Learning Based Approach for Optimal Clustering of Distributed Program's Call Flow Graph

Science.gov (United States)

Abofathi, Yousef; Zarei, Bager; Parsa, Saeed

Optimal clustering of call flow graph for reaching maximum concurrency in execution of distributable components is one of the NP-Complete problems. Learning automatas (LAs) are search tools which are used for solving many NP-Complete problems. In this paper a learning based algorithm is proposed to optimal clustering of call flow graph and appropriate distributing of programs in network level. The algorithm uses learning feature of LAs to search in state space. It has been shown that the speed of reaching to solution increases remarkably using LA in search process, and it also prevents algorithm from being trapped in local minimums. Experimental results show the superiority of proposed algorithm over others.

CALL AND COOPERATIVE LEARNING: A SOLUTION TO DEVELOP STUDENTS‟ LISTENING ABILITY

OpenAIRE

Delsa Miranty

2017-01-01

This paper aims to provide some ideas both for English teachers and target learners about how to apply CALL and Cooperative Learning as the solution to develop students‘ listening activities in the classroom. Since teachers need to understand about students‘ needs, background, age and expectations when they learn English as the foreign language in the classroom. Therefore, the English teacher should provide environment which facilitates the children to have fun di the teaching learning proces...

Sustainability in CALL Learning Environments: A Systemic Functional Grammar Approach

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Peter McDonald

2014-09-01

Full Text Available This research aims to define a sustainable resource in Computer-Assisted Language Learning (CALL. In order for a CALL resource to be sustainable it must work within existing educational curricula. This feature is a necessary prerequisite of sustainability because, despite the potential for educational change that digitalization has offered since the nineteen nineties, curricula in traditional educational institutions have not fundamentally changed, even as we move from a pre-digital society towards a digital society. Curricula have failed to incorporate CALL resources because no agreed-upon pedagogical language enables teachers to discuss CALL classroom practices. Systemic Functional Grammar (SFG can help to provide this language and bridge the gap between the needs of the curriculum and the potentiality of CALL-based resources. This paper will outline how SFG principles can be used to create a pedagogical language for CALL and it will give practical examples of how this language can be used to create sustainable resources in classroom contexts.

Information systems performance evaluation, introducing a two-level technique: Case study call centers

Directory of Open Access Journals (Sweden)

Hesham A. Baraka

2015-03-01

The objective of this paper was to introduce a new technique that can support decision makers in the call centers industry to evaluate, and analyze the performance of call centers. The technique presented is derived from the research done on measuring the success or failure of information systems. Two models are mainly adopted namely: the Delone and Mclean model first introduced in 1992 and the Design Reality Gap model introduced by Heeks in 2002. Two indices are defined to calculate the performance of the call center; the success index and the Gap Index. An evaluation tool has been developed to allow call centers managers to evaluate the performance of their call centers in a systematic analytical approach; the tool was applied on 4 call centers from different areas, simple applications such as food ordering, marketing, and sales, technical support systems, to more real time services such as the example of emergency control systems. Results showed the importance of using information systems models to evaluate complex systems as call centers. The models used allow identifying the dimensions for the call centers that are facing challenges, together with an identification of the individual indicators in these dimensions that are causing the poor performance of the call center.

Attitudes of Jordanian Undergraduate Students towards Using Computer Assisted Language Learning (CALL)

Science.gov (United States)

Saeed, Farah Jamal Abed Alrazeq; Al-Zayed, Norma Nawaf

2018-01-01

The study aimed at investigating the attitudes of Jordanian undergraduate students towards using computer assisted-language learning (CALL) and its effectiveness in the process of learning the English language. In order to fulfill the study's objective, the researchers used a questionnaire to collect data, followed-up with semi-structured…

Machine Learning Techniques in Clinical Vision Sciences.

Science.gov (United States)

Caixinha, Miguel; Nunes, Sandrina

2017-01-01

This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration

CALL AND COOPERATIVE LEARNING: A SOLUTION TO DEVELOP STUDENTS‟ LISTENING ABILITY

Directory of Open Access Journals (Sweden)

Delsa Miranty

2017-04-01

Full Text Available This paper aims to provide some ideas both for English teachers and target learners about how to apply CALL and Cooperative Learning as the solution to develop students‘ listening activities in the classroom. Since teachers need to understand about students‘ needs, background, age and expectations when they learn English as the foreign language in the classroom. Therefore, the English teacher should provide environment which facilitates the children to have fun di the teaching learning process, nice atmosphere, comfort and enjoyable to learn English and practice it both in the classroom and in the laboratory. Furthermore, this paper will provide what the teachers should do related activities such as: listening to the songs, movies, cartoon by applying STAD (Students Teams – Achievement Divisions in the classroom in order to develop students‘ listening ability both in the classroom and laboratory.

E-learning systems intelligent techniques for personalization

CERN Document Server

Klašnja-Milićević, Aleksandra; Ivanović, Mirjana; Budimac, Zoran; Jain, Lakhmi C

2017-01-01

This monograph provides a comprehensive research review of intelligent techniques for personalisation of e-learning systems. Special emphasis is given to intelligent tutoring systems as a particular class of e-learning systems, which support and improve the learning and teaching of domain-specific knowledge. A new approach to perform effective personalization based on Semantic web technologies achieved in a tutoring system is presented. This approach incorporates a recommender system based on collaborative tagging techniques that adapts to the interests and level of students' knowledge. These innovations are important contributions of this monograph. Theoretical models and techniques are illustrated on a real personalised tutoring system for teaching Java programming language. The monograph is directed to, students and researchers interested in the e-learning and personalization techniques. .

A Call to Action for Research in Digital Learning: Learning without Limits of Time, Place, Path, Pace…or Evidence

Science.gov (United States)

Cavanaugh, Cathy; Sessums, Christopher; Drexler, Wendy

2015-01-01

This essay is a call for rethinking our approach to research in digital learning. It plots a path founded in social trends and advances in education. A brief review of these trends and advances is followed by discussion of what flattened research might look like at scale. Scaling research in digital learning is crucial to advancing understanding…

Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

Science.gov (United States)

Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

2009-01-01

In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

Machine learning techniques for optical communication system optimization

DEFF Research Database (Denmark)

Zibar, Darko; Wass, Jesper; Thrane, Jakob

In this paper, machine learning techniques relevant to optical communication are presented and discussed. The focus is on applying machine learning tools to optical performance monitoring and performance prediction.......In this paper, machine learning techniques relevant to optical communication are presented and discussed. The focus is on applying machine learning tools to optical performance monitoring and performance prediction....

Storytelling: a teaching-learning technique.

Science.gov (United States)

Geanellos, R

1996-03-01

Nurses' stories, arising from the practice world, reconstruct the essence of experience as lived and provide vehicles for learning about nursing. The learning process is forwarded by combining storytelling and reflection. Reflection represents an active, purposive, contemplative and deliberative approach to learning through which learners create meaning from the learning experience. The combination of storytelling and reflection allows the creation of links between the materials at hand and prior and future learning. As a teaching-learning technique storytelling engages learners; organizes information; allows exploration of shared lived experiences without the demands, responsibilities and consequences of practice; facilitates remembering; enhances discussion, problem posing and problem solving; and aids understanding of what it is to nurse and to be a nurse.

Statistical Techniques Used in Three Applied Linguistics Journals: "Language Learning,""Applied Linguistics" and "TESOL Quarterly," 1980-1986: Implications for Readers and Researchers.

Science.gov (United States)

Teleni, Vicki; Baldauf, Richard B., Jr.

A study investigated the statistical techniques used by applied linguists and reported in three journals, "Language Learning,""Applied Linguistics," and "TESOL Quarterly," between 1980 and 1986. It was found that 47% of the published articles used statistical procedures. In these articles, 63% of the techniques used could be called basic, 28%…

A Reinforcement Learning Approach to Call Admission Control in HAPS Communication System

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Ni Shu Yan

2017-01-01

Full Text Available The large changing of link capacity and number of users caused by the movement of both platform and users in communication system based on high altitude platform station (HAPS will resulting in high dropping rate of handover and reduce resource utilization. In order to solve these problems, this paper proposes an adaptive call admission control strategy based on reinforcement learning approach. The goal of this strategy is to maximize long-term gains of system, with the introduction of cross-layer interaction and the service downgraded. In order to access different traffics adaptively, the access utility of handover traffics and new call traffics is designed in different state of communication system. Numerical simulation result shows that the proposed call admission control strategy can enhance bandwidth resource utilization and the performances of handover traffics.

Detection and Classification of Baleen Whale Foraging Calls Combining Pattern Recognition and Machine Learning Techniques

Science.gov (United States)

2016-12-01

for a call is also a time- consuming task. None of these chores bothers the analyst when applying the selection algorithm. With help from the...1365- 2907.2007.00106.x. Castellote, M., C. W. Clark, and M. O. Lammers, 2012: Acoustic and behavioural changes by fin whales (Balaenoptera physalus...separation of blue whale call types on a southern California feeding ground. Animal Behaviour , 74, 881–894, doi:10.1016/j.anbehav.2007.01.022. Rocha, R

Modern machine learning techniques and their applications in cartoon animation research

CERN Document Server

Yu, Jun

2013-01-01

The integration of machine learning techniques and cartoon animation research is fast becoming a hot topic. This book helps readers learn the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble manifold learning; multiple kernel learning; multiview subspace learning; and multiview distance metric learning. It then presents the applications of these modern machine learning techniques in cartoon animation research. With these techniques, users can efficiently utilize the cartoon materials to generate animations

Prostate Cancer Probability Prediction By Machine Learning Technique.

Science.gov (United States)

Jović, Srđan; Miljković, Milica; Ivanović, Miljan; Šaranović, Milena; Arsić, Milena

2017-11-26

The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

Neural correlates of threat perception: neural equivalence of conspecific and heterospecific mobbing calls is learned.

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Marc T Avey

Full Text Available Songbird auditory areas (i.e., CMM and NCM are preferentially activated to playback of conspecific vocalizations relative to heterospecific and arbitrary noise. Here, we asked if the neural response to auditory stimulation is not simply preferential for conspecific vocalizations but also for the information conveyed by the vocalization. Black-capped chickadees use their chick-a-dee mobbing call to recruit conspecifics and other avian species to mob perched predators. Mobbing calls produced in response to smaller, higher-threat predators contain more "D" notes compared to those produced in response to larger, lower-threat predators and thus convey the degree of threat of predators. We specifically asked whether the neural response varies with the degree of threat conveyed by the mobbing calls of chickadees and whether the neural response is the same for actual predator calls that correspond to the degree of threat of the chickadee mobbing calls. Our results demonstrate that, as degree of threat increases in conspecific chickadee mobbing calls, there is a corresponding increase in immediate early gene (IEG expression in telencephalic auditory areas. We also demonstrate that as the degree of threat increases for the heterospecific predator, there is a corresponding increase in IEG expression in the auditory areas. Furthermore, there was no significant difference in the amount IEG expression between conspecific mobbing calls or heterospecific predator calls that were the same degree of threat. In a second experiment, using hand-reared chickadees without predator experience, we found more IEG expression in response to mobbing calls than corresponding predator calls, indicating that degree of threat is learned. Our results demonstrate that degree of threat corresponds to neural activity in the auditory areas and that threat can be conveyed by different species signals and that these signals must be learned.

Neural Correlates of Threat Perception: Neural Equivalence of Conspecific and Heterospecific Mobbing Calls Is Learned

Science.gov (United States)

Avey, Marc T.; Hoeschele, Marisa; Moscicki, Michele K.; Bloomfield, Laurie L.; Sturdy, Christopher B.

2011-01-01

Songbird auditory areas (i.e., CMM and NCM) are preferentially activated to playback of conspecific vocalizations relative to heterospecific and arbitrary noise [1]–[2]. Here, we asked if the neural response to auditory stimulation is not simply preferential for conspecific vocalizations but also for the information conveyed by the vocalization. Black-capped chickadees use their chick-a-dee mobbing call to recruit conspecifics and other avian species to mob perched predators [3]. Mobbing calls produced in response to smaller, higher-threat predators contain more “D” notes compared to those produced in response to larger, lower-threat predators and thus convey the degree of threat of predators [4]. We specifically asked whether the neural response varies with the degree of threat conveyed by the mobbing calls of chickadees and whether the neural response is the same for actual predator calls that correspond to the degree of threat of the chickadee mobbing calls. Our results demonstrate that, as degree of threat increases in conspecific chickadee mobbing calls, there is a corresponding increase in immediate early gene (IEG) expression in telencephalic auditory areas. We also demonstrate that as the degree of threat increases for the heterospecific predator, there is a corresponding increase in IEG expression in the auditory areas. Furthermore, there was no significant difference in the amount IEG expression between conspecific mobbing calls or heterospecific predator calls that were the same degree of threat. In a second experiment, using hand-reared chickadees without predator experience, we found more IEG expression in response to mobbing calls than corresponding predator calls, indicating that degree of threat is learned. Our results demonstrate that degree of threat corresponds to neural activity in the auditory areas and that threat can be conveyed by different species signals and that these signals must be learned. PMID:21909363

Neural correlates of threat perception: neural equivalence of conspecific and heterospecific mobbing calls is learned.

Science.gov (United States)

Avey, Marc T; Hoeschele, Marisa; Moscicki, Michele K; Bloomfield, Laurie L; Sturdy, Christopher B

2011-01-01

Songbird auditory areas (i.e., CMM and NCM) are preferentially activated to playback of conspecific vocalizations relative to heterospecific and arbitrary noise. Here, we asked if the neural response to auditory stimulation is not simply preferential for conspecific vocalizations but also for the information conveyed by the vocalization. Black-capped chickadees use their chick-a-dee mobbing call to recruit conspecifics and other avian species to mob perched predators. Mobbing calls produced in response to smaller, higher-threat predators contain more "D" notes compared to those produced in response to larger, lower-threat predators and thus convey the degree of threat of predators. We specifically asked whether the neural response varies with the degree of threat conveyed by the mobbing calls of chickadees and whether the neural response is the same for actual predator calls that correspond to the degree of threat of the chickadee mobbing calls. Our results demonstrate that, as degree of threat increases in conspecific chickadee mobbing calls, there is a corresponding increase in immediate early gene (IEG) expression in telencephalic auditory areas. We also demonstrate that as the degree of threat increases for the heterospecific predator, there is a corresponding increase in IEG expression in the auditory areas. Furthermore, there was no significant difference in the amount IEG expression between conspecific mobbing calls or heterospecific predator calls that were the same degree of threat. In a second experiment, using hand-reared chickadees without predator experience, we found more IEG expression in response to mobbing calls than corresponding predator calls, indicating that degree of threat is learned. Our results demonstrate that degree of threat corresponds to neural activity in the auditory areas and that threat can be conveyed by different species signals and that these signals must be learned.

Investigating CALL in the Classroom: Situational Variables to Consider

Directory of Open Access Journals (Sweden)

Darlene Liutkus

2012-01-01

Full Text Available A new paradigm in second language pedagogy has Computer Assisted Language Learning (CALL playing a significant role. Much of the literature to-date claims that CALL can have a positive impact on students’ second language acquisition (SLA. Mixed method of research produces data to investigate if CALL positively affects student language proficiency, motivation and autonomy. Classroom observation of participants in their natural environment is a qualitative technique used but has situational variables that could skew results if not structured. A questionnaire is a quantitative tool that can offer insight regarding participants’ perception of performance but can contradict what the researcher has observed. This paper will take an in-depth look at variables such as: instructor’s pedagogical application; blending CALL into the curriculum; types of CALL implemented; feedback received and their implications for design of the data collection tools

Maximizing Reading Narrative Text Ability by Probing Prompting Learning Technique

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Wiwied Pratiwi

2017-12-01

Full Text Available The objective of this research was to know whether Probing Prompting Learning Technique can be used to get the maximum effect of students’ reading narrative ability in teaching and learning process. This research was applied collaborative action reEsearch, this research was done in two cycle. The subject of this research was 23 students at tenth grade of SMA Kartikatama Metro. The result of the research showed that the Probing Prompting Learning Technique is useful and effective to help students get maximum effect of their reading. Based on the results of the questionnaire obtained an average percentage of 95%, it indicated that application of Probing Prompting Learning Technique in teaching l reading was appropriately applied. In short that students’ responses toward Probing Prompting Learning Technique in teaching reading was positive. In conclusion, Probing Prompting Learning Technique can get maximum effect of students’ reading ability. In relation to the result of the reserach, some suggestion are offered to english teacher, that  the use of Probing Prompting learning Technique in teaching reading will get the maximum effect of students’ reading abilty.

APPLICABILITY OF COOPERATIVE LEARNING TECHNIQUES IN DIFFERENT CLASSROOM CONTEXTS

Directory of Open Access Journals (Sweden)

Dr. Issy Yuliasri

2017-04-01

Full Text Available This paper is based on the results of pre-test post-test, feedback questionnaire and observation during a community service program entitled ―Training on English Teaching using Cooperative Learning Techniques for Elementary and Junior High School Teachers of Sekolah Alam Arridho Semarang‖. It was an English teaching training program intended to equip the teachers with the knowledge and skills of using the different cooperative learning techniques such as jigsaw, think-pair-share, three-step interview, roundrobin braistorming, three-minute review, numbered heads together, team-pair-solo, circle the sage, dan partners. This program was participated by 8 teachers of different subjects (not only English, but most of them had good mastery of English. The objectives of this program was to improve teachers‘ skills in using the different cooperative learning techniques to vary their teaching, so that students would be more motivated to learn and improve their English skill. Besides, the training also gave the teachers the knowledge and skills to adjust their techniques with the basic competence and learning objectives to be achieved as well as with the teaching materials to be used. This was also done through workshops using cooperative learning techniques, so that the participants had real experiences of using cooperative learning techniques (learning by doing. The participants were also encouraged to explore the applicability of the techniques in their classroom contexts, in different areas of their teaching. This community service program showed very positive results. The pre-test and post-test results showed that before the training program all the participants did not know the nine cooperative techniques to be trained, but after the program they mastered the techniques as shown from the teaching-learning scenarios they developed following the test instructions. In addition, the anonymous questionnaires showed that all the participants

From Computer Assisted Language Learning (CALL) to Mobile Assisted Language Use (MALU)

Science.gov (United States)

Jarvis, Huw; Achilleos, Marianna

2013-01-01

This article begins by critiquing the long-established acronym CALL (Computer Assisted Language Learning). We then go on to report on a small-scale study which examines how student non-native speakers of English use a range of digital devices beyond the classroom in both their first (L1) and second (L2) languages. We look also at the extent to…

Call cultures in orang-utans?

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Serge A Wich

Full Text Available BACKGROUND: Several studies suggested great ape cultures, arguing that human cumulative culture presumably evolved from such a foundation. These focused on conspicuous behaviours, and showed rich geographic variation, which could not be attributed to known ecological or genetic differences. Although geographic variation within call types (accents has previously been reported for orang-utans and other primate species, we examine geographic variation in the presence/absence of discrete call types (dialects. Because orang-utans have been shown to have geographic variation that is not completely explicable by genetic or ecological factors we hypothesized that this will be similar in the call domain and predict that discrete call type variation between populations will be found. METHODOLOGY/PRINCIPAL FINDINGS: We examined long-term behavioural data from five orang-utan populations and collected fecal samples for genetic analyses. We show that there is geographic variation in the presence of discrete types of calls. In exactly the same behavioural context (nest building and infant retrieval, individuals in different wild populations customarily emit either qualitatively different calls or calls in some but not in others. By comparing patterns in call-type and genetic similarity, we suggest that the observed variation is not likely to be explained by genetic or ecological differences. CONCLUSION/SIGNIFICANCE: These results are consistent with the potential presence of 'call cultures' and suggest that wild orang-utans possess the ability to invent arbitrary calls, which spread through social learning. These findings differ substantially from those that have been reported for primates before. First, the results reported here are on dialect and not on accent. Second, this study presents cases of production learning whereas most primate studies on vocal learning were cases of contextual learning. We conclude with speculating on how these findings might

Data Mining Practical Machine Learning Tools and Techniques

CERN Document Server

Witten, Ian H; Hall, Mark A

2011-01-01

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place

Opportunities to Create Active Learning Techniques in the Classroom

Science.gov (United States)

Camacho, Danielle J.; Legare, Jill M.

2015-01-01

The purpose of this article is to contribute to the growing body of research that focuses on active learning techniques. Active learning techniques require students to consider a given set of information, analyze, process, and prepare to restate what has been learned--all strategies are confirmed to improve higher order thinking skills. Active…

Active learning techniques for librarians practical examples

CERN Document Server

Walsh, Andrew

2010-01-01

A practical work outlining the theory and practice of using active learning techniques in library settings. It explains the theory of active learning and argues for its importance in our teaching and is illustrated using a large number of examples of techniques that can be easily transferred and used in teaching library and information skills to a range of learners within all library sectors. These practical examples recognise that for most of us involved in teaching library and information skills the one off session is the norm, so we need techniques that allow us to quickly grab and hold our

Relaxation techniques for stress

Science.gov (United States)

... raise your heart rate. This is called the stress response. Relaxation techniques can help your body relax and lower your blood pressure ... also many other types of breathing techniques you can learn. In many cases, you do not need much ... including those that cause stress. Meditation has been practiced for thousands of years, ...

The Effect of Group Investigation Learning Model with Brainstroming Technique on Students Learning Outcomes

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Astiti Kade kAyu

2018-01-01

Full Text Available This study aims to determine the effect of group investigation (GI learning model with brainstorming technique on student physics learning outcomes (PLO compared to jigsaw learning model with brainstroming technique. The learning outcome in this research are the results of learning in the cognitive domain. The method used in this research is experiment with Randomised Postest Only Control Group Design. Population in this research is all students of class XI IPA SMA Negeri 9 Kupang year lesson 2015/2016. The selected sample are 40 students of class XI IPA 1 as the experimental class and 38 students of class XI IPA 2 as the control class using simple random sampling technique. The instrument used is 13 items description test. The first hypothesis was tested by using two tailed t-test. From that, it is obtained that H0 rejected which means there are differences of students physics learning outcome. The second hypothesis was tested using one tailed t-test. It is obtained that H0 rejected which means the students PLO in experiment class were higher than control class. Based on the results of this study, researchers recommend the use of GI learning models with brainstorming techniques to improve PLO, especially in the cognitive domain.

The Effect of Learning Based on Technology Model and Assessment Technique toward Thermodynamic Learning Achievement

Science.gov (United States)

Makahinda, T.

2018-02-01

The purpose of this research is to find out the effect of learning model based on technology and assessment technique toward thermodynamic achievement by controlling students intelligence. This research is an experimental research. The sample is taken through cluster random sampling with the total respondent of 80 students. The result of the research shows that the result of learning of thermodynamics of students who taught the learning model of environmental utilization is higher than the learning result of student thermodynamics taught by simulation animation, after controlling student intelligence. There is influence of student interaction, and the subject between models of technology-based learning with assessment technique to student learning result of Thermodynamics, after controlling student intelligence. Based on the finding in the lecture then should be used a thermodynamic model of the learning environment with the use of project assessment technique.

Understanding a Deep Learning Technique through a Neuromorphic System a Case Study with SpiNNaker Neuromorphic Platform

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Sugiarto Indar

2018-01-01

Full Text Available Deep learning (DL has been considered as a breakthrough technique in the field of artificial intelligence and machine learning. Conceptually, it relies on a many-layer network that exhibits a hierarchically non-linear processing capability. Some DL architectures such as deep neural networks, deep belief networks and recurrent neural networks have been developed and applied to many fields with incredible results, even comparable to human intelligence. However, many researchers are still sceptical about its true capability: can the intelligence demonstrated by deep learning technique be applied for general tasks? This question motivates the emergence of another research discipline: neuromorphic computing (NC. In NC, researchers try to identify the most fundamental ingredients that construct intelligence behaviour produced by the brain itself. To achieve this, neuromorphic systems are developed to mimic the brain functionality down to cellular level. In this paper, a neuromorphic platform called SpiNNaker is described and evaluated in order to understand its potential use as a platform for a deep learning approach. This paper is a literature review that contains comparative study on algorithms that have been implemented in SpiNNaker.

PENGARUH PERMAINAN CALL CARDS TERHADAP HASIL BELAJAR DAN AKTIVITAS PEMBELAJARAN BIOLOGI

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A. Machin

2012-10-01

Full Text Available Tujuan penelitian untuk mengetahui pengaruh permainan call cards terhadap hasil belajar dan aktivitas pembelajaran. Aktivitas pembelajaran yang diukur meliputi aktivitas individual siswa dan kinerja guru. Penelitian ini merupakan penelitian eksperimental. Hasil penelitian menunjukkan bahwa media permainan call cards berkontribusi sebesar 46% terhadap hasil belajar siswa. Hasil belajar siswa yang diberi media permainan call cards lebih baik daripada hasil belajar siswa yang tidak diberi mediapermainan call cards. Dengan demikian, media permainan call cards dapat menjadi alternatif dalam pencapaian hasil belajar biologi yang lebih baik.   Research purposes to determine the effect of call cards game against learning outcomes and learning activities. Learning activities that were measured included the activity of individual student and teacher performance. This research was experimental. The results showed that the media play call cards account for 46% of the student learning outcomes. Learning outcomes of students who were given media cards call the game better than the learning outcomes of students who were not given mediapermainan call cards. Thus, the media play call cards can be an alternative in achieving the learning outcomes of biology better.

Machine learning techniques to examine large patient databases.

Science.gov (United States)

Meyfroidt, Geert; Güiza, Fabian; Ramon, Jan; Bruynooghe, Maurice

2009-03-01

Computerization in healthcare in general, and in the operating room (OR) and intensive care unit (ICU) in particular, is on the rise. This leads to large patient databases, with specific properties. Machine learning techniques are able to examine and to extract knowledge from large databases in an automatic way. Although the number of potential applications for these techniques in medicine is large, few medical doctors are familiar with their methodology, advantages and pitfalls. A general overview of machine learning techniques, with a more detailed discussion of some of these algorithms, is presented in this review.

eLearning techniques supporting problem based learning in clinical simulation.

Science.gov (United States)

Docherty, Charles; Hoy, Derek; Topp, Helena; Trinder, Kathryn

2005-08-01

This paper details the results of the first phase of a project using eLearning to support students' learning within a simulated environment. The locus was a purpose built clinical simulation laboratory (CSL) where the School's philosophy of problem based learning (PBL) was challenged through lecturers using traditional teaching methods. a student-centred, problem based approach to the acquisition of clinical skills that used high quality learning objects embedded within web pages, substituting for lecturers providing instruction and demonstration. This encouraged student nurses to explore, analyse and make decisions within the safety of a clinical simulation. Learning was facilitated through network communications and reflection on video performances of self and others. Evaluations were positive, students demonstrating increased satisfaction with PBL, improved performance in exams, and increased self-efficacy in the performance of nursing activities. These results indicate that eLearning techniques can help students acquire clinical skills in the safety of a simulated environment within the context of a problem based learning curriculum.

Comparative Performance Analysis of Machine Learning Techniques for Software Bug Detection

OpenAIRE

Saiqa Aleem; Luiz Fernando Capretz; Faheem Ahmed

2015-01-01

Machine learning techniques can be used to analyse data from different perspectives and enable developers to retrieve useful information. Machine learning techniques are proven to be useful in terms of software bug prediction. In this paper, a comparative performance analysis of different machine learning techniques is explored f or software bug prediction on public available data sets. Results showed most of the mac ...

Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

Science.gov (United States)

Firdausiah Mansur, Andi Besse; Yusof, Norazah

2013-01-01

Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

Developing an effective corrective action process : lessons learned from operating a confidential close call reporting system

Science.gov (United States)

2013-03-05

In 2007, the Federal Railroad Administration (FRA) launched : C3RS, the Confidential Close Call Reporting System, as a : demonstration project to learn how to facilitate the effective : reporting and implementation of corrective actions, and assess t...

Learning Programming Technique through Visual Programming Application as Learning Media with Fuzzy Rating

Science.gov (United States)

Buditjahjanto, I. G. P. Asto; Nurlaela, Luthfiyah; Ekohariadi; Riduwan, Mochamad

2017-01-01

Programming technique is one of the subjects at Vocational High School in Indonesia. This subject contains theory and application of programming utilizing Visual Programming. Students experience some difficulties to learn textual learning. Therefore, it is necessary to develop media as a tool to transfer learning materials. The objectives of this…

BENCHMARKING MACHINE LEARNING TECHNIQUES FOR SOFTWARE DEFECT DETECTION

OpenAIRE

Saiqa Aleem; Luiz Fernando Capretz; Faheem Ahmed

2015-01-01

Machine Learning approaches are good in solving problems that have less information. In most cases, the software domain problems characterize as a process of learning that depend on the various circumstances and changes accordingly. A predictive model is constructed by using machine learning approaches and classified them into defective and non-defective modules. Machine learning techniques help developers to retrieve useful information after the classification and enable them to analyse data...

A machine learning model to determine the accuracy of variant calls in capture-based next generation sequencing.

Science.gov (United States)

van den Akker, Jeroen; Mishne, Gilad; Zimmer, Anjali D; Zhou, Alicia Y

2018-04-17

Next generation sequencing (NGS) has become a common technology for clinical genetic tests. The quality of NGS calls varies widely and is influenced by features like reference sequence characteristics, read depth, and mapping accuracy. With recent advances in NGS technology and software tools, the majority of variants called using NGS alone are in fact accurate and reliable. However, a small subset of difficult-to-call variants that still do require orthogonal confirmation exist. For this reason, many clinical laboratories confirm NGS results using orthogonal technologies such as Sanger sequencing. Here, we report the development of a deterministic machine-learning-based model to differentiate between these two types of variant calls: those that do not require confirmation using an orthogonal technology (high confidence), and those that require additional quality testing (low confidence). This approach allows reliable NGS-based calling in a clinical setting by identifying the few important variant calls that require orthogonal confirmation. We developed and tested the model using a set of 7179 variants identified by a targeted NGS panel and re-tested by Sanger sequencing. The model incorporated several signals of sequence characteristics and call quality to determine if a variant was identified at high or low confidence. The model was tuned to eliminate false positives, defined as variants that were called by NGS but not confirmed by Sanger sequencing. The model achieved very high accuracy: 99.4% (95% confidence interval: +/- 0.03%). It categorized 92.2% (6622/7179) of the variants as high confidence, and 100% of these were confirmed to be present by Sanger sequencing. Among the variants that were categorized as low confidence, defined as NGS calls of low quality that are likely to be artifacts, 92.1% (513/557) were found to be not present by Sanger sequencing. This work shows that NGS data contains sufficient characteristics for a machine-learning-based model to

The Differences across Distributed Leadership Practices by School Position According to the Comprehensive Assessment of Leadership for Learning (CALL)

Science.gov (United States)

Blitz, Mark H.; Modeste, Marsha

2015-01-01

The Comprehensive Assessment of Leadership for Learning (CALL) is a multi-source assessment of distributed instructional leadership. As part of the validation of CALL, researchers examined differences between teacher and leader ratings in assessing distributed leadership practices. The authors utilized a t-test for equality of means for the…

Learning Physics through Project-Based Learning Game Techniques

Science.gov (United States)

Baran, Medine; Maskan, Abdulkadir; Yasar, Seyma

2018-01-01

The aim of the present study, in which Project and game techniques are used together, is to examine the impact of project-based learning games on students' physics achievement. Participants of the study consist of 34 9th grade students (N = 34). The data were collected using achievement tests and a questionnaire. Throughout the applications, the…

IoT Security Techniques Based on Machine Learning

OpenAIRE

Xiao, Liang; Wan, Xiaoyue; Lu, Xiaozhen; Zhang, Yanyong; Wu, Di

2018-01-01

Internet of things (IoT) that integrate a variety of devices into networks to provide advanced and intelligent services have to protect user privacy and address attacks such as spoofing attacks, denial of service attacks, jamming and eavesdropping. In this article, we investigate the attack model for IoT systems, and review the IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning and reinforcement learning. We focus on the machine le...

Machine learning techniques in optical communication

DEFF Research Database (Denmark)

Zibar, Darko; Piels, Molly; Jones, Rasmus Thomas

2015-01-01

Techniques from the machine learning community are reviewed and employed for laser characterization, signal detection in the presence of nonlinear phase noise, and nonlinearity mitigation. Bayesian filtering and expectation maximization are employed within nonlinear state-space framework...

Machine learning techniques in optical communication

DEFF Research Database (Denmark)

Zibar, Darko; Piels, Molly; Jones, Rasmus Thomas

2016-01-01

Machine learning techniques relevant for nonlinearity mitigation, carrier recovery, and nanoscale device characterization are reviewed and employed. Markov Chain Monte Carlo in combination with Bayesian filtering is employed within the nonlinear state-space framework and demonstrated for parameter...

Convergence of calls as animals form social bonds, active compensation for noisy communication channels, and the evolution of vocal learning in mammals.

Science.gov (United States)

Tyack, Peter L

2008-08-01

The classic evidence for vocal production learning involves imitation of novel, often anthropogenic sounds. Among mammals, this has been reported for dolphins, elephants, harbor seals, and humans. A broader taxonomic distribution has been reported for vocal convergence, where the acoustic properties of calls from different individuals converge when they are housed together in captivity or form social bonds in the wild. Vocal convergence has been demonstrated for animals as diverse as songbirds, parakeets, hummingbirds, bats, elephants, cetaceans, and primates. For most species, call convergence is thought to reflect a group-distinctive identifier, with shared calls reflecting and strengthening social bonds. A ubiquitous function for vocal production learning that is starting to receive attention involves modifying signals to improve communication in a noisy channel. Pooling data on vocal imitation, vocal convergence, and compensation for noise suggests a wider taxonomic distribution of vocal production learning among mammals than has been generally appreciated. The wide taxonomic distribution of this evidence for vocal production learning suggests that perhaps more of the neural underpinnings for vocal production learning are in place in mammals than is usually recognized. (c) 2008 APA, all rights reserved

Impact of corpus domain for sentiment classification: An evaluation study using supervised machine learning techniques

Science.gov (United States)

Karsi, Redouane; Zaim, Mounia; El Alami, Jamila

2017-07-01

Thanks to the development of the internet, a large community now has the possibility to communicate and express its opinions and preferences through multiple media such as blogs, forums, social networks and e-commerce sites. Today, it becomes clearer that opinions published on the web are a very valuable source for decision-making, so a rapidly growing field of research called “sentiment analysis” is born to address the problem of automatically determining the polarity (Positive, negative, neutral,…) of textual opinions. People expressing themselves in a particular domain often use specific domain language expressions, thus, building a classifier, which performs well in different domains is a challenging problem. The purpose of this paper is to evaluate the impact of domain for sentiment classification when using machine learning techniques. In our study three popular machine learning techniques: Support Vector Machines (SVM), Naive Bayes and K nearest neighbors(KNN) were applied on datasets collected from different domains. Experimental results show that Support Vector Machines outperforms other classifiers in all domains, since it achieved at least 74.75% accuracy with a standard deviation of 4,08.

The application of machine learning techniques in the clinical drug therapy.

Science.gov (United States)

Meng, Huan-Yu; Jin, Wan-Lin; Yan, Cheng-Kai; Yang, Huan

2018-05-25

The development of a novel drug is an extremely complicated process that includes the target identification, design and manufacture, and proper therapy of the novel drug, as well as drug dose selection, drug efficacy evaluation, and adverse drug reaction control. Due to the limited resources, high costs, long duration, and low hit-to-lead ratio in the development of pharmacogenetics and computer technology, machine learning techniques have assisted novel drug development and have gradually received more attention by researchers. According to current research, machine learning techniques are widely applied in the process of the discovery of new drugs and novel drug targets, the decision surrounding proper therapy and drug dose, and the prediction of drug efficacy and adverse drug reactions. In this article, we discussed the history, workflow, and advantages and disadvantages of machine learning techniques in the processes mentioned above. Although the advantages of machine learning techniques are fairly obvious, the application of machine learning techniques is currently limited. With further research, the application of machine techniques in drug development could be much more widespread and could potentially be one of the major methods used in drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Three visual techniques to enhance interprofessional learning.

Science.gov (United States)

Parsell, G; Gibbs, T; Bligh, J

1998-07-01

Many changes in the delivery of healthcare in the UK have highlighted the need for healthcare professionals to learn to work together as teams for the benefit of patients. Whatever the profession or level, whether for postgraduate education and training, continuing professional development, or for undergraduates, learners should have an opportunity to learn about and with, other healthcare practitioners in a stimulating and exciting way. Learning to understand how people think, feel, and react, and the parts they play at work, both as professionals and individuals, can only be achieved through sensitive discussion and exchange of views. Teaching and learning methods must provide opportunities for this to happen. This paper describes three small-group teaching techniques which encourage a high level of learner collaboration and team-working. Learning content is focused on real-life health-care issues and strong visual images are used to stimulate lively discussion and debate. Each description includes the learning objectives of each exercise, basic equipment and resources, and learning outcomes.

Greedy Deep Dictionary Learning

OpenAIRE

Tariyal, Snigdha; Majumdar, Angshul; Singh, Richa; Vatsa, Mayank

2016-01-01

In this work we propose a new deep learning tool called deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at a time. This requires solving a simple (shallow) dictionary learning problem, the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like stacked autoencoder and deep belief network; and state of the art supervised dictionary learning t...

Massively collaborative machine learning

NARCIS (Netherlands)

Rijn, van J.N.

2016-01-01

Many scientists are focussed on building models. We nearly process all information we perceive to a model. There are many techniques that enable computers to build models as well. The field of research that develops such techniques is called Machine Learning. Many research is devoted to develop

The Ghost in the Machine: Are "Teacherless" CALL Programs Really Possible?

Science.gov (United States)

Davies, Ted; Williamson, Rodney

1998-01-01

Reflects critically on pedagogical issues in the production of computer-assisted language learning (CALL) courseware and ways CALL has affected the practice of language learning. Concludes that if CALL is to reach full potential, it must be more than a simple medium of information; it should provide a teaching/learning process, with the real…

Software Engineering and eLearning: The MuSofT Project - www.musoft.org

Directory of Open Access Journals (Sweden)

Ernst-Erich Doberkat

2005-12-01

Full Text Available eLearning supports the education in certain disciplines. Here, we report about novel eLearning concepts, techniques, and tools to support education in Software Engineering, a subdiscipline of computer science. We call this "Software Engineering eLearning". On the other side, software support is a substantial prerequisite for eLearning in any discipline. Thus, Software Engineering techniques have to be applied to develop and maintain those software systems. We call this "eLearning Software Engineering". Both aspects have been investigated in a large joint, BMBF-funded research project, termed MuSofT (Multimedia in Software Engineering. The main results are summarized in this paper.

Contemporary machine learning: techniques for practitioners in the physical sciences

Science.gov (United States)

Spears, Brian

2017-10-01

Machine learning is the science of using computers to find relationships in data without explicitly knowing or programming those relationships in advance. Often without realizing it, we employ machine learning every day as we use our phones or drive our cars. Over the last few years, machine learning has found increasingly broad application in the physical sciences. This most often involves building a model relationship between a dependent, measurable output and an associated set of controllable, but complicated, independent inputs. The methods are applicable both to experimental observations and to databases of simulated output from large, detailed numerical simulations. In this tutorial, we will present an overview of current tools and techniques in machine learning - a jumping-off point for researchers interested in using machine learning to advance their work. We will discuss supervised learning techniques for modeling complicated functions, beginning with familiar regression schemes, then advancing to more sophisticated decision trees, modern neural networks, and deep learning methods. Next, we will cover unsupervised learning and techniques for reducing the dimensionality of input spaces and for clustering data. We'll show example applications from both magnetic and inertial confinement fusion. Along the way, we will describe methods for practitioners to help ensure that their models generalize from their training data to as-yet-unseen test data. We will finally point out some limitations to modern machine learning and speculate on some ways that practitioners from the physical sciences may be particularly suited to help. This work was performed by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

A New Profile Learning Model for Recommendation System based on Machine Learning Technique

Directory of Open Access Journals (Sweden)

Shereen H. Ali

2016-03-01

Full Text Available Recommender systems (RSs have been used to successfully address the information overload problem by providing personalized and targeted recommendations to the end users. RSs are software tools and techniques providing suggestions for items to be of use to a user, hence, they typically apply techniques and methodologies from Data Mining. The main contribution of this paper is to introduce a new user profile learning model to promote the recommendation accuracy of vertical recommendation systems. The proposed profile learning model employs the vertical classifier that has been used in multi classification module of the Intelligent Adaptive Vertical Recommendation (IAVR system to discover the user’s area of interest, and then build the user’s profile accordingly. Experimental results have proven the effectiveness of the proposed profile learning model, which accordingly will promote the recommendation accuracy.

Machine Learning Techniques for Stellar Light Curve Classification

Science.gov (United States)

Hinners, Trisha A.; Tat, Kevin; Thorp, Rachel

2018-07-01

We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time-series data. We preprocessed over 94 GB of Kepler light curves from the Mikulski Archive for Space Telescopes (MAST) to classify according to 10 distinct physical properties using both representation learning and feature engineering approaches. Studies using machine learning in the field have been primarily done on simulated data, making our study one of the first to use real light-curve data for machine learning approaches. We tuned our data using previous work with simulated data as a template and achieved mixed results between the two approaches. Representation learning using a long short-term memory recurrent neural network produced no successful predictions, but our work with feature engineering was successful for both classification and regression. In particular, we were able to achieve values for stellar density, stellar radius, and effective temperature with low error (∼2%–4%) and good accuracy (∼75%) for classifying the number of transits for a given star. The results show promise for improvement for both approaches upon using larger data sets with a larger minority class. This work has the potential to provide a foundation for future tools and techniques to aid in the analysis of astrophysical data.

Precision Learning Assessment: An Alternative to Traditional Assessment Techniques.

Science.gov (United States)

Caltagirone, Paul J.; Glover, Christopher E.

1985-01-01

A continuous and curriculum-based assessment method, Precision Learning Assessment (PLA), which integrates precision teaching and norm-referenced techniques, was applied to a math computation curriculum for 214 third graders. The resulting districtwide learning curves defining average annual progress through the computation curriculum provided…

Practising What We Teach: Vocational Teachers Learn to Research through Applying Action Learning Techniques

Science.gov (United States)

Lasky, Barbara; Tempone, Irene

2004-01-01

Action learning techniques are well suited to the teaching of organisation behaviour students because of their flexibility, inclusiveness, openness, and respect for individuals. They are no less useful as a tool for change for vocational teachers, learning, of necessity, to become researchers. Whereas traditional universities have always had a…

A Comparative Analysis of Machine Learning Techniques for Credit Scoring

OpenAIRE

Nwulu, Nnamdi; Oroja, Shola; İlkan, Mustafa

2012-01-01

Abstract Credit Scoring has become an oft researched topic in light of the increasing volatility of the global economy and the recent world financial crisis. Amidst the many methods used for credit scoring, machine learning techniques are becoming increasingly popular due to their efficient and accurate nature and relative simplicity. Furthermore machine learning techniques minimize the risk of human bias and error and maximize speed as they are able to perform computation...

A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training

Directory of Open Access Journals (Sweden)

Pengfei Jia

2016-03-01

Full Text Available When an electronic nose (E-nose is used to distinguish different kinds of gases, the label information of the target gas could be lost due to some fault of the operators or some other reason, although this is not expected. Another fact is that the cost of getting the labeled samples is usually higher than for unlabeled ones. In most cases, the classification accuracy of an E-nose trained using labeled samples is higher than that of the E-nose trained by unlabeled ones, so gases without label information should not be used to train an E-nose, however, this wastes resources and can even delay the progress of research. In this work a novel multi-class semi-supervised learning technique called M-training is proposed to train E-noses with both labeled and unlabeled samples. We employ M-training to train the E-nose which is used to distinguish three indoor pollutant gases (benzene, toluene and formaldehyde. Data processing results prove that the classification accuracy of E-nose trained by semi-supervised techniques (tri-training and M-training is higher than that of an E-nose trained only with labeled samples, and the performance of M-training is better than that of tri-training because more base classifiers can be employed by M-training.

Modelling tick abundance using machine learning techniques and satellite imagery

DEFF Research Database (Denmark)

Kjær, Lene Jung; Korslund, L.; Kjelland, V.

satellite images to run Boosted Regression Tree machine learning algorithms to predict overall distribution (presence/absence of ticks) and relative tick abundance of nymphs and larvae in southern Scandinavia. For nymphs, the predicted abundance had a positive correlation with observed abundance...... the predicted distribution of larvae was mostly even throughout Denmark, it was primarily around the coastlines in Norway and Sweden. Abundance was fairly low overall except in some fragmented patches corresponding to forested habitats in the region. Machine learning techniques allow us to predict for larger...... the collected ticks for pathogens and using the same machine learning techniques to develop prevalence maps of the ScandTick region....

Skype™ Conference Calls: A Way to Promote Speaking Skills in the Teaching and Learning of English

Directory of Open Access Journals (Sweden)

Yeferson Romaña Correa

2015-01-01

Full Text Available This article presents the results of a research project on the teaching and learning of English through the use of Skype™ conference calls. The research was carried out with a group of 12 English as a foreign language adult learners in the language institute of Universidad Distrital Francisco José de Caldas, Bogotá, Colombia. The findings of this study suggest that Skype™ conference calls might be considered as an influential computer-mediated communication tool in order to promote English as a foreign language adult A1 learners’ speaking skill, especially for social interaction purposes and oral reinforcement of both language fluency and course contents outside of classroom settings.

A New Project-Based Curriculum of Design Thinking with Systems Engineering Techniques

NARCIS (Netherlands)

Haruyama, S.; Kim, S.K.; Beiter, K.A.; Dijkema, G.P.J.; De Weck, O.L.

2012-01-01

We developed a new education curriculum called "ALPS" (Active Learning Project Sequence) at Keio University that emphasizes team project-based learning and design thinking with systems engineering techniques. ALPS is a 6 month course, in which students work as a team and design and propose

Prediction of drug synergy in cancer using ensemble-based machine learning techniques

Science.gov (United States)

Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder

2018-04-01

Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

"I experienced freedom within the frame of my own narrative": The contribution of psychodrama techniques to experiential learning in teacher training

Science.gov (United States)

ter Avest, Ina

2017-02-01

To prepare Dutch students in education for critical situations in their professional life as a teacher, part of their training is to ask them to reflect upon their own experiences in their life as a child, a pupil and a student - experiences of crucial moments or with significant others which are still of the utmost importance to them. This article underlines the significance of so-called "experiential learning" in student career counselling. In this context, experiential learning is understood as an extension of in- depth reflection on critical incidents and critical persons in the biography of pre-service teachers. This reflection - customary and effective in Dutch teacher training - is a verbal process. However, this technique does not seem to be adequate for many students from other cultural backgrounds (e.g. second-generation descendants of migrant workers). By consequence, some of these students are not able to take newly offered information on board, but remain imprisoned in their own culture-related narrative, their own ethnic society of mind. Research has shown that for these students, psychodrama techniques, focusing on non-verbal and playful aspects of reflection, seem to be more suitable. The author of this article presents a sample case from a pilot study which used one of the psychodrama techniques called the empty chair. The findings of the pilot study are promising in the sense that experiencing different I- positions does seem to help students from other cultural backgrounds to develop agency in responding to hitherto unfamiliar and confusing situations.

Indico CONFERENCE: Define the Call for Abstracts

CERN Multimedia

CERN. Geneva; Ferreira, Pedro

2017-01-01

In this tutorial, you will learn how to define and open a call for abstracts. When defining a call for abstracts, you will be able to define settings related to the type of questions asked during a review of an abstract, select the users who will review the abstracts, decide when to open the call for abstracts, and more.

Learning Performance Enhancement Using Computer-Assisted Language Learning by Collaborative Learning Groups

Directory of Open Access Journals (Sweden)

Ya-huei Wang

2017-08-01

Full Text Available This study attempted to test whether the use of computer-assisted language learning (CALL and innovative collaborative learning could be more effective than the use of traditional collaborative learning in improving students’ English proficiencies. A true experimental design was used in the study. Four randomly-assigned groups participated in the study: a traditional collaborative learning group (TCLG, 34 students, an innovative collaborative learning group (ICLG, 31 students, a CALL traditional collaborative learning group (CALLTCLG, 32 students, and a CALL innovative collaborative learning group (CALLICLG, 31 students. TOEIC (Test of English for International Communication listening, reading, speaking, and writing pre-test and post-test assessments were given to all students at an interval of sixteen weeks. Multivariate analysis of covariance (MANCOVA, multivariate analysis of variance (MANOVA, and analysis of variance (ANOVA were used to analyze the data. The results revealed that students who used CALL had significantly better learning performance than those who did not. Students in innovative collaborative learning had significantly better learning performances than those in traditional collaborative learning. Additionally, students using CALL innovative collaborative learning had better learning performances than those in CALL collaborative learning, those in innovative collaborative learning, and those in traditional collaborative learning.

A preclustering-based ensemble learning technique for acute appendicitis diagnoses.

Science.gov (United States)

Lee, Yen-Hsien; Hu, Paul Jen-Hwa; Cheng, Tsang-Hsiang; Huang, Te-Chia; Chuang, Wei-Yao

2013-06-01

Acute appendicitis is a common medical condition, whose effective, timely diagnosis can be difficult. A missed diagnosis not only puts the patient in danger but also requires additional resources for corrective treatments. An acute appendicitis diagnosis constitutes a classification problem, for which a further fundamental challenge pertains to the skewed outcome class distribution of instances in the training sample. A preclustering-based ensemble learning (PEL) technique aims to address the associated imbalanced sample learning problems and thereby support the timely, accurate diagnosis of acute appendicitis. The proposed PEL technique employs undersampling to reduce the number of majority-class instances in a training sample, uses preclustering to group similar majority-class instances into multiple groups, and selects from each group representative instances to create more balanced samples. The PEL technique thereby reduces potential information loss from random undersampling. It also takes advantage of ensemble learning to improve performance. We empirically evaluate this proposed technique with 574 clinical cases obtained from a comprehensive tertiary hospital in southern Taiwan, using several prevalent techniques and a salient scoring system as benchmarks. The comparative results show that PEL is more effective and less biased than any benchmarks. The proposed PEL technique seems more sensitive to identifying positive acute appendicitis than the commonly used Alvarado scoring system and exhibits higher specificity in identifying negative acute appendicitis. In addition, the sensitivity and specificity values of PEL appear higher than those of the investigated benchmarks that follow the resampling approach. Our analysis suggests PEL benefits from the more representative majority-class instances in the training sample. According to our overall evaluation results, PEL records the best overall performance, and its area under the curve measure reaches 0.619. The

Editorial (special issue on CALL, e-learning and m-learning

Directory of Open Access Journals (Sweden)

Jo Mynard

2011-09-01

Full Text Available Technology has, in one form of another, been a part of self-access learning since the very first self-access centres (SACs of the 1980s. Some of the better-funded centres featured elaborate listening and recording machinery and (occasionally early personal computers. Early software programmes and language-learning websites available for self-access use tended to be aimed at individual study, initially following the language lab model, and were often designed to teach or test discrete language points. Of course, in 2011 programmes aimed at individual study do still exist and certainly have a place in self-access learning, particularly if a learner has identified a target language area that the software or website covers. However, in this special issue we go beyond language learning software and look at tools and technologies currently available to the learner as self-access resources.

Machine learning techniques for gait biometric recognition using the ground reaction force

CERN Document Server

Mason, James Eric; Woungang, Isaac

2016-01-01

This book focuses on how machine learning techniques can be used to analyze and make use of one particular category of behavioral biometrics known as the gait biometric. A comprehensive Ground Reaction Force (GRF)-based Gait Biometrics Recognition framework is proposed and validated by experiments. In addition, an in-depth analysis of existing recognition techniques that are best suited for performing footstep GRF-based person recognition is also proposed, as well as a comparison of feature extractors, normalizers, and classifiers configurations that were never directly compared with one another in any previous GRF recognition research. Finally, a detailed theoretical overview of many existing machine learning techniques is presented, leading to a proposal of two novel data processing techniques developed specifically for the purpose of gait biometric recognition using GRF. This book · introduces novel machine-learning-based temporal normalization techniques · bridges research gaps concerning the effect of ...

MACHINE LEARNING TECHNIQUES USED IN BIG DATA

Directory of Open Access Journals (Sweden)

STEFANIA LOREDANA NITA

2016-07-01

Full Text Available The classical tools used in data analysis are not enough in order to benefit of all advantages of big data. The amount of information is too large for a complete investigation, and the possible connections and relations between data could be missed, because it is difficult or even impossible to verify all assumption over the information. Machine learning is a great solution in order to find concealed correlations or relationships between data, because it runs at scale machine and works very well with large data sets. The more data we have, the more the machine learning algorithm is useful, because it “learns” from the existing data and applies the found rules on new entries. In this paper, we present some machine learning algorithms and techniques used in big data.

Exercise, character strengths, well-being, and learning climate in the prediction of performance over a 6-month period at a call center.

Science.gov (United States)

Moradi, Saleh; Nima, Ali A; Rapp Ricciardi, Max; Archer, Trevor; Garcia, Danilo

2014-01-01

Performance monitoring might have an adverse influence on call center agents' well-being. We investigate how performance, over a 6-month period, is related to agents' perceptions of their learning climate, character strengths, well-being (subjective and psychological), and physical activity. Agents (N = 135) self-reported perception of the learning climate (Learning Climate Questionnaire), character strengths (Values In Action Inventory Short Version), well-being (Positive Affect, Negative Affect Schedule, Satisfaction With Life Scale, Psychological Well-Being Scales Short Version), and how often/intensively they engaged in physical activity. Performance, "time on the phone," was monitored for 6 consecutive months by the same system handling the calls. Performance was positively related to having opportunities to develop, the character strengths clusters of Wisdom and Knowledge (e.g., curiosity for learning, perspective) and Temperance (e.g., having self-control, being prudent, humble, and modest), and exercise frequency. Performance was negatively related to the sense of autonomy and responsibility, contentedness, the character strengths clusters of Humanity and Love (e.g., helping others, cooperation) and Justice (e.g., affiliation, fairness, leadership), positive affect, life satisfaction and exercise Intensity. Call centers may need to create opportunities to develop to increase agents' performance and focus on individual differences in the recruitment and selection of agents to prevent future shortcomings or worker dissatisfaction. Nevertheless, performance measurement in call centers may need to include other aspects that are more attuned with different character strengths. After all, allowing individuals to put their strengths at work should empower the individual and at the end the organization itself. Finally, physical activity enhancement programs might offer considerable positive work outcomes.

Exercise, Character Strengths, Well-Being and Learning Climate in the Prediction of Performance over a Six-Month Period at a Call Center

Directory of Open Access Journals (Sweden)

Saleh eMoradi

2014-06-01

Full Text Available Background: Performance monitoring might have an adverse influence on call center agents’ well-being. We investigate how performance, over a six-month period, is related to agents’ perceptions of their learning climate, character strengths, well-being (subjective and psychological, and physical activity.Method: Agents (N = 135 self-reported perception of the learning climate (Learning Climate Questionnaire, character strengths (Values In Action Inventory Short Version, well-being (Positive Affect, Negative Affect Schedule, Satisfaction With Life Scale, Psychological Well-Being Scales Short Version, and how often/intensively they engaged in physical activity. Performance, time on the phone, was monitored for six consecutive months by the same system handling the calls. Results: Performance was positively related to having opportunities to develop, the character strengths clusters of Wisdom and Knowledge (e.g., curiosity for learning, perspective and Temperance (e.g., having self-control, being prudent, humble, and modest, and exercise frequency. Performance was negatively related to the sense of autonomy and responsibility, contentedness, the character strengths clusters of Humanity and Love (e.g., helping others, cooperation and Justice (e.g., affiliation, fairness, leadership, positive affect, life satisfaction and exercise Intensity.Conclusion: Call centers may need to create opportunities to develop to increase agents’ performance and focus on individual differences in the recruitment and selection of agents to prevent future shortcomings or worker dissatisfaction. Nevertheless, performance measurement in call centers may need to include other aspects that are more attuned with different character strengths. After all, allowing individuals to put their strengths at work should empower the individual and at the end the organization itself. Finally, physical activity enhancement programs might offer considerable positive work outcomes.

Simulation-based optimization parametric optimization techniques and reinforcement learning

CERN Document Server

Gosavi, Abhijit

2003-01-01

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. The book's objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work. Broadly speaking, the book has two parts: (1) parametric (static) optimization and (2) control (dynamic) optimization. Some of the book's special features are: *An accessible introduction to reinforcement learning and parametric-optimization techniques. *A step-by-step description of several algorithms of simulation-based optimization. *A clear and simple introduction to the methodology of neural networks. *A gentle introduction to converg...

Impact of Using CALL on Iranian EFL Learners' Vocabulary Knowledge

Science.gov (United States)

Yunus, Melor Md; Salehi, Hadi; Amini, Mahdi

2016-01-01

Computer Assisted Language Learning (CALL) integration in EFL contexts has intensified noticeably in recent years. This integration might be in different ways and for different purposes such as vocabulary acquisition, grammar learning, phonology, writing skills, etc. More explicitly, this study is an attempt to explore the effect of using CALL on…

Techniques to Promote Reflective Practice and Empowered Learning.

Science.gov (United States)

Nguyen-Truong, Connie Kim Yen; Davis, Andra; Spencer, Cassius; Rasmor, Melody; Dekker, Lida

2018-02-01

Health care environments are fraught with fast-paced critical demands and ethical dilemmas requiring decisive nursing actions. Nurse educators must prepare nursing students to practice skills, behaviors, and attitudes needed to meet the challenges of health care demands. Evidence-based, innovative, multimodal techniques with novice and seasoned nurses were incorporated into a baccalaureate (BSN) completion program (RN to-BSN) to deepen learning, complex skill building, reflective practice, teamwork, and compassion toward the experiences of others. Principles of popular education for engaged teaching-learning were applied. Nursing students experience equitable access to content through co-constructing knowledge with four creative techniques. Four creative techniques include poem reading aloud to facilitate connectedness; mindfulness to cultivate self-awareness; string figure activities to demonstrate indigenous knowledge and teamwork; and cartooning difficult subject matter. Nursing school curricula can promote a milieu for developing organizational skills to manage simultaneous priorities, practice reflectively, and develop empathy and the authenticity that effective nursing requires. [J Nurs Educ. 2018;57(2):115-120.]. Copyright 2018, SLACK Incorporated.

Optimizing of verification photographs by using the so-called tangential field technique

International Nuclear Information System (INIS)

Proske, H.; Merte, H.; Kratz, H.

1991-01-01

When irradiating under high voltage condition, verification photographs prove to be difficult to take if the Gantry position is not aligned to 0deg respectively 180deg, since the patient is being irradiated diagonally. Under these conditions it is extremely difficult to align the X-ray-cartridge vertically to the central beam of the therapeutic radiation. This results in, amongst others, misprojections, so that definite dimensions of portrayed organ structures become practical impossible to determine. This paper describes how we have solved these problems on our high voltage units (tele-gamma cobalt unit and linear-accelerator). By using simple accessories, determination of dimensions of organ structures, as shown on the verification photographs, are made possible. We illustrate our method by using the so-called tangential fields technique when irradiating mamma carcinoma. (orig.) [de

Comparative exploration of learning styles and teaching techniques between Thai and Vietnamese EFL students and instructors

Directory of Open Access Journals (Sweden)

Supalak Nakhornsri

2016-09-01

Full Text Available Learning styles have been a particular focus of a number of researchers over the past decades. Findings from various studies researching into how students learn highlight significant relationships between learners’ styles of learning and their language learning processes and achievement. This research focuses on a comparative analysis of the preferences of English learning styles and teaching techniques perceived by students from Thailand and Vietnam, and the teaching styles and techniques practiced by their instructors. The purposes were 1 to investigate the learning styles and teaching techniques students from both countries preferred, 2 to investigate the compatibility of the teaching styles and techniques practiced by instructors and those preferred by the students, 3 to specify the learning styles and teaching techniques students with high level of English proficiency preferred, and 4 to investigate the similarities of Thai and Vietnamese students’ preferences for learning styles and teaching techniques. The sample consisted of two main groups: 1 undergraduate students from King Mongkut’s University of Technology North Bangkok (KMUTNB, Thailand and Thai Nguyen University (TNU, Vietnam and 2 English instructors from both institutions. The instruments employed comprised the Students’ Preferred English Learning Style and Teaching Technique Questionnaire and the Teachers’ Practiced English Teaching Style and Technique Questionnaire. The collected data were analyzed using arithmetic means and standard deviation. The findings can contribute to the curriculum development and assist teachers to teach outside their comfort level to match the students’ preferred learning styles. In addition, the findings could better promote the courses provided for students. By understanding the learning style make-up of the students enrolled in the courses, faculty can adjust their modes of content delivery to match student preferences and maximize

The training and learning process of transseptal puncture using a modified technique.

Science.gov (United States)

Yao, Yan; Ding, Ligang; Chen, Wensheng; Guo, Jun; Bao, Jingru; Shi, Rui; Huang, Wen; Zhang, Shu; Wong, Tom

2013-12-01

As the transseptal (TS) puncture has become an integral part of many types of cardiac interventional procedures, its technique that was initial reported for measurement of left atrial pressure in 1950s, continue to evolve. Our laboratory adopted a modified technique which uses only coronary sinus catheter as the landmark to accomplishing TS punctures under fluoroscopy. The aim of this study is prospectively to evaluate the training and learning process for TS puncture guided by this modified technique. Guided by the training protocol, TS puncture was performed in 120 consecutive patients by three trainees without previous personal experience in TS catheterization and one experienced trainer as a controller. We analysed the following parameters: one puncture success rate, total procedure time, fluoroscopic time, and radiation dose. The learning curve was analysed using curve-fitting methodology. The first attempt at TS crossing was successful in 74 (82%), a second attempt was successful in 11 (12%), and 5 patients failed to puncture the interatrial septal finally. The average starting process time was 4.1 ± 0.8 min, and the estimated mean learning plateau was 1.2 ± 0.2 min. The estimated mean learning rate for process time was 25 ± 3 cases. Important aspects of learning curve can be estimated by fitting inverse curves for TS puncture. The study demonstrated that this technique was a simple, safe, economic, and effective approach for learning of TS puncture. Base on the statistical analysis, approximately 29 TS punctures will be needed for trainee to pass the steepest area of learning curve.

CALL in the Year 2000: A Look Back from 2016

Science.gov (United States)

Chapelle, Carol A.

2016-01-01

This commentary offers a brief reflection on the state of CALL in 1997, when "Language Learning & Technology" was launched with my paper entitled "CALL in the year 2000: Still in search of research paradigms?" The point of my 1997 paper was to suggest the potential value of research on second language learning for the study…

Learning-curve estimation techniques for nuclear industry

Energy Technology Data Exchange (ETDEWEB)

Vaurio, J.K.

1983-01-01

Statistical techniques are developed to estimate the progress made by the nuclear industry in learning to prevent accidents. Learning curves are derived for accident occurrence rates based on acturial data, predictions are made for the future, and compact analytical equations are obtained for the statistical accuracies of the estimates. Both maximum likelihood estimation and the method of moments are applied to obtain parameters for the learning models, and results are compared to each other and to earlier graphical and analytical results. An effective statistical test is also derived to assess the significance of trends. The models used associate learning directly to accidents, to the number of plants and to the cumulative number of operating years. Using as a data base nine core damage accidents in electricity-producing plants, it is estimated that the probability of a plant to have a serious flaw has decreased from 0.1 to 0.01 during the developmental phase of the nuclear industry. At the same time the frequency of accidents has decreased from 0.04 per reactor year to 0.0004 per reactor year.

Learning-curve estimation techniques for nuclear industry

International Nuclear Information System (INIS)

Vaurio, J.K.

1983-01-01

Statistical techniques are developed to estimate the progress made by the nuclear industry in learning to prevent accidents. Learning curves are derived for accident occurrence rates based on acturial data, predictions are made for the future, and compact analytical equations are obtained for the statistical accuracies of the estimates. Both maximum likelihood estimation and the method of moments are applied to obtain parameters for the learning models, and results are compared to each other and to earlier graphical and analytical results. An effective statistical test is also derived to assess the significance of trends. The models used associate learning directly to accidents, to the number of plants and to the cumulative number of operating years. Using as a data base nine core damage accidents in electricity-producing plants, it is estimated that the probability of a plant to have a serious flaw has decreased from 0.1 to 0.01 during the developmental phase of the nuclear industry. At the same time the frequency of accidents has decreased from 0.04 per reactor year to 0.0004 per reactor year

Learning curve estimation techniques for nuclear industry

International Nuclear Information System (INIS)

Vaurio, Jussi K.

1983-01-01

Statistical techniques are developed to estimate the progress made by the nuclear industry in learning to prevent accidents. Learning curves are derived for accident occurrence rates based on actuarial data, predictions are made for the future, and compact analytical equations are obtained for the statistical accuracies of the estimates. Both maximum likelihood estimation and the method of moments are applied to obtain parameters for the learning models, and results are compared to each other and to earlier graphical and analytical results. An effective statistical test is also derived to assess the significance of trends. The models used associate learning directly to accidents, to the number of plants and to the cumulative number of operating years. Using as a data base nine core damage accidents in electricity-producing plants, it is estimated that the probability of a plant to have a serious flaw has decreased from 0.1 to 0.01 during the developmental phase of the nuclear industry. At the same time the frequency of accidents has decreased from 0.04 per reactor year to 0.0004 per reactor year

Competitive debate classroom as a cooperative learning technique for the human resources subject

Directory of Open Access Journals (Sweden)

Guillermo A. SANCHEZ PRIETO

2018-01-01

Full Text Available The paper shows an academic debate model as a cooperative learning technique for teaching human resources at University. The general objective of this paper is to conclude if academic debate can be included in the category of cooperative learning. The Specific objective it is presenting a model to implement this technique. Thus the first part of the paper shows the concept of cooperative learning and its main characteristics. The second part presents the debate model believed to be labelled as cooperative learning. Last part concludes with the characteristics of the model that match different aspects or not of the cooperative learning.

Applying machine learning techniques for forecasting flexibility of virtual power plants

DEFF Research Database (Denmark)

MacDougall, Pamela; Kosek, Anna Magdalena; Bindner, Henrik W.

2016-01-01

network as well as the multi-variant linear regression. It is found that it is possible to estimate the longevity of flexibility with machine learning. The linear regression algorithm is, on average, able to estimate the longevity with a 15% error. However, there was a significant improvement with the ANN...... approach to investigating the longevity of aggregated response of a virtual power plant using historic bidding and aggregated behaviour with machine learning techniques. The two supervised machine learning techniques investigated and compared in this paper are, multivariate linear regression and single...... algorithm achieving, on average, a 5.3% error. This is lowered 2.4% when learning for the same virtual power plant. With this information it would be possible to accurately offer residential VPP flexibility for market operations to safely avoid causing further imbalances and financial penalties....

Machine learning techniques for persuasion dectection in conversation

OpenAIRE

Ortiz, Pedro.

2010-01-01

Approved for public release; distribution is unlimited We determined that it is possible to automatically detect persuasion in conversations using three traditional machine learning techniques, naive bayes, maximum entropy, and support vector machine. These results are the first of their kind and serve as a baseline for all future work in this field. The three techniques consistently outperformed the baseline F-score, but not at a level that would be useful for real world applications. The...

An empirical study on the performance of spectral manifold learning techniques

DEFF Research Database (Denmark)

Mysling, Peter; Hauberg, Søren; Pedersen, Kim Steenstrup

2011-01-01

In recent years, there has been a surge of interest in spectral manifold learning techniques. Despite the interest, only little work has focused on the empirical behavior of these techniques. We construct synthetic data of variable complexity and observe the performance of the techniques as they ...

Application of machine learning techniques to lepton energy reconstruction in water Cherenkov detectors

Science.gov (United States)

Drakopoulou, E.; Cowan, G. A.; Needham, M. D.; Playfer, S.; Taani, M.

2018-04-01

The application of machine learning techniques to the reconstruction of lepton energies in water Cherenkov detectors is discussed and illustrated for TITUS, a proposed intermediate detector for the Hyper-Kamiokande experiment. It is found that applying these techniques leads to an improvement of more than 50% in the energy resolution for all lepton energies compared to an approach based upon lookup tables. Machine learning techniques can be easily applied to different detector configurations and the results are comparable to likelihood-function based techniques that are currently used.

An Effective Performance Analysis of Machine Learning Techniques for Cardiovascular Disease

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Vinitha DOMINIC

2015-03-01

Full Text Available Machine learning techniques will help in deriving hidden knowledge from clinical data which can be of great benefit for society, such as reduce the number of clinical trials required for precise diagnosis of a disease of a person etc. Various areas of study are available in healthcare domain like cancer, diabetes, drugs etc. This paper focuses on heart disease dataset and how machine learning techniques can help in understanding the level of risk associated with heart diseases. Initially, data is preprocessed then analysis is done in two stages, in first stage feature selection techniques are applied on 13 commonly used attributes and in second stage feature selection techniques are applied on 75 attributes which are related to anatomic structure of the heart like blood vessels of the heart, arteries etc. Finally, validation of the reduced set of features using an exhaustive list of classifiers is done.In parallel study of the anatomy of the heart is done using the identified features and the characteristics of each class is understood. It is observed that these reduced set of features are anatomically relevant. Thus, it can be concluded that, applying machine learning techniques on clinical data is beneficial and necessary.

E-Learning System Using Segmentation-Based MR Technique for Learning Circuit Construction

Science.gov (United States)

Takemura, Atsushi

2016-01-01

This paper proposes a novel e-Learning system using the mixed reality (MR) technique for technical experiments involving the construction of electronic circuits. The proposed system comprises experimenters' mobile computers and a remote analysis system. When constructing circuits, each learner uses a mobile computer to transmit image data from the…

Help Options in CALL: A Systematic Review

Science.gov (United States)

Cardenas-Claros, Monica S.; Gruba, Paul A.

2009-01-01

This paper is a systematic review of research investigating help options in the different language skills in computer-assisted language learning (CALL). In this review, emerging themes along with is-sues affecting help option research are identified and discussed. We argue that help options in CALL are application resources that do not only seem…

Factors Affecting the Normalization of CALL in Chinese Senior High Schools

Science.gov (United States)

He, Bi; Puakpong, Nattaya; Lian, Andrew

2015-01-01

With the development of Information Technology, increasing attention has been paid to Computer Assisted Language Learning (CALL). Meanwhile, increasing enthusiasm is seen for English learning and teaching in China. Yet, few research studies have focused on the normalization of CALL in ethnically diverse areas. In response to this research gap,…

Personal recommender systems for learners in lifelong learning: requirements, techniques and model

NARCIS (Netherlands)

Drachsler, Hendrik; Hummel, Hans; Koper, Rob

2007-01-01

Drachsler, H., Hummel, H. G. K., & Koper, R. (2008). Personal recommender systems for learners in lifelong learning: requirements, techniques and model. International Journal of Learning Technology, 3(4), 404-423.

Factors Hindering the Integration of CALL in a Tertiary Institution

Directory of Open Access Journals (Sweden)

Izaham Shah Ismail

2008-12-01

Full Text Available The field of Computer Assisted Language Learning (CALL is a field that is constantly evolving as it is very much dependent on the advancement of computer technologies. With new technologies being invented almost every day, experts in the field are looking for ways to apply these new technologies in the language classroom. Despite that, teachers are said to be slow at adopting technology in their classrooms and language teachers, whether at schools or tertiary institutions, are no exception. This study attempts to investigate the factors that hinder ESL instructors at an institution of higher learning from integrating CALL in their lessons. Interviews were conducted with five ESL instructors and results revealed that factors which hinder them from integrating CALL in their teaching are universal factors such as knowledge in technology and pedagogy, computer facilities and resources, absence of exemplary integration of CALL, personal beliefs on language teaching, views on the role of a computers as teacher, and evaluation of learning outcomes.

Computer-aided auscultation learning system for nursing technique instruction.

Science.gov (United States)

Hou, Chun-Ju; Chen, Yen-Ting; Hu, Ling-Chen; Chuang, Chih-Chieh; Chiu, Yu-Hsien; Tsai, Ming-Shih

2008-01-01

Pulmonary auscultation is a physical assessment skill learned by nursing students for examining the respiratory system. Generally, a sound simulator equipped mannequin is used to group teach auscultation techniques via classroom demonstration. However, nursing students cannot readily duplicate this learning environment for self-study. The advancement of electronic and digital signal processing technologies facilitates simulating this learning environment. This study aims to develop a computer-aided auscultation learning system for assisting teachers and nursing students in auscultation teaching and learning. This system provides teachers with signal recording and processing of lung sounds and immediate playback of lung sounds for students. A graphical user interface allows teachers to control the measuring device, draw lung sound waveforms, highlight lung sound segments of interest, and include descriptive text. Effects on learning lung sound auscultation were evaluated for verifying the feasibility of the system. Fifteen nursing students voluntarily participated in the repeated experiment. The results of a paired t test showed that auscultative abilities of the students were significantly improved by using the computer-aided auscultation learning system.

RAPPORT-BUILDING THROUGH CALL IN TEACHING CHINESE AS A FOREIGN LANGUAGE: AN EXPLORATORY STUDY

Directory of Open Access Journals (Sweden)

Wenying Jiang

2005-05-01

Full Text Available Technological advances have brought about the ever-increasing utilisation of computer-assisted language learning (CALL media in the learning of a second language (L2. Computer-mediated communication, for example, provides a practical means for extending the learning of spoken language, a challenging process in tonal languages such as Chinese, beyond the realms of the classroom. In order to effectively improve spoken language competency, however, CALL applications must also reproduce the social interaction that lies at the heart of language learning and language use. This study draws on data obtained from the utilisation of CALL in the learning of L2 Chinese to explore whether this medium can be used to extend opportunities for rapport-building in language teaching beyond the face-to-face interaction of the classroom. Rapport's importance lies in its potential to enhance learning, motivate learners, and reduce learner anxiety. To date, CALL's potential in relation to this facet of social interaction remains a neglected area of research. The results of this exploratory study suggest that CALL may help foster learner-teacher rapport and that scaffolding, such as strategically composing rapport-fostering questions in sound-files, is conducive to this outcome. The study provides an instruction model for this application of CALL.

Zebra finch mates use their forebrain song system in unlearned call communication.

Science.gov (United States)

Ter Maat, Andries; Trost, Lisa; Sagunsky, Hannes; Seltmann, Susanne; Gahr, Manfred

2014-01-01

Unlearned calls are produced by all birds whereas learned songs are only found in three avian taxa, most notably in songbirds. The neural basis for song learning and production is formed by interconnected song nuclei: the song control system. In addition to song, zebra finches produce large numbers of soft, unlearned calls, among which "stack" calls are uttered frequently. To determine unequivocally the calls produced by each member of a group, we mounted miniature wireless microphones on each zebra finch. We find that group living paired males and females communicate using bilateral stack calling. To investigate the role of the song control system in call-based male female communication, we recorded the electrical activity in a premotor nucleus of the song control system in freely behaving male birds. The unique combination of acoustic monitoring together with wireless brain recording of individual zebra finches in groups shows that the neuronal activity of the song system correlates with the production of unlearned stack calls. The results suggest that the song system evolved from a brain circuit controlling simple unlearned calls to a system capable of producing acoustically rich, learned vocalizations.

Journaling; an active learning technique.

Science.gov (United States)

Blake, Tim K

2005-01-01

Journaling is a method frequently discussed in nursing literature and educational literature as an active learning technique that is meant to enhance reflective practice. Reflective practice is a means of self-examination that involves looking back over what has happened in practice in an effort to improve, or encourage professional growth. Some of the benefits of reflective practice include discovering meaning, making connections between experiences and the classroom, instilling values of the profession, gaining the perspective of others, reflection on professional roles, and development of critical thinking. A review of theory and research is discussed, as well as suggestions for implementation of journaling into coursework.

Novel Breast Imaging and Machine Learning: Predicting Breast Lesion Malignancy at Cone-Beam CT Using Machine Learning Techniques.

Science.gov (United States)

Uhlig, Johannes; Uhlig, Annemarie; Kunze, Meike; Beissbarth, Tim; Fischer, Uwe; Lotz, Joachim; Wienbeck, Susanne

2018-05-24

The purpose of this study is to evaluate the diagnostic performance of machine learning techniques for malignancy prediction at breast cone-beam CT (CBCT) and to compare them to human readers. Five machine learning techniques, including random forests, back propagation neural networks (BPN), extreme learning machines, support vector machines, and K-nearest neighbors, were used to train diagnostic models on a clinical breast CBCT dataset with internal validation by repeated 10-fold cross-validation. Two independent blinded human readers with profound experience in breast imaging and breast CBCT analyzed the same CBCT dataset. Diagnostic performance was compared using AUC, sensitivity, and specificity. The clinical dataset comprised 35 patients (American College of Radiology density type C and D breasts) with 81 suspicious breast lesions examined with contrast-enhanced breast CBCT. Forty-five lesions were histopathologically proven to be malignant. Among the machine learning techniques, BPNs provided the best diagnostic performance, with AUC of 0.91, sensitivity of 0.85, and specificity of 0.82. The diagnostic performance of the human readers was AUC of 0.84, sensitivity of 0.89, and specificity of 0.72 for reader 1 and AUC of 0.72, sensitivity of 0.71, and specificity of 0.67 for reader 2. AUC was significantly higher for BPN when compared with both reader 1 (p = 0.01) and reader 2 (p Machine learning techniques provide a high and robust diagnostic performance in the prediction of malignancy in breast lesions identified at CBCT. BPNs showed the best diagnostic performance, surpassing human readers in terms of AUC and specificity.

Effect of active learning techniques on students' choice of approach ...

African Journals Online (AJOL)

The purpose of this article is to report on empirical work, related to a techniques module, undertaken with the dental students of the University of the Western Cape, South Africa. I will relate how a range of different active learning techniques (tutorials; question papers and mock tests) assisted students to adopt a deep ...

Instructional Television: Visual Production Techniques and Learning Comprehension.

Science.gov (United States)

Silbergleid, Michael Ian

The purpose of this study was to determine if increasing levels of complexity in visual production techniques would increase the viewer's learning comprehension and the degree of likeness expressed for a college level instructional television program. A total of 119 mass communications students at the University of Alabama participated in the…

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.

Criteria for Evaluating a Game-Based CALL Platform

Science.gov (United States)

Ní Chiaráin, Neasa; Ní Chasaide, Ailbhe

2017-01-01

Game-based Computer-Assisted Language Learning (CALL) is an area that currently warrants attention, as task-based, interactive, multimodal games increasingly show promise for language learning. This area is inherently multidisciplinary--theories from second language acquisition, games, and psychology must be explored and relevant concepts from…

Evaluation of the Use of Two Teaching Techniques in Engineering

Directory of Open Access Journals (Sweden)

Jose Antonio Alvarez Salas

2014-06-01

Full Text Available This paper presents an analysis of the practical implementation of two teaching techniques so-called Problem-Based Learning and Cooperative Learning. These techniques were applied to some courses in the Department of Mechanical and Electrical Engineering and evaluated through assessment rubrics. In a sample of students and teachers, the assessment rubrics were applied to numerically evaluate the proportion of each course, in which the teacher uses traditional teaching versus teaching for meaningful learning. The results of the presented analysis allow to verify the use of these teaching techniques by professors of the Department of Mechanical and Electrical Engineering. This activity was developed as a part of the work established by the Institutional Development Plan of the Faculty of Engineering, which includes the strategic objective of developing an innovative educational model in the following ten years.

Flip-J: Development of the System for Flipped Jigsaw Supported Language Learning

Science.gov (United States)

Yamada, Masanori; Goda, Yoshiko; Hata, Kojiro; Matsukawa, Hideya; Yasunami, Seisuke

2016-01-01

This study aims to develop and evaluate a language learning system supported by the "flipped jigsaw" technique, called "Flip-J". This system mainly consists of three functions: (1) the creation of a learning material database, (2) allocation of learning materials, and (3) formation of an expert and jigsaw group. Flip-J was…

Statistical and Machine-Learning Data Mining Techniques for Better Predictive Modeling and Analysis of Big Data

CERN Document Server

Ratner, Bruce

2011-01-01

The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has

An Evaluation Framework for CALL

Science.gov (United States)

McMurry, Benjamin L.; Williams, David Dwayne; Rich, Peter J.; Hartshorn, K. James

2016-01-01

Searching prestigious Computer-assisted Language Learning (CALL) journals for references to key publications and authors in the field of evaluation yields a short list. The "American Journal of Evaluation"--the flagship journal of the American Evaluation Association--is only cited once in both the "CALICO Journal and Language…

Learning curve estimation techniques for the nuclear industry

International Nuclear Information System (INIS)

Vaurio, J.K.

1983-01-01

Statistical techniques are developed to estimate the progress made by the nuclear industry in learning to prevent accidents. Learning curves are derived for accident occurrence rates based on actuarial data, predictions are made for the future, and compact analytical equations are obtained for the statistical accuracies of the estimates. Both maximum likelihood estimation and the method of moments are applied to obtain parameters for the learning models, and results are compared to each other and to earlier graphical and analytical results. An effective statistical test is also derived to assess the significance of trends. The models used associate learning directly to accidents, to the number of plants and to the cumulative number of operating years. Using as a data base nine core damage accidents in electricity-producing plants, it is estimated that the probability of a plant to have a serious flaw has decreased from 0.1 to 0.01 during the developmental phase of the nuclear industry. At the same time the frequency of accidents has decreased from 0.04 per reactor year to 0.0004 per reactor year

Swarm Intelligence: New Techniques for Adaptive Systems to Provide Learning Support

Science.gov (United States)

Wong, Lung-Hsiang; Looi, Chee-Kit

2012-01-01

The notion of a system adapting itself to provide support for learning has always been an important issue of research for technology-enabled learning. One approach to provide adaptivity is to use social navigation approaches and techniques which involve analysing data of what was previously selected by a cluster of users or what worked for…

Language teacher education in CALL: history and perspectives

Directory of Open Access Journals (Sweden)

Ana Cristina Biondo Salomão

2013-01-01

Full Text Available Over the last years, the new technologies have changed the way we relate to information and communicate with other people, which has brought on impact to foreign language teaching and learning, and, consequently, to the area of foreign language teacher education. The abbreviation CALL (Computer Assisted Language Learning has been used to designate the processes of language teaching and learning with the use of computers, and language teacher education in CALL to name teacher education for and with the use of new technologies, since a number of authors point to the interdependence of both processes. We intend in this article to present an overview of the literature of the area of language teacher education in CALL nowadays and discuss issues related to the use of new technologies concerning its integration to teacher education and the functional and institutional roles to be taken. We also present two proposals of teacher education with the use of new technologies which are being implemented and at the same time studied in Brazil, which we believe have essential elements for the development of language teachers for and with the use of new technologies currently.

Data mining practical machine learning tools and techniques

CERN Document Server

Witten, Ian H

2005-01-01

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same

Considerations for Task Analysis Methods and Rapid E-Learning Development Techniques

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Dr. Ismail Ipek

2014-02-01

Full Text Available The purpose of this paper is to provide basic dimensions for rapid training development in e-learning courses in education and business. Principally, it starts with defining task analysis and how to select tasks for analysis and task analysis methods for instructional design. To do this, first, learning and instructional technologies as visions of the future were discussed. Second, the importance of task analysis methods in rapid e-learning was considered, with learning technologies as asynchronous and synchronous e-learning development. Finally, rapid instructional design concepts and e-learning design strategies were defined and clarified with examples, that is, all steps for effective task analysis and rapid training development techniques based on learning and instructional design approaches were discussed, such as m-learning and other delivery systems. As a result, the concept of task analysis, rapid e-learning development strategies and the essentials of online course design were discussed, alongside learner interface design features for learners and designers.

Don't Call It School

Science.gov (United States)

Robb, Daniel

2006-01-01

"Homeschooling," "deschooling," and "unschooling" are commonly used terms in the alternative-education world, but each lacks specificity. In this article, the author describes what he discovered during several visits to North Star. Known officially as North Star: Self-Directed Learning for Teens, it is not as structured as a so-called "free"…

Subliminal Cues While Teaching: HCI Technique for Enhanced Learning

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Pierre Chalfoun

2011-01-01

Full Text Available This paper presents results from an empirical study conducted with a subliminal teaching technique aimed at enhancing learner's performance in Intelligent Systems through the use of physiological sensors. This technique uses carefully designed subliminal cues (positive and miscues (negative and projects them under the learner's perceptual visual threshold. A positive cue, called answer cue, is a hint aiming to enhance the learner's inductive reasoning abilities and projected in a way to help them figure out the solution faster but more importantly better. A negative cue, called miscue, is also used and aims at obviously at the opposite (distract the learner or lead them to the wrong conclusion. The latest obtained results showed that only subliminal cues, not miscues, could significantly increase learner performance and intuition in a logic-based problem-solving task. Nonintrusive physiological sensors (EEG for recording brainwaves, blood volume pressure to compute heart rate and skin response to record skin conductivity were used to record affective and cerebral responses throughout the experiment. The descriptive analysis, combined with the physiological data, provides compelling evidence for the positive impact of answer cues on reasoning and intuitive decision making in a logic-based problem-solving paradigm.

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.

Sparse alignment for robust tensor learning.

Science.gov (United States)

Lai, Zhihui; Wong, Wai Keung; Xu, Yong; Zhao, Cairong; Sun, Mingming

2014-10-01

Multilinear/tensor extensions of manifold learning based algorithms have been widely used in computer vision and pattern recognition. This paper first provides a systematic analysis of the multilinear extensions for the most popular methods by using alignment techniques, thereby obtaining a general tensor alignment framework. From this framework, it is easy to show that the manifold learning based tensor learning methods are intrinsically different from the alignment techniques. Based on the alignment framework, a robust tensor learning method called sparse tensor alignment (STA) is then proposed for unsupervised tensor feature extraction. Different from the existing tensor learning methods, L1- and L2-norms are introduced to enhance the robustness in the alignment step of the STA. The advantage of the proposed technique is that the difficulty in selecting the size of the local neighborhood can be avoided in the manifold learning based tensor feature extraction algorithms. Although STA is an unsupervised learning method, the sparsity encodes the discriminative information in the alignment step and provides the robustness of STA. Extensive experiments on the well-known image databases as well as action and hand gesture databases by encoding object images as tensors demonstrate that the proposed STA algorithm gives the most competitive performance when compared with the tensor-based unsupervised learning methods.

Student Teachers and CALL: Personal and Pedagogical Uses and Beliefs

Science.gov (United States)

Hlas, Anne Cummings; Conroy, Kelly; Hildebrandt, Susan A.

2017-01-01

The student teaching semester affords teacher candidates the chance to apply what they have learned during their teacher preparation coursework. Therefore, it can be a prime opportunity for student teachers to use technology for their own language learning and to implement computer assisted language learning (CALL) in their instruction. This study…

The Effect of Computer Assisted Language Learning (CALL) on Performance in the Test of English for International Communication (TOEIC) Listening Module

Science.gov (United States)

van Han, Nguyen; van Rensburg, Henriette

2014-01-01

Many companies and organizations have been using the Test of English for International Communication (TOEIC) for business and commercial communication purpose in Vietnam and around the world. The present study investigated the effect of Computer Assisted Language Learning (CALL) on performance in the Test of English for International Communication…

An experimental result of estimating an application volume by machine learning techniques.

Science.gov (United States)

Hasegawa, Tatsuhito; Koshino, Makoto; Kimura, Haruhiko

2015-01-01

In this study, we improved the usability of smartphones by automating a user's operations. We developed an intelligent system using machine learning techniques that periodically detects a user's context on a smartphone. We selected the Android operating system because it has the largest market share and highest flexibility of its development environment. In this paper, we describe an application that automatically adjusts application volume. Adjusting the volume can be easily forgotten because users need to push the volume buttons to alter the volume depending on the given situation. Therefore, we developed an application that automatically adjusts the volume based on learned user settings. Application volume can be set differently from ringtone volume on Android devices, and these volume settings are associated with each specific application including games. Our application records a user's location, the volume setting, the foreground application name and other such attributes as learning data, thereby estimating whether the volume should be adjusted using machine learning techniques via Weka.

Changing teaching techniques and adapting new technologies to improve student learning in an introductory meteorology and climate course

Directory of Open Access Journals (Sweden)

E. M. Cutrim

2006-01-01

Full Text Available Responding to the call for reform in science education, changes were made in an introductory meteorology and climate course offered at a large public university. These changes were a part of a larger project aimed at deepening and extending a program of science content courses that model effective teaching strategies for prospective middle school science teachers. Therefore, revisions were made to address misconceptions about meteorological phenomena, foster deeper understanding of key concepts, encourage engagement with the text, and promote inquiry-based learning. Techniques introduced include: use of a flash cards, student reflection questionnaires, writing assignments, and interactive discussions on weather and forecast data using computer technology such as Integrated Data Viewer (IDV. The revision process is described in a case study format. Preliminary results (self-reflection by the instructor, surveys of student opinion, and measurements of student achievement, suggest student learning has been positively influenced. This study is supported by three grants: NSF grant No. 0202923, the Unidata Equipment Award, and the Lucia Harrison Endowment Fund.

Promoting Cooperative Learning in the Classroom: Comparing Explicit and Implicit Training Techniques

Directory of Open Access Journals (Sweden)

Anne Elliott

2003-07-01

Full Text Available In this study, we investigated whether providing 4th and 5th-grade students with explicit instruction in prerequisite cooperative-learning skills and techniques would enhance their academic performance and promote in them positive attitudes towards cooperative learning. Overall, students who received explicit training outperformed their peers on both the unit project and test and presented more favourable attitudes towards cooperative learning. The findings of this study support the use of explicitly instructing students about the components of cooperative learning prior to engaging in collaborative activities. Implications for teacher-education are discussed.

Generating a Spanish Affective Dictionary with Supervised Learning Techniques

Science.gov (United States)

Bermudez-Gonzalez, Daniel; Miranda-Jiménez, Sabino; García-Moreno, Raúl-Ulises; Calderón-Nepamuceno, Dora

2016-01-01

Nowadays, machine learning techniques are being used in several Natural Language Processing (NLP) tasks such as Opinion Mining (OM). OM is used to analyse and determine the affective orientation of texts. Usually, OM approaches use affective dictionaries in order to conduct sentiment analysis. These lexicons are labeled manually with affective…

Exploring machine-learning-based control plane intrusion detection techniques in software defined optical networks

Science.gov (United States)

Zhang, Huibin; Wang, Yuqiao; Chen, Haoran; Zhao, Yongli; Zhang, Jie

2017-12-01

In software defined optical networks (SDON), the centralized control plane may encounter numerous intrusion threatens which compromise the security level of provisioned services. In this paper, the issue of control plane security is studied and two machine-learning-based control plane intrusion detection techniques are proposed for SDON with properly selected features such as bandwidth, route length, etc. We validate the feasibility and efficiency of the proposed techniques by simulations. Results show an accuracy of 83% for intrusion detection can be achieved with the proposed machine-learning-based control plane intrusion detection techniques.

Investigating the Effectiveness of Computer-Assisted Language Learning (CALL) Using Google Documents in Enhancing Writing--A Study on Senior 1 Students in a Chinese Independent High School

Science.gov (United States)

Ambrose, Regina Maria; Palpanathan, Shanthini

2017-01-01

Computer-assisted language learning (CALL) has evolved through various stages in both technology as well as the pedagogical use of technology (Warschauer & Healey, 1998). Studies show that the CALL trend has facilitated students in their English language writing with useful tools such as computer based activities and word processing. Students…

Using the IGCRA (individual, group, classroom reflective action technique to enhance teaching and learning in large accountancy classes

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Cristina Poyatos

2011-02-01

Full Text Available First year accounting has generally been perceived as one of the more challenging first year business courses for university students. Various Classroom Assessment Techniques (CATs have been proposed to attempt to enrich and enhance student learning, with these studies generally positioning students as learners alone. This paper uses an educational case study approach and examines the implementation of the IGCRA (individual, group, classroom reflective action technique, a Classroom Assessment Technique, on first year accounting students’ learning performance. Building on theoretical frameworks in the areas of cognitive learning, social development, and dialogical learning, the technique uses reports to promote reflection on both learning and teaching. IGCRA was found to promote feedback on the effectiveness of student, as well as teacher satisfaction. Moreover, the results indicated formative feedback can assist to improve the learning and learning environment for a large group of first year accounting students. Clear guidelines for its implementation are provided in the paper.

Sentiment Analysis in Geo Social Streams by using Machine Learning Techniques

OpenAIRE

Twanabasu, Bikesh

2018-01-01

Treball de Final de Màster Universitari Erasmus Mundus en Tecnologia Geoespacial (Pla de 2013). Codi: SIW013. Curs acadèmic 2017-2018 Massive amounts of sentiment rich data are generated on social media in the form of Tweets, status updates, blog post, reviews, etc. Different people and organizations are using these user generated content for decision making. Symbolic techniques or Knowledge base approaches and Machine learning techniques are two main techniques used for analysis sentiment...

Linear time relational prototype based learning.

Science.gov (United States)

Gisbrecht, Andrej; Mokbel, Bassam; Schleif, Frank-Michael; Zhu, Xibin; Hammer, Barbara

2012-10-01

Prototype based learning offers an intuitive interface to inspect large quantities of electronic data in supervised or unsupervised settings. Recently, many techniques have been extended to data described by general dissimilarities rather than Euclidean vectors, so-called relational data settings. Unlike the Euclidean counterparts, the techniques have quadratic time complexity due to the underlying quadratic dissimilarity matrix. Thus, they are infeasible already for medium sized data sets. The contribution of this article is twofold: On the one hand we propose a novel supervised prototype based classification technique for dissimilarity data based on popular learning vector quantization (LVQ), on the other hand we transfer a linear time approximation technique, the Nyström approximation, to this algorithm and an unsupervised counterpart, the relational generative topographic mapping (GTM). This way, linear time and space methods result. We evaluate the techniques on three examples from the biomedical domain.

Learning Extended Finite State Machines

Science.gov (United States)

Cassel, Sofia; Howar, Falk; Jonsson, Bengt; Steffen, Bernhard

2014-01-01

We present an active learning algorithm for inferring extended finite state machines (EFSM)s, combining data flow and control behavior. Key to our learning technique is a novel learning model based on so-called tree queries. The learning algorithm uses the tree queries to infer symbolic data constraints on parameters, e.g., sequence numbers, time stamps, identifiers, or even simple arithmetic. We describe sufficient conditions for the properties that the symbolic constraints provided by a tree query in general must have to be usable in our learning model. We have evaluated our algorithm in a black-box scenario, where tree queries are realized through (black-box) testing. Our case studies include connection establishment in TCP and a priority queue from the Java Class Library.

76 Computer Assisted Language Learning (CALL) Software ...

African Journals Online (AJOL)

Ike Odimegwu

combination with other factors which may enhance or ameliorate the ... form of computer-based learning which carries two important features: .... To take some commonplace examples, a ... photographs, and even full-motion video clips.

Deep Learning Techniques for Top-Quark Reconstruction

CERN Document Server

Naderi, Kiarash

2017-01-01

Top quarks are unique probes of the standard model (SM) predictions and have the potential to be a window for physics beyond the SM (BSM). Top quarks decay to a $Wb$ pair, and the $W$ can decay in leptons or jets. In a top pair event, assigning jets to their correct source is a challenge. In this study, I studied different methods for improving top reconstruction. The main motivation was to use Deep Learning Techniques in order to enhance the precision of top reconstruction.

Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques

CSIR Research Space (South Africa)

Ngxande, Mkhuseli

2017-11-01

Full Text Available This paper presents a literature review of driver drowsiness detection based on behavioral measures using machine learning techniques. Faces contain information that can be used to interpret levels of drowsiness. There are many facial features...

The colloquial approach: An active learning technique

Science.gov (United States)

Arce, Pedro

1994-09-01

This paper addresses the very important problem of the effectiveness of teaching methodologies in fundamental engineering courses such as transport phenomena. An active learning strategy, termed the colloquial approach, is proposed in order to increase student involvement in the learning process. This methodology is a considerable departure from traditional methods that use solo lecturing. It is based on guided discussions, and it promotes student understanding of new concepts by directing the student to construct new ideas by building upon the current knowledge and by focusing on key cases that capture the essential aspects of new concepts. The colloquial approach motivates the student to participate in discussions, to develop detailed notes, and to design (or construct) his or her own explanation for a given problem. This paper discusses the main features of the colloquial approach within the framework of other current and previous techniques. Problem-solving strategies and the need for new textbooks and for future investigations based on the colloquial approach are also outlined.

Comparing the Effectiveness of Self-Learning Java Workshops with Traditional Classrooms

Science.gov (United States)

Eranki, Kiran L. N.; Moudgalya, Kannan M.

2016-01-01

In this work, we study the effectiveness of a method called Spoken Tutorial, which is a candidate technique for self-learning. The performance of college students who self-learned Java through the Spoken Tutorial method is found to be better than that of conventional learners. Although the method evaluated in this work helps both genders, females…

Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope

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Bin Xu

2017-01-01

Full Text Available This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique. Considering the system uncertainty in dynamics, neural network is employed for approximation. Minimal-learning-parameter technique is constructed to decrease the number of update parameters, and in this way the computation burden is greatly reduced. Sliding mode control is designed to cancel the effect of time-varying disturbance. The closed-loop stability analysis is established via Lyapunov approach. Simulation results are presented to demonstrate the effectiveness of the method.

Exploring Characterizations of Learning Object Repositories Using Data Mining Techniques

Science.gov (United States)

Segura, Alejandra; Vidal, Christian; Menendez, Victor; Zapata, Alfredo; Prieto, Manuel

Learning object repositories provide a platform for the sharing of Web-based educational resources. As these repositories evolve independently, it is difficult for users to have a clear picture of the kind of contents they give access to. Metadata can be used to automatically extract a characterization of these resources by using machine learning techniques. This paper presents an exploratory study carried out in the contents of four public repositories that uses clustering and association rule mining algorithms to extract characterizations of repository contents. The results of the analysis include potential relationships between different attributes of learning objects that may be useful to gain an understanding of the kind of resources available and eventually develop search mechanisms that consider repository descriptions as a criteria in federated search.

The learning conference

DEFF Research Database (Denmark)

Ravn, Ib

little support amongst serious students of learning. The professional conference as a forum for knowledge sharing is in dire need of a new learning theory and a more enlightened practice. The notion of human flourishing is offered as basis for theory, and four simple design principles for the so......The typical one-day conference attended by managers or professionals in search of inspiration is packed with PowerPoint presentations and offers little opportunity for involvement or knowledge sharing. Behind the conventional conference format lurks the transfer model of learning, which finds......-called “learning conference” are proposed: People go to conferences to 1. get concise input, 2. interpret it in the light of their ongoing concerns, 3. talk about their current projects and 4. meet the other attendees and be inspired by them. Six practical techniques that induce attendees to do these things...

Objective assessment of IP video calls with Asterisk

OpenAIRE

Kapičák, Lukáš; Nevlud, Pavel; Mikulec, Martin; Zdrálek, Jaroslav

2012-01-01

The paper deals with an objective assessment of IP video calls transmission over GSM and UMTS networks. Video transmission is affected by many factors in mobile network. Among these factors belong packet loss, latency and transmission rate of the mobile network. Network properties were simulated by Simena network simulator. Our team have developed a unique technique for finding defects in video appearing in video calls. This technique is built on modified Asterisk SW PBX with enabled video re...

Stimulating Deep Learning Using Active Learning Techniques

Science.gov (United States)

Yew, Tee Meng; Dawood, Fauziah K. P.; a/p S. Narayansany, Kannaki; a/p Palaniappa Manickam, M. Kamala; Jen, Leong Siok; Hoay, Kuan Chin

2016-01-01

When students and teachers behave in ways that reinforce learning as a spectator sport, the result can often be a classroom and overall learning environment that is mostly limited to transmission of information and rote learning rather than deep approaches towards meaningful construction and application of knowledge. A group of college instructors…

Active Learning Techniques Applied to an Interdisciplinary Mineral Resources Course.

Science.gov (United States)

Aird, H. M.

2015-12-01

An interdisciplinary active learning course was introduced at the University of Puget Sound entitled 'Mineral Resources and the Environment'. Various formative assessment and active learning techniques that have been effective in other courses were adapted and implemented to improve student learning, increase retention and broaden knowledge and understanding of course material. This was an elective course targeted towards upper-level undergraduate geology and environmental majors. The course provided an introduction to the mineral resources industry, discussing geological, environmental, societal and economic aspects, legislation and the processes involved in exploration, extraction, processing, reclamation/remediation and recycling of products. Lectures and associated weekly labs were linked in subject matter; relevant readings from the recent scientific literature were assigned and discussed in the second lecture of the week. Peer-based learning was facilitated through weekly reading assignments with peer-led discussions and through group research projects, in addition to in-class exercises such as debates. Writing and research skills were developed through student groups designing, carrying out and reporting on their own semester-long research projects around the lasting effects of the historical Ruston Smelter on the biology and water systems of Tacoma. The writing of their mini grant proposals and final project reports was carried out in stages to allow for feedback before the deadline. Speakers from industry were invited to share their specialist knowledge as guest lecturers, and students were encouraged to interact with them, with a view to employment opportunities. Formative assessment techniques included jigsaw exercises, gallery walks, placemat surveys, think pair share and take-home point summaries. Summative assessment included discussion leadership, exams, homeworks, group projects, in-class exercises, field trips, and pre-discussion reading exercises

Using the 5E Learning Cycle with Metacognitive Technique to Enhance Students’ Mathematical Critical Thinking Skills

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Runisah Runisah

2017-02-01

Full Text Available This study aims to describe enhancement and achievement of mathematical critical thinking skills of students who received the 5E Learning Cycle with Metacognitive technique, the 5E Learning Cycle, and conventional learning. This study use experimental method with pretest-posttest control group design. Population are junior high school students in Indramayu city, Indonesia. Sample are three classes of eighth grade students from high level school and three classes from medium level school. The study reveal that in terms of overall, mathematical critical thinking skills enhancement and achievement of students who received the 5E Learning Cycle with Metacognitive technique is better than students who received the 5E Learning Cycle and conventional learning. Mathematical critical thinking skills of students who received the 5E Learning Cycle is better than students who received conventional learning. There is no interaction effect between learning model and school level toward enhancement and achievement of students’ mathematical critical thinking skills.

The impact of machine learning techniques in the study of bipolar disorder: A systematic review.

Science.gov (United States)

Librenza-Garcia, Diego; Kotzian, Bruno Jaskulski; Yang, Jessica; Mwangi, Benson; Cao, Bo; Pereira Lima, Luiza Nunes; Bermudez, Mariane Bagatin; Boeira, Manuela Vianna; Kapczinski, Flávio; Passos, Ives Cavalcante

2017-09-01

Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. We found 757 abstracts and included 51 studies in our review. Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls. We also found studies that assessed the prediction of clinical outcomes and studies using unsupervised machine learning to build more consistent clinical phenotypes of bipolar disorder. We concluded that given the clinical heterogeneity of samples of patients with BD, machine learning techniques may provide clinicians and researchers with important insights in fields such as diagnosis, personalized treatment and prognosis orientation. Copyright © 2017 Elsevier Ltd. All rights reserved.

“Computer Assisted Language Learning” (CALL

Directory of Open Access Journals (Sweden)

Nazlı Gündüz

2005-10-01

Full Text Available This article will provide an overview of computers; an overview of the history of CALL, itspros and cons, the internet, World Wide Web, Multimedia, and research related to the uses of computers in the language classroom. Also, it also aims to provide some background for the beginnerson using the Internet in language classes today. It discusses some of the common types of Internetactivities that are being used today, what the minimum requirements are for using the Internet forlanguage learning, and some easy activities you can adapt for your classes. Some special terminology related to computers will also be used in this paper. For example, computer assisted language learning(CALL refers to the sets of instructions which need to be loaded into the computer for it to be able to work in the language classroom. It should be borne in mind that CALL does not refer to the use of acomputer by a teacher to type out a worksheet or a class list or preparing his/her own teaching alone.Hardware refers to any computer equipment used, including the computer itself, the keyboard, screen (or the monitor, the disc-drive, and the printer. Software (computer programs refers to the sets of instructions which need to be loaded into the computer for it to be able to work.

Comparison of Machine Learning Techniques in Inferring Phytoplankton Size Classes

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Shuibo Hu

2018-03-01

Full Text Available The size of phytoplankton not only influences its physiology, metabolic rates and marine food web, but also serves as an indicator of phytoplankton functional roles in ecological and biogeochemical processes. Therefore, some algorithms have been developed to infer the synoptic distribution of phytoplankton cell size, denoted as phytoplankton size classes (PSCs, in surface ocean waters, by the means of remotely sensed variables. This study, using the NASA bio-Optical Marine Algorithm Data set (NOMAD high performance liquid chromatography (HPLC database, and satellite match-ups, aimed to compare the effectiveness of modeling techniques, including partial least square (PLS, artificial neural networks (ANN, support vector machine (SVM and random forests (RF, and feature selection techniques, including genetic algorithm (GA, successive projection algorithm (SPA and recursive feature elimination based on support vector machine (SVM-RFE, for inferring PSCs from remote sensing data. Results showed that: (1 SVM-RFE worked better in selecting sensitive features; (2 RF performed better than PLS, ANN and SVM in calibrating PSCs retrieval models; (3 machine learning techniques produced better performance than the chlorophyll-a based three-component method; (4 sea surface temperature, wind stress, and spectral curvature derived from the remote sensing reflectance at 490, 510, and 555 nm were among the most sensitive features to PSCs; and (5 the combination of SVM-RFE feature selection techniques and random forests regression was recommended for inferring PSCs. This study demonstrated the effectiveness of machine learning techniques in selecting sensitive features and calibrating models for PSCs estimations with remote sensing.

Conversation Analysis in Computer-Assisted Language Learning

Science.gov (United States)

González-Lloret, Marta

2015-01-01

The use of Conversation Analysis (CA) in the study of technology-mediated interactions is a recent methodological addition to qualitative research in the field of Computer-assisted Language Learning (CALL). The expansion of CA in Second Language Acquisition research, coupled with the need for qualitative techniques to explore how people interact…

Virtual Learning Environments on the Go: CALL Meets MALL

Science.gov (United States)

Arús Hita, Jorge

2016-01-01

This paper presents "Eating out," a Moodle-based digital learning resource for English as a Foreign Language (EFL) teaching that can be run both on computers and mobile devices. It is argued that Mobile Assisted Language Learning (MALL) resources do not necessarily need to be specifically designed for such platforms. Rather, a carefully…

Integrating CALL into an Iranian EAP Course: Constraints and Affordances

Science.gov (United States)

Mehran, Parisa; Alizadeh, Mehrasa

2015-01-01

Iranian universities have recently displayed a growing interest in integrating Computer-Assisted Language Learning (CALL) into teaching/learning English. The English for Academic Purposes (EAP) context, however, is not keeping pace with the current changes since EAP courses are strictly text-based and exam-oriented, and little research has thus…

An Educational Data Mining Approach to Concept Map Construction for Web based Learning

Directory of Open Access Journals (Sweden)

Anal ACHARYA

2017-01-01

Full Text Available This aim of this article is to study the use of Educational Data Mining (EDM techniques in constructing concept maps for organizing knowledge in web based learning systems whereby studying their synergistic effects in enhancing learning. This article first provides a tutorial based introduction to EDM. The applicability of web based learning systems in enhancing the efficiency of EDM techniques in real time environment is investigated. Web based learning systems often use a tool for organizing knowledge. This article explores the use of one such tool called concept map for this purpose. The pioneering works by various researchers who proposed web based learning systems in personalized and collaborative environment in this arena are next presented. A set of parameters are proposed based on which personalized and collaborative learning applications may be generalized and their performances compared. It is found that personalized learning environment uses EDM techniques more exhaustively compared to collaborative learning for concept map construction in web based environment. This article can be used as a starting point for freshers who would like to use EDM techniques for concept map construction for web based learning purposes.

Using Machine Learning Techniques in the Analysis of Oceanographic Data

Science.gov (United States)

Falcinelli, K. E.; Abuomar, S.

2017-12-01

Acoustic Doppler Current Profilers (ADCPs) are oceanographic tools capable of collecting large amounts of current profile data. Using unsupervised machine learning techniques such as principal component analysis, fuzzy c-means clustering, and self-organizing maps, patterns and trends in an ADCP dataset are found. Cluster validity algorithms such as visual assessment of cluster tendency and clustering index are used to determine the optimal number of clusters in the ADCP dataset. These techniques prove to be useful in analysis of ADCP data and demonstrate potential for future use in other oceanographic applications.

Analysing CMS transfers using Machine Learning techniques

CERN Document Server

Diotalevi, Tommaso

2016-01-01

LHC experiments transfer more than 10 PB/week between all grid sites using the FTS transfer service. In particular, CMS manages almost 5 PB/week of FTS transfers with PhEDEx (Physics Experiment Data Export). FTS sends metrics about each transfer (e.g. transfer rate, duration, size) to a central HDFS storage at CERN. The work done during these three months, here as a Summer Student, involved the usage of ML techniques, using a CMS framework called DCAFPilot, to process this new data and generate predictions of transfer latencies on all links between Grid sites. This analysis will provide, as a future service, the necessary information in order to proactively identify and maybe fix latency issued transfer over the WLCG.

Study of CT image texture using deep learning techniques

Science.gov (United States)

Dutta, Sandeep; Fan, Jiahua; Chevalier, David

2018-03-01

For CT imaging, reduction of radiation dose while improving or maintaining image quality (IQ) is currently a very active research and development topic. Iterative Reconstruction (IR) approaches have been suggested to be able to offer better IQ to dose ratio compared to the conventional Filtered Back Projection (FBP) reconstruction. However, it has been widely reported that often CT image texture from IR is different compared to that from FBP. Researchers have proposed different figure of metrics to quantitate the texture from different reconstruction methods. But there is still a lack of practical and robust method in the field for texture description. This work applied deep learning method for CT image texture study. Multiple dose scans of a 20cm diameter cylindrical water phantom was performed on Revolution CT scanner (GE Healthcare, Waukesha) and the images were reconstructed with FBP and four different IR reconstruction settings. The training images generated were randomly allotted (80:20) to a training and validation set. An independent test set of 256-512 images/class were collected with the same scan and reconstruction settings. Multiple deep learning (DL) networks with Convolution, RELU activation, max-pooling, fully-connected, global average pooling and softmax activation layers were investigated. Impact of different image patch size for training was investigated. Original pixel data as well as normalized image data were evaluated. DL models were reliably able to classify CT image texture with accuracy up to 99%. Results show that the deep learning techniques suggest that CT IR techniques may help lower the radiation dose compared to FBP.

Machine learning of molecular properties: Locality and active learning

Science.gov (United States)

Gubaev, Konstantin; Podryabinkin, Evgeny V.; Shapeev, Alexander V.

2018-06-01

In recent years, the machine learning techniques have shown great potent1ial in various problems from a multitude of disciplines, including materials design and drug discovery. The high computational speed on the one hand and the accuracy comparable to that of density functional theory on another hand make machine learning algorithms efficient for high-throughput screening through chemical and configurational space. However, the machine learning algorithms available in the literature require large training datasets to reach the chemical accuracy and also show large errors for the so-called outliers—the out-of-sample molecules, not well-represented in the training set. In the present paper, we propose a new machine learning algorithm for predicting molecular properties that addresses these two issues: it is based on a local model of interatomic interactions providing high accuracy when trained on relatively small training sets and an active learning algorithm of optimally choosing the training set that significantly reduces the errors for the outliers. We compare our model to the other state-of-the-art algorithms from the literature on the widely used benchmark tests.

Machine Learning Techniques for Prediction of Early Childhood Obesity.

Science.gov (United States)

Dugan, T M; Mukhopadhyay, S; Carroll, A; Downs, S

2015-01-01

This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.

Nonpasserine bird produces soft calls and pays retaliation cost

OpenAIRE

Paweł Ręk; Tomasz S. Osiejuk

2011-01-01

Low-amplitude vocalizations produced during aggressive encounters, courtship, or both (quiet/soft songs) have been described for many species of song-learning passerines; however, such signals have not been studied among nonlearning birds. During aggressive interactions, apart from using the broadcast call, male corncrakes (Crex crex) produce a low-amplitude, gurgling--mewing call, which appears to be equivalent to soft songs of songbirds. Previous studies have shown that low-amplitude vocali...

COMPUTER ASSISTED LANGUAGE LEARNING (CALL: ITS PROSPECTS AND CONSEQUENCES FOR NIGERIAN LANGUAGES / L'APPRENTISSAGE DE LA LANGUE À L'AIDE DES TICE: PERSPECTIVES ET CONSÉQUENCES POUR LES LANGUES NIGÉRIENNES / ÎNVĂŢAREA LIMBII CU TIC: PERSPECTIVE ŞI CONSECINŢE PENTRU LIMBILE NIGERIENE

Directory of Open Access Journals (Sweden)

Oyè Táíwò

2014-11-01

Full Text Available The shift in language learning today is from “classical teaching environment” to “self-learning environment”. In Nigeria today, although CALL efforts are made by schools and individuals, these effort are geared towards the English language learning other than Nigerian languages. This paper seeks to explore the development of CALL for Nigerian Languages and the challenges of running CALL in Nigeria. The results indicate that CALL for Nigerian languages is needed and should be promoted. CALL in Nigerian can only be successful if the shortcomings of CALL are recognized and the mitigating circumstances tackled. Adequate arrangements must be made to manage CALL and Teacher-Assisted language learning (TALL in consideration of the socioeconomic impact of CALL on the teachers Nigerian languages. The attitude of Nigerians towards Nigerian languages should be positive. The government, corporate bodies and individuals must intervene in CALL programs in schools so as to control the resulting high tuition fee.

Toward accelerating landslide mapping with interactive machine learning techniques

Science.gov (United States)

Stumpf, André; Lachiche, Nicolas; Malet, Jean-Philippe; Kerle, Norman; Puissant, Anne

2013-04-01

Despite important advances in the development of more automated methods for landslide mapping from optical remote sensing images, the elaboration of inventory maps after major triggering events still remains a tedious task. Image classification with expert defined rules typically still requires significant manual labour for the elaboration and adaption of rule sets for each particular case. Machine learning algorithm, on the contrary, have the ability to learn and identify complex image patterns from labelled examples but may require relatively large amounts of training data. In order to reduce the amount of required training data active learning has evolved as key concept to guide the sampling for applications such as document classification, genetics and remote sensing. The general underlying idea of most active learning approaches is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and/or the data structure to iteratively select the most valuable samples that should be labelled by the user and added in the training set. With relatively few queries and labelled samples, an active learning strategy should ideally yield at least the same accuracy than an equivalent classifier trained with many randomly selected samples. Our study was dedicated to the development of an active learning approach for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. The developed approach is a region-based query heuristic that enables to guide the user attention towards few compact spatial batches rather than distributed points resulting in time savings of 50% and more compared to standard active learning techniques. The approach was tested with multi-temporal and multi-sensor satellite images capturing recent large scale triggering events in Brazil and China and demonstrated balanced user's and producer's accuracies between 74% and 80%. The assessment also

Call Forecasting for Inbound Call Center

Directory of Open Access Journals (Sweden)

Peter Vinje

2009-01-01

Full Text Available In a scenario of inbound call center customer service, the ability to forecast calls is a key element and advantage. By forecasting the correct number of calls a company can predict staffing needs, meet service level requirements, improve customer satisfaction, and benefit from many other optimizations. This project will show how elementary statistics can be used to predict calls for a specific company, forecast the rate at which calls are increasing/decreasing, and determine if the calls may stop at some point.

A Comprehensive Review and meta-analysis on Applications of Machine Learning Techniques in Intrusion Detection

Directory of Open Access Journals (Sweden)

Manojit Chattopadhyay

2018-05-01

Full Text Available Securing a machine from various cyber-attacks has been of serious concern for researchers, statutory bodies such as governments, business organizations and users in both wired and wireless media. However, during the last decade, the amount of data handling by any device, particularly servers, has increased exponentially and hence the security of these devices has become a matter of utmost concern. This paper attempts to examine the challenges in the application of machine learning techniques to intrusion detection. We review different inherent issues in defining and applying the machine learning techniques to intrusion detection. We also attempt to identify the best technological solution for changing usage pattern by comparing different machine learning techniques on different datasets and summarizing their performance using various performance metrics. This paper highlights the research challenges and future trends of intrusion detection in dynamic scenarios of intrusion detection problems in diverse network technologies.

Machine Learning Techniques for Modelling Short Term Land-Use Change

Directory of Open Access Journals (Sweden)

Mileva Samardžić-Petrović

2017-11-01

Full Text Available The representation of land use change (LUC is often achieved by using data-driven methods that include machine learning (ML techniques. The main objectives of this research study are to implement three ML techniques, Decision Trees (DT, Neural Networks (NN, and Support Vector Machines (SVM for LUC modeling, in order to compare these three ML techniques and to find the appropriate data representation. The ML techniques are applied on the case study of LUC in three municipalities of the City of Belgrade, the Republic of Serbia, using historical geospatial data sets and considering nine land use classes. The ML models were built and assessed using two different time intervals. The information gain ranking technique and the recursive attribute elimination procedure were implemented to find the most informative attributes that were related to LUC in the study area. The results indicate that all three ML techniques can be used effectively for short-term forecasting of LUC, but the SVM achieved the highest agreement of predicted changes.

Using support vector machines in the multivariate state estimation technique

International Nuclear Information System (INIS)

Zavaljevski, N.; Gross, K.C.

1999-01-01

One approach to validate nuclear power plant (NPP) signals makes use of pattern recognition techniques. This approach often assumes that there is a set of signal prototypes that are continuously compared with the actual sensor signals. These signal prototypes are often computed based on empirical models with little or no knowledge about physical processes. A common problem of all data-based models is their limited ability to make predictions on the basis of available training data. Another problem is related to suboptimal training algorithms. Both of these potential shortcomings with conventional approaches to signal validation and sensor operability validation are successfully resolved by adopting a recently proposed learning paradigm called the support vector machine (SVM). The work presented here is a novel application of SVM for data-based modeling of system state variables in an NPP, integrated with a nonlinear, nonparametric technique called the multivariate state estimation technique (MSET), an algorithm developed at Argonne National Laboratory for a wide range of nuclear plant applications

An empirical analysis of the corporate call decision

International Nuclear Information System (INIS)

Carlson, M.D.

1998-01-01

An economic study of the the behaviour of financial managers of utility companies was presented. The study examined whether or not an option pricing based model of the call decision does a better job of explaining callable preferred share prices and call decisions compared to other models. In this study, the Rust (1987) empirical technique was extended to include the use of information from preferred share prices in addition to the call decisions. Reasonable estimates were obtained from data of shares of the Pacific Gas and Electric Company (PGE) for the transaction costs associated with a call. It was concluded that the managers of the PGE clearly take into account the value of the option to delay the call when making their call decisions

Novel Machine Learning-Based Techniques for Efficient Resource Allocation in Next Generation Wireless Networks

KAUST Repository

AlQuerm, Ismail A.

2018-02-21

There is a large demand for applications of high data rates in wireless networks. These networks are becoming more complex and challenging to manage due to the heterogeneity of users and applications specifically in sophisticated networks such as the upcoming 5G. Energy efficiency in the future 5G network is one of the essential problems that needs consideration due to the interference and heterogeneity of the network topology. Smart resource allocation, environmental adaptivity, user-awareness and energy efficiency are essential features in the future networks. It is important to support these features at different networks topologies with various applications. Cognitive radio has been found to be the paradigm that is able to satisfy the above requirements. It is a very interdisciplinary topic that incorporates flexible system architectures, machine learning, context awareness and cooperative networking. Mitola’s vision about cognitive radio intended to build context-sensitive smart radios that are able to adapt to the wireless environment conditions while maintaining quality of service support for different applications. Artificial intelligence techniques including heuristics algorithms and machine learning are the shining tools that are employed to serve the new vision of cognitive radio. In addition, these techniques show a potential to be utilized in an efficient resource allocation for the upcoming 5G networks’ structures such as heterogeneous multi-tier 5G networks and heterogeneous cloud radio access networks due to their capability to allocate resources according to real-time data analytics. In this thesis, we study cognitive radio from a system point of view focusing closely on architectures, artificial intelligence techniques that can enable intelligent radio resource allocation and efficient radio parameters reconfiguration. We propose a modular cognitive resource management architecture, which facilitates a development of flexible control for

Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

Science.gov (United States)

Lee, Hanbong

2016-01-01

Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

Prediction of lung cancer patient survival via supervised machine learning classification techniques.

Science.gov (United States)

Lynch, Chip M; Abdollahi, Behnaz; Fuqua, Joshua D; de Carlo, Alexandra R; Bartholomai, James A; Balgemann, Rayeanne N; van Berkel, Victor H; Frieboes, Hermann B

2017-12-01

Outcomes for cancer patients have been previously estimated by applying various machine learning techniques to large datasets such as the Surveillance, Epidemiology, and End Results (SEER) program database. In particular for lung cancer, it is not well understood which types of techniques would yield more predictive information, and which data attributes should be used in order to determine this information. In this study, a number of supervised learning techniques is applied to the SEER database to classify lung cancer patients in terms of survival, including linear regression, Decision Trees, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and a custom ensemble. Key data attributes in applying these methods include tumor grade, tumor size, gender, age, stage, and number of primaries, with the goal to enable comparison of predictive power between the various methods The prediction is treated like a continuous target, rather than a classification into categories, as a first step towards improving survival prediction. The results show that the predicted values agree with actual values for low to moderate survival times, which constitute the majority of the data. The best performing technique was the custom ensemble with a Root Mean Square Error (RMSE) value of 15.05. The most influential model within the custom ensemble was GBM, while Decision Trees may be inapplicable as it had too few discrete outputs. The results further show that among the five individual models generated, the most accurate was GBM with an RMSE value of 15.32. Although SVM underperformed with an RMSE value of 15.82, statistical analysis singles the SVM as the only model that generated a distinctive output. The results of the models are consistent with a classical Cox proportional hazards model used as a reference technique. We conclude that application of these supervised learning techniques to lung cancer data in the SEER database may be of use to estimate patient survival time

New Techniques for Deep Learning with Geospatial Data using TensorFlow, Earth Engine, and Google Cloud Platform

Science.gov (United States)

Hancher, M.

2017-12-01

Recent years have seen promising results from many research teams applying deep learning techniques to geospatial data processing. In that same timeframe, TensorFlow has emerged as the most popular framework for deep learning in general, and Google has assembled petabytes of Earth observation data from a wide variety of sources and made them available in analysis-ready form in the cloud through Google Earth Engine. Nevertheless, developing and applying deep learning to geospatial data at scale has been somewhat cumbersome to date. We present a new set of tools and techniques that simplify this process. Our approach combines the strengths of several underlying tools: TensorFlow for its expressive deep learning framework; Earth Engine for data management, preprocessing, postprocessing, and visualization; and other tools in Google Cloud Platform to train TensorFlow models at scale, perform additional custom parallel data processing, and drive the entire process from a single familiar Python development environment. These tools can be used to easily apply standard deep neural networks, convolutional neural networks, and other custom model architectures to a variety of geospatial data structures. We discuss our experiences applying these and related tools to a range of machine learning problems, including classic problems like cloud detection, building detection, land cover classification, as well as more novel problems like illegal fishing detection. Our improved tools will make it easier for geospatial data scientists to apply modern deep learning techniques to their own problems, and will also make it easier for machine learning researchers to advance the state of the art of those techniques.

Tourism websites in English as a source for the autonomous learning of specialized terminology: A CALL application

Directory of Open Access Journals (Sweden)

Ángel Felices Lago

2016-04-01

Full Text Available For years now, it has been an unquestioned fact that a large majority of textbooks available in English for Tourism, either in book format, CD-Rom or web site access are based on situations and professional contexts connected with the Anglo-Saxon environment, even though the vast majority of graduates in Tourism in Spain (and other countries end up working in the region (autonomous community of origin or in the province of reference for studies. There is, therefore, a clear dysfunction between the textbooks available in the market and the materials and situations that the students will face in their professional future. However, the Internet now allows us to exploit the availability of vast quantities of local resources (websites, blogs, etc. with their corresponding versions in English, which include tourist information referring to, for example, hotels, restaurants, historical and artistic heritage sites, tour operators, travel agencies, trade fairs or specialized services at the national, regional or communal levels. All these sites offer a special showcase of all the linguistic resources available (be they lexical, syntactic or terminological that the learners must acquire for their professional development. Consequently, the purpose of this study is to offer the results of the computer-assisted language learning (CALL project entitled Autonomous Learning of Specialized Vocabulary in English for Tourism (http://wdb.ugr.es/~afelices/, which takes into consideration the previous premises in order to promote, as its title indicates, autonomous learning in a more realistic professional context and to serve as a model for the development of similar e-learning platforms in other regions or countries.

Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques.

Science.gov (United States)

Wang, Guanjin; Lam, Kin-Man; Deng, Zhaohong; Choi, Kup-Sze

2015-08-01

Bladder cancer is a common cancer in genitourinary malignancy. For muscle invasive bladder cancer, surgical removal of the bladder, i.e. radical cystectomy, is in general the definitive treatment which, unfortunately, carries significant morbidities and mortalities. Accurate prediction of the mortality of radical cystectomy is therefore needed. Statistical methods have conventionally been used for this purpose, despite the complex interactions of high-dimensional medical data. Machine learning has emerged as a promising technique for handling high-dimensional data, with increasing application in clinical decision support, e.g. cancer prediction and prognosis. Its ability to reveal the hidden nonlinear interactions and interpretable rules between dependent and independent variables is favorable for constructing models of effective generalization performance. In this paper, seven machine learning methods are utilized to predict the 5-year mortality of radical cystectomy, including back-propagation neural network (BPN), radial basis function (RBFN), extreme learning machine (ELM), regularized ELM (RELM), support vector machine (SVM), naive Bayes (NB) classifier and k-nearest neighbour (KNN), on a clinicopathological dataset of 117 patients of the urology unit of a hospital in Hong Kong. The experimental results indicate that RELM achieved the highest average prediction accuracy of 0.8 at a fast learning speed. The research findings demonstrate the potential of applying machine learning techniques to support clinical decision making. Copyright © 2015 Elsevier Ltd. All rights reserved.

A comparison of machine learning techniques for predicting downstream acid mine drainage

CSIR Research Space (South Africa)

van Zyl, TL

2014-07-01

Full Text Available windowing approach over historical values to generate a prediction for the current value. We evaluate a number of Machine Learning techniques as regressors including Support Vector Regression, Random Forests, Stochastic Gradient Decent Regression, Linear...

Exploring the Earth Using Deep Learning Techniques

Science.gov (United States)

Larraondo, P. R.; Evans, B. J. K.; Antony, J.

2016-12-01

Research using deep neural networks have significantly matured in recent times, and there is now a surge in interest to apply such methods to Earth systems science and the geosciences. When combined with Big Data, we believe there are opportunities for significantly transforming a number of areas relevant to researchers and policy makers. In particular, by using a combination of data from a range of satellite Earth observations as well as computer simulations from climate models and reanalysis, we can gain new insights into the information that is locked within the data. Global geospatial datasets describe a wide range of physical and chemical parameters, which are mostly available using regular grids covering large spatial and temporal extents. This makes them perfect candidates to apply deep learning methods. So far, these techniques have been successfully applied to image analysis through the use of convolutional neural networks. However, this is only one field of interest, and there is potential for many more use cases to be explored. The deep learning algorithms require fast access to large amounts of data in the form of tensors and make intensive use of CPU in order to train its models. The Australian National Computational Infrastructure (NCI) has recently augmented its Raijin 1.2 PFlop supercomputer with hardware accelerators. Together with NCI's 3000 core high performance OpenStack cloud, these computational systems have direct access to NCI's 10+ PBytes of datasets and associated Big Data software technologies (see http://geonetwork.nci.org.au/ and http://nci.org.au/systems-services/national-facility/nerdip/). To effectively use these computing infrastructures requires that both the data and software are organised in a way that readily supports the deep learning software ecosystem. Deep learning software, such as the open source TensorFlow library, has allowed us to demonstrate the possibility of generating geospatial models by combining information from

Machine-learning techniques applied to antibacterial drug discovery.

Science.gov (United States)

Durrant, Jacob D; Amaro, Rommie E

2015-01-01

The emergence of drug-resistant bacteria threatens to revert humanity back to the preantibiotic era. Even now, multidrug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, tens of thousands of lives lost. As many pharmaceutical companies have abandoned antibiotic development in search of more lucrative therapeutics, academic researchers are uniquely positioned to fill the pipeline. Traditional high-throughput screens and lead-optimization efforts are expensive and labor intensive. Computer-aided drug-discovery techniques, which are cheaper and faster, can accelerate the identification of novel antibiotics, leading to improved hit rates and faster transitions to preclinical and clinical testing. The current review describes two machine-learning techniques, neural networks and decision trees, that have been used to identify experimentally validated antibiotics. We conclude by describing the future directions of this exciting field. © 2015 John Wiley & Sons A/S.

Towards large-scale FAME-based bacterial species identification using machine learning techniques.

Science.gov (United States)

Slabbinck, Bram; De Baets, Bernard; Dawyndt, Peter; De Vos, Paul

2009-05-01

In the last decade, bacterial taxonomy witnessed a huge expansion. The swift pace of bacterial species (re-)definitions has a serious impact on the accuracy and completeness of first-line identification methods. Consequently, back-end identification libraries need to be synchronized with the List of Prokaryotic names with Standing in Nomenclature. In this study, we focus on bacterial fatty acid methyl ester (FAME) profiling as a broadly used first-line identification method. From the BAME@LMG database, we have selected FAME profiles of individual strains belonging to the genera Bacillus, Paenibacillus and Pseudomonas. Only those profiles resulting from standard growth conditions have been retained. The corresponding data set covers 74, 44 and 95 validly published bacterial species, respectively, represented by 961, 378 and 1673 standard FAME profiles. Through the application of machine learning techniques in a supervised strategy, different computational models have been built for genus and species identification. Three techniques have been considered: artificial neural networks, random forests and support vector machines. Nearly perfect identification has been achieved at genus level. Notwithstanding the known limited discriminative power of FAME analysis for species identification, the computational models have resulted in good species identification results for the three genera. For Bacillus, Paenibacillus and Pseudomonas, random forests have resulted in sensitivity values, respectively, 0.847, 0.901 and 0.708. The random forests models outperform those of the other machine learning techniques. Moreover, our machine learning approach also outperformed the Sherlock MIS (MIDI Inc., Newark, DE, USA). These results show that machine learning proves very useful for FAME-based bacterial species identification. Besides good bacterial identification at species level, speed and ease of taxonomic synchronization are major advantages of this computational species

Sex differences in the representation of call stimuli in a songbird secondary auditory area.

Science.gov (United States)

Giret, Nicolas; Menardy, Fabien; Del Negro, Catherine

2015-01-01

Understanding how communication sounds are encoded in the central auditory system is critical to deciphering the neural bases of acoustic communication. Songbirds use learned or unlearned vocalizations in a variety of social interactions. They have telencephalic auditory areas specialized for processing natural sounds and considered as playing a critical role in the discrimination of behaviorally relevant vocal sounds. The zebra finch, a highly social songbird species, forms lifelong pair bonds. Only male zebra finches sing. However, both sexes produce the distance call when placed in visual isolation. This call is sexually dimorphic, is learned only in males and provides support for individual recognition in both sexes. Here, we assessed whether auditory processing of distance calls differs between paired males and females by recording spiking activity in a secondary auditory area, the caudolateral mesopallium (CLM), while presenting the distance calls of a variety of individuals, including the bird itself, the mate, familiar and unfamiliar males and females. In males, the CLM is potentially involved in auditory feedback processing important for vocal learning. Based on both the analyses of spike rates and temporal aspects of discharges, our results clearly indicate that call-evoked responses of CLM neurons are sexually dimorphic, being stronger, lasting longer, and conveying more information about calls in males than in females. In addition, how auditory responses vary among call types differ between sexes. In females, response strength differs between familiar male and female calls. In males, temporal features of responses reveal a sensitivity to the bird's own call. These findings provide evidence that sexual dimorphism occurs in higher-order processing areas within the auditory system. They suggest a sexual dimorphism in the function of the CLM, contributing to transmit information about the self-generated calls in males and to storage of information about the

Sex differences in the representation of call stimuli in a songbird secondary auditory area

Directory of Open Access Journals (Sweden)

Nicolas eGiret

2015-10-01

Full Text Available Understanding how communication sounds are encoded in the central auditory system is critical to deciphering the neural bases of acoustic communication. Songbirds use learned or unlearned vocalizations in a variety of social interactions. They have telencephalic auditory areas specialized for processing natural sounds and considered as playing a critical role in the discrimination of behaviorally relevant vocal sounds. The zebra finch, a highly social songbird species, forms lifelong pair bonds. Only male zebra finches sing. However, both sexes produce the distance call when placed in visual isolation. This call is sexually dimorphic, is learned only in males and provides support for individual recognition in both sexes. Here, we assessed whether auditory processing of distance calls differs between paired males and females by recording spiking activity in a secondary auditory area, the caudolateral mesopallium (CLM, while presenting the distance calls of a variety of individuals, including the bird itself, the mate, familiar and unfamiliar males and females. In males, the CLM is potentially involved in auditory feedback processing important for vocal learning. Based on both the analyses of spike rates and temporal aspects of discharges, our results clearly indicate that call-evoked responses of CLM neurons are sexually dimorphic, being stronger, lasting longer and conveying more information about calls in males than in females. In addition, how auditory responses vary among call types differ between sexes. In females, response strength differs between familiar male and female calls. In males, temporal features of responses reveal a sensitivity to the bird’s own call. These findings provide evidence that sexual dimorphism occurs in higher-order processing areas within the auditory system. They suggest a sexual dimorphism in the function of the CLM, contributing to transmit information about the self-generated calls in males and to storage of

Survey as a group interactive teaching technique

Directory of Open Access Journals (Sweden)

Ana GOREA

2017-03-01

Full Text Available Smooth running of the educational process and the results depend a great deal on the methods used. The methodology of teaching offers a great variety of teaching techniques that the teacher can make use of in the teaching/learning process. Such techniques as brainstorming, the cube, KLW, case study, Venn diagram, and many other are familiar to the teachers and they use them effectively in the classroom. The present article proposes a technique called ‘survey’, which has been successfully used by the author as a student-centered speaking activity in foreign language classes. It has certain advantages especially if used in large groups. It can be adapted for any other discipline in the case when the teacher wishes to offer the students space for cooperative activity and creativity.

Machine learning and medicine: book review and commentary.

Science.gov (United States)

Koprowski, Robert; Foster, Kenneth R

2018-02-01

This article is a review of the book "Master machine learning algorithms, discover how they work and implement them from scratch" (ISBN: not available, 37 USD, 163 pages) edited by Jason Brownlee published by the Author, edition, v1.10 http://MachineLearningMastery.com . An accompanying commentary discusses some of the issues that are involved with use of machine learning and data mining techniques to develop predictive models for diagnosis or prognosis of disease, and to call attention to additional requirements for developing diagnostic and prognostic algorithms that are generally useful in medicine. Appendix provides examples that illustrate potential problems with machine learning that are not addressed in the reviewed book.

The Effects of the CALL Model on College English Reading Teaching

Directory of Open Access Journals (Sweden)

Dan Zhang

2017-12-01

Full Text Available Computer Assisted Language Learning (CALL is an important concept in English teaching method reform. College students’ English reading ability is an important indicator in the evaluation on the college students’ English proficiency. Therefore, this paper applies the CALL model in English reading teaching. Firstly, it introduces the application and development prospect of the CALL model, and analyzes its advantages and disadvantages; secondly, it analyzes the present situation of college English teaching and its influencing factors and then designs an application example to integrate the CALL model with different aspect of English reading. Finally, it analyzes the teaching results of college English reading under the CALL model. Therefore, in both theory and practice, this paper proves the effectiveness and innovativeness of the CALL model.

Training of passive systems matter using the Project Oriented Learning technique in Ciudad de Mexico del Tecnologico de Monterey; Ensenanza de la materia de sistemas pasivos en el cuarto semestre de la carrera de arquitectura plan 99 bajo la tecnica didactica project oriented learning (Pol), en el campus Ciudad de Mexico del Tecnologico de Monterrey

Energy Technology Data Exchange (ETDEWEB)

Aguayo, G. R.

2004-07-01

Environmental design or solar architecture is one of the subjects taught in the fourth semester of Architecture at the Instituto Tecnologico y de Estudios Superiores de Monterrey (ITESM), using the Project Oriented Learning technique. This paper explains the way Environmental Design is part of what is called the 4th semester block in the Architecture curriculum using POL technique, the way this subject was taught before as opposed to the way it is currently taught, which are the roles of the actors involved, the main characteristics of the technique itself, and how the above responds to the 2005 Mission Statement of the ITESM. (Author)

Solar photovoltaic power forecasting using optimized modified extreme learning machine technique

Directory of Open Access Journals (Sweden)

Manoja Kumar Behera

2018-06-01

Full Text Available Prediction of photovoltaic power is a significant research area using different forecasting techniques mitigating the effects of the uncertainty of the photovoltaic generation. Increasingly high penetration level of photovoltaic (PV generation arises in smart grid and microgrid concept. Solar source is irregular in nature as a result PV power is intermittent and is highly dependent on irradiance, temperature level and other atmospheric parameters. Large scale photovoltaic generation and penetration to the conventional power system introduces the significant challenges to microgrid a smart grid energy management. It is very critical to do exact forecasting of solar power/irradiance in order to secure the economic operation of the microgrid and smart grid. In this paper an extreme learning machine (ELM technique is used for PV power forecasting of a real time model whose location is given in the Table 1. Here the model is associated with the incremental conductance (IC maximum power point tracking (MPPT technique that is based on proportional integral (PI controller which is simulated in MATLAB/SIMULINK software. To train single layer feed-forward network (SLFN, ELM algorithm is implemented whose weights are updated by different particle swarm optimization (PSO techniques and their performance are compared with existing models like back propagation (BP forecasting model. Keywords: PV array, Extreme learning machine, Maximum power point tracking, Particle swarm optimization, Craziness particle swarm optimization, Accelerate particle swarm optimization, Single layer feed-forward network

Exploring Graduate Students' Perspectives towards Using Gamification Techniques in Online Learning

Directory of Open Access Journals (Sweden)

Daniah ALABBASI

2017-07-01

Full Text Available Teachers and educational institutions are attempting to find an appropriate strategy to motivate as well as engage students in the learning process. Institutions are encouraging the use of gamification in education for the purpose of improving the intrinsic motivation as well as engagement. However, the students’ perspective of the issue is under-investigated. The purpose of this research study was to explore graduate students’ perspectives toward the use of gamification techniques in online learning. The study used exploratory research and survey as the data collection tool. Forty-seven graduate students (n = 47 enrolled in an instructional technology program studied in a learning management system that supports gamification (TalentLMS. The average total percentages were calculated for each survey section to compose the final perspective of the included students. The results showed a positive perception toward the use of gamification tools in online learning among graduate students. Students require effort-demanding, challenging, sophisticated learning systems that increase competency, enhance recall memory, concentration, attentiveness, commitment, and social interaction. Limitations of the study are identified, which highlights the need for further research on the subject matter.

76 FR 45334 - Innovative Techniques for Delivering ITS Learning; Request for Information

Science.gov (United States)

2011-07-28

... adult learners? 5. Are you aware of any ITS training applications that work on a mobile phone or smart... DEPARTMENT OF TRANSPORTATION Research and Innovative Technology Administration Innovative Techniques for Delivering ITS Learning; Request for Information AGENCY: Research and Innovative Technology...

Interdisciplinary Research at the Intersection of CALL, NLP, and SLA: Methodological Implications from an Input Enhancement Project

Science.gov (United States)

Ziegler, Nicole; Meurers, Detmar; Rebuschat, Patrick; Ruiz, Simón; Moreno-Vega, José L.; Chinkina, Maria; Li, Wenjing; Grey, Sarah

2017-01-01

Despite the promise of research conducted at the intersection of computer-assisted language learning (CALL), natural language processing, and second language acquisition, few studies have explored the potential benefits of using intelligent CALL systems to deepen our understanding of the process and products of second language (L2) learning. The…

Evaluating the Motivational Impact of CALL Systems: Current Practices and Future Directions

Science.gov (United States)

Bodnar, Stephen; Cucchiarini, Catia; Strik, Helmer; van Hout, Roeland

2016-01-01

A major aim of computer-assisted language learning (CALL) is to create computer environments that facilitate students' second language (L2) acquisition. To achieve this aim, CALL employs technological innovations to create novel types of language practice. Evaluations of the new practice types serve the important role of distinguishing effective…

ISOLATED SPEECH RECOGNITION SYSTEM FOR TAMIL LANGUAGE USING STATISTICAL PATTERN MATCHING AND MACHINE LEARNING TECHNIQUES

Directory of Open Access Journals (Sweden)

VIMALA C.

2015-05-01

Full Text Available In recent years, speech technology has become a vital part of our daily lives. Various techniques have been proposed for developing Automatic Speech Recognition (ASR system and have achieved great success in many applications. Among them, Template Matching techniques like Dynamic Time Warping (DTW, Statistical Pattern Matching techniques such as Hidden Markov Model (HMM and Gaussian Mixture Models (GMM, Machine Learning techniques such as Neural Networks (NN, Support Vector Machine (SVM, and Decision Trees (DT are most popular. The main objective of this paper is to design and develop a speaker-independent isolated speech recognition system for Tamil language using the above speech recognition techniques. The background of ASR system, the steps involved in ASR, merits and demerits of the conventional and machine learning algorithms and the observations made based on the experiments are presented in this paper. For the above developed system, highest word recognition accuracy is achieved with HMM technique. It offered 100% accuracy during training process and 97.92% for testing process.

Improving face image extraction by using deep learning technique

Science.gov (United States)

Xue, Zhiyun; Antani, Sameer; Long, L. R.; Demner-Fushman, Dina; Thoma, George R.

2016-03-01

The National Library of Medicine (NLM) has made a collection of over a 1.2 million research articles containing 3.2 million figure images searchable using the Open-iSM multimodal (text+image) search engine. Many images are visible light photographs, some of which are images containing faces ("face images"). Some of these face images are acquired in unconstrained settings, while others are studio photos. To extract the face regions in the images, we first applied one of the most widely-used face detectors, a pre-trained Viola-Jones detector implemented in Matlab and OpenCV. The Viola-Jones detector was trained for unconstrained face image detection, but the results for the NLM database included many false positives, which resulted in a very low precision. To improve this performance, we applied a deep learning technique, which reduced the number of false positives and as a result, the detection precision was improved significantly. (For example, the classification accuracy for identifying whether the face regions output by this Viola- Jones detector are true positives or not in a test set is about 96%.) By combining these two techniques (Viola-Jones and deep learning) we were able to increase the system precision considerably, while avoiding the need to manually construct a large training set by manual delineation of the face regions.

Call mimicry by eastern towhees and its significance in relation to auditory learning

Science.gov (United States)

Jon S. Greenlaw; Clifford E. Shackelford; Raymond E. Brown

1998-01-01

The authors document cases of eastern towhees (Pipilo erythrophthalmus) using mimicked alarm calls from three presumptive models (blue jay (Cyanocitta cristata), brown thrasher (Toxostoma rufum), and American robin (Turdus migratorius)). In four instances, male towhees employed heterospecific calls without substitution in their own call repertoires. Three birds (New...

Learning outcomes and effective communication techniques for hazard recognition learning programmes in the transportation thrust area.

CSIR Research Space (South Africa)

Krige, PD

2001-12-01

Full Text Available on South African mines ............................................ 32 4.3 People development and training techniques associated with confidence, attitudes and leadership............................................ 34 Page 4 4.4 Recommended learning... to rules and procedures, safety commitment of management, supervision style, organising for safety, equipment design and maintenance. Only the last two are engineering issues. The trend is clear. Improvements in engineering design have significantly...

A Human/Computer Learning Network to Improve Biodiversity Conservation and Research

OpenAIRE

Kelling, Steve; Gerbracht, Jeff; Fink, Daniel; Lagoze, Carl; Wong, Weng-Keen; Yu, Jun; Damoulas, Theodoros; Gomes, Carla

2012-01-01

In this paper we describe eBird, a citizen-science project that takes advantage of the human observational capacity to identify birds to species, which is then used to accurately represent patterns of bird occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this a Human-Computer Learning Network,...

Student’s Perceptions on Simulation as Part of Experiential Learning in Approaches, Methods, and Techniques (AMT Course

Directory of Open Access Journals (Sweden)

Marselina Karina Purnomo

2017-03-01

Full Text Available Simulation is a part of Experiential Learning which represents certain real-life events. In this study, simulation is used as a learning activity in Approaches, Methods, and Techniques (AMT course which is one of the courses in English Language Education Study Program (ELESP of Sanata Dharma University. Since simulation represents the real-life events, it encourages students to apply the approaches, methods, and techniques being studied based on the real-life classroom. Several experts state that students are able to involve their personal experiences through simulation which additionally is believed to create a meaningful learning in the class. This study aimed to discover ELESP students’ perceptions toward simulation as a part of Experiential Learning in AMT course. From the findings, it could be inferred that students agreed that simulation in class was important for students’ learning for it formed a meaningful learning in class.  DOI: https://doi.org/10.24071/llt.2017.200104

Analisis Intensi Berprestasi Mahasiswa Binus Online Learning

Directory of Open Access Journals (Sweden)

Agus Putranto

2013-12-01

Full Text Available The utilization of e-learning can improve learning effectiveness and flexibility. Universitas Bina Nusantara (BINUS provides an online learning program called BINUS Online learning. It offers the student to do a study through online activities, where the students are couraged to learn individually. This research was conducted to obtain a perspective of student intention from BINUS Online learning to mark good achievement. Non experimental method with quatitative approached is implemented where the respondents are students of Information System department and Marketing Management Department. The sampling techniques used is non probability purposive sampling. All variables are measured using questionnaire based on Fishbein and Ajzen model. The data of the research are analyzed using descriptive statistics and multiple linier regression analysis. The results show that the intention to mark an achievement of students of BINUS Online learning is very high. It means that BINUS Online learning Program students have strong intention to get good marks and study achievements.

The Game Embedded CALL System to Facilitate English Vocabulary Acquisition and Pronunciation

Science.gov (United States)

Young, Shelley Shwu-Ching; Wang, Yi-Hsuan

2014-01-01

The aim of this study is to make a new attempt to explore the potential of integrating game strategies with automatic speech recognition technologies to provide learners with individual opportunities for English pronunciation learning. The study developed the Game Embedded CALL (GeCALL) system with two activities for on-line speaking practice. For…

Neutrality as Obstructionist in Academic Activism: Calling Bullshit

Science.gov (United States)

Rose, Barbara J.

2018-01-01

In this essay, the author uses experiences as a teacher educator and learning from a historically significant activism movement to (a) compare values in activism-centered and education-centered organizations, (b) posit ways that the concept of neutrality weakens academic activism, and (c) call for teacher education curricula and practices that…

Young doctors' problem solving strategies on call may be improved.

Science.gov (United States)

Michelsen, Jens; Malchow-Møller, Axel; Charles, Peder; Eika, Berit

2013-03-01

The first year following graduation from medical school is challenging as learning from books changes to workplace-based learning. Analysis and reflection on experience may ease this transition. We used Significant Event Analysis (SEA) as a tool to explore what pre-registration house officers (PRHOs) consider successful and problematic events, and to identify what problem-solving strategies they employ. A senior house officer systematically led the PRHO through the SEA of one successful and one problematic event following a night call. The PRHO wrote answers to questions about diagnosis, what happened, how he or she contributed and what knowledge-gaining activities the PRHO would prioritise before the next call. By using an inductive, thematic data analysis, we identified five problem-solving strategies: non-analytical reasoning, analytical reasoning, communication with patients, communication with colleagues and professional behaviour. On average, 1.5 strategies were used in the successful events and 1.2 strategies in the problematic events. Most PRHOs were unable to suggest activities other than reading textbooks. SEA was valuable for the identification of PRHOs' problem-solving strategies in a natural setting. PRHOs should be assisted in increasing their repertoire of strategies, and they should also be helped to "learn to learn" as they were largely unable to point to new learning strategies. not relevant. not relevant.

Neural robust stabilization via event-triggering mechanism and adaptive learning technique.

Science.gov (United States)

Wang, Ding; Liu, Derong

2018-06-01

The robust control synthesis of continuous-time nonlinear systems with uncertain term is investigated via event-triggering mechanism and adaptive critic learning technique. We mainly focus on combining the event-triggering mechanism with adaptive critic designs, so as to solve the nonlinear robust control problem. This can not only make better use of computation and communication resources, but also conduct controller design from the view of intelligent optimization. Through theoretical analysis, the nonlinear robust stabilization can be achieved by obtaining an event-triggered optimal control law of the nominal system with a newly defined cost function and a certain triggering condition. The adaptive critic technique is employed to facilitate the event-triggered control design, where a neural network is introduced as an approximator of the learning phase. The performance of the event-triggered robust control scheme is validated via simulation studies and comparisons. The present method extends the application domain of both event-triggered control and adaptive critic control to nonlinear systems possessing dynamical uncertainties. Copyright © 2018 Elsevier Ltd. All rights reserved.

An Interactive Learning Environment for Teaching the Imperative and Object-Oriented Programming Techniques in Various Learning Contexts

Science.gov (United States)

Xinogalos, Stelios

The acquisition of problem-solving and programming skills in the era of knowledge society seems to be particularly important. Due to the intrinsic difficulty of acquiring such skills various educational tools have been developed. Unfortunately, most of these tools are not utilized. In this paper we present the programming microworlds Karel and objectKarel that support the procedural-imperative and Object-Oriented Programming (OOP) techniques and can be used for supporting the teaching and learning of programming in various learning contexts and audiences. The paper focuses on presenting the pedagogical features that are common to both environments and mainly on presenting the potential uses of these environments.

Turchin's Relation for Call-by-Name Computations: A Formal Approach

OpenAIRE

Antonina Nepeivoda

2016-01-01

Supercompilation is a program transformation technique that was first described by V. F. Turchin in the 1970s. In supercompilation, Turchin's relation as a similarity relation on call-stack configurations is used both for call-by-value and call-by-name semantics to terminate unfolding of the program being transformed. In this paper, we give a formal grammar model of call-by-name stack behaviour. We classify the model in terms of the Chomsky hierarchy and then formally prove that Turchin's rel...

INTEGRATING CORPUS-BASED RESOURCES AND NATURAL LANGUAGE PROCESSING TOOLS INTO CALL

Directory of Open Access Journals (Sweden)

Pascual Cantos Gomez

2002-06-01

Full Text Available This paper ainis at presenting a survey of computational linguistic tools presently available but whose potential has been neither fully considered not exploited to its full in modern CALL. It starts with a discussion on the rationale of DDL to language learning, presenting typical DDL-activities. DDL-software and potential extensions of non-typical DDL-software (electronic dictionaries and electronic dictionary facilities to DDL . An extended section is devoted to describe NLP-technology and how it can be integrated into CALL, within already existing software or as stand alone resources. A range of NLP-tools is presentcd (MT programs, taggers, lemn~atizersp, arsers and speech technologies with special emphasis on tagged concordancing. The paper finishes with a number of reflections and ideas on how language technologies can be used efficiently within the language learning context and how extensive exploration and integration of these technologies might change and extend both modern CAI,I, and the present language learning paradigiii..

Mean-field learning for satisfactory solutions

KAUST Repository

Tembine, Hamidou

2013-12-01

One of the fundamental challenges in distributed interactive systems is to design efficient, accurate, and fair solutions. In such systems, a satisfactory solution is an innovative approach that aims to provide all players with a satisfactory payoff anytime and anywhere. In this paper we study fully distributed learning schemes for satisfactory solutions in games with continuous action space. Considering games where the payoff function depends only on own-action and an aggregate term, we show that the complexity of learning systems can be significantly reduced, leading to the so-called mean-field learning. We provide sufficient conditions for convergence to a satisfactory solution and we give explicit convergence time bounds. Then, several acceleration techniques are used in order to improve the convergence rate. We illustrate numerically the proposed mean-field learning schemes for quality-of-service management in communication networks. © 2013 IEEE.

[Learning experience of acupuncture technique from professor ZHANG Jin].

Science.gov (United States)

Xue, Hongsheng; Zhang, Jin

2017-08-12

As a famous acupuncturist in the world, professor ZHANG Jin believes the key of acupuncture technique is the use of force, and the understanding of the "concentrating the force into needle body" is essential to understand the essence of acupuncture technique. With deep study of Huangdi Neijing ( The Inner Canon of Huangdi ) and Zhenjiu Dacheng ( Compendium of Acupuncture and Moxibustion ), the author further learned professor ZHANG Jin 's theory and operation specification of "concentrating force into needle body, so the force arriving before and together with needle". The whole-body force should be subtly focused on the tip of needle, and gentle force at tip of needle could get significant reinforcing and reducing effect. In addition, proper timing at tip of needle could start reinforcing and reducing effect, lead qi to disease location, and achieve superior clinical efficacy.

THE BLENDED LEARNING OF ELECTRICITY USING LEARNING OBJECTS IN ENGINEERING

Directory of Open Access Journals (Sweden)

Lilia Maria Siqueira

2010-09-01

Full Text Available This work presents a proposal for the blended learning of Electricity education in Engineering, using resources called learning objects. The experience occurred with students enrolled on the Electrical Engineering and Computer Engineering courses at PUCPR University. It made possible the contact with interdisciplinary themes related to the study of electricity and the professional curriculum contents. The learning objects, offered during the semester, were anchored on PUCPR’s proprietary virtual educational environment, called Eureka. The students’ evaluation results showed that the study through learning objects in a virtual environment is significant for learning.

Selection of suitable e-learning approach using TOPSIS technique with best ranked criteria weights

Science.gov (United States)

Mohammed, Husam Jasim; Kasim, Maznah Mat; Shaharanee, Izwan Nizal Mohd

2017-11-01

This paper compares the performances of four rank-based weighting assessment techniques, Rank Sum (RS), Rank Reciprocal (RR), Rank Exponent (RE), and Rank Order Centroid (ROC) on five identified e-learning criteria to select the best weights method. A total of 35 experts in a public university in Malaysia were asked to rank the criteria and to evaluate five e-learning approaches which include blended learning, flipped classroom, ICT supported face to face learning, synchronous learning, and asynchronous learning. The best ranked criteria weights are defined as weights that have the least total absolute differences with the geometric mean of all weights, were then used to select the most suitable e-learning approach by using TOPSIS method. The results show that RR weights are the best, while flipped classroom approach implementation is the most suitable approach. This paper has developed a decision framework to aid decision makers (DMs) in choosing the most suitable weighting method for solving MCDM problems.

THE GAME TECHNIQUE NTCHNIQUE STIMULATING LEARNING ACTIVITY OF JUNIOR STUDENTS SPECIALIZING IN ECONOMICS

Directory of Open Access Journals (Sweden)

Juri. S. Ezrokh

2014-01-01

Full Text Available The research is aimed at specifying and developing the modern control system of current academic achievements of junior university students; and the main task is to find the adequate ways for stimulating the junior students’ learning activities, and estimating their individual achievements.Methods: The author applies his own assessment method for estimating and stimulating students’ learning outcomes, based on the rating-point system of gradually obtained points building up a student’s integrated learning outcomes.Results: The research findings prove that implementation of the given method can increase the motivational, multiplicative and controlling components of the learning process.Scientific novelty: The method in question is based on the new original game approach to controlling procedures and stimulation of learning motivation of the economic profile students.Practical significance: The recommended technique can intensify the incentivebased training activities both in and outside a classroom, developing thereby students’ professional and personal qualities.

Diminishing-cues retrieval practice: A memory-enhancing technique that works when regular testing doesn't.

Science.gov (United States)

Fiechter, Joshua L; Benjamin, Aaron S

2017-08-28

Retrieval practice has been shown to be a highly effective tool for enhancing memory, a fact that has led to major changes to educational practice and technology. However, when initial learning is poor, initial retrieval practice is unlikely to be successful and long-term benefits of retrieval practice are compromised or nonexistent. Here, we investigate the benefit of a scaffolded retrieval technique called diminishing-cues retrieval practice (Finley, Benjamin, Hays, Bjork, & Kornell, Journal of Memory and Language, 64, 289-298, 2011). Under learning conditions that favored a strong testing effect, diminishing cues and standard retrieval practice both enhanced memory performance relative to restudy. Critically, under learning conditions where standard retrieval practice was not helpful, diminishing cues enhanced memory performance substantially. These experiments demonstrate that diminishing-cues retrieval practice can widen the range of conditions under which testing can benefit memory, and so can serve as a model for the broader application of testing-based techniques for enhancing learning.

New CALL-SLA Research Interfaces for the 21st Century: Towards Equitable Multilingualism

Science.gov (United States)

Ortega, Lourdes

2017-01-01

The majority of the world is multilingual, but inequitably multilingual, and much of the world is also technologized, but inequitably so. Thus, researchers in the fields of computer-assisted language learning (CALL) and second language acquisition (SLA) would profit from considering multilingualism and social justice when envisioning new CALL-SLA…

Machine Learning or Information Retrieval Techniques for Bug Triaging: Which is better?

Directory of Open Access Journals (Sweden)

Anjali Goyal

2017-07-01

Full Text Available Bugs are the inevitable part of a software system. Nowadays, large software development projects even release beta versions of their products to gather bug reports from users. The collected bug reports are then worked upon by various developers in order to resolve the defects and make the final software product more reliable. The high frequency of incoming bugs makes the bug handling a difficult and time consuming task. Bug assignment is an integral part of bug triaging that aims at the process of assigning a suitable developer for the reported bug who corrects the source code in order to resolve the bug. There are various semi and fully automated techniques to ease the task of bug assignment. This paper presents the current state of the art of various techniques used for bug report assignment. Through exhaustive research, the authors have observed that machine learning and information retrieval based bug assignment approaches are most popular in literature. A deeper investigation has shown that the trend of techniques is taking a shift from machine learning based approaches towards information retrieval based approaches. Therefore, the focus of this work is to find the reason behind the observed drift and thus a comparative analysis is conducted on the bug reports of the Mozilla, Eclipse, Gnome and Open Office projects in the Bugzilla repository. The results of the study show that the information retrieval based technique yields better efficiency in recommending the developers for bug reports.

Challenges of Using Learning Analytics Techniques to Support Mobile Learning

Science.gov (United States)

Arrigo, Marco; Fulantelli, Giovanni; Taibi, Davide

2015-01-01

Evaluation of Mobile Learning remains an open research issue, especially as regards the activities that take place outside the classroom. In this context, Learning Analytics can provide answers, and offer the appropriate tools to enhance Mobile Learning experiences. In this poster we introduce a task-interaction framework, using learning analytics…

Turchin's Relation for Call-by-Name Computations: A Formal Approach

Directory of Open Access Journals (Sweden)

Antonina Nepeivoda

2016-07-01

Full Text Available Supercompilation is a program transformation technique that was first described by V. F. Turchin in the 1970s. In supercompilation, Turchin's relation as a similarity relation on call-stack configurations is used both for call-by-value and call-by-name semantics to terminate unfolding of the program being transformed. In this paper, we give a formal grammar model of call-by-name stack behaviour. We classify the model in terms of the Chomsky hierarchy and then formally prove that Turchin's relation can terminate all computations generated by the model.

Learning Progressions as Tools for Assessment and Learning

Science.gov (United States)

Shepard, Lorrie A.

2018-01-01

This article addresses the teaching and learning side of the learning progressions literature, calling out for measurement specialists the knowledge most needed when collaborating with subject-matter experts in the development of learning progressions. Learning progressions are one of the strongest instantiations of principles from "Knowing…

Group Guidance Services with Self-Regulation Technique to Improve Student Learning Motivation in Junior High School (JHS)

Science.gov (United States)

Pranoto, Hadi; Atieka, Nurul; Wihardjo, Sihadi Darmo; Wibowo, Agus; Nurlaila, Siti; Sudarmaji

2016-01-01

This study aims at: determining students motivation before being given a group guidance with self-regulation technique, determining students' motivation after being given a group counseling with self-regulation technique, generating a model of group counseling with self-regulation technique to improve motivation of learning, determining the…

On the Edge: Intelligent CALL in the 1990s.

Science.gov (United States)

Underwood, John

1989-01-01

Examines the possibilities of developing computer-assisted language learning (CALL) based on the best of modern technology, arguing that artificial intelligence (AI) strategies will radically improve the kinds of exercises that can be performed. Recommends combining AI technology with other tools for delivering instruction, such as simulation and…

Self-Supervised Video Representation Learning With Odd-One-Out Networks : CVPR 2017 : 21-26 July 2016, Honolulu, Hawaii : proceedings

NARCIS (Netherlands)

Fernando, B.; Bilen, H.; Gavves, E.; Gould, S.

2017-01-01

We propose a new self-supervised CNN pre-training technique based on a novel auxiliary task called odd-one-out learning. In this task, the machine is asked to identify the unrelated or odd element from a set of otherwise related elements. We apply this technique to self-supervised video

Applying perceptual and adaptive learning techniques for teaching introductory histopathology

Directory of Open Access Journals (Sweden)

Sally Krasne

2013-01-01

Full Text Available Background: Medical students are expected to master the ability to interpret histopathologic images, a difficult and time-consuming process. A major problem is the issue of transferring information learned from one example of a particular pathology to a new example. Recent advances in cognitive science have identified new approaches to address this problem. Methods: We adapted a new approach for enhancing pattern recognition of basic pathologic processes in skin histopathology images that utilizes perceptual learning techniques, allowing learners to see relevant structure in novel cases along with adaptive learning algorithms that space and sequence different categories (e.g. diagnoses that appear during a learning session based on each learner′s accuracy and response time (RT. We developed a perceptual and adaptive learning module (PALM that utilized 261 unique images of cell injury, inflammation, neoplasia, or normal histology at low and high magnification. Accuracy and RT were tracked and integrated into a "Score" that reflected students rapid recognition of the pathologies and pre- and post-tests were given to assess the effectiveness. Results: Accuracy, RT and Scores significantly improved from the pre- to post-test with Scores showing much greater improvement than accuracy alone. Delayed post-tests with previously unseen cases, given after 6-7 weeks, showed a decline in accuracy relative to the post-test for 1 st -year students, but not significantly so for 2 nd -year students. However, the delayed post-test scores maintained a significant and large improvement relative to those of the pre-test for both 1 st and 2 nd year students suggesting good retention of pattern recognition. Student evaluations were very favorable. Conclusion: A web-based learning module based on the principles of cognitive science showed an evidence for improved recognition of histopathology patterns by medical students.

Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data

Directory of Open Access Journals (Sweden)

Laura Cornejo-Bueno

2017-11-01

Full Text Available Wind Power Ramp Events (WPREs are large fluctuations of wind power in a short time interval, which lead to strong, undesirable variations in the electric power produced by a wind farm. Its accurate prediction is important in the effort of efficiently integrating wind energy in the electric system, without affecting considerably its stability, robustness and resilience. In this paper, we tackle the problem of predicting WPREs by applying Machine Learning (ML regression techniques. Our approach consists of using variables from atmospheric reanalysis data as predictive inputs for the learning machine, which opens the possibility of hybridizing numerical-physical weather models with ML techniques for WPREs prediction in real systems. Specifically, we have explored the feasibility of a number of state-of-the-art ML regression techniques, such as support vector regression, artificial neural networks (multi-layer perceptrons and extreme learning machines and Gaussian processes to solve the problem. Furthermore, the ERA-Interim reanalysis from the European Center for Medium-Range Weather Forecasts is the one used in this paper because of its accuracy and high resolution (in both spatial and temporal domains. Aiming at validating the feasibility of our predicting approach, we have carried out an extensive experimental work using real data from three wind farms in Spain, discussing the performance of the different ML regression tested in this wind power ramp event prediction problem.

Approximate multi-state reliability expressions using a new machine learning technique

International Nuclear Information System (INIS)

Rocco S, Claudio M.; Muselli, Marco

2005-01-01

The machine-learning-based methodology, previously proposed by the authors for approximating binary reliability expressions, is now extended to develop a new algorithm, based on the procedure of Hamming Clustering, which is capable to deal with multi-state systems and any success criterion. The proposed technique is presented in details and verified on literature cases: experiment results show that the new algorithm yields excellent predictions

Sculpting with people – An experiential learning technique

DEFF Research Database (Denmark)

Andersen, Helle Elisabeth; Larsen, Kirsten Vendelbo

2015-01-01

At Department of Nursing, University College Lillebaelt in Denmark we use an experiential technique called sculpting in our simulation program. Sculpting is a kind of non-verbal role play in which participants are given a certain character and create a 'sculpture' by arranging family members......, social circles and professionals in ways which reflect the quality of the relationships of the people involved. The aim of this study is to further describe the sculpting exercise and present a small scale evaluation study using a qualitative descriptive design. An evaluation sheet was formulated...... by the authors and filled out by 114 Danish third-year nursing students. The results show that sculpting is experienced as emotionally demanding, but in a good way. It is experienced as an eye-opener that helps to identify the possible complex and emotional dynamics in a family experiencing critical illness...

CRDM motion analysis using machine learning technique

International Nuclear Information System (INIS)

Nishimura, Takuya; Nakayama, Hiroyuki; Saitoh, Mayumi; Yaguchi, Seiji

2017-01-01

Magnetic jack type Control Rod Drive Mechanism (CRDM) for pressurized water reactor (PWR) plant operates control rods in response to electrical signals from a reactor control system. CRDM operability is evaluated by quantifying armature's response of closed/opened time which means interval time between coil energizing/de-energizing points and armature closed/opened points. MHI has already developed an automatic CRDM motion analysis and applied it to actual plants so far. However, CRDM operational data has wide variation depending on their characteristics such as plant condition, plant, etc. In the existing motion analysis, there is an issue of analysis accuracy for applying a single analysis technique to all plant conditions, plants, etc. In this study, MHI investigated motion analysis using machine learning (Random Forests) which is flexibly accommodated to CRDM operational data with wide variation, and is improved analysis accuracy. (author)

SPAM CLASSIFICATION BASED ON SUPERVISED LEARNING USING MACHINE LEARNING TECHNIQUES

Directory of Open Access Journals (Sweden)

T. Hamsapriya

2011-12-01

Full Text Available E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. The flaws in the e-mail protocols and the increasing amount of electronic business and financial transactions directly contribute to the increase in e-mail-based threats. Email spam is one of the major problems of the today’s Internet, bringing financial damage to companies and annoying individual users. Spam emails are invading users without their consent and filling their mail boxes. They consume more network capacity as well as time in checking and deleting spam mails. The vast majority of Internet users are outspoken in their disdain for spam, although enough of them respond to commercial offers that spam remains a viable source of income to spammers. While most of the users want to do right think to avoid and get rid of spam, they need clear and simple guidelines on how to behave. In spite of all the measures taken to eliminate spam, they are not yet eradicated. Also when the counter measures are over sensitive, even legitimate emails will be eliminated. Among the approaches developed to stop spam, filtering is the one of the most important technique. Many researches in spam filtering have been centered on the more sophisticated classifier-related issues. In recent days, Machine learning for spam classification is an important research issue. The effectiveness of the proposed work is explores and identifies the use of different learning algorithms for classifying spam messages from e-mail. A comparative analysis among the algorithms has also been presented.

AI techniques in geomagnetic storm forecasting

Science.gov (United States)

Lundstedt, Henrik

This review deals with how geomagnetic storms can be predicted with the use of Artificial Intelligence (AI) techniques. Today many different Al techniques have been developed, such as symbolic systems (expert and fuzzy systems) and connectionism systems (neural networks). Even integrations of AI techniques exist, so called Intelligent Hybrid Systems (IHS). These systems are capable of learning the mathematical functions underlying the operation of non-linear dynamic systems and also to explain the knowledge they have learned. Very few such powerful systems exist at present. Two such examples are the Magnetospheric Specification Forecast Model of Rice University and the Lund Space Weather Model of Lund University. Various attempts to predict geomagnetic storms on long to short-term are reviewed in this article. Predictions of a month to days ahead most often use solar data as input. The first SOHO data are now available. Due to the high temporal and spatial resolution new solar physics have been revealed. These SOHO data might lead to a breakthrough in these predictions. Predictions hours ahead and shorter rely on real-time solar wind data. WIND gives us real-time data for only part of the day. However, with the launch of the ACE spacecraft in 1997, real-time data during 24 hours will be available. That might lead to the second breakthrough for predictions of geomagnetic storms.

Fostering students’ thinking skill and social attitude through STAD cooperative learning technique on tenth grade students of chemistry class

Science.gov (United States)

Kriswintari, D.; Yuanita, L.; Widodo, W.

2018-04-01

The aim of this study was to develop chemistry learning package using Student Teams Achievement Division (STAD) cooperative learning technique to foster students’ thinking skills and social attitudes. The chemistry learning package consisting of lesson plan, handout, students’ worksheet, thinking skill test, and observation sheet of social attitude was developed using the Dick and Carey model. Research subject of this study was chemistry learning package using STAD which was tried out on tenth grade students of SMA Trimurti Surabaya. The tryout was conducted using the one-group pre-test post-test design. Data was collected through observation, test, and questionnaire. The obtained data were analyzed using descriptive qualitative analysis. The findings of this study revealed that the developed chemistry learning package using STAD cooperative learning technique was categorized valid, practice and effective to be implemented in the classroom to foster students’ thinking skill and social attitude.

There's a Call for You. Whenever the Phone Rings. The Helping Hand Series.

Science.gov (United States)

Nash, Claire

This booklet is intended to assist employees in the hotel and catering industry in learning to make effective use of the telephone in their jobs. The first two sections review some unpleasant experiences that a person can have when calling another organization or when receiving a business call from someone. The importance of the impression created…

Quality in e-learning

DEFF Research Database (Denmark)

Masoumi, Davoud; Lindstrom, Berner

2012-01-01

With the growing demand for e-learning along with striving for excellence associated with globalization, there are worldwide calls for enhancing and assuring quality in e-learning, specifically in the context of the developing countries. Such calls for quality enhancement, accountability, added v...

Professionals calling in lifelong learning centers

Directory of Open Access Journals (Sweden)

Victor Manuel Monteiro Seco

2013-06-01

Full Text Available Purpose: This study aims to understand how the way people see their work and the authentizotic character of their organizational climate contribute to the building of a Great Place to Work. Design/methodology/approach: This paper presents the results of a quantitative investigation that correlate the perceptions of organizational climate and the work orientations of professionals with different occupations on Portuguese lifelong education centers. Findings: The study indicates that all the core elements of an authentizotic organization contribute to explain what people potentially expect from their companies:  adequate  material  conditions  plus  a  meaningful contribution. Practical implications: The study has implications in the future for National Qualification Agency directors, education politicians and human resource managers who are responsible for providing good expectations within a healthy context of talent retention. Originality/value: The novel contribution of this paper is the finding that employee’s work orientations and authentizotic climate are related to each other in a Lifelong learning Center in the public education sector.

Deep Learning with Dynamic Computation Graphs

OpenAIRE

Looks, Moshe; Herreshoff, Marcello; Hutchins, DeLesley; Norvig, Peter

2017-01-01

Neural networks that compute over graph structures are a natural fit for problems in a variety of domains, including natural language (parse trees) and cheminformatics (molecular graphs). However, since the computation graph has a different shape and size for every input, such networks do not directly support batched training or inference. They are also difficult to implement in popular deep learning libraries, which are based on static data-flow graphs. We introduce a technique called dynami...

Acquiring organizational learning norms: a contingency approach for understanding deutero learning

NARCIS (Netherlands)

Wijnhoven, Alphonsus B.J.M.

2001-01-01

'The Learning Organization' is a configuration of learning norms (called a learning prototype here), which is seldom related to varying levels of learning needs. This article assumes that organizational environmental complexity and dynamics define four learning needs levels. Consequently, four

Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers

OpenAIRE

Rosenberg, Ishai; Shabtai, Asaf; Rokach, Lior; Elovici, Yuval

2017-01-01

In this paper, we present a black-box attack against API call based machine learning malware classifiers, focusing on generating adversarial sequences combining API calls and static features (e.g., printable strings) that will be misclassified by the classifier without affecting the malware functionality. We show that this attack is effective against many classifiers due to the transferability principle between RNN variants, feed forward DNNs, and traditional machine learning classifiers such...

Revitalizing pathology laboratories in a gastrointestinal pathophysiology course using multimedia and team-based learning techniques.

Science.gov (United States)

Carbo, Alexander R; Blanco, Paola G; Graeme-Cooke, Fiona; Misdraji, Joseph; Kappler, Steven; Shaffer, Kitt; Goldsmith, Jeffrey D; Berzin, Tyler; Leffler, Daniel; Najarian, Robert; Sepe, Paul; Kaplan, Jennifer; Pitman, Martha; Goldman, Harvey; Pelletier, Stephen; Hayward, Jane N; Shields, Helen M

2012-05-15

In 2008, we changed the gastrointestinal pathology laboratories in a gastrointestinal pathophysiology course to a more interactive format using modified team-based learning techniques and multimedia presentations. The results were remarkably positive and can be used as a model for pathology laboratory improvement in any organ system. Over a two-year period, engaging and interactive pathology laboratories were designed. The initial restructuring of the laboratories included new case material, Digital Atlas of Video Education Project videos, animations and overlays. Subsequent changes included USMLE board-style quizzes at the beginning of each laboratory, with individual readiness assessment testing and group readiness assessment testing, incorporation of a clinician as a co-teacher and role playing for the student groups. Student responses for pathology laboratory contribution to learning improved significantly compared to baseline. Increased voluntary attendance at pathology laboratories was observed. Spontaneous student comments noted the positive impact of the laboratories on their learning. Pathology laboratory innovations, including modified team-based learning techniques with individual and group self-assessment quizzes, multimedia presentations, and paired teaching by a pathologist and clinical gastroenterologist led to improvement in student perceptions of pathology laboratory contributions to their learning and better pathology faculty evaluations. These changes can be universally applied to other pathology laboratories to improve student satisfaction. Copyright © 2012 Elsevier GmbH. All rights reserved.

Active Learning to Improve Fifth Grade Mathematics Achievement in Banten

Directory of Open Access Journals (Sweden)

Andri Suherman

2011-12-01

Full Text Available Teaching for active learning is a pedagogical technique that has been actively promoted in Indonesian education through government reform efforts and international development assistance projects for decades. Recently, elementary schools in Banten province received training in active learning instructional strategies from the USAID-funded project, Decentralized Basic Education 2. Post-training evaluations conducted by lecturers from the University of Sultan Ageng Tirtayasa (UNTIRTA: Universitas Sultan Ageng Tirtayasa suggested that teachers were successfully employing active learning strategies in some subjects, but not mathematics. In order to understand the difficulties teachers were having in teaching for active learning in mathematics, and to assist them in using active learning strategies, a team of lecturers from UNTIRTA designed and carried out an action research project to train teachers in an elementary school in the city of Cilegon to use a technique called Magic Fingers in teaching Grade 5 multiplication. During the course of the project the research team discovered that teachers were having problems transferring knowledge gained from training in one context and subject to other school subjects and contexts. Key Words: Mathematics, Teaching for Active Learning, Indonesia, Banten

CLIL and CALL for a teacher’s expertise: an international training experience

Directory of Open Access Journals (Sweden)

Letizia Cinganotto

2016-07-01

Full Text Available The paper deals with the link between Content and Language Integrated Learning (CLIL and Computer Assisted Language Learning (CALL, that is the use of ICT to enhance language teaching-learning and the teaching of subject content in a foreign language. Starting from this background, the paper describes an online training initiative promoted by the authors within an international community of peers, made up of teachers, trainers and educators from all over the world, named “Techno-CLIL for EVO 2016”. The initiative was aimed at supporting and guiding participants to discover and experiment digital tools for CLIL lessons, offering the opportunity to share ideas, materials, good practices in an international perspective. Particular attention is devoted to the personal and professional enrichment and growth this training pathway may have helped the 5.000 participants to achieve.CLIL e CALL nell’expertise del docente: un’esperienza di formazione internazionaleIl contributo focalizza l’attenzione sulla correlazione tra Content and Language Integrated Learning (CLIL e Computer Assisted Language Learning (CALL, cioè l’uso delle tecnologie per una maggiore efficacia dell’insegnamento-apprendimento delle lingue o di contenuti disciplinari veicolati in lingua straniera. Partendo da questo background, il contributo descrive una iniziativa di formazione online in lingua inglese promossa dalle autrici all’interno di un contesto internazionale, costituito da una comunità di pratica di docenti, formatori, educatori di tutto il mondo, denominata “Techno-CLIL for EVO 2016”. L’iniziativa mirava a sensibilizzare e guidare i partecipanti nella scoperta e sperimentazione della didattica CLIL in modalità digitale, offrendo l’opportunità di un confronto ed uno scambio di idee, materiali, buone pratiche in una prospettiva internazionale. Particolarmente significative le ricadute che questo percorso ha comportato per i circa 5.000 partecipanti

Translating and transforming (a) CALL for leadership for learning

DEFF Research Database (Denmark)

Weinreich, Elvi; Bjerg, Helle

2015-01-01

"The paper pursues the argument that the process of translation is not solely a linguistic exercise. It also implies methodological and conceptual questions related to the translation and as such transformation of general and theoretical research based models of leadership for learning...

IMPROVING THE STUDENTS‘ READING COMPREHENSION THROUGH KNOW-WANT-LEARN (KWL TECHNIQUE TO TEACH ANALYTICAL EXPOSITION ( Class Action Research

Directory of Open Access Journals (Sweden)

Meike Imelda Wachyu

2017-12-01

Full Text Available This study is aimed at finding out the impacts of the use of Know-Want-Learn technique in improving the reading comprehension to teach analytical exposition among eleventh grade students of SMA N 2 Indramayu in the academic year of 2017/2018. The study was action research in two research cycles. In the study, the researcher collaborated with the English teachers and the students. The data of this study were qualitative in nature supported by quantitative data. Qualitative data were obtained from the results of classroom observation and collaborators‘ discussion. Quantitative data were obtained from pre-test and post test results. The instruments for collecting the data were observation guides, interview guides, and the pre-test and posttest. The data were in the form of field notes, interview transcripts, and the scores of the students‘ pre-test and posttest. The results of the two cycles show that the use of Know-WantLearn technique is effective to improve the students‘ reading comprehension. It is supported by the qualitative data which show that (1 Know-Want-Learn technique can help the teacher to scaffold the students‘ comprehension of the text by focusing on the steps before, during, and after reading; (2 Know-Want-Learn technique can help the students to preview the text, assess what they have learned after reading, and motivate their interest in reading; (3 The kind of activities given such as preeteaching vocabulary, using skimming and scanning, using fix-up strategies, and guessing meaning can help the students to read the text efficiently.

An ELT's Solution to Combat Plagiarism: "Birth" of CALL.

Science.gov (United States)

Sabieh, Christine

One English-as-a Second-Language professor fought plagiarism using computer assisted language learning (CALL). She succeeded in getting half of her class to write documented research papers free of plagiarism. Although all of the students claimed to know how to avoid plagiarizing, 35 percent presented the work with minor traces of plagiarism. The…

Combining machine learning and matching techniques to improve causal inference in program evaluation.

Science.gov (United States)

Linden, Ariel; Yarnold, Paul R

2016-12-01

Program evaluations often utilize various matching approaches to emulate the randomization process for group assignment in experimental studies. Typically, the matching strategy is implemented, and then covariate balance is assessed before estimating treatment effects. This paper introduces a novel analytic framework utilizing a machine learning algorithm called optimal discriminant analysis (ODA) for assessing covariate balance and estimating treatment effects, once the matching strategy has been implemented. This framework holds several key advantages over the conventional approach: application to any variable metric and number of groups; insensitivity to skewed data or outliers; and use of accuracy measures applicable to all prognostic analyses. Moreover, ODA accepts analytic weights, thereby extending the methodology to any study design where weights are used for covariate adjustment or more precise (differential) outcome measurement. One-to-one matching on the propensity score was used as the matching strategy. Covariate balance was assessed using standardized difference in means (conventional approach) and measures of classification accuracy (ODA). Treatment effects were estimated using ordinary least squares regression and ODA. Using empirical data, ODA produced results highly consistent with those obtained via the conventional methodology for assessing covariate balance and estimating treatment effects. When ODA is combined with matching techniques within a treatment effects framework, the results are consistent with conventional approaches. However, given that it provides additional dimensions and robustness to the analysis versus what can currently be achieved using conventional approaches, ODA offers an appealing alternative. © 2016 John Wiley & Sons, Ltd.

[Motor capacities involved in the psychomotor skills of the cardiopulmonary resuscitation technique: recommendations for the teaching-learning process].

Science.gov (United States)

Miyadahira, A M

2001-12-01

It is a bibliographic study about the identification of the motor capacities involved in the psychomotor skills of the cardiopulmonary resuscitation (CPR) which aims to obtain subsidies to the planning of the teaching-learning process of this skill. It was found that: the motor capacities involved in the psychomotor skill of the CPR technique are predominantly cognitive and motor, involving 9 perceptive-motor capacities and 8 physical proficiency capacities. The CPR technique is a psychomotor skill classified as open, done in series and categorized as a thin and global skill and the teaching-learning process of the CPR technique has an elevated degree of complexity.

The Significant Incidents and Close Calls in Human Space Flight Chart: Lessons Learned Gone Viral

Science.gov (United States)

Wood, Bill; Pate, Dennis; Thelen, David

2010-01-01

This presentation will explore the surprising history and events that transformed a mundane spreadsheet of historical spaceflight incidents into a popular and widely distributed visual compendium of lessons learned. The Significant Incidents and Close Calls in Human Space Flight Chart (a.k.a. The Significant Incidents Chart) is a popular and visually captivating reference product that has arisen from the work of the Johnson Space Center (JSC) Safety and Mission Assurance (S&MA) Flight Safety Office (FSO). It began as an internal tool intended to increase our team s awareness of historical and modern space flight incidents. Today, the chart is widely recognized across the agency as a reference tool. It appears in several training and education programs. It is used in familiarization training in the JSC Building 9 Mockup Facility and is seen by hundreds of center visitors each week. The chart visually summarizes injuries, fatalities, and close calls sustained during the continuing development of human space flight. The poster-sized chart displays over 100 total events that have direct connections to human space flight endeavors. The chart is updated periodically. The update process itself has become a collaborative effort. Many people, spanning multiple NASA organizations, have provided suggestions for additional entries. The FSO maintains a growing list of subscribers who have requested to receive updates. The presenters will discuss the origins and motivations behind the significant incidents chart. A review of the inclusion criteria used to select events will be offered. We will address how the chart is used today by S&MA and offer a vision of how it might be used by other organizations now and in the future. Particular emphasis will be placed on features of the chart that have met with broad acceptance and have helped spread awareness of the most important lessons in human spaceflight.

Markerless gating for lung cancer radiotherapy based on machine learning techniques

International Nuclear Information System (INIS)

Lin Tong; Li Ruijiang; Tang Xiaoli; Jiang, Steve B; Dy, Jennifer G

2009-01-01

In lung cancer radiotherapy, radiation to a mobile target can be delivered by respiratory gating, for which we need to know whether the target is inside or outside a predefined gating window at any time point during the treatment. This can be achieved by tracking one or more fiducial markers implanted inside or near the target, either fluoroscopically or electromagnetically. However, the clinical implementation of marker tracking is limited for lung cancer radiotherapy mainly due to the risk of pneumothorax. Therefore, gating without implanted fiducial markers is a promising clinical direction. We have developed several template-matching methods for fluoroscopic marker-less gating. Recently, we have modeled the gating problem as a binary pattern classification problem, in which principal component analysis (PCA) and support vector machine (SVM) are combined to perform the classification task. Following the same framework, we investigated different combinations of dimensionality reduction techniques (PCA and four nonlinear manifold learning methods) and two machine learning classification methods (artificial neural networks-ANN and SVM). Performance was evaluated on ten fluoroscopic image sequences of nine lung cancer patients. We found that among all combinations of dimensionality reduction techniques and classification methods, PCA combined with either ANN or SVM achieved a better performance than the other nonlinear manifold learning methods. ANN when combined with PCA achieves a better performance than SVM in terms of classification accuracy and recall rate, although the target coverage is similar for the two classification methods. Furthermore, the running time for both ANN and SVM with PCA is within tolerance for real-time applications. Overall, ANN combined with PCA is a better candidate than other combinations we investigated in this work for real-time gated radiotherapy.

Learning theories and skills in online second language teaching and learning

DEFF Research Database (Denmark)

Petersen, Karen Bjerg

2014-01-01

For decades foreign and second language teachers have taken advantage of the technology development and ensuing possibilities to use e-learning facilities for language training. Since the 1980s, the use of computer assisted language learning (CALL), Internet, web 2.0, and various kinds of e-learning...... in Denmark with special attention towards the development of web-based materials for Danish pronunciation. This paper sets out to introduce differences between the international and Danish use of web-based language learning and teaching. Finally, dilemmas and challenges for the use of CALL, IT, and web 2.0 in...

Classification of Phishing Email Using Random Forest Machine Learning Technique

Directory of Open Access Journals (Sweden)

Andronicus A. Akinyelu

2014-01-01

Full Text Available Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved phishing email classifier with better prediction accuracy and fewer numbers of features. From a dataset consisting of 2000 phishing and ham emails, a set of prominent phishing email features (identified from the literature were extracted and used by the machine learning algorithm with a resulting classification accuracy of 99.7% and low false negative (FN and false positive (FP rates.

Boosting compound-protein interaction prediction by deep learning.

Science.gov (United States)

Tian, Kai; Shao, Mingyu; Wang, Yang; Guan, Jihong; Zhou, Shuigeng

2016-11-01

The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in some applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets. Copyright © 2016 Elsevier Inc. All rights reserved.

Memorization techniques: Using mnemonics to learn fifth grade science terms

Science.gov (United States)

Garcia, Juan O.

The purpose of this study was to determine whether mnemonic instruction could assist students in learning fifth-grade science terminology more effectively than traditional-study methods of recall currently in practice The task was to examine if fifth-grade students were able to learn a mnemonic and then use it to understand science vocabulary; subsequently, to determine if students were able to remember the science terms after a period of time. The problem is that in general, elementary school students are not being successful in science achievement at the fifth grade level. In view of this problem, if science performance is increased at the elementary level, then it is likely that students will be successful when tested at the 8th and 10th grade in science with the Texas Assessment of Knowledge and Skills (TAKS) in the future. Two research questions were posited: (1) Is there a difference in recall achievement when a mnemonic such as method of loci, pegword method, or keyword method is used in learning fifth-grade science vocabulary as compared to the traditional-study method? (2) If using a mnemonic in learning fifth-grade science vocabulary was effective on recall achievement, would this achievement be maintained over a span of time? The need for this study was to assist students in learning science terms and concepts for state accountability purposes. The first assumption was that memorization techniques are not commonly applied in fifth-grade science classes in elementary schools. A second assumption was that mnemonic devices could be used successfully in learning science terms and increase long term retention. The first limitation was that the study was conducted on one campus in one school district in South Texas which limited the generalization of the study. The second limitation was that it included random assigned intact groups as opposed to random student assignment to fifth-grade classroom groups.

Comparison of two different techniques of cooperative learning approach: Undergraduates' conceptual understanding in the context of hormone biochemistry.

Science.gov (United States)

Mutlu, Ayfer

2018-03-01

The purpose of the research was to compare the effects of two different techniques of the cooperative learning approach, namely Team-Game Tournament and Jigsaw, on undergraduates' conceptual understanding in a Hormone Biochemistry course. Undergraduates were randomly assigned to Group 1 (N = 23) and Group 2 (N = 29). Instructions were accomplished using Team-Game Tournament in Group 1 and Jigsaw in Group 2. Before the instructions, all groups were informed about cooperative learning and techniques, their responsibilities in the learning process and accessing of resources. Instructions were conducted under the guidance of the researcher for nine weeks and the Hormone Concept Test developed by the researcher was used before and after the instructions for data collection. According to the results, while both techniques improved students' understanding, Jigsaw was more effective than Team-Game Tournament. © 2017 by The International Union of Biochemistry and Molecular Biology, 46(2):114-120, 2018. © 2017 The International Union of Biochemistry and Molecular Biology.

Application of Machine Learning Techniques in Aquaculture

OpenAIRE

Rahman, Akhlaqur; Tasnim, Sumaira

2014-01-01

In this paper we present applications of different machine learning algorithms in aquaculture. Machine learning algorithms learn models from historical data. In aquaculture historical data are obtained from farm practices, yields, and environmental data sources. Associations between these different variables can be obtained by applying machine learning algorithms to historical data. In this paper we present applications of different machine learning algorithms in aquaculture applications.

Frogs Call at a Higher Pitch in Traffic Noise

Directory of Open Access Journals (Sweden)

Kirsten M. Parris

2009-06-01

Full Text Available Male frogs call to attract females for mating and to defend territories from rival males. Female frogs of some species prefer lower-pitched calls, which indicate larger, more experienced males. Acoustic interference occurs when background noise reduces the active distance or the distance over which an acoustic signal can be detected. Birds are known to call at a higher pitch or frequency in urban noise, decreasing acoustic interference from low-frequency noise. Using Bayesian linear regression, we investigated the effect of traffic noise on the pitch of advertisement calls in two species of frogs, the southern brown tree frog (Litoria ewingii and the common eastern froglet (Crinia signifera. We found evidence that L. ewingii calls at a higher pitch in traffic noise, with an average increase in dominant frequency of 4.1 Hz/dB of traffic noise, and a total effect size of 123 Hz. This frequency shift is smaller than that observed in birds, but is still large enough to be detected by conspecific frogs and confer a significant benefit to the caller. Mathematical modelling predicted a 24% increase in the active distance of a L. ewingii call in traffic noise with a frequency shift of this size. Crinia signifera may also call at a higher pitch in traffic noise, but more data are required to be confident of this effect. Because frog calls are innate rather than learned, the frequency shift demonstrated by L. ewingii may represent an evolutionary adaptation to noisy conditions. The phenomenon of frogs calling at a higher pitch in traffic noise could therefore constitute an intriguing trade-off between audibility and attractiveness to potential mates.

Big data - modelling of midges in Europa using machine learning techniques and satellite imagery

DEFF Research Database (Denmark)

Cuellar, Ana Carolina; Kjær, Lene Jung; Skovgaard, Henrik

2017-01-01

coordinates of each trap, start and end dates of trapping. We used 120 environmental predictor variables together with Random Forest machine learning algorithms to predict the overall species distribution (probability of occurrence) and monthly abundance in Europe. We generated maps for every month...... and the Obsoletus group, although abundance was generally higher for a longer period of time for C. imicula than for the Obsoletus group. Using machine learning techniques, we were able to model the spatial distribution in Europe for C. imicola and the Obsoletus group in terms of abundance and suitability...

The Internet, Language Learning, and International Dialogue: Constructing Online Foreign Language Learning Websites

Science.gov (United States)

Kartal, Erdogan; Uzun, Levent

2010-01-01

In the present study we call attention to the close connection between languages and globalization, and we also emphasize the importance of the Internet and online websites in foreign language teaching and learning as unavoidable elements of computer assisted language learning (CALL). We prepared a checklist by which we investigated 28 foreign…

Introducing E-Learning in a Norwegian Service Company with Participatory Design and Evolutionary Prototyping Techniques

OpenAIRE

Mørch , Anders I.; Engen , Bård Ketil; Hansen Åsand , Hege-René; Brynhildsen , Camilla; Tødenes , Ida

2004-01-01

Over a 2-year period, we have participated in the introduction of e-learning in a Norwegian service company, a gas station division of an oil company. This company has an advanced computer network infrastructure for communication and information sharing, but the primary task of the employees is serving customers. We identify some challenges to introducing e-learning in this kind of environment. A primary emphasis has been on using participatory design techniques during the planning stages and...

A framework for detection of malicious software in Android handheld systems using machine learning techniques

OpenAIRE

Torregrosa García, Blas

2015-01-01

The present study aims at designing and developing new approaches to detect malicious applications in Android-based devices. More precisely, MaLDroide (Machine Learning-based Detector for Android malware), a framework for detection of Android malware based on machine learning techniques, is introduced here. It is devised to identify malicious applications. Este trabajo tiene como objetivo el diseño y el desarrollo de nuevas formas de detección de aplicaciones maliciosas en los dispositivos...

Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

Science.gov (United States)

Lee, Hanbong; Malik, Waqar; Jung, Yoon C.

2016-01-01

Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

Addressee Errors in ATC Communications: The Call Sign Problem

Science.gov (United States)

Monan, W. P.

1983-01-01

Communication errors involving aircraft call signs were portrayed in reports of 462 hazardous incidents voluntarily submitted to the ASRS during an approximate four-year period. These errors resulted in confusion, disorder, and uncoordinated traffic conditions and produced the following types of operational anomalies: altitude deviations, wrong-way headings, aborted takeoffs, go arounds, runway incursions, missed crossing altitude restrictions, descents toward high terrain, and traffic conflicts in flight and on the ground. Analysis of the report set resulted in identification of five categories of errors involving call signs: (1) faulty radio usage techniques, (2) call sign loss or smearing due to frequency congestion, (3) confusion resulting from similar sounding call signs, (4) airmen misses of call signs leading to failures to acknowledge or readback, and (5) controller failures regarding confirmation of acknowledgements or readbacks. These error categories are described in detail and several associated hazard mitigating measures that might be aken are considered.

The Application of the Complex Field of Geodesy to an Entrance Level College Course using Cognitive Learning Techniques.

Science.gov (United States)

Menard, J.; Beall King, A.; Larson, P. B.

2017-12-01

The study of the shape of the Earth is called geodesy. It is a complex and rich field, encompassing GPS, the development of satellites to measure Earth, and the many applications of these measurements to better understand our planet. What is the best way to explain complex concepts to an entry-level college student, such as geodesy or gravitation? What is the most efficient way to peek a student's interest in an abstract field? Two people are walking side by side on a crowded street. Do they talk? Do they look at each other? Do they laugh together? Do they touch? Even though the bond between these two people cannot necessarily be physically seen, it is possible, by looking at their behavior towards each other, to determine whether or not they know each other. If they do, they are attracted to one another, walking together in the same direction, exchanging ideas or laughs. The Moon attracts the Earth's oceans, forming tides. The Earth attracts the Moon into staying in orbit. They are attracted to each other by the invisible yet quantifiable force of gravitation. In order to ensure that first year college students understand the concept and applications of geodesy, and find interest in the field, several teaching and learning techniques must be used. These techniques are compared to one another in terms of efficiency both by comparing the students' success through quizzes and discussions, and by comparing the students' enjoyment of and interest in the class through evaluations at the beginning and end of each class in order to assess how much material was learned, understood, and retained. This study is conducted via a short course with volunteer students. The course is a combination of lecture, discussion, experiments, and field work. Quizzes are used to evaluate not the students, but their improvement as a result of the efficacy of the teaching method. In class group and one on one discussions are used as the main part of the final grade.

Learning Analytics: The next frontier for computer assisted language learning in big data age

Directory of Open Access Journals (Sweden)

Yu Qinglan

2015-01-01

Full Text Available Learning analytics (LA has been applied to various learning environments, though it is quite new in the field of computer assisted language learning (CALL. This article attempts to examine the application of learning analytics in the upcoming big data age. It starts with an introduction and application of learning analytics in other fields, followed by a retrospective review of historical interaction between learning and media in CALL, and a penetrating analysis on why people would go to learning analytics to increase the efficiency of foreign language education. As approved in previous research, new technology, including big data mining and analysis, would inevitably enhance the learning of foreign languages. Potential changes that learning analytics would bring to Chinese foreign language education and researches are also presented in the article.

Application of Cognitive and Socio-Cultural Theories in CALL

Directory of Open Access Journals (Sweden)

Mustafa Akın Güngör

2011-06-01

Full Text Available Since it is quite difficult in traditional learning atmospheres for the learners to be exposed to the target language adequately in foreign language acquisition, CALL in which virtual environment is designed in more appropriate way has given rise. Two main paradigms, cognitive model and socio-cultural theory, have also been adopted in CALL. Moreover, rather than applying one theory, combination of these paradigms is unavoidable. However, application of this combination is challenging in practice, as these two theories have different principles. Furthermore, when it comes to online education, it turns into more challenging process. In this poster this combination is presented with the help of sample applications from Gazi University.

A review on machine learning principles for multi-view biological data integration.

Science.gov (United States)

Li, Yifeng; Wu, Fang-Xiang; Ngom, Alioune

2018-03-01

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

Control-flow analysis of function calls and returns by abstract interpretation

DEFF Research Database (Denmark)

Midtgaard, Jan; Jensen, Thomas P.

2012-01-01

Abstract interpretation techniques are used to derive a control-flow analysis for a simple higher-order functional language. The analysis approximates the interprocedural control-flow of both function calls and returns in the presence of first-class functions and tail-call optimization. In additi...... a rational reconstruction of a constraint-based CFA from abstract interpretation principles....

A new learning paradigm: learning using privileged information.

Science.gov (United States)

Vapnik, Vladimir; Vashist, Akshay

2009-01-01

In the Afterword to the second edition of the book "Estimation of Dependences Based on Empirical Data" by V. Vapnik, an advanced learning paradigm called Learning Using Hidden Information (LUHI) was introduced. This Afterword also suggested an extension of the SVM method (the so called SVM(gamma)+ method) to implement algorithms which address the LUHI paradigm (Vapnik, 1982-2006, Sections 2.4.2 and 2.5.3 of the Afterword). See also (Vapnik, Vashist, & Pavlovitch, 2008, 2009) for further development of the algorithms. In contrast to the existing machine learning paradigm where a teacher does not play an important role, the advanced learning paradigm considers some elements of human teaching. In the new paradigm along with examples, a teacher can provide students with hidden information that exists in explanations, comments, comparisons, and so on. This paper discusses details of the new paradigm and corresponding algorithms, introduces some new algorithms, considers several specific forms of privileged information, demonstrates superiority of the new learning paradigm over the classical learning paradigm when solving practical problems, and discusses general questions related to the new ideas.

An Island Called Cuba

Directory of Open Access Journals (Sweden)

Jean Stubbs

2011-06-01

Full Text Available Review of: An Island Called Home: Returning to Jewish Cuba. Ruth Behar, photographs by Humberto Mayol. New Brunswick NJ: Rutgers University Press, 2007. xiii + 297 pp. (Cloth US$ 29.95 Fidel Castro: My Life: A Spoken Autobiography. Fidel Castro & Ignacio Ramonet. New York: Scribner/Simon & Schuster, 2008. vii + 724 pp. (Paper US$ 22.00, e-book US$ 14.99 Cuba: What Everyone Needs to Know. Julia E. Sweig. New York: Oxford University Press, 2009. xiv + 279 pp. (Paper US$ 16.95 [First paragraph] These three ostensibly very different books tell a compelling story of each author’s approach, as much as the subject matter itself. Fidel Castro: My Life: A Spoken Autobiography is based on a series of long interviews granted by the then-president of Cuba, Fidel Castro, to Spanish-Franco journalist Ignacio Ramonet. Cuba: What Everyone Needs to Know, by U.S. political analyst Julia Sweig, is one of a set country series, and, like Ramonet’s, presented in question/answer format. An Island Called Home: Returning to Jewish Cuba, with a narrative by Cuban-American anthropologist Ruth Behar and photographs by Cuban photographer Humberto Mayol, is a retrospective/introspective account of the Jewish presence in Cuba. While from Ramonet and Sweig we learn much about the revolutionary project, Behar and Mayol convey the lived experience of the small Jewish community against that backdrop.

Development and Experimental Evaluation of Machine-Learning Techniques for an Intelligent Hairy Scalp Detection System

Directory of Open Access Journals (Sweden)

Wei-Chien Wang

2018-05-01

Full Text Available Deep learning has become the most popular research subject in the fields of artificial intelligence (AI and machine learning. In October 2013, MIT Technology Review commented that deep learning was a breakthrough technology. Deep learning has made progress in voice and image recognition, image classification, and natural language processing. Prior to deep learning, decision tree, linear discriminant analysis (LDA, support vector machines (SVM, k-nearest neighbors algorithm (K-NN, and ensemble learning were popular in solving classification problems. In this paper, we applied the previously mentioned and deep learning techniques to hairy scalp images. Hairy scalp problems are usually diagnosed by non-professionals in hair salons, and people with such problems may be advised by these non-professionals. Additionally, several common scalp problems are similar; therefore, non-experts may provide incorrect diagnoses. Hence, scalp problems have worsened. In this work, we implemented and compared the deep-learning method, the ImageNet-VGG-f model Bag of Words (BOW, with machine-learning classifiers, and histogram of oriented gradients (HOG/pyramid histogram of oriented gradients (PHOG with machine-learning classifiers. The tools from the classification learner apps were used for hairy scalp image classification. The results indicated that deep learning can achieve an accuracy of 89.77% when the learning rate is 1 × 10−4, and this accuracy is far higher than those achieved by BOW with SVM (80.50% and PHOG with SVM (53.0%.

LVQ-SMOTE - Learning Vector Quantization based Synthetic Minority Over-sampling Technique for biomedical data.

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Nakamura, Munehiro; Kajiwara, Yusuke; Otsuka, Atsushi; Kimura, Haruhiko

2013-10-02

Over-sampling methods based on Synthetic Minority Over-sampling Technique (SMOTE) have been proposed for classification problems of imbalanced biomedical data. However, the existing over-sampling methods achieve slightly better or sometimes worse result than the simplest SMOTE. In order to improve the effectiveness of SMOTE, this paper presents a novel over-sampling method using codebooks obtained by the learning vector quantization. In general, even when an existing SMOTE applied to a biomedical dataset, its empty feature space is still so huge that most classification algorithms would not perform well on estimating borderlines between classes. To tackle this problem, our over-sampling method generates synthetic samples which occupy more feature space than the other SMOTE algorithms. Briefly saying, our over-sampling method enables to generate useful synthetic samples by referring to actual samples taken from real-world datasets. Experiments on eight real-world imbalanced datasets demonstrate that our proposed over-sampling method performs better than the simplest SMOTE on four of five standard classification algorithms. Moreover, it is seen that the performance of our method increases if the latest SMOTE called MWMOTE is used in our algorithm. Experiments on datasets for β-turn types prediction show some important patterns that have not been seen in previous analyses. The proposed over-sampling method generates useful synthetic samples for the classification of imbalanced biomedical data. Besides, the proposed over-sampling method is basically compatible with basic classification algorithms and the existing over-sampling methods.

EFL Teachers' Knowledge of the Use and Development of Computer-Assisted Language Learning (CALL) Materials

Science.gov (United States)

Dashtestani, Reza

2014-01-01

Even though there are a plethora of CALL materials available to EFL teachers nowadays, very limited attention has been directed toward the issue that most EFL teachers are merely the consumers of CALL materials. The main challenge is to equip EFL teachers with the required CALL materials development skills to enable them to be contributors to CALL…

Algorithm Building and Learning Programming Languages Using a New Educational Paradigm

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Jain, Anshul K.; Singhal, Manik; Gupta, Manu Sheel

2011-08-01

This research paper presents a new concept of using a single tool to associate syntax of various programming languages, algorithms and basic coding techniques. A simple framework has been programmed in Python that helps students learn skills to develop algorithms, and implement them in various programming languages. The tool provides an innovative and a unified graphical user interface for development of multimedia objects, educational games and applications. It also aids collaborative learning amongst students and teachers through an integrated mechanism based on Remote Procedure Calls. The paper also elucidates an innovative method for code generation to enable students to learn the basics of programming languages using drag-n-drop methods for image objects.

Analysed potential of big data and supervised machine learning techniques in effectively forecasting travel times from fused data

Directory of Open Access Journals (Sweden)

Ivana Šemanjski

2015-12-01

Full Text Available Travel time forecasting is an interesting topic for many ITS services. Increased availability of data collection sensors increases the availability of the predictor variables but also highlights the high processing issues related to this big data availability. In this paper we aimed to analyse the potential of big data and supervised machine learning techniques in effectively forecasting travel times. For this purpose we used fused data from three data sources (Global Positioning System vehicles tracks, road network infrastructure data and meteorological data and four machine learning techniques (k-nearest neighbours, support vector machines, boosting trees and random forest. To evaluate the forecasting results we compared them in-between different road classes in the context of absolute values, measured in minutes, and the mean squared percentage error. For the road classes with the high average speed and long road segments, machine learning techniques forecasted travel times with small relative error, while for the road classes with the small average speeds and segment lengths this was a more demanding task. All three data sources were proven itself to have a high impact on the travel time forecast accuracy and the best results (taking into account all road classes were achieved for the k-nearest neighbours and random forest techniques.

Computer-aided classification of lung nodules on computed tomography images via deep learning technique

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Hua KL

2015-08-01

Full Text Available Kai-Lung Hua,1 Che-Hao Hsu,1 Shintami Chusnul Hidayati,1 Wen-Huang Cheng,2 Yu-Jen Chen3 1Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, 2Research Center for Information Technology Innovation, Academia Sinica, 3Department of Radiation Oncology, MacKay Memorial Hospital, Taipei, Taiwan Abstract: Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are present. The management of small lung nodules noted on computed tomography scan is controversial due to uncertain tumor characteristics. A conventional computer-aided diagnosis (CAD scheme requires several image processing and pattern recognition steps to accomplish a quantitative tumor differentiation result. In such an ad hoc image analysis pipeline, every step depends heavily on the performance of the previous step. Accordingly, tuning of classification performance in a conventional CAD scheme is very complicated and arduous. Deep learning techniques, on the other hand, have the intrinsic advantage of an automatic exploitation feature and tuning of performance in a seamless fashion. In this study, we attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques. Specifically, we introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Two baseline methods with feature computing steps were implemented for comparison. The experimental results suggest that deep learning methods could achieve better discriminative results and hold promise in the CAD application domain. Keywords: nodule classification, deep learning, deep belief network, convolutional neural network

Multiple-Choice Testing Using Immediate Feedback--Assessment Technique (IF AT®) Forms: Second-Chance Guessing vs. Second-Chance Learning?

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Merrel, Jeremy D.; Cirillo, Pier F.; Schwartz, Pauline M.; Webb, Jeffrey A.

2015-01-01

Multiple choice testing is a common but often ineffective method for evaluating learning. A newer approach, however, using Immediate Feedback Assessment Technique (IF AT®, Epstein Educational Enterprise, Inc.) forms, offers several advantages. In particular, a student learns immediately if his or her answer is correct and, in the case of an…

Classification of breast tumour using electrical impedance and machine learning techniques.

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Al Amin, Abdullah; Parvin, Shahnaj; Kadir, M A; Tahmid, Tasmia; Alam, S Kaisar; Siddique-e Rabbani, K

2014-06-01

When a breast lump is detected through palpation, mammography or ultrasonography, the final test for characterization of the tumour, whether it is malignant or benign, is biopsy. This is invasive and carries hazards associated with any surgical procedures. The present work was undertaken to study the feasibility for such characterization using non-invasive electrical impedance measurements and machine learning techniques. Because of changes in cell morphology of malignant and benign tumours, changes are expected in impedance at a fixed frequency, and versus frequency of measurement. Tetrapolar impedance measurement (TPIM) using four electrodes at the corners of a square region of sides 4 cm was used for zone localization. Data of impedance in two orthogonal directions, measured at 5 and 200 kHz from 19 subjects, and their respective slopes with frequency were subjected to machine learning procedures through the use of feature plots. These patients had single or multiple tumours of various types in one or both breasts, and four of them had malignant tumours, as diagnosed by core biopsy. Although size and depth of the tumours are expected to affect the measurements, this preliminary work ignored these effects. Selecting 12 features from the above measurements, feature plots were drawn for the 19 patients, which displayed considerable overlap between malignant and benign cases. However, based on observed qualitative trend of the measured values, when all the feature values were divided by respective ages, the two types of tumours separated out reasonably well. Using K-NN classification method the results obtained are, positive prediction value: 60%, negative prediction value: 93%, sensitivity: 75%, specificity: 87% and efficacy: 84%, which are very good for such a test on a small sample size. Study on a larger sample is expected to give confidence in this technique, and further improvement of the technique may have the ability to replace biopsy.

Classification of breast tumour using electrical impedance and machine learning techniques

International Nuclear Information System (INIS)

Amin, Abdullah Al; Parvin, Shahnaj; Kadir, M A; Tahmid, Tasmia; Alam, S Kaisar; Siddique-e Rabbani, K

2014-01-01

When a breast lump is detected through palpation, mammography or ultrasonography, the final test for characterization of the tumour, whether it is malignant or benign, is biopsy. This is invasive and carries hazards associated with any surgical procedures. The present work was undertaken to study the feasibility for such characterization using non-invasive electrical impedance measurements and machine learning techniques. Because of changes in cell morphology of malignant and benign tumours, changes are expected in impedance at a fixed frequency, and versus frequency of measurement. Tetrapolar impedance measurement (TPIM) using four electrodes at the corners of a square region of sides 4 cm was used for zone localization. Data of impedance in two orthogonal directions, measured at 5 and 200 kHz from 19 subjects, and their respective slopes with frequency were subjected to machine learning procedures through the use of feature plots. These patients had single or multiple tumours of various types in one or both breasts, and four of them had malignant tumours, as diagnosed by core biopsy. Although size and depth of the tumours are expected to affect the measurements, this preliminary work ignored these effects. Selecting 12 features from the above measurements, feature plots were drawn for the 19 patients, which displayed considerable overlap between malignant and benign cases. However, based on observed qualitative trend of the measured values, when all the feature values were divided by respective ages, the two types of tumours separated out reasonably well. Using K-NN classification method the results obtained are, positive prediction value: 60%, negative prediction value: 93%, sensitivity: 75%, specificity: 87% and efficacy: 84%, which are very good for such a test on a small sample size. Study on a larger sample is expected to give confidence in this technique, and further improvement of the technique may have the ability to replace biopsy. (paper)

Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays.

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Hiremath, Shivayogi V; Chen, Weidong; Wang, Wei; Foldes, Stephen; Yang, Ying; Tyler-Kabara, Elizabeth C; Collinger, Jennifer L; Boninger, Michael L

2015-01-01

A brain-computer interface (BCI) system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.

Co-Labeling for Multi-View Weakly Labeled Learning.

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Xu, Xinxing; Li, Wen; Xu, Dong; Tsang, Ivor W

2016-06-01

It is often expensive and time consuming to collect labeled training samples in many real-world applications. To reduce human effort on annotating training samples, many machine learning techniques (e.g., semi-supervised learning (SSL), multi-instance learning (MIL), etc.) have been studied to exploit weakly labeled training samples. Meanwhile, when the training data is represented with multiple types of features, many multi-view learning methods have shown that classifiers trained on different views can help each other to better utilize the unlabeled training samples for the SSL task. In this paper, we study a new learning problem called multi-view weakly labeled learning, in which we aim to develop a unified approach to learn robust classifiers by effectively utilizing different types of weakly labeled multi-view data from a broad range of tasks including SSL, MIL and relative outlier detection (ROD). We propose an effective approach called co-labeling to solve the multi-view weakly labeled learning problem. Specifically, we model the learning problem on each view as a weakly labeled learning problem, which aims to learn an optimal classifier from a set of pseudo-label vectors generated by using the classifiers trained from other views. Unlike traditional co-training approaches using a single pseudo-label vector for training each classifier, our co-labeling approach explores different strategies to utilize the predictions from different views, biases and iterations for generating the pseudo-label vectors, making our approach more robust for real-world applications. Moreover, to further improve the weakly labeled learning on each view, we also exploit the inherent group structure in the pseudo-label vectors generated from different strategies, which leads to a new multi-layer multiple kernel learning problem. Promising results for text-based image retrieval on the NUS-WIDE dataset as well as news classification and text categorization on several real-world multi

Evaluation of undergraduate clinical learning experiences in the subject of pediatric dentistry using critical incident technique.

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Vyawahare, S; Banda, N R; Choubey, S; Parvekar, P; Barodiya, A; Dutta, S

2013-01-01

In pediatric dentistry, the experiences of dental students may help dental educators better prepare graduates to treat the children. Research suggests that student's perceptions should be considered in any discussion of their education, but there has been no systematic examination of India's undergraduate dental students learning experiences. This qualitative investigation aimed to gather and analyze information about experiences in pediatric dentistry from the students' viewpoint using critical incident technique (CIT). The sample group for this investigation came from all 240 3rd and 4th year dental students from all the four dental colleges in Indore. Using CIT, participants were asked to describe at least one positive and one negative experience in detail. They described 308 positive and 359 negative experiences related to the pediatric dentistry clinic. Analysis of the data resulted in the identification of four key factors related to their experiences: 1) The instructor; 2) the patient; 3) the learning process; and 4) the learning environment. The CIT is a useful data collection and analysis technique that provides rich, useful data and has many potential uses in dental education.

Learning mediastinoscopy: the need for education, experience and modern techniques--interdependency of the applied technique and surgeon's training level.

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Walles, Thorsten; Friedel, Godehard; Stegherr, Tobias; Steger, Volker

2013-04-01

Mediastinoscopy represents the gold standard for invasive mediastinal staging. While learning and teaching the surgical technique are challenging due to the limited accessibility of the operation field, both benefited from the implementation of video-assisted techniques. However, it has not been established yet whether video-assisted mediastinoscopy improves the mediastinal staging in itself. Retrospective single-centre cohort analysis of 657 mediastinoscopies performed at a specialized tertiary care thoracic surgery unit from 1994 to 2006. The number of specimens obtained per procedure and per lymph node station (2, 4, 7, 8 for mediastinoscopy and 2-9 for open lymphadenectomy), the number of lymph node stations examined, sensitivity and negative predictive value with a focus on the technique employed (video-assisted vs standard technique) and the surgeon's experience were calculated. Overall sensitivity was 60%, accuracy was 90% and negative predictive value 88%. With the conventional technique, experience alone improved sensitivity from 49 to 57% and it was predominant at the paratracheal right region (from 62 to 82%). But with the video-assisted technique, experienced surgeons rose sensitivity from 57 to 79% in contrast to inexperienced surgeons who lowered sensitivity from 49 to 33%. We found significant differences concerning (i) the total number of specimens taken, (ii) the amount of lymph node stations examined, (iii) the number of specimens taken per lymph node station and (iv) true positive mediastinoscopies. The video-assisted technique can significantly improve the results of mediastinoscopy. A thorough education on the modern video-assisted technique is mandatory for thoracic surgeons until they can fully exhaust its potential.

Estimation of Alpine Skier Posture Using Machine Learning Techniques

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Bojan Nemec

2014-10-01

Full Text Available High precision Global Navigation Satellite System (GNSS measurements are becoming more and more popular in alpine skiing due to the relatively undemanding setup and excellent performance. However, GNSS provides only single-point measurements that are defined with the antenna placed typically behind the skier’s neck. A key issue is how to estimate other more relevant parameters of the skier’s body, like the center of mass (COM and ski trajectories. Previously, these parameters were estimated by modeling the skier’s body with an inverted-pendulum model that oversimplified the skier’s body. In this study, we propose two machine learning methods that overcome this shortcoming and estimate COM and skis trajectories based on a more faithful approximation of the skier’s body with nine degrees-of-freedom. The first method utilizes a well-established approach of artificial neural networks, while the second method is based on a state-of-the-art statistical generalization method. Both methods were evaluated using the reference measurements obtained on a typical giant slalom course and compared with the inverted-pendulum method. Our results outperform the results of commonly used inverted-pendulum methods and demonstrate the applicability of machine learning techniques in biomechanical measurements of alpine skiing.

Using machine learning techniques to differentiate acute coronary syndrome

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Sougand Setareh

2015-02-01

Full Text Available Backgroud: Acute coronary syndrome (ACS is an unstable and dynamic process that includes unstable angina, ST elevation myocardial infarction, and non-ST elevation myocardial infarction. Despite recent technological advances in early diognosis of ACS, differentiating between different types of coronary diseases in the early hours of admission is controversial. The present study was aimed to accurately differentiate between various coronary events, using machine learning techniques. Such methods, as a subset of artificial intelligence, include algorithms that allow computers to learn and play a major role in treatment decisions. Methods: 1902 patients diagnosed with ACS and admitted to hospital were selected according to Euro Heart Survey on ACS. Patients were classified based on decision tree J48. Bagging aggregation algorithms was implemented to increase the efficiency of algorithm. Results: The performance of classifiers was estimated and compared based on their accuracy computed from confusion matrix. The accuracy rates of decision tree and bagging algorithm were calculated to be 91.74% and 92.53%, respectively. Conclusion: The proposed methods used in this study proved to have the ability to identify various ACS. In addition, using matrix of confusion, an acceptable number of subjects with acute coronary syndrome were identified in each class.

Learning L2 German vocabulary through reading: the effect of three enhancement techniques compared

NARCIS (Netherlands)

Peters, E.; Hulstijn, J.H.; Sercu, L.; Lutjeharms, M.

2009-01-01

This study investigated three techniques designed to increase the chances that second language (L2) readers look up and learn unfamiliar words during and after reading an L2 text. Participants in the study, 137 college students in Belgium (L1 = Dutch, L2 = German), were randomly assigned to one of

Blended call center with idling times during the call service

NARCIS (Netherlands)

Legros, Benjamin; Jouini, Oualid; Koole, Ger

We consider a blended call center with calls arriving over time and an infinitely backlogged amount of outbound jobs. Inbound calls have a non-preemptive priority over outbound jobs. The inbound call service is characterized by three successive stages where the second one is a break; i.e., there is

Investigating Teachers’ and Students’ Beliefs and Assumptions about CALL Programme at Caledonian College of Engineering

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Holi Ibrahim Holi Ali

2012-01-01

Full Text Available This study is set to investigate students’ and teachers’ perceptions and assumptions about newly implemented CALL Programme at the School of Foundation Studies, Caledonian College of Engineering, Oman. Two versions of questionnaire were administered to 24 teachers and 90 students to collect their beliefs and assumption about CALL programame. The results shows that the great majority of the students report that CALL is very interesting, motivating and useful to them and they learn a lot form it. However, the number of CALL hours should be increased, lab should be equipped and arranged in user friendly way, assessment should be integrated into CALL, and smart boards, black boards should be incorporated into the programme.

Multidisciplinary Responses to the Sexual Victimization of Children: Use of Control Phone Calls.

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Canavan, J William; Borowski, Christine; Essex, Stacy; Perkowski, Stefan

2017-10-01

This descriptive study addresses the question of the value of one-party consent phone calls regarding the sexual victimization of children. The authors reviewed 4 years of experience with children between the ages of 3 and 18 years selected for the control phone calls after a forensic interview by the New York State Police forensic interviewer. The forensic interviewer identified appropriate cases for control phone calls considering New York State law, the child's capacity to make the call, the presence of another person to make the call and a supportive residence. The control phone call process has been extremely effective forensically. Offenders choose to avoid trial by taking a plea bargain thereby dramatically speeding up the criminal judicial and family court processes. An additional outcome of the control phone call is the alleged offender's own words saved the child from the trauma of testifying in court. The control phone call reduced the need for children to repeat their stories to various interviewers. A successful control phone call gives the child a sense of vindication. This technique is the only technique that preserves the actual communication pattern between the alleged victim and the alleged offender. This can be of great value to the mental health professionals working with both the child and the alleged offender. Cautions must be considered regarding potential serious adverse effects on the child. The multidisciplinary team members must work together in the control phone call. The descriptive nature of this study did not allow the authors adequate demographic data, a subject that should be addressed in future prospective study.

Can Learning Motivation Predict Learning Achievement? A Case Study of a Mobile Game-Based English Learning Approach

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Tsai, Chia-Hui; Cheng, Ching-Hsue; Yeh, Duen-Yian; Lin, Shih-Yun

2017-01-01

This study applied a quasi-experimental design to investigate the influence and predictive power of learner motivation for achievement, employing a mobile game-based English learning approach. A system called the Happy English Learning System, integrating learning material into a game-based context, was constructed and installed on mobile devices…

Blended Learning

OpenAIRE

Bauerová, Andrea

2013-01-01

This thesis is focused on a new approach of education called blended learning. The history and developement of Blended Learning is described in the first part. Then the methods and tools of Blended Learning are evaluated and compared to the traditional methods of education. At the final part an efficient developement of the educational programs is emphasized.

Gotta Go, Mom’s Calling: Dolphin (Tursiops truncatus Mothers Use Individually Distinctive Acoustic Signals To Call Their Calves

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Stan A. Kuczaj II

2015-02-01

Full Text Available Dolphin calves often wander away from their mothers, which can compromise their safety and survival. Mothers can retrieve their calves by actively pursuing them or by signaling their wandering calves to return. However, little is known about the retrieval techniques employed by mothers in specific calf recall contexts. We experimentally investigated maternal calf retrieval methods by assessing behavioral and acoustic strategies employed by three Atlantic bottlenose dolphin mothers to elicit their calf’s return in a controlled, non-threatening setting. Three mothers were asked to retrieve their calves on cue in this setting, and could do so however they chose. Mothers were much more likely to use energetically less costly acoustic signals than physical retrievals. Each mother produced individually distinctive calls that incorporated the mother’s signature whistle but often also involved additional whistles and clicks. The dolphin mothers’ use of individually distinctive calls to request a calf’s return is consistent with the notion that other dolphins can distinguish such calls and provides additional support for the notion that dolphin communication is flexible rather than fixed.

Edu-mining: A Machine Learning Approach

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Srimani, P. K.; Patil, Malini M.

2011-12-01

Mining Educational data is an emerging interdisciplinary research area that mainly deals with the development of methods to explore the data stored in educational institutions. The educational data is referred as Edu-DATA. Queries related to Edu-DATA are of practical interest as SQL approach is insufficient and needs to be focused in a different way. The paper aims at developing a technique called Edu-MINING which converts raw data coming from educational institutions using data mining techniques into useful information. The discovered knowledge will have a great impact on the educational research and practices. Edu-MINING explores Edu-DATA, discovers new knowledge and suggests useful methods to improve the quality of education with regard to teaching-learning process. This is illustrated through a case study.

Full-Physics Inverse Learning Machine for Satellite Remote Sensing Retrievals

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Loyola, D. G.

2017-12-01

The satellite remote sensing retrievals are usually ill-posed inverse problems that are typically solved by finding a state vector that minimizes the residual between simulated data and real measurements. The classical inversion methods are very time-consuming as they require iterative calls to complex radiative-transfer forward models to simulate radiances and Jacobians, and subsequent inversion of relatively large matrices. In this work we present a novel and extremely fast algorithm for solving inverse problems called full-physics inverse learning machine (FP-ILM). The FP-ILM algorithm consists of a training phase in which machine learning techniques are used to derive an inversion operator based on synthetic data generated using a radiative transfer model (which expresses the "full-physics" component) and the smart sampling technique, and an operational phase in which the inversion operator is applied to real measurements. FP-ILM has been successfully applied to the retrieval of the SO2 plume height during volcanic eruptions and to the retrieval of ozone profile shapes from UV/VIS satellite sensors. Furthermore, FP-ILM will be used for the near-real-time processing of the upcoming generation of European Sentinel sensors with their unprecedented spectral and spatial resolution and associated large increases in the amount of data.

Predicting genome-wide redundancy using machine learning

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Shasha Dennis E

2010-11-01

Full Text Available Abstract Background Gene duplication can lead to genetic redundancy, which masks the function of mutated genes in genetic analyses. Methods to increase sensitivity in identifying genetic redundancy can improve the efficiency of reverse genetics and lend insights into the evolutionary outcomes of gene duplication. Machine learning techniques are well suited to classifying gene family members into redundant and non-redundant gene pairs in model species where sufficient genetic and genomic data is available, such as Arabidopsis thaliana, the test case used here. Results Machine learning techniques that combine multiple attributes led to a dramatic improvement in predicting genetic redundancy over single trait classifiers alone, such as BLAST E-values or expression correlation. In withholding analysis, one of the methods used here, Support Vector Machines, was two-fold more precise than single attribute classifiers, reaching a level where the majority of redundant calls were correctly labeled. Using this higher confidence in identifying redundancy, machine learning predicts that about half of all genes in Arabidopsis showed the signature of predicted redundancy with at least one but typically less than three other family members. Interestingly, a large proportion of predicted redundant gene pairs were relatively old duplications (e.g., Ks > 1, suggesting that redundancy is stable over long evolutionary periods. Conclusions Machine learning predicts that most genes will have a functionally redundant paralog but will exhibit redundancy with relatively few genes within a family. The predictions and gene pair attributes for Arabidopsis provide a new resource for research in genetics and genome evolution. These techniques can now be applied to other organisms.

A Review of Multimedia Glosses and Their Effects on L2 Vocabulary Acquisition in CALL Literature

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Mohsen, Mohammed Ali; Balakumar, M.

2011-01-01

This article reviews the literature of multimedia glosses in computer assisted language learning (CALL) and their effects on L2 vocabulary acquisition during the past seventeen years. Several studies have touched on this area to examine the potential of multimedia in a CALL environment in aiding L2 vocabulary acquisition. In this review, the…

Leaders Learning Orientation and the HCM-turn in call centres

DEFF Research Database (Denmark)

Gnaur, Dorina

2013-01-01

as a significant leadership quality that promotes reflexivity in the ongoing processes of interpretation and meaning creation enhancing the human dimension in the production of service. Learning orientation will be related to high-commitment management (HCM) as a way to reconcile the logics of efficiency...

Reflexive Photography, Attitudes, Behavior, and CALL: ITAs Improving Spoken English Intelligibility

Science.gov (United States)

Wallace, Lara

2015-01-01

Research in the field of Computer-Assisted Language Learning (CALL) has frequently taken a top-down approach when investigating learners' attitudes and behavior, both in the course as well as for their personal use. Suggestions are given for use of technology, and future research (Beatty, 2010; Levy & Stockwell, 2006). One perspective that has…

The Short Readings Project: A CALL Reading Activity Utilizing Vocabulary Recycling

Science.gov (United States)

Johnson, Andrew; Heffernan, Neil

2006-01-01

In 2003 multimedia-based English Trailers (www.english-trailers.com) joined the vast array of web sites dedicated to language learning enabling students, either autonomously or in a CALL classroom, to study English via movie commercials. To assist students in comprehending 10 trailers found on the site, the authors created the Short Readings…

Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data

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Pathak, Jaideep; Lu, Zhixin; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

2017-12-01

We use recent advances in the machine learning area known as "reservoir computing" to formulate a method for model-free estimation from data of the Lyapunov exponents of a chaotic process. The technique uses a limited time series of measurements as input to a high-dimensional dynamical system called a "reservoir." After the reservoir's response to the data is recorded, linear regression is used to learn a large set of parameters, called the "output weights." The learned output weights are then used to form a modified autonomous reservoir designed to be capable of producing an arbitrarily long time series whose ergodic properties approximate those of the input signal. When successful, we say that the autonomous reservoir reproduces the attractor's "climate." Since the reservoir equations and output weights are known, we can compute the derivatives needed to determine the Lyapunov exponents of the autonomous reservoir, which we then use as estimates of the Lyapunov exponents for the original input generating system. We illustrate the effectiveness of our technique with two examples, the Lorenz system and the Kuramoto-Sivashinsky (KS) equation. In the case of the KS equation, we note that the high dimensional nature of the system and the large number of Lyapunov exponents yield a challenging test of our method, which we find the method successfully passes.

Learning to learn: self-managed learning

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Jesús Miranda Izquierdo

2006-09-01

Full Text Available Thi is article analyzes the potentialities and weaknesses that non directive Pedagogy presents, an example of the so called self managed pedagogy, whose postulates are good to analyze for the contributions that this position can make to the search of new ways of learning.

Calibration and statistical techniques for building an interactive screen for learning of alphabets by children

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Riby Abraham Boby

2017-05-01

Full Text Available This article focuses on the implementation details of a portable interactive device called Image-projective Desktop Varnamala Trainer. The device uses a projector to produce a virtual display on a flat surface. For enabling interaction, the information about a user’s hand movement is obtained from a single two-dimensional scanning laser range finder in contrast with a camera sensor used in many earlier applications. A generalized calibration process to obtain exact transformation from projected screen coordinate system to sensor coordinate system is proposed in this article and implemented for enabling interaction. This permits production of large interactive displays with minimal cost. Additionally, it makes the entire system portable, that is, display can be produced on any planar surface like floor, tabletop, and so on. The calibration and its performance have been evaluated by varying screen sizes and the number of points used for calibration. The device was successfully calibrated for different screens. A novel learning-based methodology for predicting a user’s behaviour was then realized to improve the system’s performance. This has been experimentally evaluated, and the overall accuracy of prediction was about 96%. An application was then designed for this set-up to improve the learning of alphabets by the children through an interactive audiovisual feedback system. It uses a game-based methodology to help students learn in a fun way. Currently, it has bilingual (Hindi and English user interface to enable learning of alphabets and elementary mathematics. A user survey was conducted after demonstrating it to school children. The survey results are very encouraging. Additionally, a study to ascertain the improvement in the learning outcome of the children was done. The results clearly indicate an improvement in the learning outcome of the children who used the device over those who did not.

Help Options for L2 Listening in CALL: A Research Agenda

Science.gov (United States)

Cross, Jeremy

2017-01-01

In this article, I present an agenda for researching help options for second language (L2) listening in computer-assisted language learning (CALL) environments. I outline several theories which researchers in the area draw on, then present common points of concern identified from a review of related literature. This serves as a means to…

Campbell's monkeys concatenate vocalizations into context-specific call sequences

Science.gov (United States)

Ouattara, Karim; Lemasson, Alban; Zuberbühler, Klaus

2009-01-01

Primate vocal behavior is often considered irrelevant in modeling human language evolution, mainly because of the caller's limited vocal control and apparent lack of intentional signaling. Here, we present the results of a long-term study on Campbell's monkeys, which has revealed an unrivaled degree of vocal complexity. Adult males produced six different loud call types, which they combined into various sequences in highly context-specific ways. We found stereotyped sequences that were strongly associated with cohesion and travel, falling trees, neighboring groups, nonpredatory animals, unspecific predatory threat, and specific predator classes. Within the responses to predators, we found that crowned eagles triggered four and leopards three different sequences, depending on how the caller learned about their presence. Callers followed a number of principles when concatenating sequences, such as nonrandom transition probabilities of call types, addition of specific calls into an existing sequence to form a different one, or recombination of two sequences to form a third one. We conclude that these primates have overcome some of the constraints of limited vocal control by combinatorial organization. As the different sequences were so tightly linked to specific external events, the Campbell's monkey call system may be the most complex example of ‘proto-syntax’ in animal communication known to date. PMID:20007377

Classifying Structures in the ISM with Machine Learning Techniques

Science.gov (United States)

Beaumont, Christopher; Goodman, A. A.; Williams, J. P.

2011-01-01

The processes which govern molecular cloud evolution and star formation often sculpt structures in the ISM: filaments, pillars, shells, outflows, etc. Because of their morphological complexity, these objects are often identified manually. Manual classification has several disadvantages; the process is subjective, not easily reproducible, and does not scale well to handle increasingly large datasets. We have explored to what extent machine learning algorithms can be trained to autonomously identify specific morphological features in molecular cloud datasets. We show that the Support Vector Machine algorithm can successfully locate filaments and outflows blended with other emission structures. When the objects of interest are morphologically distinct from the surrounding emission, this autonomous classification achieves >90% accuracy. We have developed a set of IDL-based tools to apply this technique to other datasets.

Does Teaching Mnemonics for Vocabulary Learning Make a Difference? Putting the Keyword Method and the Word Part Technique to the Test

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Wei, Zheng

2015-01-01

The present research tested the effectiveness of the word part technique in comparison with the keyword method and self-strategy learning. One hundred and twenty-one Chinese year-one university students were randomly assigned to one of the three learning conditions: word part, keyword or self-strategy learning condition. Half of the target words…

Evaluation of undergraduate clinical learning experiences in the subject of pediatric dentistry using critical incident technique

Directory of Open Access Journals (Sweden)

S Vyawahare

2013-01-01

Full Text Available Introduction: In pediatric dentistry, the experiences of dental students may help dental educators better prepare graduates to treat the children. Research suggests that student′s perceptions should be considered in any discussion of their education, but there has been no systematic examination of India′s undergraduate dental students learning experiences. Aim: This qualitative investigation aimed to gather and analyze information about experiences in pediatric dentistry from the students′ viewpoint using critical incident technique (CIT. Study Design: The sample group for this investigation came from all 240 3 rd and 4 th year dental students from all the four dental colleges in Indore. Using CIT, participants were asked to describe at least one positive and one negative experience in detail. Results: They described 308 positive and 359 negative experiences related to the pediatric dentistry clinic. Analysis of the data resulted in the identification of four key factors related to their experiences: 1 The instructor; 2 the patient; 3 the learning process; and 4 the learning environment. Conclusion: The CIT is a useful data collection and analysis technique that provides rich, useful data and has many potential uses in dental education.

Wireless recording of the calls of Rousettus aegyptiacus and their reproduction using electrostatic transducers

International Nuclear Information System (INIS)

Whiteley, S M; Hayward, G; Pierce, S G; Waters, D A; Farr, I

2010-01-01

Bats are capable of imaging their surroundings in great detail using echolocation. To apply similar methods to human engineering systems requires the capability to measure and recreate the signals used, and to understand the processing applied to returning echoes. In this work, the emitted and reflected echolocation signals of Rousettus aegyptiacus are recorded while the bat is in flight, using a wireless sensor mounted on the bat. The sensor is designed to replicate the acoustic gain control which bats are known to use, applying a gain to returning echoes that is dependent on the incurred time delay. Employing this technique allows emitted and reflected echolocation calls, which have a wide dynamic range, to be recorded. The recorded echoes demonstrate the complexity of environment reconstruction using echolocation. The sensor is also used to make accurate recordings of the emitted calls, and these calls are recreated in the laboratory using custom-built wideband electrostatic transducers, allied with a spectral equalization technique. This technique is further demonstrated by recreating multi-harmonic bioinspired FM chirps. The ability to record and accurately synthesize echolocation calls enables the exploitation of biological signals in human engineering systems for sonar, materials characterization and imaging.

Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project.

Science.gov (United States)

Sakr, Sherif; Elshawi, Radwa; Ahmed, Amjad M; Qureshi, Waqas T; Brawner, Clinton A; Keteyian, Steven J; Blaha, Michael J; Al-Mallah, Mouaz H

2017-12-19

Prior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined categories. The aim of this study is to present an evaluation and comparison of how machine learning techniques can be applied on medical records of cardiorespiratory fitness and how the various techniques differ in terms of capabilities of predicting medical outcomes (e.g. mortality). We use data of 34,212 patients free of known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems Between 1991 and 2009 and had a complete 10-year follow-up. Seven machine learning classification techniques were evaluated: Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Naïve Bayesian Classifier (BC), Bayesian Network (BN), K-Nearest Neighbor (KNN) and Random Forest (RF). In order to handle the imbalanced dataset used, the Synthetic Minority Over-Sampling Technique (SMOTE) is used. Two set of experiments have been conducted with and without the SMOTE sampling technique. On average over different evaluation metrics, SVM Classifier has shown the lowest performance while other models like BN, BC and DT performed better. The RF classifier has shown the best performance (AUC = 0.97) among all models trained using the SMOTE sampling. The results show that various ML techniques can significantly vary in terms of its performance for the different evaluation metrics. It is also not necessarily that the more complex the ML model, the more prediction accuracy can be achieved. The prediction performance of all models trained with SMOTE is much better than the performance of models trained without SMOTE. The study shows the potential of machine learning methods for predicting all-cause mortality using cardiorespiratory fitness

Computer Assisted Language Learning. Routledge Studies in Computer Assisted Language Learning

Science.gov (United States)

Pennington, Martha

2011-01-01

Computer-assisted language learning (CALL) is an approach to language teaching and learning in which computer technology is used as an aid to the presentation, reinforcement and assessment of material to be learned, usually including a substantial interactive element. This books provides an up-to date and comprehensive overview of…

Tracking Active Learning in the Medical School Curriculum: A Learning-Centered Approach.

Science.gov (United States)

McCoy, Lise; Pettit, Robin K; Kellar, Charlyn; Morgan, Christine

2018-01-01

Medical education is moving toward active learning during large group lecture sessions. This study investigated the saturation and breadth of active learning techniques implemented in first year medical school large group sessions. Data collection involved retrospective curriculum review and semistructured interviews with 20 faculty. The authors piloted a taxonomy of active learning techniques and mapped learning techniques to attributes of learning-centered instruction. Faculty implemented 25 different active learning techniques over the course of 9 first year courses. Of 646 hours of large group instruction, 476 (74%) involved at least 1 active learning component. The frequency and variety of active learning components integrated throughout the year 1 curriculum reflect faculty familiarity with active learning methods and their support of an active learning culture. This project has sparked reflection on teaching practices and facilitated an evolution from teacher-centered to learning-centered instruction.

Understanding a Deep Learning Technique through a Neuromorphic System a Case Study with SpiNNaker Neuromorphic Platform

OpenAIRE

Sugiarto Indar; Pasila Felix

2018-01-01

Deep learning (DL) has been considered as a breakthrough technique in the field of artificial intelligence and machine learning. Conceptually, it relies on a many-layer network that exhibits a hierarchically non-linear processing capability. Some DL architectures such as deep neural networks, deep belief networks and recurrent neural networks have been developed and applied to many fields with incredible results, even comparable to human intelligence. However, many researchers are still scept...

Active Learning Using Hint Information.

Science.gov (United States)

Li, Chun-Liang; Ferng, Chun-Sung; Lin, Hsuan-Tien

2015-08-01

The abundance of real-world data and limited labeling budget calls for active learning, an important learning paradigm for reducing human labeling efforts. Many recently developed active learning algorithms consider both uncertainty and representativeness when making querying decisions. However, exploiting representativeness with uncertainty concurrently usually requires tackling sophisticated and challenging learning tasks, such as clustering. In this letter, we propose a new active learning framework, called hinted sampling, which takes both uncertainty and representativeness into account in a simpler way. We design a novel active learning algorithm within the hinted sampling framework with an extended support vector machine. Experimental results validate that the novel active learning algorithm can result in a better and more stable performance than that achieved by state-of-the-art algorithms. We also show that the hinted sampling framework allows improving another active learning algorithm designed from the transductive support vector machine.

The Impact of Using Note Taking's Techniques on the Students' Learning

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Asrar Jabir Edan

2017-03-01

Full Text Available It is often said that the worst pen is better than the best memory and regardless of how good the students' memory might be, they need to take notes during the lesson or lecture because it is impossible to remember all the details later on. This is so easy to use technique which requires a brief record of important information can help students not only recall what has been said in the class, but also to achieve their learning goals and provide a useful summary of the material to be revised especially before the test. Unfortunately, it is noticed that most of the students, especially at the secondary stage, neglect this important skill. Most of them don’t often write notes unless they are told to do so by the teacher or depend only on the textbooks forgetting that not all the material mentioned during the lesson found in them as some are explanations to the complex and abstract ones and others are related to the teacher's experience in the subject matter. In fact, note taking skill is part of the learning process and to be useful, students need to learn how to do it effectively and what to record because not all what is said is important. This requires acquiring more than one skill on the part of the learners and more effort on the part of the teacher to teach them how to do it properly. For the above reasons, more light will be shed in this research on this topic followed by an experiment and a test to evaluate its effectiveness in learning

The Effect of Semantic Mapping as a Vocabulary Instruction Technique on EFL Learners with Different Perceptual Learning Styles

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Esmaeel Abdollahzadeh

2009-05-01

Full Text Available Traditional and modern vocabulary instruction techniques have been introduced in the past few decades to improve the learners’ performance in reading comprehension. Semantic mapping, which entails drawing learners’ attention to the interrelationships among lexical items through graphic organizers, is claimed to enhance vocabulary learning significantly. However, whether this technique suits all types of learners has not been adequately investigated. This study examines the effectiveness of employing semantic mapping versus traditional approaches in vocabulary instruction to EFL learners with different perceptual modalities. A modified version of Reid’s (1987 perceptual learning style questionnaire was used to determine the learners’ modality types. The results indicate that semantic mapping in comparison to the traditional approaches significantly enhances vocabulary learning of EFL learners. However, although visual learners slightly outperformed other types of learners on the post-test, no significant differences were observed among intermediate learners with different perceptual modalities employing semantic mapping for vocabulary practice.

Current breathomics-a review on data pre-processing techniques and machine learning in metabolomics breath analysis

DEFF Research Database (Denmark)

Smolinska, A.; Hauschild, A. C.; Fijten, R. R. R.

2014-01-01

been extensively developed. Yet, the application of machine learning methods for fingerprinting VOC profiles in the breathomics is still in its infancy. Therefore, in this paper, we describe the current state of the art in data pre-processing and multivariate analysis of breathomics data. We start...... different conditions (e.g. disease stage, treatment). Independently of the utilized analytical method, the most important question, 'which VOCs are discriminatory?', remains the same. Answers can be given by several modern machine learning techniques (multivariate statistics) and, therefore, are the focus...

DNA Cryptography and Deep Learning using Genetic Algorithm with NW algorithm for Key Generation.

Science.gov (United States)

Kalsi, Shruti; Kaur, Harleen; Chang, Victor

2017-12-05

Cryptography is not only a science of applying complex mathematics and logic to design strong methods to hide data called as encryption, but also to retrieve the original data back, called decryption. The purpose of cryptography is to transmit a message between a sender and receiver such that an eavesdropper is unable to comprehend it. To accomplish this, not only we need a strong algorithm, but a strong key and a strong concept for encryption and decryption process. We have introduced a concept of DNA Deep Learning Cryptography which is defined as a technique of concealing data in terms of DNA sequence and deep learning. In the cryptographic technique, each alphabet of a letter is converted into a different combination of the four bases, namely; Adenine (A), Cytosine (C), Guanine (G) and Thymine (T), which make up the human deoxyribonucleic acid (DNA). Actual implementations with the DNA don't exceed laboratory level and are expensive. To bring DNA computing on a digital level, easy and effective algorithms are proposed in this paper. In proposed work we have introduced firstly, a method and its implementation for key generation based on the theory of natural selection using Genetic Algorithm with Needleman-Wunsch (NW) algorithm and Secondly, a method for implementation of encryption and decryption based on DNA computing using biological operations Transcription, Translation, DNA Sequencing and Deep Learning.

Brain Computer Interface Learning for Systems Based on Electrocorticography and Intracortical Microelectrode Arrays

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Shivayogi V Hiremath

2015-06-01

Full Text Available A brain-computer interface (BCI system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.

Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique.

Science.gov (United States)

Zhao, Xiaowei; Ning, Qiao; Chai, Haiting; Ma, Zhiqiang

2015-06-07

As a widespread type of protein post-translational modifications (PTMs), succinylation plays an important role in regulating protein conformation, function and physicochemical properties. Compared with the labor-intensive and time-consuming experimental approaches, computational predictions of succinylation sites are much desirable due to their convenient and fast speed. Currently, numerous computational models have been developed to identify PTMs sites through various types of two-class machine learning algorithms. These methods require both positive and negative samples for training. However, designation of the negative samples of PTMs was difficult and if it is not properly done can affect the performance of computational models dramatically. So that in this work, we implemented the first application of positive samples only learning (PSoL) algorithm to succinylation sites prediction problem, which was a special class of semi-supervised machine learning that used positive samples and unlabeled samples to train the model. Meanwhile, we proposed a novel succinylation sites computational predictor called SucPred (succinylation site predictor) by using multiple feature encoding schemes. Promising results were obtained by the SucPred predictor with an accuracy of 88.65% using 5-fold cross validation on the training dataset and an accuracy of 84.40% on the independent testing dataset, which demonstrated that the positive samples only learning algorithm presented here was particularly useful for identification of protein succinylation sites. Besides, the positive samples only learning algorithm can be applied to build predictors for other types of PTMs sites with ease. A web server for predicting succinylation sites was developed and was freely accessible at http://59.73.198.144:8088/SucPred/. Copyright © 2015 Elsevier Ltd. All rights reserved.

Developing an instrument to measure emotional behaviour abilities of meaningful learning through the Delphi technique.

Science.gov (United States)

Cadorin, Lucia; Bagnasco, Annamaria; Tolotti, Angela; Pagnucci, Nicola; Sasso, Loredana

2017-09-01

To identify items for a new instrument that measures emotional behaviour abilities of meaningful learning, according to Fink's Taxonomy. Meaningful learning is an active process that promotes a wider and deeper understanding of concepts. It is the result of an interaction between new and previous knowledge and produces a long-term change of knowledge and skills. To measure meaningful learning capability, it is very important in the education of health professionals to identify problems or special learning needs. For this reason, it is necessary to create valid instruments. A Delphi Study technique was implemented in four phases by means of e-mail. The study was conducted from April-September 2015. An expert panel consisting of ten researchers with experience in Fink's Taxonomy was established to identify the items of the instrument. Data were analysed for conceptual description and item characteristics and attributes were rated. Expert consensus was sought in each of these phases. An 87·5% consensus cut-off was established. After four rounds, consensus was obtained for validation of the content of the instrument 'Assessment of Meaningful learning Behavioural and Emotional Abilities'. This instrument consists of 56 items evaluated on a 6-point Likert-type scale. Foundational Knowledge, Application, Integration, Human Dimension, Caring and Learning How to Learn were the six major categories explored. This content validated tool can help educators (teachers, trainers and tutors) to identify and improve the strategies to support students' learning capability, which could increase their awareness of and/or responsibility in the learning process. © 2017 John Wiley & Sons Ltd.

Examining Mobile Learning Trends 2003-2008: A Categorical Meta-Trend Analysis Using Text Mining Techniques

Science.gov (United States)

Hung, Jui-Long; Zhang, Ke

2012-01-01

This study investigated the longitudinal trends of academic articles in Mobile Learning (ML) using text mining techniques. One hundred and nineteen (119) refereed journal articles and proceedings papers from the SCI/SSCI database were retrieved and analyzed. The taxonomies of ML publications were grouped into twelve clusters (topics) and four…

Critique: Can Children with AD/HD Learn Relaxation and Breathing Techniques through Biofeedback Video Games?

Science.gov (United States)

Wright, Craig; Conlon, Elizabeth

2009-01-01

This article presents a critique on K. Amon and A. Campbell's "Can children with AD/HD learn relaxation and breathing techniques through biofeedback video games?". Amon and Campbell reported a successful trial of a commercially available biofeedback program, "The Wild Divine", in reducing symptoms of Attention-Deficit/Hyperactivity Disorder (ADHD)…

Enhancing the Design and Analysis of Flipped Learning Strategies

Science.gov (United States)

Jenkins, Martin; Bokosmaty, Rena; Brown, Melanie; Browne, Chris; Gao, Qi; Hanson, Julie; Kupatadze, Ketevan

2017-01-01

There are numerous calls in the literature for research into the flipped learning approach to match the flood of popular media articles praising its impact on student learning and educational outcomes. This paper addresses those calls by proposing pedagogical strategies that promote active learning in "flipped" approaches and improved…

Integrating SQ4R Technique with Graphic Postorganizers in the Science Learning of Earth and Space

OpenAIRE

Djudin, Tomo; Amir, R

2018-01-01

This study examined the effect of integrating SQ4R reading technique with graphic post organizers on the students' Earth and Space Science learning achievement and development of metacognitive knowledge. The pretest-posttest non-equivalent control group design was employed in this quasi-experimental method. The sample which consists of 103 seventh grade of secondary school students of SMPN 1 Pontianak was drawn by using intact group random sampling technique. An achievement test and a questio...

Tracking Active Learning in the Medical School Curriculum: A Learning-Centered Approach

Science.gov (United States)

McCoy, Lise; Pettit, Robin K; Kellar, Charlyn; Morgan, Christine

2018-01-01

Background: Medical education is moving toward active learning during large group lecture sessions. This study investigated the saturation and breadth of active learning techniques implemented in first year medical school large group sessions. Methods: Data collection involved retrospective curriculum review and semistructured interviews with 20 faculty. The authors piloted a taxonomy of active learning techniques and mapped learning techniques to attributes of learning-centered instruction. Results: Faculty implemented 25 different active learning techniques over the course of 9 first year courses. Of 646 hours of large group instruction, 476 (74%) involved at least 1 active learning component. Conclusions: The frequency and variety of active learning components integrated throughout the year 1 curriculum reflect faculty familiarity with active learning methods and their support of an active learning culture. This project has sparked reflection on teaching practices and facilitated an evolution from teacher-centered to learning-centered instruction. PMID:29707649

Dynamic call center routing policies using call waiting and agent idle times

NARCIS (Netherlands)

Chan, W.; Koole, G.M.; L'Ecuyer, P.

2014-01-01

We study call routing policies for call centers with multiple call types and multiple agent groups. We introduce new weight-based routing policies where each pair (call type, agent group) is given a matching priority defined as an affine combination of the longest waiting time for that call type and

The learning curve of the three-port two-instrument complete thoracoscopic lobectomy for lung cancer—A feasible technique worthy of popularization

Directory of Open Access Journals (Sweden)

Yu-Jen Cheng

2015-07-01

Conclusion: Three-port complete thoracoscopic lobectomy with the two-instrument technique is feasible for lung cancer treatment. The length of the learning curve consisted of 28 cases. This TPTI technique should be popularized.

Particle identification at LHCb: new calibration techniques and machine learning classification algorithms

CERN Multimedia

CERN. Geneva

2018-01-01

Particle identification (PID) plays a crucial role in LHCb analyses. Combining information from LHCb subdetectors allows one to distinguish between various species of long-lived charged and neutral particles. PID performance directly affects the sensitivity of most LHCb measurements. Advanced multivariate approaches are used at LHCb to obtain the best PID performance and control systematic uncertainties. This talk highlights recent developments in PID that use innovative machine learning techniques, as well as novel data-driven approaches which ensure that PID performance is well reproduced in simulation.

Laparoscopic colorectal surgery in learning curve: Role of implementation of a standardized technique and recovery protocol. A cohort study

Directory of Open Access Journals (Sweden)

Gaetano Luglio

2015-06-01

Conclusion: Proper laparoscopic colorectal surgery is safe and leads to excellent results in terms of recovery and short term outcomes, even in a learning curve setting. Key factors for better outcomes and shortening the learning curve seem to be the adoption of a standardized technique and training model along with the strict supervision of an expert colorectal surgeon.

The simulated early learning of cervical spine manipulation technique utilising mannequins.

Science.gov (United States)

Chapman, Peter D; Stomski, Norman J; Losco, Barrett; Walker, Bruce F

2015-01-01

Trivial pain or minor soreness commonly follows neck manipulation and has been estimated at one in three treatments. In addition, rare catastrophic events can occur. Some of these incidents have been ascribed to poor technique where the neck is rotated too far. The aims of this study were to design an instrument to measure competency of neck manipulation in beginning students when using a simulation mannequin, and then examine the suitability of using a simulation mannequin to teach the early psychomotor skills for neck chiropractic manipulative therapy. We developed an initial set of questionnaire items and then used an expert panel to assess an instrument for neck manipulation competency among chiropractic students. The study sample comprised all 41 fourth year 2014 chiropractic students at Murdoch University. Students were randomly allocated into either a usual learning or mannequin group. All participants crossed over to undertake the alternative learning method after four weeks. A chi-square test was used to examine differences between groups in the proportion of students achieving an overall pass mark at baseline, four weeks, and eight weeks. This study was conducted between January and March 2014. We successfully developed an instrument of measurement to assess neck manipulation competency in chiropractic students. We then randomised 41 participants to first undertake either "usual learning" (n = 19) or "mannequin learning" (n = 22) for early neck manipulation training. There were no significant differences between groups in the overall pass rate at baseline (χ(2) = 0.10, p = 0.75), four weeks (χ(2) = 0.40, p = 0.53), and eight weeks (χ(2) = 0.07, p = 0.79). This study demonstrates that the use of a mannequin does not affect the manipulation competency grades of early learning students at short term follow up. Our findings have potentially important safety implications as the results indicate that students could initially

Online transfer learning with extreme learning machine

Science.gov (United States)

Yin, Haibo; Yang, Yun-an

2017-05-01

In this paper, we propose a new transfer learning algorithm for online training. The proposed algorithm, which is called Online Transfer Extreme Learning Machine (OTELM), is based on Online Sequential Extreme Learning Machine (OSELM) while it introduces Semi-Supervised Extreme Learning Machine (SSELM) to transfer knowledge from the source to the target domain. With the manifold regularization, SSELM picks out instances from the source domain that are less relevant to those in the target domain to initialize the online training, so as to improve the classification performance. Experimental results demonstrate that the proposed OTELM can effectively use instances in the source domain to enhance the learning performance.

Calling behavior of mass-reared and wild Anastrepha serpentina (Diptera: Tephritidae).

Science.gov (United States)

Castrejón-Gómez, Victor R; Lascares, Shaila; Malo, Edi A; Toledo, Jorge; Rojas, Julio C

2007-08-01

The calling behavior of mass-reared and wild males of Anastrepha serpentina (Wiedemann) (Diptera: Tephritidae) was studied both in the laboratory and in field cage tests. In the laboratory, density (1, 5, and 10 males per container), age, and hour of day significantly affected calling behavior. Mass-reared males called independently of density, whereas wild males only called at densities of 5 and 10 individuals. Males of both strains started calling when they were 5-7 d old. The daily pattern of male calling was similar in both strains, starting at 0730 hours, and reaching a peak at 1330-1630 hours. Field cage tests showed that mass-reared males started calling when they were 5d old; the period of peak calling was when males were 8-9 d old. In contrast, wild males began calling when they were 10 d old, reaching peaks when males were 13, 15, and 18 d old. Wild males tended to form leks to call during each day of the experiment, whereas mass-reared males only formed leks during 2 d, both strains displaying very low levels. During field cage tests, males, independently of strain, displayed two calling peaks, one peak in the morning and one peak in the afternoon, whereas males observed in the laboratory only showed a single calling peak. The results are discussed in view of the effects of mass rearing A. serpentina males in relation to potential use of the sterile insect technique.

Phishtest: Measuring the Impact of Email Headers on the Predictive Accuracy of Machine Learning Techniques

Science.gov (United States)

Tout, Hicham

2013-01-01

The majority of documented phishing attacks have been carried by email, yet few studies have measured the impact of email headers on the predictive accuracy of machine learning techniques in detecting email phishing attacks. Research has shown that the inclusion of a limited subset of email headers as features in training machine learning…

A data-driven predictive approach for drug delivery using machine learning techniques.

Directory of Open Access Journals (Sweden)

Yuanyuan Li

Full Text Available In drug delivery, there is often a trade-off between effective killing of the pathogen, and harmful side effects associated with the treatment. Due to the difficulty in testing every dosing scenario experimentally, a computational approach will be helpful to assist with the prediction of effective drug delivery methods. In this paper, we have developed a data-driven predictive system, using machine learning techniques, to determine, in silico, the effectiveness of drug dosing. The system framework is scalable, autonomous, robust, and has the ability to predict the effectiveness of the current drug treatment and the subsequent drug-pathogen dynamics. The system consists of a dynamic model incorporating both the drug concentration and pathogen population into distinct states. These states are then analyzed using a temporal model to describe the drug-cell interactions over time. The dynamic drug-cell interactions are learned in an adaptive fashion and used to make sequential predictions on the effectiveness of the dosing strategy. Incorporated into the system is the ability to adjust the sensitivity and specificity of the learned models based on a threshold level determined by the operator for the specific application. As a proof-of-concept, the system was validated experimentally using the pathogen Giardia lamblia and the drug metronidazole in vitro.

Failure of operant control of vocal learning in budgerigars

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Yoshimasa Seki

2018-02-01

Full Text Available Budgerigars were trained by operant conditioning to produce contact calls immediately after hearing a stimulus contact call. In Experiments 1 and 2, playback stimuli were chosen from two different contact call classes from the bird’s repertoire. Once this task was learned, the birds were then tested with other probe stimulus calls from its repertoire, which differed from the original calls drawn from the two classes. Birds failed to mimic the probe stimuli but instead produced one of the two call classes as in the training sessions, showing that birds learned that each stimulus call served as a discriminative stimulus but not as a vocal template for imitation. In Experiment 3, birds were then trained with stimulus calls falling along a 24-step acoustic gradient which varied between the two sounds representing the two contact call categories. As before, birds obtained a reward when the bird’s vocalization matched that of the stimulus above a criterion level. Since the first step and the last step in the gradient were the birds’ original contact calls,　these two patterns were easily matched. Intermediate contact calls in the gradient were much harder for the birds to match. After extensive training, one bird learned to produce contact calls that had only a modest similarity to the intermediate contact calls along the gradient. In spite of remarkable vocal plasticity under natural conditions, operant conditioning methods with budgerigars, even after extensive training and rigorous control of vocal discriminative stimuli, failed to show vocal learning.

Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete

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Palika Chopra

2018-01-01

Full Text Available A comparative analysis for the prediction of compressive strength of concrete at the ages of 28, 56, and 91 days has been carried out using machine learning techniques via “R” software environment. R is digging out a strong foothold in the statistical realm and is becoming an indispensable tool for researchers. The dataset has been generated under controlled laboratory conditions. Using R miner, the most widely used data mining techniques decision tree (DT model, random forest (RF model, and neural network (NN model have been used and compared with the help of coefficient of determination (R2 and root-mean-square error (RMSE, and it is inferred that the NN model predicts with high accuracy for compressive strength of concrete.

Learning the „Look-at-you-go” Moment in Corporate Governance Negotiation Techniques

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Clara VOLINTIRU

2015-06-01

Full Text Available This article explores in an interdisciplinary manner the way concepts are learned or internalized, depending on the varying means of transmission, as well as on the sequencing in which the information is transmitted. In this sense, we build on the constructivist methodology framework in assessing concept acquisition in academic disciplines, at an advanced level. We also present the evolution of certain negotiation techniques, from traditional setting, to less predictable ones. This assessment is compared to a specific Pop Culture case study in which we find an expressive representation of negotiation techniques. Our methodology employs both focus groups and experimental design to test the relative positioning of theoretical concept acquisition (TCA as opposed to expressive concept-acquisition (ECA. Our findings suggest that while expressive concept acquisition (ECA via popular culture representations enhances the students understanding of negotiation techniques, this can only happen in circumstances in which a theoretical concept acquisition (TCA is pre-existent.

Learning about “wicked” problems in the Global South. Creating a film-based learning environment with “Visual Problem Appraisal”

NARCIS (Netherlands)

Witteveen, L.M.; Lie, R.

2012-01-01

The current complexity of sustainable development in the Global South calls for the design of learning strategies that can deal with this complexity. One such innovative learning strategy, called Visual Problem Appraisal (VPA), is highlighted in this article. The strategy is termed visual as it

Exploring Machine Learning Techniques Using Patient Interactions in Online Health Forums to Classify Drug Safety

Science.gov (United States)

Chee, Brant Wah Kwong

2011-01-01

This dissertation explores the use of personal health messages collected from online message forums to predict drug safety using natural language processing and machine learning techniques. Drug safety is defined as any drug with an active safety alert from the US Food and Drug Administration (FDA). It is believed that this is the first…

Reinforcement learning techniques for controlling resources in power networks

Science.gov (United States)

Kowli, Anupama Sunil

As power grids transition towards increased reliance on renewable generation, energy storage and demand response resources, an effective control architecture is required to harness the full functionalities of these resources. There is a critical need for control techniques that recognize the unique characteristics of the different resources and exploit the flexibility afforded by them to provide ancillary services to the grid. The work presented in this dissertation addresses these needs. Specifically, new algorithms are proposed, which allow control synthesis in settings wherein the precise distribution of the uncertainty and its temporal statistics are not known. These algorithms are based on recent developments in Markov decision theory, approximate dynamic programming and reinforcement learning. They impose minimal assumptions on the system model and allow the control to be "learned" based on the actual dynamics of the system. Furthermore, they can accommodate complex constraints such as capacity and ramping limits on generation resources, state-of-charge constraints on storage resources, comfort-related limitations on demand response resources and power flow limits on transmission lines. Numerical studies demonstrating applications of these algorithms to practical control problems in power systems are discussed. Results demonstrate how the proposed control algorithms can be used to improve the performance and reduce the computational complexity of the economic dispatch mechanism in a power network. We argue that the proposed algorithms are eminently suitable to develop operational decision-making tools for large power grids with many resources and many sources of uncertainty.

Research On C4.5 As One Of The Inductive Learning Techniques

OpenAIRE

Yıldırım, Savaş

2003-01-01

The thesis in hand deals with C4.5 (Decision Tree Construction Algorithm) as one of the most significant techniques of machine learning, and how it differs from its older version ID3. With this aim in mind, not only the approaches provided by C4.5 but also other approaches are examined. The decision tree algorithms are useful in a variety of spheres from defense to medicine or economics; and bear a vital importance for decision support systems in these areas. Written by Quinlan in 1993 in C p...

Development of self-learning Monte Carlo technique for more efficient modeling of nuclear logging measurements

International Nuclear Information System (INIS)

Zazula, J.M.

1988-01-01

The self-learning Monte Carlo technique has been implemented to the commonly used general purpose neutron transport code MORSE, in order to enhance sampling of the particle histories that contribute to a detector response. The parameters of all the biasing techniques available in MORSE, i.e. of splitting, Russian roulette, source and collision outgoing energy importance sampling, path length transformation and additional biasing of the source angular distribution are optimized. The learning process is iteratively performed after each batch of particles, by retrieving the data concerning the subset of histories that passed the detector region and energy range in the previous batches. This procedure has been tested on two sample problems in nuclear geophysics, where an unoptimized Monte Carlo calculation is particularly inefficient. The results are encouraging, although the presented method does not directly minimize the variance and the convergence of our algorithm is restricted by the statistics of successful histories from previous random walk. Further applications for modeling of the nuclear logging measurements seem to be promising. 11 refs., 2 figs., 3 tabs. (author)

Learning to Support Learning Together: An Experience with the Soft Systems Methodology

Science.gov (United States)

Sanchez, Adolfo; Mejia, Andres

2008-01-01

An action research approach called soft systems methodology (SSM) was used to foster organisational learning in a school regarding the role of the learning support department within the school and its relation with the normal teaching-learning activities. From an initial situation of lack of coordination as well as mutual misunderstanding and…

Classification of Cytochrome P450 1A2 Inhibitors and Non-Inhibitors by Machine Learning Techniques

DEFF Research Database (Denmark)

Vasanthanathan, Poongavanam; Taboureau, Olivier; Oostenbrink, Chris

2009-01-01

of CYP1A2 inhibitors and non-inhibitors. Training and test sets consisted of about 400 and 7000 compounds, respectively. Various machine learning techniques, like binary QSAR, support vector machine (SVM), random forest, kappa nearest neighbors (kNN), and decision tree methods were used to develop...

The Internet, Language Learning, And International Dialogue: 
Constructing Online Foreign Language Learning Websites

OpenAIRE

KARTAL, Erdogan; UZUN, Levent

2015-01-01

In the present study we call attention to the close connection between languages and globalization, and we also emphasize the importance of the Internet and online websites in foreign language teaching and learning as unavoidable elements of computer assisted language learning (CALL). We prepared a checklist by which we investigated 28 foreign language teaching websites (4 from each of seven languages including English, French, German, Italian, Russian, Spanish and Turkish). The participants ...

Multisubject Learning for Common Spatial Patterns in Motor-Imagery BCI

Directory of Open Access Journals (Sweden)

Dieter Devlaminck

2011-01-01

Full Text Available Motor-imagery-based brain-computer interfaces (BCIs commonly use the common spatial pattern filter (CSP as preprocessing step before feature extraction and classification. The CSP method is a supervised algorithm and therefore needs subject-specific training data for calibration, which is very time consuming to collect. In order to reduce the amount of calibration data that is needed for a new subject, one can apply multitask (from now on called multisubject machine learning techniques to the preprocessing phase. Here, the goal of multisubject learning is to learn a spatial filter for a new subject based on its own data and that of other subjects. This paper outlines the details of the multitask CSP algorithm and shows results on two data sets. In certain subjects a clear improvement can be seen, especially when the number of training trials is relatively low.

Smart Training, Smart Learning: The Role of Cooperative Learning in Training for Youth Services.

Science.gov (United States)

Doll, Carol A.

1997-01-01

Examines cooperative learning in youth services and adult education. Discusses characteristics of cooperative learning techniques; specific cooperative learning techniques (brainstorming, mini-lecture, roundtable technique, send-a-problem problem solving, talking chips technique, and three-step interview); and the role of the trainer. (AEF)

A Cultural Psychological Approach to Analyze Intercultural Learning: Potential and Limits of the Structure Formation Technique

Directory of Open Access Journals (Sweden)

Doris Weidemann

2009-01-01

Full Text Available Despite the huge interest in sojourner adjustment, there is still a lack of qualitative as well as of longitudinal research that would offer more detailed insights into intercultural learning processes during overseas stays. The present study aims to partly fill that gap by documenting changes in knowledge structures and general living experiences of fifteen German sojourners in Taiwan in a longitudinal, cultural-psychological study. As part of a multimethod design a structure formation technique was used to document subjective theories on giving/losing face and their changes over time. In a second step results from this study are compared to knowledge-structures of seven long-term German residents in Taiwan, and implications for the conceptualization of intercultural learning will be proposed. Finally, results from both studies serve to discuss the potential and limits of structure formation techniques in the field of intercultural communication research. URN: urn:nbn:de:0114-fqs0901435

Fluid approximation analysis of a call center model with time-varying arrivals and after-call work

Directory of Open Access Journals (Sweden)

Yosuke Kawai

2015-12-01

Full Text Available Important features to be included in queueing-theoretic models of the call center operation are multiple servers, impatient customers, time-varying arrival process, and operator’s after-call work (ACW. We propose a fluid approximation technique for the queueing model with these features by extending the analysis of a similar model without ACW recently developed by Liu and Whitt (2012. Our model assumes that the service for each quantum of fluid consists of a sequence of two stages, the first stage for the conversation with a customer and the second stage for the ACW. When the duration of each stage has exponential, hyperexponential or hypo-exponential distribution, we derive the time-dependent behavior of the content of fluid in each stage of service as well as that in the waiting room. Numerical examples are shown to illustrate the system performance for the cases in which the input rate and/or the number of servers vary in sinusoidal fashion as well as in adaptive ways and in stationary cases.

Locomotion training of legged robots using hybrid machine learning techniques

Science.gov (United States)

Simon, William E.; Doerschuk, Peggy I.; Zhang, Wen-Ran; Li, Andrew L.

1995-01-01

In this study artificial neural networks and fuzzy logic are used to control the jumping behavior of a three-link uniped robot. The biped locomotion control problem is an increment of the uniped locomotion control. Study of legged locomotion dynamics indicates that a hierarchical controller is required to control the behavior of a legged robot. A structured control strategy is suggested which includes navigator, motion planner, biped coordinator and uniped controllers. A three-link uniped robot simulation is developed to be used as the plant. Neurocontrollers were trained both online and offline. In the case of on-line training, a reinforcement learning technique was used to train the neurocontroller to make the robot jump to a specified height. After several hundred iterations of training, the plant output achieved an accuracy of 7.4%. However, when jump distance and body angular momentum were also included in the control objectives, training time became impractically long. In the case of off-line training, a three-layered backpropagation (BP) network was first used with three inputs, three outputs and 15 to 40 hidden nodes. Pre-generated data were presented to the network with a learning rate as low as 0.003 in order to reach convergence. The low learning rate required for convergence resulted in a very slow training process which took weeks to learn 460 examples. After training, performance of the neurocontroller was rather poor. Consequently, the BP network was replaced by a Cerebeller Model Articulation Controller (CMAC) network. Subsequent experiments described in this document show that the CMAC network is more suitable to the solution of uniped locomotion control problems in terms of both learning efficiency and performance. A new approach is introduced in this report, viz., a self-organizing multiagent cerebeller model for fuzzy-neural control of uniped locomotion is suggested to improve training efficiency. This is currently being evaluated for a possible

Facilitation of social learning in teacher education: the ‘Dimensions of Social Learning Framework’

NARCIS (Netherlands)

de Laat, M.M.; Vrieling, E.; van den Beemt, A.A.J.; McDonald, J.; Cater-Steel, A.

2017-01-01

To understand the organization of social learning by groups in practice, this chapter elaborates on the use of a framework of dimensions and indicators to explore social learning within (prospective) teacher groups. The applied framework that we call the ‘Dimensions of Social Learning (DSL)

Investigating the Effects of Group Investigation (GI and Cooperative Integrated Reading and Comprehension (CIRC as the Cooperative Learning Techniques on Learner's Reading Comprehension

Directory of Open Access Journals (Sweden)

Mohammad Amin Karafkan

2015-11-01

Full Text Available Cooperative learning consists of some techniques for helping students work together more effectively. This study investigated the effects of Group Investigation (GI and Cooperative Integrated Reading and Composition (CIRC as cooperative learning techniques on Iranian EFL learners’ reading comprehension at an intermediate level. The participants of the study were 207 male students who studied at an intermediate level at ILI. The participants were randomly assigned into three equal groups: one control group and two experimental groups. The control group was instructed via conventional technique following an individualistic instructional approach. One experimental group received GI technique. The other experimental group received CIRC technique. The findings showed that there was a meaningful difference between the mean of the reading comprehension score of GI experimental group and CRIC experimental group. CRIC technique is more effective than GI technique in enhancing the reading comprehension test scores of students.

Adaptive Landmark-Based Navigation System Using Learning Techniques

DEFF Research Database (Denmark)

Zeidan, Bassel; Dasgupta, Sakyasingha; Wörgötter, Florentin

2014-01-01

The goal-directed navigational ability of animals is an essential prerequisite for them to survive. They can learn to navigate to a distal goal in a complex environment. During this long-distance navigation, they exploit environmental features, like landmarks, to guide them towards their goal. In...... hexapod robots. As a result, it allows the robots to successfully learn to navigate to distal goals in complex environments.......The goal-directed navigational ability of animals is an essential prerequisite for them to survive. They can learn to navigate to a distal goal in a complex environment. During this long-distance navigation, they exploit environmental features, like landmarks, to guide them towards their goal....... Inspired by this, we develop an adaptive landmark-based navigation system based on sequential reinforcement learning. In addition, correlation-based learning is also integrated into the system to improve learning performance. The proposed system has been applied to simulated simple wheeled and more complex...

Sentiment analysis: a comparison of deep learning neural network algorithm with SVM and naϊve Bayes for Indonesian text

Science.gov (United States)

Calvin Frans Mariel, Wahyu; Mariyah, Siti; Pramana, Setia

2018-03-01

Deep learning is a new era of machine learning techniques that essentially imitate the structure and function of the human brain. It is a development of deeper Artificial Neural Network (ANN) that uses more than one hidden layer. Deep Learning Neural Network has a great ability on recognizing patterns from various data types such as picture, audio, text, and many more. In this paper, the authors tries to measure that algorithm’s ability by applying it into the text classification. The classification task herein is done by considering the content of sentiment in a text which is also called as sentiment analysis. By using several combinations of text preprocessing and feature extraction techniques, we aim to compare the precise modelling results of Deep Learning Neural Network with the other two commonly used algorithms, the Naϊve Bayes and Support Vector Machine (SVM). This algorithm comparison uses Indonesian text data with balanced and unbalanced sentiment composition. Based on the experimental simulation, Deep Learning Neural Network clearly outperforms the Naϊve Bayes and SVM and offers a better F-1 Score while for the best feature extraction technique which improves that modelling result is Bigram.

Toward Personalized Vibrotactile Support When Learning Motor Skills

Directory of Open Access Journals (Sweden)

Olga C. Santos

2017-01-01

Full Text Available Personal tracking technologies allow sensing of the physical activity carried out by people. Data flows collected with these sensors are calling for big data techniques to support data collection, integration and analysis, aimed to provide personalized support when learning motor skills through varied multisensorial feedback. In particular, this paper focuses on vibrotactile feedback as it can take advantage of the haptic sense when supporting the physical interaction to be learnt. Despite each user having different needs, when providing this vibrotactile support, personalization issues are hardly taken into account, but the same response is delivered to each and every user of the system. The challenge here is how to design vibrotactile user interfaces for adaptive learning of motor skills. TORMES methodology is proposed to facilitate the elicitation of this personalized support. The resulting systems are expected to dynamically adapt to each individual user’s needs by monitoring, comparing and, when appropriate, correcting in a personalized way how the user should move when practicing a predefined movement, for instance, when performing a sport technique or playing a musical instrument.

Toward Mobile Assisted Language Learning Apps for Professionals That Integrate Learning into the Daily Routine

Science.gov (United States)

Pareja-Lora, Antonio; Arús-Hita, Jorge; Read, Timothy; Rodríguez-Arancón, Pilar; Calle-Martínez, Cristina; Pomposo, Lourdes; Martín-Monje, Elena; Bárcena, Elena

2013-01-01

In this short paper, we present some initial work on Mobile Assisted Language Learning (MALL) undertaken by the ATLAS research group. ATLAS embraced this multidisciplinary field cutting across Mobile Learning and Computer Assisted Language Learning (CALL) as a natural step in their quest to find learning formulas for professional English that…

47 CFR 22.921 - 911 call processing procedures; 911-only calling mode.

Science.gov (United States)

2010-10-01

... programming in the mobile unit that determines the handling of a non-911 call and permit the call to be... CARRIER SERVICES PUBLIC MOBILE SERVICES Cellular Radiotelephone Service § 22.921 911 call processing procedures; 911-only calling mode. Mobile telephones manufactured after February 13, 2000 that are capable of...

A Severe Weather Laboratory Exercise for an Introductory Weather and Climate Class Using Active Learning Techniques

Science.gov (United States)

Grundstein, Andrew; Durkee, Joshua; Frye, John; Andersen, Theresa; Lieberman, Jordan

2011-01-01

This paper describes a new severe weather laboratory exercise for an Introductory Weather and Climate class, appropriate for first and second year college students (including nonscience majors), that incorporates inquiry-based learning techniques. In the lab, students play the role of meteorologists making forecasts for severe weather. The…

Pervasive Learning

DEFF Research Database (Denmark)

Helms, Niels Henrik; Larsen, Lasse Juel

2009-01-01

, it is not a specific place where you can access scarce information. Pervasive or ubiquitous communication opens up for taking the organizing and design of learning landscapes a step further. Furthermore it calls for theoretical developments, which can open up for a deeper understanding of the relationship between...... emerging contexts, design of contexts and learning....

Triple-Tube-Ostomy: A Novel Technique for the Surgical Treatment of Iatrogenic Duodenal Perforation

Directory of Open Access Journals (Sweden)

Nobuaki Fujikuni

2011-12-01

Full Text Available Although duodenal perforation is currently an infrequent complication of medical procedures, its incidence in the future predictably will increase as endoscopic treatment of duodenal neoplasms becomes more frequently used. In some cases, duodenal perforation is difficult to treat even surgically. We report here a novel technique called ‘triple-tube-ostomy’ for the treatment of iatrogenic duodenal perforation. Since November 2009, there have been three cases of iatrogenic perforation of the duodenum, due to various causes, which we have treated with our novel technique. The main principles of the technique are biliary diversion, decompression of the duodenum, and early enteral nutrition. All patients who underwent the triple-tube-ostomy procedure had good postoperative courses, with few complications. The novel surgical technique we describe in this report is safe, reliable, easy to learn and perform, and led to a good postoperative course in all cases where we performed it.

Measurement of Employability Skills on Teaching Factory Learning

Science.gov (United States)

Subekti, S.; Ana, A.

2018-02-01

Vocational High Schools as one of the educational institutions that has the responsibility in preparing skilled labors has a challenge to improve the quality of human resources as a candidate for skilled labors, to compete and survive in a changing climate of work. BPS noted an increase in the number of non-worker population (BAK) in 2015-2017 on vocational graduates as many as 564,272 people. The ability to adapt and maintain jobs in a variety of conditions is called employability skills. This study purpose to measure the development of employability skills of communication skills, problem-solving skills and teamwork skills on the implementation of teaching factory learning in SMK Negeri 1 Cibadak, THPH Skills Program on bakery competency. This research uses mixed method, with concurrent triangulation mix methods research design. Data collection techniques used interviews and questionnaires. The result shows that there are increasing students’ employability skills in communication skills, problem solving skills, and teamwork skills in teaching factory learning. Principles of learning that apply learning by doing student centering and learning arrangements such as situations and conditions in the workplace have an impact on improving student employability skills.

Performance, Cognitive Load, and Behaviour of Technology-Assisted English Listening Learning: From CALL to MALL

Science.gov (United States)

Chang, Chi-Cheng; Warden, Clyde A.; Liang, Chaoyun; Chou, Pao-Nan

2018-01-01

This study examines differences in English listening comprehension, cognitive load, and learning behaviour between outdoor ubiquitous learning and indoor computer-assisted learning. An experimental design, employing a pretest-posttest control group is employed. Randomly assigned foreign language university majors joined either the experimental…

Community-Based Art Education and Performance: Pointing to a Place Called Home

Science.gov (United States)

Washington, G. E.

2011-01-01

Can art make a difference? This is a call for a new sense of interconnectivity among visual art programs in and out of schools. This common ground will be found in the embodiment of performance, critical reflection, and social change within art learning. One goal of this article is to encourage educators to use the "verbs of art" for…

Rethinking expansive learning

DEFF Research Database (Denmark)

Kolbæk, Ditte; Lundh Snis, Ulrika

Abstract: This paper analyses an online community of master’s students taking a course in ICT and organisational learning. The students initiated and facilitated an educational design for organisational learning called Proactive Review in the organisation where they are employed. By using an online...... discussion forum on Google groups, they created new ways of reflecting and learning. We used netnography to select qualitative postings from the online community and expansive learning concepts for data analysis. The findings show how students changed practices of organisational learning...

An anonymization technique using intersected decision trees

Directory of Open Access Journals (Sweden)

Sam Fletcher

2015-07-01

Full Text Available Data mining plays an important role in analyzing the massive amount of data collected in today’s world. However, due to the public’s rising awareness of privacy and lack of trust in organizations, suitable Privacy Preserving Data Mining (PPDM techniques have become vital. A PPDM technique provides individual privacy while allowing useful data mining. We present a novel noise addition technique called Forest Framework, two novel data quality evaluation techniques called EDUDS and EDUSC, and a security evaluation technique called SERS. Forest Framework builds a decision forest from a dataset and preserves all the patterns (logic rules of the forest while adding noise to the dataset. We compare Forest Framework to its predecessor, Framework, and another established technique, GADP. Our comparison is done using our three evaluation criteria, as well as Prediction Accuracy. Our experimental results demonstrate the success of our proposed extensions to Framework and the usefulness of our evaluation criteria.

Machine Learning Techniques for Arterial Pressure Waveform Analysis

Directory of Open Access Journals (Sweden)

João Cardoso

2013-05-01

Full Text Available The Arterial Pressure Waveform (APW can provide essential information about arterial wall integrity and arterial stiffness. Most of APW analysis frameworks individually process each hemodynamic parameter and do not evaluate inter-dependencies in the overall pulse morphology. The key contribution of this work is the use of machine learning algorithms to deal with vectorized features extracted from APW. With this purpose, we follow a five-step evaluation methodology: (1 a custom-designed, non-invasive, electromechanical device was used in the data collection from 50 subjects; (2 the acquired position and amplitude of onset, Systolic Peak (SP, Point of Inflection (Pi and Dicrotic Wave (DW were used for the computation of some morphological attributes; (3 pre-processing work on the datasets was performed in order to reduce the number of input features and increase the model accuracy by selecting the most relevant ones; (4 classification of the dataset was carried out using four different machine learning algorithms: Random Forest, BayesNet (probabilistic, J48 (decision tree and RIPPER (rule-based induction; and (5 we evaluate the trained models, using the majority-voting system, comparatively to the respective calculated Augmentation Index (AIx. Classification algorithms have been proved to be efficient, in particular Random Forest has shown good accuracy (96.95% and high area under the curve (AUC of a Receiver Operating Characteristic (ROC curve (0.961. Finally, during validation tests, a correlation between high risk labels, retrieved from the multi-parametric approach, and positive AIx values was verified. This approach gives allowance for designing new hemodynamic morphology vectors and techniques for multiple APW analysis, thus improving the arterial pulse understanding, especially when compared to traditional single-parameter analysis, where the failure in one parameter measurement component, such as Pi, can jeopardize the whole evaluation.

CALL--Enhanced L2 Listening Skills--Aiming for Automatization in a Multimedia Environment

Science.gov (United States)

Mayor, Maria Jesus Blasco

2009-01-01

Computer Assisted Language Learning (CALL) and L2 listening comprehension skill training are bound together for good. A neglected macroskill for decades, developing listening comprehension skill is now considered crucial for L2 acquisition. Thus this paper makes an attempt to offer latest information on processing theories and L2 listening…

Game Art Complete All-in-One; Learn Maya, 3ds Max, ZBrush, and Photoshop Winning Techniques

CERN Document Server

Gahan, Andrew

2008-01-01

A compilation of key chapters from the top Focal game art books available today - in the areas of Max, Maya, Photoshop, and ZBrush. The chapters provide the CG Artist with an excellent sampling of essential techniques that every 3D artist needs to create stunning game art. Game artists will be able to master the modeling, rendering, rigging, and texturing techniques they need - with advice from Focal's best and brightest authors. Artists can learn hundreds of tips, tricks and shortcuts in Max, Maya, Photoshop, ZBrush - all within the covers of one complete, inspiring reference

Who is that masked educator? Deconstructing the teaching and learning processes of an innovative humanistic simulation technique.

Science.gov (United States)

McAllister, Margaret; Searl, Kerry Reid; Davis, Susan

2013-12-01

Simulation learning in nursing has long made use of mannequins, standardized actors and role play to allow students opportunity to practice technical body-care skills and interventions. Even though numerous strategies have been developed to mimic or amplify clinical situations, a common problem that is difficult to overcome in even the most well-executed simulation experiences, is that students may realize the setting is artificial and fail to fully engage, remember or apply the learning. Another problem is that students may learn technical competence but remain uncertain about communicating with the person. Since communication capabilities are imperative in human service work, simulation learning that only achieves technical competence in students is not fully effective for the needs of nursing education. Furthermore, while simulation learning is a burgeoning space for innovative practices, it has been criticized for the absence of a basis in theory. It is within this context that an innovative simulation learning experience named "Mask-Ed (KRS simulation)", has been deconstructed and the active learning components examined. Establishing a theoretical basis for creative teaching and learning practices provides an understanding of how, why and when simulation learning has been effective and it may help to distinguish aspects of the experience that could be improved. Three conceptual theoretical fields help explain the power of this simulation technique: Vygotskian sociocultural learning theory, applied theatre and embodiment. Copyright © 2013 Elsevier Ltd. All rights reserved.

Learning about “wicked” problems in the Global South. Creating a film-based learning environment with “Visual Problem Appraisal”

OpenAIRE

Loes Witteveen; Rico Lie

2012-01-01

The current complexity of sustainable development in the Global South calls for the design of learning strategies that can deal with this complexity. One such innovative learning strategy, called Visual Problem Appraisal (VPA), is highlighted in this article. The strategy is termed visual as it creates a learning environment that is film-based. VPA enhances the analysis of complex issues, and facilitates stakeholder dialogue and action planning. The strategy is used in workshops dealing with ...

submitter Studies of CMS data access patterns with machine learning techniques

CERN Document Server

De Luca, Silvia

This thesis presents a study of the Grid data access patterns in distributed analysis in the CMS experiment at the LHC accelerator. This study ranges from the deep analysis of the historical patterns of access to the most relevant data types in CMS, to the exploitation of a supervised Machine Learning classification system to set-up a machinery able to eventually predict future data access patterns - i.e. the so-called dataset “popularity” of the CMS datasets on the Grid - with focus on specific data types. All the CMS workflows run on the Worldwide LHC Computing Grid (WCG) computing centers (Tiers), and in particular the distributed analysis systems sustains hundreds of users and applications submitted every day. These applications (or “jobs”) access different data types hosted on disk storage systems at a large set of WLCG Tiers. The detailed study of how this data is accessed, in terms of data types, hosting Tiers, and different time periods, allows to gain precious insight on storage occupancy ove...

Learning Inverse Rig Mappings by Nonlinear Regression.

Science.gov (United States)

Holden, Daniel; Saito, Jun; Komura, Taku

2017-03-01

We present a framework to design inverse rig-functions-functions that map low level representations of a character's pose such as joint positions or surface geometry to the representation used by animators called the animation rig. Animators design scenes using an animation rig, a framework widely adopted in animation production which allows animators to design character poses and geometry via intuitive parameters and interfaces. Yet most state-of-the-art computer animation techniques control characters through raw, low level representations such as joint angles, joint positions, or vertex coordinates. This difference often stops the adoption of state-of-the-art techniques in animation production. Our framework solves this issue by learning a mapping between the low level representations of the pose and the animation rig. We use nonlinear regression techniques, learning from example animation sequences designed by the animators. When new motions are provided in the skeleton space, the learned mapping is used to estimate the rig controls that reproduce such a motion. We introduce two nonlinear functions for producing such a mapping: Gaussian process regression and feedforward neural networks. The appropriate solution depends on the nature of the rig and the amount of data available for training. We show our framework applied to various examples including articulated biped characters, quadruped characters, facial animation rigs, and deformable characters. With our system, animators have the freedom to apply any motion synthesis algorithm to arbitrary rigging and animation pipelines for immediate editing. This greatly improves the productivity of 3D animation, while retaining the flexibility and creativity of artistic input.

Tracking urban human activity from mobile phone calling patterns.

Science.gov (United States)

Monsivais, Daniel; Ghosh, Asim; Bhattacharya, Kunal; Dunbar, Robin I M; Kaski, Kimmo

2017-11-01

Timings of human activities are marked by circadian clocks which in turn are entrained to different environmental signals. In an urban environment the presence of artificial lighting and various social cues tend to disrupt the natural entrainment with the sunlight. However, it is not completely understood to what extent this is the case. Here we exploit the large-scale data analysis techniques to study the mobile phone calling activity of people in large cities to infer the dynamics of urban daily rhythms. From the calling patterns of about 1,000,000 users spread over different cities but lying inside the same time-zone, we show that the onset and termination of the calling activity synchronizes with the east-west progression of the sun. We also find that the onset and termination of the calling activity of users follows a yearly dynamics, varying across seasons, and that its timings are entrained to solar midnight. Furthermore, we show that the average mid-sleep time of people living in urban areas depends on the age and gender of each cohort as a result of biological and social factors.

Tracking urban human activity from mobile phone calling patterns.

Directory of Open Access Journals (Sweden)

Daniel Monsivais

2017-11-01

Full Text Available Timings of human activities are marked by circadian clocks which in turn are entrained to different environmental signals. In an urban environment the presence of artificial lighting and various social cues tend to disrupt the natural entrainment with the sunlight. However, it is not completely understood to what extent this is the case. Here we exploit the large-scale data analysis techniques to study the mobile phone calling activity of people in large cities to infer the dynamics of urban daily rhythms. From the calling patterns of about 1,000,000 users spread over different cities but lying inside the same time-zone, we show that the onset and termination of the calling activity synchronizes with the east-west progression of the sun. We also find that the onset and termination of the calling activity of users follows a yearly dynamics, varying across seasons, and that its timings are entrained to solar midnight. Furthermore, we show that the average mid-sleep time of people living in urban areas depends on the age and gender of each cohort as a result of biological and social factors.

Machine learning and statistical techniques : an application to the prediction of insolvency in Spanish non-life insurance companies

OpenAIRE

Díaz, Zuleyka; Segovia, María Jesús; Fernández, José

2005-01-01

Prediction of insurance companies insolvency has arisen as an important problem in the field of financial research. Most methods applied in the past to tackle this issue are traditional statistical techniques which use financial ratios as explicative variables. However, these variables often do not satisfy statistical assumptions, which complicates the application of the mentioned methods. In this paper, a comparative study of the performance of two non-parametric machine learning techniques ...

Medical students benefit from the use of ultrasound when learning peripheral IV techniques.

Science.gov (United States)

Osborn, Scott R; Borhart, Joelle; Antonis, Michael S

2012-03-06

Recent studies support high success rates after a short learning period of ultrasound IV technique, and increased patient and provider satisfaction when using ultrasound as an adjunct to peripheral IV placement. No study to date has addressed the efficacy for instructing ultrasound-naive providers. We studied the introduction of ultrasound to the teaching technique of peripheral IV insertion on first- and second-year medical students. This was a prospective, randomized, and controlled trial. A total of 69 medical students were randomly assigned to the control group with a classic, landmark-based approach (n = 36) or the real-time ultrasound-guided group (n = 33). Both groups observed a 20-min tutorial on IV placement using both techniques and then attempted vein cannulation. Students were given a survey to report their results and observations by a 10-cm visual analog scale. The survey response rate was 100%. In the two groups, 73.9% stated that they attempted an IV previously, and 63.7% of students had used an ultrasound machine prior to the study. None had used ultrasound for IV access prior to our session. The average number of attempts at cannulation was 1.42 in either group. There was no difference between the control and ultrasound groups in terms of number of attempts (p = 0.31). In both groups, 66.7% of learners were able to cannulate in one attempt, 21.7% in two attempts, and 11.6% in three attempts. The study group commented that they felt they gained more knowledge from the experience (p students feel they learn more when using ultrasound after a 20-min tutorial to place IVs and cannulation of the vein feels easier. Success rates are comparable between the traditional and ultrasound teaching approaches.

Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

Science.gov (United States)

Kanchymalay, Kasturi; Salim, N.; Sukprasert, Anupong; Krishnan, Ramesh; Raba'ah Hashim, Ummi

2017-08-01

The aim of this paper was to study the correlation between crude palm oil (CPO) price, selected vegetable oil prices (such as soybean oil, coconut oil, and olive oil, rapeseed oil and sunflower oil), crude oil and the monthly exchange rate. Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques. Monthly CPO prices, selected vegetable oil prices, crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques. The results were assessed by using criteria of root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE) and Direction of accuracy (DA). Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method.

Use of the Learning together technique associated to the theory of significative learning

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Ester López Donoso

2008-09-01

Full Text Available This article deals with an experimental research, regarding a qualitative and quantitative design, applied to a group of students of General Physics course during the first semester of the university career of Engineering. Historically, students of this course present learning difficulties that directly affect their performance, conceptualization and permanence in the university. The present methodology integrates the collaborative learning, denominated Learning Together", with the theory of significant learning to avoid the above-written difficulties. Results of this research show that the proposed methodology works properly, especially to improve the conceptualization.

Mobile Robot Navigation Based on Q-Learning Technique

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Lazhar Khriji

2011-03-01

Full Text Available This paper shows how Q-learning approach can be used in a successful way to deal with the problem of mobile robot navigation. In real situations where a large number of obstacles are involved, normal Q-learning approach would encounter two major problems due to excessively large state space. First, learning the Q-values in tabular form may be infeasible because of the excessive amount of memory needed to store the table. Second, rewards in the state space may be so sparse that with random exploration they will only be discovered extremely slowly. In this paper, we propose a navigation approach for mobile robot, in which the prior knowledge is used within Q-learning. We address the issue of individual behavior design using fuzzy logic. The strategy of behaviors based navigation reduces the complexity of the navigation problem by dividing them in small actions easier for design and implementation. The Q-Learning algorithm is applied to coordinate between these behaviors, which make a great reduction in learning convergence times. Simulation and experimental results confirm the convergence to the desired results in terms of saved time and computational resources.

Implications of Multimodal Learning Models for foreign language teaching and learning

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Miguel Farías

2011-04-01

Full Text Available This literature review article approaches the topic of information and communications technologies from the perspective of their impact on the language learning process, with particular emphasis on the most appropriate designs of multimodal texts as informed by models of multimodal learning. The first part contextualizes multimodality within the fields of discourse studies, the psychology of learning and CALL; the second, deals with multimodal conceptions of reading and writing by discussing hypertextuality and literacy. A final section outlines the possible implications of multimodal learning models for foreign language teaching and learning.

A Call for Expanding Inclusive Student Engagement in SoTL

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Peter Felten

2013-09-01

of student-faculty partnerships focused on inquiry into teaching and learning. However, some students tend to be privileged in SoTL initiatives while others are discouraged, implicitly or explicitly, from engaging in this work. In this paper, we consider why certain students tend to be excluded from SoTL, summarize the possible developmental gains made by students and faculty when diverse student voices are included, and highlight strategies for generating a more inclusive SoTL. We call for expanding student engagement in SoTL by encouraging a diversity of student voices to engage in co-inquiry with faculty. Inclusive engagement has tremendous potential to enhance student and faculty learning, to deepen SoTL initiatives, and to help redress the exclusionary practices that too often occur in higher education.

Application and evaluation of a combination of socratice and learning through discussion techniques.

Science.gov (United States)

van Aswegen, E J; Brink, H I; Steyn, P J

2001-11-01

This article has its genesis in the inquirer's interest in the need for internalizing critical thinking, creative thinking and reflective skills in adult learners. As part of a broader study the inquirer used a combination of two techniques over a period of nine months, namely: Socratic discussion/questioning and Learning Through Discussion Technique. The inquirer within this inquiry elected mainly qualitative methods, because they were seen as more adaptable to dealing with multiple realities and more sensitive and adaptable to the many shaping influences and value patterns that may be encountered (Lincoln & Guba, 1989). Purposive sampling was used and sample size (n = 10) was determined by the willingness of potential participants to enlist in the chosen techniques. Feedback from participants was obtained: (1) verbally after each discussion session, and (2) in written format after completion of the course content. The final/summative evaluation was obtained through a semi-structured questionnaire. This was deemed necessary, in that the participants were already studying for the end of the year examination. For the purpose of this condensed report the inquirer reflected only on the feedback obtained with the help of the questionnaire. The empirical study showed that in spite of various adaptation problems experienced, eight (8) of the ten (10) participants felt positive toward the applied techniques.

Status of the Usage of Active Learning and Teaching Method and Techniques by Social Studies Teachers

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Akman, Özkan

2016-01-01

The purpose of this study was to determine the active learning and teaching methods and techniques which are employed by the social studies teachers working in state schools of Turkey. This usage status was assessed using different variables. This was a case study, wherein the research was limited to 241 social studies teachers. These teachers…

Interactive lesion segmentation with shape priors from offline and online learning.

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Shepherd, Tony; Prince, Simon J D; Alexander, Daniel C

2012-09-01

In medical image segmentation, tumors and other lesions demand the highest levels of accuracy but still call for the highest levels of manual delineation. One factor holding back automatic segmentation is the exemption of pathological regions from shape modelling techniques that rely on high-level shape information not offered by lesions. This paper introduces two new statistical shape models (SSMs) that combine radial shape parameterization with machine learning techniques from the field of nonlinear time series analysis. We then develop two dynamic contour models (DCMs) using the new SSMs as shape priors for tumor and lesion segmentation. From training data, the SSMs learn the lower level shape information of boundary fluctuations, which we prove to be nevertheless highly discriminant. One of the new DCMs also uses online learning to refine the shape prior for the lesion of interest based on user interactions. Classification experiments reveal superior sensitivity and specificity of the new shape priors over those previously used to constrain DCMs. User trials with the new interactive algorithms show that the shape priors are directly responsible for improvements in accuracy and reductions in user demand.

Conventional and Piecewise Growth Modeling Techniques: Applications and Implications for Investigating Head Start Children's Early Literacy Learning

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Hindman, Annemarie H.; Cromley, Jennifer G.; Skibbe, Lori E.; Miller, Alison L.

2011-01-01

This article reviews the mechanics of conventional and piecewise growth models to demonstrate the unique affordances of each technique for examining the nature and predictors of children's early literacy learning during the transition from preschool through first grade. Using the nationally representative Family and Child Experiences Survey…

Learning Theories and Skills in Online Second Language Teaching and Learning: Dilemmas and Challenges

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Petersen, Karen Bjerg

2014-01-01

For decades foreign and second language teachers have taken advantage of the technology development and ensuing possibilities to use e-learning facilities for language training. Since the 1980s, the use of computer assisted language learning (CALL), Internet, web 2.0, and various kinds of e-learning technology has been developed and researched…

Viewing Teaching Techniques in Enhancing Viewing Comprehension Skills of Undergraduate Students in Literature

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Ruth Ortega-Dela Cruz

2017-06-01

Full Text Available In today’s generation of digital natives, students find it unexciting to sit down and read literary texts in its entirety. Thus, students nowadays hardly understand and appreciate literature as a subject. This calls for additional challenge to teachers who had been used to deliver their lessons or any subject matter in traditional ways. This study used descriptive-correlational research design to unveil the most effective viewing teaching techniques that will help develop the viewing comprehension skills of literature students in a tertiary level. Findings reveal how literature students perceive the use of movies or videos as a powerful viewing teaching technique. Result of chi-square test of independence indicates the viewing teaching techniques are not significantly related to students’ viewing comprehension. This suggests that no single teaching technique suits the learners’ interest and ability to learn. Therefore, teachers can make use of technology combined with other viewing techniques to ensure that students will find a love for literature that will help establish their academic success in the future.

Integrating Collaborative and Decentralized Models to Support Ubiquitous Learning

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Barbosa, Jorge Luis Victória; Barbosa, Débora Nice Ferrari; Rigo, Sandro José; de Oliveira, Jezer Machado; Rabello, Solon Andrade, Jr.

2014-01-01

The application of ubiquitous technologies in the improvement of education strategies is called Ubiquitous Learning. This article proposes the integration between two models dedicated to support ubiquitous learning environments, called Global and CoolEdu. CoolEdu is a generic collaboration model for decentralized environments. Global is an…

Computational intelligence for technology enhanced learning

Energy Technology Data Exchange (ETDEWEB)

Xhafa, Fatos [Polytechnic Univ. of Catalonia, Barcelona (Spain). Dept. of Languages and Informatics Systems; Caballe, Santi; Daradoumis, Thanasis [Open Univ. of Catalonia, Barcelona (Spain). Dept. of Computer Sciences Multimedia and Telecommunications; Abraham, Ajith [Machine Intelligence Research Labs (MIR Labs), Auburn, WA (United States). Scientific Network for Innovation and Research Excellence; Juan Perez, Angel Alejandro (eds.) [Open Univ. of Catalonia, Barcelona (Spain). Dept. of Information Sciences

2010-07-01

E-Learning has become one of the most wide spread ways of distance teaching and learning. Technologies such as Web, Grid, and Mobile and Wireless networks are pushing teaching and learning communities to find new and intelligent ways of using these technologies to enhance teaching and learning activities. Indeed, these new technologies can play an important role in increasing the support to teachers and learners, to shorten the time to learning and teaching; yet, it is necessary to use intelligent techniques to take advantage of these new technologies to achieve the desired support to teachers and learners and enhance learners' performance in distributed learning environments. The chapters of this volume bring advances in using intelligent techniques for technology enhanced learning as well as development of e-Learning applications based on such techniques and supported by technology. Such intelligent techniques include clustering and classification for personalization of learning, intelligent context-aware techniques, adaptive learning, data mining techniques and ontologies in e-Learning systems, among others. Academics, scientists, software developers, teachers and tutors and students interested in e-Learning will find this book useful for their academic, research and practice activity. (orig.)

THE PUZZLE TECHNIQUE, COOPERATIVE LEARNING STRATEGY TO IMPROVE ACADEMIC PERFORMANCE

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M.ª José Mayorga Fernández

2012-04-01

Full Text Available This  article  presents  an  innovative  experience  carried  out  in  the  subject Pedagogical Bases of Special Education, a 4.5 credit core subject taught at the second year of the Degree in Physical Education Teacher Training (to be extinguish, based on the use of a methodological strategic in accordance with the new demands of the EEES. With the experience we pursue a double purpose: firstly, to present the technique of jigsaw or puzzle as a useful methodological strategy for university learning and, on the other hand, to show whether this strategy improves students results. Comparing the results with students previous year results shows that the performance of students who participated in the innovative experience has improved considerably, increasing their motivation and involvement towards the task.

Survey of Analysis of Crime Detection Techniques Using Data Mining and Machine Learning

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Prabakaran, S.; Mitra, Shilpa

2018-04-01

Data mining is the field containing procedures for finding designs or patterns in a huge dataset, it includes strategies at the convergence of machine learning and database framework. It can be applied to various fields like future healthcare, market basket analysis, education, manufacturing engineering, crime investigation etc. Among these, crime investigation is an interesting application to process crime characteristics to help the society for a better living. This paper survey various data mining techniques used in this domain. This study may be helpful in designing new strategies for crime prediction and analysis.

“Call for Prices”: Strategic Implications of Raising Consumers' Costs

OpenAIRE

Preyas S. Desai; Anand Krishnamoorthy; Preethika Sainam

2010-01-01

Many consumer durable retailers often do not advertise their prices and instead ask consumers to call them for prices. It is easy to see that this practice increases the consumers' cost of learning the prices of products they are considering, yet firms commonly use such practices. Not advertising prices may reduce the firm's advertising costs, but the strategic effects of doing so are not clear. Our objective is to examine the strategic effects of this practice. In particular, how does making...

The learning technique. Theoretical considerations for planning lessons wit h a strategic learning approach

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Dania Regueira Martínez

2014-03-01

Full Text Available This article presents the learning task considered as the unit of smaller organization level in the teaching-learning process that conditions in its systemic structuring, the learning actions, for the students acquisition of the content, by means of the development of the reflection and the metacognitiv e regulation when they conscious ly or partially plan different types of learning strategies in the ir realization, with the objective to solv e the pedagogic professional problems that are p resented in the disciplines they receive and in its research task during the direction o f the teaching-learning process.

Developing CALL for heritage languages: The 7 Keys of the Dragon

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Anthi Revithiadou

2015-09-01

Full Text Available n this article we present an interactive extensible software, The 7 Keys of the Dragon, for the teaching/learning of Albanian and Russian to students that attend primary and secondary education in Greece with the respective languages as their heritage languages. We address the key challenges we encountered during the conceptualization phase of the project development and the specific design choices we implemented in order to accommodate them. Drawing on recent research on the role of Computer-Assisted Language Learning (CALL applications for young bilingual populations, we aimed at creating a user friendly environment with a clear pedagogical orientation. Furthermore, given that games in language learning are associated with intrinsic motivation and meaningful exposure to the target language, we have integrated a fairy-tale background narrative, a game-inspired reward system, and two cartoon-like assistant characters to stimulate the user’s involvement in the learning tasks. Five chapters for each target language were created, each comprising a text, a variety of scaffolding material and quizzes. The software is designed to provide real-time automatic correction of quizzes and allow for easy expansion with additional quizzes and texts. A separate application for teachers facilitates essay correction and commenting on the students’ language learning progress and achievements.

Effect of Ability Grouping in Reciprocal Teaching Technique of Collaborative Learning on Individual Achievements and Social Skills

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Sumadi; Degeng, I Nyoman S.; Sulthon; Waras

2017-01-01

This research focused on effects of ability grouping in reciprocal teaching technique of collaborative learning on individual achievements dan social skills. The results research showed that (1) there are differences in individual achievement significantly between high group of homogeneous, middle group of homogeneous, low group of homogeneous,…

Machine Learning with Squared-Loss Mutual Information

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Masashi Sugiyama

2012-12-01

Full Text Available Mutual information (MI is useful for detecting statistical independence between random variables, and it has been successfully applied to solving various machine learning problems. Recently, an alternative to MI called squared-loss MI (SMI was introduced. While ordinary MI is the Kullback–Leibler divergence from the joint distribution to the product of the marginal distributions, SMI is its Pearson divergence variant. Because both the divergences belong to the ƒ-divergence family, they share similar theoretical properties. However, a notable advantage of SMI is that it can be approximated from data in a computationally more efficient and numerically more stable way than ordinary MI. In this article, we review recent development in SMI approximation based on direct density-ratio estimation and SMI-based machine learning techniques such as independence testing, dimensionality reduction, canonical dependency analysis, independent component analysis, object matching, clustering, and causal inference.

Scoping Study of Machine Learning Techniques for Visualization and Analysis of Multi-source Data in Nuclear Safeguards

Energy Technology Data Exchange (ETDEWEB)

Cui, Yonggang

2018-05-07

In implementation of nuclear safeguards, many different techniques are being used to monitor operation of nuclear facilities and safeguard nuclear materials, ranging from radiation detectors, flow monitors, video surveillance, satellite imagers, digital seals to open source search and reports of onsite inspections/verifications. Each technique measures one or more unique properties related to nuclear materials or operation processes. Because these data sets have no or loose correlations, it could be beneficial to analyze the data sets together to improve the effectiveness and efficiency of safeguards processes. Advanced visualization techniques and machine-learning based multi-modality analysis could be effective tools in such integrated analysis. In this project, we will conduct a survey of existing visualization and analysis techniques for multi-source data and assess their potential values in nuclear safeguards.

Education and learning: what's on the horizon?

Science.gov (United States)

Pilcher, Jobeth

2014-01-01

Numerous organizations have called for significant changes in education for health care professionals. The call has included the need to incorporate evidence-based as well as innovative strategies. Previous articles in this column have focused primarily on evidence-based teaching strategies, including concept mapping, brain-based learning strategies, methods of competency assessment, and so forth. This article shifts the focus to new ways of thinking about knowledge and education. The article will also introduce evolving, innovative, less commonly used learning strategies and provide a peek into the future of learning.

Metaphysics and Learning

Science.gov (United States)

Verran, Helen

2007-01-01

Is it possible to learn and simultaneously articulate the metaphysical basis of that learning? In my contribution to the forum I tell of how I came to recognise that bilingual Yoruba children could articulate the contrasting metaphysical framings of Yoruba and English numbering. The story introduces an arena I call "ontics" that recognises the…

Machine learning with quantum relative entropy

International Nuclear Information System (INIS)

Tsuda, Koji

2009-01-01

Density matrices are a central tool in quantum physics, but it is also used in machine learning. A positive definite matrix called kernel matrix is used to represent the similarities between examples. Positive definiteness assures that the examples are embedded in an Euclidean space. When a positive definite matrix is learned from data, one has to design an update rule that maintains the positive definiteness. Our update rule, called matrix exponentiated gradient update, is motivated by the quantum relative entropy. Notably, the relative entropy is an instance of Bregman divergences, which are asymmetric distance measures specifying theoretical properties of machine learning algorithms. Using the calculus commonly used in quantum physics, we prove an upperbound of the generalization error of online learning.

Machine learning with quantum relative entropy

Energy Technology Data Exchange (ETDEWEB)

Tsuda, Koji [Max Planck Institute for Biological Cybernetics, Spemannstr. 38, Tuebingen, 72076 (Germany)], E-mail: koji.tsuda@tuebingen.mpg.de

2009-12-01

Density matrices are a central tool in quantum physics, but it is also used in machine learning. A positive definite matrix called kernel matrix is used to represent the similarities between examples. Positive definiteness assures that the examples are embedded in an Euclidean space. When a positive definite matrix is learned from data, one has to design an update rule that maintains the positive definiteness. Our update rule, called matrix exponentiated gradient update, is motivated by the quantum relative entropy. Notably, the relative entropy is an instance of Bregman divergences, which are asymmetric distance measures specifying theoretical properties of machine learning algorithms. Using the calculus commonly used in quantum physics, we prove an upperbound of the generalization error of online learning.

Statistical learning techniques applied to epidemiology: a simulated case-control comparison study with logistic regression

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Land Walker H

2011-01-01

Full Text Available Abstract Background When investigating covariate interactions and group associations with standard regression analyses, the relationship between the response variable and exposure may be difficult to characterize. When the relationship is nonlinear, linear modeling techniques do not capture the nonlinear information content. Statistical learning (SL techniques with kernels are capable of addressing nonlinear problems without making parametric assumptions. However, these techniques do not produce findings relevant for epidemiologic interpretations. A simulated case-control study was used to contrast the information embedding characteristics and separation boundaries produced by a specific SL technique with logistic regression (LR modeling representing a parametric approach. The SL technique was comprised of a kernel mapping in combination with a perceptron neural network. Because the LR model has an important epidemiologic interpretation, the SL method was modified to produce the analogous interpretation and generate odds ratios for comparison. Results The SL approach is capable of generating odds ratios for main effects and risk factor interactions that better capture nonlinear relationships between exposure variables and outcome in comparison with LR. Conclusions The integration of SL methods in epidemiology may improve both the understanding and interpretation of complex exposure/disease relationships.

Students' perceptions of effective learning experiences in dental school: a qualitative study using a critical incident technique.

Science.gov (United States)

Victoroff, Kristin Zakariasen; Hogan, Sarah

2006-02-01

Students' views of their educational experience can be an important source of information for curriculum assessment. Although quantitative methods, particularly surveys, are frequently used to gather such data, fewer studies have employed qualitative methods to examine students' dental education experiences. The purpose of this study is to explore characteristics of effective learning experiences in dental school using a qualitative method. All third-year (seventy) and fourth-year (seventy) dental students enrolled in one midwestern dental school were invited to participate. Fifty-three dental students (thirty-five male and eighteen female; thirty-two third-year and twenty-one fourth-year) were interviewed using a critical incident interview technique. Each student was asked to describe a specific, particularly effective learning incident that he or she had experienced in dental school and a specific, particularly ineffective learning incident, for comparison. Each interview was audiotaped. Students were assured that only the interviewer and one additional researcher would have access to the tapes. Data analysis resulted in identification of key themes in the data describing characteristics of effective learning experiences. The following characteristics of effective learning experiences were identified: 1) instructor characteristics (personal qualities, "checking-in" with students, and an interactive style); 2) characteristics of the learning process (focus on the "big picture," modeling and demonstrations, opportunities to apply new knowledge, high-quality feedback, focus, specificity and relevance, and peer interactions); and 3) learning environment (culture of the learning environment, technology). Common themes emerged across a wide variety of learning incidents. Although additional research is needed, the characteristics of effective learning experiences identified in this study may have implications for individual course design and for the dental school

Perceiving a calling, living a calling, and job satisfaction: testing a moderated, multiple mediator model.

Science.gov (United States)

Duffy, Ryan D; Bott, Elizabeth M; Allan, Blake A; Torrey, Carrie L; Dik, Bryan J

2012-01-01

The current study examined the relation between perceiving a calling, living a calling, and job satisfaction among a diverse group of employed adults who completed an online survey (N = 201). Perceiving a calling and living a calling were positively correlated with career commitment, work meaning, and job satisfaction. Living a calling moderated the relations of perceiving a calling with career commitment and work meaning, such that these relations were more robust for those with a stronger sense they were living their calling. Additionally, a moderated, multiple mediator model was run to examine the mediating role of career commitment and work meaning in the relation of perceiving a calling and job satisfaction, while accounting for the moderating role of living a calling. Results indicated that work meaning and career commitment fully mediated the relation between perceiving a calling and job satisfaction. However, the indirect effects of work meaning and career commitment were only significant for individuals with high levels of living a calling, indicating the importance of living a calling in the link between perceiving a calling and job satisfaction. Implications for research and practice are discussed. (c) 2012 APA, all rights reserved.

Education for a Learning Society.

Science.gov (United States)

Tempero, Howard E., Ed.

The essays contained in this booklet are 1) "Education for a 'Learning Society': The Challenge" by Ernest Bayles in which he calls for focus on learning to live, developing skills of reflection and judgment applicable to vital issues, and reflective teaching; 2) "Teacher Education in a Learning Society" in which David Turney demands teacher…

Call Center Capacity Planning

DEFF Research Database (Denmark)

Nielsen, Thomas Bang

in order to relate the results to the service levels used in call centers. Furthermore, the generic nature of the approximation is demonstrated by applying it to a system incorporating a dynamic priority scheme. In the last paper Optimization of overflow policies in call centers, overflows between agent......The main topics of the thesis are theoretical and applied queueing theory within a call center setting. Call centers have in recent years become the main means of communication between customers and companies, and between citizens and public institutions. The extensively computerized infrastructure...... in modern call centers allows for a high level of customization, but also induces complicated operational processes. The size of the industry together with the complex and labor intensive nature of large call centers motivates the research carried out to understand the underlying processes. The customizable...

Teacher’s leadership in learning processes at kindergartens

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Maria Dominika Niron

2017-09-01

Full Text Available The aim of this study was to find out the effective kindergarten teacher’s behaviour in influencing, mobilizing, and developing students in teaching learning process. This research was phenomenological qualitative research. The main instruments of this research were the researcher and observation manual. The focus of this research was the way teachers teach in the learning process in group A of Indriyasana Kindergarten, Indriarini Kindergarten, and ABA Pokoh Kindergarten. The data validity of this research was tested by using repeated observation, resource triangulation, and technique triangulation. The componential data was analyzed by employing inductive technique from Spradley’s qualitative model and Miles and Huberman analysis model. The result of the research showed that teacher’s effective ways to influence, mobilize, and develop students in teaching learning process are as follows: 1. Reciting yell, clap yell, and asking students to sing. The content of yell, clap yell, and song was appropriate with values which were developed based on vision, mission, and the goal of Kindergarten institution. Yells, clap yell, and song were democratic and they were the form of the value of learning leadership. 2. In some situations, there was a tendency where the teacher used more autocratic way to influence, mobilize, and develop students in learning process such as the verbal way in which teacher call students’ name and non-verbal way in which teacher put his index finger on his lip as a sign to ask students to be quiet. The other non-verbal ways were: shaking head as a sign of disagreement, raising thumb as a sign of reinforcement, and nodding as a sign of agreement. Sometimes, teachers also used laissez-fair methods such as neglecting students/letting students behave as they want. Keywords: leadership, teacher’s leadership behaviour, learning process in Kindergarten

E-learning project assessment: A new approach through the analysis of learners’ posts on social media

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A. Caione

2016-04-01

Full Text Available E-learning assessment is a key aspect in the overall e-learning process. There are several parameters to consider during the assessment. In recent years, several sets of factors, called Critical Success Factors, have been defined to provide a structural approach to assessment. They focus on many aspects but, in our view, they do not properly consider student satisfaction with courses. In e-learning applications, student opinion must be examined where it is expressed: on e-learning course social pages and/or social pages outside the platform but specific to the e-learning course. The problem is that these resources are unstructured and thus it is important to structure these resources before using them for assessment. In this paper, we discuss a proposal that can capture student opinion from social pages, combining several techniques, such as Natural Language Processing, Information Extraction; ontologies that help us to understand what and how students discuss about e-learning courses.

Cultivating Critical Game Makers in Digital Game-Based Learning: Learning from the Arts

Science.gov (United States)

Denham, André R.; Guyotte, Kelly W.

2018-01-01

Digital games have the potential of being a transformative tool for applying constructionist principles to learning within formal and informal learning settings. Unfortunately, most recent attention has focused on instructionist games. Connected gaming provides a tantalizing alternative approach by calling for the development of games that are…

Application and evaluation of a combination of socratice and learning through discussion techniques

Directory of Open Access Journals (Sweden)

EJ van Aswegen

2001-09-01

Full Text Available This article has its genesis in the inquirer’s interest in the need for internalizing critical thinking, creative thinking and reflective skills in adult learners. As part of a broader study the inquirer used a combination of two techniques over a period of nine months, namely: Socratic discussion/questioning and Learning Through Discussion Technique. The inquirer within this inquiry elected mainly qualitative methods, because they were seen as more adaptable to dealing with multiple realities and more sensitive and adaptable to the many shaping influences and value patterns that may be encountered (Lincoln & Guba, 1989. Purposive sampling was used and sample size (n =10 was determined by the willingness of potential participants to enlist in the chosen techniques. Feedback from participants was obtained: (1 verbally after each discussion session, and (2 in written format after completion of the course content. The final/ summative evaluation was obtained through a semi-structured questionnaire. This was deemed necessary, in that the participants were already studying for the end of the year examination. For the purpose of this condensed report the inquirer reflected only on the feedback obtained with the help of the questionnaire. The empirical study showed that in spite of various adaptation problems experienced, eight (8 of the ten (10 participants felt positive toward the applied techniques.

A hybrid stock trading framework integrating technical analysis with machine learning techniques

Directory of Open Access Journals (Sweden)

Rajashree Dash

2016-03-01

Full Text Available In this paper, a novel decision support system using a computational efficient functional link artificial neural network (CEFLANN and a set of rules is proposed to generate the trading decisions more effectively. Here the problem of stock trading decision prediction is articulated as a classification problem with three class values representing the buy, hold and sell signals. The CEFLANN network used in the decision support system produces a set of continuous trading signals within the range 0–1 by analyzing the nonlinear relationship exists between few popular technical indicators. Further the output trading signals are used to track the trend and to produce the trading decision based on that trend using some trading rules. The novelty of the approach is to engender the profitable stock trading decision points through integration of the learning ability of CEFLANN neural network with the technical analysis rules. For assessing the potential use of the proposed method, the model performance is also compared with some other machine learning techniques such as Support Vector Machine (SVM, Naive Bayesian model, K nearest neighbor model (KNN and Decision Tree (DT model.

Calle Blanco

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Gonzalo Cerda Brintrup

1988-06-01

Full Text Available Importante arteria, que comunica el sector del puerto con la plaza. Las más imponentes construcciones se sucedían de un modo continuo, encaramándose a ambos lados de la empinada calle. Antes del gran incendio de 1936 grandes casonas de madera destacaban en calle Irarrázabal y en la esquina de ésta con calle Blanco, la más hermosa construcción pertenecía a don Alberto Oyarzún y la casa vecina hacia Blanco era de don Mateo Miserda, limitada por arriba con la casa de don Augusto Van Der Steldt y ésta era seguida de la casa de don David Barrientos provista de cuatro cúpulas en las esquinas y de un amplio corredor en el frontis. Todas estas construcciones de madera fueron destruidas en el gran incendio de 1936.

Does the Medium Really Matter in L2 Development? The Validity of Call Research Designs

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Cerezo, Luis; Baralt, Melissa; Suh, Bo-Ram; Leow, Ronald P.

2014-01-01

Currently, an increasing number of educational institutions are redefining second/foreign language (L2) classrooms by enhancing--or even replacing--traditional face-to-face (FTF) instruction with computer-assisted language learning (CALL). However, are these curricular decisions supported by research? Overall, a cursory review of empirical studies…

Learning about “wicked” problems in the Global South. Creating a film-based learning environment with “Visual Problem Appraisal”

Directory of Open Access Journals (Sweden)

Loes Witteveen

2012-03-01

Full Text Available The current complexity of sustainable development in the Global South calls for the design of learning strategies that can deal with this complexity. One such innovative learning strategy, called Visual Problem Appraisal (VPA, is highlighted in this article. The strategy is termed visual as it creates a learning environment that is film-based. VPA enhances the analysis of complex issues, and facilitates stakeholder dialogue and action planning. The strategy is used in workshops dealing with problem analysis and policy design, and involves the participants “meeting” stakeholders through filmed narratives. The article demonstrates the value of using film in multi stakeholder learning environments addressing issues concerning sustainable development.

Gradient phonological inconsistency affects vocabulary learning.

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Muench, Kristin L; Creel, Sarah C

2013-09-01

Learners frequently experience phonologically inconsistent input, such as exposure to multiple accents. Yet, little is known about the consequences of phonological inconsistency for language learning. The current study examines vocabulary acquisition with different degrees of phonological inconsistency, ranging from no inconsistency (e.g., both talkers call a picture /vig/) to mild but detectable inconsistency (e.g., one talker calls a picture a /vig/, and the other calls it a /vIg/), up to extreme inconsistency (e.g., the same picture is both a /vig/ and a /dIdʒ/). Previous studies suggest that learners readily extract consistent phonological patterns, given variable input. However, in Experiment 1, adults acquired phonologically inconsistent vocabularies more slowly than phonologically consistent ones. Experiment 2 examined whether word-form inconsistency alone, without phonological competition, was a source of learning difficulty. Even without phonological competition, listeners learned faster in 1 accent than in 2 accents, but they also learned faster in 2 accents (/vig/ = /vIg/) than with completely different labels (/vig/ = /dIdʒ/). Overall, results suggest that learners exposed to multiple accents may experience difficulty learning when 2 forms mismatch by more than 1 phonological feature, plus increased phonological competition due to a greater number of word forms. Implications for learning from variable input are discussed. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Automatic Classification of Sub-Techniques in Classical Cross-Country Skiing Using a Machine Learning Algorithm on Micro-Sensor Data

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Ole Marius Hoel Rindal

2017-12-01

Full Text Available The automatic classification of sub-techniques in classical cross-country skiing provides unique possibilities for analyzing the biomechanical aspects of outdoor skiing. This is currently possible due to the miniaturization and flexibility of wearable inertial measurement units (IMUs that allow researchers to bring the laboratory to the field. In this study, we aimed to optimize the accuracy of the automatic classification of classical cross-country skiing sub-techniques by using two IMUs attached to the skier’s arm and chest together with a machine learning algorithm. The novelty of our approach is the reliable detection of individual cycles using a gyroscope on the skier’s arm, while a neural network machine learning algorithm robustly classifies each cycle to a sub-technique using sensor data from an accelerometer on the chest. In this study, 24 datasets from 10 different participants were separated into the categories training-, validation- and test-data. Overall, we achieved a classification accuracy of 93.9% on the test-data. Furthermore, we illustrate how an accurate classification of sub-techniques can be combined with data from standard sports equipment including position, altitude, speed and heart rate measuring systems. Combining this information has the potential to provide novel insight into physiological and biomechanical aspects valuable to coaches, athletes and researchers.

The Effect of using Teams Games Tournaments (TGT Technique for Learning Mathematics in Bangladesh

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Abdus Salam

2015-07-01

Full Text Available Games-based learning has captured the interest of educationalists and industrialists who seek to reveal the characteristics of computer games as they are perceived by some to be a potentially effective approach for teaching and learning. Despite this interest in using games-based learning, there is a dearth of studies context of gaming and education in third world countries. This study investigated the effects of game playing on performance and attitudes of students towards mathematics of Grade VIII. The study was undergone by implementing TGT technique for the experimental group and typical lecture-based approach for the control group. A same achievement test was employed as in both pretest and post test, an inventory of attitudes towards mathematics were applied for the pretest and post test on TGT experimental and control group, an attitude scale on computer games was employed for the TGT experimental group, a semi-structured interview for teacher and an FGD guideline for students were applied to serving the purpose of research objectives. After three-weeks of intervention, it had been found out that TGT experimental group students had achieved a significant learning outcome than lecture based control group students. Attitude towards mathematics were differed to a certain positive extent on TGT experimental group. On the basis of findings of this study, some recommendations were made to overcome the barriers of integrating web-based game playing in a classroom.

Deep learning ensemble with asymptotic techniques for oscillometric blood pressure estimation.

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Lee, Soojeong; Chang, Joon-Hyuk

2017-11-01

This paper proposes a deep learning based ensemble regression estimator with asymptotic techniques, and offers a method that can decrease uncertainty for oscillometric blood pressure (BP) measurements using the bootstrap and Monte-Carlo approach. While the former is used to estimate SBP and DBP, the latter attempts to determine confidence intervals (CIs) for SBP and DBP based on oscillometric BP measurements. This work originally employs deep belief networks (DBN)-deep neural networks (DNN) to effectively estimate BPs based on oscillometric measurements. However, there are some inherent problems with these methods. First, it is not easy to determine the best DBN-DNN estimator, and worthy information might be omitted when selecting one DBN-DNN estimator and discarding the others. Additionally, our input feature vectors, obtained from only five measurements per subject, represent a very small sample size; this is a critical weakness when using the DBN-DNN technique and can cause overfitting or underfitting, depending on the structure of the algorithm. To address these problems, an ensemble with an asymptotic approach (based on combining the bootstrap with the DBN-DNN technique) is utilized to generate the pseudo features needed to estimate the SBP and DBP. In the first stage, the bootstrap-aggregation technique is used to create ensemble parameters. Afterward, the AdaBoost approach is employed for the second-stage SBP and DBP estimation. We then use the bootstrap and Monte-Carlo techniques in order to determine the CIs based on the target BP estimated using the DBN-DNN ensemble regression estimator with the asymptotic technique in the third stage. The proposed method can mitigate the estimation uncertainty such as large the standard deviation of error (SDE) on comparing the proposed DBN-DNN ensemble regression estimator with the DBN-DNN single regression estimator, we identify that the SDEs of the SBP and DBP are reduced by 0.58 and 0.57  mmHg, respectively. These

The Effect of Jigsaw Technique on 6th Graders' Learning of Force and Motion Unit and Their Science Attitudes and Motivation

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Ural, Evrim; Ercan, Orhan; Gençoglan, Durdu Mehmet

2017-01-01

The study aims to investigate the effects of jigsaw technique on 6th graders' learning of "Force and Motion" unit, their science learning motivation and their attitudes towards science classes. The sample of the study consisted of 49 6th grade students from two different classes taking the Science and Technology course at a government…

Myths about Technology-Supported Professional Learning

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Killion, Joellen; Treacy, Barbara

2014-01-01

The future of professional learning is shaped by its present and past. As new technologies emerge to increase affordability, access, and appropriateness of professional learning, three beliefs are visible in current practices related to online learning. Each contains a premise that merits identification and examination. The authors call these…

A Construction System for CALL Materials from TV News with Captions

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Kobayashi, Satoshi; Tanaka, Takashi; Mori, Kazumasa; Nakagawa, Seiichi

Many language learning materials have been published. In language learning, although repetition training is obviously necessary, it is difficult to maintain the learner's interest/motivation using existing learning materials, because those materials are limited in their scope and contents. In addition, we doubt whether the speech sounds used in most materials are natural in various situations. Nowadays, some TV news programs (CNN, ABC, PBS, NHK, etc.) have closed/open captions corresponding to the announcer's speech. We have developed a system that makes Computer Assisted Language Learning (CALL) materials for both English learning by Japanese and Japanese learning by foreign students from such captioned newscasts. This system computes the synchronization between captions and speech by using HMMs and a forced alignment algorithm. Materials made by the system have following functions: full/partial text caption display, repetition listening, consulting an electronic dictionary, display of the user's/announcer's sound waveform and pitch contour, and automatic construction of a dictation test. Materials have following advantages: materials present polite and natural speech, various and timely topics. Furthermore, the materials have the following possibility: automatic creation of listening/understanding tests, and storage/retrieval of the many materials. In this paper, firstly, we present the organization of the system. Then, we describe results of questionnaires on trial use of the materials. As the result, we got enough accuracy on the synchronization between captions and speech. Speaking totally, we encouraged to research this system.

Multiresolutional schemata for unsupervised learning of autonomous robots for 3D space operation

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Lacaze, Alberto; Meystel, Michael; Meystel, Alex

1994-01-01

This paper describes a novel approach to the development of a learning control system for autonomous space robot (ASR) which presents the ASR as a 'baby' -- that is, a system with no a priori knowledge of the world in which it operates, but with behavior acquisition techniques that allows it to build this knowledge from the experiences of actions within a particular environment (we will call it an Astro-baby). The learning techniques are rooted in the recursive algorithm for inductive generation of nested schemata molded from processes of early cognitive development in humans. The algorithm extracts data from the environment and by means of correlation and abduction, it creates schemata that are used for control. This system is robust enough to deal with a constantly changing environment because such changes provoke the creation of new schemata by generalizing from experiences, while still maintaining minimal computational complexity, thanks to the system's multiresolutional nature.

Case studies in nanny state name-calling: what can we learn?

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Magnusson, R S

2015-08-01

The 'nanny state' has become a popular metaphor in debates about public health regulation. It fulfils a particular role in that debate: to caution government against taking action. This paper presents case studies of nanny state criticisms, using them to identify a series of contextual features that may assist in better understanding, evaluating and where appropriate, resisting the rhetorical force of nanny state criticisms. The case studies presented include Rush Limbaugh's reactions to Michelle Obama's efforts to encourage American food companies to market healthier food to children; Christopher Hitchens' critique of New York City Mayor Michael Bloomberg's public health policies; and the reaction of neoliberal think tanks to Australia's plain tobacco packaging legislation. These case studies do not provide a basis for making generalisations about the practice of 'nanny state name-calling'. Nor do they preclude debate about the appropriate limits of government action. However, in appropriate cases they may assist policy-makers and public health advocates to contest the framing of public health interventions as unwarranted incursions into the private lives of individuals. One important lesson from these case studies is that the principal concern of nanny state critics is not loss of freedom as such, but the role of the state. The nanny state critique is ultimately a call for the state to be agnostic about the health of citizens, allowing market forces to dominate. Although the nanny state critique is not new, it is a significant challenge to government efforts to address lifestyle-influenced risk factors for non-communicable diseases, including tobacco use, harmful use of alcohol, and unhealthy diet. Copyright © 2015 The Author. Published by Elsevier Ltd.. All rights reserved.

What can health care marketing learn from bank marketing?

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Mindak, W A

1988-01-01

A useful technique in assessing opportunities for international marketers is called "lead lag" analysis. It suggests that one can predict developments, such as demand patterns, in one country by looking at an analogous country. Applying such a technique to the domestic scene, what could we predict about the development and application of marketing to the health care sector if we looked at an analogous service such as banking? Many experts believe that health care is following in the footsteps of banking and point to environmental similarities such as changes in government regulation, new forms of nontraditional competition, increased concern about retail sectors, and pressures on scarce resources. Are there lessons that health care marketers can learn from bankers that might help them avoid some false starts or expensive mistakes?

Analysis on the Metrics used in Optimizing Electronic Business based on Learning Techniques

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Irina-Steliana STAN

2014-09-01

Full Text Available The present paper proposes a methodology of analyzing the metrics related to electronic business. The drafts of the optimizing models include KPIs that can highlight the business specific, if only they are integrated by using learning-based techniques. Having set the most important and high-impact elements of the business, the models should get in the end the link between them, by automating business flows. The human resource will be found in the situation of collaborating more and more with the optimizing models which will translate into high quality decisions followed by profitability increase.

Lesion Detection in CT Images Using Deep Learning Semantic Segmentation Technique

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Kalinovsky, A.; Liauchuk, V.; Tarasau, A.

2017-05-01

In this paper, the problem of automatic detection of tuberculosis lesion on 3D lung CT images is considered as a benchmark for testing out algorithms based on a modern concept of Deep Learning. For training and testing of the algorithms a domestic dataset of 338 3D CT scans of tuberculosis patients with manually labelled lesions was used. The algorithms which are based on using Deep Convolutional Networks were implemented and applied in three different ways including slice-wise lesion detection in 2D images using semantic segmentation, slice-wise lesion detection in 2D images using sliding window technique as well as straightforward detection of lesions via semantic segmentation in whole 3D CT scans. The algorithms demonstrate superior performance compared to algorithms based on conventional image analysis methods.

Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques.

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Shirin Enshaeifar

Full Text Available The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM. TIHM is a technology assisted monitoring system that uses Internet of Things (IoT enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients' routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%.

Distance Learning

National Research Council Canada - National Science Library

Braddock, Joseph

1997-01-01

A study reviewing the existing Army Distance Learning Plan (ADLP) and current Distance Learning practices, with a focus on the Army's training and educational challenges and the benefits of applying Distance Learning techniques...

MEDLINE MeSH Indexing: Lessons Learned from Machine Learning and Future Directions

DEFF Research Database (Denmark)

Jimeno-Yepes, Antonio; Mork, James G.; Wilkowski, Bartlomiej

2012-01-01

and analyzed the issues when using standard machine learning algorithms. We show that in some cases machine learning can improve the annotations already recommended by MTI, that machine learning based on low variance methods achieves better performance and that each MeSH heading presents a different behavior......Map and a k-NN approach called PubMed Related Citations (PRC). Our motivation is to improve the quality of MTI based on machine learning. Typical machine learning approaches fit this indexing task into text categorization. In this work, we have studied some Medical Subject Headings (MeSH) recommended by MTI...

Learning Theory and Prosocial Behavior

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Rosenhan, D. L.

1972-01-01

Although theories of learning which stress the role of reinforcement can help us understand altruistic behaviors, it seems clear that a more complete comprehension calls for an expansion of our notions of learning, such that they incorporate affect and cognition. (Author/JM)

Perceiving a Calling, Living a Calling, and Job Satisfaction: Testing a Moderated, Multiple Mediator Model

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Duffy, Ryan D.; Bott, Elizabeth M.; Allan, Blake A.; Torrey, Carrie L.; Dik, Bryan J.

2012-01-01

The current study examined the relation between perceiving a calling, living a calling, and job satisfaction among a diverse group of employed adults who completed an online survey (N = 201). Perceiving a calling and living a calling were positively correlated with career commitment, work meaning, and job satisfaction. Living a calling moderated…

An Automated Self-Learning Quantification System to Identify Visible Areas in Capsule Endoscopy Images.

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Hashimoto, Shinichi; Ogihara, Hiroyuki; Suenaga, Masato; Fujita, Yusuke; Terai, Shuji; Hamamoto, Yoshihiko; Sakaida, Isao

2017-08-01

Visibility in capsule endoscopic images is presently evaluated through intermittent analysis of frames selected by a physician. It is thus subjective and not quantitative. A method to automatically quantify the visibility on capsule endoscopic images has not been reported. Generally, when designing automated image recognition programs, physicians must provide a training image; this process is called supervised learning. We aimed to develop a novel automated self-learning quantification system to identify visible areas on capsule endoscopic images. The technique was developed using 200 capsule endoscopic images retrospectively selected from each of three patients. The rate of detection of visible areas on capsule endoscopic images between a supervised learning program, using training images labeled by a physician, and our novel automated self-learning program, using unlabeled training images without intervention by a physician, was compared. The rate of detection of visible areas was equivalent for the supervised learning program and for our automatic self-learning program. The visible areas automatically identified by self-learning program correlated to the areas identified by an experienced physician. We developed a novel self-learning automated program to identify visible areas in capsule endoscopic images.

Hello, Who is Calling?: Can Words Reveal the Social Nature of Conversations?

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Stark, Anthony; Shafran, Izhak; Kaye, Jeffrey

2012-01-01