WorldWideScience

Sample records for learning methods specifically

  1. "Mastery Learning" Como Metodo Psicoeducativo para Ninos con Problemas Especificos de Aprendizaje. ("Mastery Learning" as a Psychoeducational Method for Children with Specific Learning Problems.)

    Science.gov (United States)

    Coya, Liliam de Barbosa; Perez-Coffie, Jorge

    1982-01-01

    "Mastery Learning" was compared with the "conventional" method of teaching reading skills to Puerto Rican children with specific learning disabilities. The "Mastery Learning" group showed significant gains in the cognitive and affective domains. Results suggested Mastery Learning is a more effective method of teaching…

  2. Defining the Undefinable: Operationalization of Methods to Identify Specific Learning Disabilities among Practicing School Psychologists

    Science.gov (United States)

    Cottrell, Joseph M.; Barrett, Courtenay A.

    2016-01-01

    Accurate and consistent identification of students with specific learning disabilities (SLDs) is crucial; however, state and district guidelines regarding identification methods lack operationalization and are inconsistent throughout the United States. In the current study, the authors surveyed 471 school psychologists about "school" SLD…

  3. Learning Specific Content in Technology Education: Learning Study as a Collaborative Method in Swedish Preschool Class Using Hands-On Material

    Science.gov (United States)

    Kilbrink, Nina; Bjurulf, Veronica; Blomberg, Ingela; Heidkamp, Anja; Hollsten, Ann-Christin

    2014-01-01

    This article describes the process of a learning study conducted in technology education in a Swedish preschool class. The learning study method used in this study is a collaborative method, where researchers and teachers work together as a team concerning teaching and learning about a specific learning object. The object of learning in this study…

  4. Learning a specific content in technology education : Learning Study as collaborative method in Swedish preschool class using hands-on material 

    OpenAIRE

    Kilbrink, Nina; Bjurulf, Veronica; Blomberg, Ingela; Heidkamp, Anja; Hollsten, Ann-Christin

    2014-01-01

    This article describes the process of a learning study conducted in technology education in a Swedish preschool class. The learning study method used in this study is a collaborative method, where researchers and teachers work together as a team concerning teaching and learning about a specific learning object. The object of learning in this study concerns strong constructions and framed structures. This article describes how this learning study was conducted and discusses reflections made du...

  5. Software specification methods

    CERN Document Server

    Habrias, Henri

    2010-01-01

    This title provides a clear overview of the main methods, and has a practical focus that allows the reader to apply their knowledge to real-life situations. The following are just some of the techniques covered: UML, Z, TLA+, SAZ, B, OMT, VHDL, Estelle, SDL and LOTOS.

  6. Cooperative Learning as a Democratic Learning Method

    Science.gov (United States)

    Erbil, Deniz Gökçe; Kocabas, Ayfer

    2018-01-01

    In this study, the effects of applying the cooperative learning method on the students' attitude toward democracy in an elementary 3rd-grade life studies course was examined. Over the course of 8 weeks, the cooperative learning method was applied with an experimental group, and traditional methods of teaching life studies in 2009, which was still…

  7. Machine learning methods for planning

    CERN Document Server

    Minton, Steven

    1993-01-01

    Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning.Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credi

  8. Active Learning Methods

    Science.gov (United States)

    Zayapragassarazan, Z.; Kumar, Santosh

    2012-01-01

    Present generation students are primarily active learners with varied learning experiences and lecture courses may not suit all their learning needs. Effective learning involves providing students with a sense of progress and control over their own learning. This requires creating a situation where learners have a chance to try out or test their…

  9. Qualitative methods in workplace learning

    OpenAIRE

    Fabritius, Hannele

    2015-01-01

    Methods of learning in the workplace will be introduced. The methods are connect to competence development and to the process of conducting development discussions in a dialogical way. The tools developed and applied are a fourfold table, a cycle of work identity, a plan of personal development targets, a learning meeting and a learning map. The methods introduced will aim to better learning at work.

  10. Greek Young Adults with Specific Learning Disabilities Seeking Learning Assessments

    Science.gov (United States)

    Bonti, Eleni; Bampalou, Christina E.; Kouimtzi, Eleni M.; Kyritsis, Zacharias

    2018-01-01

    The purpose of this study is to investigate the reasons why Greek young adults with Specific Learning Disabilities (SLD) seek learning assessments. The study sample consisted of 106 adults meeting Diagnostic and Statistical Manual of Mental Disorders criteria for SLD. Data were collected through self-report records (clinical interview) of adults…

  11. Adolescents with specific learning disabilities - perceptions of specific learning disabilities in the environment of secondary schools

    OpenAIRE

    Pospíšilová, Zuzana

    2012-01-01

    The thesis focuses on adolescents with specific learning disabilities in the milieu of secondary schools. It is divided into a theoretical part and an empirical part. The first part introduces a topic of specific learning disabilities in the developmental stage of adolescence. It first describes the most relevant aspects of adolescent development. The attention is then paid to typical manifestations of specific learning disabilities in adolescence, and also to secondary symptoms usually conne...

  12. Conduct disorders as a result of specific learning disorders

    OpenAIRE

    VOKROJOVÁ, Nela

    2012-01-01

    This thesis focuses on relationship between specific learning disorders and conduct disorders in puberty. The theoretical part explains the basic terms apearing in the thesis such as specific learning disorders, conduct disorders, puberty and prevention of conduct disorder formation. It presents Czech and foreign research which have already been done in this and related areas. The empirical part uses a quantitative method to measure anxiety and occurrence of conduct disorders in second grade ...

  13. Learning LM Specificity for Ganglion Cells

    Science.gov (United States)

    Ahumada, Albert J.

    2015-01-01

    Unsupervised learning models have been proposed based on experience (Ahumada and Mulligan, 1990;Wachtler, Doi, Lee and Sejnowski, 2007) that allow the cortex to develop units with LM specific color opponent receptive fields like the blob cells reported by Hubel and Wiesel on the basis of visual experience. These models used ganglion cells with LM indiscriminate wiring as inputs to the learning mechanism, which was presumed to occur at the cortical level.

  14. Geometrical methods in learning theory

    International Nuclear Information System (INIS)

    Burdet, G.; Combe, Ph.; Nencka, H.

    2001-01-01

    The methods of information theory provide natural approaches to learning algorithms in the case of stochastic formal neural networks. Most of the classical techniques are based on some extremization principle. A geometrical interpretation of the associated algorithms provides a powerful tool for understanding the learning process and its stability and offers a framework for discussing possible new learning rules. An illustration is given using sequential and parallel learning in the Boltzmann machine

  15. e-Learning Business Research Methods

    Science.gov (United States)

    Cowie, Jonathan

    2004-01-01

    This paper outlines the development of a generic Business Research Methods course from a simple name in a box to a full e-Learning web based module. It highlights particular issues surrounding the nature of the discipline and the integration of a large number of cross faculty subject specific research methods courses into a single generic module.…

  16. Cognitive Clusters in Specific Learning Disorder.

    Science.gov (United States)

    Poletti, Michele; Carretta, Elisa; Bonvicini, Laura; Giorgi-Rossi, Paolo

    The heterogeneity among children with learning disabilities still represents a barrier and a challenge in their conceptualization. Although a dimensional approach has been gaining support, the categorical approach is still the most adopted, as in the recent fifth edition of the Diagnostic and Statistical Manual of Mental Disorders. The introduction of the single overarching diagnostic category of specific learning disorder (SLD) could underemphasize interindividual clinical differences regarding intracategory cognitive functioning and learning proficiency, according to current models of multiple cognitive deficits at the basis of neurodevelopmental disorders. The characterization of specific cognitive profiles associated with an already manifest SLD could help identify possible early cognitive markers of SLD risk and distinct trajectories of atypical cognitive development leading to SLD. In this perspective, we applied a cluster analysis to identify groups of children with a Diagnostic and Statistical Manual-based diagnosis of SLD with similar cognitive profiles and to describe the association between clusters and SLD subtypes. A sample of 205 children with a diagnosis of SLD were enrolled. Cluster analyses (agglomerative hierarchical and nonhierarchical iterative clustering technique) were used successively on 10 core subtests of the Wechsler Intelligence Scale for Children-Fourth Edition. The 4-cluster solution was adopted, and external validation found differences in terms of SLD subtype frequencies and learning proficiency among clusters. Clinical implications of these findings are discussed, tracing directions for further studies.

  17. Airing method-specific advertisements.

    Science.gov (United States)

    Yaser, Y

    1992-06-01

    The Turkish Family Health and Planning Foundation initiated the commercial marketing of contraceptives in 1989 as part of a Contraceptive Social Marketing (CSM) program to make available low-cost contraceptives. In 1988 modern methods were used by 31% and traditional methods by 32.3%, while 36.6% used no contraceptives. Only 6.2% were current pill users mainly because of health reasons since high-dose pills dominated the market. A 1990 survey among urban consumers indicated a 94% awareness of contraceptive methods, 76.1% of current use, and preference for the IUD. The side effects of the pill were cited for disliking it, and the condom was rated higher. The CSM project aims at popularizing low-dose pills by explaining the differences and benefits regarding high-dose pills. It collaborated with manufacturers: Schering, Wyeth, Organon, and Eczacibasi Ilac. In 1991 a TV and radio advertisement campaign started that involves the low-dose products Microgynon, Triquilar, Desolet, Lo-Ovral, and Tri-Nordial. The introduction of the Okey condom by Eczacibasi Ilac. In June 1991 also entailed extensive promotion with newspaper ads and TV spots after getting official permission. 1.3 million condoms were sold in the 1st 2 months in 13,000 retail outlets, and 4 million more were projected to be sold. A shift of the attitude of supermarket owners allowing stocking of condoms and the support of the Turkish Ministry of Health, USAID, and the Turkish Radio and Television Bureau has facilitated the CSM project implementation that will profoundly affect family planning in Turkey.

  18. Reflexive Learning through Visual Methods

    DEFF Research Database (Denmark)

    Frølunde, Lisbeth

    2014-01-01

    What. This chapter concerns how visual methods and visual materials can support visually oriented, collaborative, and creative learning processes in education. The focus is on facilitation (guiding, teaching) with visual methods in learning processes that are designerly or involve design. Visual...... methods are exemplified through two university classroom cases about collaborative idea generation processes. The visual methods and materials in the cases are photo elicitation using photo cards, and modeling with LEGO Serious Play sets. Why. The goal is to encourage the reader, whether student...... or professional, to facilitate with visual methods in a critical, reflective, and experimental way. The chapter offers recommendations for facilitating with visual methods to support playful, emergent designerly processes. The chapter also has a critical, situated perspective. Where. This chapter offers case...

  19. Teaching Foreign Languages to Pupils with Specific Learning Disability

    OpenAIRE

    VOLDÁNOVÁ, Veronika

    2015-01-01

    This diploma thesis deals with the topic of specific learning disability. In the theoretical part I define the term specific learning disability and I mention the related terms. I deal with the history, types and causes of specific learning disability, further I describe the possibilities of diagnostics and re-education concerning specific learning disability. I also attend to the situation of a pupil in the family and school background. The main attention is especially paid to teaching forei...

  20. 34 CFR 300.307 - Specific learning disabilities.

    Science.gov (United States)

    2010-07-01

    ... 34 Education 2 2010-07-01 2010-07-01 false Specific learning disabilities. 300.307 Section 300.307... Educational Placements Additional Procedures for Identifying Children with Specific Learning Disabilities § 300.307 Specific learning disabilities. (a) General. A State must adopt, consistent with § 300.309...

  1. A Time to Define: Making the Specific Learning Disability Definition Prescribe Specific Learning Disability

    Science.gov (United States)

    Kavale, Kenneth A.; Spaulding, Lucinda S.; Beam, Andrea P.

    2009-01-01

    Unlike other special education categories defined in U.S. law (Individuals with Disabilities Education Act), the definition of specific learning disability (SLD) has not changed since first proposed in 1968. Thus, although the operational definition of SLD has responded to new knowledge and understanding about the construct, the formal definition…

  2. Decomposition methods for unsupervised learning

    DEFF Research Database (Denmark)

    Mørup, Morten

    2008-01-01

    This thesis presents the application and development of decomposition methods for Unsupervised Learning. It covers topics from classical factor analysis based decomposition and its variants such as Independent Component Analysis, Non-negative Matrix Factorization and Sparse Coding...... methods and clustering problems is derived both in terms of classical point clustering but also in terms of community detection in complex networks. A guiding principle throughout this thesis is the principle of parsimony. Hence, the goal of Unsupervised Learning is here posed as striving for simplicity...... in the decompositions. Thus, it is demonstrated how a wide range of decomposition methods explicitly or implicitly strive to attain this goal. Applications of the derived decompositions are given ranging from multi-media analysis of image and sound data, analysis of biomedical data such as electroencephalography...

  3. Are Students' Learning Styles Discipline Specific?

    Science.gov (United States)

    Jones, Cheryl; Reichard, Carla; Mokhtari, Kouider

    2003-01-01

    This study examines the extent to which community college students' learning style preferences vary as a function of discipline. Reports significant differences in students' learning style preferences across disciplines, but not by gender. Adds that student learning style preferences varied by academic performance as measured by gender. Discusses…

  4. [Specific learning disabilities - from DSM-IV to DSM-5].

    Science.gov (United States)

    Schulte-Körne, Gerd

    2014-09-01

    The publication of the DSM-5 means changes in the classification and recommendations for diagnosis of specific learning disabilities. Dyslexia and dyscalculia have been reintroduced into the DSM. Three specific learning disorders - impairment in reading, impairment in the written expression, and impairment in mathematics, described by subskills - are now part of the DSM-5. Three subcomponents of the reading disorder are expressly differentiated: word reading accuracy, reading rate, and fluency and reading comprehension. Impaired subskills of the specific learning disorder with impairment in written expression are spelling accuracy, grammar and punctuation accuracy, and clarity and organization of written expression. Four subskills are found in the mathematics disorder: number sense, memorization of arithmetic facts, accurate or fluent calculation, and accurate math reasoning. Each impaired academic domain and subskill should be recorded. A description of the severity degree was also included. The diagnosis is based on a variety of methods, including medical history, clinical interview, school report, teacher evaluation, rating scales, and psychometric tests. The IQ discrepancy criterion was abandoned, though that of age or class discrepancy criterion was retained. The application of a discrepancy is recommended by 1 to 2.5 SD. All three specific developmental disorders are common (prevalence 5 %-15 %), occur early during the first years of formal schooling, and persist into adulthood.

  5. Specific learning disorder: prevalence and gender differences.

    Directory of Open Access Journals (Sweden)

    Kristina Moll

    Full Text Available Comprehensive models of learning disorders have to consider both isolated learning disorders that affect one learning domain only, as well as comorbidity between learning disorders. However, empirical evidence on comorbidity rates including all three learning disorders as defined by DSM-5 (deficits in reading, writing, and mathematics is scarce. The current study assessed prevalence rates and gender ratios for isolated as well as comorbid learning disorders in a representative sample of 1633 German speaking children in 3rd and 4th Grade. Prevalence rates were analysed for isolated as well as combined learning disorders and for different deficit criteria, including a criterion for normal performance. Comorbid learning disorders occurred as frequently as isolated learning disorders, even when stricter cutoff criteria were applied. The relative proportion of isolated and combined disorders did not change when including a criterion for normal performance. Reading and spelling deficits differed with respect to their association with arithmetic problems: Deficits in arithmetic co-occurred more often with deficits in spelling than with deficits in reading. In addition, comorbidity rates for arithmetic and reading decreased when applying stricter deficit criteria, but stayed high for arithmetic and spelling irrespective of the chosen deficit criterion. These findings suggest that the processes underlying the relationship between arithmetic and reading might differ from those underlying the relationship between arithmetic and spelling. With respect to gender ratios, more boys than girls showed spelling deficits, while more girls were impaired in arithmetic. No gender differences were observed for isolated reading problems and for the combination of all three learning disorders. Implications of these findings for assessment and intervention of learning disorders are discussed.

  6. Statistical learning methods: Basics, control and performance

    Energy Technology Data Exchange (ETDEWEB)

    Zimmermann, J. [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)]. E-mail: zimmerm@mppmu.mpg.de

    2006-04-01

    The basics of statistical learning are reviewed with a special emphasis on general principles and problems for all different types of learning methods. Different aspects of controlling these methods in a physically adequate way will be discussed. All principles and guidelines will be exercised on examples for statistical learning methods in high energy and astrophysics. These examples prove in addition that statistical learning methods very often lead to a remarkable performance gain compared to the competing classical algorithms.

  7. Statistical learning methods: Basics, control and performance

    International Nuclear Information System (INIS)

    Zimmermann, J.

    2006-01-01

    The basics of statistical learning are reviewed with a special emphasis on general principles and problems for all different types of learning methods. Different aspects of controlling these methods in a physically adequate way will be discussed. All principles and guidelines will be exercised on examples for statistical learning methods in high energy and astrophysics. These examples prove in addition that statistical learning methods very often lead to a remarkable performance gain compared to the competing classical algorithms

  8. Comparing Three Patterns of Strengths and Weaknesses Models for the Identification of Specific Learning Disabilities

    Science.gov (United States)

    Miller, Daniel C.; Maricle, Denise E.; Jones, Alicia M.

    2016-01-01

    Processing Strengths and Weaknesses (PSW) models have been proposed as a method for identifying specific learning disabilities. Three PSW models were examined for their ability to predict expert identified specific learning disabilities cases. The Dual Discrepancy/Consistency Model (DD/C; Flanagan, Ortiz, & Alfonso, 2013) as operationalized by…

  9. Methodes for identification of specific language impairment

    Directory of Open Access Journals (Sweden)

    Toktam Maleki Shahmahmood

    2014-06-01

    Full Text Available Background and Aim: Specific language impiarment (SLI is one of the most prevalent developmental language disorders its diagnosis is a problematic issue among researchers and clinicians because of the heterogeneity of language profiles in the affected population and overlapping with other developmental language disorders. The aim of this study was to review the suggested diagnostic criteria for this disorder, controversies about these criteria and identify the most accurate diagnostic methods.Methods: Published article from 1980 to 2012 in bibliographic and publisher databases including Pubmed, Google scholar, Cochran library, Web of Science, ProQuest, Springer, Oxford, Science direct, Ovid, Iran Medex and Magiran about the diagnostic methods for discriminating preschoool children with specific language impiarment from normal developing children were reviewd in this article. These keywords were used for research: “specific language impairment”, “SLI”, “diagnosis or identification”, “standardized tests”, and “tests for language development”.Conclusion: The results of this study show inspite of agreement of researchers and clinicians about exclusionary criteria as one basic part of the diagnosis of specific language impiarment , there is no consensus about the other part, inclusionary criteria. Different studies used different inclusionary criteria which can be divided to categories of clincal judgment, discrepancy-based criteria, standardized testing, clinical markers and markers from spontaneous speech samples. Advantages, disadvantages, and clinical applicability of each diagnostic method are discussed in this article.

  10. Cognitive Clusters in Specific Learning Disorder

    Science.gov (United States)

    Poletti, Michele; Carretta, Elisa; Bonvicini, Laura; Giorgi-Rossi, Paolo

    2018-01-01

    The heterogeneity among children with learning disabilities still represents a barrier and a challenge in their conceptualization. Although a dimensional approach has been gaining support, the categorical approach is still the most adopted, as in the recent fifth edition of the "Diagnostic and Statistical Manual of Mental Disorders." The…

  11. Optimisation of technical specifications using probabilistic methods

    International Nuclear Information System (INIS)

    Ericsson, G.; Knochenhauer, M.; Hultqvist, G.

    1986-01-01

    During the last few years the development of methods for modifying and optimising nuclear power plant Technical Specifications (TS) for plant operations has received increased attention. Probalistic methods in general, and the plant and system models of probabilistic safety assessment (PSA) in particular, seem to provide the most forceful tools for optimisation. This paper first gives some general comments on optimisation, identifying important parameters and then gives a description of recent Swedish experiences from the use of nuclear power plant PSA models and results for TS optimisation

  12. Specific methods on in-pile gammametry

    International Nuclear Information System (INIS)

    Pointud, M.-L.; Michel, Francois.

    1979-01-01

    The gammametry technique in nuclear research reactors has evolved by the adequation of its means and the quality of its results since its beginnings in 1972. We do not propose here to make a detailed presentation, nor describe the kinds of well known results that can henceforth be attained with it in a conventional manner; our intention, on the other hand, is to describe a few specific methods developed for using it in the SILOE reactor of the CEN/G [fr

  13. New e-learning method using databases

    Directory of Open Access Journals (Sweden)

    Andreea IONESCU

    2012-10-01

    Full Text Available The objective of this paper is to present a new e-learning method that use databases. The solution could pe implemented for any typeof e-learning system in any domain. The article will purpose a solution to improve the learning process for virtual classes.

  14. Working Memory Functioning in Children with Learning Disorders and Specific Language Impairment

    Science.gov (United States)

    Schuchardt, Kirsten; Bockmann, Ann-Katrin; Bornemann, Galina; Maehler, Claudia

    2013-01-01

    Purpose: On the basis of Baddeley's working memory model (1986), we examined working memory functioning in children with learning disorders with and without specific language impairment (SLI). We pursued the question whether children with learning disorders exhibit similar working memory deficits as children with additional SLI. Method: In…

  15. Learning Science, Learning about Science, Doing Science: Different Goals Demand Different Learning Methods

    Science.gov (United States)

    Hodson, Derek

    2014-01-01

    This opinion piece paper urges teachers and teacher educators to draw careful distinctions among four basic learning goals: learning science, learning about science, doing science and learning to address socio-scientific issues. In elaboration, the author urges that careful attention is paid to the selection of teaching/learning methods that…

  16. Perceptual learning is specific to the trained structure of information.

    Science.gov (United States)

    Cohen, Yamit; Daikhin, Luba; Ahissar, Merav

    2013-12-01

    What do we learn when we practice a simple perceptual task? Many studies have suggested that we learn to refine or better select the sensory representations of the task-relevant dimension. Here we show that learning is specific to the trained structural regularities. Specifically, when this structure is modified after training with a fixed temporal structure, performance regresses to pretraining levels, even when the trained stimuli and task are retained. This specificity raises key questions as to the importance of low-level sensory modifications in the learning process. We trained two groups of participants on a two-tone frequency discrimination task for several days. In one group, a fixed reference tone was consistently presented in the first interval (the second tone was higher or lower), and in the other group the same reference tone was consistently presented in the second interval. When following training, these temporal protocols were switched between groups, performance of both groups regressed to pretraining levels, and further training was needed to attain postlearning performance. ERP measures, taken before and after training, indicated that participants implicitly learned the temporal regularity of the protocol and formed an attentional template that matched the trained structure of information. These results are consistent with Reverse Hierarchy Theory, which posits that even the learning of simple perceptual tasks progresses in a top-down manner, hence can benefit from temporal regularities at the trial level, albeit at the potential cost that learning may be specific to these regularities.

  17. Methods for control over learning individual trajectory

    Science.gov (United States)

    Mitsel, A. A.; Cherniaeva, N. V.

    2015-09-01

    The article discusses models, methods and algorithms of determining student's optimal individual educational trajectory. A new method of controlling the learning trajectory has been developed as a dynamic model of learning trajectory control, which uses score assessment to construct a sequence of studied subjects.

  18. The Guided Autobiography Method: A Learning Experience

    Science.gov (United States)

    Thornton, James E.

    2008-01-01

    This article discusses the proposition that learning is an unexplored feature of the guided autobiography method and its developmental exchange. Learning, conceptualized and explored as the embedded and embodied processes, is essential in narrative activities of the guided autobiography method leading to psychosocial development and growth in…

  19. Active teaching methods, studying responses and learning

    DEFF Research Database (Denmark)

    Christensen, Hans Peter; Vigild, Martin Etchells; Thomsen, Erik Vilain

    2010-01-01

    Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed.......Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed....

  20. Microgenetic Learning Analytics Methods: Workshop Report

    Science.gov (United States)

    Aghababyan, Ani; Martin, Taylor; Janisiewicz, Philip; Close, Kevin

    2016-01-01

    Learning analytics is an emerging discipline and, as such, benefits from new tools and methodological approaches. This work reviews and summarizes our workshop on microgenetic data analysis techniques using R, held at the second annual Learning Analytics Summer Institute in Cambridge, Massachusetts, on 30 June 2014. Specifically, this paper…

  1. Introducing the Collaborative E-Learning Design Method (CoED)

    DEFF Research Database (Denmark)

    Ryberg, Thomas; Buus, Lillian; Nyvang, Tom

    2015-01-01

    In this chapter, a specific learning design method is introduced and explained, namely the Collaborative E-learning Design method (CoED), which has been developed through various projects in “e-Learning Lab – Centre for User Driven Innovation, Learning and Design” (Nyvang & Georgsen, 2007). We br...

  2. A predictive validity study of the Learning Style Questionnaire (LSQ) using multiple, specific learning criteria

    NARCIS (Netherlands)

    Kappe, F.R.; Boekholt, L.; den Rooyen, C.; van der Flier, H.

    2009-01-01

    Multiple and specific learning criteria were used to examine the predictive validity of the Learning Style Questionnaire (LSQ). Ninety-nine students in a college of higher learning in The Netherlands participated in a naturally occurring field study. The students were categorized into one of four

  3. Academic Achievement and Memory Differences among Specific Learning Disabilities Subtypes

    Science.gov (United States)

    Carmichael, Jessica A.; Fraccaro, Rebecca L.; Miller, Daniel C.; Maricle, Denise E.

    2014-01-01

    Reading, writing, and math are academic skills involving a number of different executive functions, particularly working memory. Children with specific learning disabilities (SLD) may present myriad academic difficulties, depending on their specific area(s) of processing weakness. is study examined differences in academic achievement and working…

  4. Computational learning on specificity-determining residue-nucleotide interactions

    KAUST Repository

    Wong, Ka-Chun; Li, Yue; Peng, Chengbin; Moses, Alan M.; Zhang, Zhaolei

    2015-01-01

    The protein–DNA interactions between transcription factors and transcription factor binding sites are essential activities in gene regulation. To decipher the binding codes, it is a long-standing challenge to understand the binding mechanism across different transcription factor DNA binding families. Past computational learning studies usually focus on learning and predicting the DNA binding residues on protein side. Taking into account both sides (protein and DNA), we propose and describe a computational study for learning the specificity-determining residue-nucleotide interactions of different known DNA-binding domain families. The proposed learning models are compared to state-of-the-art models comprehensively, demonstrating its competitive learning performance. In addition, we describe and propose two applications which demonstrate how the learnt models can provide meaningful insights into protein–DNA interactions across different DNA binding families.

  5. Computational learning on specificity-determining residue-nucleotide interactions

    KAUST Repository

    Wong, Ka-Chun

    2015-11-02

    The protein–DNA interactions between transcription factors and transcription factor binding sites are essential activities in gene regulation. To decipher the binding codes, it is a long-standing challenge to understand the binding mechanism across different transcription factor DNA binding families. Past computational learning studies usually focus on learning and predicting the DNA binding residues on protein side. Taking into account both sides (protein and DNA), we propose and describe a computational study for learning the specificity-determining residue-nucleotide interactions of different known DNA-binding domain families. The proposed learning models are compared to state-of-the-art models comprehensively, demonstrating its competitive learning performance. In addition, we describe and propose two applications which demonstrate how the learnt models can provide meaningful insights into protein–DNA interactions across different DNA binding families.

  6. Sequence-specific procedural learning deficits in children with specific language impairment.

    Science.gov (United States)

    Hsu, Hsinjen Julie; Bishop, Dorothy V M

    2014-05-01

    This study tested the procedural deficit hypothesis of specific language impairment (SLI) by comparing children's performance in two motor procedural learning tasks and an implicit verbal sequence learning task. Participants were 7- to 11-year-old children with SLI (n = 48), typically developing age-matched children (n = 20) and younger typically developing children matched for receptive grammar (n = 28). In a serial reaction time task, the children with SLI performed at the same level as the grammar-matched children, but poorer than age-matched controls in learning motor sequences. When tested with a motor procedural learning task that did not involve learning sequential relationships between discrete elements (i.e. pursuit rotor), the children with SLI performed comparably with age-matched children and better than younger grammar-matched controls. In addition, poor implicit learning of word sequences in a verbal memory task (the Hebb effect) was found in the children with SLI. Together, these findings suggest that SLI might be characterized by deficits in learning sequence-specific information, rather than generally weak procedural learning. © 2014 The Authors. Developmental Science Published by John Wiley & Sons Ltd.

  7. Kernel methods for deep learning

    OpenAIRE

    Cho, Youngmin

    2012-01-01

    We introduce a new family of positive-definite kernels that mimic the computation in large neural networks. We derive the different members of this family by considering neural networks with different activation functions. Using these kernels as building blocks, we also show how to construct other positive-definite kernels by operations such as composition, multiplication, and averaging. We explore the use of these kernels in standard models of supervised learning, such as support vector mach...

  8. Learning speaker-specific characteristics with a deep neural architecture.

    Science.gov (United States)

    Chen, Ke; Salman, Ahmad

    2011-11-01

    Speech signals convey various yet mixed information ranging from linguistic to speaker-specific information. However, most of acoustic representations characterize all different kinds of information as whole, which could hinder either a speech or a speaker recognition (SR) system from producing a better performance. In this paper, we propose a novel deep neural architecture (DNA) especially for learning speaker-specific characteristics from mel-frequency cepstral coefficients, an acoustic representation commonly used in both speech recognition and SR, which results in a speaker-specific overcomplete representation. In order to learn intrinsic speaker-specific characteristics, we come up with an objective function consisting of contrastive losses in terms of speaker similarity/dissimilarity and data reconstruction losses used as regularization to normalize the interference of non-speaker-related information. Moreover, we employ a hybrid learning strategy for learning parameters of the deep neural networks: i.e., local yet greedy layerwise unsupervised pretraining for initialization and global supervised learning for the ultimate discriminative goal. With four Linguistic Data Consortium (LDC) benchmarks and two non-English corpora, we demonstrate that our overcomplete representation is robust in characterizing various speakers, no matter whether their utterances have been used in training our DNA, and highly insensitive to text and languages spoken. Extensive comparative studies suggest that our approach yields favorite results in speaker verification and segmentation. Finally, we discuss several issues concerning our proposed approach.

  9. Activating teaching methods, studying responses and learning

    OpenAIRE

    Christensen, Hans Peter; Vigild, Martin E.; Thomsen, Erik; Szabo, Peter; Horsewell, Andy

    2009-01-01

    Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed. Peer Reviewed

  10. Vulnerability Assessment by Learning Attack Specifications in Graphs

    NARCIS (Netherlands)

    Nunes Leal Franqueira, V.; Lopes, Raul H.C.

    This paper presents an evolutionary approach for learning attack specifications that describe attack scenarios. The objective is to find vulnerabilities in computer networks which minimise the cost of an attack with maximum impact. Although we focus on Insider Threat, the proposed approach applies

  11. Assessing the Learning Path Specification: a Pragmatic Quality Approach

    NARCIS (Netherlands)

    Janssen, José; Berlanga, Adriana; Heyenrath, Stef; Martens, Harrie; Vogten, Hubert; Finders, Anton; Herder, Eelco; Hermans, Henry; Melero, Javier; Schaeps, Leon; Koper, Rob

    2010-01-01

    Janssen, J., Berlanga, A. J., Heyenrath, S., Martens, H., Vogten, H., Finders, A., Herder, E., Hermans, H., Melero Gallardo, J., Schaeps, L., & Koper, R. (2010). Assessing the Learning Path Specification: a Pragmatic Quality Approach. Journal of Universal Computer Science, 16(21), 3191-3209.

  12. Specific Learning Difficulties--What Teachers Need to Know

    Science.gov (United States)

    Hudson, Diana

    2015-01-01

    This book clearly explains what Specific Learning Difficulties (SpLD) are, and describes the symptoms of conditions most commonly encountered in the mainstream classroom: dyslexia, dyspraxia, dyscalculia, dysgraphia, Autism Spectrum Disorder, ADHD, and OCD. The author provides an overview of the strengths and weaknesses commonly associated with…

  13. Effect of Methods of Learning and Self Regulated Learning toward Outcomes of Learning Social Studies

    Science.gov (United States)

    Tjalla, Awaluddin; Sofiah, Evi

    2015-01-01

    This research aims to reveal the influence of learning methods and self-regulated learning on students learning scores for Social Studies object. The research was done in Islamic Junior High School (MTs Manba'ul Ulum), Batuceper City Tangerang using quasi-experimental method. The research employed simple random technique to 28 students. Data were…

  14. In silico machine learning methods in drug development.

    Science.gov (United States)

    Dobchev, Dimitar A; Pillai, Girinath G; Karelson, Mati

    2014-01-01

    Machine learning (ML) computational methods for predicting compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties are being increasingly applied in drug discovery and evaluation. Recently, machine learning techniques such as artificial neural networks, support vector machines and genetic programming have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic targets. These methods are particularly useful for screening compound libraries of diverse chemical structures, "noisy" and high-dimensional data to complement QSAR methods, and in cases of unavailable receptor 3D structure to complement structure-based methods. A variety of studies have demonstrated the potential of machine-learning methods for predicting compounds as potential drug candidates. The present review is intended to give an overview of the strategies and current progress in using machine learning methods for drug design and the potential of the respective model development tools. We also regard a number of applications of the machine learning algorithms based on common classes of diseases.

  15. Impact of Computer Aided Learning on Children with Specific Learning Disabilities

    OpenAIRE

    The Spastic Society Of Karnataka , Bangalore

    2004-01-01

    Study conducted by The Spastics Society of Karnataka on behalf of Azim Premji Foundation to assess the effectiveness of computers in enhancing learning for children with specific learning disabilities. Azim Premji Foundation is not liable for any direct or indirect loss or damage whatsoever arising from the use or access of any information, interpretation and conclusions that may be printed in this report.; Study to assess the effectiveness of computers in enhancing learning for children with...

  16. Formal specification level concepts, methods, and algorithms

    CERN Document Server

    Soeken, Mathias

    2015-01-01

    This book introduces a new level of abstraction that closes the gap between the textual specification of embedded systems and the executable model at the Electronic System Level (ESL). Readers will be enabled to operate at this new, Formal Specification Level (FSL), using models which not only allow significant verification tasks in this early stage of the design flow, but also can be extracted semi-automatically from the textual specification in an interactive manner.  The authors explain how to use these verification tasks to check conceptual properties, e.g. whether requirements are in conflict, as well as dynamic behavior, in terms of execution traces. • Serves as a single-source reference to a new level of abstraction for embedded systems, known as the Formal Specification Level (FSL); • Provides a variety of use cases which can be adapted to readers’ specific design flows; • Includes a comprehensive illustration of Natural Language Processing (NLP) techniques, along with examples of how to i...

  17. The specificity of learned parallelism in dual-memory retrieval.

    Science.gov (United States)

    Strobach, Tilo; Schubert, Torsten; Pashler, Harold; Rickard, Timothy

    2014-05-01

    Retrieval of two responses from one visually presented cue occurs sequentially at the outset of dual-retrieval practice. Exclusively for subjects who adopt a mode of grouping (i.e., synchronizing) their response execution, however, reaction times after dual-retrieval practice indicate a shift to learned retrieval parallelism (e.g., Nino & Rickard, in Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 373-388, 2003). In the present study, we investigated how this learned parallelism is achieved and why it appears to occur only for subjects who group their responses. Two main accounts were considered: a task-level versus a cue-level account. The task-level account assumes that learned retrieval parallelism occurs at the level of the task as a whole and is not limited to practiced cues. Grouping response execution may thus promote a general shift to parallel retrieval following practice. The cue-level account states that learned retrieval parallelism is specific to practiced cues. This type of parallelism may result from cue-specific response chunking that occurs uniquely as a consequence of grouped response execution. The results of two experiments favored the second account and were best interpreted in terms of a structural bottleneck model.

  18. Active learning methods for interactive image retrieval.

    Science.gov (United States)

    Gosselin, Philippe Henri; Cord, Matthieu

    2008-07-01

    Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.

  19. Polyunsaturated fatty acids (PUFAs) for children with specific learning disorders.

    Science.gov (United States)

    Tan, May Loong; Ho, Jacqueline J; Teh, Keng Hwang

    2016-09-28

    About 5% of school children have a specific learning disorder, defined as unexpected failure to acquire adequate abilities in reading, writing or mathematics that is not a result of reduced intellectual ability, inadequate teaching or social deprivation. Of these events, 80% are reading disorders. Polyunsaturated fatty acids (PUFAs), in particular, omega-3 and omega-6 fatty acids, which normally are abundant in the brain and in the retina, are important for learning. Some children with specific learning disorders have been found to be deficient in these PUFAs, and it is argued that supplementation of PUFAs may help these children improve their learning abilities. 1. To assess effects on learning outcomes of supplementation of polyunsaturated fatty acids (PUFAs) for children with specific learning disorders.2. To determine whether adverse effects of supplementation of PUFAs are reported in these children. In November 2015, we searched CENTRAL, Ovid MEDLINE, Embase, PsycINFO, 10 other databases and two trials registers. We also searched the reference lists of relevant articles. Randomised controlled trials (RCTs) or quasi-RCTs comparing PUFAs with placebo or no treatment in children younger than 18 years with specific learning disabilities, as diagnosed in accordance with the fifth (or earlier) edition of theDiagnostic and Statistical Manual of Mental Disorders (DSM-5), or the 10th (or earlier) revision of the International Classification of Diseases (ICD-10) or equivalent criteria. We included children with coexisting developmental disorders such as attention deficit hyperactivity disorder (ADHD) or autism. Two review authors (MLT and KHT) independently screened the titles and abstracts of articles identified by the search and eliminated all studies that did not meet the inclusion criteria. We contacted study authors to ask for missing information and clarification, when needed. We used the GRADE approach to assess the quality of evidence. Two small studies

  20. Deep Learning and Bayesian Methods

    Directory of Open Access Journals (Sweden)

    Prosper Harrison B.

    2017-01-01

    Full Text Available A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.

  1. Learning styles: The learning methods of air traffic control students

    Science.gov (United States)

    Jackson, Dontae L.

    In the world of aviation, air traffic controllers are an integral part in the overall level of safety that is provided. With a number of controllers reaching retirement age, the Air Traffic Collegiate Training Initiative (AT-CTI) was created to provide a stronger candidate pool. However, AT-CTI Instructors have found that a number of AT-CTI students are unable to memorize types of aircraft effectively. This study focused on the basic learning styles (auditory, visual, and kinesthetic) of students and created a teaching method to try to increase memorization in AT-CTI students. The participants were asked to take a questionnaire to determine their learning style. Upon knowing their learning styles, participants attended two classroom sessions. The participants were given a presentation in the first class, and divided into a control and experimental group for the second class. The control group was given the same presentation from the first classroom session while the experimental group had a group discussion and utilized Middle Tennessee State University's Air Traffic Control simulator to learn the aircraft types. Participants took a quiz and filled out a survey, which tested the new teaching method. An appropriate statistical analysis was applied to determine if there was a significant difference between the control and experimental groups. The results showed that even though the participants felt that the method increased their learning, there was no significant difference between the two groups.

  2. Pragmatics of Contemporary Teaching and Learning Methods

    Directory of Open Access Journals (Sweden)

    Ryszard Józef Panfil

    2013-09-01

    Full Text Available The dynamics of the environment in which educational institutions operate have a significant influence on the basic activity of these institutions, i.e. the process of educating, and particularly teaching and learning methods used during that process: traditional teaching, tutoring, mentoring and coaching. The identity of an educational institution and the appeal of its services depend on how flexible, diverse and adaptable is the educational process it offers as a core element of its services. Such a process is determined by how its pragmatism is displayed in the operational relativism of methods, their applicability, as well as practical dimension of achieved results and values. Based on the above premises, this publication offers a pragmatic-systemic identification of contemporary teaching and learning methods, while taking into account the differences between them and the scope of their compatibility. Secondly, using the case of sport coaches’ education, the author exemplifies the pragmatic theory of perception of contemporary teaching and learning methods.

  3. Category Specificity in Normal Episodic Learning: Applications to Object Recognition and Category-Specific Agnosia

    Science.gov (United States)

    Bukach, Cindy M.; Bub, Daniel N.; Masson, Michael E. J.; Lindsay, D. Stephen

    2004-01-01

    Studies of patients with category-specific agnosia (CSA) have given rise to multiple theories of object recognition, most of which assume the existence of a stable, abstract semantic memory system. We applied an episodic view of memory to questions raised by CSA in a series of studies examining normal observers' recall of newly learned attributes…

  4. Deep Learning and Bayesian Methods

    OpenAIRE

    Prosper Harrison B.

    2017-01-01

    A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such meth...

  5. Characterizing Reinforcement Learning Methods through Parameterized Learning Problems

    Science.gov (United States)

    2011-06-03

    extraneous. The agent could potentially adapt these representational aspects by applying methods from feature selection ( Kolter and Ng, 2009; Petrik et al...611–616. AAAI Press. Kolter , J. Z. and Ng, A. Y. (2009). Regularization and feature selection in least-squares temporal difference learning. In A. P

  6. The learning continuum based on student's level of competence and specific pedagogical learning material on physiological aspects from teachers's opinions

    Science.gov (United States)

    Hadi, Ria Fitriyani; Subali, Bambang

    2017-08-01

    The scope of learning continuum at the conceptual knowledge is formulated based on the student's level of competence and specific pedagogical learning material. The purpose of this study is to develop a learning continuum of specific pedagogical material aspects of physiology targeted for students in primary and secondary education. This research was conducted in Province of Yogyakarta Special Region from October 2016 to January 2017. The method used in this study was survey method. The data were collected using questionnaire that had been validated from the aspects of construct validity and experts judgements. Respondents in this study consist of 281 Science/Biology teachers at Public Junior and Senior High Schools in the Province of Yogyakarta Special Region which spread in Yogyakarta city and 4 regencies namely Sleman, Bantul, Kulonprogo, and Gunungkidul. The data were taken using a census. Data were analyzed using a descriptive analysis technique. The results show the learning continuum of physiology based on teachers's opinion from grade VII, VIII, and IX are taught in grade VII, VIII, IX and X on level of C2 (understanding) and the learning continuum of physiology based on teachers's opinion from grade X, XI and XII are taught in grade X and XI on level of C2 (understanding), C3 (applying), and C4 (analyzing) based on teachers's opinions. The conclusion is that many teachers refer to the existing curriculum rather than their own original idea for developing learning continuum.

  7. Tracking by Machine Learning Methods

    CERN Document Server

    Jofrehei, Arash

    2015-01-01

    Current track reconstructing methods start with two points and then for each layer loop through all possible hits to find proper hits to add to that track. Another idea would be to use this large number of already reconstructed events and/or simulated data and train a machine on this data to find tracks given hit pixels. Training time could be long but real time tracking is really fast Simulation might not be as realistic as real data but tacking has been done for that with 100 percent efficiency while by using real data we would probably be limited to current efficiency.

  8. Machine Learning Identification of Protein Properties Useful for Specific Applications

    KAUST Repository

    Khamis, Abdullah

    2016-03-31

    Proteins play critical roles in cellular processes of living organisms. It is therefore important to identify and characterize their key properties associated with their functions. Correlating protein’s structural, sequence and physicochemical properties of its amino acids (aa) with protein functions could identify some of the critical factors governing the specific functionality. We point out that not all functions of even well studied proteins are known. This, complemented by the huge increase in the number of newly discovered and predicted proteins, makes challenging the experimental characterization of the whole spectrum of possible protein functions for all proteins of interest. Consequently, the use of computational methods has become more attractive. Here we address two questions. The first one is how to use protein aa sequence and physicochemical properties to characterize a family of proteins. The second one focuses on how to use transcription factor (TF) protein’s domains to enhance accuracy of predicting TF DNA binding sites (TFBSs). To address the first question, we developed a novel method using computational representation of proteins based on characteristics of different protein regions (N-terminal, M-region and C-terminal) and combined these with the properties of protein aa sequences. We show that this description provides important biological insight about characterization of the protein functional groups. Using feature selection techniques, we identified key properties of proteins that allow for very accurate characterization of different protein families. We demonstrated efficiency of our method in application to a number of antimicrobial peptide families. To address the second question we developed another novel method that uses a combination of aa properties of DNA binding domains of TFs and their TFBS properties to develop machine learning models for predicting TFBSs. Feature selection is used to identify the most relevant characteristics

  9. Specification, authoring and prototyping of personalised workplace learning solutions

    DEFF Research Database (Denmark)

    Dolog, Peter; Kravcik, Milos; Cristea, Alexandra

    2007-01-01

    The main goal of this document is to survey the existing approaches for the authoring and engineering of personalisation and adaptation in e-learning systems. This document enables the comparison of various methods and techniques, and facilitates their integration or reuse. It offers a cohesive r...

  10. IP-MLI: An Independency of Learning Materials from Platforms in a Mobile Learning using Intelligent Method

    Directory of Open Access Journals (Sweden)

    Mohammed Abdallh Otair

    2006-06-01

    Full Text Available Attempting to deliver a monolithic mobile learning system is too inflexible in view of the heterogeneous mixture of hardware and services available and the desirability of facility blended approaches to learning delivery, and how to build learning materials to run on all platforms[1]. This paper proposes a framework of mobile learning system using an intelligent method (IP-MLI . A fuzzy matching method is used to find suitable learning material design. It will provide a best matching for each specific platform type for each learner. The main contribution of the proposed method is to use software layer to insulate learning materials from device-specific features. Consequently, many versions of learning materials can be designed to work on many platform types.

  11. Managing specific learning disability in schools in India.

    Science.gov (United States)

    Karande, Sunil; Sholapurwala, Rukhshana; Kulkarni, Madhuri

    2011-07-01

    Specific learning disability (dyslexia, dysgraphia, and dyscalculia) afflicts 5-15% of school-going children. Over the last decade; awareness about this invisible handicap has grown in India. However, much needs to be done to ensure that each afflicted child gets an opportunity to achieve his or her full academic potential in regular mainstream schools. In order to achieve this ideal scenario, all regular classroom teachers should be sensitized to suspect, and trained to screen for this disability when the child is in primary school. School managements should become proactive to set up resource rooms and employ special educators to ensure that these children receive regular and affordable remedial education; and be diligent in ensuring that these children get the mandatory provisions both during school and board examinations. Once specific learning disability is recognized as a disability by the Government of India, these children with the backing of the Right to Education Act, would be able to benefit significantly.

  12. Specific decontamination methods: water nozzle, cavitation erosion

    International Nuclear Information System (INIS)

    Boulitrop, D.; Gauchon, J.P.; Lecoffre, Y.

    1984-05-01

    The erosion and decontamination tests carried out in the framework of this study, allowed to specify the fields favourable to the use of the high pressure jet taking into account the determinant parameters that are the pressure and the target-nozzle distance. The previous spraying of gels with chemical reagents (sulfuric acid anf hydrazine) allows to get better decontamination factors. Then, the feasibility study of a decontamination method by cavitation erosion is presented. Gelled compounds for decontamination have been developed; their decontamination quality has been evaluated by comparative contamination tests in laboratory and decontamination tests of samples of materials used in nuclear industry; this last method is adapted to remote handling devices and produces a low quantity of secondary effluents, so it allows to clean high contaminated installation on the site without additional exposure of the personnel [fr

  13. Effectiveness of Memantine in Improvement of Cognitive Deficits in Specific Learning Disorder

    Directory of Open Access Journals (Sweden)

    Elham Ahmadi Zahrani

    2016-12-01

    Full Text Available Abstract Background: Specific learning disorder is a neurodevelopmental disorder characterized by persistent difficulties in learning academic skills in reading, written expression, or mathematics. This study was performed to investigate the effectiveness of memantine in the relief of cognitive deficits (selective attention, sustained attention, and working memory in specific learning disorder. Materials and Methods: This study is a clinical trial. Of all children 8-12 years referred to Amir Kabir Hospital 94 patients diagnosed with specific learning disorder based on DSMV diagnostic interview referred by specialist and randomly divided by two groups, memantine and placebo. Cognitive deficits before and after treatment were measured with continuous performance test, Stroop test and Wechsler Digit Span forward and reverse and Corsi test. Results: Multivariate analysis of variance showed a significant difference in error when answering, omission answer and corrected answer in continuous performance test, but this difference is not significant in response time. Difference in forward, reverse and collected auditory was significant and not significant in the auditory span. In active visual working memory at corsi cube test, difference was significant (p <0.05. Conclusion: The results showed that memantine in improvement of sustained attention, auditory working memory and visual working memory, is effective, while in selective attention is not effective and according to similarities of learning disorder and Attention deficit / Hyperactivity disorder (ADHD and the effectiveness of memantine in improvement of symptoms of ADHD, we can also use this drug in improvement of cognitive deficits of specific learning disorder.

  14. Using budget-friendly methods to analyze sport specific movements

    Science.gov (United States)

    Jackson, Lindsay; Williams, Sarah; Ferrara, Davon

    2015-03-01

    When breaking down the physics behind sport specific movements, athletes, usually professional, are often assessed in multimillion-dollar laboratories and facilities. Budget-friendly methods, such as video analysis using low-cost cameras, iPhone sensors, or inexpensive force sensors can make this process more accessible to amateur athletes, which in-turn can give insight into injury mechanisms. Here we present a comparison of two methods of determining the forces experienced by a cheerleader during co-education stunting and soccer goalies while side-diving. For the cheerleader, accelerometer measurements were taken by an iPhone 5 and compared to video analysis. The measurements done on the soccer players were taken using FlexiForce force sensors and again compared to video analysis. While these budget-friendly methods could use some refining, they show promise for producing usable measurements for possibly increasing our understanding of injury in amateur players. Furthermore, low-cost physics experiments with sports can foster an active learning environment for students with minimum physics and mathematical background.

  15. A Scale Development for Teacher Competencies on Cooperative Learning Method

    Science.gov (United States)

    Kocabas, Ayfer; Erbil, Deniz Gokce

    2017-01-01

    Cooperative learning method is a learning method studied both in Turkey and in the world for long years as an active learning method. Although cooperative learning method takes place in training programs, it cannot be implemented completely in the direction of its principles. The results of the researches point out that teachers have problems with…

  16. Anxiety levels in mothers of children with specific learning disability

    Directory of Open Access Journals (Sweden)

    Karande S

    2009-01-01

    Full Text Available Background : Parents of children with specific learning disability (SpLD undergo stress in coping with their child′s condition. Aim : To measure the levels of anxiety and find out the cause of anxiety in mothers of children with SpLD at time of diagnosis. Settings and Design : Prospective rating-scale and interview-based study conducted in our clinic. Materials and Methods : One hundred mothers of children (70 boys, 30 girls with SpLD were interviewed using the Hamilton anxiety rating scale (HAM-A and a semi-structured questionnaire. Detailed clinical and demographic data of mothers were noted. Statistical Analysis : Chi-square test or unpaired student′s t-test was applied wherever applicable. Results : The mean age of mothers was 40.14 years (±SD 4.94, range 25.07-54.0, 73% belonged to upper or upper middle socioeconomic strata of society, 67% were graduates or postgraduates, 58% were full-time home-makers, and 33% lived in joint families. Levels of anxiety were absent in 24%, mild in 75%, and moderate in 1% of mothers. Their mean total anxiety score was 5.65 (±SD 4.75, range 0-21, mean psychic anxiety score was 3.92 (±SD 3.11, range 0-13, and mean somatic anxiety score was 1.76 (±SD 2.05, range 0-10. Their common worries were related to child′s poor school performance (95%, child′s future (90%, child′s behavior (51%, and visits to our clinic (31%. Conclusion : Most mothers of children with SpLD have already developed mild anxiety levels by the time this hidden disability is diagnosed. These anxieties should be addressed by counseling to ensure optimum rehabilitation of these children.

  17. ASPECTS REGARDING NEUROMARKETING SPECIFIC RESEARCH METHODS

    Directory of Open Access Journals (Sweden)

    MIRELA-CRISTINA VOICU

    2012-05-01

    Full Text Available Information is one of the most important resources that a company must posses. Some informations are hidden deep in the black box - the mind of the consumer. Neuromarketing helps us find what is inside of the black box without troubling the consumer with questions that he doesn’t want to answer or that he can’t answer. Today because of an extensive research in mapping cortical and subcortical activity in association with behaviors and thoughts, the confidence in neurological data is growing. Thanks to discoveries in perceptual sciences we can identify the parts of the brain that are responsible for the phenomena that we experience daily. For all those interested in information obtained through neuromarketing techniques it becomes evident that there are corresponding neural substrates of consumer decision making process and these substrates can be observed, measured, and possibly manipulated. The following paper reveals some important aspects of the use of neuromarketing in studying consumer behavior by presenting the concepts, methods and techniques used under this sophisticated name, the limitations and advantages of using neuromarketing techniques and the importance of this type of information in decision making process at a company level.

  18. Learning Method, Facilities And Infrastructure, And Learning Resources In Basic Networking For Vocational School

    OpenAIRE

    Pamungkas, Bian Dwi

    2017-01-01

    This study aims to examine the contribution of learning methods on learning output, the contribution of facilities and infrastructure on output learning, the contribution of learning resources on learning output, and the contribution of learning methods, the facilities and infrastructure, and learning resources on learning output. The research design is descriptive causative, using a goal-oriented assessment approach in which the assessment focuses on assessing the achievement of a goal. The ...

  19. Enriching behavioral ecology with reinforcement learning methods.

    Science.gov (United States)

    Frankenhuis, Willem E; Panchanathan, Karthik; Barto, Andrew G

    2018-02-13

    This article focuses on the division of labor between evolution and development in solving sequential, state-dependent decision problems. Currently, behavioral ecologists tend to use dynamic programming methods to study such problems. These methods are successful at predicting animal behavior in a variety of contexts. However, they depend on a distinct set of assumptions. Here, we argue that behavioral ecology will benefit from drawing more than it currently does on a complementary collection of tools, called reinforcement learning methods. These methods allow for the study of behavior in highly complex environments, which conventional dynamic programming methods do not feasibly address. In addition, reinforcement learning methods are well-suited to studying how biological mechanisms solve developmental and learning problems. For instance, we can use them to study simple rules that perform well in complex environments. Or to investigate under what conditions natural selection favors fixed, non-plastic traits (which do not vary across individuals), cue-driven-switch plasticity (innate instructions for adaptive behavioral development based on experience), or developmental selection (the incremental acquisition of adaptive behavior based on experience). If natural selection favors developmental selection, which includes learning from environmental feedback, we can also make predictions about the design of reward systems. Our paper is written in an accessible manner and for a broad audience, though we believe some novel insights can be drawn from our discussion. We hope our paper will help advance the emerging bridge connecting the fields of behavioral ecology and reinforcement learning. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

  20. Learning Unknown Structure in CRFs via Adaptive Gradient Projection Method

    Directory of Open Access Journals (Sweden)

    Wei Xue

    2016-08-01

    Full Text Available We study the problem of fitting probabilistic graphical models to the given data when the structure is not known. More specifically, we focus on learning unknown structure in conditional random fields, especially learning both the structure and parameters of a conditional random field model simultaneously. To do this, we first formulate the learning problem as a convex minimization problem by adding an l_2-regularization to the node parameters and a group l_1-regularization to the edge parameters, and then a gradient-based projection method is proposed to solve it which combines an adaptive stepsize selection strategy with a nonmonotone line search. Extensive simulation experiments are presented to show the performance of our approach in solving unknown structure learning problems.

  1. Early Language Learning: Complexity and Mixed Methods

    Science.gov (United States)

    Enever, Janet, Ed.; Lindgren, Eva, Ed.

    2017-01-01

    This is the first collection of research studies to explore the potential for mixed methods to shed light on foreign or second language learning by young learners in instructed contexts. It brings together recent studies undertaken in Cameroon, China, Croatia, Ethiopia, France, Germany, Italy, Kenya, Mexico, Slovenia, Spain, Sweden, Tanzania and…

  2. Keystone Method: A Learning Paradigm in Mathematics

    Science.gov (United States)

    Siadat, M. Vali; Musial, Paul M.; Sagher, Yoram

    2008-01-01

    This study reports the effects of an integrated instructional program (the Keystone Method) on the students' performance in mathematics and reading, and tracks students' persistence and retention. The subject of the study was a large group of students in remedial mathematics classes at the college, willing to learn but lacking basic educational…

  3. Students' Ideas on Cooperative Learning Method

    Science.gov (United States)

    Yoruk, Abdulkadir

    2016-01-01

    Aim of this study is to investigate students' ideas on cooperative learning method. For that purpose students who are studying at elementary science education program are distributed into two groups through an experimental design. Factors threaten the internal validity are either eliminated or reduced to minimum value. Data analysis is done…

  4. Suggestology as an Effective Language Learning Method.

    Science.gov (United States)

    MaCoy, Katherine W.

    The methods used and the results obtained by means of the accelerated language learning techniques developed by Georgi Lozanov, Director of the Institute of Suggestology in Bulgaria, are discussed. The following topics are included: (1) discussion of hypermnesia, "super memory," and the reasons foreign languages were chosen for purposes…

  5. Effects of Jigsaw Learning Method on Students’ Self-Efficacy and Motivation to Learn

    OpenAIRE

    Dwi Nur Rachmah

    2017-01-01

    Jigsaw learning as a cooperative learning method, according to the results of some studies, can improve academic skills, social competence, behavior in learning, and motivation to learn. However, in some other studies, there are different findings regarding the effect of jigsaw learning method on self-efficacy. The purpose of this study is to examine the effects of jigsaw learning method on self-efficacy and motivation to learn in psychology students at the Faculty of Medicine, Universitas La...

  6. Color image definition evaluation method based on deep learning method

    Science.gov (United States)

    Liu, Di; Li, YingChun

    2018-01-01

    In order to evaluate different blurring levels of color image and improve the method of image definition evaluation, this paper proposed a method based on the depth learning framework and BP neural network classification model, and presents a non-reference color image clarity evaluation method. Firstly, using VGG16 net as the feature extractor to extract 4,096 dimensions features of the images, then the extracted features and labeled images are employed in BP neural network to train. And finally achieve the color image definition evaluation. The method in this paper are experimented by using images from the CSIQ database. The images are blurred at different levels. There are 4,000 images after the processing. Dividing the 4,000 images into three categories, each category represents a blur level. 300 out of 400 high-dimensional features are trained in VGG16 net and BP neural network, and the rest of 100 samples are tested. The experimental results show that the method can take full advantage of the learning and characterization capability of deep learning. Referring to the current shortcomings of the major existing image clarity evaluation methods, which manually design and extract features. The method in this paper can extract the images features automatically, and has got excellent image quality classification accuracy for the test data set. The accuracy rate is 96%. Moreover, the predicted quality levels of original color images are similar to the perception of the human visual system.

  7. Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization.

    Science.gov (United States)

    Gao, Shenghua; Tsang, Ivor Wai-Hung; Ma, Yi

    2014-02-01

    This paper targets fine-grained image categorization by learning a category-specific dictionary for each category and a shared dictionary for all the categories. Such category-specific dictionaries encode subtle visual differences among different categories, while the shared dictionary encodes common visual patterns among all the categories. To this end, we impose incoherence constraints among the different dictionaries in the objective of feature coding. In addition, to make the learnt dictionary stable, we also impose the constraint that each dictionary should be self-incoherent. Our proposed dictionary learning formulation not only applies to fine-grained classification, but also improves conventional basic-level object categorization and other tasks such as event recognition. Experimental results on five data sets show that our method can outperform the state-of-the-art fine-grained image categorization frameworks as well as sparse coding based dictionary learning frameworks. All these results demonstrate the effectiveness of our method.

  8. Project Oriented Immersion Learning: Method and Results

    DEFF Research Database (Denmark)

    Icaza, José I.; Heredia, Yolanda; Borch, Ole M.

    2005-01-01

    A pedagogical approach called “project oriented immersion learning” is presented and tested on a graduate online course. The approach combines the Project Oriented Learning method with immersion learning in a virtual enterprise. Students assumed the role of authors hired by a fictitious publishing...... house that develops digital products including e-books, tutorials, web sites and so on. The students defined the problem that their product was to solve; choose the type of product and the content; and built the product following a strict project methodology. A wiki server was used as a platform to hold...

  9. The method of global learning in teaching foreign languages

    Directory of Open Access Journals (Sweden)

    Tatjana Dragovič

    2001-12-01

    Full Text Available The authors describe the method of global learning of foreign languages, which is based on the principles of neurolinguistic programming (NLP. According to this theory, the educator should use the method of the so-called periphery learning, where students learn relaxation techniques and at the same time they »incidentally « or subconsciously learn a foreign language. The method of global learning imitates successful strategies of learning in early childhood and therefore creates a relaxed attitude towards learning. Global learning is also compared with standard methods.

  10. Solar adaptive optics: specificities, lessons learned, and open alternatives

    Science.gov (United States)

    Montilla, I.; Marino, J.; Asensio Ramos, A.; Collados, M.; Montoya, L.; Tallon, M.

    2016-07-01

    First on sky adaptive optics experiments were performed on the Dunn Solar Telescope on 1979, with a shearing interferometer and limited success. Those early solar adaptive optics efforts forced to custom-develop many components, such as Deformable Mirrors and WaveFront Sensors, which were not available at that time. Later on, the development of the correlation Shack-Hartmann marked a breakthrough in solar adaptive optics. Since then, successful Single Conjugate Adaptive Optics instruments have been developed for many solar telescopes, i.e. the National Solar Observatory, the Vacuum Tower Telescope and the Swedish Solar Telescope. Success with the Multi Conjugate Adaptive Optics systems for GREGOR and the New Solar Telescope has proved to be more difficult to attain. Such systems have a complexity not only related to the number of degrees of freedom, but also related to the specificities of the Sun, used as reference, and the sensing method. The wavefront sensing is performed using correlations on images with a field of view of 10", averaging wavefront information from different sky directions, affecting the sensing and sampling of high altitude turbulence. Also due to the low elevation at which solar observations are performed we have to include generalized fitting error and anisoplanatism, as described by Ragazzoni and Rigaut, as non-negligible error sources in the Multi Conjugate Adaptive Optics error budget. For the development of the next generation Multi Conjugate Adaptive Optics systems for the Daniel K. Inouye Solar Telescope and the European Solar Telescope we still need to study and understand these issues, to predict realistically the quality of the achievable reconstruction. To improve their designs other open issues have to be assessed, i.e. possible alternative sensing methods to avoid the intrinsic anisoplanatism of the wide field correlation Shack-Hartmann, new parameters to estimate the performance of an adaptive optics solar system, alternatives to

  11. The future of the IMS Learning Design specification: a critical look

    NARCIS (Netherlands)

    Sloep, Peter

    2009-01-01

    P. B. Sloep (2009). The future of the IMS Learning Design specification: a critical look. Presentation at the IMS Learning Design seminar 'The future of IMS Learning Design'. December, 10, 2009, Wollongong, Australia: University of Wollongong.

  12. Specific learning disability in mathematics: a comprehensive review.

    Science.gov (United States)

    Soares, Neelkamal; Evans, Teresa; Patel, Dilip R

    2018-01-01

    Math skills are necessary for success in the childhood educational and future adult work environment. This article reviews the changing terminology for specific learning disabilities (SLD) in math and describes the emerging genetics and neuroimaging studies that relate to individuals with math disability (MD). It is important to maintain a developmental perspective on MD, as presentation changes with age, instruction, and the different models (educational and medical) of identification. Intervention requires a systematic approach to screening and remediation that has evolved with more evidence-based literature. Newer directions in behavioral, educational and novel interventions are described.

  13. Unsupervised behaviour-specific dictionary learning for abnormal event detection

    DEFF Research Database (Denmark)

    Ren, Huamin; Liu, Weifeng; Olsen, Søren Ingvor

    2015-01-01

    the training data is only a small proportion of the surveillance data. Therefore, we propose behavior-specific dictionaries (BSD) through unsupervised learning, pursuing atoms from the same type of behavior to represent one behavior dictionary. To further improve the dictionary by introducing information from...... potential infrequent normal patterns, we refine the dictionary by searching ‘missed atoms’ that have compact coefficients. Experimental results show that our BSD algorithm outperforms state-of-the-art dictionaries in abnormal event detection on the public UCSD dataset. Moreover, BSD has less false alarms...

  14. The lasting effects of process-specific versus stimulus-specific learning during infancy.

    Science.gov (United States)

    Hadley, Hillary; Pickron, Charisse B; Scott, Lisa S

    2015-09-01

    The capacity to tell the difference between two faces within an infrequently experienced face group (e.g. other species, other race) declines from 6 to 9 months of age unless infants learn to match these faces with individual-level names. Similarly, the use of individual-level labels can also facilitate differentiation of a group of non-face objects (strollers). This early learning leads to increased neural specialization for previously unfamiliar face or object groups. The current investigation aimed to determine whether early conceptual learning between 6 and 9 months leads to sustained behavioral advantages and neural changes in these same children at 4-6 years of age. Results suggest that relative to a control group of children with no previous training and to children with infant category-level naming experience, children with early individual-level training exhibited faster response times to human faces. Further, individual-level training with a face group - but not an object group - led to more adult-like neural responses for human faces. These results suggest that early individual-level learning results in long-lasting process-specific effects, which benefit categories that continue to be perceived and recognized at the individual level (e.g. human faces). © 2014 John Wiley & Sons Ltd.

  15. Human reinforcement learning subdivides structured action spaces by learning effector-specific values.

    Science.gov (United States)

    Gershman, Samuel J; Pesaran, Bijan; Daw, Nathaniel D

    2009-10-28

    Humans and animals are endowed with a large number of effectors. Although this enables great behavioral flexibility, it presents an equally formidable reinforcement learning problem of discovering which actions are most valuable because of the high dimensionality of the action space. An unresolved question is how neural systems for reinforcement learning-such as prediction error signals for action valuation associated with dopamine and the striatum-can cope with this "curse of dimensionality." We propose a reinforcement learning framework that allows for learned action valuations to be decomposed into effector-specific components when appropriate to a task, and test it by studying to what extent human behavior and blood oxygen level-dependent (BOLD) activity can exploit such a decomposition in a multieffector choice task. Subjects made simultaneous decisions with their left and right hands and received separate reward feedback for each hand movement. We found that choice behavior was better described by a learning model that decomposed the values of bimanual movements into separate values for each effector, rather than a traditional model that treated the bimanual actions as unitary with a single value. A decomposition of value into effector-specific components was also observed in value-related BOLD signaling, in the form of lateralized biases in striatal correlates of prediction error and anticipatory value correlates in the intraparietal sulcus. These results suggest that the human brain can use decomposed value representations to "divide and conquer" reinforcement learning over high-dimensional action spaces.

  16. Addiction memory as a specific, individually learned memory imprint.

    Science.gov (United States)

    Böning, J

    2009-05-01

    The construct of "addiction memory" (AM) and its importance for relapse occurrence has been the subject of discussion for the past 30 years. Neurobiological findings from "social neuroscience" and biopsychological learning theory, in conjunction with construct-valid behavioral pharmacological animal models, can now also provide general confirmation of addiction memory as a pathomorphological correlate of addiction disorders. Under multifactorial influences, experience-driven neuronal learning and memory processes of emotional and cognitive processing patterns in the specific individual "set" and "setting" play an especially pivotal role in this connection. From a neuropsychological perspective, the episodic (biographical) memory, located at the highest hierarchical level, is of central importance for the formation of the AM in certain structural and functional areas of the brain and neuronal networks. Within this context, neuronal learning and conditioning processes take place more or less unconsciously and automatically in the preceding long-term-memory systems (in particular priming and perceptual memory). They then regulate the individually programmed addiction behavior implicitly and thus subsequently stand for facilitated recollection of corresponding, previously stored cues or context situations. This explains why it is so difficult to treat an addiction memory, which is embedded above all in the episodic memory, from the molecular carrier level via the neuronal pattern level through to the psychological meaning level, and has thus meanwhile become a component of personality.

  17. Psychological Co-morbidity in Children with Specific Learning Disorders.

    Science.gov (United States)

    Sahoo, Manoj K; Biswas, Haritha; Padhy, Susanta Kumar

    2015-01-01

    Children under 19 years of age constitute over 40% of India's population and information about their mental health needs is a national imperative. Children with specific learning disorders (SLDs) exhibit academic difficulties disproportionate to their intellectual capacities. Prevalence of SLD ranges from 2% to 10%. Dyslexia (developmental reading disorder) is the most common type, affecting 80% of all SLD. About 30% of learning disabled children have behavioral and emotional problems, which range from attention deficit hyperactivity disorder (most common) to depression, anxiety, suicide etc., to substance abuse (least common). Co-occurrence of such problems with SLD further adds to the academic difficulty. In such instances, diagnosis is difficult and tricky; improvement in academics demands comprehensive holistic treatment approach. SLD remains a large public health problem because of under-recognition, inadequate treatment and therefore merits greater effort to understand the co-morbidities, especially in the Indian population. As the literature is scarce regarding co-morbid conditions in learning disability in Indian scenario, the present study has tried to focus on Indian population. The educational concessions (recent most) given to such children by Central Board of Secondary Education, New Delhi are referred to. The issues to be addressed by the family physicians are: Low level of awareness among families and teachers, improper dissemination of accurate information about psychological problems, available help seeking avenues, need to develop service delivery models in rural and urban areas and focus on the integration of mental health and primary care keeping such co-morbidity in mind.

  18. 19 CFR 134.43 - Methods of marking specific articles.

    Science.gov (United States)

    2010-04-01

    ... 19 Customs Duties 1 2010-04-01 2010-04-01 false Methods of marking specific articles. 134.43...; DEPARTMENT OF THE TREASURY COUNTRY OF ORIGIN MARKING Method and Location of Marking Imported Articles § 134.43 Methods of marking specific articles. (a) Marking previously required by certain provisions of the...

  19. Parallelization of the ROOT Machine Learning Methods

    CERN Document Server

    Vakilipourtakalou, Pourya

    2016-01-01

    Today computation is an inseparable part of scientific research. Specially in Particle Physics when there is a classification problem like discrimination of Signals from Backgrounds originating from the collisions of particles. On the other hand, Monte Carlo simulations can be used in order to generate a known data set of Signals and Backgrounds based on theoretical physics. The aim of Machine Learning is to train some algorithms on known data set and then apply these trained algorithms to the unknown data sets. However, the most common framework for data analysis in Particle Physics is ROOT. In order to use Machine Learning methods, a Toolkit for Multivariate Data Analysis (TMVA) has been added to ROOT. The major consideration in this report is the parallelization of some TMVA methods, specially Cross-Validation and BDT.

  20. Machine Learning Methods for Production Cases Analysis

    Science.gov (United States)

    Mokrova, Nataliya V.; Mokrov, Alexander M.; Safonova, Alexandra V.; Vishnyakov, Igor V.

    2018-03-01

    Approach to analysis of events occurring during the production process were proposed. Described machine learning system is able to solve classification tasks related to production control and hazard identification at an early stage. Descriptors of the internal production network data were used for training and testing of applied models. k-Nearest Neighbors and Random forest methods were used to illustrate and analyze proposed solution. The quality of the developed classifiers was estimated using standard statistical metrics, such as precision, recall and accuracy.

  1. d-Cycloserine reduces context specificity of sexual extinction learning.

    Science.gov (United States)

    Brom, Mirte; Laan, Ellen; Everaerd, Walter; Spinhoven, Philip; Trimbos, Baptist; Both, Stephanie

    2015-11-01

    d-Cycloserine (DCS) enhances extinction processes in animals. Although classical conditioning is hypothesized to play a pivotal role in the aetiology of appetitive motivation problems, no research has been conducted on the effect of DCS on the reduction of context specificity of extinction in human appetitive learning, while facilitation hereof is relevant in the context of treatment of problematic reward-seeking behaviors. Female participants were presented with two conditioned stimuli (CSs) that either predicted (CS+) or did not predict (CS-) a potential sexual reward (unconditioned stimulus (US); genital vibrostimulation). Conditioning took place in context A and extinction in context B. Subjects received DCS (125mg) or placebo directly after the experiment on day 1 in a randomized, double-blind, between-subject fashion (Placebo n=31; DCS n=31). Subsequent testing for CS-evoked conditioned responses (CRs) in both the conditioning (A) and the extinction context (B) took place 24h later on day 2. Drug effects on consolidation were then assessed by comparing the recall of sexual extinction memories between the DCS and the placebo groups. Post learning administration of DCS facilitates sexual extinction memory consolidation and affects extinction's fundamental context specificity, evidenced by reduced conditioned genital and subjective sexual responses, relative to placebo, for presentations of the reward predicting cue 24h later outside the extinction context. DCS makes appetitive extinction memories context-independent and prevents the return of conditioned response. NMDA receptor glycine site agonists may be potential pharmacotherapies for the prevention of relapse of appetitive motivation disorders with a learned component. Copyright © 2015 Elsevier Inc. All rights reserved.

  2. Decentralized indirect methods for learning automata games.

    Science.gov (United States)

    Tilak, Omkar; Martin, Ryan; Mukhopadhyay, Snehasis

    2011-10-01

    We discuss the application of indirect learning methods in zero-sum and identical payoff learning automata games. We propose a novel decentralized version of the well-known pursuit learning algorithm. Such a decentralized algorithm has significant computational advantages over its centralized counterpart. The theoretical study of such a decentralized algorithm requires the analysis to be carried out in a nonstationary environment. We use a novel bootstrapping argument to prove the convergence of the algorithm. To our knowledge, this is the first time that such analysis has been carried out for zero-sum and identical payoff games. Extensive simulation studies are reported, which demonstrate the proposed algorithm's fast and accurate convergence in a variety of game scenarios. We also introduce the framework of partial communication in the context of identical payoff games of learning automata. In such games, the automata may not communicate with each other or may communicate selectively. This comprehensive framework has the capability to model both centralized and decentralized games discussed in this paper.

  3. Implementing Collaborative Learning Methods in the Political Science Classroom

    Science.gov (United States)

    Wolfe, Angela

    2012-01-01

    Collaborative learning is one, among other, active learning methods, widely acclaimed in higher education. Consequently, instructors in fields that lack pedagogical training often implement new learning methods such as collaborative learning on the basis of trial and error. Moreover, even though the benefits in academic circles are broadly touted,…

  4. Alignment-free genome tree inference by learning group-specific distance metrics.

    Science.gov (United States)

    Patil, Kaustubh R; McHardy, Alice C

    2013-01-01

    Understanding the evolutionary relationships between organisms is vital for their in-depth study. Gene-based methods are often used to infer such relationships, which are not without drawbacks. One can now attempt to use genome-scale information, because of the ever increasing number of genomes available. This opportunity also presents a challenge in terms of computational efficiency. Two fundamentally different methods are often employed for sequence comparisons, namely alignment-based and alignment-free methods. Alignment-free methods rely on the genome signature concept and provide a computationally efficient way that is also applicable to nonhomologous sequences. The genome signature contains evolutionary signal as it is more similar for closely related organisms than for distantly related ones. We used genome-scale sequence information to infer taxonomic distances between organisms without additional information such as gene annotations. We propose a method to improve genome tree inference by learning specific distance metrics over the genome signature for groups of organisms with similar phylogenetic, genomic, or ecological properties. Specifically, our method learns a Mahalanobis metric for a set of genomes and a reference taxonomy to guide the learning process. By applying this method to more than a thousand prokaryotic genomes, we showed that, indeed, better distance metrics could be learned for most of the 18 groups of organisms tested here. Once a group-specific metric is available, it can be used to estimate the taxonomic distances for other sequenced organisms from the group. This study also presents a large scale comparison between 10 methods--9 alignment-free and 1 alignment-based.

  5. A Fast Optimization Method for General Binary Code Learning.

    Science.gov (United States)

    Shen, Fumin; Zhou, Xiang; Yang, Yang; Song, Jingkuan; Shen, Heng; Tao, Dacheng

    2016-09-22

    Hashing or binary code learning has been recognized to accomplish efficient near neighbor search, and has thus attracted broad interests in recent retrieval, vision and learning studies. One main challenge of learning to hash arises from the involvement of discrete variables in binary code optimization. While the widely-used continuous relaxation may achieve high learning efficiency, the pursued codes are typically less effective due to accumulated quantization error. In this work, we propose a novel binary code optimization method, dubbed Discrete Proximal Linearized Minimization (DPLM), which directly handles the discrete constraints during the learning process. Specifically, the discrete (thus nonsmooth nonconvex) problem is reformulated as minimizing the sum of a smooth loss term with a nonsmooth indicator function. The obtained problem is then efficiently solved by an iterative procedure with each iteration admitting an analytical discrete solution, which is thus shown to converge very fast. In addition, the proposed method supports a large family of empirical loss functions, which is particularly instantiated in this work by both a supervised and an unsupervised hashing losses, together with the bits uncorrelation and balance constraints. In particular, the proposed DPLM with a supervised `2 loss encodes the whole NUS-WIDE database into 64-bit binary codes within 10 seconds on a standard desktop computer. The proposed approach is extensively evaluated on several large-scale datasets and the generated binary codes are shown to achieve very promising results on both retrieval and classification tasks.

  6. M-Learning: Implications in Learning Domain Specificities, Adaptive Learning, Feedback, Augmented Reality, and the Future of Online Learning

    Science.gov (United States)

    Squires, David R.

    2014-01-01

    The aim of this paper is to examine the potential and effectiveness of m-learning in the field of Education and Learning domains. The purpose of this research is to illustrate how mobile technology can and is affecting novel change in instruction, from m-learning and the link to adaptive learning, to the uninitiated learner and capacities of…

  7. Psychological co-morbidity in children with specific learning disorders

    Directory of Open Access Journals (Sweden)

    Manoj K Sahoo

    2015-01-01

    Full Text Available Children under 19 years of age constitute over 40% of India′s population and information about their mental health needs is a national imperative. Children with specific learning disorders (SLDs exhibit academic difficulties disproportionate to their intellectual capacities. Prevalence of SLD ranges from 2% to 10%. Dyslexia (developmental reading disorder is the most common type, affecting 80% of all SLD. About 30% of learning disabled children have behavioral and emotional problems, which range from attention deficit hyperactivity disorder (most common to depression, anxiety, suicide etc., to substance abuse (least common. Co-occurrence of such problems with SLD further adds to the academic difficulty. In such instances, diagnosis is difficult and tricky; improvement in academics demands comprehensive holistic treatment approach. SLD remains a large public health problem because of under-recognition, inadequate treatment and therefore merits greater effort to understand the co-morbidities, especially in the Indian population. As the literature is scarce regarding co-morbid conditions in learning disability in Indian scenario, the present study has tried to focus on Indian population. The educational concessions (recent most given to such children by Central Board of Secondary Education, New Delhi are referred to. The issues to be addressed by the family physicians are: Low level of awareness among families and teachers, improper dissemination of accurate information about psychological problems, available help seeking avenues, need to develop service delivery models in rural and urban areas and focus on the integration of mental health and primary care keeping such co-morbidity in mind.

  8. The 8 Learning Events Model: a Pedagogic Conceptual Tool Supporting Diversification of Learning Methods

    NARCIS (Netherlands)

    Verpoorten, Dominique; Poumay, M; Leclercq, D

    2006-01-01

    Please, cite this publication as: Verpoorten, D., Poumay, M., & Leclercq, D. (2006). The 8 Learning Events Model: a Pedagogic Conceptual Tool Supporting Diversification of Learning Methods. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence

  9. Specific learning disabilities in children: deficits and neuropsychological profile.

    Science.gov (United States)

    Kohli, Adarsh; Malhotra, Savita; Mohanty, Manju; Khehra, Nitasha; Kaur, Manreet

    2005-06-01

    The public is gradually becoming aware of specific learning disabilities (SLDs), which are very often the cause of academic difficulties. The aim of the study was to assess the SLDs in the clinic population at the Child and Adolescent Psychiatry Clinic at the Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh using the National Institute of Mental Health and Neurosciences SLD index and subsequently to assess the children's neuropsychological functions using a battery of tests. Thirty-five children in the age range of 7-14 years (both boys and girls) were recruited as the cohort, diagnosed clinically and assessed using the battery of tests for SLDs and neuropsychological tests consisting of the PGIMER memory scale for children, the Wisconsin card sorting test, the Bender visuo-motor gestalt test and Malin's intelligence scale for Indian children. The study revealed deficits in language and writing skills and impairments in specific areas of memory, executive functions and perceptuo-motor tasks. Identification of SLDs is useful in drawing up a treatment plan specific for a particular child.

  10. Teaching learning methods of an entrepreneurship curriculum

    Directory of Open Access Journals (Sweden)

    KERAMAT ESMI

    2015-10-01

    Full Text Available Introduction: One of the most significant elements of entrepreneurship curriculum design is teaching-learning methods, which plays a key role in studies and researches related to such a curriculum. It is the teaching method, and systematic, organized and logical ways of providing lessons that should be consistent with entrepreneurship goals and contents, and should also be developed according to the learners’ needs. Therefore, the current study aimed to introduce appropriate, modern, and effective methods of teaching entrepreneurship and their validation Methods: This is a mixed method research of a sequential exploratory kind conducted through two stages: a developing teaching methods of entrepreneurship curriculum, and b validating developed framework. Data were collected through “triangulation” (study of documents, investigating theoretical basics and the literature, and semi-structured interviews with key experts. Since the literature on this topic is very rich, and views of the key experts are vast, directed and summative content analysis was used. In the second stage, qualitative credibility of research findings was obtained using qualitative validation criteria (credibility, confirmability, and transferability, and applying various techniques. Moreover, in order to make sure that the qualitative part is reliable, reliability test was used. Moreover, quantitative validation of the developed framework was conducted utilizing exploratory and confirmatory factor analysis methods and Cronbach’s alpha. The data were gathered through distributing a three-aspect questionnaire (direct presentation teaching methods, interactive, and practical-operational aspects with 29 items among 90 curriculum scholars. Target population was selected by means of purposive sampling and representative sample. Results: Results obtained from exploratory factor analysis showed that a three factor structure is an appropriate method for describing elements of

  11. Statistical learning methods in high-energy and astrophysics analysis

    Energy Technology Data Exchange (ETDEWEB)

    Zimmermann, J. [Forschungszentrum Juelich GmbH, Zentrallabor fuer Elektronik, 52425 Juelich (Germany) and Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)]. E-mail: zimmerm@mppmu.mpg.de; Kiesling, C. [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)

    2004-11-21

    We discuss several popular statistical learning methods used in high-energy- and astro-physics analysis. After a short motivation for statistical learning we present the most popular algorithms and discuss several examples from current research in particle- and astro-physics. The statistical learning methods are compared with each other and with standard methods for the respective application.

  12. Statistical learning methods in high-energy and astrophysics analysis

    International Nuclear Information System (INIS)

    Zimmermann, J.; Kiesling, C.

    2004-01-01

    We discuss several popular statistical learning methods used in high-energy- and astro-physics analysis. After a short motivation for statistical learning we present the most popular algorithms and discuss several examples from current research in particle- and astro-physics. The statistical learning methods are compared with each other and with standard methods for the respective application

  13. [Study on commercial specification of atractylodes based on Delphi method].

    Science.gov (United States)

    Wang, Hao; Chen, Li-Xiao; Huang, Lu-Qi; Zhang, Tian-Tian; Li, Ying; Zheng, Yu-Guang

    2016-03-01

    This research adopts "Delphi method" to evaluate atractylodes traditional traits and rank correlation. By using methods of mathematical statistics the relationship of the traditional identification indicators and atractylodes goods rank correlation was analyzed, It is found that the main characteristics affectingatractylodes commodity specifications and grades of main characters wereoil points of transaction,color of transaction,color of surface,grain of transaction,texture of transaction andspoilage. The study points out that the original "seventy-six kinds of medicinal materials commodity specification standards of atractylodes differentiate commodity specification" is not in conformity with the actual market situation, we need to formulate corresponding atractylodes medicinal products specifications and grades.This study combined with experimental results "Delphi method" and the market actual situation, proposed the new draft atractylodes commodity specifications and grades, as the new atractylodes commodity specifications and grades standards. It provides a reference and theoretical basis. Copyright© by the Chinese Pharmaceutical Association.

  14. Learning phacoemulsification. Results of different teaching methods.

    Directory of Open Access Journals (Sweden)

    Hennig Albrecht

    2004-01-01

    Full Text Available We report the learning curves of three eye surgeons converting from sutureless extracapsular cataract extraction to phacoemulsification using different teaching methods. Posterior capsule rupture (PCR as a per-operative complication and visual outcome of the first 100 operations were analysed. The PCR rate was 4% and 15% in supervised and unsupervised surgery respectively. Likewise, an uncorrected visual acuity of > or = 6/18 on the first postoperative day was seen in 62 (62% of patients and in 22 (22% in supervised and unsupervised surgery respectively.

  15. Impact of an education program on parental knowledge of specific learning disability

    OpenAIRE

    Karande Sunil; Mehta Vishal; Kulkarni Madhuri

    2007-01-01

    Background :A supportive home environment is one of the factors that can favorably determine the outcome of specific learning disability (SpLD) in a school-going child. However, there is no reliable information available on parental knowledge about SpLD. Aims :To investigate parental knowledge of SpLD and to evaluate the impact of an educational intervention on it. Settings and Design : Prospective questionnaire-based study conducted in our clinic. Materials and Methods : From April to Novemb...

  16. Subsampled Hessian Newton Methods for Supervised Learning.

    Science.gov (United States)

    Wang, Chien-Chih; Huang, Chun-Heng; Lin, Chih-Jen

    2015-08-01

    Newton methods can be applied in many supervised learning approaches. However, for large-scale data, the use of the whole Hessian matrix can be time-consuming. Recently, subsampled Newton methods have been proposed to reduce the computational time by using only a subset of data for calculating an approximation of the Hessian matrix. Unfortunately, we find that in some situations, the running speed is worse than the standard Newton method because cheaper but less accurate search directions are used. In this work, we propose some novel techniques to improve the existing subsampled Hessian Newton method. The main idea is to solve a two-dimensional subproblem per iteration to adjust the search direction to better minimize the second-order approximation of the function value. We prove the theoretical convergence of the proposed method. Experiments on logistic regression, linear SVM, maximum entropy, and deep networks indicate that our techniques significantly reduce the running time of the subsampled Hessian Newton method. The resulting algorithm becomes a compelling alternative to the standard Newton method for large-scale data classification.

  17. Teaching learning methods of an entrepreneurship curriculum.

    Science.gov (United States)

    Esmi, Keramat; Marzoughi, Rahmatallah; Torkzadeh, Jafar

    2015-10-01

    One of the most significant elements of entrepreneurship curriculum design is teaching-learning methods, which plays a key role in studies and researches related to such a curriculum. It is the teaching method, and systematic, organized and logical ways of providing lessons that should be consistent with entrepreneurship goals and contents, and should also be developed according to the learners' needs. Therefore, the current study aimed to introduce appropriate, modern, and effective methods of teaching entrepreneurship and their validation. This is a mixed method research of a sequential exploratory kind conducted through two stages: a) developing teaching methods of entrepreneurship curriculum, and b) validating developed framework. Data were collected through "triangulation" (study of documents, investigating theoretical basics and the literature, and semi-structured interviews with key experts). Since the literature on this topic is very rich, and views of the key experts are vast, directed and summative content analysis was used. In the second stage, qualitative credibility of research findings was obtained using qualitative validation criteria (credibility, confirmability, and transferability), and applying various techniques. Moreover, in order to make sure that the qualitative part is reliable, reliability test was used. Moreover, quantitative validation of the developed framework was conducted utilizing exploratory and confirmatory factor analysis methods and Cronbach's alpha. The data were gathered through distributing a three-aspect questionnaire (direct presentation teaching methods, interactive, and practical-operational aspects) with 29 items among 90 curriculum scholars. Target population was selected by means of purposive sampling and representative sample. Results obtained from exploratory factor analysis showed that a three factor structure is an appropriate method for describing elements of teaching-learning methods of entrepreneurship curriculum

  18. Influence on Learning of a Collaborative Learning Method Comprising the Jigsaw Method and Problem-based Learning (PBL).

    Science.gov (United States)

    Takeda, Kayoko; Takahashi, Kiyoshi; Masukawa, Hiroyuki; Shimamori, Yoshimitsu

    2017-01-01

    Recently, the practice of active learning has spread, increasingly recognized as an essential component of academic studies. Classes incorporating small group discussion (SGD) are conducted at many universities. At present, assessments of the effectiveness of SGD have mostly involved evaluation by questionnaires conducted by teachers, by peer assessment, and by self-evaluation of students. However, qualitative data, such as open-ended descriptions by students, have not been widely evaluated. As a result, we have been unable to analyze the processes and methods involved in how students acquire knowledge in SGD. In recent years, due to advances in information and communication technology (ICT), text mining has enabled the analysis of qualitative data. We therefore investigated whether the introduction of a learning system comprising the jigsaw method and problem-based learning (PBL) would improve student attitudes toward learning; we did this by text mining analysis of the content of student reports. We found that by applying the jigsaw method before PBL, we were able to improve student attitudes toward learning and increase the depth of their understanding of the area of study as a result of working with others. The use of text mining to analyze qualitative data also allowed us to understand the processes and methods by which students acquired knowledge in SGD and also changes in students' understanding and performance based on improvements to the class. This finding suggests that the use of text mining to analyze qualitative data could enable teachers to evaluate the effectiveness of various methods employed to improve learning.

  19. COOPERATIVE LEARNING IN DISTANCE LEARNING: A MIXED METHODS STUDY

    Directory of Open Access Journals (Sweden)

    Lori Kupczynski

    2012-07-01

    Full Text Available Distance learning has facilitated innovative means to include Cooperative Learning (CL in virtual settings. This study, conducted at a Hispanic-Serving Institution, compared the effectiveness of online CL strategies in discussion forums with traditional online forums. Quantitative and qualitative data were collected from 56 graduate student participants. Quantitative results revealed no significant difference on student success between CL and Traditional formats. The qualitative data revealed that students in the cooperative learning groups found more learning benefits than the Traditional group. The study will benefit instructors and students in distance learning to improve teaching and learning practices in a virtual classroom.

  20. Machine learning methods for metabolic pathway prediction

    Directory of Open Access Journals (Sweden)

    Karp Peter D

    2010-01-01

    Full Text Available Abstract Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations.

  1. Machine learning methods for metabolic pathway prediction

    Science.gov (United States)

    2010-01-01

    Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML) methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations. PMID:20064214

  2. Student Achievement in Basic College Mathematics: Its Relationship to Learning Style and Learning Method

    Science.gov (United States)

    Gunthorpe, Sydney

    2006-01-01

    From the assumption that matching a student's learning style with the learning method best suited for the student, it follows that developing courses that correlate learning method with learning style would be more successful for students. Albuquerque Technical Vocational Institute (TVI) in New Mexico has attempted to provide students with more…

  3. Two Undergraduate Process Modeling Courses Taught Using Inductive Learning Methods

    Science.gov (United States)

    Soroush, Masoud; Weinberger, Charles B.

    2010-01-01

    This manuscript presents a successful application of inductive learning in process modeling. It describes two process modeling courses that use inductive learning methods such as inquiry learning and problem-based learning, among others. The courses include a novel collection of multi-disciplinary complementary process modeling examples. They were…

  4. The Method of High School English Word Learning

    Institute of Scientific and Technical Information of China (English)

    吴博涵

    2016-01-01

    Most Chinese students are not interested in English learning, especially English words. In this paper, I focus on English vocabulary learning, for example, the study of high school students English word learning method, and also introduce several ways to make vocabulary memory becomes more effective. The purpose is to make high school students grasp more English word learning skills.

  5. Nurse practitioner preferences for distance education methods related to learning style, course content, and achievement.

    Science.gov (United States)

    Andrusyszyn, M A; Cragg, C E; Humbert, J

    2001-04-01

    The relationships among multiple distance delivery methods, preferred learning style, content, and achievement was sought for primary care nurse practitioner students. A researcher-designed questionnaire was completed by 86 (71%) participants, while 6 engaged in follow-up interviews. The results of the study included: participants preferred learning by "considering the big picture"; "setting own learning plans"; and "focusing on concrete examples." Several positive associations were found: learning on own with learning by reading, and setting own learning plans; small group with learning through discussion; large group with learning new things through hearing and with having learning plans set by others. The most preferred method was print-based material and the least preferred method was audio tape. The most suited method for content included video teleconferencing for counseling, political action, and transcultural issues; and video tape for physical assessment. Convenience, self-direction, and timing of learning were more important than delivery method or learning style. Preferred order of learning was reading, discussing, observing, doing, and reflecting. Recommended considerations when designing distance courses include a mix of delivery methods, specific content, outcomes, learner characteristics, and state of technology.

  6. System and method for deriving a process-based specification

    Science.gov (United States)

    Hinchey, Michael Gerard (Inventor); Rash, James Larry (Inventor); Rouff, Christopher A. (Inventor)

    2009-01-01

    A system and method for deriving a process-based specification for a system is disclosed. The process-based specification is mathematically inferred from a trace-based specification. The trace-based specification is derived from a non-empty set of traces or natural language scenarios. The process-based specification is mathematically equivalent to the trace-based specification. Code is generated, if applicable, from the process-based specification. A process, or phases of a process, using the features disclosed can be reversed and repeated to allow for an interactive development and modification of legacy systems. The process is applicable to any class of system, including, but not limited to, biological and physical systems, electrical and electro-mechanical systems in addition to software, hardware and hybrid hardware-software systems.

  7. Machine learning-based patient specific prompt-gamma dose monitoring in proton therapy

    Science.gov (United States)

    Gueth, P.; Dauvergne, D.; Freud, N.; Létang, J. M.; Ray, C.; Testa, E.; Sarrut, D.

    2013-07-01

    Online dose monitoring in proton therapy is currently being investigated with prompt-gamma (PG) devices. PG emission was shown to be correlated with dose deposition. This relationship is mostly unknown under real conditions. We propose a machine learning approach based on simulations to create optimized treatment-specific classifiers that detect discrepancies between planned and delivered dose. Simulations were performed with the Monte-Carlo platform Gate/Geant4 for a spot-scanning proton therapy treatment and a PG camera prototype currently under investigation. The method first builds a learning set of perturbed situations corresponding to a range of patient translation. This set is then used to train a combined classifier using distal falloff and registered correlation measures. Classifier performances were evaluated using receiver operating characteristic curves and maximum associated specificity and sensitivity. A leave-one-out study showed that it is possible to detect discrepancies of 5 mm with specificity and sensitivity of 85% whereas using only distal falloff decreases the sensitivity down to 77% on the same data set. The proposed method could help to evaluate performance and to optimize the design of PG monitoring devices. It is generic: other learning sets of deviations, other measures and other types of classifiers could be studied to potentially reach better performance. At the moment, the main limitation lies in the computation time needed to perform the simulations.

  8. Machine learning-based patient specific prompt-gamma dose monitoring in proton therapy

    International Nuclear Information System (INIS)

    Gueth, P; Freud, N; Létang, J M; Sarrut, D; Dauvergne, D; Ray, C; Testa, E

    2013-01-01

    Online dose monitoring in proton therapy is currently being investigated with prompt-gamma (PG) devices. PG emission was shown to be correlated with dose deposition. This relationship is mostly unknown under real conditions. We propose a machine learning approach based on simulations to create optimized treatment-specific classifiers that detect discrepancies between planned and delivered dose. Simulations were performed with the Monte-Carlo platform Gate/Geant4 for a spot-scanning proton therapy treatment and a PG camera prototype currently under investigation. The method first builds a learning set of perturbed situations corresponding to a range of patient translation. This set is then used to train a combined classifier using distal falloff and registered correlation measures. Classifier performances were evaluated using receiver operating characteristic curves and maximum associated specificity and sensitivity. A leave-one-out study showed that it is possible to detect discrepancies of 5 mm with specificity and sensitivity of 85% whereas using only distal falloff decreases the sensitivity down to 77% on the same data set. The proposed method could help to evaluate performance and to optimize the design of PG monitoring devices. It is generic: other learning sets of deviations, other measures and other types of classifiers could be studied to potentially reach better performance. At the moment, the main limitation lies in the computation time needed to perform the simulations. (paper)

  9. [Multilingualism and child psychiatry: on differential diagnoses of language disorder, specific learning disorder, and selective mutism].

    Science.gov (United States)

    Tamiya, Satoshi

    2014-01-01

    Multilingualism poses unique psychiatric problems, especially in the field of child psychiatry. The author discusses several linguistic and transcultural issues in relation to Language Disorder, Specific Learning Disorder and Selective Mutism. Linguistic characteristics of multiple language development, including so-called profile effects and code-switching, need to be understood for differential diagnosis. It is also emphasized that Language Disorder in a bilingual person is not different or worse than that in a monolingual person. Second language proficiency, cultural background and transfer from the first language all need to be considered in an evaluation for Specific Learning Disorder. Selective Mutism has to be differentiated from the silent period observed in the normal successive bilingual development. The author concludes the review by remarking on some caveats around methods of language evaluation in a multilingual person.

  10. Methods of checking general safety criteria in UML statechart specifications

    International Nuclear Information System (INIS)

    Pap, Zsigmond; Majzik, Istvan; Pataricza, Andras; Szegi, Andras

    2005-01-01

    This paper describes methods and tools for safety analysis of UML statechart specifications. A comprehensive set of general safety criteria including completeness and consistency is applied in automated analysis. Analysis techniques are based on OCL expressions, graph transformations and reachability analysis. Two canonical intermediate representations of the statechart specification are introduced. They are suitable for straightforward implementation of checker methods and for the support of the proof of the correctness and soundness of the applied analysis. One of them also serves as a basis of the metamodel of a variant of UML statecharts proposed for the specification of safety-critical control systems. The analysis is extended to object-oriented specifications. Examples illustrate the application of the checker methods implemented by an automated tool-set

  11. Teaching and learning methods in IVET

    DEFF Research Database (Denmark)

    Aarkrog, Vibe

    The cases deals about learner centered learning in a commercial program and a technical program.......The cases deals about learner centered learning in a commercial program and a technical program....

  12. The research on business rules classification and specification methods

    OpenAIRE

    Baltrušaitis, Egidijus

    2005-01-01

    The work is based on the research of business rules classification and specification methods. The basics of business rules approach are discussed. The most common business rules classification and modeling methods are analyzed. Business rules modeling techniques and tools for supporting them in the information systems are presented. Basing on the analysis results business rules classification method is proposed. Templates for every business rule type are presented. Business rules structuring ...

  13. An online supervised learning method based on gradient descent for spiking neurons.

    Science.gov (United States)

    Xu, Yan; Yang, Jing; Zhong, Shuiming

    2017-09-01

    The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified in the current research. Although the existing GDB multi-spike learning (or spike sequence learning) methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence learning method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses. The method constructs error function and calculates the adjustment of synaptic weights as soon as the neurons emit a spike during their running process. We analyze and synthesize desired and actual output spikes to select appropriate input spikes in the calculation of weight adjustment in this paper. The experimental results show that our method obviously improves learning performance compared with the offline learning manner and has certain advantage on learning accuracy compared with other learning methods. Stronger learning ability determines that the method has large pattern storage capacity. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Machine Learning and Data Mining Methods in Diabetes Research.

    Science.gov (United States)

    Kavakiotis, Ioannis; Tsave, Olga; Salifoglou, Athanasios; Maglaveras, Nicos; Vlahavas, Ioannis; Chouvarda, Ioanna

    2017-01-01

    The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

  15. Evaluation of a Didactic Method for the Active Learning of Greedy Algorithms

    Science.gov (United States)

    Esteban-Sánchez, Natalia; Pizarro, Celeste; Velázquez-Iturbide, J. Ángel

    2014-01-01

    An evaluation of the educational effectiveness of a didactic method for the active learning of greedy algorithms is presented. The didactic method sets students structured-inquiry challenges to be addressed with a specific experimental method, supported by the interactive system GreedEx. This didactic method has been refined over several years of…

  16. Exploring Service Learning Outcomes in Students: A Mixed Methods Study for Nursing

    Science.gov (United States)

    Martin, John F.

    2017-01-01

    This mixed methods study exploring student outcomes of service learning experiences is inter-disciplinary, near the intersection of higher education research, moral development, and nursing. The specific problem examined in this study is that service learning among university students is utilized by educators, but largely without a full…

  17. Evaluation Methods on Usability of M-Learning Environments

    Directory of Open Access Journals (Sweden)

    Teresa Magal-Royo

    2007-10-01

    Full Text Available Nowadays there are different evaluation methods focused in the assessment of the usability of telematic methods. The assessment of 3rd generation web environments evaluates the effectiveness and usability of application with regard to the user needs. Wireless usability and, specifically in mobile phones, is concentrated in the validation of the features and tools management using conventional interactive environments. There is not a specific and suitable criterion to evaluate created environments and m-learning platforms, where the restricted and sequential representation is a fundamental aspect to be considered.The present paper exposes the importance of the conventional usability methods to verify both: the employed contents in wireless formats, and the possible interfaces from the conception phases, to the validations of the platform with such characteristics.The development of usability adapted inspection could be complemented with the Remote’s techniques of usability testing, which are being carried out these days in the mobile devices field and which pointed out the need to apply common criteria in the validation of non-located learning scenarios.

  18. An Entry Point for Formal Methods: Specification and Analysis of Event Logs

    Directory of Open Access Journals (Sweden)

    Howard Barringer

    2010-03-01

    Full Text Available Formal specification languages have long languished, due to the grave scalability problems faced by complete verification methods. Runtime verification promises to use formal specifications to automate part of the more scalable art of testing, but has not been widely applied to real systems, and often falters due to the cost and complexity of instrumentation for online monitoring. In this paper we discuss work in progress to apply an event-based specification system to the logging mechanism of the Mars Science Laboratory mission at JPL. By focusing on log analysis, we exploit the "instrumentation" already implemented and required for communicating with the spacecraft. We argue that this work both shows a practical method for using formal specifications in testing and opens interesting research avenues, including a challenging specification learning problem.

  19. Exploring Culture-Specific Learning Styles in Accounting Education

    Science.gov (United States)

    Sikkema, Seth E.; Sauerwein, Joshua A.

    2015-01-01

    Purpose: The purpose of this paper is to review whether culture affects accounting students' learning processes to identify practical guidance for accounting educators facing a culturally diverse classroom. In spite of a significant literature thread in accounting education on student learning, relatively, little emphasis has been placed on…

  20. Anxiety levels in mothers of children with specific learning disability.

    Science.gov (United States)

    Karande, S; Kumbhare, N; Kulkarni, M; Shah, N

    2009-01-01

    Parents of children with specific learning disability (SpLD) undergo stress in coping with their child's condition. To measure the levels of anxiety and find out the cause of anxiety in mothers of children with SpLD at time of diagnosis. Prospective rating-scale and interview-based study conducted in our clinic. One hundred mothers of children (70 boys, 30 girls) with SpLD were interviewed using the Hamilton anxiety rating scale (HAM-A) and a semi-structured questionnaire. Detailed clinical and demographic data of mothers were noted. Chi-square test or unpaired student's t-test was applied wherever applicable. The mean age of mothers was 40.14 years (+/-SD 4.94, range 25.07-54.0), 73% belonged to upper or upper middle socioeconomic strata of society, 67% were graduates or postgraduates, 58% were full-time home-makers, and 33% lived in joint families. Levels of anxiety were absent in 24%, mild in 75%, and moderate in 1% of mothers. Their mean total anxiety score was 5.65 (+/-SD 4.75, range 0-21), mean psychic anxiety score was 3.92 (+/-SD 3.11, range 0-13), and mean somatic anxiety score was 1.76 (+/-SD 2.05, range 0-10). Their common worries were related to child's poor school performance (95%), child's future (90%), child's behavior (51%), and visits to our clinic (31%). Most mothers of children with SpLD have already developed mild anxiety levels by the time this hidden disability is diagnosed. These anxieties should be addressed by counseling to ensure optimum rehabilitation of these children.

  1. Tumor specific glycoproteins and method for detecting tumorigenic cancers

    International Nuclear Information System (INIS)

    Davidson, E.A.; Bolmer, S.D.

    1981-01-01

    The detection of tumour specific glycoproteins (TSGP) in human sera often indicates the presence of a malignant tumour in a patient. The distinguishing characteristics of TSGP isolated from the blood sera of cancer patients are described in detail together with methods of TSGP isolation and purification. Details are also given of radioimmunoassay techniques capable of detecting very low levels of serum TSGP with high specificity. (U.K.)

  2. A convenient method to synthesize specifically labelled cholesterol with tritium

    International Nuclear Information System (INIS)

    Malik, S.; Kenny, M.; Ahmad, S.; Washington Univ., Seattle, WA

    1992-01-01

    A simple method is described to label cholesterol with tritium. Cholesterol was first oxidized to 5-cholesten-3-one which was then purified by HPLC. Its structure was established by electron impact (EI) mass spectrometry and 1 H-NMR spectroscopy. The ketone was reduced with NaB 3 H 4 to give specifically labelled cholesterol (C-3 3 H) at low specific activity. (author)

  3. Do students’ styles of learning affect how they adapt to learning methods and to the learning environment?

    OpenAIRE

    Topal, Kenan; Sarıkaya, Özlem; Basturk, Ramazan; Buke, Akile

    2015-01-01

    Objectives: The process of development and evaluation of undergraduate medical education programs should include analysis of learners’ characteristics, needs, and perceptions about learning methods. This study aims to evaluate medical students’ perceptions about problem-based learning methods and to compare these results with their individual learning styles.Materials and Methods: The survey was conducted at Marmara University Medical School where problem-based learning was implemented in the...

  4. Measuring the surgical 'learning curve': methods, variables and competency.

    Science.gov (United States)

    Khan, Nuzhath; Abboudi, Hamid; Khan, Mohammed Shamim; Dasgupta, Prokar; Ahmed, Kamran

    2014-03-01

    To describe how learning curves are measured and what procedural variables are used to establish a 'learning curve' (LC). To assess whether LCs are a valuable measure of competency. A review of the surgical literature pertaining to LCs was conducted using the Medline and OVID databases. Variables should be fully defined and when possible, patient-specific variables should be used. Trainee's prior experience and level of supervision should be quantified; the case mix and complexity should ideally be constant. Logistic regression may be used to control for confounding variables. Ideally, a learning plateau should reach a predefined/expert-derived competency level, which should be fully defined. When the group splitting method is used, smaller cohorts should be used in order to narrow the range of the LC. Simulation technology and competence-based objective assessments may be used in training and assessment in LC studies. Measuring the surgical LC has potential benefits for patient safety and surgical education. However, standardisation in the methods and variables used to measure LCs is required. Confounding variables, such as participant's prior experience, case mix, difficulty of procedures and level of supervision, should be controlled. Competency and expert performance should be fully defined. © 2013 The Authors. BJU International © 2013 BJU International.

  5. Cultivating Collaborations: Site Specific Design for Embodied Science Learning.

    Science.gov (United States)

    Gill, Katherine; Glazier, Jocelyn; Towns, Betsy

    2018-05-21

    Immersion in well-designed outdoor environments can foster the habits of mind that enable critical and authentic scientific questions to take root in students' minds. Here we share two design cases in which careful, collaborative, and intentional design of outdoor learning environments for informal inquiry provide people of all ages with embodied opportunities to learn about the natural world, developing the capacity for understanding ecology and the ability to empathize, problem-solve and reflect. Embodied learning, as facilitated by and in well-designed outdoor learning environments, leads students to develop new ways of seeing, new scientific questions, new ways to connect with ideas, with others and new ways of thinking about the natural world. Using examples from our collaborative practices as experiential learning designers, we illustrate how creating the habits of mind critical to creating scientists, science-interested, and science-aware individuals benefits from providing students spaces to engage in embodied learning in nature. We show how public landscapes designed in creative partnerships between educators, scientists, designers and the public have potential to amplify science learning for all.

  6. Deep learning versus traditional machine learning methods for aggregated energy demand prediction

    NARCIS (Netherlands)

    Paterakis, N.G.; Mocanu, E.; Gibescu, M.; Stappers, B.; van Alst, W.

    2018-01-01

    In this paper the more advanced, in comparison with traditional machine learning approaches, deep learning methods are explored with the purpose of accurately predicting the aggregated energy consumption. Despite the fact that a wide range of machine learning methods have been applied to

  7. Finding protein sites using machine learning methods

    Directory of Open Access Journals (Sweden)

    Jaime Leonardo Bobadilla Molina

    2003-07-01

    Full Text Available The increasing amount of protein three-dimensional (3D structures determined by x-ray and NMR technologies as well as structures predicted by computational methods results in the need for automated methods to provide inital annotations. We have developed a new method for recognizing sites in three-dimensional protein structures. Our method is based on a previosly reported algorithm for creating descriptions of protein microenviroments using physical and chemical properties at multiple levels of detail. The recognition method takes three inputs: 1. A set of control nonsites that share some structural or functional role. 2. A set of control nonsites that lack this role. 3. A single query site. A support vector machine classifier is built using feature vectors where each component represents a property in a given volume. Validation against an independent test set shows that this recognition approach has high sensitivity and specificity. We also describe the results of scanning four calcium binding proteins (with the calcium removed using a three dimensional grid of probe points at 1.25 angstrom spacing. The system finds the sites in the proteins giving points at or near the blinding sites. Our results show that property based descriptions along with support vector machines can be used for recognizing protein sites in unannotated structures.

  8. SPECIFIC METHOD OF RISK ASSESSMENT IN TOURISM ENTERPRISES

    Directory of Open Access Journals (Sweden)

    Andreea ARMEAN

    2014-12-01

    Full Text Available The objective of this paper is to present an innovative method of risk assessment for tourism businesses. The contribution to literature is the novelty of this method of following paths: is an ante-factum assessment not post-factum; risk assessment is based on perception rather than results; is based on specific risks tourism enterprises not on the overall risks. Is an asset-research methodology and consists in generating its own method of risk assessment based on the ideas summarized from the literature studied. The aim established is tourism enterprises from Romania. The data necessary for the application of this method will result from applying to top level management of tourism enterprises, a questionnaire about risk perception. The results from this study will help identify and measure the risks specific to tourism enterprises. The applicability of the results is to improve risk management in these enterprises.

  9. Preparing Students for Flipped or Team-Based Learning Methods

    Science.gov (United States)

    Balan, Peter; Clark, Michele; Restall, Gregory

    2015-01-01

    Purpose: Teaching methods such as Flipped Learning and Team-Based Learning require students to pre-learn course materials before a teaching session, because classroom exercises rely on students using self-gained knowledge. This is the reverse to "traditional" teaching when course materials are presented during a lecture, and students are…

  10. Learning Technology Specification: Principles for Army Training Designers and Developers

    Science.gov (United States)

    2013-09-01

    Bowers & Bowers, 2010; Moreno, 2006; Shönborn, 2011; Watkins & Hufnagel, 2007). • Interactive technologies can help maintain student engagement when...modified to better suit the trainee characteristics, learning objectives, and environmental constraints. • To maintain student engagement when...learning styles (e.g., auditory, visual, tactile) 1 2 3 4 5 Improves student engagement 1 2 3 4 5 Please list any additional factors that are

  11. Specific surface area evaluation method by using scanning electron microscopy

    International Nuclear Information System (INIS)

    Petrescu, Camelia; Petrescu, Cristian; Axinte, Adrian

    2000-01-01

    Ceramics are among the most interesting materials for a large category of applications, including both industry and health. Among the characteristic of the ceramic materials, the specific surface area is often difficult to evaluate.The paper presents a method of evaluation for the specific surface area of two ceramic powders by means of scanning electron microscopy measurements and an original method of computing the specific surface area.Cumulative curves are used to calculate the specific surface area under assumption that the values of particles diameters follow a normal logarithmic distribution. For two powder types, X7R and NPO the results are the following: - for the density ρ (g/cm 2 ), 5.5 and 6.0, respectively; - for the average diameter D bar (μm), 0.51 and 0.53, respectively; - for σ, 1.465 and 1.385, respectively; - for specific surface area (m 2 /g), 1.248 and 1.330, respectively. The obtained results are in good agreement with the values measured by conventional methods. (authors)

  12. Effects of Jigsaw Learning Method on Students’ Self-Efficacy and Motivation to Learn

    Directory of Open Access Journals (Sweden)

    Dwi Nur Rachmah

    2017-12-01

    Full Text Available Jigsaw learning as a cooperative learning method, according to the results of some studies, can improve academic skills, social competence, behavior in learning, and motivation to learn. However, in some other studies, there are different findings regarding the effect of jigsaw learning method on self-efficacy. The purpose of this study is to examine the effects of jigsaw learning method on self-efficacy and motivation to learn in psychology students at the Faculty of Medicine, Universitas Lambung Mangkurat. The method used in the study is the experimental method using one group pre-test and post-test design. The results of the measurements before and after the use of jigsaw learning method were compared using paired samples t-test. The results showed that there is a difference in students’ self-efficacy and motivation to learn before and after subjected to the treatments; therefore, it can be said that jigsaw learning method had significant effects on self-efficacy and motivation to learn. The application of jigsaw learning model in a classroom with large number of students was the discussion of this study.

  13. Time-trends in method-specific suicide rates compared with the availability of specific compounds

    DEFF Research Database (Denmark)

    Nordentoft, Merete; Qin, Ping; Helweg-Larsen, Karin

    2006-01-01

    Restriction of means for suicide is an important part of suicide preventive strategies in different countries. All suicides in Denmark between 1970 and 2000 were examined with regard to method used for suicide. Overall suicide mortality and method-specific suicide mortality was compared...... in the number of suicides by self-poisoning with these compounds. Restricted access occurred concomittantly with a 55% decrease in suicide rate....

  14. A Swarm-Based Learning Method Inspired by Social Insects

    Science.gov (United States)

    He, Xiaoxian; Zhu, Yunlong; Hu, Kunyuan; Niu, Ben

    Inspired by cooperative transport behaviors of ants, on the basis of Q-learning, a new learning method, Neighbor-Information-Reference (NIR) learning method, is present in the paper. This is a swarm-based learning method, in which principles of swarm intelligence are strictly complied with. In NIR learning, the i-interval neighbor's information, namely its discounted reward, is referenced when an individual selects the next state, so that it can make the best decision in a computable local neighborhood. In application, different policies of NIR learning are recommended by controlling the parameters according to time-relativity of concrete tasks. NIR learning can remarkably improve individual efficiency, and make swarm more "intelligent".

  15. Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation.

    Science.gov (United States)

    Roy, Snehashis; He, Qing; Sweeney, Elizabeth; Carass, Aaron; Reich, Daniel S; Prince, Jerry L; Pham, Dzung L

    2015-09-01

    Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting where different tissues are expected to be located, and a hard segmentation. Unlike most atlas-based classification methods that require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject-specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole-brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available state-of-the art approaches.

  16. Arts-based Methods and Organizational Learning

    DEFF Research Database (Denmark)

    This thematic volume explores the relationship between the arts and learning in various educational contexts and across cultures, but with a focus on higher education and organizational learning. Arts-based interventions are at the heart of this volume, which addresses how they are conceived, des...

  17. Reasons and Methods to Learn the Management

    Science.gov (United States)

    Li, Hongxin; Ding, Mengchun

    2010-01-01

    Reasons for learning the management include (1) perfecting the knowledge structure, (2) the management is the base of all organizations, (3) one person may be the manager or the managed person, (4) the management is absolutely not simple knowledge, and (5) the learning of the theoretical knowledge of the management can not be replaced by the…

  18. PYRAMID METHOD OF DISTANCE LEARNING IN HIGER EDUCATION

    Directory of Open Access Journals (Sweden)

    Дмитрий Васильевич Сенашенко

    2017-12-01

    Full Text Available The article deals with modern methods of distance learning in the corporate sector. On the specifics of the application of the described methods is their classification and be subject to review their specific differences based on the features and applications of these techniques given the characteristics of the organization of teaching in higher education, a conclusion about their preferred sides, which can be used in distance education. Later in the article, taking into account the above factors, it is proposed an innovative method of formation of educational programs. In view of the similarity of the rendered appearance of the pyramids, this technique proposed name “pyramid”. Offered by the authors, this technique is best synthesis of the best features of the previously described in the article for the online teaching methods. In the future, we are given a detailed description and conducted a preliminary analysis of the applicability of this technique to the training process in the Russian Federation. The analysis describes the eight alleged authors of distance education problems of high school that this method can help to solve.

  19. Aligning professional skills and active learning methods: an application for information and communications technology engineering

    Science.gov (United States)

    Llorens, Ariadna; Berbegal-Mirabent, Jasmina; Llinàs-Audet, Xavier

    2017-07-01

    Engineering education is facing new challenges to effectively provide the appropriate skills to future engineering professionals according to market demands. This study proposes a model based on active learning methods, which is expected to facilitate the acquisition of the professional skills most highly valued in the information and communications technology (ICT) market. The theoretical foundations of the study are based on the specific literature on active learning methodologies. The Delphi method is used to establish the fit between learning methods and generic skills required by the ICT sector. An innovative proposition is therefore presented that groups the required skills in relation to the teaching method that best develops them. The qualitative research suggests that a combination of project-based learning and the learning contract is sufficient to ensure a satisfactory skills level for this profile of engineers.

  20. One-trial overshadowing: Evidence for fast specific fear learning in humans.

    Science.gov (United States)

    Haesen, Kim; Beckers, Tom; Baeyens, Frank; Vervliet, Bram

    2017-03-01

    Adaptive defensive actions necessitate a fear learning system that is both fast and specific. Fast learning serves to minimize the number of threat confrontations, while specific learning ensures that the acquired fears are tied to threat-relevant cues only. In Pavlovian fear conditioning, fear acquisition is typically studied via repetitive pairings of a single cue with an aversive experience, which is not optimal for the examination of fast specific fear learning. In this study, we adopted the one-trial overshadowing procedure from basic learning research, in which a combination of two visual cues is presented once and paired with an aversive electrical stimulation. Using on-line shock expectancy ratings, skin conductance reactivity and startle reflex modulation as indices of fear learning, we found evidence of strong fear after a single conditioning trial (fast learning) as well as attenuated fear responding when only half of the trained stimulus combination was presented (specific learning). Moreover, specificity of fear responding tended to correlate with levels of state and trait anxiety. These results suggest that one-trial overshadowing can be used as a model to study fast specific fear learning in humans and individual differences therein. Copyright © 2016 Elsevier Ltd. All rights reserved.

  1. Question presentation methods for paired-associate learning

    NARCIS (Netherlands)

    Engel, F.L.; Geerings, M.P.W.

    1988-01-01

    Four different methods of question presentation, in interactive computeraided learning of Dutch-English word pairs are evaluated experimentally. These methods are: 1) the 'open-question method', 2) the 'multiple-choice method', 3) the 'sequential method' and 4) the 'true/ false method'. When

  2. SU-F-I-12: Region-Specific Dictionary Learning for Low-Dose X-Ray CT Reconstruction

    International Nuclear Information System (INIS)

    Xu, Q; Han, H; Xing, L

    2016-01-01

    Purpose: Dictionary learning based method has attracted more and more attentions in low-dose CT due to the superior performance on suppressing noise and preserving structural details. Considering the structures and noise vary from region to region in one imaging object, we propose a region-specific dictionary learning method to improve the low-dose CT reconstruction. Methods: A set of normal-dose images was used for dictionary learning. Segmentations were performed on these images, so that the training patch sets corresponding to different regions can be extracted out. After that, region-specific dictionaries were learned from these training sets. For the low-dose CT reconstruction, a conventional reconstruction, such as filtered back-projection (FBP), was performed firstly, and then segmentation was followed to segment the image into different regions. Sparsity constraints of each region based on its dictionary were used as regularization terms. The regularization parameters were selected adaptively according to different regions. A low-dose human thorax dataset was used to evaluate the proposed method. The single dictionary based method was performed for comparison. Results: Since the lung region is very different from the other part of thorax, two dictionaries corresponding to lung region and the rest part of thorax respectively were learned to better express the structural details and avoid artifacts. With only one dictionary some artifact appeared in the body region caused by the spot atoms corresponding to the structures in the lung region. And also some structure in the lung regions cannot be recovered well by only one dictionary. The quantitative indices of the result by the proposed method were also improved a little compared to the single dictionary based method. Conclusion: Region-specific dictionary can make the dictionary more adaptive to different region characteristics, which is much desirable for enhancing the performance of dictionary learning

  3. SU-F-I-12: Region-Specific Dictionary Learning for Low-Dose X-Ray CT Reconstruction

    Energy Technology Data Exchange (ETDEWEB)

    Xu, Q; Han, H; Xing, L [Stanford University School of Medicine, Stanford, CA (United States)

    2016-06-15

    Purpose: Dictionary learning based method has attracted more and more attentions in low-dose CT due to the superior performance on suppressing noise and preserving structural details. Considering the structures and noise vary from region to region in one imaging object, we propose a region-specific dictionary learning method to improve the low-dose CT reconstruction. Methods: A set of normal-dose images was used for dictionary learning. Segmentations were performed on these images, so that the training patch sets corresponding to different regions can be extracted out. After that, region-specific dictionaries were learned from these training sets. For the low-dose CT reconstruction, a conventional reconstruction, such as filtered back-projection (FBP), was performed firstly, and then segmentation was followed to segment the image into different regions. Sparsity constraints of each region based on its dictionary were used as regularization terms. The regularization parameters were selected adaptively according to different regions. A low-dose human thorax dataset was used to evaluate the proposed method. The single dictionary based method was performed for comparison. Results: Since the lung region is very different from the other part of thorax, two dictionaries corresponding to lung region and the rest part of thorax respectively were learned to better express the structural details and avoid artifacts. With only one dictionary some artifact appeared in the body region caused by the spot atoms corresponding to the structures in the lung region. And also some structure in the lung regions cannot be recovered well by only one dictionary. The quantitative indices of the result by the proposed method were also improved a little compared to the single dictionary based method. Conclusion: Region-specific dictionary can make the dictionary more adaptive to different region characteristics, which is much desirable for enhancing the performance of dictionary learning

  4. Efficient model learning methods for actor-critic control.

    Science.gov (United States)

    Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik

    2012-06-01

    We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.

  5. Individual differences in implicit motor learning: task specificity in sensorimotor adaptation and sequence learning.

    Science.gov (United States)

    Stark-Inbar, Alit; Raza, Meher; Taylor, Jordan A; Ivry, Richard B

    2017-01-01

    In standard taxonomies, motor skills are typically treated as representative of implicit or procedural memory. We examined two emblematic tasks of implicit motor learning, sensorimotor adaptation and sequence learning, asking whether individual differences in learning are correlated between these tasks, as well as how individual differences within each task are related to different performance variables. As a prerequisite, it was essential to establish the reliability of learning measures for each task. Participants were tested twice on a visuomotor adaptation task and on a sequence learning task, either the serial reaction time task or the alternating reaction time task. Learning was evident in all tasks at the group level and reliable at the individual level in visuomotor adaptation and the alternating reaction time task but not in the serial reaction time task. Performance variability was predictive of learning in both domains, yet the relationship was in the opposite direction for adaptation and sequence learning. For the former, faster learning was associated with lower variability, consistent with models of sensorimotor adaptation in which learning rates are sensitive to noise. For the latter, greater learning was associated with higher variability and slower reaction times, factors that may facilitate the spread of activation required to form predictive, sequential associations. Interestingly, learning measures of the different tasks were not correlated. Together, these results oppose a shared process for implicit learning in sensorimotor adaptation and sequence learning and provide insight into the factors that account for individual differences in learning within each task domain. We investigated individual differences in the ability to implicitly learn motor skills. As a prerequisite, we assessed whether individual differences were reliable across test sessions. We found that two commonly used tasks of implicit learning, visuomotor adaptation and the

  6. Ensemble Machine Learning Methods and Applications

    CERN Document Server

    Ma, Yunqian

    2012-01-01

    It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face detection and are now being applied in areas as diverse as object trackingand bioinformatics.   Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including various contributions from researchers in leading industrial research labs. At once a solid theoretical study and a practical guide, the volume is a windfall for r...

  7. Specific activity measurement of 64Cu: A comparison of methods

    International Nuclear Information System (INIS)

    Mastren, Tara; Guthrie, James; Eisenbeis, Paul; Voller, Tom; Mebrahtu, Efrem; Robertson, J. David; Lapi, Suzanne E.

    2014-01-01

    Effective specific activity of 64 Cu (amount of radioactivity per µmol metal) is important in order to determine purity of a particular 64 Cu lot and to assist in optimization of the purification process. Metal impurities can affect effective specific activity and therefore it is important to have a simple method that can measure trace amounts of metals. This work shows that ion chromatography (IC) yields similar results to ICP mass spectrometry for copper, nickel and iron contaminants in 64 Cu production solutions. - Highlights: • Comparison of TETA titration, ICP mass spectrometry, and ion chromatography to measure specific activity. • Validates ion chromatography by using ICP mass spectrometry as the “gold standard”. • Shows different types and amounts of metal impurities present in 64 Cu

  8. A novel affinity purification method to isolate peptide specific antibodies

    DEFF Research Database (Denmark)

    Karlsen, Alan E; Lernmark, A; Kofod, Hans

    1990-01-01

    Site-specific, high affinity polyclonal antisera are effectively and successfully produced by immunizing rabbits with synthetic peptides. The use of these antisera in subsequent immune analysis is often limited because of non-specific binding. We describe a new and simple method to effectively...... affinity-purify anti-peptide antibodies. To test our system, rabbits were immunized with model peptides representing sequences of the putative rabbit growth hormone receptor and several HLA-DQ beta-chain molecules. Polystyrene plastic beads were coated with peptides. Immune serum was incubated...... with the beads and after a wash step the bound antibodies were eluted in 1 M acetic acid. The eluted material was composed predominantly of intact immunoglobulin as evidenced by the presence of heavy and light chain bands in SDS-PAGE. The eluted antibodies were peptide specific in ELISA and bound only to intact...

  9. Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries.

    Science.gov (United States)

    Ma, Xiao H; Jia, Jia; Zhu, Feng; Xue, Ying; Li, Ze R; Chen, Yu Z

    2009-05-01

    Machine learning methods have been explored as ligand-based virtual screening tools for facilitating drug lead discovery. These methods predict compounds of specific pharmacodynamic, pharmacokinetic or toxicological properties based on their structure-derived structural and physicochemical properties. Increasing attention has been directed at these methods because of their capability in predicting compounds of diverse structures and complex structure-activity relationships without requiring the knowledge of target 3D structure. This article reviews current progresses in using machine learning methods for virtual screening of pharmacodynamically active compounds from large compound libraries, and analyzes and compares the reported performances of machine learning tools with those of structure-based and other ligand-based (such as pharmacophore and clustering) virtual screening methods. The feasibility to improve the performance of machine learning methods in screening large libraries is discussed.

  10. Formal Methods for Abstract Specifications – A Comparison of Concepts

    DEFF Research Database (Denmark)

    Instenberg, Martin; Schneider, Axel; Schnetter, Sabine

    2006-01-01

    In industry formal methods are becoming increasingly important for the verification of hardware and software designs. However current practice for specification of system and protocol functionality on high level of abstraction is textual description. For verification of the system behavior manual...... inspections and tests are usual means. To facilitate the introduction of formal methods in the development process of complex systems and protocols, two different tools evolved from research activities – UPPAAL and SpecEdit – have been investigated and compared regarding their concepts and functionality...

  11. Methods for Optimizing CRISPR-Cas9 Genome Editing Specificity

    Science.gov (United States)

    Tycko, Josh; Myer, Vic E.; Hsu, Patrick D.

    2016-01-01

    Summary Advances in the development of delivery, repair, and specificity strategies for the CRISPR-Cas9 genome engineering toolbox are helping researchers understand gene function with unprecedented precision and sensitivity. CRISPR-Cas9 also holds enormous therapeutic potential for the treatment of genetic disorders by directly correcting disease-causing mutations. Although the Cas9 protein has been shown to bind and cleave DNA at off-target sites, the field of Cas9 specificity is rapidly progressing with marked improvements in guide RNA selection, protein and guide engineering, novel enzymes, and off-target detection methods. We review important challenges and breakthroughs in the field as a comprehensive practical guide to interested users of genome editing technologies, highlighting key tools and strategies for optimizing specificity. The genome editing community should now strive to standardize such methods for measuring and reporting off-target activity, while keeping in mind that the goal for specificity should be continued improvement and vigilance. PMID:27494557

  12. Adaptive e-learning methods and IMS Learning Design. An integrated approach

    NARCIS (Netherlands)

    Burgos, Daniel; Specht, Marcus

    2006-01-01

    Please, cite this publication as: Burgos, D., & Specht, M. (2006). Adaptive e-learning methods and IMS Learning Design. In Kinshuk, R. Koper, P. Kommers, P. Kirschner, D. G. Sampson & W. Didderen (Eds.), Proceedings of the 6th IEEE International Conference on Advanced Learning Technologies (pp.

  13. An Innovative Teaching Method To Promote Active Learning: Team-Based Learning

    Science.gov (United States)

    Balasubramanian, R.

    2007-12-01

    Traditional teaching practice based on the textbook-whiteboard- lecture-homework-test paradigm is not very effective in helping students with diverse academic backgrounds achieve higher-order critical thinking skills such as analysis, synthesis, and evaluation. Consequently, there is a critical need for developing a new pedagogical approach to create a collaborative and interactive learning environment in which students with complementary academic backgrounds and learning skills can work together to enhance their learning outcomes. In this presentation, I will discuss an innovative teaching method ('Team-Based Learning (TBL)") which I recently developed at National University of Singapore to promote active learning among students in the environmental engineering program with learning abilities. I implemented this new educational activity in a graduate course. Student feedback indicates that this pedagogical approach is appealing to most students, and promotes active & interactive learning in class. Data will be presented to show that the innovative teaching method has contributed to improved student learning and achievement.

  14. Reduced autobiographical memory specificity is associated with impaired discrimination learning in anxiety disorder patients

    Science.gov (United States)

    Lenaert, Bert; Boddez, Yannick; Vervliet, Bram; Schruers, Koen; Hermans, Dirk

    2015-01-01

    Associative learning plays an important role in the development of anxiety disorders, but a thorough understanding of the variables that impact such learning is still lacking. We investigated whether individual differences in autobiographical memory specificity are related to discrimination learning and generalization. In an associative learning task, participants learned the association between two pictures of female faces and a non-aversive outcome. Subsequently, six morphed pictures functioning as generalization stimuli (GSs) were introduced. In a sample of healthy participants (Study 1), we did not find evidence for differences in discrimination learning as a function of memory specificity. In a sample of anxiety disorder patients (Study 2), individuals who were characterized by low memory specificity showed deficient discrimination learning relative to high specific individuals. In contrast to previous findings, results revealed no effect of memory specificity on generalization. These results indicate that impaired discrimination learning, previously shown in patients suffering from an anxiety disorder, may be—in part—due to limited memory specificity. Together, these studies emphasize the importance of incorporating cognitive variables in associative learning theories and their implications for the development of anxiety disorders. In addition, re-analyses of the data (Study 3) showed that patients suffering from panic disorder showed higher outcome expectancies in the presence of the stimulus that was never followed by an outcome during discrimination training, relative to patients suffering from other anxiety disorders and healthy participants. Because we used a neutral, non-aversive outcome (i.e., drawing of a lightning bolt), these data suggest that learning abnormalities in panic disorder may not be restricted to fear learning, but rather reflect a more general associative learning deficit that also manifests in fear irrelevant contexts. PMID

  15. Deep kernel learning method for SAR image target recognition

    Science.gov (United States)

    Chen, Xiuyuan; Peng, Xiyuan; Duan, Ran; Li, Junbao

    2017-10-01

    With the development of deep learning, research on image target recognition has made great progress in recent years. Remote sensing detection urgently requires target recognition for military, geographic, and other scientific research. This paper aims to solve the synthetic aperture radar image target recognition problem by combining deep and kernel learning. The model, which has a multilayer multiple kernel structure, is optimized layer by layer with the parameters of Support Vector Machine and a gradient descent algorithm. This new deep kernel learning method improves accuracy and achieves competitive recognition results compared with other learning methods.

  16. A Review on Different Virtual Learning Methods in Pharmacy Education

    Directory of Open Access Journals (Sweden)

    Amin Noori

    2015-10-01

    Full Text Available Virtual learning is a type of electronic learning system based on the web. It models traditional in- person learning by providing virtual access to classes, tests, homework, feedbacks and etc. Students and teachers can interact through chat rooms or other virtual environments. Web 2.0 services are usually used for this method. Internet audio-visual tools, multimedia systems, a disco CD-ROMs, videotapes, animation, video conferencing, and interactive phones can all be used to deliver data to the students. E-learning can occur in or out of the classroom. It is time saving with lower costs compared to traditional methods. It can be self-paced, it is suitable for distance learning and it is flexible. It is a great learning style for continuing education and students can independently solve their problems but it has its disadvantages too. Thereby, blended learning (combination of conventional and virtual education is being used worldwide and has improved knowledge, skills and confidence of pharmacy students.The aim of this study is to review, discuss and introduce different methods of virtual learning for pharmacy students.Google scholar, Pubmed and Scupus databases were searched for topics related to virtual, electronic and blended learning and different styles like computer simulators, virtual practice environment technology, virtual mentor, virtual patient, 3D simulators, etc. are discussed in this article.Our review on different studies on these areas shows that the students are highly satisfied withvirtual and blended types of learning.

  17. [Detection and specific studies in procedural learning difficulties].

    Science.gov (United States)

    Magallón, S; Narbona, J

    2009-02-27

    The main disabilities in non-verbal learning disorder (NLD) are: the acquisition and automating of motor and cognitive processes, visual spatial integration, motor coordination, executive functions, difficulty in comprehension of the context, and social skills. AIMS. To review the research to date on NLD, and to discuss whether the term 'procedural learning disorder' (PLD) would be more suitable to refer to NLD. A considerable amount of research suggests a neurological correlate of PLD with dysfunctions in the 'posterior' attention system, or the right hemisphere, or the cerebellum. Even if it is said to be difficult the delimitation between NLD and other disorders or syndromes like Asperger syndrome, certain characteristics contribute to differential diagnosis. Intervention strategies for the PLD must lead to the development of motor automatisms and problem solving strategies, including social skills. The basic dysfunction in NLD affects to implicit learning of routines, automating of motor skills and cognitive strategies that spare conscious resources in daily behaviours. These limitations are partly due to a dysfunction in non-declarative procedural memory. Various dimensions of language are also involved: context comprehension, processing of the spatial and emotional indicators of verbal language, language inferences, prosody, organization of the inner speech, use of language and non-verbal communication; this is why the diagnostic label 'PLD' would be more appropriate, avoiding the euphemistic adjective 'non-verbal'.

  18. A Comparison between the Effect of Cooperative Learning Teaching Method and Lecture Teaching Method on Students' Learning and Satisfaction Level

    Science.gov (United States)

    Mohammadjani, Farzad; Tonkaboni, Forouzan

    2015-01-01

    The aim of the present research is to investigate a comparison between the effect of cooperative learning teaching method and lecture teaching method on students' learning and satisfaction level. The research population consisted of all the fourth grade elementary school students of educational district 4 in Shiraz. The statistical population…

  19. Unsupervised process monitoring and fault diagnosis with machine learning methods

    CERN Document Server

    Aldrich, Chris

    2013-01-01

    This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data

  20. Generalized framework for context-specific metabolic model extraction methods

    Directory of Open Access Journals (Sweden)

    Semidán eRobaina Estévez

    2014-09-01

    Full Text Available Genome-scale metabolic models are increasingly applied to investigate the physiology not only of simple prokaryotes, but also eukaryotes, such as plants, characterized with compartmentalized cells of multiple types. While genome-scale models aim at including the entirety of known metabolic reactions, mounting evidence has indicated that only a subset of these reactions is active in a given context, including: developmental stage, cell type, or environment. As a result, several methods have been proposed to reconstruct context-specific models from existing genome-scale models by integrating various types of high-throughput data. Here we present a mathematical framework that puts all existing methods under one umbrella and provides the means to better understand their functioning, highlight similarities and differences, and to help users in selecting a most suitable method for an application.

  1. Application of machine learning methods in bioinformatics

    Science.gov (United States)

    Yang, Haoyu; An, Zheng; Zhou, Haotian; Hou, Yawen

    2018-05-01

    Faced with the development of bioinformatics, high-throughput genomic technology have enabled biology to enter the era of big data. [1] Bioinformatics is an interdisciplinary, including the acquisition, management, analysis, interpretation and application of biological information, etc. It derives from the Human Genome Project. The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets.[2]. This paper analyzes and compares various algorithms of machine learning and their applications in bioinformatics.

  2. Are students' impressions of improved learning through active learning methods reflected by improved test scores?

    Science.gov (United States)

    Everly, Marcee C

    2013-02-01

    To report the transformation from lecture to more active learning methods in a maternity nursing course and to evaluate whether student perception of improved learning through active-learning methods is supported by improved test scores. The process of transforming a course into an active-learning model of teaching is described. A voluntary mid-semester survey for student acceptance of the new teaching method was conducted. Course examination results, from both a standardized exam and a cumulative final exam, among students who received lecture in the classroom and students who had active learning activities in the classroom were compared. Active learning activities were very acceptable to students. The majority of students reported learning more from having active-learning activities in the classroom rather than lecture-only and this belief was supported by improved test scores. Students who had active learning activities in the classroom scored significantly higher on a standardized assessment test than students who received lecture only. The findings support the use of student reflection to evaluate the effectiveness of active-learning methods and help validate the use of student reflection of improved learning in other research projects. Copyright © 2011 Elsevier Ltd. All rights reserved.

  3. Interference in Ballistic Motor Learning: Specificity and Role of Sensory Error Signals

    Science.gov (United States)

    Lundbye-Jensen, Jesper; Petersen, Tue Hvass; Rothwell, John C.; Nielsen, Jens Bo

    2011-01-01

    Humans are capable of learning numerous motor skills, but newly acquired skills may be abolished by subsequent learning. Here we ask what factors determine whether interference occurs in motor learning. We speculated that interference requires competing processes of synaptic plasticity in overlapping circuits and predicted specificity. To test this, subjects learned a ballistic motor task. Interference was observed following subsequent learning of an accuracy-tracking task, but only if the competing task involved the same muscles and movement direction. Interference was not observed from a non-learning task suggesting that interference requires competing learning. Subsequent learning of the competing task 4 h after initial learning did not cause interference suggesting disruption of early motor memory consolidation as one possible mechanism underlying interference. Repeated transcranial magnetic stimulation (rTMS) of corticospinal motor output at intensities below movement threshold did not cause interference, whereas suprathreshold rTMS evoking motor responses and (re)afferent activation did. Finally, the experiments revealed that suprathreshold repetitive electrical stimulation of the agonist (but not antagonist) peripheral nerve caused interference. The present study is, to our knowledge, the first to demonstrate that peripheral nerve stimulation may cause interference. The finding underscores the importance of sensory feedback as error signals in motor learning. We conclude that interference requires competing plasticity in overlapping circuits. Interference is remarkably specific for circuits involved in a specific movement and it may relate to sensory error signals. PMID:21408054

  4. Visual Perceptual Learning and its Specificity and Transfer: A New Perspective

    Directory of Open Access Journals (Sweden)

    Cong Yu

    2011-05-01

    Full Text Available Visual perceptual learning is known to be location and orientation specific, and is thus assumed to reflect the neuronal plasticity in the early visual cortex. However, in recent studies we created “Double training” and “TPE” procedures to demonstrate that these “fundamental” specificities of perceptual learning are in some sense artifacts and that learning can completely transfer to a new location or orientation. We proposed a rule-based learning theory to reinterpret perceptual learning and its specificity and transfer: A high-level decision unit learns the rules of performing a visual task through training. However, the learned rules cannot be applied to a new location or orientation automatically because the decision unit cannot functionally connect to new visual inputs with sufficient strength because these inputs are unattended or even suppressed during training. It is double training and TPE training that reactivate these new inputs, so that the functional connections can be strengthened to enable rule application and learning transfer. Currently we are investigating the properties of perceptual learning free from the bogus specificities, and the results provide some preliminary but very interesting insights into how training reshapes the functional connections between the high-level decision units and sensory inputs in the brain.

  5. Learning strategies and general cognitive ability as predictors of gender- specific academic achievement.

    Science.gov (United States)

    Ruffing, Stephanie; Wach, F-Sophie; Spinath, Frank M; Brünken, Roland; Karbach, Julia

    2015-01-01

    Recent research has revealed that learning behavior is associated with academic achievement at the college level, but the impact of specific learning strategies on academic success as well as gender differences therein are still not clear. Therefore, the aim of this study was to investigate gender differences in the incremental contribution of learning strategies over general cognitive ability in the prediction of academic achievement. The relationship between these variables was examined by correlation analyses. A set of t-tests was used to test for gender differences in learning strategies, whereas structural equation modeling as well as multi-group analyses were applied to investigate the incremental contribution of learning strategies for male and female students' academic performance. The sample consisted of 461 students (mean age = 21.2 years, SD = 3.2). Correlation analyses revealed that general cognitive ability as well as the learning strategies effort, attention, and learning environment were positively correlated with academic achievement. Gender differences were found in the reported application of many learning strategies. Importantly, the prediction of achievement in structural equation modeling revealed that only effort explained incremental variance (10%) over general cognitive ability. Results of multi-group analyses showed no gender differences in this prediction model. This finding provides further knowledge regarding gender differences in learning research and the specific role of learning strategies for academic achievement. The incremental assessment of learning strategy use as well as gender-differences in their predictive value contributes to the understanding and improvement of successful academic development.

  6. Specific developmental disorders. The language-learning continuum.

    Science.gov (United States)

    Swank, L K

    1999-01-01

    The goal of this article is to inform and educate those who work with children who present with language-learning disorders about phonologic processing deficits, because this area has been shown to have a significant impact on children and adults who exhibit reading disabilities. Mental health professionals who work with children with reading problems need to be aware of what is known about this source of reading disorders and the implications of this knowledge for prevention and treatment. Advocating for appropriate instruction for children with reading problems is an important role mental health professionals can play in working with this population.

  7. IMS Learning Design Specification (version 1.0)

    NARCIS (Netherlands)

    Koper, Rob; Olivier, Bill; Anderson, Thor

    2003-01-01

    Information Model is the core document with the actual specification, the other documents and schema's are derived from the Information Model. When you see any inconsistency between documents, look at the Information Model for the correct interpretation.

  8. Nuclear data for specific problems. Part 1: Methods

    International Nuclear Information System (INIS)

    Leszczynski, Francisco

    1999-01-01

    The growing volume of basic nuclear data, methods and codes for processing these data, and the wide variety of problems where these data and codes are required, oblige to have an efficient system for managing all this information. In this work we present a new methodology for nuclear data processing, applied to neutron and photon transport calculations for specific problems. The base of the new methodology is the analysis of the requirements, following the chain: Problem-Components-Materials-Elements-Isotopes-Process-Tests-Final product (a library with processed data). This order is the inverse of the normal order followed up to date where, for performing a specific calculation, the first step is the choice of an existing data library for general purposes, without the previous steps of pre-processing data, and tests of the final library. Then, the used data are limited to the isotope content of this library, and the adaptation of material compositions and components to the data availability is necessary , performing finally the required calculations in a rather approximated form, depending on the available data. An interactive computer program for PC , is developed, for managing all the information generated by nuclear data processing, with the additional advantage of having a help tool for performing the needed analysis, before processing data calculations for specific applications. These analyses are based on the particular characteristics of each application, and the processed information of previous cases, is stored in conveniently designed data bases for an easy inspection of its contents. By means of an example of application of the new method, in this paper the methods of analysis and calculations and the tools used (computer programs, data bases and documents) are describes. (author)

  9. The learning continuum of ecology based on teachers' opinion about student's level of competence and specific pedagogical learning material

    Science.gov (United States)

    Pramesti, Indah Cahaya; Subali, Bambang

    2017-08-01

    This study aims at designing learning continuum for developing a curriculum based on teachers' opinion about student's level of competence and specific pedagogical learning material on ecological aspect targeted for students of Primary and Secondary Education. This research is a descriptive research using survey methods. The researchers conducted a census by distributing questionnaires that had been validated from the aspects of construct validity and experts judgements to 147 natural science teachers at junior high school and 134 Biology teachers at senior high school as a population throughout 4 regencies and 1 city in Yogyakarta Special Region.. Data analysis techniques used descriptive analysis. In conclusion, teacher's opinion is influenced by curriculum that exist today. According to the opinions of Natural Science teachers at Junior High School, most of the ecological aspects such as characteristics of biomes, characteristics of ecosystems, characteristics of communities, characteristics of populations, etc. should be taught in grade VII with the level of competence: to understand (C2), while Biology teachers at Senior High School state that the ecological aspect should be taught in class X with the level of competence: to understand (C2), apply (C3) and analyze (C4). Teachers should be a privy in the formulation of the curriculum, so they're not only accept and apply the existing curriculum but also give opinions to improve the curriculum, especially in terms of ecology.

  10. Exploring gender differences on general and specific computer self-efficacy in mobile learning adoption

    OpenAIRE

    Bao, Yukun; Xiong, Tao; Hu, Zhongyi; Kibelloh, Mboni

    2014-01-01

    Reasons for contradictory findings regarding the gender moderate effect on computer self-efficacy in the adoption of e-learning/mobile learning are limited. Recognizing the multilevel nature of the computer self-efficacy (CSE), this study attempts to explore gender differences in the adoption of mobile learning, by extending the Technology Acceptance Model (TAM) with general and specific CSE. Data collected from 137 university students were tested against the research model using the structur...

  11. The Effect of Task-based Teaching on Incidental Vocabulary Learning in English for Specific Purposes

    OpenAIRE

    FALLAHRAFIE, Zahra; RAHMANY, Ramin; SADEGHI, Bahador

    2015-01-01

    Abstract. Learning vocabulary is an essential part of language learning linking the four skills of speaking, listening, reading and writing together. This paper considers the incidental vocabulary teaching and learning within the framework of task-based activities in the hope of improving learners’ vocabulary acquiring in English for Specific Purposes courses (ESP), concentrating on Mechanical Engineering students at Islamic Azad University of Hashtgerd, Iran. A total number of 55 male and fe...

  12. Comparison of executive functions in students with and without specific learning disability with the characteristic reading and writing

    Directory of Open Access Journals (Sweden)

    Saba Hasanvandi

    2017-03-01

    Full Text Available Background: The aim of present study was to investigate executive functions included of working memory, organization-planning and reasoning in the children with and without specific learning disability with the characteristic reading and writing. Materials and methods: The design of this research was Ex-Post Facto design. Statistical population was all male students of third grade primary schools in Tehran which were referred to education institution with diagnosis special learning disorders in educational centers. The sample included of 90 students chosen and assigned into 3 groups of 30 students, included of: children who had specific learning disability with characteristic reading, children who had specific learning disability with characteristic writing, normal children were selected by systematic randomized sampling and 3 groups were compared. The data instruments were: Wechsler’ subtests of similarities and digit differences, Andre Ray test, in formal (unofficial reading and dictation test. The obtained data were analyzed with ANOVA. Results: The results showed that there was difference between the group of normal children and other group in executive functions including working memory, organization-planning and reasoning (P<0.05. Also there was difference between two children groups with specific learning disability with  characteristic reading and writing in working memory and reasoning, whereas for organization-planning parameter there were not seen any differences between these two groups (P<0.05. Conclusion: Regarding to obtained results, it is recommended to adoption some ways for improvements of working memory, organization-planning and reasoning

  13. Interference in ballistic motor learning: specificity and role of sensory error signals

    DEFF Research Database (Denmark)

    Lundbye-Jensen, Jesper; Petersen, Tue Hvass; Rothwell, John C

    2011-01-01

    Humans are capable of learning numerous motor skills, but newly acquired skills may be abolished by subsequent learning. Here we ask what factors determine whether interference occurs in motor learning. We speculated that interference requires competing processes of synaptic plasticity in overlap......Humans are capable of learning numerous motor skills, but newly acquired skills may be abolished by subsequent learning. Here we ask what factors determine whether interference occurs in motor learning. We speculated that interference requires competing processes of synaptic plasticity...... in overlapping circuits and predicted specificity. To test this, subjects learned a ballistic motor task. Interference was observed following subsequent learning of an accuracy-tracking task, but only if the competing task involved the same muscles and movement direction. Interference was not observed from a non......-learning task suggesting that interference requires competing learning. Subsequent learning of the competing task 4 h after initial learning did not cause interference suggesting disruption of early motor memory consolidation as one possible mechanism underlying interference. Repeated transcranial magnetic...

  14. Generating method-specific Reference Ranges - A harmonious outcome?

    Science.gov (United States)

    Lee, Graham R; Griffin, Alison; Halton, Kieran; Fitzgibbon, Maria C

    2017-12-01

    When laboratory Reference Ranges (RR) do not reflect analytical methodology, result interpretation can cause misclassification of patients and inappropriate management. This can be mitigated by determining and implementing method-specific RRs, which was the main objective of this study. Serum was obtained from healthy volunteers (Male + Female, n > 120) attending hospital health-check sessions during June and July 2011. Pseudo-anonymised aliquots were stored (at - 70 °C) prior t° analysis on Abbott ARCHITECT c16000 chemistry and i 2000SR immunoassay analysers. Data were stratified by gender where appropriate. Outliers were excluded statistically (Tukey method) to generate non-parametric RRs (2.5th + 97.5th percentiles). RRs were compared to those quoted by Abbott and UK Pathology Harmony (PH) where possible. For 7 selected tests, RRs were verified using a data mining approach. For chemistry tests (n = 23), Upper or Lower Reference Limits (LRL or URL) were > 20% different from Abbott ranges in 25% of tests (11% from PH ranges) but in 38% for immunoassay tests (n = 13). RRs (mmol/L) for sodium (138-144), potassium (3.8-4.9) and chloride (102-110) were considerably narrower than PH ranges (133-146, 3.5-5.0 and 95-108, respectively). The gender difference for ferritin (M: 29-441, F: 8-193 ng/mL) was more pronounced than reported by Abbott (M: 22-275, F: 5-204 ng/mL). Verification studies showed good agreement for chemistry tests (mean [SD] difference = 0.4% [1.2%]) but less so for immunoassay tests (27% [29%]), particularly for TSH (LRL). Where resource permits, we advocate using method-specific RRs in preference to other sources, particularly where method bias and lack of standardisation limits RR transferability and harmonisation.

  15. An Analytical framework of social learning facilitated by participatory methods

    NARCIS (Netherlands)

    Scholz, G.; Dewulf, A.; Pahl-Wostl, C.

    2014-01-01

    Social learning among different stakeholders is often a goal in problem solving contexts such as environmental management. Participatory methods (e.g., group model-building and role playing games) are frequently assumed to stimulate social learning. Yet understanding if and why this assumption is

  16. Blended learning – integrating E-learning with traditional learning methods in teaching basic medical science

    OpenAIRE

    J.G. Bagi; N.K. Hashilkar

    2014-01-01

    Background: Blended learning includes an integration of face to face classroom learning with technology enhanced online material. It provides the convenience, speed and cost effectiveness of e-learning with the personal touch of traditional learning. Objective: The objective of the present study was to assess the effectiveness of a combination of e-learning module and traditional teaching (Blended learning) as compared to traditional teaching alone to teach acid base homeostasis to Phase I MB...

  17. Learning Methods for Radial Basis Functions Networks

    Czech Academy of Sciences Publication Activity Database

    Neruda, Roman; Kudová, Petra

    2005-01-01

    Roč. 21, - (2005), s. 1131-1142 ISSN 0167-739X R&D Projects: GA ČR GP201/03/P163; GA ČR GA201/02/0428 Institutional research plan: CEZ:AV0Z10300504 Keywords : radial basis function networks * hybrid supervised learning * genetic algorithms * benchmarking Subject RIV: BA - General Mathematics Impact factor: 0.555, year: 2005

  18. Machine Learning Methods to Predict Diabetes Complications.

    Science.gov (United States)

    Dagliati, Arianna; Marini, Simone; Sacchi, Lucia; Cogni, Giulia; Teliti, Marsida; Tibollo, Valentina; De Cata, Pasquale; Chiovato, Luca; Bellazzi, Riccardo

    2018-03-01

    One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients. Such pipeline comprises clinical center profiling, predictive model targeting, predictive model construction and model validation. After having dealt with missing data by means of random forest (RF) and having applied suitable strategies to handle class imbalance, we have used Logistic Regression with stepwise feature selection to predict the onset of retinopathy, neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years from the first visit at the Hospital Center for Diabetes (not from the diagnosis). Considered variables are gender, age, time from diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), hypertension, and smoking habit. Final models, tailored in accordance with the complications, provided an accuracy up to 0.838. Different variables were selected for each complication and time scenario, leading to specialized models easy to translate to the clinical practice.

  19. Kernel-Based Learning for Domain-Specific Relation Extraction

    Science.gov (United States)

    Basili, Roberto; Giannone, Cristina; Del Vescovo, Chiara; Moschitti, Alessandro; Naggar, Paolo

    In a specific process of business intelligence, i.e. investigation on organized crime, empirical language processing technologies can play a crucial role. The analysis of transcriptions on investigative activities, such as police interrogatories, for the recognition and storage of complex relations among people and locations is a very difficult and time consuming task, ultimately based on pools of experts. We discuss here an inductive relation extraction platform that opens the way to much cheaper and consistent workflows. The presented empirical investigation shows that accurate results, comparable to the expert teams, can be achieved, and parametrization allows to fine tune the system behavior for fitting domain-specific requirements.

  20. Periodic local MP2 method employing orbital specific virtuals

    International Nuclear Information System (INIS)

    Usvyat, Denis; Schütz, Martin; Maschio, Lorenzo

    2015-01-01

    We introduce orbital specific virtuals (OSVs) to represent the truncated pair-specific virtual space in periodic local Møller-Plesset perturbation theory of second order (LMP2). The OSVs are constructed by diagonalization of the LMP2 amplitude matrices which correspond to diagonal Wannier-function (WF) pairs. Only a subset of these OSVs is adopted for the subsequent OSV-LMP2 calculation, namely, those with largest contribution to the diagonal pair correlation energy and with the accumulated value of these contributions reaching a certain accuracy. The virtual space for a general (non diagonal) pair is spanned by the union of the two OSV sets related to the individual WFs of the pair. In the periodic LMP2 method, the diagonal LMP2 amplitude matrices needed for the construction of the OSVs are calculated in the basis of projected atomic orbitals (PAOs), employing very large PAO domains. It turns out that the OSVs are excellent to describe short range correlation, yet less appropriate for long range van der Waals correlation. In order to compensate for this bias towards short range correlation, we augment the virtual space spanned by the OSVs by the most diffuse PAOs of the corresponding minimal PAO domain. The Fock and overlap matrices in OSV basis are constructed in the reciprocal space. The 4-index electron repulsion integrals are calculated by local density fitting and, for distant pairs, via multipole approximation. New procedures for determining the fit-domains and the distant-pair lists, leading to higher efficiency in the 4-index integral evaluation, have been implemented. Generally, and in contrast to our previous PAO based periodic LMP2 method, the OSV-LMP2 method does not require anymore great care in the specification of the individual domains (to get a balanced description when calculating energy differences) and is in that sense a black box procedure. Discontinuities in potential energy surfaces, which may occur for PAO-based calculations if one is not

  1. Periodic local MP2 method employing orbital specific virtuals

    Energy Technology Data Exchange (ETDEWEB)

    Usvyat, Denis, E-mail: denis.usvyat@chemie.uni-regensburg.de; Schütz, Martin, E-mail: martin.schuetz@chemie.uni-regensburg.de [Institute for Physical and Theoretical Chemistry, Universität Regensburg, Universitätsstraße 31, D-93040 Regensburg (Germany); Maschio, Lorenzo, E-mail: lorenzo.maschio@unito.it [Dipartimento di Chimica, and Centre of Excellence NIS (Nanostructured Interfaces and Surfaces), Università di Torino, via Giuria 5, I-10125 Torino (Italy)

    2015-09-14

    We introduce orbital specific virtuals (OSVs) to represent the truncated pair-specific virtual space in periodic local Møller-Plesset perturbation theory of second order (LMP2). The OSVs are constructed by diagonalization of the LMP2 amplitude matrices which correspond to diagonal Wannier-function (WF) pairs. Only a subset of these OSVs is adopted for the subsequent OSV-LMP2 calculation, namely, those with largest contribution to the diagonal pair correlation energy and with the accumulated value of these contributions reaching a certain accuracy. The virtual space for a general (non diagonal) pair is spanned by the union of the two OSV sets related to the individual WFs of the pair. In the periodic LMP2 method, the diagonal LMP2 amplitude matrices needed for the construction of the OSVs are calculated in the basis of projected atomic orbitals (PAOs), employing very large PAO domains. It turns out that the OSVs are excellent to describe short range correlation, yet less appropriate for long range van der Waals correlation. In order to compensate for this bias towards short range correlation, we augment the virtual space spanned by the OSVs by the most diffuse PAOs of the corresponding minimal PAO domain. The Fock and overlap matrices in OSV basis are constructed in the reciprocal space. The 4-index electron repulsion integrals are calculated by local density fitting and, for distant pairs, via multipole approximation. New procedures for determining the fit-domains and the distant-pair lists, leading to higher efficiency in the 4-index integral evaluation, have been implemented. Generally, and in contrast to our previous PAO based periodic LMP2 method, the OSV-LMP2 method does not require anymore great care in the specification of the individual domains (to get a balanced description when calculating energy differences) and is in that sense a black box procedure. Discontinuities in potential energy surfaces, which may occur for PAO-based calculations if one is not

  2. Placement and Achievement of Urban Hispanic Middle Schoolers with Specific Learning Disabilities

    Science.gov (United States)

    Barrocas, Lisa; Cramer, Elizabeth D.

    2014-01-01

    This study examined achievement gains in reading and math for Hispanic middle school students with specific learning disabilities in inclusive versus segregated settings in a large urban school district. The authors report learning gains for students with and without disabilities in inclusive versus segregated settings. Results indicate no…

  3. Principals' Perceptions of Instructional Leadership for Middle School Students of Color with Specific Learning Disabilities

    Science.gov (United States)

    Shannon-Luster, Beverly

    2013-01-01

    Instructional leadership is the most important responsibility for principals and the most vulnerable students in need of productive instructional leadership are students of color with specific learning disabilities. Instructional leaders are challenged with creating supportive learning environments and school cultures that promotes the education…

  4. Understanding Impulsivity among Children with Specific Learning Disabilities in Inclusion Schools

    Science.gov (United States)

    Al-Dababneh, Kholoud Adeeb; Al-Zboon, Eman K.

    2018-01-01

    Impulsive behavior is a characteristic of children with specific learning disabilities (SLD), and is related to learning ability. The present study aims to identify impulsivity behavior in children with SLD who attend inclusion schools, from their resource room teachers' perspectives. A 31-item questionnaire that addressed four subscales was…

  5. Are Language Learning Websites Special? Towards a Research Agenda for Discipline-Specific Usability

    Science.gov (United States)

    Shield, Lesley; Kukulska-Hulme, Agnes

    2006-01-01

    With the intention of defining an initial research agenda for discipline-specific factors in the usability of e-learning websites, this article focuses on the example of foreign language learning. First, general notions and concepts of usability are analyzed, and the term "pedagogical usability" is proposed as a means of focusing on the close…

  6. Context Fear Learning Specifically Activates Distinct Populations of Neurons in Amygdala and Hypothalamus

    Science.gov (United States)

    Trogrlic, Lidia; Wilson, Yvette M.; Newman, Andrew G.; Murphy, Mark

    2011-01-01

    The identity and distribution of neurons that are involved in any learning or memory event is not known. In previous studies, we identified a discrete population of neurons in the lateral amygdala that show learning-specific activation of a c-"fos"-regulated transgene following context fear conditioning. Here, we have extended these studies to…

  7. Identifying Learning Patterns of Children at Risk for Specific Reading Disability

    Science.gov (United States)

    Barbot, Baptiste; Krivulskaya, Suzanna; Hein, Sascha; Reich, Jodi; Thuma, Philip E.; Grigorenko, Elena L.

    2016-01-01

    Differences in learning patterns of vocabulary acquisition in children at risk (+SRD) and not at risk (-SRD) for Specific Reading Disability (SRD) were examined using a microdevelopmental paradigm applied to the multi-trial Foreign Language Learning Task (FLLT; Baddeley et al., 1995). The FLLT was administered to 905 children from rural…

  8. D-Cycloserine reduces context specificity of sexual extinction learning

    NARCIS (Netherlands)

    Brom, M.; Laan, E.; Everaerd, W.; Spinhoven, P.; Trimbos, B.; Both, S.

    2015-01-01

    BACKGROUND: d-Cycloserine (DCS) enhances extinction processes in animals. Although classical conditioning is hypothesized to play a pivotal role in the aetiology of appetitive motivation problems, no research has been conducted on the effect of DCS on the reduction of context specificity of

  9. d-Cycloserine reduces context specificity of sexual extinction learning

    NARCIS (Netherlands)

    Brom, Mirte; Laan, Ellen; Everaerd, Walter; Spinhoven, Philip; Trimbos, Baptist; Both, Stephanie

    2015-01-01

    d-Cycloserine (DCS) enhances extinction processes in animals. Although classical conditioning is hypothesized to play a pivotal role in the aetiology of appetitive motivation problems, no research has been conducted on the effect of DCS on the reduction of context specificity of extinction in human

  10. Working Memory Deficits in Children with Specific Learning Disorders

    Science.gov (United States)

    Schuchardt, Kirsten; Maehler, Claudia; Hasselhorn, Marcus

    2008-01-01

    This article examines working memory functioning in children with specific developmental disorders of scholastic skills as defined by ICD-10. Ninety-seven second to fourth graders with a minimum IQ of 80 are compared using a 2 x 2 factorial (dyscalculia vs. no dyscalculia; dyslexia vs. no dyslexia) design. An extensive test battery assesses the…

  11. Lessons learned applying CASE methods/tools to Ada software development projects

    Science.gov (United States)

    Blumberg, Maurice H.; Randall, Richard L.

    1993-01-01

    This paper describes the lessons learned from introducing CASE methods/tools into organizations and applying them to actual Ada software development projects. This paper will be useful to any organization planning to introduce a software engineering environment (SEE) or evolving an existing one. It contains management level lessons learned, as well as lessons learned in using specific SEE tools/methods. The experiences presented are from Alpha Test projects established under the STARS (Software Technology for Adaptable and Reliable Systems) project. They reflect the front end efforts by those projects to understand the tools/methods, initial experiences in their introduction and use, and later experiences in the use of specific tools/methods and the introduction of new ones.

  12. Self-Esteem among Boys with and without Specific Learning Disabilities.

    Science.gov (United States)

    Bingham, Grace

    1980-01-01

    The self-esteem of 120 males with and without specific learning disabilities, at each of two levels of development (preadolescent and adolescent) was measured using Coopersmith Self-esteem Inventory. (MP)

  13. Learning in Non-Stationary Environments Methods and Applications

    CERN Document Server

    Lughofer, Edwin

    2012-01-01

    Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences.   Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dyna...

  14. Methodical Specifics of Thermal Experiments with Thin Carbon Reinforced Plates

    Directory of Open Access Journals (Sweden)

    O. V. Denisov

    2015-01-01

    Full Text Available Polymer composite materials (CM are widely used in creation of large space constructions, especially reflectors of space antennas. Composite materials should provide high level of specific stiffness and strength for space structures. Thermal conductivity in reinforcement plane is a significant factor in case of irregular heating space antennas. Nowadays, data on CM reinforcement plane thermal conductivity are limited and existing methods of its defining are imperfect. Basically, traditional methods allow us to define thermal conductivity in perpendicular direction towards the reinforcement plane on the samples of round or rectangular plate. In addition, the thickness of standard samples is larger than space antenna thickness. Consequently, new methods are required. Method of contact heating, which was developed by BMSTU specialists with long hollow carbon beam, could be a perspective way. This article is devoted to the experimental method of contact heating on the thin carbon plates.Thermal tests were supposed to provide a non-stationary temperature field with a gradient being co-directional with the plane reinforcement in the material sample. Experiments were conducted in vacuum chamber to prevent unstructured convection. Experimental thermo-grams processing were calculated by 1-d thermal model for a thin plate. Influence of uncertainty of experimental parameters, such as (radiation emission coefficients of sample surface, glue, temperature sensors and uncertainty of sensors placement on the result of defined thermal conductivity has been estimated. New data on the thermal conductivity in reinforcement plane were obtained within 295 - 375 K temperature range, which can be used to design and develop reflectors of precision space antennas. In the future it is expedient to conduct tests of thin-wall plates from carbon fiber-reinforced plastic in wide temperature range, especially in the low-range temperatures.

  15. Comparative Analysis of Kernel Methods for Statistical Shape Learning

    National Research Council Canada - National Science Library

    Rathi, Yogesh; Dambreville, Samuel; Tannenbaum, Allen

    2006-01-01

    .... In this work, we perform a comparative analysis of shape learning techniques such as linear PCA, kernel PCA, locally linear embedding and propose a new method, kernelized locally linear embedding...

  16. Implementing Adaptive Educational Methods with IMS Learning Design

    NARCIS (Netherlands)

    Specht, Marcus; Burgos, Daniel

    2006-01-01

    Please, cite this publication as: Specht, M. & Burgos, D. (2006). Implementing Adaptive Educational Methods with IMS Learning Design. Proceedings of Adaptive Hypermedia. June, Dublin, Ireland. Retrieved June 30th, 2006, from http://dspace.learningnetworks.org

  17. Specific Deficit in Implicit Motor Sequence Learning following Spinal Cord Injury.

    Directory of Open Access Journals (Sweden)

    Ayala Bloch

    Full Text Available Physical and psychosocial rehabilitation following spinal cord injury (SCI leans heavily on learning and practicing new skills. However, despite research relating motor sequence learning to spinal cord activity and clinical observations of impeded skill-learning after SCI, implicit procedural learning following spinal cord damage has not been examined.To test the hypothesis that spinal cord injury (SCI in the absence of concomitant brain injury is associated with a specific implicit motor sequence learning deficit that cannot be explained by depression or impairments in other cognitive measures.Ten participants with SCI in T1-T11, unharmed upper limb motor and sensory functioning, and no concomitant brain injury were compared to ten matched control participants on measures derived from the serial reaction time (SRT task, which was used to assess implicit motor sequence learning. Explicit generation of the SRT sequence, depression, and additional measures of learning, memory, and intelligence were included to explore the source and specificity of potential learning deficits.There was no between-group difference in baseline reaction time, indicating that potential differences between the learning curves of the two groups could not be attributed to an overall reduction in response speed in the SCI group. Unlike controls, the SCI group showed no decline in reaction time over the first six blocks of the SRT task and no advantage for the initially presented sequence over the novel interference sequence. Meanwhile, no group differences were found in explicit learning, depression, or any additional cognitive measures.The dissociation between impaired implicit learning and intact declarative memory represents novel empirical evidence of a specific implicit procedural learning deficit following SCI, with broad implications for rehabilitation and adjustment.

  18. Specific binding-adsorbent assay method and test means

    International Nuclear Information System (INIS)

    1981-01-01

    A description is given of an improved specific binding assay method and test means employing a nonspecific adsorbent for the substance to be determined, particularly hepatitis B surface (HBsub(s)) antigen, in its free state or additionally in the form of its immune complex. The invention is illustrated by 1) the radioimmunoadsorbent assay for HBsub(s) antigen, 2) the radioimmunoadsorbent assay for HBsub(s) antigen in the form of immune complex with antibody, 3) a study of adsorption characteristics of various anion exchange materials for HBsub(s) antigen, 4) the use of hydrophobic adsorbents in a radioimmunoadsorbent assay for HBsub(s) antigen and 5) the radioimmunoadsorbent assay for antibody to HBsub(s) antigen. The advantages of the present method for detecting HBsub(s) antigen compared to previous methods include the manufacturing advantages of eliminating the need for insolubilised anti-HBsub(s) and the advantages of a single incubation step, fewer manipulations, storability of adsorbent materials, increased sensitivity and versatility of detecting HBsub(s) antigen in the form of its immune complex if desired. (U.K.)

  19. Implementation of Active Learning Method in Unit Operations II Subject

    OpenAIRE

    Ma'mun, Sholeh

    2018-01-01

    ABSTRACT: Active Learning Method which requires students to take an active role in the process of learning in the classroom has been applied in Department of Chemical Engineering, Faculty of Industrial Technology, Islamic University of Indonesia for Unit Operations II subject in the Even Semester of Academic Year 2015/2016. The purpose of implementation of the learning method is to assist students in achieving competencies associated with the Unit Operations II subject and to help in creating...

  20. Studying depression using imaging and machine learning methods

    Directory of Open Access Journals (Sweden)

    Meenal J. Patel

    2016-01-01

    Full Text Available Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1 presents a background on depression, imaging, and machine learning methodologies; (2 reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3 suggests directions for future depression-related studies.

  1. Studying depression using imaging and machine learning methods.

    Science.gov (United States)

    Patel, Meenal J; Khalaf, Alexander; Aizenstein, Howard J

    2016-01-01

    Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.

  2. Incorporating deep learning with convolutional neural networks and position specific scoring matrices for identifying electron transport proteins.

    Science.gov (United States)

    Le, Nguyen-Quoc-Khanh; Ho, Quang-Thai; Ou, Yu-Yen

    2017-09-05

    In several years, deep learning is a modern machine learning technique using in a variety of fields with state-of-the-art performance. Therefore, utilization of deep learning to enhance performance is also an important solution for current bioinformatics field. In this study, we try to use deep learning via convolutional neural networks and position specific scoring matrices to identify electron transport proteins, which is an important molecular function in transmembrane proteins. Our deep learning method can approach a precise model for identifying of electron transport proteins with achieved sensitivity of 80.3%, specificity of 94.4%, and accuracy of 92.3%, with MCC of 0.71 for independent dataset. The proposed technique can serve as a powerful tool for identifying electron transport proteins and can help biologists understand the function of the electron transport proteins. Moreover, this study provides a basis for further research that can enrich a field of applying deep learning in bioinformatics. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  3. Working memory and novel word learning in children with hearing impairment and children with specific language impairment.

    Science.gov (United States)

    Hansson, K; Forsberg, J; Löfqvist, A; Mäki-Torkko, E; Sahlén, B

    2004-01-01

    Working memory is considered to influence a range of linguistic skills, i.e. vocabulary acquisition, sentence comprehension and reading. Several studies have pointed to limitations of working memory in children with specific language impairment. Few studies, however, have explored the role of working memory for language deficits in children with hearing impairment. The first aim was to compare children with mild-to-moderate bilateral sensorineural hearing impairment, children with a preschool diagnosis of specific language impairment and children with normal language development, aged 9-12 years, for language and working memory. The special focus was on the role of working memory in learning new words for primary school age children. The assessment of working memory included tests of phonological short-term memory and complex working memory. Novel word learning was assessed according to the methods of. In addition, a range of language tests was used to assess language comprehension, output phonology and reading. Children with hearing impairment performed significantly better than children with a preschool diagnosis of specific language impairment on tasks assessing novel word learning, complex working memory, sentence comprehension and reading accuracy. No significant correlation was found between phonological short-term memory and novel word learning in any group. The best predictor of novel word learning in children with specific language impairment and in children with hearing impairment was complex working memory. Furthermore, there was a close relationship between complex working memory and language in children with a preschool diagnosis of specific language impairment but not in children with hearing impairment. Complex working memory seems to play a significant role in vocabulary acquisition in primary school age children. The interpretation is that the results support theories suggesting a weakened influence of phonological short-term memory on novel word

  4. Working memory deficits in children with specific learning disorders.

    Science.gov (United States)

    Schuchardt, Kirsten; Maehler, Claudia; Hasselhorn, Marcus

    2008-01-01

    This article examines working memory functioning in children with specific developmental disorders of scholastic skills as defined by ICD-10. Ninety-seven second to fourth graders with a minimum IQ of 80 are compared using a 2 x 2 factorial (dyscalculia vs. no dyscalculia; dyslexia vs. no dyslexia) design. An extensive test battery assesses the three subcomponents of working memory described by Baddeley (1986): phonological loop, visual-spatial sketchpad, and central executive. Children with dyscalculia show deficits in visual-spatial memory; children with dyslexia show deficits in phonological and central executive functioning. When controlling for the influence of the phonological loop on the performance of the central executive, however, the effect is no longer significant. Although children with both reading and arithmetic disorders are consistently outperformed by all other groups, there is no significant interaction between the factors dyscalculia and dyslexia.

  5. Domain-specific and domain-general constraints on word and sequence learning.

    Science.gov (United States)

    Archibald, Lisa M D; Joanisse, Marc F

    2013-02-01

    The relative influences of language-related and memory-related constraints on the learning of novel words and sequences were examined by comparing individual differences in performance of children with and without specific deficits in either language or working memory. Children recalled lists of words in a Hebbian learning protocol in which occasional lists repeated, yielding improved recall over the course of the task on the repeated lists. The task involved presentation of pictures of common nouns followed immediately by equivalent presentations of the spoken names. The same participants also completed a paired-associate learning task involving word-picture and nonword-picture pairs. Hebbian learning was observed for all groups. Domain-general working memory constrained immediate recall, whereas language abilities impacted recall in the auditory modality only. In addition, working memory constrained paired-associate learning generally, whereas language abilities disproportionately impacted novel word learning. Overall, all of the learning tasks were highly correlated with domain-general working memory. The learning of nonwords was additionally related to general intelligence, phonological short-term memory, language abilities, and implicit learning. The results suggest that distinct associations between language- and memory-related mechanisms support learning of familiar and unfamiliar phonological forms and sequences.

  6. Active teaching methods, studying responses and learning

    DEFF Research Database (Denmark)

    Christensen, Hans Peter; Vigild, Martin Etchells; Thomsen, Erik Vilain

    Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching.......Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching....

  7. Methods of reducing non-specific adsorption in microfluidic biosensors

    International Nuclear Information System (INIS)

    Choi, Seokheun; Chae, Junseok

    2010-01-01

    Non-specific adsorption (NSA) of biomolecules is a persistent challenge in microfluidic biosensors. Microfluidic biosensors often have immobilized bioreceptors such as antibodies, enzymes, DNAs, etc, via linker molecules such as SAMs (self-assembled monolayers) to enhance immobilization. However, the linker molecules are very susceptible to NSA, causing false responses and decreasing sensitivity. In this paper, we present design methods to reduce the NSA of alkanethiol SAMs, which are popular linker molecules on microfluidic biosensors. Three design parameters were studied for two different chain-length SAMs (n = 2 and 10): (i) SAM incubation time, (ii) surface roughness [0.8 nm and 4.4 nm RMS (root mean square)] and (iii) gold crystal re-growth along (1 1 1) the target orientation. NSA was monitored by surface plasmon resonance (SPR). The results suggest that increased SAM incubation time reduces NSA, and that short-chain SAMs respond more favorably than the long-chain SAMs. Both SAMs were shown to be sensitive to surface roughness, and long-chain SAMs reduced NSA by 75%. Gold crystal re-growth along (1 1 1) the target orientation profoundly reduced NSA on the short-chain SAM. On a gold surface where surface roughness was 0.8 nm and there was strong directional alignment along the (1 1 1) gold crystal, final concentrations of nonspecifically bound proteins were 0.05 ng mm −2 (fibrinogen) and 0.075 ng mm −2 (lysozyme)—significantly lower than other known methods. The results show that optimizing three parameters (SAM incubation time, gold surface roughness and gold crystal orientation) improved SAM sensitivity for fibrinogen–anti-fibrinogen conjugates by a factor of 5 in 2.94 pM, suggesting that the methods are effective for reducing NSA in microfluidic biosensors.

  8. Developing a Blended Learning-Based Method for Problem-Solving in Capability Learning

    Science.gov (United States)

    Dwiyogo, Wasis D.

    2018-01-01

    The main objectives of the study were to develop and investigate the implementation of blended learning based method for problem-solving. Three experts were involved in the study and all three had stated that the model was ready to be applied in the classroom. The implementation of the blended learning-based design for problem-solving was…

  9. SMALL GROUP LEARNING METHODS AND THEIR EFFECT ON LEARNERS’ RELATIONSHIPS

    Directory of Open Access Journals (Sweden)

    Radka Borůvková

    2016-04-01

    Full Text Available Building relationships in the classroom is an essential part of any teacher's career. Having healthy teacher-to-learner and learner-to-learner relationships is an effective way to help prevent pedagogical failure, social conflict and quarrelsome behavior. Many strategies are available that can be used to achieve good long-lasting relationships in the classroom setting. Successful teachers’ pedagogical work in the classroom requires detailed knowledge of learners’ relationships. Good understanding of the relationships is necessary, especially in the case of teenagers’ class. This sensitive period of adolescence demands attention of all teachers who should deal with the problems of their learners. Special care should be focused on children that are out of their classmates’ interest (so called isolated learners or isolates in such class and on possibilities to integrate them into the class. Natural idea how to do it is that of using some modern non-traditional teaching/learning methods, especially the methods based on work in small groups involving learners’ cooperation. Small group education (especially problem-based learning, project-based learning, cooperative learning, collaborative learning or inquire-based learning as one of these methods involves a high degree of interaction. The effectiveness of learning groups is determined by the extent to which the interaction enables members to clarify their own understanding, build upon each other's contributions, sift out meanings, ask and answer questions. An influence of this kind of methods (especially cooperative learning (CL on learners’ relationships was a subject of the further described research. Within the small group education, students work with their classmates to solve complex and authentic problems that help develop content knowledge as well as problem-solving, reasoning, communication, and self-assessment skills. The aim of the research was to answer the question: Can the

  10. Computerization of Hungarian reforestation manual with machine learning methods

    Science.gov (United States)

    Czimber, Kornél; Gálos, Borbála; Mátyás, Csaba; Bidló, András; Gribovszki, Zoltán

    2017-04-01

    Hungarian forests are highly sensitive to the changing climate, especially to the available precipitation amount. Over the past two decades several drought damages were observed for tree species which are in the lower xeric limit of their distribution. From year to year these affected forest stands become more difficult to reforest with the same native species because these are not able to adapt to the increasing probability of droughts. The climate related parameter set of the Hungarian forest stand database needs updates. Air humidity that was formerly used to define the forest climate zones is not measured anymore and its value based on climate model outputs is highly uncertain. The aim was to develop a novel computerized and objective method to describe the species-specific climate conditions that is essential for survival, growth and optimal production of the forest ecosystems. The method is expected to project the species spatial distribution until 2100 on the basis of regional climate model simulations. Until now, Hungarian forest managers have been using a carefully edited spreadsheet for reforestation purposes. Applying binding regulations this spreadsheet prescribes the stand-forming and admixed tree species and their expected growth rate for each forest site types. We are going to present a new machine learning based method to replace the former spreadsheet. We took into great consideration of various methods, such as maximum likelihood, Bayesian networks, Fuzzy logic. The method calculates distributions, setups classification, which can be validated and modified by experts if necessary. Projected climate change conditions makes necessary to include into this system an additional climate zone that does not exist in our region now, as well as new options for potential tree species. In addition to or instead of the existing ones, the influence of further limiting parameters (climatic extremes, soil water retention) are also investigated. Results will be

  11. Human reinforcement learning subdivides structured action spaces by learning effector-specific values

    OpenAIRE

    Gershman, Samuel J.; Pesaran, Bijan; Daw, Nathaniel D.

    2009-01-01

    Humans and animals are endowed with a large number of effectors. Although this enables great behavioral flexibility, it presents an equally formidable reinforcement learning problem of discovering which actions are most valuable, due to the high dimensionality of the action space. An unresolved question is how neural systems for reinforcement learning – such as prediction error signals for action valuation associated with dopamine and the striatum – can cope with this “curse of dimensionality...

  12. Think Pair Share (TPS as Method to Improve Student’s Learning Motivation and Learning Achievement

    Directory of Open Access Journals (Sweden)

    Hetika Hetika

    2018-03-01

    Full Text Available This research aims to find out the application of Think Pair Share (TPS learning method in improving learning motivation and learning achievement in the subject of Introduction to Accounting I of the Accounting Study Program students of Politeknik Harapan Bersama. The Method of data collection in this study used observation method, test method, and documentation method. The research instruments used observation sheet, questionnaire and test question. This research used Class Action Research Design which is an action implementation oriented research, with the aim of improving quality or problem solving in a group by carefully and observing the success rate due to the action. The method of analysis used descriptive qualitative and quantitative analysis method. The results showed that the application of Think Pair Share Learning (TPS Method can improve the Learning Motivation and Achievement. Before the implementation of the action, the obtained score is 67% then in the first cycle increases to 72%, and in the second cycle increasws to 80%. In addition, based on questionnaires distributed to students, it also increases the score of Accounting Learning Motivation where the score in the first cycle of 76% increases to 79%. In addition, in the first cycle, the score of pre test and post test of the students has increased from 68.86 to 76.71 while in the second cycle the score of pre test and post test of students has increased from 79.86 to 84.86.

  13. Projection specificity in heterogeneous locus coeruleus cell populations: implications for learning and memory

    Science.gov (United States)

    Uematsu, Akira; Tan, Bao Zhen

    2015-01-01

    Noradrenergic neurons in the locus coeruleus (LC) play a critical role in many functions including learning and memory. This relatively small population of cells sends widespread projections throughout the brain including to a number of regions such as the amygdala which is involved in emotional associative learning and the medial prefrontal cortex which is important for facilitating flexibility when learning rules change. LC noradrenergic cells participate in both of these functions, but it is not clear how this small population of neurons modulates these partially distinct processes. Here we review anatomical, behavioral, and electrophysiological studies to assess how LC noradrenergic neurons regulate these different aspects of learning and memory. Previous work has demonstrated that subpopulations of LC noradrenergic cells innervate specific brain regions suggesting heterogeneity of function in LC neurons. Furthermore, noradrenaline in mPFC and amygdala has distinct effects on emotional learning and cognitive flexibility. Finally, neural recording data show that LC neurons respond during associative learning and when previously learned task contingencies change. Together, these studies suggest a working model in which distinct and potentially opposing subsets of LC neurons modulate particular learning functions through restricted efferent connectivity with amygdala or mPFC. This type of model may provide a general framework for understanding other neuromodulatory systems, which also exhibit cell type heterogeneity and projection specificity. PMID:26330494

  14. Deep learning methods for protein torsion angle prediction.

    Science.gov (United States)

    Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

    2017-09-18

    Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

  15. Investigation of distinctive characteristics of children with specific learning disorder and borderline intellectual functioning

    Directory of Open Access Journals (Sweden)

    Selcuk Ozkan

    Full Text Available Abstract Background Borderline intelligence function (BIF and specific learning disorder (SLD are common diagnoses in children who are brought up for learning problems and school failure. Objective The aim of our study was to determine whether there were distinctive aspects of cognitive testing routinely used in evaluating SLD and BIF and investigate emotion regulation skills and minor neurologic symptoms. Method Sixty children (30 SLD and 30 BIF who are currently attending primary school are selected for study. Visual Aural Digit Span Test – Form B, Gessel Figure Drawing Test, Bender Gestalt Visual Motor Perception Test, WISC-R, Emotion Regulation Scale (ERS and Neurological Evaluation Scale (NES was administered. Results There was no statistically significant difference between groups in cognitive tests. The emotional regulation ability measured by the emotional regulation subscale was better in the SLD group than the BIF group (p = 0.014. In the NES, sensory integration (p = 0.008, motor coordination (p = 0.047 and other (p < 0.001 subscales showed higher scores in the BIF group. Discussion It has been shown that cognitive tests don’t have distinguishing features in the evaluation of SLD and BIF. Emotion regulation subscale score of ERS and sensory integration, motor coordination, and total scores of NES can be used in both discrimination of groups.

  16. TEACHING METHODS IN MBA AND LIFELONG LEARNING PROGRAMMES FOR MANAGERS

    Directory of Open Access Journals (Sweden)

    Jarošová, Eva

    2017-09-01

    Full Text Available Teaching methods in MBA and Lifelong Learning Programmes (LLP for managers should be topically relevant in terms of content as well as the teaching methods used. In terms of the content, the integral part of MBA and Lifelong Learning Programmes for managers should be the development of participants’ leadership competencies and their understanding of current leadership concepts. The teaching methods in educational programmes for managers as adult learners should correspond to the strategy of learner-centred teaching that focuses on the participants’ learning process and their active involvement in class. The focus on the participants’ learning process also raises questions about whether the programme’s participants perceive the teaching methods used as useful and relevant for their development as leaders. The paper presents the results of the analysis of the responses to these questions in a sample of 54 Czech participants in the MBA programme and of lifelong learning programmes at the University of Economics, Prague. The data was acquired based on written or electronically submitted questionnaires. The data was analysed in relation to the usefulness of the teaching methods for understanding the concepts of leadership, leadership skills development as well as respondents’ personal growth. The results show that the respondents most valued the methods that enabled them to get feedback, activated them throughout the programme and got them involved in discussions with others in class. Implications for managerial education practices are discussed.

  17. Statistical word learning in children with autism spectrum disorder and specific language impairment.

    Science.gov (United States)

    Haebig, Eileen; Saffran, Jenny R; Ellis Weismer, Susan

    2017-11-01

    Word learning is an important component of language development that influences child outcomes across multiple domains. Despite the importance of word knowledge, word-learning mechanisms are poorly understood in children with specific language impairment (SLI) and children with autism spectrum disorder (ASD). This study examined underlying mechanisms of word learning, specifically, statistical learning and fast-mapping, in school-aged children with typical and atypical development. Statistical learning was assessed through a word segmentation task and fast-mapping was examined in an object-label association task. We also examined children's ability to map meaning onto newly segmented words in a third task that combined exposure to an artificial language and a fast-mapping task. Children with SLI had poorer performance on the word segmentation and fast-mapping tasks relative to the typically developing and ASD groups, who did not differ from one another. However, when children with SLI were exposed to an artificial language with phonemes used in the subsequent fast-mapping task, they successfully learned more words than in the isolated fast-mapping task. There was some evidence that word segmentation abilities are associated with word learning in school-aged children with typical development and ASD, but not SLI. Follow-up analyses also examined performance in children with ASD who did and did not have a language impairment. Children with ASD with language impairment evidenced intact statistical learning abilities, but subtle weaknesses in fast-mapping abilities. As the Procedural Deficit Hypothesis (PDH) predicts, children with SLI have impairments in statistical learning. However, children with SLI also have impairments in fast-mapping. Nonetheless, they are able to take advantage of additional phonological exposure to boost subsequent word-learning performance. In contrast to the PDH, children with ASD appear to have intact statistical learning, regardless of

  18. FLIPPED CLASSROOM LEARNING METHOD TO IMPROVE CARING AND LEARNING OUTCOME IN FIRST YEAR NURSING STUDENT

    Directory of Open Access Journals (Sweden)

    Ni Putu Wulan Purnama Sari

    2017-08-01

    Full Text Available Background and Purpose: Caring is the essence of nursing profession. Stimulation of caring attitude should start early. Effective teaching methods needed to foster caring attitude and improve learning achievement. This study aimed to explain the effect of applying flipped classroom learning method for improving caring attitude and learning achievement of new student nurses at nursing institutions in Surabaya. Method: This is a pre-experimental study using the one group pretest posttest and posttest only design. Population was all new student nurses on nursing institutions in Surabaya. Inclusion criteria: female, 18-21 years old, majoring in nursing on their own volition and being first choice during students selection process, status were active in the even semester of 2015/2016 academic year. Sample size was 67 selected by total sampling. Variables: 1 independent: application of flipped classroom learning method; 2 dependent: caring attitude, learning achievement. Instruments: teaching plan, assignment descriptions, presence list, assignment assessment rubrics, study materials, questionnaires of caring attitude. Data analysis: paired and one sample t test. Ethical clearance was available. Results: Most respondents were 20 years old (44.8%, graduated from high school in Surabaya (38.8%, living with parents (68.7% in their homes (64.2%. All data were normally distributed. Flipped classroom learning method could improve caring attitude by 4.13%. Flipped classroom learning method was proved to be effective for improving caring attitude (p=0.021 and learning achievement (p=0.000. Conclusion and Recommendation: Flipped classroom was effective for improving caring attitude and learning achievement of new student nurse. It is recommended to use mix-method and larger sample for further study.

  19. Science Learning Cycle Method to Enhance the Conceptual Understanding and the Learning Independence on Physics Learning

    Science.gov (United States)

    Sulisworo, Dwi; Sutadi, Novitasari

    2017-01-01

    There have been many studies related to the implementation of cooperative learning. However, there are still many problems in school related to the learning outcomes on science lesson, especially in physics. The aim of this study is to observe the application of science learning cycle (SLC) model on improving scientific literacy for secondary…

  20. Approximation methods for efficient learning of Bayesian networks

    CERN Document Server

    Riggelsen, C

    2008-01-01

    This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.

  1. Experts in Teams – An experiential learning method

    DEFF Research Database (Denmark)

    Johansen, Steffen Kjær

    2017-01-01

    T becomes a learning method rather than a teaching method. Besides discussing the pedagogical characteristics of EiT, the study also gives a general introduction to EiT as it was taught at SDU fall 2016 as well as a brief review of the basic theory behind experiential learning. As such this study serves...... courses. Most of the practical courses are group work along the lines of project based learning. EiT is in a way both. It is a practical course in as much as our students get hands-on experience with interdisciplinary team work and innovation processes. EiT is a theoretical course in as much as our...... both as an introduction to e.g. new teachers of EiT but also as a starting point for a clarification of the features that makes EiT an experiential learning endeavor....

  2. Research on demand-oriented Business English learning method

    Directory of Open Access Journals (Sweden)

    Zhou Yuan

    2016-01-01

    Full Text Available Business English is integrated with visual-audio-oral English, which focuses on the application for English listening and speaking skills in common business occasions, and acquire business knowledge and improve skills through English. This paper analyzes the Business English Visual-audio-oral Course, and learning situation of higher vocational students’ learning objectives, interests, vocabulary, listening and speaking, and focuses on the research of effective methods to guide the higher vocational students to learn Business English Visual-audio-oral Course, master Business English knowledge, and improve communicative competence of Business English.

  3. Statistical Learning in Specific Language Impairment and Autism Spectrum Disorder: A Meta-Analysis

    Directory of Open Access Journals (Sweden)

    Rita Obeid

    2016-08-01

    Full Text Available Impairments in statistical learning might be a common deficit among individuals with Specific Language Impairment (SLI and Autism Spectrum Disorder (ASD. Using meta-analysis, we examined statistical learning in SLI (14 studies, 15 comparisons and ASD (13 studies, 20 comparisons to evaluate this hypothesis. Effect sizes were examined as a function of diagnosis across multiple statistical learning tasks (Serial Reaction Time, Contextual Cueing, Artificial Grammar Learning, Speech Stream, Observational Learning, Probabilistic Classification. Individuals with SLI showed deficits in statistical learning relative to age-matched controls g = .47, 95% CI [.28, .66], p < .001. In contrast, statistical learning was intact in individuals with ASD relative to controls, g = –.13, 95% CI [–.34, .08], p = .22. Effect sizes did not vary as a function of task modality or participant age. Our findings inform debates about overlapping social-communicative difficulties in children with SLI and ASD by suggesting distinct underlying mechanisms. In line with the procedural deficit hypothesis (Ullman & Pierpont, 2005, impaired statistical learning may account for phonological and syntactic difficulties associated with SLI. In contrast, impaired statistical learning fails to account for the social-pragmatic difficulties associated with ASD.

  4. Code-specific learning rules improve action selection by populations of spiking neurons.

    Science.gov (United States)

    Friedrich, Johannes; Urbanczik, Robert; Senn, Walter

    2014-08-01

    Population coding is widely regarded as a key mechanism for achieving reliable behavioral decisions. We previously introduced reinforcement learning for population-based decision making by spiking neurons. Here we generalize population reinforcement learning to spike-based plasticity rules that take account of the postsynaptic neural code. We consider spike/no-spike, spike count and spike latency codes. The multi-valued and continuous-valued features in the postsynaptic code allow for a generalization of binary decision making to multi-valued decision making and continuous-valued action selection. We show that code-specific learning rules speed up learning both for the discrete classification and the continuous regression tasks. The suggested learning rules also speed up with increasing population size as opposed to standard reinforcement learning rules. Continuous action selection is further shown to explain realistic learning speeds in the Morris water maze. Finally, we introduce the concept of action perturbation as opposed to the classical weight- or node-perturbation as an exploration mechanism underlying reinforcement learning. Exploration in the action space greatly increases the speed of learning as compared to exploration in the neuron or weight space.

  5. [Which learning methods are expected for ultrasound training? Blended learning on trial].

    Science.gov (United States)

    Röhrig, S; Hempel, D; Stenger, T; Armbruster, W; Seibel, A; Walcher, F; Breitkreutz, R

    2014-10-01

    Current teaching methods in graduate and postgraduate training often include frontal presentations. Especially in ultrasound education not only knowledge but also sensomotory and visual skills need to be taught. This requires new learning methods. This study examined which types of teaching methods are preferred by participants in ultrasound training courses before, during and after the course by analyzing a blended learning concept. It also investigated how much time trainees are willing to spend on such activities. A survey was conducted at the end of a certified ultrasound training course. Participants were asked to complete a questionnaire based on a visual analogue scale (VAS) in which three categories were defined: category (1) vote for acceptance with a two thirds majority (VAS 67-100%), category (2) simple acceptance (50-67%) and category (3) rejection (learning program with interactive elements, short presentations (less than 20 min), incorporating interaction with the audience, hands-on sessions in small groups, an alternation between presentations and hands-on-sessions, live demonstrations and quizzes. For post-course learning, interactive and media-assisted approaches were preferred, such as e-learning, films of the presentations and the possibility to stay in contact with instructors in order to discuss the results. Participants also voted for maintaining a logbook for documentation of results. The results of this study indicate the need for interactive learning concepts and blended learning activities. Directors of ultrasound courses may consider these aspects and are encouraged to develop sustainable learning pathways.

  6. Research on demand-oriented Business English learning method

    OpenAIRE

    Zhou Yuan

    2016-01-01

    Business English is integrated with visual-audio-oral English, which focuses on the application for English listening and speaking skills in common business occasions, and acquire business knowledge and improve skills through English. This paper analyzes the Business English Visual-audio-oral Course, and learning situation of higher vocational students’ learning objectives, interests, vocabulary, listening and speaking, and focuses on the research of effective methods to guide the higher voca...

  7. Future Competencies and Learning Methods in Engineering Education

    DEFF Research Database (Denmark)

    Kolmos, Anette

    2002-01-01

    What are the competencies for tommorow´s enginnering education and the implications of these regarding the choice of teaching content and learning methods? The paper analyses two trends: the traditional and the techo-science approach. These two trends are based on technological innovation...... and change processes and impact on educational content and methods....

  8. Empowering and Engaging Students in Learning Research Methods

    Science.gov (United States)

    Liu, Shuang; Breit, Rhonda

    2013-01-01

    The capacity to conduct research is essential for university graduates to survive and thrive in their future career. However, research methods courses have often been considered by students as "abstract", "uninteresting", and "hard". Thus, motivating students to engage in the process of learning research methods has become a crucial challenge for…

  9. Computer-enhanced visual learning method: a paradigm to teach and document surgical skills.

    Science.gov (United States)

    Maizels, Max; Mickelson, Jennie; Yerkes, Elizabeth; Maizels, Evelyn; Stork, Rachel; Young, Christine; Corcoran, Julia; Holl, Jane; Kaplan, William E

    2009-09-01

    Changes in health care are stimulating residency training programs to develop new methods for teaching surgical skills. We developed Computer-Enhanced Visual Learning (CEVL) as an innovative Internet-based learning and assessment tool. The CEVL method uses the educational procedures of deliberate practice and performance to teach and learn surgery in a stylized manner. CEVL is a learning and assessment tool that can provide students and educators with quantitative feedback on learning a specific surgical procedure. Methods involved examine quantitative data of improvement in surgical skills. Herein, we qualitatively describe the method and show how program directors (PDs) may implement this technique in their residencies. CEVL allows an operation to be broken down into teachable components. The process relies on feedback and remediation to improve performance, with a focus on learning that is applicable to the next case being performed. CEVL has been shown to be effective for teaching pediatric orchiopexy and is being adapted to additional adult and pediatric procedures and to office examination skills. The CEVL method is available to other residency training programs.

  10. Construction of the Questionnaire on Foreign Language Learning Strategies in Specific Croatian Context.

    Science.gov (United States)

    Božinović, Nikolina; Sindik, Joško

    2017-03-01

    Learning strategies are special thoughts or behaviours that individuals use to understand, learn or retain new information, according to the point of view of O’Malley & Chamot. The other view, promoted by Oxford, believes learning strategies are specific actions taken by the learner to make learning easier, faster, more enjoyable, and more transferrable to new situations of language learning and use. The use of appropriate strategies ensures greater success in language learning. The aim of the research was to establish metric characteristics of the Questionnaire on learning strategies created by the author, in line with the template of the original SILL questionnaire (Strategy Inventory for Language Learning). The research was conducted at the Rochester Institute of Technology Croatia on a sample of 201 participants who learned German, Spanish, French and Italian as a foreign language. The results have shown that one-component latent dimensions which describe the space of foreign language learning strategies according to Oxford’s classification, have metric characteristics which are low, but still satisfactory (reliability and validity). All dimensions of learning strategies appeared not to be adequately defined. Therefore, we excluded compensation strategies and merged social and affective strategies into social-affective strategies into the unique dimension. Overall, this version of Oxford’s original questionnaire, based on Oxford’s theoretical construct, applied on Croatian students, clearly shows that current version of the questionnaire has poor metric characteristics. One of the explanations of the results obtained could be positioned in multicultural context and intercultural dialogue. Namely, particular social, political and economic context in Croatia could shape even foreign language learning strategies.

  11. An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data.

    Science.gov (United States)

    Liu, Yuzhe; Gopalakrishnan, Vanathi

    2017-03-01

    Many clinical research datasets have a large percentage of missing values that directly impacts their usefulness in yielding high accuracy classifiers when used for training in supervised machine learning. While missing value imputation methods have been shown to work well with smaller percentages of missing values, their ability to impute sparse clinical research data can be problem specific. We previously attempted to learn quantitative guidelines for ordering cardiac magnetic resonance imaging during the evaluation for pediatric cardiomyopathy, but missing data significantly reduced our usable sample size. In this work, we sought to determine if increasing the usable sample size through imputation would allow us to learn better guidelines. We first review several machine learning methods for estimating missing data. Then, we apply four popular methods (mean imputation, decision tree, k-nearest neighbors, and self-organizing maps) to a clinical research dataset of pediatric patients undergoing evaluation for cardiomyopathy. Using Bayesian Rule Learning (BRL) to learn ruleset models, we compared the performance of imputation-augmented models versus unaugmented models. We found that all four imputation-augmented models performed similarly to unaugmented models. While imputation did not improve performance, it did provide evidence for the robustness of our learned models.

  12. Case study teaching method improves student performance and perceptions of learning gains.

    Science.gov (United States)

    Bonney, Kevin M

    2015-05-01

    Following years of widespread use in business and medical education, the case study teaching method is becoming an increasingly common teaching strategy in science education. However, the current body of research provides limited evidence that the use of published case studies effectively promotes the fulfillment of specific learning objectives integral to many biology courses. This study tested the hypothesis that case studies are more effective than classroom discussions and textbook reading at promoting learning of key biological concepts, development of written and oral communication skills, and comprehension of the relevance of biological concepts to everyday life. This study also tested the hypothesis that case studies produced by the instructor of a course are more effective at promoting learning than those produced by unaffiliated instructors. Additionally, performance on quantitative learning assessments and student perceptions of learning gains were analyzed to determine whether reported perceptions of learning gains accurately reflect academic performance. The results reported here suggest that case studies, regardless of the source, are significantly more effective than other methods of content delivery at increasing performance on examination questions related to chemical bonds, osmosis and diffusion, mitosis and meiosis, and DNA structure and replication. This finding was positively correlated to increased student perceptions of learning gains associated with oral and written communication skills and the ability to recognize connections between biological concepts and other aspects of life. Based on these findings, case studies should be considered as a preferred method for teaching about a variety of concepts in science courses.

  13. Case Study Teaching Method Improves Student Performance and Perceptions of Learning Gains

    Directory of Open Access Journals (Sweden)

    Kevin M. Bonney

    2015-02-01

    Full Text Available Following years of widespread use in business and medical education, the case study teaching method is becoming an increasingly common teaching strategy in science education. However, the current body of research provides limited evidence that the use of published case studies effectively promotes the fulfillment of specific learning objectives integral to many biology courses. This study tested the hypothesis that case studies are more effective than classroom discussions and textbook reading at promoting learning of key biological concepts, development of written and oral communication skills, and comprehension of the relevance of biological concepts to everyday life. This study also tested the hypothesis that case studies produced by the instructor of a course are more effective at promoting learning than those produced by unaffiliated instructors. Additionally, performance on quantitative learning assessments and student perceptions of learning gains were analyzed to determine whether reported perceptions of learning gains accurately reflect academic performance. The results reported here suggest that case studies, regardless of the source, are significantly more effective than other methods of content delivery at increasing performance on examination questions related to chemical bonds, osmosis and diffusion, mitosis and meiosis, and DNA structure and replication. This finding was positively correlated to increased student perceptions of learning gains associated with oral and written communication skills and the ability to recognize connections between biological concepts and other aspects of life. Based on these findings, case studies should be considered as a preferred method for teaching about a variety of concepts in science courses.

  14. Radiochemical methods. Analytical chemistry by open learning

    Energy Technology Data Exchange (ETDEWEB)

    Geary, W.J.; James, A.M. (ed.)

    1986-01-01

    This book presents the analytical uses of radioactive isotopes within the context of radiochemistry as a whole. It is designed for scientists with relatively little background knowledge of the subject. Thus the initial emphasis is on developing the basic concepts of radioactive decay, particularly as they affect the potential usage of radioisotopes. Discussion of the properties of various types of radiation, and of factors such as half-life, is related to practical considerations such as counting and preparation methods, and handling/disposal problems. Practical aspects are then considered in more detail, and the various radioanalytical methods are outlined with particular reference to their applicability. The approach is 'user friendly' and the use of self assessment questions allows the reader to test his/her understanding of individual sections easily. For those who wish to develop their knowledge further, a reading list is provided.

  15. Uncertain Photometric Redshifts with Deep Learning Methods

    Science.gov (United States)

    D'Isanto, A.

    2017-06-01

    The need for accurate photometric redshifts estimation is a topic that has fundamental importance in Astronomy, due to the necessity of efficiently obtaining redshift information without the need of spectroscopic analysis. We propose a method for determining accurate multi-modal photo-z probability density functions (PDFs) using Mixture Density Networks (MDN) and Deep Convolutional Networks (DCN). A comparison with a Random Forest (RF) is performed.

  16. Monte Carlo methods for preference learning

    DEFF Research Database (Denmark)

    Viappiani, P.

    2012-01-01

    Utility elicitation is an important component of many applications, such as decision support systems and recommender systems. Such systems query the users about their preferences and give recommendations based on the system’s belief about the utility function. Critical to these applications is th...... is the acquisition of prior distribution about the utility parameters and the possibility of real time Bayesian inference. In this paper we consider Monte Carlo methods for these problems....

  17. Choosing Learning Methods Suitable for Teaching and Learning in Computer Science

    Science.gov (United States)

    Taylor, Estelle; Breed, Marnus; Hauman, Ilette; Homann, Armando

    2013-01-01

    Our aim is to determine which teaching methods students in Computer Science and Information Systems prefer. There are in total 5 different paradigms (behaviorism, cognitivism, constructivism, design-based and humanism) with 32 models between them. Each model is unique and states different learning methods. Recommendations are made on methods that…

  18. Developing tools for the safety specification in risk management plans: lessons learned from a pilot project.

    Science.gov (United States)

    Cooper, Andrew J P; Lettis, Sally; Chapman, Charlotte L; Evans, Stephen J W; Waller, Patrick C; Shakir, Saad; Payvandi, Nassrin; Murray, Alison B

    2008-05-01

    Following the adoption of the ICH E2E guideline, risk management plans (RMP) defining the cumulative safety experience and identifying limitations in safety information are now required for marketing authorisation applications (MAA). A collaborative research project was conducted to gain experience with tools for presenting and evaluating data in the safety specification. This paper presents those tools found to be useful and the lessons learned from their use. Archive data from a successful MAA were utilised. Methods were assessed for demonstrating the extent of clinical safety experience, evaluating the sensitivity of the clinical trial data to detect treatment differences and identifying safety signals from adverse event and laboratory data to define the extent of safety knowledge with the drug. The extent of clinical safety experience was demonstrated by plots of patient exposure over time. Adverse event data were presented using dot plots, which display the percentages of patients with the events of interest, the odds ratio, and 95% confidence interval. Power and confidence interval plots were utilised for evaluating the sensitivity of the clinical database to detect treatment differences. Box and whisker plots were used to display laboratory data. This project enabled us to identify new evidence-based methods for presenting and evaluating clinical safety data. These methods represent an advance in the way safety data from clinical trials can be analysed and presented. This project emphasises the importance of early and comprehensive planning of the safety package, including evaluation of the use of epidemiology data.

  19. Using principles of learning to inform language therapy design for children with specific language impairment.

    Science.gov (United States)

    Alt, Mary; Meyers, Christina; Ancharski, Alexandra

    2012-01-01

    Language treatment for children with specific language impairment (SLI) often takes months to achieve moderate results. Interventions often do not incorporate the principles that are known to affect learning in unimpaired learners. To outline some key findings about learning in typical populations and to suggest a model of how they might be applied to language treatment design as a catalyst for further research and discussion. Three main principles of implicit learning are reviewed: variability, complexity and sleep-dependent consolidation. After explaining these principles, evidence is provided as to how they influence learning tasks in unimpaired learners. Information is reviewed on principles of learning as they apply to impaired populations, current treatment designs are also reviewed that conform to the principles, and ways in which principles of learning might be incorporated into language treatment design are demonstrated. This paper provides an outline for how theoretical knowledge might be applied to clinical practice in an effort to promote discussion. Although the authors look forward to more specific details on how the principles of learning relate to impaired populations, there is ample evidence to suggest that these principles should be considered during treatment design. © 2012 Royal College of Speech and Language Therapists.

  20. Machine Learning Methods for Attack Detection in the Smart Grid.

    Science.gov (United States)

    Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatos Tunay; Kulkarni, Sanjeev R; Poor, H Vincent

    2016-08-01

    Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.

  1. Image Classification Workflow Using Machine Learning Methods

    Science.gov (United States)

    Christoffersen, M. S.; Roser, M.; Valadez-Vergara, R.; Fernández-Vega, J. A.; Pierce, S. A.; Arora, R.

    2016-12-01

    Recent increases in the availability and quality of remote sensing datasets have fueled an increasing number of scientifically significant discoveries based on land use classification and land use change analysis. However, much of the software made to work with remote sensing data products, specifically multispectral images, is commercial and often prohibitively expensive. The free to use solutions that are currently available come bundled up as small parts of much larger programs that are very susceptible to bugs and difficult to install and configure. What is needed is a compact, easy to use set of tools to perform land use analysis on multispectral images. To address this need, we have developed software using the Python programming language with the sole function of land use classification and land use change analysis. We chose Python to develop our software because it is relatively readable, has a large body of relevant third party libraries such as GDAL and Spectral Python, and is free to install and use on Windows, Linux, and Macintosh operating systems. In order to test our classification software, we performed a K-means unsupervised classification, Gaussian Maximum Likelihood supervised classification, and a Mahalanobis Distance based supervised classification. The images used for testing were three Landsat rasters of Austin, Texas with a spatial resolution of 60 meters for the years of 1984 and 1999, and 30 meters for the year 2015. The testing dataset was easily downloaded using the Earth Explorer application produced by the USGS. The software should be able to perform classification based on any set of multispectral rasters with little to no modification. Our software makes the ease of land use classification using commercial software available without an expensive license.

  2. A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

    Directory of Open Access Journals (Sweden)

    Zekić-Sušac Marijana

    2014-09-01

    Full Text Available Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

  3. The «PBL WORKING ENVIRONMENT» as interactive and expert system to learn the problem-based learning method

    Directory of Open Access Journals (Sweden)

    Susana Correnti

    2016-01-01

    Full Text Available The «PBL working environment» is a virtual environment developed in the framework of SCENE project (profeSsional development for an effeCtive PBL approach: a practical experiENce through ICT-enabled lEarning solution, co-funded by the European Lifelong Learning Program. The «PBL working environment» is devoted to prepare headmasters and teachers of secondary and vocational schools to use Problem-Based Learning (PBL pedagogy effectively. It is a student-centered pedagogy where learners are «actively» engaged in real world problems to solve or challenges to meet. Students develop problem-solving, self-directed learning and team skills. The «PBL working environment» is an virtual tool including three main elements: e-learning platform, virtual facilitator and PBL repository. Teachers, trainers and headmasters/school managers learn the PBL pedagogy by attending an on-line course (e-learning platform delivered through the «inductive method». It allows learners to experience PBL approach, by practicing it stage by stage, and then learn to turn practice into theory by abstracting their experience to build a theoretical understanding. Since generating the proper scenario is the most critical aspect of PBL, after benefiting from the on-line course, users can benefit from a further support: the Virtual Facilitator. It provides tips and hints on how correctly design a problem scenario and by asking questions to collect data on user's specific needs. The Virtual Facilitator is able to provide a/or more suitable example(s which match as closest as possible the teacher/trainer need. Finally, users can share problem scenarios and projects of different subjects of studies and with different characteristics uploaded and downloaded in the PBL repository.

  4. Qualitative and quantitative revaluation of specific learning disabilities: a multicentric study.

    Science.gov (United States)

    Operto, Francesca F; Mazza, Roberta; Buttiglione, Maura; Craig, Francesco; Frolli, Alessandro; Pisano, Simone; Margari, Lucia; Coppola, Giangennaro

    2018-04-12

    Specific learning disabilities are disorders that affect the instrumental skills of academic learning, leaving intact the general intellectual functioning. It is possible to distinguish: dyslexia, dysorthography, dysgraphia, and dyscalculia. The diagnosis is made according to DSMV. The aim of this study is to evaluate the implementation of Law N° 170 following a diagnosis of specific learning disabilities in children and their evolution over time. The sample under examination consists of 75 children, 56 males and 18 females aged 7,8 to 16 years, with a diagnosis of specific learning disabilities; a revaluation was carried outthrough the use of standardized instruments according to age and school attended. A twopart questionnaire was proposed: the first part turned to the parents/carers of the child and the second part turned to the boy himself. The improvement parameter has been linked, through a statistical analysis of univarianza with intelligence quotient, age, application of the law 10 October 2010 n 170, rehabilitative paths and attending afterschool program. Most of the guys are followed at school by the application of the law 170 and, outside school, by attending speech and neuropsychological therapy and after school. Going to investigate the actual use of the measures put in place by the school, it is evident a partial and incomplete application of Law 170. The most suitable measures for these children are pedagogical measures in order to make them integrate with the group class and strengthen their capacities through specific measures provided by a specific legislative decree.

  5. Teaching numerical methods with IPython notebooks and inquiry-based learning

    KAUST Repository

    Ketcheson, David I.

    2014-01-01

    A course in numerical methods should teach both the mathematical theory of numerical analysis and the craft of implementing numerical algorithms. The IPython notebook provides a single medium in which mathematics, explanations, executable code, and visualizations can be combined, and with which the student can interact in order to learn both the theory and the craft of numerical methods. The use of notebooks also lends itself naturally to inquiry-based learning methods. I discuss the motivation and practice of teaching a course based on the use of IPython notebooks and inquiry-based learning, including some specific practical aspects. The discussion is based on my experience teaching a Masters-level course in numerical analysis at King Abdullah University of Science and Technology (KAUST), but is intended to be useful for those who teach at other levels or in industry.

  6. Perceptual learning of basic visual features remains task specific with Training-Plus-Exposure (TPE) training.

    Science.gov (United States)

    Cong, Lin-Juan; Wang, Ru-Jie; Yu, Cong; Zhang, Jun-Yun

    2016-01-01

    Visual perceptual learning is known to be specific to the trained retinal location, feature, and task. However, location and feature specificity can be eliminated by double-training or TPE training protocols, in which observers receive additional exposure to the transfer location or feature dimension via an irrelevant task besides the primary learning task Here we tested whether these new training protocols could even make learning transfer across different tasks involving discrimination of basic visual features (e.g., orientation and contrast). Observers practiced a near-threshold orientation (or contrast) discrimination task. Following a TPE training protocol, they also received exposure to the transfer task via performing suprathreshold contrast (or orientation) discrimination in alternating blocks of trials in the same sessions. The results showed no evidence for significant learning transfer to the untrained near-threshold contrast (or orientation) discrimination task after discounting the pretest effects and the suprathreshold practice effects. These results thus do not support a hypothetical task-independent component in perceptual learning of basic visual features. They also set the boundary of the new training protocols in their capability to enable learning transfer.

  7. Learning to spell from reading: general knowledge about spelling patterns influences memory for specific words.

    Science.gov (United States)

    Pacton, Sébastien; Borchardt, Gaëlle; Treiman, Rebecca; Lété, Bernard; Fayol, Michel

    2014-05-01

    Adults often learn to spell words during the course of reading for meaning, without intending to do so. We used an incidental learning task in order to study this process. Spellings that contained double n, r and t which are common doublets in French, were learned more readily by French university students than spellings that contained less common but still legal doublets. When recalling or recognizing the latter, the students sometimes made transposition errors, doubling a consonant that often doubles in French rather than the consonant that was originally doubled (e.g., tiddunar recalled as tidunnar). The results, found in three experiments using different nonwords and different types of instructions, show that people use general knowledge about the graphotactic patterns of their writing system together with word-specific knowledge to reconstruct spellings that they learn from reading. These processes contribute to failures and successes in memory for spellings, as in other domains.

  8. Histogram specification as a method of density modification

    International Nuclear Information System (INIS)

    Harrison, R.W.

    1988-01-01

    A new method for improving the quality and extending the resolution of Fourier maps is described. The method is based on a histogram analysis of the electron density. The distribution of electron density values in the map is forced to be 'ideal'. The 'ideal' distribution is assumed to be Gaussian. The application of the method to improve the electron density map for the protein Acinetobacter asparaginase, which is a tetrameric enzyme of molecular weight 140000 daltons, is described. (orig.)

  9. Comparisons and Analyses of Gifted Students' Characteristics and Learning Methods

    Science.gov (United States)

    Lu, Jiamei; Li, Daqi; Stevens, Carla; Ye, Renmin

    2017-01-01

    Using PISA 2009, an international education database, this study compares gifted and talented (GT) students in three groups with normal (non-GT) students by examining student characteristics, reading, schooling, learning methods, and use of strategies for understanding and memorizing. Results indicate that the GT and non-GT gender distributions…

  10. Identification of alternative method of teaching and learning the ...

    African Journals Online (AJOL)

    This study examines alternative method of teaching and learning of the concept of diffusion. An improvised U-shape glass tube called ionic mobility tube was used to observed and measure the rate of movement of divalent metal ions in an aqueous medium in the absence of an electric current. The study revealed that the ...

  11. Kernel Methods for Machine Learning with Life Science Applications

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie

    Kernel methods refer to a family of widely used nonlinear algorithms for machine learning tasks like classification, regression, and feature extraction. By exploiting the so-called kernel trick straightforward extensions of classical linear algorithms are enabled as long as the data only appear a...

  12. Research on Language Learning Strategies: Methods, Findings, and Instructional Issues.

    Science.gov (United States)

    Oxford, Rebecca; Crookall, David

    1989-01-01

    Surveys research on formal and informal second-language learning strategies, covering the effectiveness of research methods involving making lists, interviews and thinking aloud, note-taking, diaries, surveys, and training. Suggestions for future and improved research are presented. (131 references) (CB)

  13. Second-Order Learning Methods for a Multilayer Perceptron

    International Nuclear Information System (INIS)

    Ivanov, V.V.; Purehvdorzh, B.; Puzynin, I.V.

    1994-01-01

    First- and second-order learning methods for feed-forward multilayer neural networks are studied. Newton-type and quasi-Newton algorithms are considered and compared with commonly used back-propagation algorithm. It is shown that, although second-order algorithms require enhanced computer facilities, they provide better convergence and simplicity in usage. 13 refs., 2 figs., 2 tabs

  14. Educational integrating projects as a method of interactive learning

    Directory of Open Access Journals (Sweden)

    Иван Николаевич Куринин

    2013-12-01

    Full Text Available The article describes a method of interactive learning based on educational integrating projects. Some examples of content of such projects for the disciplines related to the study of information and Internet technologies and their application in management are presented.

  15. Learning by Designing Interview Methods in Special Education

    DEFF Research Database (Denmark)

    Jönsson, Lise Høgh

    2017-01-01

    , and people with learning disabilities worked together to develop five new visual and digital methods for interviewing in special education. Thereby not only enhancing the students’ competences, knowledge and proficiency in innovation and research, but also proposing a new teaching paradigm for university...

  16. Arabic Supervised Learning Method Using N-Gram

    Science.gov (United States)

    Sanan, Majed; Rammal, Mahmoud; Zreik, Khaldoun

    2008-01-01

    Purpose: Recently, classification of Arabic documents is a real problem for juridical centers. In this case, some of the Lebanese official journal documents are classified, and the center has to classify new documents based on these documents. This paper aims to study and explain the useful application of supervised learning method on Arabic texts…

  17. A Simulator to Enhance Teaching and Learning of Mining Methods ...

    African Journals Online (AJOL)

    Audio visual education that incorporates devices and materials which involve sight, sound, or both has become a sine qua non in recent times in the teaching and learning process. An automated physical model of mining methods aided with video instructions was designed and constructed by harnessing locally available ...

  18. Optimization of technical specifications by use of probabilistic methods

    International Nuclear Information System (INIS)

    Laakso, K.

    1990-01-01

    The Technical Specifications of a nuclear power plant specify the limits for plant operation from the safety point of view. These operational safety rules were originally defined on the basis of deterministic analyses and engineering judgement. As experience has accumulated, it has proved necessary to consider problems and make specific modifications in these rules. Developments in probabilistic safety assessment have provided a new tool to analyse, present and compare the risk effects of proposed rule modificatons. The main areas covered in the project are operational decisions in failure situations, preventive maintenance during power operation and surveillance tests of standby safety systems. (author)

  19. A Learning Model for L/M Specificity in Ganglion Cells

    Science.gov (United States)

    Ahumada, Albert J.

    2016-01-01

    An unsupervised learning model for developing LM specific wiring at the ganglion cell level would support the research indicating LM specific wiring at the ganglion cell level (Reid and Shapley, 2002). Removing the contributions to the surround from cells of the same cone type improves the signal-to-noise ratio of the chromatic signals. The unsupervised learning model used is Hebbian associative learning, which strengthens the surround input connections according to the correlation of the output with the input. Since the surround units of the same cone type as the center are redundant with the center, their weights end up disappearing. This process can be thought of as a general mechanism for eliminating unnecessary cells in the nervous system.

  20. Towards sophisticated learning from EHRs: increasing prediction specificity and accuracy using clinically meaningful risk criteria.

    Science.gov (United States)

    Vasiljeva, Ieva; Arandjelovic, Ognjen

    2016-08-01

    Computer based analysis of Electronic Health Records (EHRs) has the potential to provide major novel insights of benefit both to specific individuals in the context of personalized medicine, as well as on the level of population-wide health care and policy. The present paper introduces a novel algorithm that uses machine learning for the discovery of longitudinal patterns in the diagnoses of diseases. Two key technical novelties are introduced: one in the form of a novel learning paradigm which enables greater learning specificity, and another in the form of a risk driven identification of confounding diagnoses. We present a series of experiments which demonstrate the effectiveness of the proposed techniques, and which reveal novel insights regarding the most promising future research directions.

  1. The Keyimage Method of Learning Sound-Symbol Correspondences: A Case Study of Learning Written Khmer

    Directory of Open Access Journals (Sweden)

    Elizabeth Lavolette

    2009-01-01

    Full Text Available I documented my strategies for learning sound-symbol correspondences during a Khmer course. I used a mnemonic strategy that I call the keyimage method. In this method, a character evokes an image (the keyimage, which evokes the corresponding sound. For example, the keyimage for the character 2 could be a swan with its head tucked in. This evokes the sound "kaw" that a swan makes, which sounds similar to the Khmer sound corresponding to 2. The method has some similarities to the keyword method. Considering the results of keyword studies, I hypothesize that the keyimage method is more effective than rote learning and that peer-generated keyimages are more effective than researcher- or teacher-generated keyimages, which are more effective than learner-generated ones. In Dr. Andrew Cohen's plenary presentation at the Hawaii TESOL 2007 conference, he mentioned that more case studies are needed on learning strategies (LSs. One reason to study LSs is that what learners do with input to produce output is unclear, and knowing what strategies learners use may help us understand that process (Dornyei, 2005, p. 170. Hopefully, we can use that knowledge to improve language learning, perhaps by teaching learners to use the strategies that we find. With that in mind, I have examined the LSs that I used in studying Khmer as a foreign language, focusing on learning the syllabic alphabet.

  2. Linking actions and objects: Context-specific learning of novel weight priors.

    Science.gov (United States)

    Trewartha, Kevin M; Flanagan, J Randall

    2017-06-01

    Distinct explicit and implicit memory processes support weight predictions used when lifting objects and making perceptual judgments about weight, respectively. The first time that an object is encountered weight is predicted on the basis of learned associations, or priors, linking size and material to weight. A fundamental question is whether the brain maintains a single, global representation of priors, or multiple representations that can be updated in a context specific way. A second key question is whether the updating of priors, or the ability to scale lifting forces when repeatedly lifting unusually weighted objects requires focused attention. To investigate these questions we compared the adaptability of weight predictions used when lifting objects and judging their weights in different groups of participants who experienced size-weight inverted objects passively (with the objects placed on the hands) or actively (where participants lift the objects) under full or divided attention. To assess weight judgments we measured the size-weight illusion after every 20 trials of experience with the inverted objects both passively and actively. The attenuation of the illusion that arises when lifting inverted object was found to be context-specific such that the attenuation was larger when the mode of interaction with the inverted objects matched the method of assessment of the illusion. Dividing attention during interaction with the inverted objects had no effect on attenuation of the illusion, but did slow the rate at which lifting forces were scaled to the weight inverted objects. These findings suggest that the brain stores multiple representations of priors that are context specific, and that focused attention is important for scaling lifting forces, but not for updating weight predictions used when judging object weight. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Method to predict process signals to learn for SVM

    International Nuclear Information System (INIS)

    Minowa, Hirotsugu; Gofuku, Akio

    2013-01-01

    Study of diagnostic system using machine learning to reduce the incidents of the plant is in advance because an accident causes large damage about human, economic and social loss. There is a problem that 2 performances between a classification performance and generalization performance on the machine diagnostic machine is exclusive. However, multi agent diagnostic system makes it possible to use a diagnostic machine specialized either performance by multi diagnostic machines can be used. We propose method to select optimized variables to improve classification performance. The method can also be used for other supervised learning machine but Support Vector Machine. This paper reports that our method and result of evaluation experiment applied our method to output 40% of Monju. (author)

  4. Quality of life of parents of children with newly diagnosed specific learning disability

    Directory of Open Access Journals (Sweden)

    Karande S

    2009-01-01

    Full Text Available Background: Poor school performance in children causes significant stress to parents. Aims: To analyze the quality of life (QOL of parents having a child with newly diagnosed specific learning disability (SpLD and to evaluate the impact of clinical and socio-demographic characteristics on their QOL. Design: Cross-sectional questionnaire-based study. Setting: Learning disability clinic in tertiary care hospital. Materials and Methods: From June 2006 to February 2007, 150 parents (either mother or father of children consecutively diagnosed as having SpLD were enrolled. Parent′s QOL was measured by the WHOQOL-100 instrument which is a generic instrument containing 25 facets of QOL organized in six domains. Statistical Analysis Used: Independent samples t-test, one-way analysis of variance, and multiple regression analysis were carried out for statistical significance. Results: Mean age of parents was 42.6 years (SD 5.5; mothers to fathers ratio 1.3:1; and 19 (12.7% were currently ill. Only four WHOQOL-100 domains (psychological > social relationships > environment > spiritual and five WHOQOL-100 facets (leisur > pfeel > energy > esteem > sex contributed significantly to their "overall" QOL. Female gender, being currently ill, being in paid work, and having a male child were characteristics that independently predicted a poor domain/facet QOL score. Conclusions: The present study has identified domains and facets that need to be addressed by counselors for improving overall QOL of these parents. Initiating these measures would also improve the home environment and help in the rehabilitation of children with SpLD.

  5. Formal methods in design and verification of functional specifications

    International Nuclear Information System (INIS)

    Vaelisuo, H.

    1995-01-01

    It is claimed that formal methods should be applied already when specifying the functioning of the control/monitoring system, i.e. when planning how to implement the desired operation of the plant. Formal methods are seen as a way to mechanize and thus automate part of the planning. All mathematical methods which can be applied on related problem solving should be considered as formal methods. Because formal methods can only support the designer, not replace him/her, they must be integrated into a design support tool. Such a tool must also aid the designer in getting the correct conception of the plant and its behaviour. The use of a hypothetic design support tool is illustrated to clarify the requirements such a tool should fulfill. (author). 3 refs, 5 figs

  6. Computer-Assisted Mathematics Instruction for Students with Specific Learning Disability: A Review of the Literature

    Science.gov (United States)

    Stultz, Sherry L.

    2017-01-01

    This review was conducted to evaluate the current body of scholarly research regarding the use of computer-assisted instruction (CAI) to teach mathematics to students with specific learning disability (SLD). For many years, computers are utilized for educational purposes. However, the effectiveness of CAI for teaching mathematics to this specific…

  7. The Effectiveness of Computer-Assisted Instruction for Teaching Mathematics to Students with Specific Learning Disability

    Science.gov (United States)

    Stultz, Sherry L.

    2013-01-01

    Using computers to teach students is not a new idea. Computers have been utilized for educational purposes for over 80 years. However, the effectiveness of these programs for teaching mathematics to students with specific learning disability is unclear. This study was undertaken to determine if computer-assisted instruction was as effective as…

  8. Extracurricular Activities and the Development of Social Skills in Children with Intellectual and Specific Learning Disabilities

    Science.gov (United States)

    Brooks, B. A.; Floyd, F.; Robins, D. L.; Chan, W. Y.

    2015-01-01

    Background: Children with intellectual disability and specific learning disabilities often lack age-appropriate social skills, which disrupts their social functioning. Because of the limited effectiveness of classroom mainstreaming and social skills training for these children, it is important to explore alternative opportunities for social skill…

  9. Graduation Prospects of College Students with Specific Learning Disorder and Students with Mental Health Related Disabilities

    Science.gov (United States)

    Jorgensen, Mary; Budd, Jillian; Fichten, Catherine S.; Nguyen, Mai N.; Havel, Alice

    2018-01-01

    This study's goal was to compare aspects related to academic persistence of two groups of college students with non-visible disabilities: 110 Canadian two and four-year college students--55 with mental health related disabilities and 55 with Specific Learning Disorder (LD). Results show that students with mental health related disabilities were…

  10. Personalization for Specific Users : Designing Decision Support Systems to Support Stimulating Learning Environments

    NARCIS (Netherlands)

    Maruster, Laura; Faber, Niels R.; van Haren, Rob J.; Salvendy, G; Smith, MJ

    2009-01-01

    Creating adaptive systems becomes increasingly attractive in the context of specific groups of users, such as agricultural users. This group of users seems to differ with respect to information processing, knowledge management and learning styles. In this work we aim to offer directions toward

  11. Parents' Perspectives on Coping with Duchenne Muscular Dystrophy and Concomitant Specific Learning Disabilities

    Science.gov (United States)

    Webb, Carol L.

    2005-01-01

    This study addresses parental perspectives and coping strategies related to Duchenne muscular dystrophy and specific learning disabilities. Data were collected through individual semi-structured in-depth interviews with fifteen sets of parents. Participants were selected based on variables such as age of children, number of children with both…

  12. The Legal Meaning of Specific Learning Disability for IDEA Eligibility: The Latest Case Law

    Science.gov (United States)

    Zirkel, Perry A.

    2013-01-01

    Specific learning disability (SLD), although moderately declining in recent years, continues to be the largest of the eligibility classifications under the Individuals with Disabilities Education Act (IDEA; NCES, 2012). The recognition of response to intervention (RTI) in the 2004 amendments of the IDEA as an approach for identifying students with…

  13. Executive Functioning and Psychopathology in Psychotherapy for Adolescents with Specific Learning Disorders

    Science.gov (United States)

    Kopelman-Rubin, Daphne; Klomek, Anat Brunstein; Al-Yagon, Michal; Mufson, Laura; Apter, Alan; Mikulincer, Mario

    2017-01-01

    This study examined the contribution of executive functioning (EF) to improvements in psychiatric symptomatology following I Can Succeed (ICS; Kopelman-Rubin, 2012) psychotherapy, a skill-enhancement intervention designed to target EF and socio-emotional aspects of specific learning disabilities (SLD). Forty adolescents with SLD underwent ICS in…

  14. Working Memory and Learning in Children with Developmental Coordination Disorder and Specific Language Impairment

    Science.gov (United States)

    Alloway, Tracy Packiam; Archibald, Lisa

    2008-01-01

    The authors compared 6- to 11-year-olds with developmental coordination disorder (DCD) and those with specific language impairment (SLI) on measures of memory (verbal and visuospatial short-term and working memory) and learning (reading and mathematics). Children with DCD with typical language skills were impaired in all four areas of memory…

  15. Internet-Specific Epistemic Beliefs and Self-Regulated Learning in Online Academic Information Searching

    Science.gov (United States)

    Chiu, Yen-Lin; Liang, Jyh-Chong; Tsai, Chin-Chung

    2013-01-01

    Epistemic beliefs have been considered as important components of the self-regulatory model; however, their relationships with self-regulated learning processes in the Internet context need further research. The main purpose of this study was to examine the relationships between Internet-specific epistemic belief dimensions and self-regulated…

  16. Evaluating the Impact of Dyslexia Laws on the Identification of Specific Learning Disability and Dyslexia

    Science.gov (United States)

    Phillips, B. Anne Barber; Odegard, Timothy N.

    2017-01-01

    Dyslexia is a specific learning disability that impacts word reading accuracy and/or reading fluency. Over half of the states in the USA have passed legislation intended to promote better identification of individuals with dyslexia. To date, no study has been conducted to investigate the potential impact of state laws on the identification of…

  17. Training School Psychologists to Identify Specific Learning Disabilities: A Content Analysis of Syllabi

    Science.gov (United States)

    Barrett, Courtenay A.; Cottrell, Joseph M.; Newman, Daniel S.; Pierce, Benjamin G.; Anderson, Alisha

    2015-01-01

    Approximately 2.4 million children receive special education services for specific learning disabilities (SLDs), and school psychologists are key contributors to the SLD eligibility decision-making process. The Individuals with Disabilities Education Act (2004) enabled local education agencies to use response to intervention (RTI) instead of the…

  18. The effects of inspecting and constructing part-task-specific visualizations on team and individual learning

    NARCIS (Netherlands)

    Slof, Bert; Erkens, Gijsbert; Kirschner, Paul A.; Helms-Lorenz, Michelle

    This study examined whether inspecting and constructing different part-task-specific visualizations differentially affects learning. To this end, a complex business-economics problem was structured into three phase-related part-tasks: (1) determining core concepts, (2) proposing multiple solutions,

  19. Specific Learning Disabilities in DSM-5: Are the Changes for Better or Worse?

    Science.gov (United States)

    Tannock, Rosemary

    2013-01-01

    DSM-5, the fifth edition of the American Psychiatric Association's "Diagnostic and Statistical Manual of Mental Disorders," was published in May 2013, amidst a storm of controversy. This article focuses on changes made to the diagnostic criteria for Specific Learning Disorders (SLD). Primary criticisms of the changes in the SLD concern…

  20. Fostering Self-Concept and Interest for Statistics through Specific Learning Environments

    Science.gov (United States)

    Sproesser, Ute; Engel, Joachim; Kuntze, Sebastian

    2016-01-01

    Supporting motivational variables such as self-concept or interest is an important goal of schooling as they relate to learning and achievement. In this study, we investigated whether specific interest and self-concept related to the domains of statistics and mathematics can be fostered through a four-lesson intervention focusing on statistics.…

  1. Suicide Attempts among Individuals with Specific Learning Disorders: An Underrecognized Issue

    Science.gov (United States)

    Fuller-Thomson, Esme; Carroll, Samara Z.; Yang, Wook

    2018-01-01

    Several studies have linked specific learning disorders (SLDs) with suicidal ideation, but less is known about the disorders' association with suicide attempts. This gap in the literature is addressed via the 2012 nationally representative Canadian Community Health Survey (n = 21,744). The prevalence of lifetime suicide attempts among those with…

  2. E-Learning Lifecycles:How Communities and Context can affect E-learning Specifications and Tool Design

    Directory of Open Access Journals (Sweden)

    Michael Magee

    2004-10-01

    Full Text Available The development of a large body of e-learning specifications, such as IMS and SCORM, has led to the proposal for a new way to facilitate content workflow. This involves the movement of educational digital content and the knowledge of pedagogical communities into an online space. Several projects have looked at the theoretical structure of these specifications. They implemented a series of tools in order to examine and research the issues around the actual usage of these specifications. The CAREO, ALOHA and ALOHA 2 projects were designed to expose both individual users and whole institutions to these ideas. Initial research into the result of those interactions indicates that there is some utility in the adoption of e-learning specifications. The future success of them will depend on their ability to adapt and meet the needs of the educational community as they begin to adopt, use and evolve the way they use the specifications and the tools created around them.

  3. Studying depression using imaging and machine learning methods

    OpenAIRE

    Patel, Meenal J.; Khalaf, Alexander; Aizenstein, Howard J.

    2015-01-01

    Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presen...

  4. E-learning as new method of medical education.

    Science.gov (United States)

    Masic, Izet

    2008-01-01

    NONE DECLARED Distance learning refers to use of technologies based on health care delivered on distance and covers areas such as electronic health, tele-health (e-health), telematics, telemedicine, tele-education, etc. For the need of e-health, telemedicine, tele-education and distance learning there are various technologies and communication systems from standard telephone lines to the system of transmission digitalized signals with modem, optical fiber, satellite links, wireless technologies, etc. Tele-education represents health education on distance, using Information Communication Technologies (ICT), as well as continuous education of a health system beneficiaries and use of electronic libraries, data bases or electronic data with data bases of knowledge. Distance learning (E-learning) as a part of tele-education has gained popularity in the past decade; however, its use is highly variable among medical schools and appears to be more common in basic medical science courses than in clinical education. Distance learning does not preclude traditional learning processes; frequently it is used in conjunction with in-person classroom or professional training procedures and practices. Tele-education has mostly been used in biomedical education as a blended learning method, which combines tele-education technology with traditional instructor-led training, where, for example, a lecture or demonstration is supplemented by an online tutorial. Distance learning is used for self-education, tests, services and for examinations in medicine i.e. in terms of self-education and individual examination services. The possibility of working in the exercise mode with image files and questions is an attractive way of self education. Automated tracking and reporting of learners' activities lessen faculty administrative burden. Moreover, e-learning can be designed to include outcomes assessment to determine whether learning has occurred. This review article evaluates the current

  5. Frank Gilbreth and health care delivery method study driven learning.

    Science.gov (United States)

    Towill, Denis R

    2009-01-01

    The purpose of this article is to look at method study, as devised by the Gilbreths at the beginning of the twentieth century, which found early application in hospital quality assurance and surgical "best practice". It has since become a core activity in all modern methods, as applied to healthcare delivery improvement programmes. The article traces the origin of what is now currently and variously called "business process re-engineering", "business process improvement" and "lean healthcare" etc., by different management gurus back to the century-old pioneering work of Frank Gilbreth. The outcome is a consistent framework involving "width", "length" and "depth" dimensions within which healthcare delivery systems can be analysed, designed and successfully implemented to achieve better and more consistent performance. Healthcare method (saving time plus saving motion) study is best practised as co-joint action learning activity "owned" by all "players" involved in the re-engineering process. However, although process mapping is a key step forward, in itself it is no guarantee of effective re-engineering. It is not even the beginning of the end of the change challenge, although it should be the end of the beginning. What is needed is innovative exploitation of method study within a healthcare organisational learning culture accelerated via the Gilbreth Knowledge Flywheel. It is shown that effective healthcare delivery pipeline improvement is anchored into a team approach involving all "players" in the system especially physicians. A comprehensive process study, constructive dialogue, proper and highly professional re-engineering plus managed implementation are essential components. Experience suggests "learning" is thereby achieved via "natural groups" actively involved in healthcare processes. The article provides a proven method for exploiting Gilbreths' outputs and their many successors in enabling more productive evidence-based healthcare delivery as summarised

  6. Histogram specification as a method of density modification

    Energy Technology Data Exchange (ETDEWEB)

    Harrison, R.W.

    1988-12-01

    A new method for improving the quality and extending the resolution of Fourier maps is described. The method is based on a histogram analysis of the electron density. The distribution of electron density values in the map is forced to be 'ideal'. The 'ideal' distribution is assumed to be Gaussian. The application of the method to improve the electron density map for the protein Acinetobacter asparaginase, which is a tetrameric enzyme of molecular weight 140000 daltons, is described.

  7. Use of probabilistic methods for the improvement of technical specifications

    International Nuclear Information System (INIS)

    Gros, G.; Mattei, J.M.

    1987-11-01

    The technical specifications set the requirements related to the availability of equipment. In order to limit the risk when an unavailability is noticed during power operation, an allowed outage time is determined based on a fixed limit imposed on the increase of calculated core melt probability when the equipment is considered as unavailable. An example related to the main electric equipment is presented. In case of cold shutdown condition, the operations carried out lead to numerous programmed unavailabilities of equipment. For the examination of a project of technical specifications defining the necessary equipment in the diffrent phases encountered, evaluation of the increase of the core melt probability due to the unavaibilities has been made

  8. Non-Gaussian Methods for Causal Structure Learning.

    Science.gov (United States)

    Shimizu, Shohei

    2018-05-22

    Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be studied. Nevertheless, in many cases, classical methods for causal structure learning are not capable of estimating the causal structure of variables. This is because it explicitly or implicitly assumes Gaussianity of data and typically utilizes only the covariance structure. In many applications, however, non-Gaussian data are often obtained, which means that more information may be contained in the data distribution than the covariance matrix is capable of containing. Thus, many new methods have recently been proposed for using the non-Gaussian structure of data and inferring the causal structure of variables. This paper introduces prevention scientists to such causal structure learning methods, particularly those based on the linear, non-Gaussian, acyclic model known as LiNGAM. These non-Gaussian data analysis tools can fully estimate the underlying causal structures of variables under assumptions even in the presence of unobserved common causes. This feature is in contrast to other approaches. A simulated example is also provided.

  9. Waste classification and methods applied to specific disposal sites

    International Nuclear Information System (INIS)

    Rogers, V.C.

    1979-01-01

    An adequate definition of the classes of radioactive wastes is necessary to regulating the disposal of radioactive wastes. A classification system is proposed in which wastes are classified according to characteristics relating to their disposal. Several specific sites are analyzed with the methodology in order to gain insights into the classification of radioactive wastes. Also presented is the analysis of ocean dumping as it applies to waste classification. 5 refs

  10. Comparative Evaluations of Four Specification Methods for Real-Time Systems

    Science.gov (United States)

    1989-12-01

    December 1989 Comparative Evaluations of Four Specification Methods for Real - Time Systems David P. Wood William G. Wood Specification and Design Methods...Methods for Real - Time Systems Abstract: A number of methods have been proposed in the last decade for the specification of system and software requirements...and software specification for real - time systems . Our process for the identification of methods that meet the above criteria is described in greater

  11. Aggregative Learning Method and Its Application for Communication Quality Evaluation

    Science.gov (United States)

    Akhmetov, Dauren F.; Kotaki, Minoru

    2007-12-01

    In this paper, so-called Aggregative Learning Method (ALM) is proposed to improve and simplify the learning and classification abilities of different data processing systems. It provides a universal basis for design and analysis of mathematical models of wide class. A procedure was elaborated for time series model reconstruction and analysis for linear and nonlinear cases. Data approximation accuracy (during learning phase) and data classification quality (during recall phase) are estimated from introduced statistic parameters. The validity and efficiency of the proposed approach have been demonstrated through its application for monitoring of wireless communication quality, namely, for Fixed Wireless Access (FWA) system. Low memory and computation resources were shown to be needed for the procedure realization, especially for data classification (recall) stage. Characterized with high computational efficiency and simple decision making procedure, the derived approaches can be useful for simple and reliable real-time surveillance and control system design.

  12. Learning and retention of quantum concepts with different teaching methods

    Science.gov (United States)

    Deslauriers, Louis; Wieman, Carl

    2011-06-01

    We measured mastery and retention of conceptual understanding of quantum mechanics in a modern physics course. This was studied for two equivalent cohorts of students taught with different pedagogical approaches using the Quantum Mechanics Conceptual Survey. We measured the impact of pedagogical approach both on the original conceptual learning and on long-term retention. The cohort of students who had a very highly rated traditional lecturer scored 19% lower than the equivalent cohort that was taught using interactive engagement methods. However, the amount of retention was very high for both cohorts, showing only a few percent decrease in scores when retested 6 and 18 months after completion of the course and with no exposure to the material in the interim period. This high level of retention is in striking contrast to the retention measured for more factual learning from university courses and argues for the value of emphasizing conceptual learning.

  13. The Graduating European Dentist: Contemporaneous Methods of Teaching, Learning and Assessment in Dental Undergraduate Education.

    Science.gov (United States)

    Field, J C; Walmsley, A D; Paganelli, C; McLoughlin, J; Szep, S; Kavadella, A; Manzanares Cespedes, M C; Davies, J R; DeLap, E; Levy, G; Gallagher, J; Roger-Leroi, V; Cowpe, J G

    2017-12-01

    It is often the case that good teachers just "intuitively" know how to teach. Whilst that may be true, there is now a greater need to understand the various processes that underpin both the ways in which a curriculum is delivered, and the way in which the students engage with learning; curricula need to be designed to meet the changing needs of our new graduates, providing new, and robust learning opportunities, and be communicated effectively to both staff and students. The aim of this document is to draw together robust and contemporaneous methods of teaching, learning and assessment that help to overcome some of the more traditional barriers within dental undergraduate programmes. The methods have been chosen to map specifically to The Graduating European Dentist, and should be considered in parallel with the benchmarking process that educators and institutions employ locally. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  14. Sunspot drawings handwritten character recognition method based on deep learning

    Science.gov (United States)

    Zheng, Sheng; Zeng, Xiangyun; Lin, Ganghua; Zhao, Cui; Feng, Yongli; Tao, Jinping; Zhu, Daoyuan; Xiong, Li

    2016-05-01

    High accuracy scanned sunspot drawings handwritten characters recognition is an issue of critical importance to analyze sunspots movement and store them in the database. This paper presents a robust deep learning method for scanned sunspot drawings handwritten characters recognition. The convolution neural network (CNN) is one algorithm of deep learning which is truly successful in training of multi-layer network structure. CNN is used to train recognition model of handwritten character images which are extracted from the original sunspot drawings. We demonstrate the advantages of the proposed method on sunspot drawings provided by Chinese Academy Yunnan Observatory and obtain the daily full-disc sunspot numbers and sunspot areas from the sunspot drawings. The experimental results show that the proposed method achieves a high recognition accurate rate.

  15. A diagram retrieval method with multi-label learning

    Science.gov (United States)

    Fu, Songping; Lu, Xiaoqing; Liu, Lu; Qu, Jingwei; Tang, Zhi

    2015-01-01

    In recent years, the retrieval of plane geometry figures (PGFs) has attracted increasing attention in the fields of mathematics education and computer science. However, the high cost of matching complex PGF features leads to the low efficiency of most retrieval systems. This paper proposes an indirect classification method based on multi-label learning, which improves retrieval efficiency by reducing the scope of compare operation from the whole database to small candidate groups. Label correlations among PGFs are taken into account for the multi-label classification task. The primitive feature selection for multi-label learning and the feature description of visual geometric elements are conducted individually to match similar PGFs. The experiment results show the competitive performance of the proposed method compared with existing PGF retrieval methods in terms of both time consumption and retrieval quality.

  16. Learning Method and Its Influence on Nutrition Study Results Throwing the Ball

    Science.gov (United States)

    Samsudin; Nugraha, Bayu

    2015-01-01

    This study aimed to know the difference between playing and learning methods of exploratory learning methods to learning outcomes throwing the ball. In addition, this study also aimed to determine the effect of nutritional status of these two learning methods mentioned above. This research was conducted at SDN Cipinang Besar Selatan 16 Pagi East…

  17. Effects of practice schedule and task specificity on the adaptive process of motor learning.

    Science.gov (United States)

    Barros, João Augusto de Camargo; Tani, Go; Corrêa, Umberto Cesar

    2017-10-01

    This study investigated the effects of practice schedule and task specificity based on the perspective of adaptive process of motor learning. For this purpose, tasks with temporal and force control learning requirements were manipulated in experiments 1 and 2, respectively. Specifically, the task consisted of touching with the dominant hand the three sequential targets with specific movement time or force for each touch. Participants were children (N=120), both boys and girls, with an average age of 11.2years (SD=1.0). The design in both experiments involved four practice groups (constant, random, constant-random, and random-constant) and two phases (stabilisation and adaptation). The dependent variables included measures related to the task goal (accuracy and variability of error of the overall movement and force patterns) and movement pattern (macro- and microstructures). Results revealed a similar error of the overall patterns for all groups in both experiments and that they adapted themselves differently in terms of the macro- and microstructures of movement patterns. The study concludes that the effects of practice schedules on the adaptive process of motor learning were both general and specific to the task. That is, they were general to the task goal performance and specific regarding the movement pattern. Copyright © 2017 Elsevier B.V. All rights reserved.

  18. Advanced Steel Microstructural Classification by Deep Learning Methods.

    Science.gov (United States)

    Azimi, Seyed Majid; Britz, Dominik; Engstler, Michael; Fritz, Mario; Mücklich, Frank

    2018-02-01

    The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Network (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.

  19. Hybrid Method for Mobile learning Cooperative: Study of Timor Leste

    Science.gov (United States)

    da Costa Tavares, Ofelia Cizela; Suyoto; Pranowo

    2018-02-01

    In the modern world today the decision support system is very useful to help in solving a problem, so this study discusses the learning process of savings and loan cooperatives in Timor Leste. The purpose of the observation is that the people of Timor Leste are still in the process of learning the use DSS for good saving and loan cooperative process. Based on existing research on the Timor Leste community on credit cooperatives, a mobile application will be built that will help the cooperative learning process in East Timorese society. The methods used for decision making are AHP (Analytical Hierarchy Process) and SAW (simple additive Weighting) method to see the result of each criterion and the weight of the value. The result of this research is mobile leaning cooperative in decision support system by using SAW and AHP method. Originality Value: Changed the two methods of mobile application development using AHP and SAW methods to help the decision support system process of a savings and credit cooperative in Timor Leste.

  20. Hybrid Method for Mobile learning Cooperative: Study of Timor Leste

    Directory of Open Access Journals (Sweden)

    da Costa Tavares Ofelia Cizela

    2018-01-01

    Full Text Available In the modern world today the decision support system is very useful to help in solving a problem, so this study discusses the learning process of savings and loan cooperatives in Timor Leste. The purpose of the observation is that the people of Timor Leste are still in the process of learning the use DSS for good saving and loan cooperative process. Based on existing research on the Timor Leste community on credit cooperatives, a mobile application will be built that will help the cooperative learning process in East Timorese society. The methods used for decision making are AHP (Analytical Hierarchy Process and SAW (simple additive Weighting method to see the result of each criterion and the weight of the value. The result of this research is mobile leaning cooperative in decision support system by using SAW and AHP method. Originality Value: Changed the two methods of mobile application development using AHP and SAW methods to help the decision support system process of a savings and credit cooperative in Timor Leste.

  1. First-order and higher order sequence learning in specific language impairment.

    Science.gov (United States)

    Clark, Gillian M; Lum, Jarrad A G

    2017-02-01

    A core claim of the procedural deficit hypothesis of specific language impairment (SLI) is that the disorder is associated with poor implicit sequence learning. This study investigated whether implicit sequence learning problems in SLI are present for first-order conditional (FOC) and higher order conditional (HOC) sequences. Twenty-five children with SLI and 27 age-matched, nonlanguage-impaired children completed 2 serial reaction time tasks. On 1 version, the sequence to be implicitly learnt comprised a FOC sequence and on the other a HOC sequence. Results showed that the SLI group learned the HOC sequence (η p ² = .285, p = .005) but not the FOC sequence (η p ² = .099, p = .118). The control group learned both sequences (FOC η p ² = .497, HOC η p 2= .465, ps < .001). The SLI group's difficulty learning the FOC sequence is consistent with the procedural deficit hypothesis. However, the study provides new evidence that multiple mechanisms may underpin the learning of FOC and HOC sequences. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  2. Agent-specific learning signals for self-other distinction during mentalising.

    Directory of Open Access Journals (Sweden)

    Sam Ereira

    2018-04-01

    Full Text Available Humans have a remarkable ability to simulate the minds of others. How the brain distinguishes between mental states attributed to self and mental states attributed to someone else is unknown. Here, we investigated how fundamental neural learning signals are selectively attributed to different agents. Specifically, we asked whether learning signals are encoded in agent-specific neural patterns or whether a self-other distinction depends on encoding agent identity separately from this learning signal. To examine this, we tasked subjects to learn continuously 2 models of the same environment, such that one was selectively attributed to self and the other was selectively attributed to another agent. Combining computational modelling with magnetoencephalography (MEG enabled us to track neural representations of prediction errors (PEs and beliefs attributed to self, and of simulated PEs and beliefs attributed to another agent. We found that the representational pattern of a PE reliably predicts the identity of the agent to whom the signal is attributed, consistent with a neural self-other distinction implemented via agent-specific learning signals. Strikingly, subjects exhibiting a weaker neural self-other distinction also had a reduced behavioural capacity for self-other distinction and displayed more marked subclinical psychopathological traits. The neural self-other distinction was also modulated by social context, evidenced in a significantly reduced decoding of agent identity in a nonsocial control task. Thus, we show that self-other distinction is realised through an encoding of agent identity intrinsic to fundamental learning signals. The observation that the fidelity of this encoding predicts psychopathological traits is of interest as a potential neurocomputational psychiatric biomarker.

  3. Machine learning methods without tears: a primer for ecologists.

    Science.gov (United States)

    Olden, Julian D; Lawler, Joshua J; Poff, N LeRoy

    2008-06-01

    Machine learning methods, a family of statistical techniques with origins in the field of artificial intelligence, are recognized as holding great promise for the advancement of understanding and prediction about ecological phenomena. These modeling techniques are flexible enough to handle complex problems with multiple interacting elements and typically outcompete traditional approaches (e.g., generalized linear models), making them ideal for modeling ecological systems. Despite their inherent advantages, a review of the literature reveals only a modest use of these approaches in ecology as compared to other disciplines. One potential explanation for this lack of interest is that machine learning techniques do not fall neatly into the class of statistical modeling approaches with which most ecologists are familiar. In this paper, we provide an introduction to three machine learning approaches that can be broadly used by ecologists: classification and regression trees, artificial neural networks, and evolutionary computation. For each approach, we provide a brief background to the methodology, give examples of its application in ecology, describe model development and implementation, discuss strengths and weaknesses, explore the availability of statistical software, and provide an illustrative example. Although the ecological application of machine learning approaches has increased, there remains considerable skepticism with respect to the role of these techniques in ecology. Our review encourages a greater understanding of machin learning approaches and promotes their future application and utilization, while also providing a basis from which ecologists can make informed decisions about whether to select or avoid these approaches in their future modeling endeavors.

  4. Affordances of telecollaboration tools for English for Specific Purposes online learning

    Directory of Open Access Journals (Sweden)

    Ana Sevilla-Pavón

    2016-11-01

    Full Text Available This paper explores students’ perceptions of the affordances of different telecollaboration tools used in an innovation project for English for Specific Purposes online learning carried out between the University of Valencia (Spain and Wofford College (South Carolina, United States during the school year 2015-2016. Different tools for synchronous and asynchronous communication were used. The asynchronous tools included a discussion forum, a wiki, social networking websites and Google forms; while the tools used for synchronous communication were text, voice and video chat, videoconferencing tools and Google Drive. All the tools were accessible through the online platform used in the project, Google+. By using these tools, students from both sides of the Atlantic Ocean carried out a number of activities and tasks through online telecollaborative methods, involving both synchronous and asynchronous communication. The tasks completed by students through the use of the different tools were aimed at fostering distance online collaboration among American and Spanish students for the development of their linguistic, intercultural and digital literacies.

  5. Crop-Specific Grafting Methods, Rootstocks and Scheduling-Tomato

    Science.gov (United States)

    Grafting has gained popularity as a method to manage plant diseases previously controlled by soil fumigation with methyl bromide. Some of the most significant soilborne pest problems for which resistant rootstocks may be beneficial include root-knot nematodes, Verticillium wilt, and southern blight....

  6. Electrospinning versus fibre production methods: from specifics to technological convergence.

    Science.gov (United States)

    Luo, C J; Stoyanov, Simeon D; Stride, E; Pelan, E; Edirisinghe, M

    2012-07-07

    Academic and industrial research on nanofibres is an area of increasing global interest, as seen in the continuously multiplying number of research papers and patents and the broadening range of chemical, medical, electrical and environmental applications. This in turn expands the size of the market opportunity and is reflected in the significant rise of entrepreneurial activities and investments in the field. Electrospinning is probably the most researched top-down method to form nanofibres from a remarkable range of organic and inorganic materials. It is well known and discussed in many comprehensive studies, so why this review? As we read about yet another "novel" method producing multifunctional nanomaterials in grams or milligrams in the laboratory, there is hardly any research addressing how these methods can be safely, consistently and cost-effectively up-scaled. Despite two decades of governmental and private investment, the productivity of nanofibre forming methods is still struggling to meet the increasing demand. This hinders the further integration of nanofibres into practical large-scale applications and limits current uses to niche-markets. Looking into history, this large gap between supply and demand of synthetic fibres was seen and addressed in conventional textile production a century ago. The remarkable achievement was accomplished via extensive collaborative research between academia and industry, applying ingenious solutions and technological convergence from polymer chemistry, physical chemistry, materials science and engineering disciplines. Looking into the present, current advances in electrospinning and nanofibre production are showing similar interdisciplinary technological convergence, and knowledge of industrial textile processing is being combined with new developments in nanofibre forming methods. Moreover, many important parameters in electrospinning and nanofibre spinning methods overlap parameters extensively studied in industrial

  7. Enhanced Assessment Technology and Neurocognitive Aspects of Specific Learning Disorder with Impairment in Mathematics.

    Directory of Open Access Journals (Sweden)

    Marios A. Pappas

    2018-02-01

    Full Text Available Specific Learning Disorder with impairment in Mathematics (Developmental Dyscalculia is a complex learning disorder which affects arithmetic skills, symbolic magnitude processing, alertness, flexibility in problem solving and maintained attention. Neuro-cognitive studies revealed that such difficulties in children with DD could be related to poor Working Memory and attention deficits. Furthermore, neuroimaging studies indicate that brain structure differences in children with DD compared to typically developing children could affect mathematical performance. In this study we present the cognitive profile of Dyscalculia, as well as the neuropsychological aspects of the deficit, with special reference to the utilization of enhanced assessment technology such as computerized neuropsychological tools and neuroimaging techniques.

  8. Grammar predicts procedural learning and consolidation deficits in children with Specific Language Impairment.

    Science.gov (United States)

    Hedenius, Martina; Persson, Jonas; Tremblay, Antoine; Adi-Japha, Esther; Veríssimo, João; Dye, Cristina D; Alm, Per; Jennische, Margareta; Bruce Tomblin, J; Ullman, Michael T

    2011-01-01

    The Procedural Deficit Hypothesis (PDH) posits that Specific Language Impairment (SLI) can be largely explained by abnormalities of brain structures that subserve procedural memory. The PDH predicts impairments of procedural memory itself, and that such impairments underlie the grammatical deficits observed in the disorder. Previous studies have indeed reported procedural learning impairments in SLI, and have found that these are associated with grammatical difficulties. The present study extends this research by examining consolidation and longer-term procedural sequence learning in children with SLI. The Alternating Serial Reaction Time (ASRT) task was given to children with SLI and typically developing (TD) children in an initial learning session and an average of three days later to test for consolidation and longer-term learning. Although both groups showed evidence of initial sequence learning, only the TD children showed clear signs of consolidation, even though the two groups did not differ in longer-term learning. When the children were re-categorized on the basis of grammar deficits rather than broader language deficits, a clearer pattern emerged. Whereas both the grammar impaired and normal grammar groups showed evidence of initial sequence learning, only those with normal grammar showed consolidation and longer-term learning. Indeed, the grammar-impaired group appeared to lose any sequence knowledge gained during the initial testing session. These findings held even when controlling for vocabulary or a broad non-grammatical language measure, neither of which were associated with procedural memory. When grammar was examined as a continuous variable over all children, the same relationships between procedural memory and grammar, but not vocabulary or the broader language measure, were observed. Overall, the findings support and further specify the PDH. They suggest that consolidation and longer-term procedural learning are impaired in SLI, but that these

  9. Grammar Predicts Procedural Learning and Consolidation Deficits in Children with Specific Language Impairment

    Science.gov (United States)

    Hedenius, Martina; Persson, Jonas; Tremblay, Antoine; Adi-Japha, Esther; Veríssimo, João; Dye, Cristina D.; Alm, Per; Jennische, Margareta; Tomblin, J. Bruce; Ullman, Michael T.

    2011-01-01

    The Procedural Deficit Hypothesis (PDH) posits that Specific Language Impairment (SLI) can be largely explained by abnormalities of brain structures that subserve procedural memory. The PDH predicts impairments of procedural memory itself, and that such impairments underlie the grammatical deficits observed in the disorder. Previous studies have indeed reported procedural learning impairments in SLI, and have found that these are associated with grammatical difficulties. The present study extends this research by examining the consolidation and longer-term procedural sequence learning in children with SLI. The Alternating Serial Reaction Time (ASRT) task was given to children with SLI and typically-developing (TD) children in an initial learning session and an average of three days later to test for consolidation and longer-term learning. Although both groups showed evidence of initial sequence learning, only the TD children showed clear signs of consolidation, even though the two groups did not differ in longer-term learning. When the children were re-categorized on the basis of grammar deficits rather than broader language deficits, a clearer pattern emerged. Whereas both the grammar impaired and normal grammar groups showed evidence of initial sequence learning, only those with normal grammar showed consolidation and longer-term learning. Indeed, the grammar-impaired group appeared to lose any sequence knowledge gained during the initial testing session. These findings held even when controlling for vocabulary or a broad non-grammatical language measure, neither of which were associated with procedural memory. When grammar was examined as a continuous variable over all children, the same relationships between procedural memory and grammar, but not vocabulary or the broader language measure, were observed. Overall, the findings support and further specify the PDH. They suggest that consolidation and longer-term procedural learning are impaired in SLI, but that

  10. Different levels of food restriction reveal genotype-specific differences in learning a visual discrimination task.

    Directory of Open Access Journals (Sweden)

    Kalina Makowiecki

    Full Text Available In behavioural experiments, motivation to learn can be achieved using food rewards as positive reinforcement in food-restricted animals. Previous studies reduce animal weights to 80-90% of free-feeding body weight as the criterion for food restriction. However, effects of different degrees of food restriction on task performance have not been assessed. We compared learning task performance in mice food-restricted to 80 or 90% body weight (BW. We used adult wildtype (WT; C57Bl/6j and knockout (ephrin-A2⁻/⁻ mice, previously shown to have a reverse learning deficit. Mice were trained in a two-choice visual discrimination task with food reward as positive reinforcement. When mice reached criterion for one visual stimulus (80% correct in three consecutive 10 trial sets they began the reverse learning phase, where the rewarded stimulus was switched to the previously incorrect stimulus. For the initial learning and reverse phase of the task, mice at 90%BW took almost twice as many trials to reach criterion as mice at 80%BW. Furthermore, WT 80 and 90%BW groups significantly differed in percentage correct responses and learning strategy in the reverse learning phase, whereas no differences between weight restriction groups were observed in ephrin-A2⁻/⁻ mice. Most importantly, genotype-specific differences in reverse learning strategy were only detected in the 80%BW groups. Our results indicate that increased food restriction not only results in better performance and a shorter training period, but may also be necessary for revealing behavioural differences between experimental groups. This has important ethical and animal welfare implications when deciding extent of diet restriction in behavioural studies.

  11. The Effect of Using Cooperative Learning Method on Tenth Grade Students' Learning Achievement and Attitude towards Biology

    Science.gov (United States)

    Rabgay, Tshewang

    2018-01-01

    The study investigated the effect of using cooperative learning method on tenth grade students' learning achievement in biology and their attitude towards the subject in a Higher Secondary School in Bhutan. The study used a mixed method approach. The quantitative component included an experimental design where cooperative learning was the…

  12. Introduction of active learning method in learning physiology by MBBS students.

    Science.gov (United States)

    Gilkar, Suhail Ahmad; Lone, Shabiruddin; Lone, Riyaz Ahmad

    2016-01-01

    Active learning has received considerable attention over the past several years, often presented or perceived as a radical change from traditional instruction methods. Current research on learning indicates that using a variety of teaching strategies in the classroom increases student participation and learning. To introduce active learning methodology, i.e., "jigsaw technique" in undergraduate medical education and assess the student and faculty response to it. This study was carried out in the Department of Physiology in a Medical College of North India. A topic was chosen and taught using one of the active learning methods (ALMs), i.e., jigsaw technique. An instrument (questionnaire) was developed in English through an extensive review of literature and was properly validated. The students were asked to give their response on a five-point Likert scale. The feedback was kept anonymous. Faculty also provided their feedback in a separately provided feedback proforma. The data were collected, compiled, and analyzed. Of 150 students of MBBS-first year batch 2014, 142 participated in this study along with 14 faculty members of the Physiology Department. The majority of the students (>90%) did welcome the introduction of ALM and strongly recommended the use of such methods in teaching many more topics in future. 100% faculty members were of the opinion that many more topics shall be taken up using ALMs. This study establishes the fact that both the medical students and faculty want a change from the traditional way of passive, teacher-centric learning, to the more active teaching-learning techniques.

  13. Non-adjacent dependency learning in Cantonese-speaking\\ud children with and without a history of specific language\\ud impairment

    OpenAIRE

    Iao, L-S; Ng, LY; Wong, AMY; Lee, OT

    2017-01-01

    Purpose: This study investigated non-adjacent dependency learning in Cantonese-speaking children with and without a history of Specific Language Impairment (SLI) in an artificial linguistic context.\\ud \\ud Method: Sixteen Cantonese-speaking children with SLI history and 16 Cantonese-speaking children with typical language development (TLD) were tested with a non-adjacent dependency learning task using artificial languages that mimic Cantonese.\\ud \\ud Results: Children with TLD performed above...

  14. Data Mining and Machine Learning Methods for Dementia Research.

    Science.gov (United States)

    Li, Rui

    2018-01-01

    Patient data in clinical research often includes large amounts of structured information, such as neuroimaging data, neuropsychological test results, and demographic variables. Given the various sources of information, we can develop computerized methods that can be a great help to clinicians to discover hidden patterns in the data. The computerized methods often employ data mining and machine learning algorithms, lending themselves as the computer-aided diagnosis (CAD) tool that assists clinicians in making diagnostic decisions. In this chapter, we review state-of-the-art methods used in dementia research, and briefly introduce some recently proposed algorithms subsequently.

  15. A Pharmacy Preregistration Course Using Online Teaching and Learning Methods

    Science.gov (United States)

    McDowell, Jenny; Marriott, Jennifer L.; Calandra, Angela; Duncan, Gregory

    2009-01-01

    Objective To design and evaluate a preregistration course utilizing asynchronous online learning as the primary distance education delivery method. Design Online course components including tutorials, quizzes, and moderated small-group asynchronous case-based discussions were implemented. Online delivery was supplemented with self-directed and face-to-face learning. Assessment Pharmacy graduates who had completed the course in 2004 and 2005 were surveyed. The majority felt they had benefited from all components of the course, and that online delivery provided benefits including increased peer support, shared learning, and immediate feedback on performance. A majority of the first cohort reported that the workload associated with asynchronous online discussions was too great. The course was altered in 2005 to reduce the online component. Participant satisfaction improved, and most felt that the balance of online to face-to-face delivery was appropriate. Conclusion A new pharmacy preregistration course was successfully implemented. Online teaching and learning was well accepted and appeared to deliver benefits over traditional distance education methods once workload issues were addressed. PMID:19777092

  16. Clinical and academic profile of children with specific learning disorder-mixed type: An Indian study

    Directory of Open Access Journals (Sweden)

    Anamika Sahu

    2017-01-01

    Full Text Available Background: Specific learning disorder (SLD in the past decade has gained recognition as a disabling condition among children by parents and teachers in India. However, there are still gaps in knowledge about its clinical presentation and understanding. Therefore, the present study was planned to evaluate the clinical and academic profile of children with SLD. Methods: The sample comprised 30 children with their age range between 7 and 12 years with a diagnosis of SLD-mixed type. All children were assessed through specifically designed structured pro forma for clinical details (i.e., nature of birth, developmental milestones, and comorbidities and academic history (i.e., history of failure, promoted in next class, repetition in the class, school change, etc. and SLD-comprehensive battery. Results: The mean age of the participants was 9.6 years (standard deviation [SD] = 1.5. 76.7% of participants were male and their mean years of education was 4.7 (SD = 1.5. Thirty percent of children had a history of delayed developmental milestones in terms of speech (16.7%, walking (6.7% and in speech and walking (6.7%, 23% of children had comorbid conditions of attention-deficit/hyperactivity disorder/attention-deficit disorder. Thirty percent of children repeated classes in their academic career. Conclusions: A significant number of children had delayed milestones and other problems. Moreover, it is important to understand the clinical and academic profile in the cultural context so that early identification and intervention can be planned.

  17. Sensitivity and specificity of machine learning classifiers and spectral domain OCT for the diagnosis of glaucoma.

    Science.gov (United States)

    Vidotti, Vanessa G; Costa, Vital P; Silva, Fabrício R; Resende, Graziela M; Cremasco, Fernanda; Dias, Marcelo; Gomi, Edson S

    2012-06-15

    Purpose. To investigate the sensitivity and specificity of machine learning classifiers (MLC) and spectral domain optical coherence tomography (SD-OCT) for the diagnosis of glaucoma. Methods. Sixty-two patients with early to moderate glaucomatous visual field damage and 48 healthy individuals were included. All subjects underwent a complete ophthalmologic examination, achromatic standard automated perimetry, and RNFL imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec, Inc., Dublin, California, USA). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters. Subsequently, the following MLCs were tested: Classification Tree (CTREE), Random Forest (RAN), Bagging (BAG), AdaBoost M1 (ADA), Ensemble Selection (ENS), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Naive-Bayes (NB), and Support Vector Machine (SVM). Areas under the ROC curves (aROCs) obtained for each parameter and each MLC were compared. Results. The mean age was 57.0±9.2 years for healthy individuals and 59.9±9.0 years for glaucoma patients (p=0.103). Mean deviation values were -4.1±2.4 dB for glaucoma patients and -1.5±1.6 dB for healthy individuals (pposition (0.765), and 6 o'clock position (0.754). The aROCs from classifiers varied from 0.785 (ADA) to 0.818 (BAG). The aROC obtained with BAG was not significantly different from the aROC obtained with the best single SD-OCT parameter (p=0.93). Conclusions. The SD-OCT showed good diagnostic accuracy in a group of patients with early glaucoma. In this series, MLCs did not improve the sensitivity and specificity of SD-OCT for the diagnosis of glaucoma.

  18. Methodological proposal for environmental impact evaluation since different specific methods

    International Nuclear Information System (INIS)

    Leon Pelaez, Juan Diego; Lopera Arango Gabriel Jaime

    1999-01-01

    Some conceptual and practical elements related to environmental impact evaluation are described and related to the preparation of technical reports (environmental impact studies and environmental management plans) to be presented to environmental authorities for obtaining the environmental permits for development projects. In the first part of the document a summary of the main aspects of normative type is made that support the studies of environmental impact in Colombia. We propose a diagram for boarding and elaboration of the evaluation of environmental impact, which begins with the description of the project and of the environmental conditions in the area of the same. Passing then to identify the impacts through a method matricial and continuing with the quantitative evaluation of the same. For which we propose the use of the method developed by Arboleda (1994). Also we propose to qualify the activities of the project and the components of the environment in their relative importance, by means of a method here denominated agglomerate evaluation. Which allows finding those activities more impacting and the mostly impacted components. Lastly it is presented some models for the elaboration and presentation of the environmental management plans. The pursuit programs and those of environmental supervision

  19. Improve Biomedical Information Retrieval using Modified Learning to Rank Methods.

    Science.gov (United States)

    Xu, Bo; Lin, Hongfei; Lin, Yuan; Ma, Yunlong; Yang, Liang; Wang, Jian; Yang, Zhihao

    2016-06-14

    In these years, the number of biomedical articles has increased exponentially, which becomes a problem for biologists to capture all the needed information manually. Information retrieval technologies, as the core of search engines, can deal with the problem automatically, providing users with the needed information. However, it is a great challenge to apply these technologies directly for biomedical retrieval, because of the abundance of domain specific terminologies. To enhance biomedical retrieval, we propose a novel framework based on learning to rank. Learning to rank is a series of state-of-the-art information retrieval techniques, and has been proved effective in many information retrieval tasks. In the proposed framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents, but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate the effectiveness of our framework for biomedical information retrieval.

  20. Predicting Solar Activity Using Machine-Learning Methods

    Science.gov (United States)

    Bobra, M.

    2017-12-01

    Of all the activity observed on the Sun, two of the most energetic events are flares and coronal mass ejections. However, we do not, as of yet, fully understand the physical mechanism that triggers solar eruptions. A machine-learning algorithm, which is favorable in cases where the amount of data is large, is one way to [1] empirically determine the signatures of this mechanism in solar image data and [2] use them to predict solar activity. In this talk, we discuss the application of various machine learning algorithms - specifically, a Support Vector Machine, a sparse linear regression (Lasso), and Convolutional Neural Network - to image data from the photosphere, chromosphere, transition region, and corona taken by instruments aboard the Solar Dynamics Observatory in order to predict solar activity on a variety of time scales. Such an approach may be useful since, at the present time, there are no physical models of flares available for real-time prediction. We discuss our results (Bobra and Couvidat, 2015; Bobra and Ilonidis, 2016; Jonas et al., 2017) as well as other attempts to predict flares using machine-learning (e.g. Ahmed et al., 2013; Nishizuka et al. 2017) and compare these results with the more traditional techniques used by the NOAA Space Weather Prediction Center (Crown, 2012). We also discuss some of the challenges in using machine-learning algorithms for space science applications.

  1. Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models

    NARCIS (Netherlands)

    Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A.; van t Veld, Aart A.

    2012-01-01

    PURPOSE: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. METHODS AND MATERIALS: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator

  2. The Context-Specific Conceptions of Learning in Case-Based Accounting Assignments, Students' Characteristics and Performance

    Science.gov (United States)

    Moilanen, Sinikka

    2017-01-01

    The present study contributes to accounting education literature by describing context-specific conceptions of learning related to case assignments, and by exploring the associations between the conceptions of learning, students' characteristics and performance. The data analysed consist of 1320 learning diaries of 336 students, connected with…

  3. An Improved Sparse Representation over Learned Dictionary Method for Seizure Detection.

    Science.gov (United States)

    Li, Junhui; Zhou, Weidong; Yuan, Shasha; Zhang, Yanli; Li, Chengcheng; Wu, Qi

    2016-02-01

    Automatic seizure detection has played an important role in the monitoring, diagnosis and treatment of epilepsy. In this paper, a patient specific method is proposed for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. This seizure detection method is based on sparse representation with online dictionary learning and elastic net constraint. The online learned dictionary could sparsely represent the testing samples more accurately, and the elastic net constraint which combines the 11-norm and 12-norm not only makes the coefficients sparse but also avoids over-fitting problem. First, the EEG signals are preprocessed using wavelet filtering and differential filtering, and the kernel function is applied to make the samples closer to linearly separable. Then the dictionaries of seizure and nonseizure are respectively learned from original ictal and interictal training samples with online dictionary optimization algorithm to compose the training dictionary. After that, the test samples are sparsely coded over the learned dictionary and the residuals associated with ictal and interictal sub-dictionary are calculated, respectively. Eventually, the test samples are classified as two distinct categories, seizure or nonseizure, by comparing the reconstructed residuals. The average segment-based sensitivity of 95.45%, specificity of 99.08%, and event-based sensitivity of 94.44% with false detection rate of 0.23/h and average latency of -5.14 s have been achieved with our proposed method.

  4. Extracurricular activities and the development of social skills in children with intellectual and specific learning disabilities.

    Science.gov (United States)

    Brooks, B A; Floyd, F; Robins, D L; Chan, W Y

    2015-07-01

    Children with intellectual disability and specific learning disabilities often lack age-appropriate social skills, which disrupts their social functioning. Because of the limited effectiveness of classroom mainstreaming and social skills training for these children, it is important to explore alternative opportunities for social skill acquisition. Participation in social activities is positively related to children's social adjustment, but little is known about the benefits of activity participation for children with intellectual and specific learning disabilities. This study investigated the association between frequency and type of social activity participation and the social competence of 8-11-year-old children with intellectual disability (n = 40) and specific learning disabilities (n = 53), in comparison with typically developing peers (n = 24). More time involved in unstructured activities, but not structured activities, was associated with higher levels of social competence for all children. This association was strongest for children with intellectual disability, suggesting that participation in unstructured social activities was most beneficial for these children. Future research on the quality of involvement is necessary to further understand specific aspects of unstructured activities that might facilitate social development. © 2014 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

  5. Ants use partner specific odors to learn to recognize a mutualistic partner.

    Directory of Open Access Journals (Sweden)

    Masaru K Hojo

    Full Text Available Regulation via interspecific communication is an important for the maintenance of many mutualisms. However, mechanisms underlying the evolution of partner communication are poorly understood for many mutualisms. Here we show, in an ant-lycaenid butterfly mutualism, that attendant ants selectively learn to recognize and interact cooperatively with a partner. Workers of the ant Pristomyrmex punctatus learn to associate cuticular hydrocarbons of mutualistic Narathura japonica caterpillars with food rewards and, as a result, are more likely to tend the caterpillars. However, the workers do not learn to associate the cuticular hydrocarbons of caterpillars of a non-ant-associated lycaenid, Lycaena phlaeas, with artificial food rewards. Chemical analysis revealed cuticular hydrocarbon profiles of the mutualistic caterpillars were complex compared with those of non-ant-associated caterpillars. Our results suggest that partner-recognition based on partner-specific chemical signals and cognitive abilities of workers are important mechanisms underlying the evolution and maintenance of mutualism with ants.

  6. The Learners’ Attitudes towards Using Different Learning Methods in E-Learning Portal Environment

    Directory of Open Access Journals (Sweden)

    Issham Ismail

    2011-09-01

    Full Text Available This study investigates the learners’ preference of academic, collaborative and social interaction towards interaction methods in e-learning portal. Academic interaction consists of interaction between learners and online learning resources such as online reading, online explanation, online examination and also online question answering. Collaborative interaction occurs when learners interact among themselves using online group discussion. Social interaction happens when learners and instructors participate in the session either via online text chatting or voice chatting. The study employed qualitative methodology where data were collected through questionnaire that was administered to 933 distance education students from Bachelor of Management, Bachelor of Science, Bachelor of Social Science and Bachelor of Art. The survey responses were tabulated in a 5-point Likert scale and analyzed using the Statistical Package for Social Science (SPSS Version 12.0 based on frequency and percentage distribution. The result of the study suggest that among three types of interaction, most of the student prefer academic interaction for their learning supports in e-learning portal compared to collaborative and social interaction. They wish to interact with learning content rather than interact with people. They prefer to read and learn from the resources rather than sharing knowledge among themselves and instructors via collaborative and social interaction.

  7. Realization of Chinese word segmentation based on deep learning method

    Science.gov (United States)

    Wang, Xuefei; Wang, Mingjiang; Zhang, Qiquan

    2017-08-01

    In recent years, with the rapid development of deep learning, it has been widely used in the field of natural language processing. In this paper, I use the method of deep learning to achieve Chinese word segmentation, with large-scale corpus, eliminating the need to construct additional manual characteristics. In the process of Chinese word segmentation, the first step is to deal with the corpus, use word2vec to get word embedding of the corpus, each character is 50. After the word is embedded, the word embedding feature is fed to the bidirectional LSTM, add a linear layer to the hidden layer of the output, and then add a CRF to get the model implemented in this paper. Experimental results show that the method used in the 2014 People's Daily corpus to achieve a satisfactory accuracy.

  8. A Photometric Machine-Learning Method to Infer Stellar Metallicity

    Science.gov (United States)

    Miller, Adam A.

    2015-01-01

    Following its formation, a star's metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectroscopic surveys are limited to a few x 10(exp 6) targets; photometric surveys, on the other hand, have detected > 10(exp 9) stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of approx. 120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g' learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra..

  9. A Photometric Machine-Learning Method to Infer Stellar Metallicity

    Science.gov (United States)

    Miller, Adam A.

    2015-01-01

    Following its formation, a star's metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectroscopic surveys are limited to a few x 10(exp 6) targets; photometric surveys, on the other hand, have detected > 10(exp 9) stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of approx. 120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g' machine-learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra..

  10. Recommended environmental dose calculation methods and Hanford-specific parameters

    International Nuclear Information System (INIS)

    Schreckhise, R.G.; Rhoads, K.; Napier, B.A.; Ramsdell, J.V.; Davis, J.S.

    1993-03-01

    This document was developed to support the Hanford Environmental Dose overview Panel (HEDOP). The Panel is responsible for reviewing all assessments of potential doses received by humans and other biota resulting from the actual or possible environmental releases of radioactive and other hazardous materials from facilities and/or operations belonging to the US Department of Energy on the Hanford Site in south-central Washington. This document serves as a guide to be used for developing estimates of potential radiation doses, or other measures of risk or health impacts, to people and other biota in the environs on and around the Hanford Site. It provides information to develop technically sound estimates of exposure (i.e., potential or actual) to humans or other biotic receptors that could result from the environmental transport of potentially harmful materials that have been, or could be, released from Hanford operations or facilities. Parameter values and information that are specific to the Hanford environs as well as other supporting material are included in this document

  11. Recommended environmental dose calculation methods and Hanford-specific parameters

    Energy Technology Data Exchange (ETDEWEB)

    Schreckhise, R.G.; Rhoads, K.; Napier, B.A.; Ramsdell, J.V. (Pacific Northwest Lab., Richland, WA (United States)); Davis, J.S. (Westinghouse Hanford Co., Richland, WA (United States))

    1993-03-01

    This document was developed to support the Hanford Environmental Dose overview Panel (HEDOP). The Panel is responsible for reviewing all assessments of potential doses received by humans and other biota resulting from the actual or possible environmental releases of radioactive and other hazardous materials from facilities and/or operations belonging to the US Department of Energy on the Hanford Site in south-central Washington. This document serves as a guide to be used for developing estimates of potential radiation doses, or other measures of risk or health impacts, to people and other biota in the environs on and around the Hanford Site. It provides information to develop technically sound estimates of exposure (i.e., potential or actual) to humans or other biotic receptors that could result from the environmental transport of potentially harmful materials that have been, or could be, released from Hanford operations or facilities. Parameter values and information that are specific to the Hanford environs as well as other supporting material are included in this document.

  12. Learning trajectories for speech motor performance in children with specific language impairment.

    Science.gov (United States)

    Richtsmeier, Peter T; Goffman, Lisa

    2015-01-01

    Children with specific language impairment (SLI) often perform below expected levels, including on tests of motor skill and in learning tasks, particularly procedural learning. In this experiment we examined the possibility that children with SLI might also have a motor learning deficit. Twelve children with SLI and thirteen children with typical development (TD) produced complex nonwords in an imitation task. Productions were collected across three blocks, with the first and second blocks on the same day and the third block one week later. Children's lip movements while producing the nonwords were recorded using an Optotrak camera system. Movements were then analyzed for production duration and stability. Movement analyses indicated that both groups of children produced shorter productions in later blocks (corroborated by an acoustic analysis), and the rate of change was comparable for the TD and SLI groups. A nonsignificant trend for more stable productions was also observed in both groups. SLI is regularly accompanied by a motor deficit, and this study does not dispute that. However, children with SLI learned to make more efficient productions at a rate similar to their peers with TD, revealing some modification of the motor deficit associated with SLI. The reader will learn about deficits commonly associated with specific language impairment (SLI) that often occur alongside the hallmark language deficit. The authors present an experiment showing that children with SLI improved speech motor performance at a similar rate compared to typically developing children. The implication is that speech motor learning is not impaired in children with SLI. Copyright © 2015 Elsevier Inc. All rights reserved.

  13. Comparing three experiential learning methods and their effect on medical students' attitudes to learning communication skills.

    Science.gov (United States)

    Koponen, Jonna; Pyörälä, Eeva; Isotalus, Pekka

    2012-01-01

    Despite numerous studies exploring medical students' attitudes to communication skills learning (CSL), there are apparently no studies comparing different experiential learning methods and their influence on students' attitudes. We compared medical students' attitudes to learning communication skills before and after a communication course in the data as a whole, by gender and when divided into three groups using different methods. Second-year medical students (n = 129) were randomly assigned to three groups. In group A (n = 42) the theatre in education method, in group B (n = 44) simulated patients and in group C (n = 43) role-play were used. The data were gathered before and after the course using Communication Skills Attitude Scale. Students' positive attitudes to learning communication skills (PAS; positive attitude scale) increased significantly and their negative attitudes (NAS; negative attitude scale) decreased significantly between the beginning and end of the course. Female students had more positive attitudes than the male students. There were no significant differences in the three groups in the mean scores for PAS or NAS measured before or after the course. The use of experiential methods and integrating communication skills training with visits to health centres may help medical students to appreciate the importance of CSL.

  14. Housing Value Forecasting Based on Machine Learning Methods

    OpenAIRE

    Mu, Jingyi; Wu, Fang; Zhang, Aihua

    2014-01-01

    In the era of big data, many urgent issues to tackle in all walks of life all can be solved via big data technique. Compared with the Internet, economy, industry, and aerospace fields, the application of big data in the area of architecture is relatively few. In this paper, on the basis of the actual data, the values of Boston suburb houses are forecast by several machine learning methods. According to the predictions, the government and developers can make decisions about whether developing...

  15. Learning with Generalization Capability by Kernel Methods of Bounded Complexity

    Czech Academy of Sciences Publication Activity Database

    Kůrková, Věra; Sanguineti, M.

    2005-01-01

    Roč. 21, č. 3 (2005), s. 350-367 ISSN 0885-064X R&D Projects: GA AV ČR 1ET100300419 Institutional research plan: CEZ:AV0Z10300504 Keywords : supervised learning * generalization * model complexity * kernel methods * minimization of regularized empirical errors * upper bounds on rates of approximate optimization Subject RIV: BA - General Mathematics Impact factor: 1.186, year: 2005

  16. Safety based on organisational learning (SOL) - Conceptual approach and verification of a method for event analysis

    International Nuclear Information System (INIS)

    Miller, R.; Wilpert, B.; Fahlbruch, B.

    1999-01-01

    This paper discusses a method for analysing safety-relevant events in NPP which is known as 'SOL', safety based on organisational learning. After discussion of the specific organisational and psychological problems examined in the event analysis, the analytic process using the SOL approach is explained as well as the required general setting. The SOL approach has been tested both with scientific experiments and from the practical perspective, by operators of NPPs and experts from other branches of industry. (orig./CB) [de

  17. Later learning stages in procedural memory are impaired in children with Specific Language Impairment.

    Science.gov (United States)

    Desmottes, Lise; Meulemans, Thierry; Maillart, Christelle

    2016-01-01

    According to the Procedural Deficit Hypothesis (PDH), difficulties in the procedural memory system may contribute to the language difficulties encountered by children with Specific Language Impairment (SLI). Most studies investigating the PDH have used the sequence learning paradigm; however these studies have principally focused on initial sequence learning in a single practice session. The present study sought to extend these investigations by assessing the consolidation stage and longer-term retention of implicit sequence-specific knowledge in 42 children with or without SLI. Both groups of children completed a serial reaction time task and were tested 24h and one week after practice. Results showed that children with SLI succeeded as well as children with typical development (TD) in the early acquisition stage of the sequence learning task. However, as training blocks progressed, only TD children improved their sequence knowledge while children with SLI did not appear to evolve any more. Moreover, children with SLI showed a lack of the consolidation gains in sequence knowledge displayed by the TD children. Overall, these results were in line with the predictions of the PDH and suggest that later learning stages in procedural memory are impaired in SLI. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. A Doctoral Seminar in Qualitative Research Methods: Lessons Learned

    Directory of Open Access Journals (Sweden)

    Suzanne Franco

    2016-09-01

    Full Text Available New qualitative research methods continue to emerge in response to factors such as renewed interest in mixed methods, better understanding of the importance of a researcher’s philosophical stance, as well as the increased use of technology in data collection and analysis, to name a few. As a result, those facilitating research methods courses must revisit content and instructional strategies in order to prepare well-informed researchers. Approaches range from paradigm to pragmatic emphasis. This descriptive case study of a doctoral seminar for novice qualitative researchers describes the intricacies of the syllabus of a pragmatic approach in a constructivist/social constructionist learning environment. The purpose was to document the delivery and faculty/student interactions and reactions. Noteworthy were the contradictions and frustrations in the delivery as well as in student experiences. In the end, student input led to seminal learning experiences. The confirmation of the effectiveness of a constructivist/social constructivist learning environment is applicable to higher education pedagogy in general.

  19. Deep Learning Methods for Underwater Target Feature Extraction and Recognition

    Directory of Open Access Journals (Sweden)

    Gang Hu

    2018-01-01

    Full Text Available The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved.

  20. Cognitive profiles in bilingual children born to immigrant parents and Italian monolingual native children with specific learning disorders

    Directory of Open Access Journals (Sweden)

    Riva A

    2016-12-01

    Full Text Available Anna Riva, Renata Nacinovich, Nadia Bertuletti, Valentina Montrasi, Sara Marchetti, Francesca Neri, Monica Bomba Child and Adolescent Mental Health Department, University of Milan Bicocca, San Gerardo Hospital, Monza, Italy Purpose: The aim of this study is to compare the Wechsler Intelligence Scale for Children® – fourth edition IV (WISC IV intellectual profile of two groups of children with specific learning disorder, a group of bilingual children and a group of monolingual Italian children, in order to identify possible significant differences between them. Patients and methods: A group of 48 bilingual children and a group of 48 Italian monolingual children were included in this study. A preliminary comparison showed the homogeneity of the two groups regarding learning disorder typology and sociodemographic characteristics (age at WISC IV assessment, sex and years of education in Italy with the exception of socioeconomic status. Socioeconomic status was then used as a covariate in the analysis. Results: Even if the two groups were comparable in specific learning disorder severity and, in particular, in the text comprehension performance, our findings showed that the WISC IV performances of the bilingual group were significantly worse than the Italian group in Full Scale Intelligence Quotient (P=0.03, in General Ability Index (P=0.03, in Working Memory Index (P=0.009 and in some subtests and clusters requiring advanced linguistic abilities. Conclusion: These results support the hypothesis of a weakness in metalinguistic abilities in bilingual children with specific learning disorders than monolinguals. If confirmed, this result must be considered in the rehabilitation treatment. Keywords: children, bilingualism, WISC IV, SLD

  1. Issues in Learning About and Teaching Qualitative Research Methods and Methodology in the Social Sciences

    Directory of Open Access Journals (Sweden)

    Franz Breuer

    2007-01-01

    Full Text Available For many qualitative researchers in the social sciences, learning about and teaching qualitative research methods and methodology raises a number of questions. This topic was the focus of a symposium held during the Second Berlin Summer School for Qualitative Research Methods in July 2006. In this contribution, some of the issues discussed during the symposium are taken up and extended, and some basic dimensions underlying these issues are summarized. How qualitative research methods and methodology are taught is closely linked to the ways in which qualitative researchers in the social sciences conceptualize themselves and their discipline. In the following, we distinguish between a paradigmatic and a pragmatic view. From a pragmatic point of view, qualitative research methods are considered research strategies or techniques and can be taught in the sense of recipes with specific steps to be carried out. According to a paradigmatic point of view (strongly inspired by constructivism, qualitative research methods and methodology are conceptualized as a craft to be practiced together by a "master" and an "apprentice." Moreover, the teaching of qualitative research methods also depends heavily on the institutional standing of qualitative compared to quantitative research method. Based on these considerations, five basic dimensions of learning about and teaching qualitative research methods are suggested: ways of teaching (ranging from the presentation of textbook knowledge to cognitive apprenticeship and instructors' experience with these; institutional contexts, including their development and the teaching of qualitative research methods in other than university contexts; the "fit" between personality and method, including relevant personal skills and talents; and, as a special type of instructional context that increasingly has gained importance, distance learning and its implications for learning about and teaching qualitative research methods

  2. The Goal Specificity Effect on Strategy Use and Instructional Efficiency during Computer-Based Scientific Discovery Learning

    Science.gov (United States)

    Kunsting, Josef; Wirth, Joachim; Paas, Fred

    2011-01-01

    Using a computer-based scientific discovery learning environment on buoyancy in fluids we investigated the "effects of goal specificity" (nonspecific goals vs. specific goals) for two goal types (problem solving goals vs. learning goals) on "strategy use" and "instructional efficiency". Our empirical findings close an important research gap,…

  3. Application of machine learning methods for traffic signs recognition

    Science.gov (United States)

    Filatov, D. V.; Ignatev, K. V.; Deviatkin, A. V.; Serykh, E. V.

    2018-02-01

    This paper focuses on solving a relevant and pressing safety issue on intercity roads. Two approaches were considered for solving the problem of traffic signs recognition; the approaches involved neural networks to analyze images obtained from a camera in the real-time mode. The first approach is based on a sequential image processing. At the initial stage, with the help of color filters and morphological operations (dilatation and erosion), the area containing the traffic sign is located on the image, then the selected and scaled fragment of the image is analyzed using a feedforward neural network to determine the meaning of the found traffic sign. Learning of the neural network in this approach is carried out using a backpropagation method. The second approach involves convolution neural networks at both stages, i.e. when searching and selecting the area of the image containing the traffic sign, and when determining its meaning. Learning of the neural network in the second approach is carried out using the intersection over union function and a loss function. For neural networks to learn and the proposed algorithms to be tested, a series of videos from a dash cam were used that were shot under various weather and illumination conditions. As a result, the proposed approaches for traffic signs recognition were analyzed and compared by key indicators such as recognition rate percentage and the complexity of neural networks’ learning process.

  4. Kernel methods for interpretable machine learning of order parameters

    Science.gov (United States)

    Ponte, Pedro; Melko, Roger G.

    2017-11-01

    Machine learning is capable of discriminating phases of matter, and finding associated phase transitions, directly from large data sets of raw state configurations. In the context of condensed matter physics, most progress in the field of supervised learning has come from employing neural networks as classifiers. Although very powerful, such algorithms suffer from a lack of interpretability, which is usually desired in scientific applications in order to associate learned features with physical phenomena. In this paper, we explore support vector machines (SVMs), which are a class of supervised kernel methods that provide interpretable decision functions. We find that SVMs can learn the mathematical form of physical discriminators, such as order parameters and Hamiltonian constraints, for a set of two-dimensional spin models: the ferromagnetic Ising model, a conserved-order-parameter Ising model, and the Ising gauge theory. The ability of SVMs to provide interpretable classification highlights their potential for automating feature detection in both synthetic and experimental data sets for condensed matter and other many-body systems.

  5. A cross-benchmark comparison of 87 learning to rank methods

    NARCIS (Netherlands)

    Tax, N.; Bockting, S.; Hiemstra, D.

    2015-01-01

    Learning to rank is an increasingly important scientific field that comprises the use of machine learning for the ranking task. New learning to rank methods are generally evaluated on benchmark test collections. However, comparison of learning to rank methods based on evaluation results is hindered

  6. Characteristics and Consequences of Adult Learning Methods and Strategies. Practical Evaluation Reports, Volume 2, Number 1

    Science.gov (United States)

    Trivette, Carol M.; Dunst, Carl J.; Hamby, Deborah W.; O'Herin, Chainey E.

    2009-01-01

    The effectiveness of four adult learning methods (accelerated learning, coaching, guided design, and just-in-time training) constituted the focus of this research synthesis. Findings reported in "How People Learn" (Bransford et al., 2000) were used to operationally define six adult learning method characteristics, and to code and analyze…

  7. Actively Teaching Research Methods with a Process Oriented Guided Inquiry Learning Approach

    Science.gov (United States)

    Mullins, Mary H.

    2017-01-01

    Active learning approaches have shown to improve student learning outcomes and improve the experience of students in the classroom. This article compares a Process Oriented Guided Inquiry Learning style approach to a more traditional teaching method in an undergraduate research methods course. Moving from a more traditional learning environment to…

  8. Pitch Discrimination Learning: Specificity for Pitch and Harmonic Resolvability, and Electrophysiological Correlates

    OpenAIRE

    Carcagno, Samuele; Plack, Christopher J.

    2011-01-01

    Multiple-hour training on a pitch discrimination task dramatically decreases the threshold for detecting a pitch difference between two harmonic complexes. Here, we investigated the specificity of this perceptual learning with respect to the pitch and the resolvability of the trained harmonic complex, as well as its cortical electrophysiological correlates. We trained 24 participants for 12 h on a pitch discrimination task using one of four different harmonic complexes. The complexes differed...

  9. Nitric oxide regulates input specificity of long-term depression and context dependence of cerebellar learning.

    Directory of Open Access Journals (Sweden)

    Hideaki Ogasawara

    2007-01-01

    Full Text Available Recent studies have shown that multiple internal models are acquired in the cerebellum and that these can be switched under a given context of behavior. It has been proposed that long-term depression (LTD of parallel fiber (PF-Purkinje cell (PC synapses forms the cellular basis of cerebellar learning, and that the presynaptically synthesized messenger nitric oxide (NO is a crucial "gatekeeper" for LTD. Because NO diffuses freely to neighboring synapses, this volume learning is not input-specific and brings into question the biological significance of LTD as the basic mechanism for efficient supervised learning. To better characterize the role of NO in cerebellar learning, we simulated the sequence of electrophysiological and biochemical events in PF-PC LTD by combining established simulation models of the electrophysiology, calcium dynamics, and signaling pathways of the PC. The results demonstrate that the local NO concentration is critical for induction of LTD and for its input specificity. Pre- and postsynaptic coincident firing is not sufficient for a PF-PC synapse to undergo LTD, and LTD is induced only when a sufficient amount of NO is provided by activation of the surrounding PFs. On the other hand, above-adequate levels of activity in nearby PFs cause accumulation of NO, which also allows LTD in neighboring synapses that were not directly stimulated, ruining input specificity. These findings lead us to propose the hypothesis that NO represents the relevance of a given context and enables context-dependent selection of internal models to be updated. We also predict sparse PF activity in vivo because, otherwise, input specificity would be lost.

  10. A Survey on Domain-Specific Languages for Machine Learning in Big Data

    OpenAIRE

    Portugal, Ivens; Alencar, Paulo; Cowan, Donald

    2016-01-01

    The amount of data generated in the modern society is increasing rapidly. New problems and novel approaches of data capture, storage, analysis and visualization are responsible for the emergence of the Big Data research field. Machine Learning algorithms can be used in Big Data to make better and more accurate inferences. However, because of the challenges Big Data imposes, these algorithms need to be adapted and optimized to specific applications. One important decision made by software engi...

  11. Affordances of Telecollaboration tools for English for Specific Purposes online learning

    OpenAIRE

    Sevilla Pavón, Ana

    2016-01-01

    This paper explores students’ perceptions of the affordances of different telecollaboration tools used in an innovation project for English for Specific Purposes online learning carried out between the University of Valencia (Spain) and Wofford College (South Carolina, United States) during the school year 2015-2016. Different tools for synchronous and asynchronous communication were used. The asynchronous tools included a discussion forum, a wiki, social networking websites and Google forms;...

  12. Critical thinking instruction and technology enhanced learning from the student perspective: A mixed methods research study.

    Science.gov (United States)

    Swart, Ruth

    2017-03-01

    Critical thinking is acclaimed as a valuable asset for graduates from higher education programs. Technology has advanced in quantity and quality; recognized as a requirement of 21st century learners. A mixed methods research study was undertaken, examining undergraduate nursing student engagement with critical thinking instruction, platformed on two technology-enhanced learning environments: a classroom response system face-to-face in-class and an online discussion forum out-of-class. The Community of Inquiry framed the study capturing constructivist collaborative inquiry to support learning, and facilitate critical thinking capability. Inclusion of quantitative and qualitative data sources aimed to gather a comprehensive understanding of students' development of critical thinking and engagement with technology-enhanced learning. The findings from the students' perspectives were positive toward the inclusion of technology-enhanced learning, and use in supporting their development of critical thinking. Students considered the use of two forms of technology beneficial in meeting different needs and preferences, offering varied means to actively participate in learning. They valued critical thinking instruction being intentionally aligned with subject-specific content facilitating understanding, application, and relevance of course material. While the findings are limited to student participants, the instructional strategies and technology-enhanced learning identified as beneficial can inform course design for the development of critical thinking. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Effect of Chemistry Triangle Oriented Learning Media on Cooperative, Individual and Conventional Method on Chemistry Learning Result

    Science.gov (United States)

    Latisma D, L.; Kurniawan, W.; Seprima, S.; Nirbayani, E. S.; Ellizar, E.; Hardeli, H.

    2018-04-01

    The purpose of this study was to see which method are well used with the Chemistry Triangle-oriented learning media. This quasi experimental research involves first grade of senior high school students in six schools namely each two SMA N in Solok city, in Pasaman and two SMKN in Pariaman. The sampling technique was done by Cluster Random Sampling. Data were collected by test and analyzed by one-way anova and Kruskall Wallish test. The results showed that the high school students in Solok learning taught by cooperative method is better than the results of student learning taught by conventional and Individual methods, both for students who have high initial ability and low-ability. Research in SMK showed that the overall student learning outcomes taught by conventional method is better than the student learning outcomes taught by cooperative and individual methods. Student learning outcomes that have high initial ability taught by individual method is better than student learning outcomes that are taught by cooperative method and for students who have low initial ability, there is no difference in student learning outcomes taught by cooperative, individual and conventional methods. Learning in high school in Pasaman showed no significant difference in learning outcomes of the three methods undertaken.

  14. Classification of older adults with/without a fall history using machine learning methods.

    Science.gov (United States)

    Lin Zhang; Ou Ma; Fabre, Jennifer M; Wood, Robert H; Garcia, Stephanie U; Ivey, Kayla M; McCann, Evan D

    2015-01-01

    Falling is a serious problem in an aged society such that assessment of the risk of falls for individuals is imperative for the research and practice of falls prevention. This paper introduces an application of several machine learning methods for training a classifier which is capable of classifying individual older adults into a high risk group and a low risk group (distinguished by whether or not the members of the group have a recent history of falls). Using a 3D motion capture system, significant gait features related to falls risk are extracted. By training these features, classification hypotheses are obtained based on machine learning techniques (K Nearest-neighbour, Naive Bayes, Logistic Regression, Neural Network, and Support Vector Machine). Training and test accuracies with sensitivity and specificity of each of these techniques are assessed. The feature adjustment and tuning of the machine learning algorithms are discussed. The outcome of the study will benefit the prediction and prevention of falls.

  15. A machine learning approach for efficient uncertainty quantification using multiscale methods

    Science.gov (United States)

    Chan, Shing; Elsheikh, Ahmed H.

    2018-02-01

    Several multiscale methods account for sub-grid scale features using coarse scale basis functions. For example, in the Multiscale Finite Volume method the coarse scale basis functions are obtained by solving a set of local problems over dual-grid cells. We introduce a data-driven approach for the estimation of these coarse scale basis functions. Specifically, we employ a neural network predictor fitted using a set of solution samples from which it learns to generate subsequent basis functions at a lower computational cost than solving the local problems. The computational advantage of this approach is realized for uncertainty quantification tasks where a large number of realizations has to be evaluated. We attribute the ability to learn these basis functions to the modularity of the local problems and the redundancy of the permeability patches between samples. The proposed method is evaluated on elliptic problems yielding very promising results.

  16. Application of unsupervised learning methods in high energy physics

    Energy Technology Data Exchange (ETDEWEB)

    Koevesarki, Peter; Nuncio Quiroz, Adriana Elizabeth; Brock, Ian C. [Physikalisches Institut, Universitaet Bonn, Bonn (Germany)

    2011-07-01

    High energy physics is a home for a variety of multivariate techniques, mainly due to the fundamentally probabilistic behaviour of nature. These methods generally require training based on some theory, in order to discriminate a known signal from a background. Nevertheless, new physics can show itself in ways that previously no one thought about, and in these cases conventional methods give little or no help. A possible way to discriminate between known processes (like vector bosons or top-quark production) or look for new physics is using unsupervised machine learning to extract the features of the data. A technique was developed, based on the combination of neural networks and the method of principal curves, to find a parametrisation of the non-linear correlations of the data. The feasibility of the method is shown on ATLAS data.

  17. Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics

    Directory of Open Access Journals (Sweden)

    Vladimir S. Kublanov

    2017-01-01

    Full Text Available The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following machine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation results show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated feature set search achieved higher results than data set based on the principal components.

  18. Impact of parenting practices on parent-child relationships in children with specific learning disability

    Directory of Open Access Journals (Sweden)

    S Karande

    2011-01-01

    Full Text Available Background: Parents of children with specific learning disability (SpLD undergo stress in coping up with their child′s condition. Aims: To document the parenting practices of parents having a child with newly diagnosed SpLD and to analyze their impact on parent-child relationships. Settings and Design: Cross-sectional questionnaire-based study in our clinic. Materials and Methods: From May 2007 to January 2008, 150 parents (either mother or father of children consecutively diagnosed as having SpLD were enrolled. Parenting practices and parent-child relationships were measured by the Alabama Parenting Questionnaire-Parent Form (APQ-PF and the Parent Child Relationship Questionnaire (PCRQ, respectively. Statistical Analysis Used: Pearson correlation coefficients between subscales of APQ-PF and PCRQ were computed. Multiple regression analysis was carried out for statistical significance of the clinical and demographic variables. Results: Parents who were: (i "involved" in parenting had a good "personal relationship and disciplinary warmth," (ii practicing "positive parenting" had good "warmth, personal relationship and disciplinary warmth," (iii "poorly supervising" their child′s activities lacked "warmth and personal relationship," (iv practicing "inconsistent discipline′ had a higher "power assertion" and (v practicing "corporal punishment" lacked "warmth" and had a higher "power assertion and possessiveness" in their relationships with their child. Parent being poorly educated or currently ill and child having all three types of SpLD present concomitantly or a sibling or a sibling with a chronic disability or being in class standard IX to XI were variables that independently predicted a poor parenting or parent-child relationship subscale score. Conclusions: The present study has identified parenting practices that need to be encouraged or excluded for improving parent-child relationships. Initiating these measures would help in the

  19. An Efficient Ensemble Learning Method for Gene Microarray Classification

    Directory of Open Access Journals (Sweden)

    Alireza Osareh

    2013-01-01

    Full Text Available The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

  20. Digital game based learning: A new method in teaching and learning mathematics

    Science.gov (United States)

    Hussain, Sayed Yusoff bin Syed; Hoe, Tan Wee; Idris, Muhammad Zaffwan bin

    2017-05-01

    Digital game-based learning (DGBL) had been regarded as a sound learning strategy in raising pupils' willingness and interest in many disciplines. Normally, video and digital games are used in the teaching and learning mathematics. based on literature, digital games have proven its capability in making pupils motivated and are more likely to contribute to effective learning mathematics. Hence this research aims to construct a DGBL in the teaching of Mathematics for Year 1 pupils. Then, a quasi-experimental study was carried out in a school located in Gua Musang, Kelantan, involving 39 pupils. Specifically, this article tests the effectiveness of the use of DGBL in the teaching of the topic Addition of Less than 100 on pupil's achievement. This research employed a quasi-experiment, Pre and Post Test of Non-equivalent Control Group design. The data were analysed using the Nonparametric test namely the Mann-Whitney U. The research finding shows the use of the DGBL could increase the pupils' achievement in the topic of Addition of Less than 100. In practice, this research indicates that the DBGL can utilized as an alternative reference strategy for Mathematics teacher.

  1. Time-trends in method-specific suicide rates compared with the availability of specific compounds. The Danish experience

    DEFF Research Database (Denmark)

    Nordentoft, Merete; Qin, Ping; Helweg-Larsen, Karin

    2006-01-01

    Restriction of means for suicide is an important part of suicide preventive strategies in different countries. All suicides in Denmark between 1970 and 2000 were examined with regard to method used for suicide. Overall suicide mortality and method-specific suicide mortality was compared...... in the number of suicides by self-poisoning with these compounds. Restricted access occurred concomittantly with a 55% decrease in suicide rate...

  2. Quality specifications in postgraduate medical e-learning: an integrative literature review leading to a postgraduate medical e-learning model.

    Science.gov (United States)

    De Leeuw, R A; Westerman, Michiel; Nelson, E; Ket, J C F; Scheele, F

    2016-07-08

    E-learning is driving major shifts in medical education. Prioritizing learning theories and quality models improves the success of e-learning programs. Although many e-learning quality standards are available, few are focused on postgraduate medical education. We conducted an integrative review of the current postgraduate medical e-learning literature to identify quality specifications. The literature was thematically organized into a working model. Unique quality specifications (n = 72) were consolidated and re-organized into a six-domain model that we called the Postgraduate Medical E-learning Model (Postgraduate ME Model). This model was partially based on the ISO-19796 standard, and drew on cognitive load multimedia principles. The domains of the model are preparation, software design and system specifications, communication, content, assessment, and maintenance. This review clarified the current state of postgraduate medical e-learning standards and specifications. It also synthesized these specifications into a single working model. To validate our findings, the next-steps include testing the Postgraduate ME Model in controlled e-learning settings.

  3. Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees.

    Science.gov (United States)

    Choi, Ickwon; Chung, Amy W; Suscovich, Todd J; Rerks-Ngarm, Supachai; Pitisuttithum, Punnee; Nitayaphan, Sorachai; Kaewkungwal, Jaranit; O'Connell, Robert J; Francis, Donald; Robb, Merlin L; Michael, Nelson L; Kim, Jerome H; Alter, Galit; Ackerman, Margaret E; Bailey-Kellogg, Chris

    2015-04-01

    The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release). We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.

  4. Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees.

    Directory of Open Access Journals (Sweden)

    Ickwon Choi

    2015-04-01

    Full Text Available The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release. We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.

  5. Talker-specific learning in amnesia: Insight into mechanisms of adaptive speech perception.

    Science.gov (United States)

    Trude, Alison M; Duff, Melissa C; Brown-Schmidt, Sarah

    2014-05-01

    A hallmark of human speech perception is the ability to comprehend speech quickly and effortlessly despite enormous variability across talkers. However, current theories of speech perception do not make specific claims about the memory mechanisms involved in this process. To examine whether declarative memory is necessary for talker-specific learning, we tested the ability of amnesic patients with severe declarative memory deficits to learn and distinguish the accents of two unfamiliar talkers by monitoring their eye-gaze as they followed spoken instructions. Analyses of the time-course of eye fixations showed that amnesic patients rapidly learned to distinguish these accents and tailored perceptual processes to the voice of each talker. These results demonstrate that declarative memory is not necessary for this ability and points to the involvement of non-declarative memory mechanisms. These results are consistent with findings that other social and accommodative behaviors are preserved in amnesia and contribute to our understanding of the interactions of multiple memory systems in the use and understanding of spoken language. Copyright © 2014 Elsevier Ltd. All rights reserved.

  6. Lessons learned: advantages and disadvantages of mixed method research

    DEFF Research Database (Denmark)

    Malina, Mary A.; Nørreklit, Hanne; Selto, Frank H.

    2011-01-01

    on the use and usefulness of a specialized balanced scorecard; and third, to encourage researchers to actually use multiple methods and sources of data to address the very many accounting phenomena that are not fully understood. Design/methodology/approach – This paper is an opinion piece based...... on the authors' experience conducting a series of longitudinal mixed method studies. Findings – The authors suggest that in many studies, using a mixed method approach provides the best opportunity for addressing research questions. Originality/value – This paper provides encouragement to those who may wish......Purpose – The purpose of this paper is first, to discuss the theoretical assumptions, qualities, problems and myopia of the dominating quantitative and qualitative approaches; second, to describe the methodological lessons that the authors learned while conducting a series of longitudinal studies...

  7. Machine learning methods can replace 3D profile method in classification of amyloidogenic hexapeptides

    Directory of Open Access Journals (Sweden)

    Stanislawski Jerzy

    2013-01-01

    Full Text Available Abstract Background Amyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such peptides have been experimentally found. Experimental testing of all possible aminoacid combinations is currently not feasible. Instead, they can be predicted by computational methods. 3D profile is a physicochemical-based method that has generated the most numerous dataset - ZipperDB. However, it is computationally very demanding. Here, we show that dataset generation can be accelerated. Two methods to increase the classification efficiency of amyloidogenic candidates are presented and tested: simplified 3D profile generation and machine learning methods. Results We generated a new dataset of hexapeptides, using more economical 3D profile algorithm, which showed very good classification overlap with ZipperDB (93.5%. The new part of our dataset contains 1779 segments, with 204 classified as amyloidogenic. The dataset of 6-residue sequences with their binary classification, based on the energy of the segment, was applied for training machine learning methods. A separate set of sequences from ZipperDB was used as a test set. The most effective methods were Alternating Decision Tree and Multilayer Perceptron. Both methods obtained area under ROC curve of 0.96, accuracy 91%, true positive rate ca. 78%, and true negative rate 95%. A few other machine learning methods also achieved a good performance. The computational time was reduced from 18-20 CPU-hours (full 3D profile to 0.5 CPU-hours (simplified 3D profile to seconds (machine learning. Conclusions We showed that the simplified profile generation method does not introduce an error with regard to the original method, while

  8. Machine learning methods can replace 3D profile method in classification of amyloidogenic hexapeptides.

    Science.gov (United States)

    Stanislawski, Jerzy; Kotulska, Malgorzata; Unold, Olgierd

    2013-01-17

    Amyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such peptides have been experimentally found. Experimental testing of all possible aminoacid combinations is currently not feasible. Instead, they can be predicted by computational methods. 3D profile is a physicochemical-based method that has generated the most numerous dataset - ZipperDB. However, it is computationally very demanding. Here, we show that dataset generation can be accelerated. Two methods to increase the classification efficiency of amyloidogenic candidates are presented and tested: simplified 3D profile generation and machine learning methods. We generated a new dataset of hexapeptides, using more economical 3D profile algorithm, which showed very good classification overlap with ZipperDB (93.5%). The new part of our dataset contains 1779 segments, with 204 classified as amyloidogenic. The dataset of 6-residue sequences with their binary classification, based on the energy of the segment, was applied for training machine learning methods. A separate set of sequences from ZipperDB was used as a test set. The most effective methods were Alternating Decision Tree and Multilayer Perceptron. Both methods obtained area under ROC curve of 0.96, accuracy 91%, true positive rate ca. 78%, and true negative rate 95%. A few other machine learning methods also achieved a good performance. The computational time was reduced from 18-20 CPU-hours (full 3D profile) to 0.5 CPU-hours (simplified 3D profile) to seconds (machine learning). We showed that the simplified profile generation method does not introduce an error with regard to the original method, while increasing the computational efficiency. Our new dataset

  9. Improving Nursing Students' Learning Outcomes in Fundamentals of Nursing Course through Combination of Traditional and e-Learning Methods.

    Science.gov (United States)

    Sheikhaboumasoudi, Rouhollah; Bagheri, Maryam; Hosseini, Sayed Abbas; Ashouri, Elaheh; Elahi, Nasrin

    2018-01-01

    Fundamentals of nursing course are prerequisite to providing comprehensive nursing care. Despite development of technology on nursing education, effectiveness of using e-learning methods in fundamentals of nursing course is unclear in clinical skills laboratory for nursing students. The aim of this study was to compare the effect of blended learning (combining e-learning with traditional learning methods) with traditional learning alone on nursing students' scores. A two-group post-test experimental study was administered from February 2014 to February 2015. Two groups of nursing students who were taking the fundamentals of nursing course in Iran were compared. Sixty nursing students were selected as control group (just traditional learning methods) and experimental group (combining e-learning with traditional learning methods) for two consecutive semesters. Both groups participated in Objective Structured Clinical Examination (OSCE) and were evaluated in the same way using a prepared checklist and questionnaire of satisfaction. Statistical analysis was conducted through SPSS software version 16. Findings of this study reflected that mean of midterm (t = 2.00, p = 0.04) and final score (t = 2.50, p = 0.01) of the intervention group (combining e-learning with traditional learning methods) were significantly higher than the control group (traditional learning methods). The satisfaction of male students in intervention group was higher than in females (t = 2.60, p = 0.01). Based on the findings, this study suggests that the use of combining traditional learning methods with e-learning methods such as applying educational website and interactive online resources for fundamentals of nursing course instruction can be an effective supplement for improving nursing students' clinical skills.

  10. Best practices for learning physiology: combining classroom and online methods.

    Science.gov (United States)

    Anderson, Lisa C; Krichbaum, Kathleen E

    2017-09-01

    Physiology is a requisite course for many professional allied health programs and is a foundational science for learning pathophysiology, health assessment, and pharmacology. Given the demand for online learning in the health sciences, it is important to evaluate the efficacy of online and in-class teaching methods, especially as they are combined to form hybrid courses. The purpose of this study was to compare two hybrid physiology sections in which one section was offered mostly in-class (85% in-class), and the other section was offered mostly online (85% online). The two sections in 2 yr ( year 1 and year 2 ) were compared in terms of knowledge of physiology measured in exam scores and pretest-posttest improvement, and in measures of student satisfaction with teaching. In year 1 , there were some differences on individual exam scores between the two sections, but no significant differences in mean exam scores or in pretest-posttest improvements. However, in terms of student satisfaction, the mostly in-class students in year 1 rated the instructor significantly higher than did the mostly online students. Comparisons between in-class and online students in the year 2 cohort yielded data that showed that mean exam scores were not statistically different, but pre-post changes were significantly greater in the mostly online section; student satisfaction among mostly online students also improved significantly. Education researchers must investigate effective combinations of in-class and online methods for student learning outcomes, while maintaining the flexibility and convenience that online methods provide. Copyright © 2017 the American Physiological Society.

  11. MACHINE LEARNING METHODS IN DIGITAL AGRICULTURE: ALGORITHMS AND CASES

    Directory of Open Access Journals (Sweden)

    Aleksandr Vasilyevich Koshkarov

    2018-05-01

    Full Text Available Ensuring food security is a major challenge in many countries. With a growing global population, the issues of improving the efficiency of agriculture have become most relevant. Farmers are looking for new ways to increase yields, and governments of different countries are developing new programs to support agriculture. This contributes to a more active implementation of digital technologies in agriculture, helping farmers to make better decisions, increase yields and take care of the environment. The central point is the collection and analysis of data. In the industry of agriculture, data can be collected from different sources and may contain useful patterns that identify potential problems or opportunities. Data should be analyzed using machine learning algorithms to extract useful insights. Such methods of precision farming allow the farmer to monitor individual parts of the field, optimize the consumption of water and chemicals, and identify problems quickly. Purpose: to make an overview of the machine learning algorithms used for data analysis in agriculture. Methodology: an overview of the relevant literature; a survey of farmers. Results: relevant algorithms of machine learning for the analysis of data in agriculture at various levels were identified: soil analysis (soil assessment, soil classification, soil fertility predictions, weather forecast (simulation of climate change, temperature and precipitation prediction, and analysis of vegetation (weed identification, vegetation classification, plant disease identification, crop forecasting. Practical implications: agriculture, crop production.

  12. Estimating building energy consumption using extreme learning machine method

    International Nuclear Information System (INIS)

    Naji, Sareh; Keivani, Afram; Shamshirband, Shahaboddin; Alengaram, U. Johnson; Jumaat, Mohd Zamin; Mansor, Zulkefli; Lee, Malrey

    2016-01-01

    The current energy requirements of buildings comprise a large percentage of the total energy consumed around the world. The demand of energy, as well as the construction materials used in buildings, are becoming increasingly problematic for the earth's sustainable future, and thus have led to alarming concern. The energy efficiency of buildings can be improved, and in order to do so, their operational energy usage should be estimated early in the design phase, so that buildings are as sustainable as possible. An early energy estimate can greatly help architects and engineers create sustainable structures. This study proposes a novel method to estimate building energy consumption based on the ELM (Extreme Learning Machine) method. This method is applied to building material thicknesses and their thermal insulation capability (K-value). For this purpose up to 180 simulations are carried out for different material thicknesses and insulation properties, using the EnergyPlus software application. The estimation and prediction obtained by the ELM model are compared with GP (genetic programming) and ANNs (artificial neural network) models for accuracy. The simulation results indicate that an improvement in predictive accuracy is achievable with the ELM approach in comparison with GP and ANN. - Highlights: • Buildings consume huge amounts of energy for operation. • Envelope materials and insulation influence building energy consumption. • Extreme learning machine is used to estimate energy usage of a sample building. • The key effective factors in this study are insulation thickness and K-value.

  13. A Preliminary Survey of the Preferred Learning Methods for Interpretation Students

    Science.gov (United States)

    Heinz, Michael

    2013-01-01

    There are many different methods that individuals use to learn languages like reading books or writing essays. Not all methods are equally successful for second language learners but nor do all successful learners of a second language show identical preferences for learning methods. Additionally, at the highest level of language learning various…

  14. Characterizing Engineering Learners' Preferences for Active and Passive Learning Methods

    Science.gov (United States)

    Magana, Alejandra J.; Vieira, Camilo; Boutin, Mireille

    2018-01-01

    This paper studies electrical engineering learners' preferences for learning methods with various degrees of activity. Less active learning methods such as homework and peer reviews are investigated, as well as a newly introduced very active (constructive) learning method called "slectures," and some others. The results suggest that…

  15. Teaching Sustainability Using an Active Learning Constructivist Approach: Discipline-Specific Case Studies in Higher Education

    Directory of Open Access Journals (Sweden)

    Maria Kalamas Hedden

    2017-07-01

    Full Text Available In this paper we present our rationale for using an active learning constructivist approach to teach sustainability-related topics in a higher education. To push the boundaries of ecological literacy, we also develop a theoretical model for sustainability knowledge co-creation. Drawing on the experiences of faculty at a major Southeastern University in the United States, we present case studies in architecture, engineering, geography, and marketing. Four Sustainability Faculty Fellows describe their discipline-specific case studies, all of which are project-based learning experiences, and include details regarding teaching and assessment. Easily replicated in other educational contexts, these case studies contribute to the advancement of sustainability education.

  16. Comparisons of likelihood and machine learning methods of individual classification

    Science.gov (United States)

    Guinand, B.; Topchy, A.; Page, K.S.; Burnham-Curtis, M. K.; Punch, W.F.; Scribner, K.T.

    2002-01-01

    Classification methods used in machine learning (e.g., artificial neural networks, decision trees, and k-nearest neighbor clustering) are rarely used with population genetic data. We compare different nonparametric machine learning techniques with parametric likelihood estimations commonly employed in population genetics for purposes of assigning individuals to their population of origin (“assignment tests”). Classifier accuracy was compared across simulated data sets representing different levels of population differentiation (low and high FST), number of loci surveyed (5 and 10), and allelic diversity (average of three or eight alleles per locus). Empirical data for the lake trout (Salvelinus namaycush) exhibiting levels of population differentiation comparable to those used in simulations were examined to further evaluate and compare classification methods. Classification error rates associated with artificial neural networks and likelihood estimators were lower for simulated data sets compared to k-nearest neighbor and decision tree classifiers over the entire range of parameters considered. Artificial neural networks only marginally outperformed the likelihood method for simulated data (0–2.8% lower error rates). The relative performance of each machine learning classifier improved relative likelihood estimators for empirical data sets, suggesting an ability to “learn” and utilize properties of empirical genotypic arrays intrinsic to each population. Likelihood-based estimation methods provide a more accessible option for reliable assignment of individuals to the population of origin due to the intricacies in development and evaluation of artificial neural networks. In recent years, characterization of highly polymorphic molecular markers such as mini- and microsatellites and development of novel methods of analysis have enabled researchers to extend investigations of ecological and evolutionary processes below the population level to the level of

  17. rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning

    Directory of Open Access Journals (Sweden)

    Miron B. Kursa

    2014-11-01

    Full Text Available Random ferns is a very simple yet powerful classification method originally introduced for specific computer vision tasks. In this paper, I show that this algorithm may be considered as a constrained decision tree ensemble and use this interpretation to introduce a series of modifications which enable the use of random ferns in general machine learning problems. Moreover, I extend the method with an internal error approximation and an attribute importance measure based on corresponding features of the random forest algorithm. I also present the R package rFerns containing an efficient implementation of this modified version of random ferns.

  18. Advanced methods in NDE using machine learning approaches

    Science.gov (United States)

    Wunderlich, Christian; Tschöpe, Constanze; Duckhorn, Frank

    2018-04-01

    Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition to a small printed circuit board (PCB). Still, algorithms will be trained on an ordinary PC; however, trained algorithms run on the Digital Signal Processor and the FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Some key requirements have to be fulfilled, however. A sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required. The automated testing can be done subsequently by the machine. By integrating the test data of many components along the value chain further optimization including lifetime and durability

  19. The Importance of a Teacher in a Distance Education and the Progressive Methods of Teaching in a Virtual Learning Environment

    Directory of Open Access Journals (Sweden)

    Olga Miščenko

    2014-12-01

    Full Text Available The purpose of the article is to analyze the experience of the first work years of teaching the students, who study by distance, to compare other authors’ experience and to examine the advantages of Moodle virtual learning environment (VLE, searching for new applications of it. The relevance of e-learning is noted. It is affirmed that metacognitive learning strategies are typical for learning foreign languages in virtual environment. It is said that the Internet is a tool that ensures studies by distance. It is said that raising the qualification and learning by distance allows a responsible employee to improve foreign language skills while lifelong learning. VLE adaptability for teaching and studying English is being discussed. It is stated that the Internet conditions all types of methods in the virtual environment, application, and its existence expands and deepens the learning approach. In the paper it is claimed that the Moodle VLE function is to improve the learning process to ensure a high level of expertise and the objectivity of assessment. Studying in conventional way and in the virtual environment are briefly compared. Moodle virtual learning environment application objectives to learning outcomes, emphasizing the importance of the traditional teaching methods, the student’s responsibility to call attention to the learning process and system characteristics are defined. It is noted that learning in the virtual environment is based on the principles of epistemology, therefore the Moodle system meets the didactic tasks. The virtual learning environment possibilities ensure a very good feedback and increase students’ motivation, and, consequently, that provides better knowledge. It is emphasized that while teaching by distance, the teacher’s responsibility, his role in the development of educational material and the course tasks have increased. Some specific cases for various forms of studies and exercises to perform in the

  20. Prevalence of specific learning disabilities among primary school children in a South Indian city.

    Science.gov (United States)

    Mogasale, Vijayalaxmi V; Patil, Vishwanath D; Patil, Nanasaheb M; Mogasale, Vittal

    2012-03-01

    To measure the prevalence of specific learning disabilities (SpLDs) such as dyslexia, dysgraphia and dyscalculia among primary school children in a South Indian city. A cross-sectional multi-staged stratified randomized cluster sampling study was conducted among children aged 8-11 years from third and fourth standard. A six level screening approach that commenced with identification of scholastic backwardness followed by stepwise exclusion of impaired vision and hearing, chronic medical conditions and subnormal intelligence was carried out among these children. In the final step, the remaining children were subjected to specific tests for reading, comprehension, writing and mathematical calculation. The prevalence of specific learning disabilities was 15.17% in sampled children, whereas 12.5%, 11.2% and 10.5% had dysgraphia, dyslexia and dyscalculia respectively. This study suggests that the prevalence of SpLDs is at the higher side of previous estimations in India. The study is unique due to its large geographically representative design and identification of the problem using simplified screening approach and tools, which minimizes the number and time of specialist requirement and spares the expensive investigation. This approach and tools are suitable for field situations and resource scarce settings. Based on the authors' experience, they express the need for more prevalence studies, remedial education and policy interventions to manage SpLDs at main stream educational system to improve the school performance in Indian children.

  1. Pitch discrimination learning: specificity for pitch and harmonic resolvability, and electrophysiological correlates.

    Science.gov (United States)

    Carcagno, Samuele; Plack, Christopher J

    2011-08-01

    Multiple-hour training on a pitch discrimination task dramatically decreases the threshold for detecting a pitch difference between two harmonic complexes. Here, we investigated the specificity of this perceptual learning with respect to the pitch and the resolvability of the trained harmonic complex, as well as its cortical electrophysiological correlates. We trained 24 participants for 12 h on a pitch discrimination task using one of four different harmonic complexes. The complexes differed in pitch and/or spectral resolvability of their components by the cochlea, but were filtered into the same spectral region. Cortical-evoked potentials and a behavioral measure of pitch discrimination were assessed before and after training for all the four complexes. The change in these measures was compared to that of two control groups: one trained on a level discrimination task and one without any training. The behavioral results showed that learning was partly specific to both pitch and resolvability. Training with a resolved-harmonic complex improved pitch discrimination for resolved complexes more than training with an unresolved complex. However, we did not find evidence that training with an unresolved complex leads to specific learning for unresolved complexes. Training affected the P2 component of the cortical-evoked potentials, as well as a later component (250-400 ms). No significant changes were found on the mismatch negativity (MMN) component, although a separate experiment showed that this measure was sensitive to pitch changes equivalent to the pitch discriminability changes induced by training. This result suggests that pitch discrimination training affects processes not measured by the MMN, for example, processes higher in level or parallel to those involved in MMN generation.

  2. Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence.

    Science.gov (United States)

    Ahn, Woo-Young; Vassileva, Jasmin

    2016-04-01

    Recent animal and human studies reveal distinct cognitive and neurobiological differences between opiate and stimulant addictions; however, our understanding of the common and specific effects of these two classes of drugs remains limited due to the high rates of polysubstance-dependence among drug users. The goal of the current study was to identify multivariate substance-specific markers classifying heroin dependence (HD) and amphetamine dependence (AD), by using machine-learning approaches. Participants included 39 amphetamine mono-dependent, 44 heroin mono-dependent, 58 polysubstance dependent, and 81 non-substance dependent individuals. The majority of substance dependent participants were in protracted abstinence. We used demographic, personality (trait impulsivity, trait psychopathy, aggression, sensation seeking), psychiatric (attention deficit hyperactivity disorder, conduct disorder, antisocial personality disorder, psychopathy, anxiety, depression), and neurocognitive impulsivity measures (Delay Discounting, Go/No-Go, Stop Signal, Immediate Memory, Balloon Analogue Risk, Cambridge Gambling, and Iowa Gambling tasks) as predictors in a machine-learning algorithm. The machine-learning approach revealed substance-specific multivariate profiles that classified HD and AD in new samples with high degree of accuracy. Out of 54 predictors, psychopathy was the only classifier common to both types of addiction. Important dissociations emerged between factors classifying HD and AD, which often showed opposite patterns among individuals with HD and AD. These results suggest that different mechanisms may underlie HD and AD, challenging the unitary account of drug addiction. This line of work may shed light on the development of standardized and cost-efficient clinical diagnostic tests and facilitate the development of individualized prevention and intervention programs for HD and AD. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  3. Is there a need for a specific educational scholarship for using e-learning in medical education?

    Science.gov (United States)

    Sandars, John; Goh, Poh Sun

    2016-10-01

    We propose the need for a specific educational scholarship when using e-learning in medical education. Effective e-learning has additional factors that require specific critical attention, including the design and delivery of e-learning. An important aspect is the recognition that e-learning is a complex intervention, with several interconnecting components that have to be aligned. This alignment requires an essential iterative development process with usability testing. Effectiveness of e-learning in one context may not be fully realized in another context unless there is further consideration of applicability and scalability. We recommend a participatory approach for an educational scholarship for using e-learning in medical education, such as by action research or design-based research.

  4. The experimental field work as practical learning method

    Directory of Open Access Journals (Sweden)

    Nicolás Fernández Losa

    2014-11-01

    Full Text Available This paper describes a teaching experience about experimental field work as practical learning method implemented in the subject of Organizational Behaviour. With this teaching experience we pretend to change the practical training, as well as in its evaluation process, in order to favour the development of transversal skills of students. For this purpose, the use of a practice plan, tackled through an experimental field work and carried out with the collaboration of a business organization within a work team (as organic unity of learning, arises as an alternative to the traditional method of practical teachings and allows the approach of business reality into the classroom, as well as actively promote the use of transversal skills. In particular, we develop the experience in three phases. Initially, the students, after forming a working group and define a field work project, should get the collaboration of a nearby business organization in which to obtain data on one or more functional areas of organizational behaviour. Subsequently, students carry out the field work with the realization of the scheduled visits and elaboration of a memory to establish a diagnosis of the strategy followed by the company in these functional areas in order to propose and justify alternative actions that improve existing ones. Finally, teachers assess the different field work memories and their public presentations according to evaluation rubrics, which try to objectify and unify to the maximum the evaluation criteria and serve to guide the learning process of students. The results of implementation of this teaching experience, measured through a Likert questionnaire, are very satisfactory for students.

  5. Application of blended learning in teaching statistical methods

    Directory of Open Access Journals (Sweden)

    Barbara Dębska

    2012-12-01

    Full Text Available The paper presents the application of a hybrid method (blended learning - linking traditional education with on-line education to teach selected problems of mathematical statistics. This includes the teaching of the application of mathematical statistics to evaluate laboratory experimental results. An on-line statistics course was developed to form an integral part of the module ‘methods of statistical evaluation of experimental results’. The course complies with the principles outlined in the Polish National Framework of Qualifications with respect to the scope of knowledge, skills and competencies that students should have acquired at course completion. The paper presents the structure of the course and the educational content provided through multimedia lessons made accessible on the Moodle platform. Following courses which used the traditional method of teaching and courses which used the hybrid method of teaching, students test results were compared and discussed to evaluate the effectiveness of the hybrid method of teaching when compared to the effectiveness of the traditional method of teaching.

  6. Shape-specific perceptual learning in a figure-ground segregation task.

    Science.gov (United States)

    Yi, Do-Joon; Olson, Ingrid R; Chun, Marvin M

    2006-03-01

    What does perceptual experience contribute to figure-ground segregation? To study this question, we trained observers to search for symmetric dot patterns embedded in random dot backgrounds. Training improved shape segmentation, but learning did not completely transfer either to untrained locations or to untrained shapes. Such partial specificity persisted for a month after training. Interestingly, training on shapes in empty backgrounds did not help segmentation of the trained shapes in noisy backgrounds. Our results suggest that perceptual training increases the involvement of early sensory neurons in the segmentation of trained shapes, and that successful segmentation requires perceptual skills beyond shape recognition alone.

  7. Participant Interaction in Asynchronous Learning Environments: Evaluating Interaction Analysis Methods

    Science.gov (United States)

    Blanchette, Judith

    2012-01-01

    The purpose of this empirical study was to determine the extent to which three different objective analytical methods--sequence analysis, surface cohesion analysis, and lexical cohesion analysis--can most accurately identify specific characteristics of online interaction. Statistically significant differences were found in all points of…

  8. Methods of Efficient Study Habits and Physics Learning

    Science.gov (United States)

    Zettili, Nouredine

    2010-02-01

    We want to discuss the methods of efficient study habits and how they can be used by students to help them improve learning physics. In particular, we deal with the most efficient techniques needed to help students improve their study skills. We focus on topics such as the skills of how to develop long term memory, how to improve concentration power, how to take class notes, how to prepare for and take exams, how to study scientific subjects such as physics. We argue that the students who conscientiously use the methods of efficient study habits achieve higher results than those students who do not; moreover, a student equipped with the proper study skills will spend much less time to learn a subject than a student who has no good study habits. The underlying issue here is not the quantity of time allocated to the study efforts by the students, but the efficiency and quality of actions so that the student can function at peak efficiency. These ideas were developed as part of Project IMPACTSEED (IMproving Physics And Chemistry Teaching in SEcondary Education), an outreach grant funded by the Alabama Commission on Higher Education. This project is motivated by a major pressing local need: A large number of high school physics teachers teach out of field. )

  9. A Photometric Machine-Learning Method to Infer Stellar Metallicity

    Science.gov (United States)

    Miller, Adam A.

    2015-01-01

    Following its formation, a star's metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectroscopic surveys are limited to a few x 10(exp 6) targets; photometric surveys, on the other hand, have detected > 10(exp 9) stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of approx. 120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g' < or = 18 mag), with 4500 K < or = Teff < or = 7000 K, corresponding to those with the most reliable SSPP estimates, I find that the model predicts [Fe/H] values with a root-mean-squared-error (RMSE) of approx.0.27 dex. The RMSE from this machine-learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra..

  10. Multiple instance learning tracking method with local sparse representation

    KAUST Repository

    Xie, Chengjun

    2013-10-01

    When objects undergo large pose change, illumination variation or partial occlusion, most existed visual tracking algorithms tend to drift away from targets and even fail in tracking them. To address this issue, in this study, the authors propose an online algorithm by combining multiple instance learning (MIL) and local sparse representation for tracking an object in a video system. The key idea in our method is to model the appearance of an object by local sparse codes that can be formed as training data for the MIL framework. First, local image patches of a target object are represented as sparse codes with an overcomplete dictionary, where the adaptive representation can be helpful in overcoming partial occlusion in object tracking. Then MIL learns the sparse codes by a classifier to discriminate the target from the background. Finally, results from the trained classifier are input into a particle filter framework to sequentially estimate the target state over time in visual tracking. In addition, to decrease the visual drift because of the accumulative errors when updating the dictionary and classifier, a two-step object tracking method combining a static MIL classifier with a dynamical MIL classifier is proposed. Experiments on some publicly available benchmarks of video sequences show that our proposed tracker is more robust and effective than others. © The Institution of Engineering and Technology 2013.

  11. Deep Learning Methods for Improved Decoding of Linear Codes

    Science.gov (United States)

    Nachmani, Eliya; Marciano, Elad; Lugosch, Loren; Gross, Warren J.; Burshtein, David; Be'ery, Yair

    2018-02-01

    The problem of low complexity, close to optimal, channel decoding of linear codes with short to moderate block length is considered. It is shown that deep learning methods can be used to improve a standard belief propagation decoder, despite the large example space. Similar improvements are obtained for the min-sum algorithm. It is also shown that tying the parameters of the decoders across iterations, so as to form a recurrent neural network architecture, can be implemented with comparable results. The advantage is that significantly less parameters are required. We also introduce a recurrent neural decoder architecture based on the method of successive relaxation. Improvements over standard belief propagation are also observed on sparser Tanner graph representations of the codes. Furthermore, we demonstrate that the neural belief propagation decoder can be used to improve the performance, or alternatively reduce the computational complexity, of a close to optimal decoder of short BCH codes.

  12. Machine learning methods for clinical forms analysis in mental health.

    Science.gov (United States)

    Strauss, John; Peguero, Arturo Martinez; Hirst, Graeme

    2013-01-01

    In preparation for a clinical information system implementation, the Centre for Addiction and Mental Health (CAMH) Clinical Information Transformation project completed multiple preparation steps. An automated process was desired to supplement the onerous task of manual analysis of clinical forms. We used natural language processing (NLP) and machine learning (ML) methods for a series of 266 separate clinical forms. For the investigation, documents were represented by feature vectors. We used four ML algorithms for our examination of the forms: cluster analysis, k-nearest neigh-bours (kNN), decision trees and support vector machines (SVM). Parameters for each algorithm were optimized. SVM had the best performance with a precision of 64.6%. Though we did not find any method sufficiently accurate for practical use, to our knowledge this approach to forms has not been used previously in mental health.

  13. Statistical learning modeling method for space debris photometric measurement

    Science.gov (United States)

    Sun, Wenjing; Sun, Jinqiu; Zhang, Yanning; Li, Haisen

    2016-03-01

    Photometric measurement is an important way to identify the space debris, but the present methods of photometric measurement have many constraints on star image and need complex image processing. Aiming at the problems, a statistical learning modeling method for space debris photometric measurement is proposed based on the global consistency of the star image, and the statistical information of star images is used to eliminate the measurement noises. First, the known stars on the star image are divided into training stars and testing stars. Then, the training stars are selected as the least squares fitting parameters to construct the photometric measurement model, and the testing stars are used to calculate the measurement accuracy of the photometric measurement model. Experimental results show that, the accuracy of the proposed photometric measurement model is about 0.1 magnitudes.

  14. A Learning-Based Steganalytic Method against LSB Matching Steganography

    Directory of Open Access Journals (Sweden)

    Z. Xia

    2011-04-01

    Full Text Available This paper considers the detection of spatial domain least significant bit (LSB matching steganography in gray images. Natural images hold some inherent properties, such as histogram, dependence between neighboring pixels, and dependence among pixels that are not adjacent to each other. These properties are likely to be disturbed by LSB matching. Firstly, histogram will become smoother after LSB matching. Secondly, the two kinds of dependence will be weakened by the message embedding. Accordingly, three features, which are respectively based on image histogram, neighborhood degree histogram and run-length histogram, are extracted at first. Then, support vector machine is utilized to learn and discriminate the difference of features between cover and stego images. Experimental results prove that the proposed method possesses reliable detection ability and outperforms the two previous state-of-the-art methods. Further more, the conclusions are drawn by analyzing the individual performance of three features and their fused feature.

  15. Relabeling exchange method (REM) for learning in neural networks

    Science.gov (United States)

    Wu, Wen; Mammone, Richard J.

    1994-02-01

    The supervised training of neural networks require the use of output labels which are usually arbitrarily assigned. In this paper it is shown that there is a significant difference in the rms error of learning when `optimal' label assignment schemes are used. We have investigated two efficient random search algorithms to solve the relabeling problem: the simulated annealing and the genetic algorithm. However, we found them to be computationally expensive. Therefore we shall introduce a new heuristic algorithm called the Relabeling Exchange Method (REM) which is computationally more attractive and produces optimal performance. REM has been used to organize the optimal structure for multi-layered perceptrons and neural tree networks. The method is a general one and can be implemented as a modification to standard training algorithms. The motivation of the new relabeling strategy is based on the present interpretation of dyslexia as an encoding problem.

  16. Incorporating Meaningful Gamification in a Blended Learning Research Methods Class: Examining Student Learning, Engagement, and Affective Outcomes

    Science.gov (United States)

    Tan, Meng; Hew, Khe Foon

    2016-01-01

    In this study, we investigated how the use of meaningful gamification affects student learning, engagement, and affective outcomes in a short, 3-day blended learning research methods class using a combination of experimental and qualitative research methods. Twenty-two postgraduates were randomly split into two groups taught by the same…

  17. Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

    Directory of Open Access Journals (Sweden)

    Mingjie Tan

    2015-02-01

    Full Text Available The high rate of dropout is a serious problem in E-learning program. Thus it has received extensive concern from the education administrators and researchers. Predicting the potential dropout students is a workable solution to prevent dropout. Based on the analysis of related literature, this study selected student’s personal characteristic and academic performance as input attributions. Prediction models were developed using Artificial Neural Network (ANN, Decision Tree (DT and Bayesian Networks (BNs. A large sample of 62375 students was utilized in the procedures of model training and testing. The results of each model were presented in confusion matrix, and analyzed by calculating the rates of accuracy, precision, recall, and F-measure. The results suggested all of the three machine learning methods were effective in student dropout prediction, and DT presented a better performance. Finally, some suggestions were made for considerable future research.

  18. METHOD FOR THE MEASUREMENT OF SITE-SPECIFIC TAUTOMERIC AND ZWITTERIONIC MICROSPECIES EQUILIBRIUM CONSTANTS

    Science.gov (United States)

    We describe a method for the individual measurement of simultaneously occurring, unimolecular, site-specific “microequilibrium” constants as in, for example, prototropic tautomerism and zwitterionic equilibria. Our method represents an elaboration of that of Nygren et al. (Anal. ...

  19. A Simple Deep Learning Method for Neuronal Spike Sorting

    Science.gov (United States)

    Yang, Kai; Wu, Haifeng; Zeng, Yu

    2017-10-01

    Spike sorting is one of key technique to understand brain activity. With the development of modern electrophysiology technology, some recent multi-electrode technologies have been able to record the activity of thousands of neuronal spikes simultaneously. The spike sorting in this case will increase the computational complexity of conventional sorting algorithms. In this paper, we will focus spike sorting on how to reduce the complexity, and introduce a deep learning algorithm, principal component analysis network (PCANet) to spike sorting. The introduced method starts from a conventional model and establish a Toeplitz matrix. Through the column vectors in the matrix, we trains a PCANet, where some eigenvalue vectors of spikes could be extracted. Finally, support vector machine (SVM) is used to sort spikes. In experiments, we choose two groups of simulated data from public databases availably and compare this introduced method with conventional methods. The results indicate that the introduced method indeed has lower complexity with the same sorting errors as the conventional methods.

  20. Emotional and Meta-Emotional Intelligence as Predictors of Adjustment Problems in Students with Specific Learning Disorders

    Science.gov (United States)

    D'Amico, Antonella; Guastaferro, Teresa

    2017-01-01

    The purpose of this study was to analyse adjustment problems in a group of adolescents with a Specific Learning Disorder (SLD), examining to what extent they depend on the severity level of the learning disorder and/or on the individual's level of emotional intelligence. Adjustment problems,, perceived severity levels of SLD, and emotional and…

  1. Teachers' opinion about learning continuum based on student's level of competence and specific pedagogical material in classification topics

    Science.gov (United States)

    Andriani, Aldina Eka; Subali, Bambang

    2017-08-01

    This research discusses learning continuum development for designing a curriculum. The objective of this study is to gather the opinion of public junior and senior high school teachers about learning continuum based on student's level of competence and specific pedagogical material in classification topics. This research was conducted in Yogyakarta province from October 2016 to January 2017. This research utilizes a descriptive survey method. Respondents in this study consist of 281 science teachers at junior and senior high school in Yogyakarta city and 4 regencies namely Sleman, Bantul, Kulonprogo, and Gunung Kidul. The sample were taken using a census. The collection of data used questionnaire that had been validated from the aspects of construct validity and experts judgements. Data were analyzed using a descriptive analysis technique. The results of the analysis show that the opinions of teachers regarding specific pedagogical material in classification topics of living things at the junior high school taught in grade VII to the ability level of C2 (Understanding). At senior high school level, it is taught in grade X with the ability level C2 (Understanding). Based on these results, it can be concluded that the opinions of teachers still refer to the current syllabus and curriculum so that the teachers do not have pure opinions about the student's competence level in classification topics that should be taught at the level of the grade in accordance with the level of corresponding competency.

  2. A Learning Method for Neural Networks Based on a Pseudoinverse Technique

    Directory of Open Access Journals (Sweden)

    Chinmoy Pal

    1996-01-01

    Full Text Available A theoretical formulation of a fast learning method based on a pseudoinverse technique is presented. The efficiency and robustness of the method are verified with the help of an Exclusive OR problem and a dynamic system identification of a linear single degree of freedom mass–spring problem. It is observed that, compared with the conventional backpropagation method, the proposed method has a better convergence rate and a higher degree of learning accuracy with a lower equivalent learning coefficient. It is also found that unlike the steepest descent method, the learning capability of which is dependent on the value of the learning coefficient ν, the proposed pseudoinverse based backpropagation algorithm is comparatively robust with respect to its equivalent variable learning coefficient. A combination of the pseudoinverse method and the steepest descent method is proposed for a faster, more accurate learning capability.

  3. The implications of technological learning on the prospects of specific renewable energy technologies in Europe

    International Nuclear Information System (INIS)

    Uyterlinde, M.A.; De Vries, H.J.; Junginger, H.M.

    2005-05-01

    The objective of this chapter is to examine the impact of technological learning on the diffusion of specific renewable energy technologies into the electricity market of the EU-25 until 2020, using a market simulation model (ADMIRE REBUS). It is assumed that from 2012 a harmonized trading system for renewable energy certificates will be implemented. Also it is assumed that a target of 24% renewable electricity (RES-E) in 2020 is set and met. By comparing optimistic and pessimistic endogenous technological learning scenarios, it is found that the diffusion of onshore wind energy into the market is relatively robust, regardless of technological development. However the diffusion rates of offshore wind energy and biomass gasification greatly depend on their technological development. Competition between these two options and already existing biomass combustion options largely determines the overall costs of electricity from renewables and the choice of technologies for the individual member countries. In the optimistic learning scenario, in 2020 the market price for RES-E is 1 euroct/kWh lower than in the pessimistic scenario (about 7 vs. 8 euroct/kWh). As a result, the total expenditures for RES-E market stimulation are 30% lower in the optimistic scenario. For comparison, instead of introducing a harmonized trading system, also continuation of present policies to support renewables was evaluated, assuming that the member states of the EU can fulfil their ambition levels only by exploiting their domestic renewable energy potentials (i.e. exclusion of international trade). This would require many member states to use their offshore wind potential, making the diffusion of offshore wind much less dependent on both the rate of technological learning and competition from biomass options, compared to the harmonization policy scenario

  4. Self-perceived health-related quality of life of Indian children with specific learning disability

    Directory of Open Access Journals (Sweden)

    S Karande

    2012-01-01

    Full Text Available Background: Specific learning disability (SpLD often remains undetected, resulting in the afflicted child experiencing chronic poor school performance. Aims: To measure and analyze the self-perceived health-related quality of life (HRQoL of children with newly-diagnosed SpLD. Settings and Design: Cross-sectional questionnaire-based study in our clinic. Materials and Methods: From February to December 2008, 150 children consecutively diagnosed as having SpLD were enrolled and their HRQoL documented using the DISABKIDS chronic generic module self-report version instrument. Statistical Analysis: Multiple regression analysis was carried out for determining the ′independent′ impact that each of the clinical and socio-demographic variables had on a poor facet score outcome and on a poor total score outcome. Results: Clinically significant deficits were detected in all 6 facets, namely: ′large deficits (effect size ≥−0.8′ in "social exclusion", "emotion", "limitation", "treatment", and "independence"; and ′medium deficit (effect size −0.5 to <−0.8′ in "social inclusion"; and ′large deficit′ in "total score". Multivariate analysis revealed that: (i not belonging to the upper socio-economic strata of society was an independent predictor of a poor "independence" facet outcome (P=0.010, OR=1.99, 95% CI: 1.18 to 3.37; (ii not having experienced class detainment was an independent predictor of a poor "emotion" facet outcome (P=0.008, OR=3.04, 95% CI: 1.34 to 6.85; (iii first-born status was an independent predictor of a poor "limitation" facet outcome (P=0.022, OR=2.60, 95% CI: 1.15 to 5.90; and (iv female gender was an independent predictor of a poor "social exclusion" facet outcome (P=0.024, OR=0.28, 95% CI: 0.09 to 0.85 and a poor "overall health" outcome (P=0.025, OR=0.32, 95% CI: 0.12 to 0.87. Conclusions: Children with newly-diagnosed SpLD perceive their psychosocial, physical, and overall HRQoL to be significantly compromised.

  5. Learning to see the difference specifically alters the most informative V4 neurons.

    Science.gov (United States)

    Raiguel, Steven; Vogels, Rufin; Mysore, Santosh G; Orban, Guy A

    2006-06-14

    Perceptual learning is an instance of adult plasticity whereby training in a sensory (e.g., a visual task) results in neuronal changes leading to an improved ability to perform the task. Yet studies in primary visual cortex have found that changes in neuronal response properties were relatively modest. The present study examines the effects of training in an orientation discrimination task on the response properties of V4 neurons in awake rhesus monkeys. Results indicate that the changes induced in V4 are indeed larger than those in V1. Nonspecific effects of training included a decrease in response variance, and an increase in overall orientation selectivity in V4. The orientation-specific changes involved a local steepening in the orientation tuning curve around the trained orientation that selectively improved orientation discriminability at the trained orientation. Moreover, these changes were largely confined to the population of neurons whose orientation tuning was optimal for signaling small differences in orientation at the trained orientation. Finally, the modifications were restricted to the part of the tuning curve close to the trained orientation. Thus, we conclude that it is the most informative V4 neurons, those most directly involved in the discrimination, that are specifically modified by perceptual learning.

  6. Post-Learning Sleep Transiently Boosts Context Specific Operant Extinction Memory

    Directory of Open Access Journals (Sweden)

    Marion Inostroza

    2017-04-01

    Full Text Available Operant extinction is learning to supress a previously rewarded behavior. It is known to be strongly associated with the specific context in which it was acquired, which limits the therapeutic use of operant extinction in behavioral treatments, e.g., of addiction. We examined whether sleep influences contextual memory of operant extinction over time, using two different recall tests (Recent and Remote. Rats were trained in an operant conditioning task (lever press in context A, then underwent extinction training in context B, followed by a 3-h retention period that contained either spontaneous morning sleep, morning sleep deprivation, or spontaneous evening wakefulness. A recall test was performed either immediately after the 3-h experimental retention period (Recent recall or after 48 h (Remote, in the extinction context B and in a novel context C. The two main findings were: (i at the Recent recall test, sleep in comparison with sleep deprivation and spontaneous wakefulness enhanced extinction memory but, only in the extinction context B; (ii at the Remote recall, extinction performance after sleep was enhanced in both contexts B and C to an extent comparable to levels at Recent recall in context B. Interestingly, extinction performance at Remote recall was also improved in the sleep deprivation groups in both contexts, with no difference to performance in the sleep group. Our results suggest that 3 h of post-learning sleep transiently facilitate the context specificity of operant extinction at a Recent recall. However, the improvement and contextual generalization of operant extinction memory observed in the long-term, i.e., after 48 h, does not require immediate post-learning sleep.

  7. Internet-based versus traditional teaching and learning methods.

    Science.gov (United States)

    Guarino, Salvatore; Leopardi, Eleonora; Sorrenti, Salvatore; De Antoni, Enrico; Catania, Antonio; Alagaratnam, Swethan

    2014-10-01

    The rapid and dramatic incursion of the Internet and social networks in everyday life has revolutionised the methods of exchanging data. Web 2.0 represents the evolution of the Internet as we know it. Internet users are no longer passive receivers, and actively participate in the delivery of information. Medical education cannot evade this process. Increasingly, students are using tablets and smartphones to instantly retrieve medical information on the web or are exchanging materials on their Facebook pages. Medical educators cannot ignore this continuing revolution, and therefore the traditional academic schedules and didactic schemes should be questioned. Analysing opinions collected from medical students regarding old and new teaching methods and tools has become mandatory, with a view towards renovating the process of medical education. A cross-sectional online survey was created with Google® docs and administrated to all students of our medical school. Students were asked to express their opinion on their favourite teaching methods, learning tools, Internet websites and Internet delivery devices. Data analysis was performed using spss. The online survey was completed by 368 students. Although textbooks remain a cornerstone for training, students also identified Internet websites, multimedia non-online material, such as the Encyclopaedia on CD-ROM, and other non-online computer resources as being useful. The Internet represented an important aid to support students' learning needs, but textbooks are still their resource of choice. Among the websites noted, Google and Wikipedia significantly surpassed the peer-reviewed medical databases, and access to the Internet was primarily through personal computers in preference to other Internet access devices, such as mobile phones and tablet computers. Increasingly, students are using tablets and smartphones to instantly retrieve medical information. © 2014 John Wiley & Sons Ltd.

  8. The Keyword Method of Foreign Vocabulary Learning: An Investigation of Its Generalizability. Working Paper No. 270.

    Science.gov (United States)

    Pressley, Michael; And Others

    In five experiments, college-age students of differing foreign language-learning abilities were asked to learn Latin word translations to determine the effectiveness of the keyword method of foreign language vocabulary learning. The Latin words were the types for which it has been argued that the keyword method effects would be maximized (the…

  9. LEARNING MATERIALS SELECTION FOR DIFFERENTIATED INSTRUCTION OF ENGLISH FOR SPECIFIC PURPOSES OF FUTURE PROFESSIONALS IN THE FIELD OF INFORMATION TECHNOLOGY

    Directory of Open Access Journals (Sweden)

    Oksana Synekop

    2017-09-01

    Full Text Available In conditions of differentiation the learning materials selection will optimize the training English for Specific Purposes of the future professionals in the field of information technology at university level. The purpose of the article is to define the basic unit of learning material, the factors of influence on the learning material selection, principles, criteria and the procedure of learning material selection in this paper. Reviewing the scientific achievements in the learning material selection in teaching English has become a basis for defining the factors of influence, principles and criteria in the research. The basic unit of learning material (learning English text for professional purposes is outlined. The factors of influence and principles (correspondence of learning materials to professional interests and needs of information technology students; necessary ability and accessibility; regarding the linguistic and stylistic necessity and sufficiency; availability of Internet sources information of the learning material selection are defined. Also, the qualitative criteria (authenticity; professional significance, relevance and informativeness; conformity of foreign language level and intellectual development of students; variety of genres and forms of speech, their sufficient filling by linguistic material; coherence, integrity, consistency, semantic completeness; topic conformity; situation conformity; unlimited access, reliability and exemplarity of Internet sources and the quantitative criteria (the amount of material of the learning material selection are highlighted. The process of English for Specific Purposes material selection (defining the disciplines of different cycles; defining spheres and related topics; outlining situations, communicative roles and intentions of professional communication; specifying the sources of selection; evaluating the texts; analysis of the knowledge, skills and sub-skills required for the

  10. Perception and coping with the specific learning disabilities impacts on everyday life of children with this diagnosis

    OpenAIRE

    Vilímová, Zuzana

    2015-01-01

    TITLE: Perception and coping with the specific learning disabilities impacts on everyday life of children with this diagnosis. ABSTRACT This text is focused on recognition of impacts of the specific learning disabilities on everyday life as the children with this diagnosis themselves see it and the strategies used by these children in order to cope with these disabilities. The theoretical part summarizes the necessary knowledge of the early school age developmental stage, the interaction of a...

  11. Statistical and Machine Learning forecasting methods: Concerns and ways forward.

    Science.gov (United States)

    Makridakis, Spyros; Spiliotis, Evangelos; Assimakopoulos, Vassilios

    2018-01-01

    Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions.

  12. Statistical and Machine Learning forecasting methods: Concerns and ways forward

    Science.gov (United States)

    Makridakis, Spyros; Assimakopoulos, Vassilios

    2018-01-01

    Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions. PMID:29584784

  13. Specific Methods of Information Security for Nuclear Materials Control and Accounting Automate Systems

    Directory of Open Access Journals (Sweden)

    Konstantin Vyacheslavovich Ivanov

    2013-02-01

    Full Text Available The paper is devoted to specific methods of information security for nuclear materials control and accounting automate systems which is not required of OS and DBMS certifications and allowed to programs modification for clients specific without defenses modification. System ACCORD-2005 demonstrates the realization of this method.

  14. Learning the scientific method using GloFish.

    Science.gov (United States)

    Vick, Brianna M; Pollak, Adrianna; Welsh, Cynthia; Liang, Jennifer O

    2012-12-01

    Here we describe projects that used GloFish, brightly colored, fluorescent, transgenic zebrafish, in experiments that enabled students to carry out all steps in the scientific method. In the first project, students in an undergraduate genetics laboratory course successfully tested hypotheses about the relationships between GloFish phenotypes and genotypes using PCR, fluorescence microscopy, and test crosses. In the second and third projects, students doing independent research carried out hypothesis-driven experiments that also developed new GloFish projects for future genetics laboratory students. Brianna Vick, an undergraduate student, identified causes of the different shades of color found in orange GloFish. Adrianna Pollak, as part of a high school science fair project, characterized the fluorescence emission patterns of all of the commercially available colors of GloFish (red, orange, yellow, green, blue, and purple). The genetics laboratory students carrying out the first project found that learning new techniques and applying their knowledge of genetics were valuable. However, assessments of their learning suggest that this project was not challenging to many of the students. Thus, the independent projects will be valuable as bases to widen the scope and range of difficulty of experiments available to future genetics laboratory students.

  15. Machine Learning-Empowered Biometric Methods for Biomedicine Applications

    Directory of Open Access Journals (Sweden)

    Qingxue Zhang

    2017-07-01

    Full Text Available Nowadays, pervasive computing technologies are paving a promising way for advanced smart health applications. However, a key impediment faced by wide deployment of these assistive smart devices, is the increasing privacy and security issue, such as how to protect access to sensitive patient data in the health record. Focusing on this challenge, biometrics are attracting intense attention in terms of effective user identification to enable confidential health applications. In this paper, we take special interest in two bio-potential-based biometric modalities, electrocardiogram (ECG and electroencephalogram (EEG, considering that they are both unique to individuals, and more reliable than token (identity card and knowledge-based (username/password methods. After extracting effective features in multiple domains from ECG/EEG signals, several advanced machine learning algorithms are introduced to perform the user identification task, including Neural Network, K-nearest Neighbor, Bagging, Random Forest and AdaBoost. Experimental results on two public ECG and EEG datasets show that ECG is a more robust biometric modality compared to EEG, leveraging a higher signal to noise ratio and also more distinguishable morphological patterns. Among different machine learning classifiers, the random forest greatly outperforms the others and owns an identification rate as high as 98%. This study is expected to demonstrate that properly selected biometric empowered by an effective machine learner owns a great potential, to enable confidential biomedicine applications in the era of smart digital health.

  16. E-learning support for Economic-mathematical methods

    Directory of Open Access Journals (Sweden)

    Pavel Kolman

    2009-01-01

    Full Text Available Article is describing process of creating and using of e-learning program for graphical solution of li­near programming problems that is used in the Economic mathematical methods course on Faculty of Business and Economics, MZLU. The program was created within FRVŠ 788/2008 grant and is intended for practicing of graphical solution of LP problems and allows better understanding of the li­near programming problems. In the article is on one hand described the way, how does the program work, it means how were the algorithms implemented, and on the other hand there is described way of use of that program. The program is constructed for working with integer and rational numbers. At the end of the article are shown basic statistics of programs use of students in the present form and the part-time form of study. It is mainly the number of programs downloads and comparison to another programs and students opinion on the e-learning support.

  17. Assessment of two e-learning methods teaching undergraduate students cephalometry in orthodontics.

    Science.gov (United States)

    Ludwig, B; Bister, D; Schott, T C; Lisson, J A; Hourfar, J

    2016-02-01

    Cephalometry is important for orthodontic diagnosis and treatment planning and is part of the core curriculum for training dentists. Training involves identifying anatomical landmarks. The aim of this investigation was to assess whether e-learning improves learning efficiency; a programme specifically designed for this purpose was compared to commercially available software. Thirty undergraduate students underwent traditional training of cephalometry consisting of lectures and tutorials. Tracing skills were tested immediately afterwards (T0). The students were then randomly allocated to three groups: 10 students served as control (CF); they were asked to improve their skills using the material provided so far. Ten students were given a program specifically designed for this study that was based on a power point presentation (PPT). The last group was given a commercially available program that included teaching elements (SW). The groups were tested at the end the six week training (T1). The test consisted of tracing 30 points on two radiographs and a point score improvement was calculated. The students were interviewed after the second test. Both e-learning groups improved more than the traditional group. Improvement scores were four for CF; 8.6 for PPT and 2.8 for SW. For PPT all participants improved and the student feedback was the best compared to the other groups. For the other groups some candidates worsened. Blended learning produced better learning outcomes compared to using a traditional teaching method alone. The easy to use Power Point based custom software produced better results than the commercially available software. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  18. Geocoding location expressions in Twitter messages: A preference learning method

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2014-12-01

    Full Text Available Resolving location expressions in text to the correct physical location, also known as geocoding or grounding, is complicated by the fact that so many places around the world share the same name. Correct resolution is made even more difficult when there is little context to determine which place is intended, as in a 140-character Twitter message, or when location cues from different sources conflict, as may be the case among different metadata fields of a Twitter message. We used supervised machine learning to weigh the different fields of the Twitter message and the features of a world gazetteer to create a model that will prefer the correct gazetteer candidate to resolve the extracted expression. We evaluated our model using the F1 measure and compared it to similar algorithms. Our method achieved results higher than state-of-the-art competitors.

  19. Employing Machine-Learning Methods to Study Young Stellar Objects

    Science.gov (United States)

    Moore, Nicholas

    2018-01-01

    Vast amounts of data exist in the astronomical data archives, and yet a large number of sources remain unclassified. We developed a multi-wavelength pipeline to classify infrared sources. The pipeline uses supervised machine learning methods to classify objects into the appropriate categories. The program is fed data that is already classified to train it, and is then applied to unknown catalogues. The primary use for such a pipeline is the rapid classification and cataloging of data that would take a much longer time to classify otherwise. While our primary goal is to study young stellar objects (YSOs), the applications extend beyond the scope of this project. We present preliminary results from our analysis and discuss future applications.

  20. New Learning Methods for Marine Oil Spill Response Training

    Directory of Open Access Journals (Sweden)

    Justiina Halonen

    2017-06-01

    Full Text Available In Finland the Regional Fire and Rescue Services (RFRS are responsible for near shore oil spill response and shoreline cleanup operations. In addition, they assist in other types of maritime incidents, such as search and rescue operations and fire-fighting on board. These statutory assignments require the RFRS to have capability to act both on land and at sea. As maritime incidents occur infrequently, little routine has been established. In order to improve their performance in maritime operations, the RFRS are participating in a new oil spill training programme to be launched by South-Eastern Finland University of Applied Sciences. This training programme aims to utilize new educational methods; e-learning and simulator based training. In addition to fully exploiting the existing navigational bridge simulator, radio communication simulator and crisis management simulator, an entirely new simulator is developed. This simulator is designed to model the oil recovery process; recovery method, rate and volume in various conditions with different oil types. New simulator enables creation of a comprehensive training programme covering training tasks from a distress call to the completion of an oil spill response operation. Structure of the training programme, as well as the training objectives, are based on the findings from competence and education surveys conducted in spring 2016. In these results, a need for vessel maneuvering and navigation exercises together with actual response measures training were emphasized. Also additional training for maritime radio communication, GMDSS-emergency protocols and collaboration with maritime authorities were seemed important. This paper describes new approach to the maritime operations training designed for rescue authorities, a way of learning by doing, without mobilising the vessels at sea.

  1. BEBP: An Poisoning Method Against Machine Learning Based IDSs

    OpenAIRE

    Li, Pan; Liu, Qiang; Zhao, Wentao; Wang, Dongxu; Wang, Siqi

    2018-01-01

    In big data era, machine learning is one of fundamental techniques in intrusion detection systems (IDSs). However, practical IDSs generally update their decision module by feeding new data then retraining learning models in a periodical way. Hence, some attacks that comprise the data for training or testing classifiers significantly challenge the detecting capability of machine learning-based IDSs. Poisoning attack, which is one of the most recognized security threats towards machine learning...

  2. Extremely Randomized Machine Learning Methods for Compound Activity Prediction

    Directory of Open Access Journals (Sweden)

    Wojciech M. Czarnecki

    2015-11-01

    Full Text Available Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called ‘extremely randomized methods’—Extreme Entropy Machine and Extremely Randomized Trees—for their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their ‘non-extreme’ competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure.

  3. Housing Value Forecasting Based on Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Jingyi Mu

    2014-01-01

    Full Text Available In the era of big data, many urgent issues to tackle in all walks of life all can be solved via big data technique. Compared with the Internet, economy, industry, and aerospace fields, the application of big data in the area of architecture is relatively few. In this paper, on the basis of the actual data, the values of Boston suburb houses are forecast by several machine learning methods. According to the predictions, the government and developers can make decisions about whether developing the real estate on corresponding regions or not. In this paper, support vector machine (SVM, least squares support vector machine (LSSVM, and partial least squares (PLS methods are used to forecast the home values. And these algorithms are compared according to the predicted results. Experiment shows that although the data set exists serious nonlinearity, the experiment result also show SVM and LSSVM methods are superior to PLS on dealing with the problem of nonlinearity. The global optimal solution can be found and best forecasting effect can be achieved by SVM because of solving a quadratic programming problem. In this paper, the different computation efficiencies of the algorithms are compared according to the computing times of relevant algorithms.

  4. Comprehensive, Mixed-Methods Assessment of a Blended Learning Model for Geospatial Literacy Instruction

    Science.gov (United States)

    Brodeur, J. J.; Maclachlan, J. C.; Bagg, J.; Chiappetta-Swanson, C.; Vine, M. M.; Vajoczki, S.

    2013-12-01

    Geospatial literacy -- the ability to conceptualize, capture, analyze and communicate spatial phenomena -- represents an important competency for 21st Century learners in a period of 'Geospatial Revolution'. Though relevant to in-course learning, these skills are often taught externally, placing time and resource pressures on the service providers - commonly libraries - that are relied upon to provide instruction. The emergence of online and blended modes of instruction has presented a potential means of increasing the cost-effectiveness of such activities, by simultaneously reducing instructional costs, expanding the audience for these resources, and addressing student preferences for asynchronous learning and '24-7' access. During 2011 and 2012, McMaster University Library coordinated the development, implementation and assessment of blended learning modules for geospatial literacy instruction in first-year undergraduate Social Science courses. In this paper, we present the results of a comprehensive mixed-methods approach to assess the efficacy of implementing blended learning modules to replace traditional (face-to-face), library-led, first-year undergraduate geospatial literacy instruction. Focus groups, personal interviews and an online survey were used to assess modules across dimensions of: student use, satisfaction and accessibility requirements (via Universal Instructional Design [UID] principles); instructor and teaching staff perception of pedagogical efficacy and instructional effectiveness; and, administrator cost-benefit assessment of development and implementation. Results showed that both instructors and students identified significant value in using the online modules in a blended-learning setting. Reaffirming assumptions of students' '24/7' learning preferences, over 80% of students reported using the modules on a repeat basis. Students were more likely to use the modules to better understand course content than simply to increase their grade in

  5. Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection.

    Science.gov (United States)

    Kim, Jihun; Kim, Jonghong; Jang, Gil-Jin; Lee, Minho

    2017-03-01

    Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. Application of a path sensitizing method on automated generation of test specifications for control software

    International Nuclear Information System (INIS)

    Morimoto, Yuuichi; Fukuda, Mitsuko

    1995-01-01

    An automated generation method for test specifications has been developed for sequential control software in plant control equipment. Sequential control software can be represented as sequential circuits. The control software implemented in a control equipment is designed from these circuit diagrams. In logic tests of VLSI's, path sensitizing methods are widely used to generate test specifications. But the method generates test specifications at a single time only, and can not be directly applied to sequential control software. The basic idea of the proposed method is as follows. Specifications of each logic operator in the diagrams are defined in the software design process. Therefore, test specifications of each operator in the control software can be determined from these specifications, and validity of software can be judged by inspecting all of the operators in the logic circuit diagrams. Candidates for sensitized paths, on which test data for each operator propagates, can be generated by the path sensitizing method. To confirm feasibility of the method, it was experimentally applied to control software in digital control equipment. The program could generate test specifications exactly, and feasibility of the method was confirmed. (orig.) (3 refs., 7 figs.)

  7. Perinatal exposure to dioxins perturbs learning performance of the rat in a dose-specific fashion

    Energy Technology Data Exchange (ETDEWEB)

    Hojo, R.; Rieko, H.; Masaki, K.; Junzo, Y.; Chiharu, T. [National Inst. for Environmental Studies, Tsukuba (Japan)

    2004-09-15

    Dioxins (chlorinated dibenzo-p-dioxin congeners and related compounds including coplanar PCBs) are transferred transplacentally and lactationally from mothers to the developing brain of offspring. Maternal exposure to dioxins are suspected to cause adverse effects on the advanced brain function of offspring, because Previous studies indicate that the most toxic dioxin congener, 2,3,7,8-tetrachloro-dibenzo-p-dioxin (TCDD), affected the advanced brain function of rats, even when mothers had been exposed to a relatively low level of dioxins that would not affect themselves. In coplanar PCBs, which are dioxin-like, toxic equivalency factors (TEFs) are based on similar toxicity to TCDD and on a common mechanism of action, mediated by the aromatic hydrocarbon receptor (AhR). However, non-coplanar PCBs, which are considered to be non-dioxin-like PCBs, also show adverse effects on the learning and memory functions of offspring. In the present study, we hypothesize that coplanar PCBs have two types of toxicities, one is the similar to TCDD and the other is the specific toxicity of PCB itself. To address this hypothesis, effects of maternal exposure to one of the coplanar PCBs, 3,3',4,4',5-pentachlorobiphenyl (PCB126, 1997 WHO TEF = 0.1), on learning and behavioural performance of rats assessed by schedule-controlled operant behavior (SCOB) were examined and compared to TCDD.

  8. Understanding the Effects of Time on Collaborative Learning Processes in Problem Based Learning: A Mixed Methods Study

    Science.gov (United States)

    Hommes, J.; Van den Bossche, P.; de Grave, W.; Bos, G.; Schuwirth, L.; Scherpbier, A.

    2014-01-01

    Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning…

  9. Project-Based Learning Using Discussion and Lesson-Learned Methods via Social Media Model for Enhancing Problem Solving Skills

    Science.gov (United States)

    Jewpanich, Chaiwat; Piriyasurawong, Pallop

    2015-01-01

    This research aims to 1) develop the project-based learning using discussion and lesson-learned methods via social media model (PBL-DLL SoMe Model) used for enhancing problem solving skills of undergraduate in education student, and 2) evaluate the PBL-DLL SoMe Model used for enhancing problem solving skills of undergraduate in education student.…

  10. Reporting intellectual capital in health care organizations: specifics, lessons learned, and future research perspectives.

    Science.gov (United States)

    Veltri, Stefania; Bronzetti, Giovanni; Sicoli, Graziella

    2011-01-01

    This article analyzes the concept of intellectual capital (IC) in the health sector sphere by studying the case of a major nonprofit research organization in this sector, which has for some time been publishing IC reports. In the last few years, health care organizations have been the object of great attention in the implementation and transfer of managerial models and tools; however, there is still a lack of attention paid to the strategic management of IC as a fundamental resource for supporting and enhancing performance improvement dynamics. The main aim of this article is to examine the IC reporting model used by the Center of Molecular Medicine (CMM), a Swedish health organization which is an outstanding benchmark in reporting its IC. We also consider the specifics of IC reporting for health organizations, the lessons learned by analyzing CMM's IC reporting, and future perspectives for research.

  11. Health-related quality of life of children with newly diagnosed specific learning disability.

    Science.gov (United States)

    Karande, Sunil; Bhosrekar, Kirankumar; Kulkarni, Madhuri; Thakker, Arpita

    2009-06-01

    The objective of this study was to measure health-related quality of life (HRQL) of children with newly diagnosed specific learning disability (SpLD) using the Child Health Questionnaire-Parent Form 50. We detected clinically significant deficits (effect size > or = -0.5) in 9 out of 12 domains: limitations in family activities, emotional impact on parents, social limitations as a result of emotional-behavioral problems, time impact on parents, general behavior, physical functioning, social limitations as a result of physical health, general health perceptions and mental health; and in both summary scores (psychosocial > physical). Multivariate analysis revealed having > or = 1 non-academic problem(s) (p or =1 non-academic problem(s) (p = 0.006) or first-born status (p = 0.035) predicted a poor physical summary score. HRQL is significantly compromised in children having newly diagnosed SpLD.

  12. Cross-organism learning method to discover new gene functionalities.

    Science.gov (United States)

    Domeniconi, Giacomo; Masseroli, Marco; Moro, Gianluca; Pinoli, Pietro

    2016-04-01

    Knowledge of gene and protein functions is paramount for the understanding of physiological and pathological biological processes, as well as in the development of new drugs and therapies. Analyses for biomedical knowledge discovery greatly benefit from the availability of gene and protein functional feature descriptions expressed through controlled terminologies and ontologies, i.e., of gene and protein biomedical controlled annotations. In the last years, several databases of such annotations have become available; yet, these valuable annotations are incomplete, include errors and only some of them represent highly reliable human curated information. Computational techniques able to reliably predict new gene or protein annotations with an associated likelihood value are thus paramount. Here, we propose a novel cross-organisms learning approach to reliably predict new functionalities for the genes of an organism based on the known controlled annotations of the genes of another, evolutionarily related and better studied, organism. We leverage a new representation of the annotation discovery problem and a random perturbation of the available controlled annotations to allow the application of supervised algorithms to predict with good accuracy unknown gene annotations. Taking advantage of the numerous gene annotations available for a well-studied organism, our cross-organisms learning method creates and trains better prediction models, which can then be applied to predict new gene annotations of a target organism. We tested and compared our method with the equivalent single organism approach on different gene annotation datasets of five evolutionarily related organisms (Homo sapiens, Mus musculus, Bos taurus, Gallus gallus and Dictyostelium discoideum). Results show both the usefulness of the perturbation method of available annotations for better prediction model training and a great improvement of the cross-organism models with respect to the single-organism ones

  13. Investigating quality of life and self-stigma in Hong Kong children with specific learning disabilities.

    Science.gov (United States)

    Chan, Yi; Chan, Yim Yuk; Cheng, Sui Lam; Chow, Man Yin; Tsang, Yau Wai; Lee, Clara; Lin, Chung-Ying

    2017-09-01

    Children with specific learning disabilities (SpLD) are likely to develop self-stigma and have a poor quality of life (QoL) because of their poor academic performance. Although both self-stigma and poor QoL issues are likely to be found in low academic achievers without SpLD, children with SpLD have worse situation because their diagnosis of SpLD suggests that their learning struggles are biological and permanent. Specifically, students' perception of own capabilities may be affected more by the diagnosis of SpLD than their own actual performance. We examined the self-stigma and QoL of children with SpLD in Hong Kong, a region with an academics-focused culture. Children with SpLD (n=49,M age ±SD=9.55±1.21; SpLD group) and typically developing children (n=32,M age ±SD=9.81±1.40; TD group) completed a Kid-KINDL to measure QoL and a Modified Self-Stigma Scale to measure self-stigma. All parents completed a parallel Kid-KINDL to measure QoL of their children. Compared with the TD group, the SpLD group had a higher level of self-stigma (p=0.027) and lower QoL (child-reported Kid-KINDL: p=0.001; parent-reported Kid-KINDL: plearning process of children with SpLD may be designed to overcome self-stigma and to improve QoL. In addition, the program may involve parents of the children with SpLD or other people (e.g., the peer of the children with SpLD) for improving their understanding and perceptions of SpLD. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Do students with Down syndrome have a specific learning profile for reading?

    Science.gov (United States)

    Ratz, Christoph

    2013-12-01

    The present study assessed achieved reading stages of 190 school-aged children with Down syndrome (DS, age 6-20) in Bavaria, one of the most populated federal states in Germany. Teachers described the reading stages of their students in a questionnaire. The achieved stages of reading according to the developmental model of Frith are compared to a sample of 1419 students with intellectual disability (ID) regardless of etiology, but excluding DS; thereafter parallelized ID-groups were compared. Results of the questionnaire addressed to the students' teachers showed that 20.2% of the students with DS do not read at all, 7.6% read at a logographic stage, 49.4% at an alphabetic and 22.8% at an orthographic level. Alongside these findings among the whole sample, correlations are described concerning age, gender, IQ and sociocultural background. The students with DS are then compared to other students with ID with mixed etiologies. This comparison stresses the emphasis on the alphabetic level amongst students with DS. This emphasis also exists when DS and non-DS students are parallelized in groups of ID, thus showing that students with DS and severe ID are ahead in reading, but those with mild ID are behind. Knowledge about specific literacy attainment of students with DS is vital for planning instruction, for creating learning environments, and for formulating future fields of research. Especially students with DS need specific teaching which takes their impaired verbal short term memory into account, such as learning to read in syllables. Copyright © 2013 Elsevier Ltd. All rights reserved.

  15. Cognitive deficits are a matter of emotional context: inflexible strategy use mediates context-specific learning impairments in OCD.

    Science.gov (United States)

    Zetsche, Ulrike; Rief, Winfried; Westermann, Stefan; Exner, Cornelia

    2015-01-01

    The present study examines the interplay between cognitive deficits and emotional context in obsessive-compulsive disorder (OCD) and social phobia (SP). Specifically, this study examines whether the inflexible use of efficient learning strategies in an emotional context underlies impairments in probabilistic classification learning (PCL) in OCD, and whether PCL impairments are specific to OCD. Twenty-three participants with OCD, 30 participants with SP and 30 healthy controls completed a neutral and an OCD-specific PCL task. OCD participants failed to adopt efficient learning strategies and showed fewer beneficial strategy switches than controls only in an OCD-specific context, but not in a neutral context. Additionally, OCD participants did not show any explicit memory impairments. Number of beneficial strategy switches in the OCD-specific task mediated the difference in PCL performance between OCD and control participants. Individuals with SP were impaired in both PCL tasks. In contrast to neuropsychological models postulating general cognitive impairments in OCD, the present findings suggest that it is the interaction between cognition and emotion that is impaired in OCD. Specifically, activated disorder-specific fears may impair the flexible adoption of efficient learning strategies and compromise otherwise unimpaired PCL. Impairments in PCL are not specific to OCD.

  16. Machine Learning Methods for Prediction of CDK-Inhibitors

    Science.gov (United States)

    Ramana, Jayashree; Gupta, Dinesh

    2010-01-01

    Progression through the cell cycle involves the coordinated activities of a suite of cyclin/cyclin-dependent kinase (CDK) complexes. The activities of the complexes are regulated by CDK inhibitors (CDKIs). Apart from its role as cell cycle regulators, CDKIs are involved in apoptosis, transcriptional regulation, cell fate determination, cell migration and cytoskeletal dynamics. As the complexes perform crucial and diverse functions, these are important drug targets for tumour and stem cell therapeutic interventions. However, CDKIs are represented by proteins with considerable sequence heterogeneity and may fail to be identified by simple similarity search methods. In this work we have evaluated and developed machine learning methods for identification of CDKIs. We used different compositional features and evolutionary information in the form of PSSMs, from CDKIs and non-CDKIs for generating SVM and ANN classifiers. In the first stage, both the ANN and SVM models were evaluated using Leave-One-Out Cross-Validation and in the second stage these were tested on independent data sets. The PSSM-based SVM model emerged as the best classifier in both the stages and is publicly available through a user-friendly web interface at http://bioinfo.icgeb.res.in/cdkipred. PMID:20967128

  17. Machine-learning methods in the classification of water bodies

    Directory of Open Access Journals (Sweden)

    Sołtysiak Marek

    2016-06-01

    Full Text Available Amphibian species have been considered as useful ecological indicators. They are used as indicators of environmental contamination, ecosystem health and habitat quality., Amphibian species are sensitive to changes in the aquatic environment and therefore, may form the basis for the classification of water bodies. Water bodies in which there are a large number of amphibian species are especially valuable even if they are located in urban areas. The automation of the classification process allows for a faster evaluation of the presence of amphibian species in the water bodies. Three machine-learning methods (artificial neural networks, decision trees and the k-nearest neighbours algorithm have been used to classify water bodies in Chorzów – one of 19 cities in the Upper Silesia Agglomeration. In this case, classification is a supervised data mining method consisting of several stages such as building the model, the testing phase and the prediction. Seven natural and anthropogenic features of water bodies (e.g. the type of water body, aquatic plants, the purpose of the water body (destination, position of the water body in relation to any possible buildings, condition of the water body, the degree of littering, the shore type and fishing activities have been taken into account in the classification. The data set used in this study involved information about 71 different water bodies and 9 amphibian species living in them. The results showed that the best average classification accuracy was obtained with the multilayer perceptron neural network.

  18. Recent Advances in Conotoxin Classification by Using Machine Learning Methods.

    Science.gov (United States)

    Dao, Fu-Ying; Yang, Hui; Su, Zhen-Dong; Yang, Wuritu; Wu, Yun; Hui, Ding; Chen, Wei; Tang, Hua; Lin, Hao

    2017-06-25

    Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer's disease, Parkinson's disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine. Generally, conotoxin types are confirmed when their sequence, structure, and function are experimentally validated. However, it is time-consuming and costly to acquire the structure and function information by using biochemical experiments. Therefore, it is important to develop computational tools for efficiently and effectively recognizing conotoxin types based on sequence information. In this work, we reviewed the current progress in computational identification of conotoxins in the following aspects: (i) construction of benchmark dataset; (ii) strategies for extracting sequence features; (iii) feature selection techniques; (iv) machine learning methods for classifying conotoxins; (v) the results obtained by these methods and the published tools; and (vi) future perspectives on conotoxin classification. The paper provides the basis for in-depth study of conotoxins and drug therapy research.

  19. Floor-Fractured Craters through Machine Learning Methods

    Science.gov (United States)

    Thorey, C.

    2015-12-01

    Floor-fractured craters are impact craters that have undergone post impact deformations. They are characterized by shallow floors with a plate-like or convex appearance, wide floor moats, and radial, concentric, and polygonal floor-fractures. While the origin of these deformations has long been debated, it is now generally accepted that they are the result of the emplacement of shallow magmatic intrusions below their floor. These craters thus constitute an efficient tool to probe the importance of intrusive magmatism from the lunar surface. The most recent catalog of lunar-floor fractured craters references about 200 of them, mainly located around the lunar maria Herein, we will discuss the possibility of using machine learning algorithms to try to detect new floor-fractured craters on the Moon among the 60000 craters referenced in the most recent catalogs. In particular, we will use the gravity field provided by the Gravity Recovery and Interior Laboratory (GRAIL) mission, and the topographic dataset obtained from the Lunar Orbiter Laser Altimeter (LOLA) instrument to design a set of representative features for each crater. We will then discuss the possibility to design a binary supervised classifier, based on these features, to discriminate between the presence or absence of crater-centered intrusion below a specific crater. First predictions from different classifier in terms of their accuracy and uncertainty will be presented.

  20. Does Structured Quizzing with Process Specific Feedback Lead to Learning Gains in an Active Learning Geoscience Classroom?

    Science.gov (United States)

    Palsole, S.; Serpa, L. F.

    2013-12-01

    There is a great realization that efficient teaching in the geosciences has the potential to have far reaching effects in outreach to decision and policy makers (Herbert, 2006; Manduca & Mogk, 2006). This research in turn informs educators that the geosciences by the virtue of their highly integrative nature play an important role in serving as an entry point into STEM disciplines and helping developing a new cadre of geoscientists, scientists and a general population with an understanding of science. Keeping these goals in mind we set to design introductory geoscience courses for non-majors and majors that move away from the traditional lecture models which don't necessarily contribute well to knowledge building and retention ((Handelsman et al., 2007; Hake, 1997) to a blended active learning classroom where basic concepts and didactic information is acquired online via webquests, lecturettes and virtual field trips and the face to face portions of the class are focused on problem solving exercises. The traditional way to ensure that students are prepared for the in-class activity is to have the students take a quiz online to demonstrate basic competency. In the process of redesign, we decided to leverage the technology to build quizzes that are highly structured and map to a process (formation of divergent boundaries for example) or sets of earth processes that we needed the students to know before in-class activities. The quizzes can be taken multiple times and provide process specific feedback, thus serving as a heuristic to the students to ensure they have acquired the necessary competency. The heuristic quizzes were developed and deployed over a year with the student data driving the redesign process to ensure synchronicity. Preliminary data analysis indicates a positive correlation between higher student scores on in-class application exercises and time spent on the process quizzes. An assessment of learning gains also indicate a higher degree of self

  1. Use of computerized tests to evaluate psychomotor performance in children with specific learning disabilities in comparison to normal children

    Directory of Open Access Journals (Sweden)

    Santosh Taur

    2014-01-01

    Full Text Available Background & objectives: Children with specific learning disabilities (SpLD have an unexplained difficulty in acquiring basic academic skills resulting in a significant discrepancy between their academic potential and achievements. This study was undertaken to compare the performance on a battery of six psychomotor tests of children with SpLD and those without any learning disabilities (controls using computerized tests. Methods: In this study, 25 children with SpLD and 25 controls (matched for age, socio-economic status and medium of instruction were given three training sessions over one week. Then children were asked to perform on the six computerized psychomotor tests. Results were compared between the two groups. Results: Children with SpLD fared significantly worse on finger tapping test, choice reaction test, digit picture substitution test and card sorting test compared to the controls ( p <0.05. Interpretation & conclusions: Children with SpLD have impairment of psychomotor skills like attention, sensory-motor coordination and executive functioning. Further research is needed to evaluate if the remedial education plan results in improvement in psychomotor performance of children with SpLD on these selected tests.

  2. Application of learning techniques based on kernel methods for the fault diagnosis in industrial processes

    Directory of Open Access Journals (Sweden)

    Jose M. Bernal-de-Lázaro

    2016-05-01

    Full Text Available This article summarizes the main contributions of the PhD thesis titled: "Application of learning techniques based on kernel methods for the fault diagnosis in Industrial processes". This thesis focuses on the analysis and design of fault diagnosis systems (DDF based on historical data. Specifically this thesis provides: (1 new criteria for adjustment of the kernel methods used to select features with a high discriminative capacity for the fault diagnosis tasks, (2 a proposed approach process monitoring using statistical techniques multivariate that incorporates a reinforced information concerning to the dynamics of the Hotelling's T2 and SPE statistics, whose combination with kernel methods improves the detection of small-magnitude faults; (3 an robustness index to compare the diagnosis classifiers performance taking into account their insensitivity to possible noise and disturbance on historical data.

  3. Women with learning disabilities and access to cervical screening: retrospective cohort study using case control methods

    Science.gov (United States)

    Reynolds, Fiona; Stanistreet, Debbi; Elton, Peter

    2008-01-01

    Background Several studies in the UK have suggested that women with learning disabilities may be less likely to receive cervical screening tests and a previous local study in had found that GPs considered screening unnecessary for women with learning disabilities. This study set out to ascertain whether women with learning disabilities are more likely to be ceased from a cervical screening programme than women without; and to examine the reasons given for ceasing women with learning disabilities. It was carried out in Bury, Heywood-and-Middleton and Rochdale. Methods Carried out using retrospective cohort study methods, women with learning disabilities were identified by Read code; and their cervical screening records were compared with the Call-and-Recall records of women without learning disabilities in order to examine their screening histories. Analysis was carried out using case-control methods – 1:2 (women with learning disabilities: women without learning disabilities), calculating odds ratios. Results 267 women's records were compared with the records of 534 women without learning disabilities. Women with learning disabilities had an odds ratio (OR) of 0.48 (Confidence Interval (CI) 0.38 – 0.58; X2: 72.227; p.value learning disabilities. Conclusion The reasons given for ceasing and/or not screening suggest that merely being coded as having a learning disability is not the sole reason for these actions. There are training needs among smear takers regarding appropriate reasons not to screen and providing screening for women with learning disabilities. PMID:18218106

  4. Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

    Science.gov (United States)

    Korotcov, Alexandru; Tkachenko, Valery; Russo, Daniel P; Ekins, Sean

    2017-12-04

    Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further

  5. The Implementation of Discovery Learning Method to Increase Learning Outcomes and Motivation of Student in Senior High School

    Directory of Open Access Journals (Sweden)

    Nanda Saridewi

    2017-11-01

    Full Text Available Based on data from the observation of high school students grade XI that daily low student test scores due to a lack of role of students in the learning process. This classroom action research aims to improve learning outcomes and student motivation through discovery learning method in colloidal material. This study uses the approach developed by Lewin consisting of planning, action, observation, and reflection. Data collection techniques used the questionnaires and ability tests end. Based on the research that results for students received a positive influence on learning by discovery learning model by increasing the average value of 74 students from the first cycle to 90.3 in the second cycle and increased student motivation in the form of two statements based competence (KD categories (sometimes on the first cycle and the first statement KD category in the second cycle. Thus the results of this study can be used to improve learning outcomes and student motivation

  6. Computer game-based and traditional learning method: a comparison regarding students’ knowledge retention

    Directory of Open Access Journals (Sweden)

    Rondon Silmara

    2013-02-01

    Full Text Available Abstract Background Educational computer games are examples of computer-assisted learning objects, representing an educational strategy of growing interest. Given the changes in the digital world over the last decades, students of the current generation expect technology to be used in advancing their learning requiring a need to change traditional passive learning methodologies to an active multisensory experimental learning methodology. The objective of this study was to compare a computer game-based learning method with a traditional learning method, regarding learning gains and knowledge retention, as means of teaching head and neck Anatomy and Physiology to Speech-Language and Hearing pathology undergraduate students. Methods Students were randomized to participate to one of the learning methods and the data analyst was blinded to which method of learning the students had received. Students’ prior knowledge (i.e. before undergoing the learning method, short-term knowledge retention and long-term knowledge retention (i.e. six months after undergoing the learning method were assessed with a multiple choice questionnaire. Students’ performance was compared considering the three moments of assessment for both for the mean total score and for separated mean scores for Anatomy questions and for Physiology questions. Results Students that received the game-based method performed better in the pos-test assessment only when considering the Anatomy questions section. Students that received the traditional lecture performed better in both post-test and long-term post-test when considering the Anatomy and Physiology questions. Conclusions The game-based learning method is comparable to the traditional learning method in general and in short-term gains, while the traditional lecture still seems to be more effective to improve students’ short and long-term knowledge retention.

  7. Numerical Capacities as Domain-Specific Predictors beyond Early Mathematics Learning: A Longitudinal Study

    Science.gov (United States)

    Reigosa-Crespo, Vivian; González-Alemañy, Eduardo; León, Teresa; Torres, Rosario; Mosquera, Raysil; Valdés-Sosa, Mitchell

    2013-01-01

    The first aim of the present study was to investigate whether numerical effects (Numerical Distance Effect, Counting Effect and Subitizing Effect) are domain-specific predictors of mathematics development at the end of elementary school by exploring whether they explain additional variance of later mathematics fluency after controlling for the effects of general cognitive skills, focused on nonnumerical aspects. The second aim was to address the same issues but applied to achievement in mathematics curriculum that requires solutions to fluency in calculation. These analyses assess whether the relationship found for fluency are generalized to mathematics content beyond fluency in calculation. As a third aim, the domain specificity of the numerical effects was examined by analyzing whether they contribute to the development of reading skills, such as decoding fluency and reading comprehension, after controlling for general cognitive skills and phonological processing. Basic numerical capacities were evaluated in children of 3rd and 4th grades (n=49). Mathematics and reading achievements were assessed in these children one year later. Results showed that the size of the Subitizing Effect was a significant domain-specific predictor of fluency in calculation and also in curricular mathematics achievement, but not in reading skills, assessed at the end of elementary school. Furthermore, the size of the Counting Effect also predicted fluency in calculation, although this association only approached significance. These findings contrast with proposals that the core numerical competencies measured by enumeration will bear little relationship to mathematics achievement. We conclude that basic numerical capacities constitute domain-specific predictors and that they are not exclusively “start-up” tools for the acquisition of Mathematics; but they continue modulating this learning at the end of elementary school. PMID:24255710

  8. Numerical capacities as domain-specific predictors beyond early mathematics learning: a longitudinal study.

    Science.gov (United States)

    Reigosa-Crespo, Vivian; González-Alemañy, Eduardo; León, Teresa; Torres, Rosario; Mosquera, Raysil; Valdés-Sosa, Mitchell

    2013-01-01

    The first aim of the present study was to investigate whether numerical effects (Numerical Distance Effect, Counting Effect and Subitizing Effect) are domain-specific predictors of mathematics development at the end of elementary school by exploring whether they explain additional variance of later mathematics fluency after controlling for the effects of general cognitive skills, focused on nonnumerical aspects. The second aim was to address the same issues but applied to achievement in mathematics curriculum that requires solutions to fluency in calculation. These analyses assess whether the relationship found for fluency are generalized to mathematics content beyond fluency in calculation. As a third aim, the domain specificity of the numerical effects was examined by analyzing whether they contribute to the development of reading skills, such as decoding fluency and reading comprehension, after controlling for general cognitive skills and phonological processing. Basic numerical capacities were evaluated in children of 3(rd) and 4(th) grades (n=49). Mathematics and reading achievements were assessed in these children one year later. Results showed that the size of the Subitizing Effect was a significant domain-specific predictor of fluency in calculation and also in curricular mathematics achievement, but not in reading skills, assessed at the end of elementary school. Furthermore, the size of the Counting Effect also predicted fluency in calculation, although this association only approached significance. These findings contrast with proposals that the core numerical competencies measured by enumeration will bear little relationship to mathematics achievement. We conclude that basic numerical capacities constitute domain-specific predictors and that they are not exclusively "start-up" tools for the acquisition of Mathematics; but they continue modulating this learning at the end of elementary school.

  9. Prediction of site-specific interactions in antibody-antigen complexes: the proABC method and server.

    KAUST Repository

    Olimpieri, Pier Paolo

    2013-06-26

    MOTIVATION: Antibodies or immunoglobulins are proteins of paramount importance in the immune system. They are extremely relevant as diagnostic, biotechnological and therapeutic tools. Their modular structure makes it easy to re-engineer them for specific purposes. Short of undergoing a trial and error process, these experiments, as well as others, need to rely on an understanding of the specific determinants of the antibody binding mode. RESULTS: In this article, we present a method to identify, on the basis of the antibody sequence alone, which residues of an antibody directly interact with its cognate antigen. The method, based on the random forest automatic learning techniques, reaches a recall and specificity as high as 80% and is implemented as a free and easy-to-use server, named prediction of Antibody Contacts. We believe that it can be of great help in re-design experiments as well as a guide for molecular docking experiments. The results that we obtained also allowed us to dissect which features of the antibody sequence contribute most to the involvement of specific residues in binding to the antigen. AVAILABILITY: http://www.biocomputing.it/proABC. CONTACT: anna.tramontano@uniroma1.it or paolo.marcatili@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

  10. Visual paired-associate learning: in search of material-specific effects in adult patients who have undergone temporal lobectomy.

    Science.gov (United States)

    Smith, Mary Lou; Bigel, Marla; Miller, Laurie A

    2011-02-01

    The mesial temporal lobes are important for learning arbitrary associations. It has previously been demonstrated that left mesial temporal structures are involved in learning word pairs, but it is not yet known whether comparable lesions in the right temporal lobe impair visually mediated associative learning. Patients who had undergone left (n=16) or right (n=18) temporal lobectomy for relief of intractable epilepsy and healthy controls (n=13) were administered two paired-associate learning tasks assessing their learning and memory of pairs of abstract designs or pairs of symbols in unique locations. Both patient groups had deficits in learning the designs, but only the right temporal group was impaired in recognition. For the symbol location task, differences were not found in learning, but again a recognition deficit was found for the right temporal group. The findings implicate the mesial temporal structures in relational learning. They support a material-specific effect for recognition but not for learning and recall of arbitrary visual and visual-spatial associative information. Copyright © 2010 Elsevier Inc. All rights reserved.

  11. Contingency learning is not affected by conflict experience: Evidence from a task conflict-free, item-specific Stroop paradigm.

    Science.gov (United States)

    Levin, Yulia; Tzelgov, Joseph

    2016-02-01

    A contingency learning account of the item-specific proportion congruent effect has been described as an associative stimulus-response learning process that has nothing to do with controlling the Stroop conflict. As supportive evidence, contingency learning has been demonstrated with response conflict-free stimuli, such as neutral words. However, what gives rise to response conflict and to Stroop interference in general is task conflict. The present study investigated whether task conflict can constitute a trigger or, alternatively, a booster to the contingency learning process. This was done by employing a "task conflict-free" condition (i.e., geometric shapes) and comparing it with a "task conflict" condition (i.e., neutral words). The results showed a significant contingency learning effect in both conditions, refuting the possibility that contingency learning is triggered by the presence of a task conflict. Contingency learning was also not enhanced by the task conflict experience, indicating its complete insensitivity to Stroop conflict(s). Thus, the results showed no evidence that performance optimization as a result of contingency learning is greater under conflict, implying that contingency learning is not recruited to assist the control system to overcome conflict. Copyright © 2015 Elsevier B.V. All rights reserved.

  12. L2 Vocabulary Acquisition in Children: Effects of Learning Method and Cognate Status

    Science.gov (United States)

    Tonzar, Claudio; Lotto, Lorella; Job, Remo

    2009-01-01

    In this study we investigated the effects of two learning methods (picture- or word-mediated learning) and of word status (cognates vs. noncognates) on the vocabulary acquisition of two foreign languages: English and German. We examined children from fourth and eighth grades in a school setting. After a learning phase during which L2 words were…

  13. Spatial Visualization Learning in Engineering: Traditional Methods vs. a Web-Based Tool

    Science.gov (United States)

    Pedrosa, Carlos Melgosa; Barbero, Basilio Ramos; Miguel, Arturo Román

    2014-01-01

    This study compares an interactive learning manager for graphic engineering to develop spatial vision (ILMAGE_SV) to traditional methods. ILMAGE_SV is an asynchronous web-based learning tool that allows the manipulation of objects with a 3D viewer, self-evaluation, and continuous assessment. In addition, student learning may be monitored, which…

  14. Application of a Novel Collaboration Engineering Method for Learning Design: A Case Study

    Science.gov (United States)

    Cheng, Xusen; Li, Yuanyuan; Sun, Jianshan; Huang, Jianqing

    2016-01-01

    Collaborative case studies and computer-supported collaborative learning (CSCL) play an important role in the modern education environment. A number of researchers have given significant attention to learning design in order to improve the satisfaction of collaborative learning. Although collaboration engineering (CE) is a mature method widely…

  15. Strategic Management: An Evaluation of the Use of Three Learning Methods.

    Science.gov (United States)

    Jennings, David

    2002-01-01

    A study of 46 management students compared three methods for learning strategic management: cases, simulation, and action learning through consulting projects. Simulation was superior to action learning on all outcomes and equal or superior to cases on two. Simulation gave students a central role in management and greater control of the learning…

  16. Using Problem Based Learning Methods from Engineering Education in Company Based Development

    DEFF Research Database (Denmark)

    Kofoed, Lise B.; Jørgensen, Frances

    2007-01-01

    This paper discusses how Problem-Based Learning (PBL) methods were used to support a Danish company in its efforts to become more of a 'learning organisation', characterized by sharing of knowledge and experiences. One of the central barriers to organisational learning in this company involved...

  17. Investigating Learning with an Interactive Tutorial: A Mixed-Methods Strategy

    Science.gov (United States)

    de Villiers, M. R.; Becker, Daphne

    2017-01-01

    From the perspective of parallel mixed-methods research, this paper describes interactivity research that employed usability-testing technology to analyse cognitive learning processes; personal learning styles and times; and errors-and-recovery of learners using an interactive e-learning tutorial called "Relations." "Relations"…

  18. Multi-Role Project (MRP): A New Project-Based Learning Method for STEM

    Science.gov (United States)

    Warin, Bruno; Talbi, Omar; Kolski, Christophe; Hoogstoel, Frédéric

    2016-01-01

    This paper presents the "Multi-Role Project" method (MRP), a broadly applicable project-based learning method, and describes its implementation and evaluation in the context of a Science, Technology, Engineering, and Mathematics (STEM) course. The MRP method is designed around a meta-principle that considers the project learning activity…

  19. Role of Class III phosphoinositide 3-kinase in the brain development: possible involvement in specific learning disorders.

    Science.gov (United States)

    Inaguma, Yutaka; Matsumoto, Ayumi; Noda, Mariko; Tabata, Hidenori; Maeda, Akihiko; Goto, Masahide; Usui, Daisuke; Jimbo, Eriko F; Kikkawa, Kiyoshi; Ohtsuki, Mamitaro; Momoi, Mariko Y; Osaka, Hitoshi; Yamagata, Takanori; Nagata, Koh-Ichi

    2016-10-01

    Class III phosphoinositide 3-kinase (PIK3C3 or mammalian vacuolar protein sorting 34 homolog, Vps34) regulates vesicular trafficking, autophagy, and nutrient sensing. Recently, we reported that PIK3C3 is expressed in mouse cerebral cortex throughout the developmental process, especially at early embryonic stage. We thus examined the role of PIK3C3 in the development of the mouse cerebral cortex. Acute silencing of PIK3C3 with in utero electroporation method caused positional defects of excitatory neurons during corticogenesis. Time-lapse imaging revealed that the abnormal positioning was at least partially because of the reduced migration velocity. When PIK3C3 was silenced in cortical neurons in one hemisphere, axon extension to the contralateral hemisphere was also delayed. These aberrant phenotypes were rescued by RNAi-resistant PIK3C3. Notably, knockdown of PIK3C3 did not affect the cell cycle of neuronal progenitors and stem cells at the ventricular zone. Taken together, PIK3C3 was thought to play a crucial role in corticogenesis through the regulation of excitatory neuron migration and axon extension. Meanwhile, when we performed comparative genomic hybridization on a patient with specific learning disorders, a 107 Kb-deletion was identified on 18q12.3 (nt. 39554147-39661206) that encompasses exons 5-23 of PIK3C3. Notably, the above aberrant migration and axon growth phenotypes were not rescued by the disease-related truncation mutant (172 amino acids) lacking the C-terminal kinase domain. Thus, functional defects of PIK3C3 might impair corticogenesis and relate to the pathophysiology of specific learning disorders and other neurodevelopmental disorders. Acute knockdown of Class III phosphoinositide 3-kinase (PIK3C3) evokes migration defects of excitatory neurons during corticogenesis. PIK3C3-knockdown also disrupts axon outgrowth, but not progenitor proliferation in vivo. Involvement of PIK3C3 in neurodevelopmental disorders might be an interesting future

  20. A Machine Learning Method for the Prediction of Receptor Activation in the Simulation of Synapses

    Science.gov (United States)

    Montes, Jesus; Gomez, Elena; Merchán-Pérez, Angel; DeFelipe, Javier; Peña, Jose-Maria

    2013-01-01

    Chemical synaptic transmission involves the release of a neurotransmitter that diffuses in the extracellular space and interacts with specific receptors located on the postsynaptic membrane. Computer simulation approaches provide fundamental tools for exploring various aspects of the synaptic transmission under different conditions. In particular, Monte Carlo methods can track the stochastic movements of neurotransmitter molecules and their interactions with other discrete molecules, the receptors. However, these methods are computationally expensive, even when used with simplified models, preventing their use in large-scale and multi-scale simulations of complex neuronal systems that may involve large numbers of synaptic connections. We have developed a machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the percentage of open synaptic receptors as a function of time since the release of the neurotransmitter, with considerably lower computational cost compared with the conventional Monte Carlo alternative. The method is designed to learn patterns and general principles from a corpus of previously generated Monte Carlo simulations of synapses covering a wide range of structural and functional characteristics. These patterns are later used as a predictive model of the behavior of synapses under different conditions without the need for additional computationally expensive Monte Carlo simulations. This is performed in five stages: data sampling, fold creation, machine learning, validation and curve fitting. The resulting procedure is accurate, automatic, and it is general enough to predict synapse behavior under experimental conditions that are different to the ones it has been trained on. Since our method efficiently reproduces the results that can be obtained with Monte Carlo simulations at a considerably lower computational cost, it is suitable for the simulation of high numbers of synapses and it is

  1. A machine learning method for the prediction of receptor activation in the simulation of synapses.

    Directory of Open Access Journals (Sweden)

    Jesus Montes

    Full Text Available Chemical synaptic transmission involves the release of a neurotransmitter that diffuses in the extracellular space and interacts with specific receptors located on the postsynaptic membrane. Computer simulation approaches provide fundamental tools for exploring various aspects of the synaptic transmission under different conditions. In particular, Monte Carlo methods can track the stochastic movements of neurotransmitter molecules and their interactions with other discrete molecules, the receptors. However, these methods are computationally expensive, even when used with simplified models, preventing their use in large-scale and multi-scale simulations of complex neuronal systems that may involve large numbers of synaptic connections. We have developed a machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the percentage of open synaptic receptors as a function of time since the release of the neurotransmitter, with considerably lower computational cost compared with the conventional Monte Carlo alternative. The method is designed to learn patterns and general principles from a corpus of previously generated Monte Carlo simulations of synapses covering a wide range of structural and functional characteristics. These patterns are later used as a predictive model of the behavior of synapses under different conditions without the need for additional computationally expensive Monte Carlo simulations. This is performed in five stages: data sampling, fold creation, machine learning, validation and curve fitting. The resulting procedure is accurate, automatic, and it is general enough to predict synapse behavior under experimental conditions that are different to the ones it has been trained on. Since our method efficiently reproduces the results that can be obtained with Monte Carlo simulations at a considerably lower computational cost, it is suitable for the simulation of high numbers of

  2. A deep learning method for classifying mammographic breast density categories.

    Science.gov (United States)

    Mohamed, Aly A; Berg, Wendie A; Peng, Hong; Luo, Yahong; Jankowitz, Rachel C; Wu, Shandong

    2018-01-01

    Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., "scattered density" and "heterogeneously dense". The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow. In this study, we constructed a convolutional neural network (CNN)-based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board-certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier. The AUC was 0.9421 when the CNN-model was trained from scratch on our own mammogram images, and the accuracy increased gradually along with an increased size of training samples

  3. RULE-BASE METHOD FOR ANALYSIS OF QUALITY E-LEARNING IN HIGHER EDUCATION

    Directory of Open Access Journals (Sweden)

    darsih darsih darsih

    2016-04-01

    Full Text Available ABSTRACT Assessing the quality of e-learning courses to measure the success of e-learning systems in online learning is essential. The system can be used to improve education. The study analyzes the quality of e-learning course on the web site www.kulon.undip.ac.id used a questionnaire with questions based on the variables of ISO 9126. Penilaiann Likert scale was used with a web app. Rule-base reasoning method is used to subject the quality of e-learningyang assessed. A case study conducted in four e-learning courses with 133 sample / respondents as users of the e-learning course. From the obtained results of research conducted both for the value of e-learning from each subject tested. In addition, each e-learning courses have different advantages depending on certain variables. Keywords : E-Learning, Rule-Base, Questionnaire, Likert, Measuring.

  4. A specific implicit sequence learning deficit as an underlying cause of dyslexia? Investigating the role of attention in implicit learning tasks.

    Science.gov (United States)

    Staels, Eva; Van den Broeck, Wim

    2017-05-01

    Recently, a general implicit sequence learning deficit was proposed as an underlying cause of dyslexia. This new hypothesis was investigated in the present study by including a number of methodological improvements, for example, the inclusion of appropriate control conditions. The second goal of the study was to explore the role of attentional functioning in implicit and explicit learning tasks. In a 2 × 2 within-subjects design 4 tasks were administered in 30 dyslexic and 38 control children: an implicit and explicit serial reaction time (RT) task and an implicit and explicit contextual cueing task. Attentional functioning was also administered. The entire learning curves of all tasks were analyzed using latent growth curve modeling in order to compare performances between groups and to examine the role of attentional functioning on the learning curves. The amount of implicit learning was similar for both groups. However, the dyslexic group showed slower RTs throughout the entire task. This group difference reduced and became nonsignificant after controlling for attentional functioning. Both implicit learning tasks, but none of the explicit learning tasks, were significantly affected by attentional functioning. Dyslexic children do not suffer from a specific implicit sequence learning deficit. The slower RTs of the dyslexic children throughout the entire implicit sequence learning process are caused by their comorbid attention problems and overall slowness. A key finding of the present study is that, in contrast to what was assumed for a long time, implicit learning relies on attentional resources, perhaps even more than explicit learning does. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  5. Comparison of methods applicable to evaluation of nuclear power plant technical specifications

    International Nuclear Information System (INIS)

    Cho, N.Z.; Bozoki, G.E.; Youngblood, R.W.

    1986-01-01

    This study compares three probabilistic methods based on the static fault tree analysis, time-dependent unavailability analysis, and Markov analysis, which can be used to evaluate technical specifications in nuclear power plants. They are tested on a sample problem which was devised to closely represent the important and essential characteristics that should be addressed in determination and evaluation of the technical specifications

  6. Modern Languages and Specific Learning Difficulties (SpLD): Implications of Teaching Adult Learners with Dyslexia in Distance Learning

    Science.gov (United States)

    Gallardo, Matilde; Heiser, Sarah; Arias McLaughlin, Ximena

    2015-01-01

    In modern language (ML) distance learning programmes, teachers and students use online tools to facilitate, reinforce and support independent learning. This makes it essential for teachers to develop pedagogical expertise in using online communication tools to perform their role. Teachers frequently raise questions of how best to support the needs…

  7. Approximation Methods for Inference and Learning in Belief Networks: Progress and Future Directions

    National Research Council Canada - National Science Library

    Pazzan, Michael

    1997-01-01

    .... In this research project, we have investigated methods and implemented algorithms for efficiently making certain classes of inference in belief networks, and for automatically learning certain...

  8. Multiresolution, Geometric, and Learning Methods in Statistical Image Processing, Object Recognition, and Sensor Fusion

    National Research Council Canada - National Science Library

    Willsky, Alan

    2004-01-01

    .... Our research blends methods from several fields-statistics and probability, signal and image processing, mathematical physics, scientific computing, statistical learning theory, and differential...

  9. Computer game-based and traditional learning method: a comparison regarding students' knowledge retention.

    Science.gov (United States)

    Rondon, Silmara; Sassi, Fernanda Chiarion; Furquim de Andrade, Claudia Regina

    2013-02-25

    Educational computer games are examples of computer-assisted learning objects, representing an educational strategy of growing interest. Given the changes in the digital world over the last decades, students of the current generation expect technology to be used in advancing their learning requiring a need to change traditional passive learning methodologies to an active multisensory experimental learning methodology. The objective of this study was to compare a computer game-based learning method with a traditional learning method, regarding learning gains and knowledge retention, as means of teaching head and neck Anatomy and Physiology to Speech-Language and Hearing pathology undergraduate students. Students were randomized to participate to one of the learning methods and the data analyst was blinded to which method of learning the students had received. Students' prior knowledge (i.e. before undergoing the learning method), short-term knowledge retention and long-term knowledge retention (i.e. six months after undergoing the learning method) were assessed with a multiple choice questionnaire. Students' performance was compared considering the three moments of assessment for both for the mean total score and for separated mean scores for Anatomy questions and for Physiology questions. Students that received the game-based method performed better in the pos-test assessment only when considering the Anatomy questions section. Students that received the traditional lecture performed better in both post-test and long-term post-test when considering the Anatomy and Physiology questions. The game-based learning method is comparable to the traditional learning method in general and in short-term gains, while the traditional lecture still seems to be more effective to improve students' short and long-term knowledge retention.

  10. Automatic Detection of Acromegaly From Facial Photographs Using Machine Learning Methods.

    Science.gov (United States)

    Kong, Xiangyi; Gong, Shun; Su, Lijuan; Howard, Newton; Kong, Yanguo

    2018-01-01

    Automatic early detection of acromegaly is theoretically possible from facial photographs, which can lessen the prevalence and increase the cure probability. In this study, several popular machine learning algorithms were used to train a retrospective development dataset consisting of 527 acromegaly patients and 596 normal subjects. We firstly used OpenCV to detect the face bounding rectangle box, and then cropped and resized it to the same pixel dimensions. From the detected faces, locations of facial landmarks which were the potential clinical indicators were extracted. Frontalization was then adopted to synthesize frontal facing views to improve the performance. Several popular machine learning methods including LM, KNN, SVM, RT, CNN, and EM were used to automatically identify acromegaly from the detected facial photographs, extracted facial landmarks, and synthesized frontal faces. The trained models were evaluated using a separate dataset, of which half were diagnosed as acromegaly by growth hormone suppression test. The best result of our proposed methods showed a PPV of 96%, a NPV of 95%, a sensitivity of 96% and a specificity of 96%. Artificial intelligence can automatically early detect acromegaly with a high sensitivity and specificity. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  11. Region and task-specific activation of Arc in primary motor cortex of rats following motor skill learning.

    Science.gov (United States)

    Hosp, J A; Mann, S; Wegenast-Braun, B M; Calhoun, M E; Luft, A R

    2013-10-10

    Motor learning requires protein synthesis within the primary motor cortex (M1). Here, we show that the immediate early gene Arc/Arg3.1 is specifically induced in M1 by learning a motor skill. Arc mRNA was quantified using a fluorescent in situ hybridization assay in adult Long-Evans rats learning a skilled reaching task (SRT), in rats performing reaching-like forelimb movement without learning (ACT) and in rats that were trained in the operant but not the motor elements of the task (controls). Apart from M1, Arc expression was assessed within the rostral motor area (RMA), primary somatosensory cortex (S1), striatum (ST) and cerebellum. In SRT animals, Arc mRNA levels in M1 contralateral to the trained limb were 31% higher than ipsilateral (pmotor skill learning in rats. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

  12. Sixth Grade Students' Content-Specific Competencies and Challenges in Learning the Seasons Through Modeling

    Science.gov (United States)

    Sung, Ji Young; Oh, Phil Seok

    2017-06-01

    Recent science education reform initiatives suggest that learning in science should be organized on the basis of scientists' actual practices including the development and use of models. In line with this, the current study adapted three types of modeling practices to teach two Korean 6th grade science classes the causes of the Earth's seasons. Specifically, the study aimed to identify the students' content-specific competencies and challenges based on fine-grained descriptions and analyses of two target groups' cases. Data included digital recordings of modeling-based science lessons in the two classes, the teacher's and students' artifacts, and interviews with the students. These multiple types of data were analyzed complementarily and qualitatively. It was revealed that the students had a competency in constructing models to generate the desired phenomenon (i.e., seasons). They had difficulty, however, in considering the tilt of the Earth's rotation axis as a cause of the seasons and in finding a proper way of representing the Sun's meridian altitude on a globe. But, when the students were helped and guided by the teacher and peers' interventions, they were able to revise their models in alignment with the scientific understanding of the seasons. Based on these findings, the teacher's pedagogical roles, which include using student competencies as resources, asking physical questions, and explicit guidance on experimentation skills, were recommended to support successful incorporations of modeling practices in the science classroom.

  13. Impact of international humanitarian service-learning on emerging adult social competence: A mixed-methods evaluation

    Directory of Open Access Journals (Sweden)

    Paul Schvaneveldt

    2016-09-01

    Full Text Available This article presents the results from a study into international humanitarian service-learning experiences on young adult volunteers. Specifically, the service-learning experiences of emerging adults who had served in orphanages in Latin America were assessed, in a pre- and post-test design, for their development in areas of social competency such as identity, self-efficacy, self-esteem and ethnocentric attitudes. A mixed-methods design using both qualitative and quantitative measures was used. Both qualitative and quantitative results identified significant and important impacts on the development of the social competencies of these emerging adults. In addition, several qualitative themes illustrated that longer term international service-learning experiences have a profound impact on the social competence of emerging adults. Keywords: International humanitarian service, service-learning, emerging adult competency

  14. A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease.

    Science.gov (United States)

    Peng, Bo; Wang, Suhong; Zhou, Zhiyong; Liu, Yan; Tong, Baotong; Zhang, Tao; Dai, Yakang

    2017-06-09

    Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisting diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-features-based machine learning method to detect sensitive morphometric biomarkers in Parkinson's disease (PD). Specifically, the low-level ROI features (gray matter volume, cortical thickness, etc.) and high-level correlative features (connectivity between ROIs) are integrated to construct the multilevel ROI features. Filter- and wrapper- based feature selection method and multi-kernel support vector machine (SVM) are used in the classification algorithm. T1-weighted brain magnetic resonance (MR) images of 69 PD patients and 103 normal controls from the Parkinson's Progression Markers Initiative (PPMI) dataset are included in the study. The machine learning method performs well in classification between PD patients and normal controls with an accuracy of 85.78%, a specificity of 87.79%, and a sensitivity of 87.64%. The most sensitive biomarkers between PD patients and normal controls are mainly distributed in frontal lobe, parental lobe, limbic lobe, temporal lobe, and central region. The classification performance of our method with multilevel ROI features is significantly improved comparing with other classification methods using single-level features. The proposed method shows promising identification ability for detecting morphometric biomarkers in PD, thus confirming the potentiality of our method in assisting diagnosis of the disease. Copyright © 2017 Elsevier B.V. All rights reserved.

  15. Learners with learning difficulties in mathematics : attitudes, curriculum and methods of teaching mathematics

    OpenAIRE

    2012-01-01

    D.Ed. The aim of this theses is to find out whether there is any relationship between learners' attitudes and learning difficulties in mathematics: To investigate whether learning difficulties in mathematics are associated with learners' gender. To establish the nature of teachers' perceptions of the learning problem areas in the mathematics curriculum. To find out about the teachers' views on the methods of teaching mathematics, resources, learning of mathematics, extra curricular activit...

  16. Novel-word learning deficits in Mandarin-speaking preschool children with specific language impairments.

    Science.gov (United States)

    Chen, Yuchun; Liu, Huei-Mei

    2014-01-01

    Children with SLI exhibit overall deficits in novel word learning compared to their age-matched peers. However, the manifestation of the word learning difficulty in SLI was not consistent across tasks and the factors affecting the learning performance were not yet determined. Our aim is to examine the extent of word learning difficulties in Mandarin-speaking preschool children with SLI, and to explore the potent influence of existing lexical knowledge on to the word learning process. Preschool children with SLI (n=37) and typical language development (n=33) were exposed to novel words for unfamiliar objects embedded in stories. Word learning tasks including the initial mapping and short-term repetitive learning were designed. Results revealed that Mandarin-speaking preschool children with SLI performed as well as their age-peers in the initial form-meaning mapping task. Their word learning difficulty was only evidently shown in the short-term repetitive learning task under a production demand, and their learning speed was slower than the control group. Children with SLI learned the novel words with a semantic head better in both the initial mapping and repetitive learning tasks. Moderate correlations between stand word learning performances and scores on standardized vocabulary were found after controlling for children's age and nonverbal IQ. The results suggested that the word learning difficulty in children with SLI occurred in the process of establishing a robust phonological representation at the beginning stage of word learning. Also, implicit compound knowledge is applied to aid word learning process for children with and without SLI. We also provide the empirical data to validate the relationship between preschool children's word learning performance and their existing receptive vocabulary ability. Copyright © 2013 Elsevier Ltd. All rights reserved.

  17. Rapid analysis method for the determination of 14C specific activity in irradiated graphite.

    Directory of Open Access Journals (Sweden)

    Vidmantas Remeikis

    Full Text Available 14C is one of the limiting radionuclides used in the categorization of radioactive graphite waste; this categorization is crucial in selecting the appropriate graphite treatment/disposal method. We propose a rapid analysis method for 14C specific activity determination in small graphite samples in the 1-100 μg range. The method applies an oxidation procedure to the sample, which extracts 14C from the different carbonaceous matrices in a controlled manner. Because this method enables fast online measurement and 14C specific activity evaluation, it can be especially useful for characterizing 14C in irradiated graphite when dismantling graphite moderator and reflector parts, or when sorting radioactive graphite waste from decommissioned nuclear power plants. The proposed rapid method is based on graphite combustion and the subsequent measurement of both CO2 and 14C, using a commercial elemental analyser and the semiconductor detector, respectively. The method was verified using the liquid scintillation counting (LSC technique. The uncertainty of this rapid method is within the acceptable range for radioactive waste characterization purposes. The 14C specific activity determination procedure proposed in this study takes approximately ten minutes, comparing favorably to the more complicated and time consuming LSC method. This method can be potentially used to radiologically characterize radioactive waste or used in biomedical applications when dealing with the specific activity determination of 14C in the sample.

  18. Rapid analysis method for the determination of 14C specific activity in irradiated graphite.

    Science.gov (United States)

    Remeikis, Vidmantas; Lagzdina, Elena; Garbaras, Andrius; Gudelis, Arūnas; Garankin, Jevgenij; Plukienė, Rita; Juodis, Laurynas; Duškesas, Grigorijus; Lingis, Danielius; Abdulajev, Vladimir; Plukis, Artūras

    2018-01-01

    14C is one of the limiting radionuclides used in the categorization of radioactive graphite waste; this categorization is crucial in selecting the appropriate graphite treatment/disposal method. We propose a rapid analysis method for 14C specific activity determination in small graphite samples in the 1-100 μg range. The method applies an oxidation procedure to the sample, which extracts 14C from the different carbonaceous matrices in a controlled manner. Because this method enables fast online measurement and 14C specific activity evaluation, it can be especially useful for characterizing 14C in irradiated graphite when dismantling graphite moderator and reflector parts, or when sorting radioactive graphite waste from decommissioned nuclear power plants. The proposed rapid method is based on graphite combustion and the subsequent measurement of both CO2 and 14C, using a commercial elemental analyser and the semiconductor detector, respectively. The method was verified using the liquid scintillation counting (LSC) technique. The uncertainty of this rapid method is within the acceptable range for radioactive waste characterization purposes. The 14C specific activity determination procedure proposed in this study takes approximately ten minutes, comparing favorably to the more complicated and time consuming LSC method. This method can be potentially used to radiologically characterize radioactive waste or used in biomedical applications when dealing with the specific activity determination of 14C in the sample.

  19. Linearized method: A new approach for kinetic analysis of central dopamine D2 receptor specific binding

    International Nuclear Information System (INIS)

    Watabe, Hiroshi; Hatazawa, Jun; Ishiwata, Kiichi; Ido, Tatsuo; Itoh, Masatoshi; Iwata, Ren; Nakamura, Takashi; Takahashi, Toshihiro; Hatano, Kentaro

    1995-01-01

    The authors proposed a new method (Linearized method) to analyze neuroleptic ligand-receptor specific binding in a human brain using positron emission tomography (PET). They derived the linear equation to solve four rate constants, k 3 , k 4 , k 5 , k 6 from PET data. This method does not demand radioactivity curve in plasma as an input function to brain, and can do fast calculations in order to determine rate constants. They also tested Nonlinearized method including nonlinear equations which is conventional analysis using plasma radioactivity corrected for ligand metabolites as an input function. The authors applied these methods to evaluate dopamine D 2 receptor specific binding of [ 11 C] YM-09151-2. The value of B max /K d = k 3 k 4 obtained by Linearized method was 5.72 ± 3.1 which was consistent with the value of 5.78 ± 3.4 obtained by Nonlinearized method

  20. A lifelong learning hyper-heuristic method for bin packing.

    Science.gov (United States)

    Sim, Kevin; Hart, Emma; Paechter, Ben

    2015-01-01

    We describe a novel hyper-heuristic system that continuously learns over time to solve a combinatorial optimisation problem. The system continuously generates new heuristics and samples problems from its environment; and representative problems and heuristics are incorporated into a self-sustaining network of interacting entities inspired by methods in artificial immune systems. The network is plastic in both its structure and content, leading to the following properties: it exploits existing knowledge captured in the network to rapidly produce solutions; it can adapt to new problems with widely differing characteristics; and it is capable of generalising over the problem space. The system is tested on a large corpus of 3,968 new instances of 1D bin-packing problems as well as on 1,370 existing problems from the literature; it shows excellent performance in terms of the quality of solutions obtained across the datasets and in adapting to dynamically changing sets of problem instances compared to previous approaches. As the network self-adapts to sustain a minimal repertoire of both problems and heuristics that form a representative map of the problem space, the system is further shown to be computationally efficient and therefore scalable.