WorldWideScience

Sample records for adaptive reuse learning

  1. Learning Experiences Reuse Based on an Ontology Modeling to Improve Adaptation in E-Learning Systems

    Science.gov (United States)

    Hadj M'tir, Riadh; Rumpler, Béatrice; Jeribi, Lobna; Ben Ghezala, Henda

    2014-01-01

    Current trends in e-Learning focus mainly on personalizing and adapting the learning environment and learning process. Although their increasingly number, theses researches often ignore the concepts of capitalization and reuse of learner experiences which can be exploited later by other learners. Thus, the major challenge of distance learning is…

  2. Learning for design reuse

    DEFF Research Database (Denmark)

    Duffy, Alex; Duffy, Sandra M.

    1996-01-01

    Over the past decade "design assistance", that is, where the computer is viewed as an Intelligent Design Assistant (IDA), has emerged in knowledge based design support and has formed the basic research strategy for the CAD Cantre, University of Strathclyde, since the mid-1980s. Within this philos......Over the past decade "design assistance", that is, where the computer is viewed as an Intelligent Design Assistant (IDA), has emerged in knowledge based design support and has formed the basic research strategy for the CAD Cantre, University of Strathclyde, since the mid-1980s. Within...... this philosophy, an IDA would act as a colleague to a designer, providing guidance, learning from past experiences, carrying out semi- and fully-automated tasks, explaining its reasoning and in essence complementing the designer's own natural skills, and thus leaving the ultimate decision-making, control......, and responsibility with the designer.The ability to learn and evolve has been recognized as one of the key components of an IDA for it to fully support the designer's activities. Consequently, we have been directing our research effort on two main fronts, formalizing our understanding and developing models...

  3. The Adaptive Reuse of Kirkuk Citadel

    OpenAIRE

    Mokhtar, Mustafa Sabah Saleh; Korumaz, Mustafa

    2017-01-01

    Knowledge and memory influence the interpretations of a built environment, implying particular expectations in regard to the built environments and their roles in a society. People and their culture constitute the spirits of a building and a space. Memory also can dominate many heritage users, individuals, social and political groups over many centuries. Memory and spirit of cultural heritage enriches cultural identity under the global development. The adaptive reuse of heritage buildings is ...

  4. The Adaptive Reuse of Kirkuk Citadel

    Directory of Open Access Journals (Sweden)

    Mustafa Sabah Saleh Mokhtar

    2017-12-01

    Full Text Available Knowledge and memory influence the interpretations of a built environment, implying particular expectations in regard to the built environments and their roles in a society. People and their culture constitute the spirits of a building and a space. Memory also can dominate many heritage users, individuals, social and political groups over many centuries. Memory and spirit of cultural heritage enriches cultural identity under the global development. The adaptive reuse of heritage buildings is valued for the contribution for social and environmental sustainability as well as retaining memory. The inherent value of cultural heritage components and their place within the community’s memory helps to reinforce sense of place. In conservation sense identity, memory and the relationships of people give cultural significance to historical places. Evolution of the built environments bridges past and present to the future and embrace memory. However the cities as organisms are in a dilemma along with the loss of city memories and city spirits. These collective memories that bring spirits to a place play very important role and determine the cultural significance of places. The main contribution of this study is to emphasize the importance of adaptive reuse as a carrier of spirits to have a collective memory in order to sustain the development of a place. This article explores the relations between spirit and memory of a place by focusing of adaptive reuse project in Kirkuk citadel.   Aim of this study is to question and evaluate restoration of Kirkuk Citadel in terms of urban identity and sense of place referring the early Kirkuk city and development of it. This paper also intends to put important guidelines for the future restoration projects of Kirkuk citadel – which is very urgently required – and high lights the importance of revitalizing this area, which is now the semi-dead heart of the city. The paper advocates policy makers is to increase

  5. Creating by Reusing Learning Design Solutions

    NARCIS (Netherlands)

    Hernández-Leo, Davinia; Harrer, Andreas; Dodero, Juan Manuel; Asensio-Pérez, Juan; Burgos, Daniel

    2006-01-01

    Hernández-Leo, D., Harrer, A., Dodero, J. M., Asension-Pérez, J. I., & Burgos, D. (2006). Creating by reusing Learning Design solutions. Proceedings of 8th Simposo Internacional de Informática Educativa, León, Spain: IEEE Technical Committee on Learning Technology. Retrieved October 3rd, 2006, from

  6. Patterns of Learning Object Reuse in the Connexions Repository

    Science.gov (United States)

    Duncan, S. M.

    2009-01-01

    Since the term "learning object" was first published, there has been either an explicit or implicit expectation of reuse. There has also been a lot of speculation about why learning objects are, or are not, reused. This study quantitatively examined the actual amount and type of learning object use, to include reuse, modification, and translation,…

  7. Technology and human issues in reusing learning objects

    NARCIS (Netherlands)

    Collis, Betty; Strijker, A.

    2004-01-01

    Reusing learning objects is as old as retelling a story or making use of libraries and textbooks, and in electronic form has received an enormous new impetus because of the World Wide Web and Web technologies. Are we at the brink of changing the "shape and form of learning, ... of being able to

  8. Adaptive Re-Use Principles in Historic Hotel Buildings in Melaka And George Town

    Directory of Open Access Journals (Sweden)

    Ab Wahab Lilawati

    2016-01-01

    Full Text Available Adaptive re-use of historic buildings is a process of changing the original function of the historic buildings to another function that can optimise the use of existing historic buildings. The selection of appropriate new function is an important factor in determining the success of adaptive re-use of historic buildings. However, adaptive re-use work done on historic buildings on the World Heritage Site is not an easy task due to rules and principles outlined by local and international charters that must be abide by. This research is conducted to gather the true picture of applied adaptive re-use principles that has been done on heritage hotels available in Melaka and George Town World Heritage Sites. This research is started with an inventory that led to the discovery of 35 hotels which applied the principle of adaptive re-use of historic buildings. Based on this finding, 4 historic hotels from adaptive re-use applications have been selected as the case studies. Results of the case studies carried out show that the level of conservation of heritage hotel is moderate and measures of control should be taken to ensure the privileges of heritage hotel. As a result of this research, a number of suggestions are made to ensure that adaptive re-use work done in the future will be conducted as optimum as possible according to the adaptive re-use and conservation principles.

  9. Reusing open data for learning database design through project development

    Directory of Open Access Journals (Sweden)

    Jose-Norberto MAZÓN

    2015-12-01

    Full Text Available This paper describes a novel methodology based on reusing open data for applying project-based learning in a Database Design subject of a university degree. This methodology is applied to the ARA (Alto Rendimiento Académico or High Academic Performance group taught in the degree in Computer Engineering at the University of Alicante (Spain during 2012/2013, 2013/2014, and 2014/2015. Openness philosophy implies that huge amount of data is available to students in tabular format, ready for reusing. In our teaching experience, students propose an original scenario where different open data can be reused to a specific goal. Then, it is proposed to design a database in order to manage this data in the envisioned scenario. Open data in the subject helps in instilling a creative and entrepreneur attitude in students, as well as encourages autonomous and lifelong learning. Surveys made to students at the end of each year shown that reusing open data within project-based learning methodologies makes more motivated students since they are using real data.

  10. The Suitability of Adaptive Reuse Practices on Historic Residential Buildings to National Memorials

    Directory of Open Access Journals (Sweden)

    Nor Syahila Ab Rashid

    2014-06-01

    Full Text Available In order to prolong the life of the old buildings in the country through building conservation practices, there is a tendency by the government to acquire and reuse Malaysian leadership figures’ residential buildings as memorials. However, it raises the question of whether there is any adaptive reuse guidelines to reuse historic residential buildings in Malaysia as national memorials in maintaining those buildings as an exhibition space on the history of their leadership. The absence of guidelines raises questions about how to implement the process accordingly. The objective of this research is to find the best formula for reusing historic residential buildings as national memorials based on that issue by reviewing and identify the principles of adaptive reuse practices of historic residential buildings as national memorials that implemented in Malaysia. The case studies were conducted on three samples of historic residential buildings that reused as national memorials and those buildings were selected based on a list of the study population, which are Rumah Kelahiran Tun Dr. Mahathir Mohamad (The Birthplace of Tun Dr. Mahathir Mohamad, Rumah Merdeka (Freedom House and Memorial Tun Abdul Ghafar Baba (The Tun Abdul Ghafar Baba Memorial. The sample may be determined by the sampling method and evaluated using the checklist provided. Based on the results of the case studies that were analyzed and discussed, it can be concluded that aspects of building code (local requirements as well as environmental and conservation requirements are not met in implementing adaptive reuse process on historic residential buildings to national memorials which needs suggestions for improvement.

  11. Adaptive Reuse as A Strategy Toward Urban Resilience

    OpenAIRE

    Deniz Ozge Aytac; Tulin Vural Arslan; Selen Durak

    2016-01-01

    The significance of urban development has been realized again while acute shocks and chronic stresses (earthquake or unemployment) affect cities in a negative way. Therefore, urban resilience becomes more important for economic, environmental, and social sustainability of the built environment. There is a wide range of approaches to resilience in the literature such as ecological, engineering, and adaptive systems. Unlike others, adaptive resilience establishes a co-evolutionary interaction b...

  12. Creative ideation and adaptive reuse: a solution to sustainable urban heritage conservation

    Science.gov (United States)

    Hasnain, H.; Mohseni, F.

    2018-03-01

    The rapid phenomenon of urbanization in the last century has resulted in abandonment of historic urban centers and still today attracting people to suburbs and newly developed districts. This urban expansion poses a serious threat to historic properties when people opt to move away leaving behind their heritage. The adaptive reuse of heritage is considered to be a dominant strategy for handling this issue bypassing demolition of heritage properties. However, the adaptive reuse cannot only consider the preservation of the heritage as the structural retrofit or functional revitalization but rather it needs to enunciate the image and the creative ideation that reflect upon the future of heritage. Consequently, this paper aims to examine the role of branding as an innovative source of ideation in the implication of adaptive reuse on heritage. In this regards, the case study of Zalando outlet store in Berlin is selected, which is an old building situated in the commercial district of the city in a wide range of styles and heritage buildings from the middle ages. This research uses semi-structured interviews to examine the significance of Brand making in a successful adaptive reuse process. The findings indicate that the importance of the outlet building lies not only in its physical fabric or commercial aspects, but the spirit of the place that lies in the magical essence of big labels as emblems. This underlying essence of place stimulated the adaptation of building in a way that it surpasses the physical and functional aspect of a building and makes it merge in new times and new sustainable development.

  13. The adaptive reuse of historic city centres. Bologna and Lisbon: solutions for urban regeneration

    Directory of Open Access Journals (Sweden)

    Andrea Boeri

    2016-11-01

    Full Text Available The European historic city centres are currently experiencing innovative approaches for rehabilitation of urban spaces afflicted by social and physical decay. The revitalization challenges are a consequence of the integration of contemporary technologies and solutions to achieve new requirements and of the impacts of socio-economic dynamics. Understanding and boosting the drivers connected to the cultural potential of the historic city centres can play an important role in adaptive re-use. This paper focuses on the synergy between cultural heritage and urban development, cultural heritage preservation and local economic growth, proposing adaptive reuse design practices applied in historic city centre, through the adoption of a multi-criteria methodology for heritage-led regeneration.

  14. Advancing indigent healthcare services through adaptive reuse: repurposing abandoned buildings as medical clinics for disadvantaged populations.

    Science.gov (United States)

    Elrod, James K; Fortenberry, John L

    2017-12-13

    Challenges abound for healthcare providers engaged in initiatives directed toward disadvantaged populations, with financial constraints representing one of the most prominent hardships. Society's less fortunate typically lack the means to pay for healthcare services and even when they are covered by government health insurance programs, reimbursement shortcomings often occur, placing funding burdens on the shoulders of establishments dedicated to serving those of limited means. For such charitably-minded organizations, efficiencies are required on all fronts, including one which involves significant operational costs: the physical space required for care provision. Newly constructed buildings, whether owned or leased, are expensive, consuming a significant percentage of funds that otherwise could be directed toward patient care. Such costs can even prohibit the delivery of services to indigent populations altogether. But through adaptive reuse-the practice of repurposing existing, abandoned buildings, placing them back into service in pursuit of new missions-opportunities exist to economize on this front, allowing healthcare providers to acquire operational space at a discount. In an effort to shore up related knowledge, this article profiles Willis-Knighton Health System's development of Project NeighborHealth, an indigent clinic network which was significantly bolstered by the economies associated with adaptive reuse. Despite its potential to bolster healthcare initiatives directed toward the medically underserved by presenting more affordable options for acquiring operational space, adaptive reuse remains relatively obscure, diminishing opportunities for providers to take advantage of its many benefits. By shedding light on this repurposing approach, healthcare providers will have a better understanding of adaptive reuse, enabling them to make use of the practice to improve the depth and breadth of healthcare services available to disadvantaged populations.

  15. Sharing and Adaptation of Educational Documents in E-Learning

    Directory of Open Access Journals (Sweden)

    Chekry Abderrahman

    2012-03-01

    Full Text Available Few documents can be reused among the huge number of the educational documents on the web. The exponential increase of these documents makes it almost impossible to search for relevant documents. In addition to this, e-learning is designed for public users who have different levels of knowledge and varied skills so they should be given a content that sees to their needs. This work is about adapting the content of learning with learners preferences, and give the teachers the ability to reuse a given content.

  16. Facilitating Teachers' Reuse of Mobile Assisted Language Learning Resources Using Educational Metadata

    Science.gov (United States)

    Zervas, Panagiotis; Sampson, Demetrios G.

    2014-01-01

    Mobile assisted language learning (MALL) and open access repositories for language learning resources are both topics that have attracted the interest of researchers and practitioners in technology enhanced learning (TeL). Yet, there is limited experimental evidence about possible factors that can influence and potentially enhance reuse of MALL…

  17. Advanced technology for the reuse of learning objects in a course-management system

    NARCIS (Netherlands)

    Strijker, A.; Collis, Betty

    2005-01-01

    The creation, labelling, use, and re-use of learning objects is an important area of development involving learning technology. In the higher education context, instructors typically use a course management system (CMS) to organize and manage their own learning objects. The needs and practices of

  18. Sustainable Building Assessment of Colonial Shophouses after Adaptive Reuse in Kuala Lumpur

    Directory of Open Access Journals (Sweden)

    Karam M. Al-Obaidi

    2017-10-01

    Full Text Available Kuala Lumpur, as a major capital city, has undergone a drastic transformation in the past ten years. Many heritage buildings have been sacrificed for urban renewal projects. Those located in the touristic heritage zones within Kuala Lumpur were being converted by their owners into hotels and cafés to meet current demands to sustain their incomes. This approach, however, creates several physical and environmental issues within the new adaptation. The aim of this study is to evaluate the building performance of heritage shophouses that were adapted into budget hotels. The research focuses on two case studies in a strategic and historical location of Jalan Sultan, Kuala Lumpur. At the initial stage, interviews and physical surveys were done to determine the context of this study. The authors of this research then used a triangulation method through indoor environmental condition assessment, measurements of indoor environmental conditions and occupant survey to determine the indoor building performance after the adaption. Results showed that adaptive reuse heritage buildings can perform and meet new indoor environmental requirements, but many sensitive design judgments need to be made before the adaptive reuse renovation. The research found that the use of natural light, natural ventilation, recycled materials and water efficiency have been neglected and thus, they should be prioritized and preserved to ensure a successful change of use. Conserving existing heritage buildings, while incorporating new usages with acceptable comfort, is in line with the principle of sustainability.

  19. INST7150 - Advanced Topics in Learning Object Design and Reuse, Fall 2005

    OpenAIRE

    Wiley, David

    2005-01-01

    This course is designed to help you understand and apply advanced topics in the design, creation, and reuse of learning objects. The course is structured around a practical, hands-on project using learning objects, intermingled with readings and discussion on a variety of topics.

  20. EVALUATION OF AUTHENTICITY ON THE BASIS OF THE NARA GRID IN ADAPTIVE REUSE OF MANOCHEHRI HISTORICAL HOUSE KASHAN, IRAN

    Directory of Open Access Journals (Sweden)

    Parastoo Eshrati

    2017-11-01

    Full Text Available Since it is highly desirable in the reuse of historic monuments not only to maintain their values but to promote them in contemporary life, authenticity is considered one of the measures of success in adaptive reuse projects. Nara Grid, which is designed concerning authenticity, is used as a tool to assess the authenticity of cultural heritage. This paper investigates authenticity in the adaptive reuse of Manouchehri House in Kashan based on the Nara Grid. The importance of this house lies in the fact that it is one of the first which has been reused by the private sector in this city and also has managed to encourage private sector partnership in the reuse of houses in this city and other historic cities of Iran. It has also been able to increase government trust, which shifts from top-down to a bottom-up approach in the field of cultural heritage, in the private sector. In this research, using Statistical Analysis methods, it is determined within the adaptive reuse of this house which ‘Dimensions’ and ‘Aspects’ of authenticity have received more attention and which ones received less; and also significant differences among individual’s viewpoints according to their gender, field, education, and age were investigated.

  1. Investigation on Adaptive Re-use of Heritage Building in George Town, Penang

    Directory of Open Access Journals (Sweden)

    Marhamah Abdul Hadi

    2013-09-01

    Full Text Available Adaptive re-use of heritage buildings in George Town has gained attention from their owners. Their owners either private owners or government, want to adapt their respective buildings to new usage for instance to become a gallery, museum, restaurant, boutique hotel and many more. Every heritage building that is being adapted to paper main objective is to identify the changes made in terms of structure, space and material when adapting the heritage buildings to a new usage specifically into a gallery. Two heritage buildings are chosen as case studies for this paper which are Rumah Teh Bunga and Fort Cornwallis; both buildings located in Penang, Malaysia. The changes made to these two buildings were analyzed using the guidelines provided which are Guideline for Conservation areas and Heritage Buildings and National Heritage Acts 2005. Both buildings will be analyzed using National Heritage Acts, while only Fort Cornwallis will be analyzed using Guideline for Conservation areas and Heritage Buildings. Adaptation of these two heritage buildings requires changes in structure, space and material. The changes in Rumah Teh Bunga focuses more on materials and space while changes in Fort Cornwallis emphasize more to space and addition of other structures. Analysis on the changes are made by using the guideline provided, most of the changes made to both of these heritage buildings comply the rules and regulations stated in the guideline. It was found from the data that some of the reasons on why the owner change Rumah Teh Bunga to gallery are because of its complicated procedure that involves in privatization of this building to other owner and the need to promote the heritage significance of this building to the public. As for Fort Cornwallis, the adaptation is more on strengthening its value as a fort and becoming a tourist attraction.

  2. Recommendation System for Adaptive Learning.

    Science.gov (United States)

    Chen, Yunxiao; Li, Xiaoou; Liu, Jingchen; Ying, Zhiliang

    2018-01-01

    An adaptive learning system aims at providing instruction tailored to the current status of a learner, differing from the traditional classroom experience. The latest advances in technology make adaptive learning possible, which has the potential to provide students with high-quality learning benefit at a low cost. A key component of an adaptive learning system is a recommendation system, which recommends the next material (video lectures, practices, and so on, on different skills) to the learner, based on the psychometric assessment results and possibly other individual characteristics. An important question then follows: How should recommendations be made? To answer this question, a mathematical framework is proposed that characterizes the recommendation process as a Markov decision problem, for which decisions are made based on the current knowledge of the learner and that of the learning materials. In particular, two plain vanilla systems are introduced, for which the optimal recommendation at each stage can be obtained analytically.

  3. Adaptive representations for reinforcement learning

    NARCIS (Netherlands)

    Whiteson, S.

    2010-01-01

    This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own

  4. ADAPTIVE REUSE FOR NEW SOCIAL AND MUNICIPAL FUNCTIONS AS AN ACCEPTABLE APPROACH FOR CONSERVATION OF INDUSTRIAL HERITAGE ARCHITECTURE IN THE CZECH REPUBLIC

    Directory of Open Access Journals (Sweden)

    Oleg Fetisov

    2016-04-01

    Full Text Available The present paper deals with a problem of conservation and adaptive reuse of industrial heritage architecture. The relevance and topicality of the problem of adaptive reuse of industrial heritage architecture for new social and municipal functions as the conservation concept are defined. New insights on the typology of industrial architecture are reviewed (e. g. global changes in all European industry, new concepts and technologies in manufacturing, new features of industrial architecture and their construction and typology, first results of industrialization and changes in the typology of industrial architecture in post-industrial period. General goals and tasks of conservation in context of adaptive reuse of industrial heritage architecture are defined (e. g. historical, architectural and artistic, technical. Adaptive reuse as an acceptable approach for conservation and new use is proposed and reviewed. Moreover, the logical model of adaptive reuse of industrial heritage architecture as an acceptable approach for new use has been developed. Consequently, three general methods for the conservation of industrial heritage architecture by the adaptive reuse approach are developed: historical, architectural and artistic, technical. Relevant functional methods' concepts (social concepts are defined and classified. General beneficial effect of the adaptive reuse approach is given. On the basis of analysis results of experience in adaptive reuse of industrial architecture with new social functions general conclusions are developed.

  5. E-learning Materials Development: Applying and Implementing Software Reuse Principles and Granularity Levels in the Small

    OpenAIRE

    Nabil Arman

    2010-01-01

    E-learning materials development is typically acknowledged as an expensive, complicated, and lengthy process, often producing materials that are of low quality and difficult to adaptand maintain. It has always been a challenge to identify proper e-learning materials that can be reused at a reasonable cost and effort. In this paper, software engineering reuse principlesare applied to e-learning materials development process. These principles are then applied and implemented in a prototype that...

  6. Adaptive learning and complex dynamics

    International Nuclear Information System (INIS)

    Gomes, Orlando

    2009-01-01

    In this paper, we explore the dynamic properties of a group of simple deterministic difference equation systems in which the conventional perfect foresight assumption gives place to a mechanism of adaptive learning. These systems have a common feature: under perfect foresight (or rational expectations) they all possess a unique fixed point steady state. This long-term outcome is obtained also under learning if the quality underlying the learning process is high. Otherwise, when the degree of inefficiency of the learning process is relatively strong, nonlinear dynamics (periodic and a-periodic cycles) arise. The specific properties of each one of the proposed systems is explored both in terms of local and global dynamics. One macroeconomic model is used to illustrate how the formation of expectations through learning may eventually lead to awkward long-term outcomes.

  7. ADAPTIVE REUSE FOR NEW SOCIAL AND MUNICIPAL FUNCTIONS AS AN ACCEPTABLE APPROACH FOR CONSERVATION OF INDUSTRIAL HERITAGE ARCHITECTURE IN THE CZECH REPUBLIC

    OpenAIRE

    Oleg Fetisov

    2016-01-01

    The present paper deals with a problem of conservation and adaptive reuse of industrial heritage architecture. The relevance and topicality of the problem of adaptive reuse of industrial heritage architecture for new social and municipal functions as the conservation concept are defined. New insights on the typology of industrial architecture are reviewed (e. g. global changes in all European industry, new concepts and technologies in manufacturing, new features of industrial architecture and...

  8. Learning to Adapt. Organisational Adaptation to Climate Change Impacts

    International Nuclear Information System (INIS)

    Berkhout, F.; Hertin, J.; Gann, D.M.

    2006-01-01

    Analysis of human adaptation to climate change should be based on realistic models of adaptive behaviour at the level of organisations and individuals. The paper sets out a framework for analysing adaptation to the direct and indirect impacts of climate change in business organisations with new evidence presented from empirical research into adaptation in nine case-study companies. It argues that adaptation to climate change has many similarities with processes of organisational learning. The paper suggests that business organisations face a number of obstacles in learning how to adapt to climate change impacts, especially in relation to the weakness and ambiguity of signals about climate change and the uncertainty about benefits flowing from adaptation measures. Organisations rarely adapt 'autonomously', since their adaptive behaviour is influenced by policy and market conditions, and draws on resources external to the organisation. The paper identifies four adaptation strategies that pattern organisational adaptive behaviour

  9. A New Mobile Learning Adaptation Model

    OpenAIRE

    Mohamd Hassan Hassan; Jehad Al-Sadi

    2009-01-01

    This paper introduces a new model for m- Learning context adaptation due to the need of utilizing mobile technology in education. Mobile learning; m-Learning for short; in considered to be one of the hottest topics in the educational community, many researches had been done to conceptualize this new form of learning. We are presenting a promising design for a model to adapt the learning content in mobile learning applications in order to match the learner context, preferences and the educatio...

  10. Knowledge Reuse Method to Improve the Learning of Interference-Preventive Allocation Policies in Multi-Car Elevators

    Science.gov (United States)

    Valdivielso Chian, Alex; Miyamoto, Toshiyuki

    In this letter, we introduce a knowledge reuse method to improve the performance of a learning algorithm developed to prevent interference in multi-car elevators. This method enables the algorithm to use its previously acquired experience in new learning processes. The simulation results confirm the improvement achieved in the algorithm's performance.

  11. Computerized adaptive testing item selection in computerized adaptive learning systems

    NARCIS (Netherlands)

    Eggen, Theodorus Johannes Hendrikus Maria; Eggen, T.J.H.M.; Veldkamp, B.P.

    2012-01-01

    Item selection methods traditionally developed for computerized adaptive testing (CAT) are explored for their usefulness in item-based computerized adaptive learning (CAL) systems. While in CAT Fisher information-based selection is optimal, for recovering learning populations in CAL systems item

  12. Integrative learning for practicing adaptive resource management

    Directory of Open Access Journals (Sweden)

    Craig A. McLoughlin

    2015-03-01

    Full Text Available Adaptive resource management is a learning-by-doing approach to natural resource management. Its effective practice involves the activation, completion, and regeneration of the "adaptive management cycle" while working toward achieving a flexible set of collaboratively identified objectives. This iterative process requires application of single-, double-, and triple-loop learning, to strategically modify inputs, outputs, assumptions, and hypotheses linked to improving policies, management strategies, and actions, along with transforming governance. Obtaining an appropriate balance between these three modes of learning has been difficult to achieve in practice and building capacity in this area can be achieved through an emphasis on reflexive learning, by employing adaptive feedback systems. A heuristic reflexive learning framework for adaptive resource management is presented in this manuscript. It is built on the conceptual pillars of the following: stakeholder driven adaptive feedback systems; strategic adaptive management (SAM; and hierarchy theory. The SAM Reflexive Learning Framework (SRLF emphasizes the types, roles, and transfer of information within a reflexive learning context. Its adaptive feedback systems enhance the facilitation of single-, double-, and triple-loop learning. Focus on the reflexive learning process is further fostered by streamlining objectives within and across all governance levels; incorporating multiple interlinked adaptive management cycles; having learning as an ongoing, nested process; recognizing when and where to employ the three-modes of learning; distinguishing initiating conditions for this learning; and contemplating practitioner mandates for this learning across governance levels. The SRLF is a key enabler for implementing the "adaptive management cycle," and thereby translating the theory of adaptive resource management into practice. It promotes the heuristics of adaptive management within a cohesive

  13. Computerized adaptive testing in computer assisted learning?

    NARCIS (Netherlands)

    Veldkamp, Bernard P.; Matteucci, Mariagiulia; Eggen, Theodorus Johannes Hendrikus Maria; De Wannemacker, Stefan; Clarebout, Geraldine; De Causmaecker, Patrick

    2011-01-01

    A major goal in computerized learning systems is to optimize learning, while in computerized adaptive tests (CAT) efficient measurement of the proficiency of students is the main focus. There seems to be a common interest to integrate computerized adaptive item selection in learning systems and

  14. Improving Flood Plain Management through Adaptive Learning ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    This project will explore how an adaptive learning approach can improve CBO governance ... for improving resource sustainability and productivity, and facilitate learning and an exchange ... Middlesex University Higher Education Corporation.

  15. Fire Risk Assessment of Adaptive Re-Use of Historic Shop Houses for Sleeping Accommodations in Malaysia

    Directory of Open Access Journals (Sweden)

    Mydin M.A.O.

    2014-01-01

    Full Text Available Heritage buildings were generally constructed without regard for fire risks or the requirements for fire protection, as are obligatory in new constructions. When a heritage building undergoes a change to its original function, improvements to the building’s fire safety are necessary to meet the needs of possible increases in occupancy loads and to account for fire risks related to the new usage. This research focuses on fire safety risks, fire protection and safety systems as well as the rules and regulations that an adaptive reuse heritage shop house is bound to when transitioning to a sleeping accommodation, which, in this case, means becoming a hotel. In this research, six heritage shop houses were chosen as case studies. The objectives of this research were to evaluate current fire emergency plans as well as to identify and assess possible fire hazards created by adaptive reuse of heritage shop houses to sleeping accommodations in Penang through a series of observations and interviews. The results of the research show that most of the buildings were provided with inadequate fire safety systems.

  16. Adaptive Learning Systems: Beyond Teaching Machines

    Science.gov (United States)

    Kara, Nuri; Sevim, Nese

    2013-01-01

    Since 1950s, teaching machines have changed a lot. Today, we have different ideas about how people learn, what instructor should do to help students during their learning process. We have adaptive learning technologies that can create much more student oriented learning environments. The purpose of this article is to present these changes and its…

  17. Adaptive vs. eductive learning : Theory and evidence

    NARCIS (Netherlands)

    Bao, T.; Duffy, J.

    2014-01-01

    Adaptive learning and eductive learning are two widely used ways of modeling learning behavior in macroeconomics. Both approaches yield restrictions on model parameters under which agents are able to learn a rational expectation equilibrium (REE) but these restrictions do not always overlap with one

  18. Adaptive competitive learning neural networks

    Directory of Open Access Journals (Sweden)

    Ahmed R. Abas

    2013-11-01

    Full Text Available In this paper, the adaptive competitive learning (ACL neural network algorithm is proposed. This neural network not only groups similar input feature vectors together but also determines the appropriate number of groups of these vectors. This algorithm uses a new proposed criterion referred to as the ACL criterion. This criterion evaluates different clustering structures produced by the ACL neural network for an input data set. Then, it selects the best clustering structure and the corresponding network architecture for this data set. The selected structure is composed of the minimum number of clusters that are compact and balanced in their sizes. The selected network architecture is efficient, in terms of its complexity, as it contains the minimum number of neurons. Synaptic weight vectors of these neurons represent well-separated, compact and balanced clusters in the input data set. The performance of the ACL algorithm is evaluated and compared with the performance of a recently proposed algorithm in the literature in clustering an input data set and determining its number of clusters. Results show that the ACL algorithm is more accurate and robust in both determining the number of clusters and allocating input feature vectors into these clusters than the other algorithm especially with data sets that are sparsely distributed.

  19. Learning for Climate Change Adaptation among Selected ...

    African Journals Online (AJOL)

    Learning for Climate Change Adaptation among Selected Communities of Lusaka ... This research was aimed at surveying perceptions of climate change and ... This work is licensed under a Creative Commons Attribution 3.0 License.

  20. ADAPTIVE E-LEARNING AND ITS EVALUATION

    Directory of Open Access Journals (Sweden)

    KOSTOLÁNYOVÁ, Katerina

    2012-12-01

    Full Text Available This paper introduces a complex plan for a complete system of individualized electronic instruction. The core of the system is a computer program to control teaching, the so called “virtual teacher”. The virtual teacher automatically adapts to individual student’s characteristics and their learning style. It adapts to static as well as to dynamic characteristics of the student. To manage all this it needs a database of various styles and forms of teaching as well as a sufficient amount of information about the learning style, type of memory and other characteristics of the student. The information about these characteristics, the structure of data storage and its use by the virtual teacher are also part of this paper. We also outline a methodology of adaptive study materials. We define basic rules and forms to create adaptive study materials. This adaptive e-learning system was pilot tested in learning of more than 50 students. These students filled in a learning style questionnaire at the beginning of the study and they had the option to fill in an adaptive evaluation questionnaire at the end of the study. Results of these questionnaires were analyzed. Several conclusions were concluded from this analysis to alter the methodology of adaptive study materials.

  1. Representing adaptive and adaptable Units of Learning. How to model personalized eLearning in IMS Learning Design

    OpenAIRE

    Burgos, Daniel; Tattersall, Colin; Koper, Rob

    2006-01-01

    Burgos, D., Tattersall, C., & Koper, E. J. R. (2007). Representing adaptive and adaptable Units of Learning. How to model personalized eLearning in IMS Learning Design. In B. Fernández Manjon, J. M. Sanchez Perez, J. A. Gómez Pulido, M. A. Vega Rodriguez & J. Bravo (Eds.), Computers and Education: E-learning - from theory to practice. Germany: Kluwer.

  2. Adaptive Social Learning Based on Crowdsourcing

    Science.gov (United States)

    Karataev, Evgeny; Zadorozhny, Vladimir

    2017-01-01

    Many techniques have been developed to enhance learning experience with computer technology. A particularly great influence of technology on learning came with the emergence of the web and adaptive educational hypermedia systems. While the web enables users to interact and collaborate with each other to create, organize, and share knowledge via…

  3. Inference in models with adaptive learning

    NARCIS (Netherlands)

    Chevillon, G.; Massmann, M.; Mavroeidis, S.

    2010-01-01

    Identification of structural parameters in models with adaptive learning can be weak, causing standard inference procedures to become unreliable. Learning also induces persistent dynamics, and this makes the distribution of estimators and test statistics non-standard. Valid inference can be

  4. Different Futures of Adaptive Collaborative Learning Support

    Science.gov (United States)

    Rummel, Nikol; Walker, Erin; Aleven, Vincent

    2016-01-01

    In this position paper we contrast a Dystopian view of the future of adaptive collaborative learning support (ACLS) with a Utopian scenario that--due to better-designed technology, grounded in research--avoids the pitfalls of the Dystopian version and paints a positive picture of the practice of computer-supported collaborative learning 25 years…

  5. Adaptive Hypermedia Systems for E-Learning

    Directory of Open Access Journals (Sweden)

    Aammou Souhaib

    2010-11-01

    Full Text Available The domain of traditional hypermedia is revolutionized by the arrival of the concept of adaptation. Currently the domain of Adaptive Hypermedia Systems (AHS is constantly growing. A major goal of current research is to provide a personalized educational experience that meets the needs specific to each learner (knowledge level, goals, motivation etc.... In this article we have studied the possibility of implementing traditional features of adaptive hypermedia in an open environment, and discussed the standards for describing learning objects and architectural models based on the use of ontologies as a prerequisite for such an adaptation.

  6. Reusing Water

    Science.gov (United States)

    Goals Recycling Green Purchasing Pollution Prevention Reusing Water Resources Environmental Management System Environmental Outreach Feature Stories Individual Permit for Storm Water Public Reading Room Sustainability » Reusing Water Reusing Water Millions of gallons of industrial wastewater is recycled at LANL by

  7. Fractal Adaptive Web Service for Mobile Learning

    Directory of Open Access Journals (Sweden)

    Ichraf Tirellil

    2006-06-01

    Full Text Available This paper describes our proposition for adaptive web services which is based on configurable, re-usable adaptive/personalized services. To realize our ideas, we have developed an approach for designing, implementing and maintaining personal service. This approach enables the user to accomplish an activity with a set of services answering to his preferences, his profiles and to a personalized context. In this paper, we describe the principle of our approach that we call fractal adaptation approach, and we discuss the implementation of personalization services in the context of mobile and collaborative scenario of learning. We have realized a platform in this context -a platform for mobile and collaborative learning- based on fractal adaptable web services. The platform is tested with a population of students and tutors, in order to release the gaps and the advantages of the approach suggested.

  8. Representing adaptive and adaptable Units of Learning. How to model personalized eLearning in IMS Learning Design

    NARCIS (Netherlands)

    Burgos, Daniel; Tattersall, Colin; Koper, Rob

    2006-01-01

    Burgos, D., Tattersall, C., & Koper, E. J. R. (2007). Representing adaptive and adaptable Units of Learning. How to model personalized eLearning in IMS Learning Design. In B. Fernández Manjon, J. M. Sanchez Perez, J. A. Gómez Pulido, M. A. Vega Rodriguez & J. Bravo (Eds.), Computers and Education:

  9. Adaptive Machine Aids to Learning.

    Science.gov (United States)

    Starkweather, John A.

    With emphasis on man-machine relationships and on machine evolution, computer-assisted instruction (CAI) is examined in this paper. The discussion includes the background of machine assistance to learning, the current status of CAI, directions of development, the development of criteria for successful instruction, meeting the needs of users,…

  10. Learning Transferable Features with Deep Adaptation Networks

    OpenAIRE

    Long, Mingsheng; Cao, Yue; Wang, Jianmin; Jordan, Michael I.

    2015-01-01

    Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation...

  11. Adaptive polymeric system for Hebbian type learning

    OpenAIRE

    2011-01-01

    Abstract We present the experimental realization of an adaptive polymeric system displaying a ?learning behaviour?. The system consists on a statistically organized networks of memristive elements (memory-resitors) based on polyaniline. In a such network the path followed by the current increments its conductivity, a property which makes the system able to mimic Hebbian type learning and have application in hardware neural networks. After discussing the working principle of ...

  12. Adaptive Learning in Weighted Network Games

    NARCIS (Netherlands)

    Bayer, Péter; Herings, P. Jean-Jacques; Peeters, Ronald; Thuijsman, Frank

    2017-01-01

    This paper studies adaptive learning in the class of weighted network games. This class of games includes applications like research and development within interlinked firms, crime within social networks, the economics of pollution, and defense expenditures within allied nations. We show that for

  13. Impediments to Learning Object Reuse and Openness as a Potential Solution

    OpenAIRE

    David Wiley

    2010-01-01

    The restrictions of copyright law and its accompany-ing penalties are ambient factors in our everyday lives. "Everyone knows" you’re not allowed to make a copy of a textbook, article, or simulation without permission. The technically daunting and time-consuming process of requesting permission, and the inevitable fees associated with that permission if granted, prevent law-abiding individuals and organizations from reusing ...

  14. Extensible Adaptive System for STEM Learning

    Science.gov (United States)

    2013-07-16

    Copyright 2013 Raytheon BBN Technologies Corp. All Rights Reserved ONR STEM Grand Challenge Extensible Adaptive System for STEM Learning ...Contract # N00014-12-C-0535 Raytheon BBN Technologies Corp. (BBN) Reference # 14217 In partial fulfillment of contract deliverable item # A001...Quarterly Progress Report #2 April 7, 2013 –July 6, 2013 Submitted July 16, 2013 BBN Technical POC: John Makhoul Raytheon BBN Technologies

  15. Using Data to Understand How to Better Design Adaptive Learning

    Science.gov (United States)

    Liu, Min; Kang, Jina; Zou, Wenting; Lee, Hyeyeon; Pan, Zilong; Corliss, Stephanie

    2017-01-01

    There is much enthusiasm in higher education about the benefits of adaptive learning and using big data to investigate learning processes to make data-informed educational decisions. The benefits of adaptive learning to achieve personalized learning are obvious. Yet, there lacks evidence-based research to understand how data such as user behavior…

  16. MEAT: An Authoring Tool for Generating Adaptable Learning Resources

    Science.gov (United States)

    Kuo, Yen-Hung; Huang, Yueh-Min

    2009-01-01

    Mobile learning (m-learning) is a new trend in the e-learning field. The learning services in m-learning environments are supported by fundamental functions, especially the content and assessment services, which need an authoring tool to rapidly generate adaptable learning resources. To fulfill the imperious demand, this study proposes an…

  17. Wastewater reuse

    OpenAIRE

    Milan R. Radosavljević; Vanja M. Šušteršič

    2013-01-01

    Water scarcity and water pollution are some of the crucial issues that must be addressed within local and global perspectives. One of the ways to reduce the impact of water scarcity  and to minimizine water pollution is to expand water and wastewater reuse. The local conditions including regulations, institutions, financial mechanisms, availability of local technology and stakeholder participation have a great influence on the decisions for wastewater reuse. The increasing awareness of food s...

  18. The contributing student: Learners as co-developers of learning resources for reuse in Web environments.

    NARCIS (Netherlands)

    Collis, Betty; Moonen, J.C.M.M.; Hung, David; Khine, Myint Swe

    2005-01-01

    Learners can and do become engaged in learning through intrinsic motivations without the need for a teacher or instructional designer. In the workplace, for example, workplace learning is typically seen as a process of such self-guided learning, based on the needs of the task at hand. In the school

  19. Willingness to Adopt or Reuse an E-Learning System: The Perspectives of Self-Determination and Perceived Characteristics of Innovation

    Science.gov (United States)

    Chang, Hsin Hsin; Fu, Chen Su; Huang, Ching Ying

    2017-01-01

    Adopting self-determination theory and the perceived characteristics of innovation as the theoretical background, this study investigates the school teachers' willingness to adopt and reuse an e-learning system. Three hundred and eighty-eight valid questionnaires were collected for analysis using structural equation modelling. The results…

  20. How Language Supports Adaptive Teaching through a Responsive Learning Culture

    Science.gov (United States)

    Johnston, Peter; Dozier, Cheryl; Smit, Julie

    2016-01-01

    For students to learn optimally, teachers must design classrooms that are responsive to the full range of student development. The teacher must be adaptive, but so must each student and the learning culture itself. In other words, adaptive teaching means constructing a responsive learning culture that accommodates and even capitalizes on diversity…

  1. Reuse, Repurposing and Learning Design--Lessons from the DART Project

    Science.gov (United States)

    Bond, Stephen T.; Ingram, Caroline; Ryan, Steve

    2008-01-01

    Digital Anthropological Resources for Teaching (DART) is a major project examining ways in which the use of online learning activities and repositories can enhance the teaching of anthropology and, by extension, other disciplines. This paper reports on one strand of DART activity, the development of customisable learning activities that can be…

  2. Adaptive and accelerated tracking-learning-detection

    Science.gov (United States)

    Guo, Pengyu; Li, Xin; Ding, Shaowen; Tian, Zunhua; Zhang, Xiaohu

    2013-08-01

    An improved online long-term visual tracking algorithm, named adaptive and accelerated TLD (AA-TLD) based on Tracking-Learning-Detection (TLD) which is a novel tracking framework has been introduced in this paper. The improvement focuses on two aspects, one is adaption, which makes the algorithm not dependent on the pre-defined scanning grids by online generating scale space, and the other is efficiency, which uses not only algorithm-level acceleration like scale prediction that employs auto-regression and moving average (ARMA) model to learn the object motion to lessen the detector's searching range and the fixed number of positive and negative samples that ensures a constant retrieving time, but also CPU and GPU parallel technology to achieve hardware acceleration. In addition, in order to obtain a better effect, some TLD's details are redesigned, which uses a weight including both normalized correlation coefficient and scale size to integrate results, and adjusts distance metric thresholds online. A contrastive experiment on success rate, center location error and execution time, is carried out to show a performance and efficiency upgrade over state-of-the-art TLD with partial TLD datasets and Shenzhou IX return capsule image sequences. The algorithm can be used in the field of video surveillance to meet the need of real-time video tracking.

  3. Implementing Adaptive Educational Methods with IMS Learning Design

    NARCIS (Netherlands)

    Specht, Marcus; Burgos, Daniel

    2006-01-01

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

  4. Adaptive and perceptual learning technologies in medical education and training.

    Science.gov (United States)

    Kellman, Philip J

    2013-10-01

    Recent advances in the learning sciences offer remarkable potential to improve medical education and maximize the benefits of emerging medical technologies. This article describes 2 major innovation areas in the learning sciences that apply to simulation and other aspects of medical learning: Perceptual learning (PL) and adaptive learning technologies. PL technology offers, for the first time, systematic, computer-based methods for teaching pattern recognition, structural intuition, transfer, and fluency. Synergistic with PL are new adaptive learning technologies that optimize learning for each individual, embed objective assessment, and implement mastery criteria. The author describes the Adaptive Response-Time-based Sequencing (ARTS) system, which uses each learner's accuracy and speed in interactive learning to guide spacing, sequencing, and mastery. In recent efforts, these new technologies have been applied in medical learning contexts, including adaptive learning modules for initial medical diagnosis and perceptual/adaptive learning modules (PALMs) in dermatology, histology, and radiology. Results of all these efforts indicate the remarkable potential of perceptual and adaptive learning technologies, individually and in combination, to improve learning in a variety of medical domains. Reprint & Copyright © 2013 Association of Military Surgeons of the U.S.

  5. The Dynamics of Learning and the Emergence of Distributed Adaption

    National Research Council Canada - National Science Library

    Crutchfield, James P

    2006-01-01

    .... The second goal was to adapt this single-agent learning theory and associated learning algorithms to the distributed setting in which a population of autonomous agents communicate to achieve a desired group task...

  6. Learning to adapt: Organisational adaptation to climate change impacts

    NARCIS (Netherlands)

    Berkhout, F.G.H.; Hertin, J.; Gann, D.M.

    2006-01-01

    Analysis of human adaptation to climate change should be based on realistic models of adaptive behaviour at the level of organisations and individuals. The paper sets out a framework for analysing adaptation to the direct and indirect impacts of climate change in business organisations with new

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

    Science.gov (United States)

    Squires, David R.

    2014-01-01

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

  8. Teacher-Led Design of an Adaptive Learning Environment

    Science.gov (United States)

    Mavroudi, Anna; Hadzilacos, Thanasis; Kalles, Dimitris; Gregoriades, Andreas

    2016-01-01

    This paper discusses a requirements engineering process that exemplifies teacher-led design in the case of an envisioned system for adaptive learning. Such a design poses various challenges and still remains an open research issue in the field of adaptive learning. Starting from a scenario-based elicitation method, the whole process was highly…

  9. Bayesian policy reuse

    CSIR Research Space (South Africa)

    Rosman, Benjamin

    2016-02-01

    Full Text Available Keywords Policy Reuse · Reinforcement Learning · Online Learning · Online Bandits · Transfer Learning · Bayesian Optimisation · Bayesian Decision Theory. 1 Introduction As robots and software agents are becoming more ubiquitous in many applications.... The agent has access to a library of policies (pi1, pi2 and pi3), and has previously experienced a set of task instances (τ1, τ2, τ3, τ4), as well as samples of the utilities of the library policies on these instances (the black dots indicate the means...

  10. Designing monitoring arrangements for collaborative learning about adaptation pathways

    NARCIS (Netherlands)

    Hermans, L.M.; Haasnoot, M.; ter Maat, Judith; Kwakkel, J.H.

    2017-01-01

    Adaptation pathways approaches support long-term planning under uncertainty. The use of adaptation pathways implies a systematic monitoring effort to inform future adaptation decisions. Such monitoring should feed into a long-term collaborative learning process between multiple actors at various

  11. A METHODOLOGICAL APPROACH FOR IMPLEMENTATION OF ADAPTIVE E-LEARNING

    OpenAIRE

    Valia Arnaudova; Todorka Terzieva; Asen Rahnev

    2016-01-01

    The purpose of adaptive e-Learning is to ensure effective teaching by providing an opportunity for students to connect with an environment that suits their needs, behavior, and knowledge. The reason adaptive e-Learning is important is that, for a learning process to be successful, it is necessary to consider teaching materials that address specific characteristics of the student, such as their particular goals, preferences, knowledge, and style of studying, to provide an appropriate teaching ...

  12. The Influence of Learning Behaviour on Team Adaptability

    Science.gov (United States)

    Murray, Peter A.; Millett, Bruce

    2011-01-01

    Multiple contexts shape team activities and how they learn, and group learning is a dynamic construct that reflects a repertoire of potential behaviour. The purpose of this developmental paper is to examine how better learning behaviours in semi-autonomous teams improves the level of team adaptability and performance. The discussion suggests that…

  13. Learner Open Modeling in Adaptive Mobile Learning System for Supporting Student to Learn English

    Directory of Open Access Journals (Sweden)

    Van Cong Pham

    2011-10-01

    Full Text Available This paper represents a personalized context-aware mobile learning architecture for supporting student to learn English as foreign language in order to prepare for TOEFL test. We consider how to apply open learner modeling techniques to adapt contents for different learners based on context, which includes location, amount of time to learn, the manner as well as learner's knowledge in learning progress. Through negotiation with system, the editable learner model will be updated to support adaptive engine to select adaptive contents meeting learner's demands. Empirical testing results for students who used application prototype indicate that interaction user modeling is helpful in supporting learner to learn adaptive materials.

  14. Lessons learned at West Valley during facility decontamination for re-use (1982--1988)

    International Nuclear Information System (INIS)

    Tundo, D.; Gessner, R.F.; Lawrence, R.E.

    1988-11-01

    The primary mission of the West Valley Demonstration Project (WVDP) is to solidify a large volume of high-level liquid waste (2.3 million liters -- 600,000 gallons) produced during reprocessing plant operations and stored in underground tanks. This is to be accomplished through the maximum use of existing facilities. This required a significant effort to remove existing equipment and to decontaminate areas for installation of liquid and cement processing systems in a safe environment while maintaining exposure to workers as low as reasonably achievable. The reprocessing plant occupied a building of about 33,000 m 2 (350,000 ft 2 ). When the WVDP was initiated, approximately 6 percent of the plant area was in a non-contaminated condition where personnel could function without protective clothing or radiological controls. From 1982 to 1988, an additional 64 percent of the plant was cleaned up and much of this converted to low- and high-level waste processing areas. The high-level liquid and resulting low-level liquids are now being treated in these areas using an Integrated Radwaste Treatment System (IRTS). The Project has now focused attention on installation, qualification and operation of a vitrification system which will convert the remaining high-level waste into borosilicate glass logs. The stabilized waste will be sent to a Federal Repository for long-term storage. From 1982 to 1988, about 70 technical reports were dealing with specific tasks and cleanup efforts. This report provides an overview of the decontamination and decommissioning work done in that period. The report emphasizes lessons learned during that effort. Significant advances were made in: remote and contact decontamination technology; personnel protection and training; planning and procedures; and radiological controls. 62 refs., 35 figs., 5 tabs

  15. Adaptive Trajectory Tracking Control using Reinforcement Learning for Quadrotor

    Directory of Open Access Journals (Sweden)

    Wenjie Lou

    2016-02-01

    Full Text Available Inaccurate system parameters and unpredicted external disturbances affect the performance of non-linear controllers. In this paper, a new adaptive control algorithm under the reinforcement framework is proposed to stabilize a quadrotor helicopter. Based on a command-filtered non-linear control algorithm, adaptive elements are added and learned by policy-search methods. To predict the inaccurate system parameters, a new kernel-based regression learning method is provided. In addition, Policy learning by Weighting Exploration with the Returns (PoWER and Return Weighted Regression (RWR are utilized to learn the appropriate parameters for adaptive elements in order to cancel the effect of external disturbance. Furthermore, numerical simulations under several conditions are performed, and the ability of adaptive trajectory-tracking control with reinforcement learning are demonstrated.

  16. Wastewater reuse

    Directory of Open Access Journals (Sweden)

    Milan R. Radosavljević

    2013-12-01

    Full Text Available Water scarcity and water pollution are some of the crucial issues that must be addressed within local and global perspectives. One of the ways to reduce the impact of water scarcity  and to minimizine water pollution is to expand water and wastewater reuse. The local conditions including regulations, institutions, financial mechanisms, availability of local technology and stakeholder participation have a great influence on the decisions for wastewater reuse. The increasing awareness of food safety and the influence of the countries which import food are influencing policy makers and agriculturists to improve the standards of wastewater reuse in agriculture. The environmental awareness of consumers has been putting pressure on the producers (industries to opt for environmentally sound technologies including those which conserve water and reduce the level of pollution. It may be observed that we have to move forwards to implement strategies and plans for wastewater reuse. However, their success and sustainability will depend on political will, public awareness and active support from national and international agencies to create favorable    environment for the promotion of environmentally sustainable technologies. Wastewater treatment has a long history, especially in agriculture, but also in industry and households. Poor quality of wastewater can pose a significant risk to the health of farmers and users of agricultural products. The World Health Organization (WHO is working on a project for the reuse of wastewater in agriculture. To reduce effects of human activities to the minimum, it is necessary to provide such technical and technological solutions that would on the one hand ensure complying with  the existing regulations and legislation, and on the other hand provide economically viable systems as seen through investments and operating costs. The use of wastewater The practice of using wastewater varies from country to country. Its

  17. An Adaptive E-Learning System Based on Students' Learning Styles: An Empirical Study

    Science.gov (United States)

    Drissi, Samia; Amirat, Abdelkrim

    2016-01-01

    Personalized e-learning implementation is recognized as one of the most interesting research areas in the distance web-based education. Since the learning style of each learner is different one must fit e-learning with the different needs of learners. This paper presents an approach to integrate learning styles into adaptive e-learning hypermedia.…

  18. Learning Words through Computer-Adaptive Tool

    DEFF Research Database (Denmark)

    Zhang, Chun

    2005-01-01

    construction, I stress the design of a test theory, namely, a learning algorithm. The learning algorithm is designed under such principles that users experience both 'elaborative rehearsal’ (aspects in receptive and productive learning) and 'expanding rehearsal, (memory-based learning and repetitive act...

  19. INTUITEL and the Hypercube Model - Developing Adaptive Learning Environments

    Directory of Open Access Journals (Sweden)

    Kevin Fuchs

    2016-06-01

    Full Text Available In this paper we introduce an approach for the creation of adaptive learning environments that give human-like recommendations to a learner in the form of a virtual tutor. We use ontologies defining pedagogical, didactic and learner-specific data describing a learner's progress, learning history, capabilities and the learner's current state within the learning environment. Learning recommendations are based on a reasoning process on these ontologies and can be provided in real-time. The ontologies may describe learning content from any domain of knowledge. Furthermore, we describe an approach to store learning histories as spatio-temporal trajectories and to correlate them with influencing didactic factors. We show how such analysis of spatiotemporal data can be used for learning analytics to improve future adaptive learning environments.

  20. Anticipatory Learning for Climate Change Adaptation and Resilience

    Directory of Open Access Journals (Sweden)

    Petra Tschakert

    2010-06-01

    Full Text Available This paper is a methodological contribution to emerging debates on the role of learning, particularly forward-looking (anticipatory learning, as a key element for adaptation and resilience in the context of climate change. First, we describe two major challenges: understanding adaptation as a process and recognizing the inadequacy of existing learning tools, with a specific focus on high poverty contexts and complex livelihood-vulnerability risks. Then, the article examines learning processes from a dynamic systems perspective, comparing theoretical aspects and conceptual advances in resilience thinking and action research/learning (AR/AL. Particular attention is paid to learning loops (cycles, critical reflection, spaces for learning, and power. Finally, we outline a methodological framework to facilitate iterative learning processes and adaptive decision making in practice. We stress memory, monitoring of key drivers of change, scenario planning, and measuring anticipatory capacity as crucial ingredients. Our aim is to identify opportunities and obstacles for forward-looking learning processes at the intersection of climatic uncertainty and development challenges in Africa, with the overarching objective to enhance adaptation and resilient livelihood pathways, rather than learning by shock.

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

    Science.gov (United States)

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

    2017-01-01

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

  2. Creating adaptive environment for e-learning courses

    Directory of Open Access Journals (Sweden)

    Bozidar Radenkovic

    2009-06-01

    Full Text Available In this paper we provide an approach to creating adaptive environment for e-learning courses. In the context of e-education, successful adaptation has to be performed upon learners’ characteristics. Currently, modeling and discovering users’ needs, goals, knowledge preferences and motivations is one of the most challenging tasks in e-learning systems that deal with large volumes of information. Primary goal of the research is to perform personalizing of distance education system, according to students’ learning styles. Main steps and requirements in applying business intelligence techniques in process of personalization are identified. In addition, we propose generic model and architecture of an adaptive e-learning system by describing the structure of an adaptive course and exemplify correlations among e-learning course content and different learning styles. Moreover, research that dealt with application of data mining technique in a real e-learning system was carried out. We performed adaptation of our e-learning courses using the results from the research.

  3. Quantifying Functional Reuse from Object Oriented Requirements Specifications

    NARCIS (Netherlands)

    Condori-Fernandez, Nelly; Condori-Fernández, N.; Pastor, O; Daneva, Maia; Abran, A.; Castro, J.; Quer, C.; Carvallo, J. B.; Fernandes da Silva, L.

    2008-01-01

    Software reuse is essential in improving efficiency and productivity in the software development process. This paper analyses reuse within requirements engineering phase by taking and adapting a standard functional size measurement method, COSMIC FFP. Our proposal attempts to quantify reusability

  4. LO + EPSS = Just-in-Time Reuse of Content to Support Employee Performance

    Science.gov (United States)

    Nguyen, Frank; Hanzel, Matthew

    2007-01-01

    Those involved in training know that creating instructional materials can become a tedious, repetitive process. They also know that business conditions often require training interventions to be delivered in ways that are not ideally structured or timed. This article examines the notion that learning objects can be reused and adapted for…

  5. Adaptive learning fuzzy control of a mobile robot

    International Nuclear Information System (INIS)

    Tsukada, Akira; Suzuki, Katsuo; Fujii, Yoshio; Shinohara, Yoshikuni

    1989-11-01

    In this report a problem is studied to construct a fuzzy controller for a mobile robot to move autonomously along a given reference direction curve, for which control rules are generated and acquired through an adaptive learning process. An adaptive learning fuzzy controller has been developed for a mobile robot. Good properties of the controller are shown through the travelling experiments of the mobile robot. (author)

  6. Towards adaptation in e-learning 2.0

    Science.gov (United States)

    Cristea, Alexandra I.; Ghali, Fawaz

    2011-04-01

    This paper presents several essential steps from an overall study on shaping new ways of learning and teaching, by using the synergetic merger of three different fields: Web 2.0, e-learning and adaptation (in particular, personalisation to the learner). These novel teaching and learning ways-the latter focus of this paper-are reflected in and finally adding to various versions of the My Online Teacher 2.0 adaptive system. In particular, this paper focuses on a study of how to more effectively use and combine the recommendation of peers and content adaptation to enhance the learning outcome in e-learning systems based on Web 2.0. In order to better isolate and examine the effects of peer recommendation and adaptive content presentation, we designed experiments inspecting collaboration between individuals based on recommendation of peers who have greater knowledge, and compare this to adaptive content recommendation, as well as to "simple" learning in a system with a minimum of Web 2.0 support. Overall, the results of adding peer recommendation and adaptive content presentation were encouraging, and are further discussed in detail in this paper.

  7. Adaptive strategies for cumulative cultural learning.

    Science.gov (United States)

    Ehn, Micael; Laland, Kevin

    2012-05-21

    The demographic and ecological success of our species is frequently attributed to our capacity for cumulative culture. However, it is not yet known how humans combine social and asocial learning to generate effective strategies for learning in a cumulative cultural context. Here we explore how cumulative culture influences the relative merits of various pure and conditional learning strategies, including pure asocial and social learning, critical social learning, conditional social learning and individual refiner strategies. We replicate the Rogers' paradox in the cumulative setting. However, our analysis suggests that strategies that resolved Rogers' paradox in a non-cumulative setting may not necessarily evolve in a cumulative setting, thus different strategies will optimize cumulative and non-cumulative cultural learning. Copyright © 2012 Elsevier Ltd. All rights reserved.

  8. Integrating Adaptive Games in Student-Centered Virtual Learning Environments

    Science.gov (United States)

    del Blanco, Angel; Torrente, Javier; Moreno-Ger, Pablo; Fernandez-Manjon, Baltasar

    2010-01-01

    The increasing adoption of e-Learning technology is facing new challenges, such as how to produce student-centered systems that can be adapted to each student's needs. In this context, educational video games are proposed as an ideal medium to facilitate adaptation and tracking of students' performance for assessment purposes, but integrating the…

  9. Applications of Adaptive Learning Controller to Synthetic Aperture Radar.

    Science.gov (United States)

    1985-02-01

    TERMS (Continue on retuerse if necessary and identify by block num ber) FIELD YGROUP SUB. GR. Adaptive control, aritificial intelligence , synthetic aetr1...application of Artificial Intelligence methods to Synthetic Aperture Radars (SARs) is investigated. It was shown that the neuron-like Adaptive Learning...wavelength Al SE!RI M RADAR DIVISION REFERENCES 1. Barto, A.G. and R.S. Sutton, Goal Seeking Components for Adaptive Intelligence : An Initial Assessment

  10. Urban regeneration in the context of climate change, on the example of adaptive re-use of the military complex in the Bela Crkva

    Directory of Open Access Journals (Sweden)

    Manić Božidar

    2015-01-01

    Full Text Available The challenges of sustainable development and climate change open up the new possibilities in understanding the importance, principles and methodologies of urban renewal. The paper discusses the problem of abandoned buildings and complexes and their possible reuse in the context of climate change and sustainability at an example of the study of repurposing the military barracks complex in Bela Crkva conducted in the Institute of Architecture and Urban & Spatial Planning of Serbia in 2012. The conceptual solution of the variant 2, based on the idea of climate-aware and socially responsible design, has been embedded into the basic program scheme of the new complex, which includes the establishment of a university campus, construction of experimental social housing, cultural and artistic facilities, as well as urban agriculture, including the use of alternative energy sources and respecting the principles of energy efficiency. The problems of the abandonment of military buildings and complexes, as well as the recommendations for their adaptation, taking into account the past experience and the current environmental problems, are pointed out in the concluding remarks.

  11. Design Framework for an Adaptive MOOC Enhanced by Blended Learning

    DEFF Research Database (Denmark)

    Gynther, Karsten

    2016-01-01

    The research project has developed a design framework for an adaptive MOOC that complements the MOOC format with blended learning. The design framework consists of a design model and a series of learning design principles which can be used to design in-service courses for teacher professional...

  12. PERSO: Towards an Adaptive e-Learning System

    Science.gov (United States)

    Chorfi, Henda; Jemni, Mohamed

    2004-01-01

    In today's information technology society, members are increasingly required to be up to date on new technologies, particularly for computers, regardless of their background social situation. In this context, our aim is to design and develop an adaptive hypermedia e-learning system, called PERSO (PERSOnalizing e-learning system), where learners…

  13. Student Modelling in Adaptive E-Learning Systems

    Directory of Open Access Journals (Sweden)

    Clemens Bechter

    2011-09-01

    Full Text Available Most e-Learning systems provide web-based learning so that students can access the same online courses via the Internet without adaptation, based on each student's profile and behavior. In an e-Learning system, one size does not fit all. Therefore, it is a challenge to make e-Learning systems that are suitably “adaptive”. The aim of adaptive e-Learning is to provide the students the appropriate content at the right time, means that the system is able to determine the knowledge level, keep track of usage, and arrange content automatically for each student for the best learning result. This study presents a proposed system which includes major adaptive features based on a student model. The proposed system is able to initialize the student model for determining the knowledge level of a student when the student registers for the course. After a student starts learning the lessons and doing many activities, the system can track information of the student until he/she takes a test. The student’s knowledge level, based on the test scores, is updated into the system for use in the adaptation process, which combines the student model with the domain model in order to deliver suitable course contents to the students. In this study, the proposed adaptive e-Learning system is implemented on an “Introduction to Java Programming Language” course, using LearnSquare software. After the system was tested, the results showed positive feedback towards the proposed system, especially in its adaptive capability.

  14. Towards a semantic learning model fostering learning object reusability

    OpenAIRE

    Fernandes , Emmanuel; Madhour , Hend; Wentland Forte , Maia; Miniaoui , Sami

    2005-01-01

    We try in this paper to propose a domain model for both author's and learner's needs concerning learning objects reuse. First of all, we present four key criteria for an efficient authoring tool: adaptive level of granularity, flexibility, integration and interoperability. Secondly, we introduce and describe our six-level Semantic Learning Model (SLM) designed to facilitate multi-level reuse of learning materials and search by defining a multi-layer model for metadata. Finally, after mapping ...

  15. Conformal prediction for reliable machine learning theory, adaptations and applications

    CERN Document Server

    Balasubramanian, Vineeth; Vovk, Vladimir

    2014-01-01

    The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detecti

  16. learning for Climate Change adaptation among Selected ...

    African Journals Online (AJOL)

    and coping strategies for, climate change (Gangwar, 2010). In order to adapt to the ..... and forest management were proposed the most among communities. Proposed educational ...... ethical values (McDonald, 2008). The deep meaning of ...

  17. Adaptive e-learning system using ontology

    OpenAIRE

    Yarandi, Maryam; Tawil, Abdel-Rahman; Jahankhani, Hossein

    2011-01-01

    This paper proposes an innovative ontological approach to design a personalised e-learning system which creates a tailored workflow for individual learner. Moreover, the learning content and sequencing logic is separated into content model and pedagogical model to increase the reusability and flexibility of the system.

  18. Using assistive technology adaptations to include students with learning disabilities in cooperative learning activities.

    Science.gov (United States)

    Bryant, D P; Bryant, B R

    1998-01-01

    Cooperative learning (CL) is a common instructional arrangement that is used by classroom teachers to foster academic achievement and social acceptance of students with and without learning disabilities. Cooperative learning is appealing to classroom teachers because it can provide an opportunity for more instruction and feedback by peers than can be provided by teachers to individual students who require extra assistance. Recent studies suggest that students with LD may need adaptations during cooperative learning activities. The use of assistive technology adaptations may be necessary to help some students with LD compensate for their specific learning difficulties so that they can engage more readily in cooperative learning activities. A process for integrating technology adaptations into cooperative learning activities is discussed in terms of three components: selecting adaptations, monitoring the use of the adaptations during cooperative learning activities, and evaluating the adaptations' effectiveness. The article concludes with comments regarding barriers to and support systems for technology integration, technology and effective instructional practices, and the need to consider technology adaptations for students who have learning disabilities.

  19. Adaptive nodes enrich nonlinear cooperative learning beyond traditional adaptation by links.

    Science.gov (United States)

    Sardi, Shira; Vardi, Roni; Goldental, Amir; Sheinin, Anton; Uzan, Herut; Kanter, Ido

    2018-03-23

    Physical models typically assume time-independent interactions, whereas neural networks and machine learning incorporate interactions that function as adjustable parameters. Here we demonstrate a new type of abundant cooperative nonlinear dynamics where learning is attributed solely to the nodes, instead of the network links which their number is significantly larger. The nodal, neuronal, fast adaptation follows its relative anisotropic (dendritic) input timings, as indicated experimentally, similarly to the slow learning mechanism currently attributed to the links, synapses. It represents a non-local learning rule, where effectively many incoming links to a node concurrently undergo the same adaptation. The network dynamics is now counterintuitively governed by the weak links, which previously were assumed to be insignificant. This cooperative nonlinear dynamic adaptation presents a self-controlled mechanism to prevent divergence or vanishing of the learning parameters, as opposed to learning by links, and also supports self-oscillations of the effective learning parameters. It hints on a hierarchical computational complexity of nodes, following their number of anisotropic inputs and opens new horizons for advanced deep learning algorithms and artificial intelligence based applications, as well as a new mechanism for enhanced and fast learning by neural networks.

  20. Evolutionary and adaptive learning in complex markets: a brief summary

    Science.gov (United States)

    Hommes, Cars H.

    2007-06-01

    We briefly review some work on expectations and learning in complex markets, using the familiar demand-supply cobweb model. We discuss and combine two different approaches on learning. According to the adaptive learning approach, agents behave as econometricians using time series observations to form expectations, and update the parameters as more observations become available. This approach has become popular in macro. The second approach has an evolutionary flavor and is sometimes referred to as reinforcement learning. Agents employ different forecasting strategies and evaluate these strategies based upon a fitness measure, e.g. past realized profits. In this framework, boundedly rational agents switch between different, but fixed behavioral rules. This approach has become popular in finance. We combine evolutionary and adaptive learning to model complex markets and discuss whether this theory can match empirical facts and forecasting behavior in laboratory experiments with human subjects.

  1. E-Learning and Personalized Learning Path: A Proposal Based on the Adaptive Educational Hypermedia System

    Directory of Open Access Journals (Sweden)

    Francesco Colace

    2014-03-01

    Full Text Available The E-Learning is becoming an effective approach for the improving of quality of learning. Many institutions are adopting this approach both to improve their traditional courses both to increase the potential audience. In the last period great attention is paid in the introduction of methodologies and techniques for the adaptation of learning process to the real needs of students. In this scenario the Adaptive Educational Hypermedia System can be an effective approach. Adaptive hypermedia is a promising area of research at the crossroads of hypermedia and adaptive systems. One of the most important fields where this approach can be applied is just the e-Learning. In this context the adaptive learning resources selection and sequencing is recognized as among one of the most interesting research questions. An Adaptive Educational Hypermedia System is composed by services for the management of the Knowledge Space, the definition of a User Model, the observation of student during his learning period and, as previously said, the adaptation of the learning path according to the real needs of the students. In particular the use of ontologyཿs formalism for the modeling of the ཿknowledge space࿝ related to the course can increase the sharable of learning objects among similar courses or better contextualize their role in the course. This paper addresses the design problem of an Adaptive hypermedia system by the definition of methodologies able to manage each its components, In particular an original user, learning contents, tracking strategies and adaptation model are developed. The proposed Adaptive Educational Hypermedia System has been integrated in an e-Learning platform and an experimental campaign has been conducted. In particular the proposed approach has been introduced in three different blended courses. A comparison with traditional approach has been described and the obtained results seem to be very promising.

  2. The immune system, adaptation, and machine learning

    Science.gov (United States)

    Farmer, J. Doyne; Packard, Norman H.; Perelson, Alan S.

    1986-10-01

    The immune system is capable of learning, memory, and pattern recognition. By employing genetic operators on a time scale fast enough to observe experimentally, the immune system is able to recognize novel shapes without preprogramming. Here we describe a dynamical model for the immune system that is based on the network hypothesis of Jerne, and is simple enough to simulate on a computer. This model has a strong similarity to an approach to learning and artificial intelligence introduced by Holland, called the classifier system. We demonstrate that simple versions of the classifier system can be cast as a nonlinear dynamical system, and explore the analogy between the immune and classifier systems in detail. Through this comparison we hope to gain insight into the way they perform specific tasks, and to suggest new approaches that might be of value in learning systems.

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

    NARCIS (Netherlands)

    Burgos, Daniel; Specht, Marcus

    2006-01-01

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

  4. Women, Subjectivities and Learning to Be Adaptable

    Science.gov (United States)

    Cavanagh, Jillian

    2010-01-01

    Purpose: The purpose of this paper is to advance understandings of the subjectivities that influence auxiliary-level female employees' work and learning experiences in general legal practice. Moreover, the aim is to maximise the opportunities for these workers. Design/methodology/approach: A broader critical ethnographic study investigated…

  5. Adaptation of mathematical educational content in e-learning resources

    Directory of Open Access Journals (Sweden)

    Yuliya V. Vainshtein

    2017-01-01

    Full Text Available Modern trends in the world electronic educational system development determine the necessity of adaptive learning intellectual environments and resources’ development and implementation. An upcoming trend in improvement the quality of studying mathematical disciplines is the development and application of adaptive electronic educational resources. However, the development and application experience of adaptive technologies in higher education is currently extremely limited and does not imply the usage flexibility. Adaptive educational resources in the electronic environment are electronic educational resources that provide the student with a personal educational space, filled with educational content that “adapts” to the individual characteristics of the students and provides them with the necessary information.This article focuses on the mathematical educational content adaptation algorithms development and their implementation in the e-learning system. The peculiarity of the proposed algorithms is the possibility of their application and distribution for adaptive e-learning resources construction. The novelty of the proposed approach is the three-step content organization of the adaptive algorithms for the educational content: “introductory adaptation of content”, “the current adaptation of content”, “estimative and a corrective adaptation”. For each stage of the proposed system, mathematical algorithms for educational content adaptation in adaptive e-learning resources are presented.Due to the high level of abstraction and complexity perception of mathematical disciplines, educational content is represented in the various editions of presentation that correspond to the levels of assimilation of the course material. Adaptation consists in the selection of the optimal edition of the material that best matches the individual characteristics of the student. The introduction of a three-step content organization of the adaptive

  6. Learning and adaptation: neural and behavioural mechanisms behind behaviour change

    Science.gov (United States)

    Lowe, Robert; Sandamirskaya, Yulia

    2018-01-01

    This special issue presents perspectives on learning and adaptation as they apply to a number of cognitive phenomena including pupil dilation in humans and attention in robots, natural language acquisition and production in embodied agents (robots), human-robot game play and social interaction, neural-dynamic modelling of active perception and neural-dynamic modelling of infant development in the Piagetian A-not-B task. The aim of the special issue, through its contributions, is to highlight some of the critical neural-dynamic and behavioural aspects of learning as it grounds adaptive responses in robotic- and neural-dynamic systems.

  7. Optical implementations of associative networks with versatile adaptive learning capabilities.

    Science.gov (United States)

    Fisher, A D; Lippincott, W L; Lee, J N

    1987-12-01

    Optical associative, parallel-processing architectures are being developed using a multimodule approach, where a number of smaller, adaptive, associative modules are nonlinearly interconnected and cascaded under the guidance of a variety of organizational principles to structure larger architectures for solving specific problems. A number of novel optical implementations with versatile adaptive learning capabilities are presented for the individual associative modules, including holographic configurations and five specific electrooptic configurations. The practical issues involved in real optical architectures are analyzed, and actual laboratory optical implementations of associative modules based on Hebbian and Widrow-Hoff learning rules are discussed, including successful experimental demonstrations of their operation.

  8. Soft systems thinking and social learning for adaptive management.

    Science.gov (United States)

    Cundill, G; Cumming, G S; Biggs, D; Fabricius, C

    2012-02-01

    The success of adaptive management in conservation has been questioned and the objective-based management paradigm on which it is based has been heavily criticized. Soft systems thinking and social-learning theory expose errors in the assumption that complex systems can be dispassionately managed by objective observers and highlight the fact that conservation is a social process in which objectives are contested and learning is context dependent. We used these insights to rethink adaptive management in a way that focuses on the social processes involved in management and decision making. Our approach to adaptive management is based on the following assumptions: action toward a common goal is an emergent property of complex social relationships; the introduction of new knowledge, alternative values, and new ways of understanding the world can become a stimulating force for learning, creativity, and change; learning is contextual and is fundamentally about practice; and defining the goal to be addressed is continuous and in principle never ends. We believe five key activities are crucial to defining the goal that is to be addressed in an adaptive-management context and to determining the objectives that are desirable and feasible to the participants: situate the problem in its social and ecological context; raise awareness about alternative views of a problem and encourage enquiry and deconstruction of frames of reference; undertake collaborative actions; and reflect on learning. ©2011 Society for Conservation Biology.

  9. Biomimetic molecular design tools that learn, evolve, and adapt

    Directory of Open Access Journals (Sweden)

    David A Winkler

    2017-06-01

    Full Text Available A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known “S curve”, with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine.

  10. Biomimetic molecular design tools that learn, evolve, and adapt

    Science.gov (United States)

    2017-01-01

    A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known “S curve”, with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine. PMID:28694872

  11. 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...

  12. Beyond adaptive-critic creative learning for intelligent mobile robots

    Science.gov (United States)

    Liao, Xiaoqun; Cao, Ming; Hall, Ernest L.

    2001-10-01

    Intelligent industrial and mobile robots may be considered proven technology in structured environments. Teach programming and supervised learning methods permit solutions to a variety of applications. However, we believe that to extend the operation of these machines to more unstructured environments requires a new learning method. Both unsupervised learning and reinforcement learning are potential candidates for these new tasks. The adaptive critic method has been shown to provide useful approximations or even optimal control policies to non-linear systems. The purpose of this paper is to explore the use of new learning methods that goes beyond the adaptive critic method for unstructured environments. The adaptive critic is a form of reinforcement learning. A critic element provides only high level grading corrections to a cognition module that controls the action module. In the proposed system the critic's grades are modeled and forecasted, so that an anticipated set of sub-grades are available to the cognition model. The forecasting grades are interpolated and are available on the time scale needed by the action model. The success of the system is highly dependent on the accuracy of the forecasted grades and adaptability of the action module. Examples from the guidance of a mobile robot are provided to illustrate the method for simple line following and for the more complex navigation and control in an unstructured environment. The theory presented that is beyond the adaptive critic may be called creative theory. Creative theory is a form of learning that models the highest level of human learning - imagination. The application of the creative theory appears to not only be to mobile robots but also to many other forms of human endeavor such as educational learning and business forecasting. Reinforcement learning such as the adaptive critic may be applied to known problems to aid in the discovery of their solutions. The significance of creative theory is that it

  13. Software Reuse in the Naval Open Architecture

    National Research Council Canada - National Science Library

    Greathouse, Carlus A

    2008-01-01

    This thesis describes a web-based continuous learning module (CLM) for use in introducing members of the Department of the Navy s acquisition community to software reuse in the context of Naval Open Architecture...

  14. Adaptive E- Learning System Based on Personalized Learning Style

    African Journals Online (AJOL)

    pc

    2018-03-05

    Mar 5, 2018 ... motivation to this research is to improve the learner performance and achieve the ... valuable factor for enhancing learning process by adopting an effective .... Video. Reflective Intuitive. Primer Test. Verbal Sequential. Tutorial.

  15. Generalized projective synchronization of chaotic systems via adaptive learning control

    International Nuclear Information System (INIS)

    Yun-Ping, Sun; Jun-Min, Li; Hui-Lin, Wang; Jiang-An, Wang

    2010-01-01

    In this paper, a learning control approach is applied to the generalized projective synchronisation (GPS) of different chaotic systems with unknown periodically time-varying parameters. Using the Lyapunov–Krasovskii functional stability theory, a differential-difference mixed parametric learning law and an adaptive learning control law are constructed to make the states of two different chaotic systems asymptotically synchronised. The scheme is successfully applied to the generalized projective synchronisation between the Lorenz system and Chen system. Moreover, numerical simulations results are used to verify the effectiveness of the proposed scheme. (general)

  16. Adaptive learning algorithms for vibration energy harvesting

    International Nuclear Information System (INIS)

    Ward, John K; Behrens, Sam

    2008-01-01

    By scavenging energy from their local environment, portable electronic devices such as MEMS devices, mobile phones, radios and wireless sensors can achieve greater run times with potentially lower weight. Vibration energy harvesting is one such approach where energy from parasitic vibrations can be converted into electrical energy through the use of piezoelectric and electromagnetic transducers. Parasitic vibrations come from a range of sources such as human movement, wind, seismic forces and traffic. Existing approaches to vibration energy harvesting typically utilize a rectifier circuit, which is tuned to the resonant frequency of the harvesting structure and the dominant frequency of vibration. We have developed a novel approach to vibration energy harvesting, including adaptation to non-periodic vibrations so as to extract the maximum amount of vibration energy available. Experimental results of an experimental apparatus using an off-the-shelf transducer (i.e. speaker coil) show mechanical vibration to electrical energy conversion efficiencies of 27–34%

  17. Adaptive Knowledge Management of Project-Based Learning

    Science.gov (United States)

    Tilchin, Oleg; Kittany, Mohamed

    2016-01-01

    The goal of an approach to Adaptive Knowledge Management (AKM) of project-based learning (PBL) is to intensify subject study through guiding, inducing, and facilitating development knowledge, accountability skills, and collaborative skills of students. Knowledge development is attained by knowledge acquisition, knowledge sharing, and knowledge…

  18. Adaptive Learning in Psychology: Wayfinding in the Digital Age

    Science.gov (United States)

    Dziuban, Charles D.; Moskal, Patsy D.; Cassisi, Jeffrey; Fawcett, Alexis

    2016-01-01

    This paper presents the results of a pilot study investigating the use of the Realizeit adaptive learning platform to deliver a fully online General Psychology course across two semesters. Through mutual cooperation, UCF and vendor (CCKF) researchers examined students' affective, behavioral, and cognitive reactions to the system. Student survey…

  19. Learner Profile Management for Collaborative Adaptive eLearning Application

    DEFF Research Database (Denmark)

    Alrifai, Mohammad; Dolog, Peter; Nejdl, Wolfgang

    2006-01-01

    Adaptive Learning Systems would perform better if they would be able to exchange as many relevant fragments of information about the learner as possible. The use of Web Services standards is recently gaining the attention of many researches as a promising solution for the problem of interfacing a...

  20. Active learning and adaptive sampling for non-parametric inference

    NARCIS (Netherlands)

    Castro, R.M.

    2007-01-01

    This thesis presents a general discussion of active learning and adaptive sampling. In many practical scenarios it is possible to use information gleaned from previous observations to focus the sampling process, in the spirit of the "twenty-questions" game. As more samples are collected one can

  1. Heterogeneous Users in MOOC and their Adaptive Learning Needs

    Directory of Open Access Journals (Sweden)

    María Luisa SEIN-ECHALUCE LACLETA

    2017-02-01

    Full Text Available Many research works point out the overcrowding and the heterogeneity of participant’s profiles in Massive Open Online Courses (MOOC as the main causes of their low completion rate. On the other hand, the methodologies of personalization of the learning, along next to the technologies of the information, that allows to realize techniques of adaptativity, appear in international reports as an effective way to improve the learning. This paper explores the participante’ perception of their adaptive needs in this tupe of course, as well as their relationship with different aspects of the participants, such as: profiles (gender, age, geographical location and academic level, previous experience and knowledge about the topic of the MOOC and motivation to enroll the MOOC. The study is carried out through a survey completes by the participants in the MOOC Campus of Educational Innovation. We conclude that the age or gender of the participants does not significantly influence their need for adaptive techniques in a MOOC. However, living in a Latin American country, working as a manager or enrolling in a MOOC with a specific motivation, are some of the factors that influence in the desire for adaptive techniques in a MOOC. The obtained results will contribute to improve the adaptive designs of the MOOC and will be easily transferable to any online training course, in blended or virtual learning.

  2. Supporting Student Learning in Computer Science Education via the Adaptive Learning Environment ALMA

    Directory of Open Access Journals (Sweden)

    Alexandra Gasparinatou

    2015-10-01

    Full Text Available This study presents the ALMA environment (Adaptive Learning Models from texts and Activities. ALMA supports the processes of learning and assessment via: (1 texts differing in local and global cohesion for students with low, medium, and high background knowledge; (2 activities corresponding to different levels of comprehension which prompt the student to practically implement different text-reading strategies, with the recommended activity sequence adapted to the student’s learning style; (3 an overall framework for informing, guiding, and supporting students in performing the activities; and; (4 individualized support and guidance according to student specific characteristics. ALMA also, supports students in distance learning or in blended learning in which students are submitted to face-to-face learning supported by computer technology. The adaptive techniques provided via ALMA are: (a adaptive presentation and (b adaptive navigation. Digital learning material, in accordance with the text comprehension model described by Kintsch, was introduced into the ALMA environment. This material can be exploited in either distance or blended learning.

  3. Intelligent and Adaptive Educational-Learning Systems Achievements and Trends

    CERN Document Server

    2013-01-01

    The Smart Innovation, Systems and Technologies book series encompasses the topics of knowledge, intelligence, innovation and sustainability. The aim of the series is to make available a platform for the publication of books on all aspects of single and multi-disciplinary research on these themes in order to make the latest results available in a readily-accessible form.  This book is devoted to the “Intelligent and Adaptive Educational-Learning Systems”. It privileges works that highlight key achievements and outline trends to inspire future research.  After a rigorous revision process twenty manuscripts were accepted and organized into four parts as follows: ·     Modeling: The first part embraces five chapters oriented to: 1) shape the affective behavior; 2) depict the adaptive learning curriculum; 3) predict learning achievements; 4) mine learner models to outcome optimized and adaptive e-learning objects; 5) classify learning preferences of learners. ·     Content: The second part encompas...

  4. Enhancing Student Adaption to a Case Based Learning Environment

    DEFF Research Database (Denmark)

    Jensen, Lars Peter

    2010-01-01

    these at the end of the semester, showing the development of the student in terms of adapting to the learning model. The idea will be explained more closely in the final paper. RESEARCH METHOD The research part of the experiment was carried out as action research, as the teacher of the course in the same time......INTRODUCTION Since Aalborg University (AAU) was started it has been using an educational model, where Problem Based Learning is the turning point. Each semester the students on the Engineering Educations form groups of 3-6 persons, which uses half of the study time within the semester to solve......) in groups. It appeared to be difficult for the students to adapt to two different PBL approaches at the same time, and with the project being the most popular the learning outcome of the case studies was not satisfactory after the first semester, but improved on the following semesters. In 2009...

  5. Adaptive learning by extremal dynamics and negative feedback

    International Nuclear Information System (INIS)

    Bak, Per; Chialvo, Dante R.

    2001-01-01

    We describe a mechanism for biological learning and adaptation based on two simple principles: (i) Neuronal activity propagates only through the network's strongest synaptic connections (extremal dynamics), and (ii) the strengths of active synapses are reduced if mistakes are made, otherwise no changes occur (negative feedback). The balancing of those two tendencies typically shapes a synaptic landscape with configurations which are barely stable, and therefore highly flexible. This allows for swift adaptation to new situations. Recollection of past successes is achieved by punishing synapses which have once participated in activity associated with successful outputs much less than neurons that have never been successful. Despite its simplicity, the model can readily learn to solve complicated nonlinear tasks, even in the presence of noise. In particular, the learning time for the benchmark parity problem scales algebraically with the problem size N, with an exponent k∼1.4

  6. Adaptive Management of Communication in the Chamilo System of Distant Learning

    OpenAIRE

    Yatsenko Roman Nikolaevich; Polevich Olesya V.

    2012-01-01

    The article considers the communication management within an adaptive system of distance learning. We present two-circuit interaction system of the adaptive system. We consider the implementation of management communication in distance learning system based on the platform Chamilo.

  7. Selecting Learning Tasks: Effects of Adaptation and Shared Control on Learning Efficiency and Task Involvement

    Science.gov (United States)

    Corbalan, Gemma; Kester, Liesbeth; van Merrienboer, Jeroen J. G.

    2008-01-01

    Complex skill acquisition by performing authentic learning tasks is constrained by limited working memory capacity [Baddeley, A. D. (1992). Working memory. "Science, 255", 556-559]. To prevent cognitive overload, task difficulty and support of each newly selected learning task can be adapted to the learner's competence level and perceived task…

  8. Supporting Adaptive Learning Pathways through the Use of Learning Analytics: Developments, Challenges and Future Opportunities

    Science.gov (United States)

    Mavroudi, Anna; Giannakos, Michail; Krogstie, John

    2018-01-01

    Learning Analytics (LA) and adaptive learning are inextricably linked since they both foster technology-supported learner-centred education. This study identifies developments focusing on their interplay and emphasises insufficiently investigated directions which display a higher innovation potential. Twenty-one peer-reviewed studies are…

  9. Learning from sensory and reward prediction errors during motor adaptation.

    Science.gov (United States)

    Izawa, Jun; Shadmehr, Reza

    2011-03-01

    Voluntary motor commands produce two kinds of consequences. Initially, a sensory consequence is observed in terms of activity in our primary sensory organs (e.g., vision, proprioception). Subsequently, the brain evaluates the sensory feedback and produces a subjective measure of utility or usefulness of the motor commands (e.g., reward). As a result, comparisons between predicted and observed consequences of motor commands produce two forms of prediction error. How do these errors contribute to changes in motor commands? Here, we considered a reach adaptation protocol and found that when high quality sensory feedback was available, adaptation of motor commands was driven almost exclusively by sensory prediction errors. This form of learning had a distinct signature: as motor commands adapted, the subjects altered their predictions regarding sensory consequences of motor commands, and generalized this learning broadly to neighboring motor commands. In contrast, as the quality of the sensory feedback degraded, adaptation of motor commands became more dependent on reward prediction errors. Reward prediction errors produced comparable changes in the motor commands, but produced no change in the predicted sensory consequences of motor commands, and generalized only locally. Because we found that there was a within subject correlation between generalization patterns and sensory remapping, it is plausible that during adaptation an individual's relative reliance on sensory vs. reward prediction errors could be inferred. We suggest that while motor commands change because of sensory and reward prediction errors, only sensory prediction errors produce a change in the neural system that predicts sensory consequences of motor commands.

  10. Adaptive Learning and Thinking Style to Improve E-Learning Environment Using Neural Network (ALTENN) Model

    OpenAIRE

    Dagez, Hanan Ettaher; Ambarka, Ali Elghali

    2015-01-01

     In recent years we have witnessed an increasingly heightened awareness of the potential benefits of adaptively in e-learning. This has been mainly driven by the realization that the ideal of individualized learning (i.e., learning tailored to the specific requirements and preferences of the individual) cannot be achieved, especially at a “massive” scale, using traditional approaches. In e-learning when the learning style of the student is not compatible with the teaching style of the teacher...

  11. Adaptive E-learning System in Secondary Education

    Directory of Open Access Journals (Sweden)

    Sofija Tosheva

    2012-02-01

    Full Text Available In this paper we describe an adaptive web application E-school, where students can adjust some features according to their preferences and learning style. This e-learning environment enables monitoring students progress, total time students have spent in the system, their activity on the forums, the overall achievements in lessons learned, tests performed and solutions to given projects. Personalized assistance that teacher provides in a traditional classroom is not easy to implement. Students have regular contact with teachers using e-mail tools and conversation, so teacher get mentoring role for each student. The results of exploitation of the e-learning system show positive impact in acquiring the material and improvement of student’s achievements.

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

    Directory of Open Access Journals (Sweden)

    Wei Xue

    2016-08-01

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

  13. Deep reinforcement learning for automated radiation adaptation in lung cancer.

    Science.gov (United States)

    Tseng, Huan-Hsin; Luo, Yi; Cui, Sunan; Chien, Jen-Tzung; Ten Haken, Randall K; Naqa, Issam El

    2017-12-01

    To investigate deep reinforcement learning (DRL) based on historical treatment plans for developing automated radiation adaptation protocols for nonsmall cell lung cancer (NSCLC) patients that aim to maximize tumor local control at reduced rates of radiation pneumonitis grade 2 (RP2). In a retrospective population of 114 NSCLC patients who received radiotherapy, a three-component neural networks framework was developed for deep reinforcement learning (DRL) of dose fractionation adaptation. Large-scale patient characteristics included clinical, genetic, and imaging radiomics features in addition to tumor and lung dosimetric variables. First, a generative adversarial network (GAN) was employed to learn patient population characteristics necessary for DRL training from a relatively limited sample size. Second, a radiotherapy artificial environment (RAE) was reconstructed by a deep neural network (DNN) utilizing both original and synthetic data (by GAN) to estimate the transition probabilities for adaptation of personalized radiotherapy patients' treatment courses. Third, a deep Q-network (DQN) was applied to the RAE for choosing the optimal dose in a response-adapted treatment setting. This multicomponent reinforcement learning approach was benchmarked against real clinical decisions that were applied in an adaptive dose escalation clinical protocol. In which, 34 patients were treated based on avid PET signal in the tumor and constrained by a 17.2% normal tissue complication probability (NTCP) limit for RP2. The uncomplicated cure probability (P+) was used as a baseline reward function in the DRL. Taking our adaptive dose escalation protocol as a blueprint for the proposed DRL (GAN + RAE + DQN) architecture, we obtained an automated dose adaptation estimate for use at ∼2/3 of the way into the radiotherapy treatment course. By letting the DQN component freely control the estimated adaptive dose per fraction (ranging from 1-5 Gy), the DRL automatically favored dose

  14. 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.

  15. Climate change, mitigation and adaptation with uncertainty and learning

    International Nuclear Information System (INIS)

    Ingham, Alan; Ma Jie; Ulph, Alistair

    2007-01-01

    One of the major issues in climate change policy is how to deal with the considerable uncertainty that surrounds many of the elements. Some of these uncertainties will be resolved through the process of further research. This process of learning raises a crucial timing question: should society delay taking action in anticipation of obtaining better information, or should it accelerate action, because we might learn that climate change is much more serious than expected. Much of the analysis to date has focussed on the case where the actions available to society are just the mitigation of emissions, and where there is little or no role for learning. We extend the analysis to allow for both mitigation and adaptation. We show that including adaptation weakens the effect of the irreversibility constraint and so, for this model, makes it more likely that the prospect of future learning should lead to less action now to deal with climate change. We review the empirical literature on climate change policy with uncertainty, learning, and irreversibility, and show that to date the effects on current policy are rather small, though this may reflect the particular choice of models employed

  16. Adaptation and learning: characteristic time scales of performance dynamics.

    Science.gov (United States)

    Newell, Karl M; Mayer-Kress, Gottfried; Hong, S Lee; Liu, Yeou-Teh

    2009-12-01

    A multiple time scales landscape model is presented that reveals structures of performance dynamics that were not resolved in the traditional power law analysis of motor learning. It shows the co-existence of separate processes during and between practice sessions that evolve in two independent dimensions characterized by time scales that differ by about an order of magnitude. Performance along the slow persistent dimension of learning improves often as much and sometimes more during rest (memory consolidation and/or insight generation processes) than during a practice session itself. In contrast, the process characterized by the fast, transient dimension of adaptation reverses direction between practice sessions, thereby significantly degrading performance at the beginning of the next practice session (warm-up decrement). The theoretical model fits qualitatively and quantitatively the data from Snoddy's [Snoddy, G. S. (1926). Learning and stability. Journal of Applied Psychology, 10, 1-36] classic learning study of mirror tracing and other averaged and individual data sets, and provides a new account of the processes of change in adaptation and learning. 2009 Elsevier B.V. All rights reserved.

  17. A globally convergent MC algorithm with an adaptive learning rate.

    Science.gov (United States)

    Peng, Dezhong; Yi, Zhang; Xiang, Yong; Zhang, Haixian

    2012-02-01

    This brief deals with the problem of minor component analysis (MCA). Artificial neural networks can be exploited to achieve the task of MCA. Recent research works show that convergence of neural networks based MCA algorithms can be guaranteed if the learning rates are less than certain thresholds. However, the computation of these thresholds needs information about the eigenvalues of the autocorrelation matrix of data set, which is unavailable in online extraction of minor component from input data stream. In this correspondence, we introduce an adaptive learning rate into the OJAn MCA algorithm, such that its convergence condition does not depend on any unobtainable information, and can be easily satisfied in practical applications.

  18. Adapting to managed care by becoming a learning organization.

    Science.gov (United States)

    O'Sullivan, M J

    1999-03-01

    In the tumultuous and chaotic environment of managed health care, hospital-based mental health providers need to change in fundamental ways. The traditional view of mental health organizations is a professional-bureaucratic one where actions and outcomes of planning are thought to be highly predictable. The author proposes an alternative paradigm for viewing mental health provider organizations, one based on learning theory, which accepts that the future is unknowable because of its complexity and the probabilistic nature of the world. Within this perspective, mental health care providers need to become "learning organizations" to successfully adapt to the new and evolving conditions.

  19. Preference learning with evolutionary Multivariate Adaptive Regression Spline model

    DEFF Research Database (Denmark)

    Abou-Zleikha, Mohamed; Shaker, Noor; Christensen, Mads Græsbøll

    2015-01-01

    This paper introduces a novel approach for pairwise preference learning through combining an evolutionary method with Multivariate Adaptive Regression Spline (MARS). Collecting users' feedback through pairwise preferences is recommended over other ranking approaches as this method is more appealing...... for function approximation as well as being relatively easy to interpret. MARS models are evolved based on their efficiency in learning pairwise data. The method is tested on two datasets that collectively provide pairwise preference data of five cognitive states expressed by users. The method is analysed...

  20. Adaptive Learning in Cartesian Product of Reproducing Kernel Hilbert Spaces

    OpenAIRE

    Yukawa, Masahiro

    2014-01-01

    We propose a novel adaptive learning algorithm based on iterative orthogonal projections in the Cartesian product of multiple reproducing kernel Hilbert spaces (RKHSs). The task is estimating/tracking nonlinear functions which are supposed to contain multiple components such as (i) linear and nonlinear components, (ii) high- and low- frequency components etc. In this case, the use of multiple RKHSs permits a compact representation of multicomponent functions. The proposed algorithm is where t...

  1. Operating boundaries of full-scale advanced water reuse treatment plants: many lessons learned from pilot plant experience.

    Science.gov (United States)

    Bele, C; Kumar, Y; Walker, T; Poussade, Y; Zavlanos, V

    2010-01-01

    Three Advanced Water Treatment Plants (AWTP) have recently been built in South East Queensland as part of the Western Corridor Recycled Water Project (WCRWP) producing Purified Recycled Water from secondary treated waste water for the purpose of indirect potable reuse. At Luggage Point, a demonstration plant was primarily operated by the design team for design verification. The investigation program was then extended so that the operating team could investigate possible process optimisation, and operation flexibility. Extending the demonstration plant investigation program enabled monitoring of the long term performance of the microfiltration and reverse osmosis membranes, which did not appear to foul even after more than a year of operation. The investigation primarily identified several ways to optimise the process. It highlighted areas of risk for treated water quality, such as total nitrogen. Ample and rapid swings of salinity from 850 to 3,000 mg/l-TDS were predicted to affect the RO process day-to-day operation and monitoring. Most of the setpoints used for monitoring under HACCP were determined during the pilot plant trials.

  2. Adaptation Criteria for the Personalised Delivery of Learning Materials: A Multi-Stage Empirical Investigation

    Science.gov (United States)

    Thalmann, Stefan

    2014-01-01

    Personalised e-Learning represents a major step-change from the one-size-fits-all approach of traditional learning platforms to a more customised and interactive provision of learning materials. Adaptive learning can support the learning process by tailoring learning materials to individual needs. However, this requires the initial preparation of…

  3. Masters of adaptation: learning in late life adjustments.

    Science.gov (United States)

    Roberson, Donald N

    2005-01-01

    The purpose of this research is to understand the relationship between human development in older adults and personal learning. Personal or self-directed learning (SDL) refers to a style of learning where the individual directs, controls, and evaluates what is learned. It may occur with formal classes, but most often takes place in non-formal situations. This study employed a descriptive qualitative design incorporating in-depth, semistructured interviews for data collection. The sample of 10 purposefully selected older adults from a rural area reflected diversity in gender, race, education, and employment. Data analysis was guided by the constant comparative method. The primary late life adjustments of these older adults were in response to having extra time, changes in family, and social and physical loss. This research also indicated that late life adjustments are a primary incentive for self-directed learning. The results of this study indicated that older adults become masters of adaptation through the use of self-directed learning activities.

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

    Science.gov (United States)

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

    2016-07-01

    the Strehl and the Point Spread Function used in night time adaptive optics but not really suitable to the solar systems, and new control strategies more complex than the ones used in nowadays solar Multi Conjugate Adaptive Optics systems. In this paper we summarize the lessons learned with past and current solar adaptive optics systems and focus on the discussion on the new alternatives to solve present open issues limiting their performance.

  5. Wastewater reuse. What can be learned from the Israel experience; Reutilizacion de aguas residuales. Que se puede aprender de la experiencia israeli

    Energy Technology Data Exchange (ETDEWEB)

    Juanico, M.

    2007-07-01

    In Israel, wastewater is defined as an integral part of the water resources of the country and massive water reuse has been performed for almost four decades. Today, reuse achieves 75% of the produced wastewater. The present paper analyzes the historical development of wastewater reuse in Israel, addressing main events, regulations, coexistence of projects of different sizes and characteristics, institutional organization, the contractual relationship between the urban and rural sectors, nutrients recycling, quality of the treated wastewater and the problem of salination of soils and aquifers. The paper gives an holistic and impartial description of the controversial issues that are presently discussed in the country regarding wastewater reuse. (Author) 42 refs.

  6. Online Adaptation and Over-Trial Learning in Macaque Visuomotor Control

    Science.gov (United States)

    Braun, Daniel A.; Aertsen, Ad; Paz, Rony; Vaadia, Eilon; Rotter, Stefan; Mehring, Carsten

    2011-01-01

    When faced with unpredictable environments, the human motor system has been shown to develop optimized adaptation strategies that allow for online adaptation during the control process. Such online adaptation is to be contrasted to slower over-trial learning that corresponds to a trial-by-trial update of the movement plan. Here we investigate the interplay of both processes, i.e., online adaptation and over-trial learning, in a visuomotor experiment performed by macaques. We show that simple non-adaptive control schemes fail to perform in this task, but that a previously suggested adaptive optimal feedback control model can explain the observed behavior. We also show that over-trial learning as seen in learning and aftereffect curves can be explained by learning in a radial basis function network. Our results suggest that both the process of over-trial learning and the process of online adaptation are crucial to understand visuomotor learning. PMID:21720526

  7. Greywater Treatment and Reuse

    Directory of Open Access Journals (Sweden)

    Gökhan Ekrem ÜSTÜN

    2015-07-01

    Full Text Available The aim of this study, to examine grey water treatment and reuse. For this aim, previous literature studies been research on and interpreted. Project began with study of physical, chemical and biological characteristics of the gray water. At the second part; grey water treatment and reuse were examined. At the third part; the technologies used for the methods treatment of gray water were explained. Then from costs and previous studies about grey water reuse were mentioned.

  8. Evolutionary online behaviour learning and adaptation in real robots.

    Science.gov (United States)

    Silva, Fernando; Correia, Luís; Christensen, Anders Lyhne

    2017-07-01

    Online evolution of behavioural control on real robots is an open-ended approach to autonomous learning and adaptation: robots have the potential to automatically learn new tasks and to adapt to changes in environmental conditions, or to failures in sensors and/or actuators. However, studies have so far almost exclusively been carried out in simulation because evolution in real hardware has required several days or weeks to produce capable robots. In this article, we successfully evolve neural network-based controllers in real robotic hardware to solve two single-robot tasks and one collective robotics task. Controllers are evolved either from random solutions or from solutions pre-evolved in simulation. In all cases, capable solutions are found in a timely manner (1 h or less). Results show that more accurate simulations may lead to higher-performing controllers, and that completing the optimization process in real robots is meaningful, even if solutions found in simulation differ from solutions in reality. We furthermore demonstrate for the first time the adaptive capabilities of online evolution in real robotic hardware, including robots able to overcome faults injected in the motors of multiple units simultaneously, and to modify their behaviour in response to changes in the task requirements. We conclude by assessing the contribution of each algorithmic component on the performance of the underlying evolutionary algorithm.

  9. Classification of multiple sclerosis lesions using adaptive dictionary learning.

    Science.gov (United States)

    Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

    2015-12-01

    This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

  10. Rich open educational resources for personal and inquiry learning : Agile creation, sharing and reuse in educational social media platforms

    NARCIS (Netherlands)

    Rodríguez-Triana, María Jesús; Govaerts, Sten; Halimi, Wissam; Holzer, Adrian; Salzmann, Christophe; Vozniuk, Andrii; De Jong, Ton; Sotirou, Sofoklis; Gillet, Denis

    2015-01-01

    Open Educational Resources (OERs) are freely accessible, openly licensed multimedia documents or interactive tools that can be typically integrated in Learning Management Systems to support courses. With social media platforms becoming the central piece of the students' digital ecosystem, there is

  11. Algebraic and adaptive learning in neural control systems

    Science.gov (United States)

    Ferrari, Silvia

    A systematic approach is developed for designing adaptive and reconfigurable nonlinear control systems that are applicable to plants modeled by ordinary differential equations. The nonlinear controller comprising a network of neural networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design. A critical observation is that the gradients of the functions defined by the neural networks must equal corresponding linear gain matrices at chosen operating points. On-line training is based on a dual heuristic adaptive critic architecture that improves control for large, coupled motions by accounting for actual plant dynamics and nonlinear effects. An action network computes the optimal control law; a critic network predicts the derivative of the cost-to-go with respect to the state. Both networks are algebraically initialized based on prior knowledge of satisfactory pointwise linear controllers and continue to adapt on line during full-scale simulations of the plant. On-line training takes place sequentially over discrete periods of time and involves several numerical procedures. A backpropagating algorithm called Resilient Backpropagation is modified and successfully implemented to meet these objectives, without excessive computational expense. This adaptive controller is as conservative as the linear designs and as effective as a global nonlinear controller. The method is successfully implemented for the full-envelope control of a six-degree-of-freedom aircraft simulation. The results show that the on-line adaptation brings about improved performance with respect to the initialization phase during aircraft maneuvers that involve large-angle and coupled dynamics, and parameter variations.

  12. Classification and Comparison of Architecture Evolution Reuse Knowledge - A Systematic Review

    DEFF Research Database (Denmark)

    Ahmad, Aakash; Jamshidi, Pooyan; Pahl, Claus

    2014-01-01

    patterns (34% of selected studies) represent a predominant solution, followed by evolution styles (25%) and adaptation strategies and policies (22%) to enable application of reuse knowledge. Empirical methods for acquisition of reuse knowledge represent 19% including pattern discovery, configuration...

  13. Clinical quality needs complex adaptive systems and machine learning.

    Science.gov (United States)

    Marsland, Stephen; Buchan, Iain

    2004-01-01

    The vast increase in clinical data has the potential to bring about large improvements in clinical quality and other aspects of healthcare delivery. However, such benefits do not come without cost. The analysis of such large datasets, particularly where the data may have to be merged from several sources and may be noisy and incomplete, is a challenging task. Furthermore, the introduction of clinical changes is a cyclical task, meaning that the processes under examination operate in an environment that is not static. We suggest that traditional methods of analysis are unsuitable for the task, and identify complexity theory and machine learning as areas that have the potential to facilitate the examination of clinical quality. By its nature the field of complex adaptive systems deals with environments that change because of the interactions that have occurred in the past. We draw parallels between health informatics and bioinformatics, which has already started to successfully use machine learning methods.

  14. Learning deep features with adaptive triplet loss for person reidentification

    Science.gov (United States)

    Li, Zhiqiang; Sang, Nong; Chen, Kezhou; Gao, Changxin; Wang, Ruolin

    2018-03-01

    Person reidentification (re-id) aims to match a specified person across non-overlapping cameras, which remains a very challenging problem. While previous methods mostly focus on feature extraction or metric learning, this paper makes the attempt in jointly learning both the global full-body and local body-parts features of the input persons with a multichannel convolutional neural network (CNN) model, which is trained by an adaptive triplet loss function that serves to minimize the distance between the same person and maximize the distance between different persons. The experimental results show that our approach achieves very promising results on the large-scale Market-1501 and DukeMTMC-reID datasets.

  15. Water Reuse Reconsidered

    Science.gov (United States)

    Environmental Science and Technology, 1975

    1975-01-01

    The Second National Conference on Complete WateReuse stressed better planning, management, and use of water. The sessions covered: water reuse and its problems; water's interface with air and land, and modification of these interactions by the imposition of energy; and heavy metals in the environment and methods for their removal. (BT)

  16. Water Reclamation and Reuse.

    Science.gov (United States)

    Smith, Daniel W.

    1978-01-01

    Presents a literature review of water reclamation and reuse. This review covers: (1) water resources planning; (2) agriculture and irrigation; (3) ground recharge; (4) industrial reuse; (5) health considerations; and (6) technology developments. A list of 217 references is also presented. (HM)

  17. How adaptive learning affects evolution: reviewing theory on the Baldwin effect

    NARCIS (Netherlands)

    Sznajder, B.; Sabelis, M.W.; Egas, M.

    2012-01-01

    We review models of the Baldwin effect, i.e., the hypothesis that adaptive learning (i.e., learning to improve fitness) accelerates genetic evolution of the phenotype. Numerous theoretical studies scrutinized the hypothesis that a non-evolving ability of adaptive learning accelerates evolution of

  18. The influence of student characteristics on the use of adaptive e-learning material

    NARCIS (Netherlands)

    van Seters, J. R.; Ossevoort, M. A.; Tramper, J.; Goedhart, M. J.

    Adaptive e-learning materials can help teachers to educate heterogeneous student groups. This study provides empirical data about the way academic students differ in their learning when using adaptive e-learning materials. Ninety-four students participated in the study. We determined characteristics

  19. Adapting online learning for Canada's Northern public health workforce

    Directory of Open Access Journals (Sweden)

    Marnie Bell

    2013-08-01

    Full Text Available Background . Canada's North is a diverse, sparsely populated land, where inequalities and public health issues are evident, particularly for Aboriginal people. The Northern public health workforce is a unique mix of professional and paraprofessional workers. Few have formal public health education. From 2009 to 2012, the Public Health Agency of Canada (PHAC collaborated with a Northern Advisory Group to develop and implement a strategy to strengthen public health capacity in Canada's 3 northern territories. Access to relevant, effective continuing education was identified as a key issue. Challenges include diverse educational and cultural backgrounds of public health workers, geographical isolation and variable technological infrastructure across the north. Methods . PHAC's Skills Online program offers Internet-based continuing education modules for public health professionals. In partnership with the Northern Advisory Group, PHAC conducted 3 pilots between 2008 and 2012 to assess the appropriateness of the Skills Online program for Northern/Aboriginal public health workers. Module content and delivery modalities were adapted for the pilots. Adaptations included adding Inuit and Northern public health examples and using video and teleconference discussions to augment the online self-study component. Results . Findings from the pilots were informative and similar to those from previous Skills Online pilots with learners in developing countries. Online learning is effective in bridging the geographical barriers in remote locations. Incorporating content on Northern and Aboriginal health issues facilitates engagement in learning. Employer support facilitates the recruitment and retention of learners in an online program. Facilitator assets included experience as a public health professional from the north, and flexibility to use modified approaches to support and measure knowledge acquisition and application, especially for First Nations, Inuit and

  20. Adapting online learning for Canada's Northern public health workforce.

    Science.gov (United States)

    Bell, Marnie; MacDougall, Karen

    2013-01-01

    Canada's North is a diverse, sparsely populated land, where inequalities and public health issues are evident, particularly for Aboriginal people. The Northern public health workforce is a unique mix of professional and paraprofessional workers. Few have formal public health education. From 2009 to 2012, the Public Health Agency of Canada (PHAC) collaborated with a Northern Advisory Group to develop and implement a strategy to strengthen public health capacity in Canada's 3 northern territories. Access to relevant, effective continuing education was identified as a key issue. Challenges include diverse educational and cultural backgrounds of public health workers, geographical isolation and variable technological infrastructure across the north. PHAC's Skills Online program offers Internet-based continuing education modules for public health professionals. In partnership with the Northern Advisory Group, PHAC conducted 3 pilots between 2008 and 2012 to assess the appropriateness of the Skills Online program for Northern/Aboriginal public health workers. Module content and delivery modalities were adapted for the pilots. Adaptations included adding Inuit and Northern public health examples and using video and teleconference discussions to augment the online self-study component. Findings from the pilots were informative and similar to those from previous Skills Online pilots with learners in developing countries. Online learning is effective in bridging the geographical barriers in remote locations. Incorporating content on Northern and Aboriginal health issues facilitates engagement in learning. Employer support facilitates the recruitment and retention of learners in an online program. Facilitator assets included experience as a public health professional from the north, and flexibility to use modified approaches to support and measure knowledge acquisition and application, especially for First Nations, Inuit and Metis learners. Results demonstrate that

  1. Reinforcement Learning Based Novel Adaptive Learning Framework for Smart Grid Prediction

    Directory of Open Access Journals (Sweden)

    Tian Li

    2017-01-01

    Full Text Available Smart grid is a potential infrastructure to supply electricity demand for end users in a safe and reliable manner. With the rapid increase of the share of renewable energy and controllable loads in smart grid, the operation uncertainty of smart grid has increased briskly during recent years. The forecast is responsible for the safety and economic operation of the smart grid. However, most existing forecast methods cannot account for the smart grid due to the disabilities to adapt to the varying operational conditions. In this paper, reinforcement learning is firstly exploited to develop an online learning framework for the smart grid. With the capability of multitime scale resolution, wavelet neural network has been adopted in the online learning framework to yield reinforcement learning and wavelet neural network (RLWNN based adaptive learning scheme. The simulations on two typical prediction problems in smart grid, including wind power prediction and load forecast, validate the effectiveness and the scalability of the proposed RLWNN based learning framework and algorithm.

  2. Adaptive Learning in Medical Education: The Final Piece of Technology Enhanced Learning?

    Science.gov (United States)

    Sharma, Neel; Doherty, Iain; Dong, Chaoyan

    2017-09-01

    Technology enhanced learning (TEL) is now common practice in the field of medical education. One of the primary examples of its use is that of high fidelity simulation and computerised mannequins. Further examples include online learning modules, electronic portfolios, virtual patient interactions, massive open online courses and the flipped classroom movement. The rise of TEL has occurred primarily due to the ease of internet access enabling the retrieval and sharing of information in an instant. Furthermore, the compact nature of internet ready devices such as smartphones and laptops has meant that access to information can occur anytime and anywhere. From an educational perspective however, the current utilisation of TEL has been hindered by its lack of understanding of learners' needs. This is concerning, particularly as evidence highlights that during medical training, each individual learner has their own learning requirements and often achieves competency at different rates. In view of this, there has been interest in ensuring TEL is more learner aware and that the learning process should be more personalised. Adaptive learning can aim to achieve this by ensuring content is delivered according to the needs of the learner. This commentary highlights the move towards adaptive learning and the benefits of such an intervention.

  3. Breast image feature learning with adaptive deconvolutional networks

    Science.gov (United States)

    Jamieson, Andrew R.; Drukker, Karen; Giger, Maryellen L.

    2012-03-01

    Feature extraction is a critical component of medical image analysis. Many computer-aided diagnosis approaches employ hand-designed, heuristic lesion extracted features. An alternative approach is to learn features directly from images. In this preliminary study, we explored the use of Adaptive Deconvolutional Networks (ADN) for learning high-level features in diagnostic breast mass lesion images with potential application to computer-aided diagnosis (CADx) and content-based image retrieval (CBIR). ADNs (Zeiler, et. al., 2011), are recently-proposed unsupervised, generative hierarchical models that decompose images via convolution sparse coding and max pooling. We trained the ADNs to learn multiple layers of representation for two breast image data sets on two different modalities (739 full field digital mammography (FFDM) and 2393 ultrasound images). Feature map calculations were accelerated by use of GPUs. Following Zeiler et. al., we applied the Spatial Pyramid Matching (SPM) kernel (Lazebnik, et. al., 2006) on the inferred feature maps and combined this with a linear support vector machine (SVM) classifier for the task of binary classification between cancer and non-cancer breast mass lesions. Non-linear, local structure preserving dimension reduction, Elastic Embedding (Carreira-Perpiñán, 2010), was then used to visualize the SPM kernel output in 2D and qualitatively inspect image relationships learned. Performance was found to be competitive with current CADx schemes that use human-designed features, e.g., achieving a 0.632+ bootstrap AUC (by case) of 0.83 [0.78, 0.89] for an ultrasound image set (1125 cases).

  4. Reduce, reuse and recycle

    CSIR Research Space (South Africa)

    Afrika, M

    2010-10-01

    Full Text Available The adoption of the internationally accepted waste management hierarchy (Sakai et al, 1996) into South African policy has changed the focus from “end of pipe” waste management towards waste minimisation (reuse, recycling and cleaner production...

  5. Challenges in adapting imitation and reinforcement learning to compliant robots

    Directory of Open Access Journals (Sweden)

    Calinon Sylvain

    2011-12-01

    Full Text Available There is an exponential increase of the range of tasks that robots are forecasted to accomplish. (Reprogramming these robots becomes a critical issue for their commercialization and for their applications to real-world scenarios in which users without expertise in robotics wish to adapt the robot to their needs. This paper addresses the problem of designing userfriendly human-robot interfaces to transfer skills in a fast and efficient manner. This paper presents recent work conducted at the Learning and Interaction group at ADVR-IIT, ranging from skill acquisition through kinesthetic teaching to self-refinement strategies initiated from demonstrations. Our group started to explore the use of imitation and exploration strategies that can take advantage of the compliant capabilities of recent robot hardware and control architectures.

  6. Adaptive Global Innovative Learning Environment for Glioblastoma: GBM AGILE.

    Science.gov (United States)

    Alexander, Brian M; Ba, Sujuan; Berger, Mitchel S; Berry, Donald A; Cavenee, Webster K; Chang, Susan M; Cloughesy, Timothy F; Jiang, Tao; Khasraw, Mustafa; Li, Wenbin; Mittman, Robert; Poste, George H; Wen, Patrick Y; Yung, W K Alfred; Barker, Anna D

    2018-02-15

    Glioblastoma (GBM) is a deadly disease with few effective therapies. Although much has been learned about the molecular characteristics of the disease, this knowledge has not been translated into clinical improvements for patients. At the same time, many new therapies are being developed. Many of these therapies have potential biomarkers to identify responders. The result is an enormous amount of testable clinical questions that must be answered efficiently. The GBM Adaptive Global Innovative Learning Environment (GBM AGILE) is a novel, multi-arm, platform trial designed to address these challenges. It is the result of the collective work of over 130 oncologists, statisticians, pathologists, neurosurgeons, imagers, and translational and basic scientists from around the world. GBM AGILE is composed of two stages. The first stage is a Bayesian adaptively randomized screening stage to identify effective therapies based on impact on overall survival compared with a common control. This stage also finds the population in which the therapy shows the most promise based on clinical indication and biomarker status. Highly effective therapies transition in an inferentially seamless manner in the identified population to a second confirmatory stage. The second stage uses fixed randomization to confirm the findings from the first stage to support registration. Therapeutic arms with biomarkers may be added to the trial over time, while others complete testing. The design of GBM AGILE enables rapid clinical testing of new therapies and biomarkers to speed highly effective therapies to clinical practice. Clin Cancer Res; 24(4); 737-43. ©2017 AACR . ©2017 American Association for Cancer Research.

  7. Water Reuse: Using Reclaimed Water For Irrigation

    OpenAIRE

    Haering, Kathryn; Evanylo, Gregory K.; Benham, Brian Leslie, 1960-; Goatley, Michael

    2009-01-01

    Describes water reuse and reclaimed water, explains how reclaimed water is produced, options for water reuse, water reuse regulations, and agronomic concerns with water reuse, and provides several case studies of water reuse.

  8. Evaluation framework based on fuzzy measured method in adaptive learning systems

    OpenAIRE

    Houda Zouari Ounaies, ,; Yassine Jamoussi; Henda Hajjami Ben Ghezala

    2008-01-01

    Currently, e-learning systems are mainly web-based applications and tackle a wide range of users all over the world. Fitting learners’ needs is considered as a key issue to guaranty the success of these systems. Many researches work on providing adaptive systems. Nevertheless, evaluation of the adaptivity is still in an exploratory phase. Adaptation methods are a basic factor to guaranty an effective adaptation. This issue is referred as meta-adaptation in numerous researches. In our research...

  9. Learner Characteristic Based Learning Effort Curve Mode: The Core Mechanism on Developing Personalized Adaptive E-Learning Platform

    Science.gov (United States)

    Hsu, Pi-Shan

    2012-01-01

    This study aims to develop the core mechanism for realizing the development of personalized adaptive e-learning platform, which is based on the previous learning effort curve research and takes into account the learner characteristics of learning style and self-efficacy. 125 university students from Taiwan are classified into 16 groups according…

  10. 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…

  11. Exploring the Effects of Intercultural Learning on Cross-Cultural Adaptation in a Study Abroad Context

    Science.gov (United States)

    Tsai, Yau

    2011-01-01

    This study targets Asian students studying abroad and explores the effects of intercultural learning on their cross-cultural adaptation by drawing upon a questionnaire survey. On the one hand, the results of this study find that under the influence of intercultural learning, students respond differently in their cross-cultural adaptation and no…

  12. Effectiveness of Adaptive Assessment versus Learner Control in a Multimedia Learning System

    Science.gov (United States)

    Chen, Ching-Huei; Chang, Shu-Wei

    2015-01-01

    The purpose of this study was to explore the effectiveness of adaptive assessment versus learner control in a multimedia learning system designed to help secondary students learn science. Unlike other systems, this paper presents a workflow of adaptive assessment following instructional materials that better align with learners' cognitive…

  13. The influence of student characteristics on the use of adaptive e-learning material

    NARCIS (Netherlands)

    Seters, van J.R.; Ossevoort, M.A.; Tramper, J.; Goedhart, M.J.

    2012-01-01

    Adaptive e-learning materials can help teachers to educate heterogeneous student groups. This study provides empirical data about the way academic students differ in their learning when using adaptive elearning materials. Ninety-four students participated in the study. We determined characteristics

  14. An Adaptive Approach to Managing Knowledge Development in a Project-Based Learning Environment

    Science.gov (United States)

    Tilchin, Oleg; Kittany, Mohamed

    2016-01-01

    In this paper we propose an adaptive approach to managing the development of students' knowledge in the comprehensive project-based learning (PBL) environment. Subject study is realized by two-stage PBL. It shapes adaptive knowledge management (KM) process and promotes the correct balance between personalized and collaborative learning. The…

  15. Examining the Role of Emotional Intelligence between Organizational Learning and Adaptive Performance in Indian Manufacturing Industries

    Science.gov (United States)

    Pradhan, Rabindra Kumar; Jena, Lalatendu Kesari; Singh, Sanjay Kumar

    2017-01-01

    Purpose: The purpose of this study is to examine the relationship between organisational learning and adaptive performance. Furthermore, the study investigates the moderating role of emotional intelligence in the perspective of organisational learning for addressing adaptive performance of executives employed in manufacturing organisations.…

  16. OPUS One: An Intelligent Adaptive Learning Environment Using Artificial Intelligence Support

    Science.gov (United States)

    Pedrazzoli, Attilio

    2010-06-01

    AI based Tutoring and Learning Path Adaptation are well known concepts in e-Learning scenarios today and increasingly applied in modern learning environments. In order to gain more flexibility and to enhance existing e-learning platforms, the OPUS One LMS Extension package will enable a generic Intelligent Tutored Adaptive Learning Environment, based on a holistic Multidimensional Instructional Design Model (PENTHA ID Model), allowing AI based tutoring and adaptation functionality to existing Web-based e-learning systems. Relying on "real time" adapted profiles, it allows content- / course authors to apply a dynamic course design, supporting tutored, collaborative sessions and activities, as suggested by modern pedagogy. The concept presented combines a personalized level of surveillance, learning activity- and learning path adaptation suggestions to ensure the students learning motivation and learning success. The OPUS One concept allows to implement an advanced tutoring approach combining "expert based" e-tutoring with the more "personal" human tutoring function. It supplies the "Human Tutor" with precise, extended course activity data and "adaptation" suggestions based on predefined subject matter rules. The concept architecture is modular allowing a personalized platform configuration.

  17. The effect of adaptive versus static practicing on student learning - evidence from a randomized field experiment

    NARCIS (Netherlands)

    van Klaveren, Chris; Vonk, Sebastiaan; Cornelisz, Ilja

    2017-01-01

    Schools and governments are increasingly investing in adaptive practice software. To date, the evidence whether adaptivity improves learning outcomes is limited and mixed. A large-scale randomized control trial is conducted in Dutch secondary schools to evaluate the effectiveness of an adaptive

  18. Fundamentals of Adaptive Intelligent Tutoring Systems for Self-Regulated Learning

    Science.gov (United States)

    2015-03-01

    ARL-SR-0318 ● MAR 2015 US Army Research Laboratory Fundamentals of Adaptive Intelligent Tutoring Systems for Self-Regulated...Adaptive Intelligent Tutoring Systems for Self-Regulated Learning by Robert A Sottilare Human Research and Engineering Directorate, ARL...TITLE AND SUBTITLE Fundamentals of Adaptive Intelligent Tutoring Systems for Self-Regulated Learning 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c

  19. The use of and obstacles to social learning in climate change adaptation initiatives in South Africa

    OpenAIRE

    Mudombi, Shakespear; Fabricius, Christo; Van Zyl-Bulitta, Verena; Patt, Anthony

    2017-01-01

    Global environmental change will have major impacts on ecosystems and human livelihoods while challenging the adaptive capacity of individuals and communities. Social learning, an ongoing adaptive process of knowledge generation, reflection and synthesis, may enhance people’s awareness about climate change and its impacts, with positive outcomes for their adaptive capacity. The objectives of this study were to assess the prevalence of factors promoting social learning in climate change adapta...

  20. Beneficial reuse '97

    International Nuclear Information System (INIS)

    Anon.

    1997-01-01

    The annual Beneficial Reuse Conference was conducted in Knoxville, Tennessee from August 5-7, 1997. Now in its fifth year, this conference has become the national forum for discussing the beneficial reuse and recycle of contaminated buildings, equipment and resources, and the fabrication of useful products from such resources. As in the past, the primary goal of Beneficial Reuse ''97 was to provide a forum for the practitioners of pollution prevention, decontamination and decommissioning, waste minimization, reindustrialization, asset management, privatization and recycling to share their successes and failures, as well as their innovative strategies and operational experiences with the assembled group of stakeholders. Separate abstracts have been indexed into the database for contributions to this conference proceedings

  1. Context-Adaptive Learning Designs by Using Semantic Web Services

    Science.gov (United States)

    Dietze, Stefan; Gugliotta, Alessio; Domingue, John

    2007-01-01

    IMS Learning Design (IMS-LD) is a promising technology aimed at supporting learning processes. IMS-LD packages contain the learning process metadata as well as the learning resources. However, the allocation of resources--whether data or services--within the learning design is done manually at design-time on the basis of the subjective appraisals…

  2. The Effects of Reflective Activities on Skill Adaptation in a Work-Related Instrumental Learning Setting

    Science.gov (United States)

    Roessger, Kevin M.

    2014-01-01

    In work-related instrumental learning contexts, the role of reflective activities is unclear. Kolb's experiential learning theory and Mezirow's transformative learning theory predict skill adaptation as an outcome. This prediction was tested by manipulating reflective activities and assessing participants' response and error rates during novel…

  3. Constructive, Self-Regulated, Situated, and Collaborative Learning: An Approach for the Acquisition of Adaptive Competence

    Science.gov (United States)

    de Corte, Erik

    2012-01-01

    In today's learning society, education must focus on fostering adaptive competence (AC) defined as the ability to apply knowledge and skills flexibly in different contexts. In this article, four major types of learning are discussed--constructive, self-regulated, situated, and collaborative--in relation to what students must learn in order to…

  4. A Framework for Adaptive Learning Design in a Web-Conferencing Environment

    Science.gov (United States)

    Bower, Matt

    2016-01-01

    Many recent technologies provide the ability to dynamically adjust the interface depending on the emerging cognitive and collaborative needs of the learning episode. This means that educators can adaptively re-design the learning environment during the lesson, rather than purely relying on preemptive learning design thinking. Based on a…

  5. Information-educational environment with adaptive control of learning process

    Science.gov (United States)

    Modjaev, A. D.; Leonova, N. M.

    2017-01-01

    Recent years, a new scientific branch connected with the activities in social sphere management developing intensively and it is called "Social Cybernetics". In the framework of this scientific branch, theory and methods of management of social sphere are formed. Considerable attention is paid to the management, directly in real time. However, the decision of such management tasks is largely constrained by the lack of or insufficiently deep study of the relevant sections of the theory and methods of management. The article discusses the use of cybernetic principles in solving problems of control in social systems. Applying to educational activities a model of composite interrelated objects representing the behaviour of students at various stages of educational process is introduced. Statistical processing of experimental data obtained during the actual learning process is being done. If you increase the number of features used, additionally taking into account the degree and nature of variability of levels of current progress of students during various types of studies, new properties of students' grouping are discovered. L-clusters were identified, reflecting the behaviour of learners with similar characteristics during lectures. It was established that the characteristics of the clusters contain information about the dynamics of learners' behaviour, allowing them to be used in additional lessons. The ways of solving the problem of adaptive control based on the identified dynamic characteristics of the learners are planned.

  6. Learning as You Journey: Anishinaabe Perception of Social-ecological Environments and Adaptive Learning

    Directory of Open Access Journals (Sweden)

    Iain Davidson-Hunt

    2003-12-01

    Full Text Available This paper explores the linkages between social-ecological resilience and adaptive learning. We refer to adaptive learning as a method to capture the two-way relationship between people and their social-ecological environment. In this paper, we focus on traditional ecological knowledge. Research was undertaken with the Anishinaabe people of Iskatewizaagegan No. 39 Independent First Nation, in northwestern Ontario, Canada. The research was carried out over two field seasons, with verification workshops following each field season. The methodology was based on site visits and transects determined by the elders as appropriate to answer a specific question, find specific plants, or locate plant communities. During site visits and transect walks, research themes such as plant nomenclature, plant use, habitat descriptions, biogeophysical landscape vocabulary, and place names were discussed. Working with elders allowed us to record a rich set of vocabulary to describe the spatial characteristics of the biogeophysical landscape. However, elders also directed our attention to places they knew through personal experiences and journeys and remembered from stories and collective history. We documented elders' perceptions of the temporal dynamics of the landscape through discussion of disturbance events and cycles. Again, elders drew our attention to the ways in which time was marked by cultural references to seasons and moons. The social memory of landscape dynamics was documented as a combination of biogeophysical structures and processes, along with the stories by which Iskatewizaagegan people wrote their histories upon the land. Adaptive learning for social-ecological resilience, as suggested by this research, requires maintaining the web of relationships of people and places. Such relationships allow social memory to frame creativity, while allowing knowledge to evolve in the face of change. Social memory does not actually evolve directly out of

  7. Aligning the economic modeling of software reuse with reuse practices

    NARCIS (Netherlands)

    Postmus, D.; Meijler, 27696

    In contrast to current practices where software reuse is applied recursively and reusable assets are tailored trough parameterization or specialization, existing reuse economic models assume that (i) the cost of reusing a software asset depends on its size and (ii) reusable assets are developed from

  8. Reuse, Reduce, Recycle.

    Science.gov (United States)

    Briscoe, Georgia

    1991-01-01

    Discussion of recycling paper in law libraries is also applicable to other types of libraries. Results of surveys of law libraries that investigated recycling practices in 1987 and again in 1990 are reported, and suggestions for reducing the amount of paper used and reusing as much as possible are offered. (LRW)

  9. Promoter reuse in prokaryotes

    NARCIS (Netherlands)

    Nijveen, H.; Matus-Garcia, M.; Passel, van M.W.J.

    2012-01-01

    Anecdotal evidence shows promoters being reused separate from their downstream gene, thus providing a mechanism for the efficient and rapid rewiring of a gene’s transcriptional regulation. We have identified over 4000 groups of highly similar promoters using a conservative sequence similarity search

  10. 2012 Guidelines for Water Reuse

    Science.gov (United States)

    This manual is a revision of the "2004 Water Reuse Guidelines." This document is a summary of reuse guidelines, with supporting information, for the benefit of utilities of utilities and regulatory agencies, particularly EPA.

  11. How assessment and reflection relate to more effective learning in adaptive management

    Directory of Open Access Journals (Sweden)

    Harry Biggs

    2011-05-01

    Two other studies in the Kruger National Park, which have examined learning specifically, are also discussed. One of them suggests that in a complex environment, learning necessarily has a dual nature, with each component of seven contrasting pairs of the aspects of learning in partial tension with the other. We use these dualities to further probe assessment, reflection, inter-relatedness and learning in the cases presented. Each contrasting aspect of a ‘learning duality’ turns out to emphasise either assessment or reflection, which reinforces the idea that both are needed to facilitate sufficient learning for successful adaptive management. We hope this analysis can act as a springboard for further study, practice and reflection on these important and often underrated components of adaptive management. Conservation implications: The better understanding of assessment and reflection as being largely separate but complementary actions will assist adaptive management practitioners to give explicit attention to both, and to relate them better to each other.

  12. Web Log Pre-processing and Analysis for Generation of Learning Profiles in Adaptive E-learning

    Directory of Open Access Journals (Sweden)

    Radhika M. Pai

    2016-03-01

    Full Text Available Adaptive E-learning Systems (AESs enhance the efficiency of online courses in education by providing personalized contents and user interfaces that changes according to learner’s requirements and usage patterns. This paper presents the approach to generate learning profile of each learner which helps to identify the learning styles and provide Adaptive User Interface which includes adaptive learning components and learning material. The proposed method analyzes the captured web usage data to identify the learning profile of the learners. The learning profiles are identified by an algorithmic approach that is based on the frequency of accessing the materials and the time spent on the various learning components on the portal. The captured log data is pre-processed and converted into standard XML format to generate learners sequence data corresponding to the different sessions and time spent. The learning style model adopted in this approach is Felder-Silverman Learning Style Model (FSLSM. This paper also presents the analysis of learner’s activities, preprocessed XML files and generated sequences.

  13. Web Log Pre-processing and Analysis for Generation of Learning Profiles in Adaptive E-learning

    Directory of Open Access Journals (Sweden)

    Radhika M. Pai

    2016-04-01

    Full Text Available Adaptive E-learning Systems (AESs enhance the efficiency of online courses in education by providing personalized contents and user interfaces that changes according to learner’s requirements and usage patterns. This paper presents the approach to generate learning profile of each learner which helps to identify the learning styles and provide Adaptive User Interface which includes adaptive learning components and learning material. The proposed method analyzes the captured web usage data to identify the learning profile of the learners. The learning profiles are identified by an algorithmic approach that is based on the frequency of accessing the materials and the time spent on the various learning components on the portal. The captured log data is pre-processed and converted into standard XML format to generate learners sequence data corresponding to the different sessions and time spent. The learning style model adopted in this approach is Felder-Silverman Learning Style Model (FSLSM. This paper also presents the analysis of learner’s activities, preprocessed XML files and generated sequences.

  14. Impact of learning adaptability and time management disposition on study engagement among Chinese baccalaureate nursing students.

    Science.gov (United States)

    Liu, Jing-Ying; Liu, Yan-Hui; Yang, Ji-Peng

    2014-01-01

    The aim of this study was to explore the relationships among study engagement, learning adaptability, and time management disposition in a sample of Chinese baccalaureate nursing students. A convenient sample of 467 baccalaureate nursing students was surveyed in two universities in Tianjin, China. Students completed a questionnaire that included their demographic information, Chinese Utrecht Work Engagement Scale-Student Questionnaire, Learning Adaptability Scale, and Adolescence Time Management Disposition Scale. One-way analysis of variance tests were used to assess the relationship between certain characteristics of baccalaureate nursing students. Pearson correlation was performed to test the correlation among study engagement, learning adaptability, and time management disposition. Hierarchical linear regression analyses were performed to explore the mediating role of time management disposition. The results revealed that study engagement (F = 7.20, P < .01) and learning adaptability (F = 4.41, P < .01) differed across grade groups. Learning adaptability (r = 0.382, P < .01) and time management disposition (r = 0.741, P < .01) were positively related with study engagement. Time management disposition had a partially mediating effect on the relationship between study engagement and learning adaptability. The findings implicate that educators should not only promote interventions to increase engagement of baccalaureate nursing students but also focus on development, investment in adaptability, and time management. Copyright © 2014 Elsevier Inc. All rights reserved.

  15. Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment.

    Science.gov (United States)

    Walter, Carina; Rosenstiel, Wolfgang; Bogdan, Martin; Gerjets, Peter; Spüler, Martin

    2017-01-01

    In this paper, we demonstrate a closed-loop EEG-based learning environment, that adapts instructional learning material online, to improve learning success in students during arithmetic learning. The amount of cognitive workload during learning is crucial for successful learning and should be held in the optimal range for each learner. Based on EEG data from 10 subjects, we created a prediction model that estimates the learner's workload to obtain an unobtrusive workload measure. Furthermore, we developed an interactive learning environment that uses the prediction model to estimate the learner's workload online based on the EEG data and adapt the difficulty of the learning material to keep the learner's workload in an optimal range. The EEG-based learning environment was used by 13 subjects to learn arithmetic addition in the octal number system, leading to a significant learning effect. The results suggest that it is feasible to use EEG as an unobtrusive measure of cognitive workload to adapt the learning content. Further it demonstrates that a promptly workload prediction is possible using a generalized prediction model without the need for a user-specific calibration.

  16. The use of and obstacles to social learning in climate change adaptation initiatives in South Africa

    Directory of Open Access Journals (Sweden)

    Shakespear Mudombi

    2017-03-01

    Full Text Available Global environmental change will have major impacts on ecosystems and human livelihoods while challenging the adaptive capacity of individuals and communities. Social learning, an ongoing adaptive process of knowledge generation, reflection and synthesis, may enhance people’s awareness about climate change and its impacts, with positive outcomes for their adaptive capacity. The objectives of this study were to assess the prevalence of factors promoting social learning in climate change adaptation initiatives in South Africa. An online survey was used to obtain the views of decision makers in government and non-governmental organisations about the presence of personal factors and organisational factors that promote social learning. Descriptive analysis was used to assess these issues. The findings provide some evidence of social learning in climate change adaptation projects in South Africa, with the majority of respondents indicating that personal social learning indicators were present. Mechanisms for improved conflict resolution were, however, less prevalent. The organisational and governance-related barriers to implementation also presented significant challenges. Some of the main organisational barriers were short timeframes for implementing projects, inadequate financial resources, political interference, shortcomings in governance systems and lack of knowledge and expertise in organisations. There is a need for organisations to promote social learning by ensuring that their organisational environment and governance structures are conducive for their employees to embrace social learning. This will help contribute to the overall success of climate change adaptation initiatives.

  17. Enhancing Adaptive Capacity in Food Systems: Learning at Farmers' Markets in Sweden

    Directory of Open Access Journals (Sweden)

    Rebecka Milestad

    2010-09-01

    Full Text Available This article examines how local food systems in the form of farmers' markets can enhance adaptive capacity and build social-ecological resilience. It does this by exploring the learning potential among farmers and customers. Learning can enable actors to adapt successfully and thus build adaptive capacity. Three forms of learning are investigated: instrumental, communicative, and emancipatory. These forms of learning constitute the foundation for lasting changes of behaviors. Local food systems are characterized by close links and opportunities for face-to-face interactions between consumers and producers of food, and are also institutions where farmers and customers can express and act upon their ethical values concerning food. However, local food systems are still a marginal phenomenon and cannot be accessed by all consumers. Interviews were held with customers and farmers, and the interactions between farmers and customers were observed at two farmers' markets in Sweden. Customers and farmers were found to learn and adapt to each other due to the opportunities offered by the farmers' markets. We found that farmers and customers learned in the instrumental and communicative domains, but could not confirm emancipatory learning. We concluded that the feedback between customers and farmers offers the potential for learning, which in turn contributes to adaptive capacity. This can be a driving force for building resilience in the food system.

  18. Learning analytics in practice: The effects of adaptive educational technology Snappet on students' arithmetic skills

    NARCIS (Netherlands)

    Molenaar, I.; Knoop-van Campen, C.A.N.

    2016-01-01

    Even though the recent influx of tablets in primary education goes together with the vision that educational technology empowered with learning analytics will revolutionize education, empirical results supporting this claim are scares. Adaptive educational technology Snappet combines extracted and

  19. ADAPTATION OF TEACHING PROCESS BASED ON A STUDENTS INDIVIDUAL LEARNING NEEDS

    Directory of Open Access Journals (Sweden)

    TAKÁCS, Ondřej

    2011-03-01

    Full Text Available Development of current society requires integration of information technology to every sector, including education. The idea of adaptive teaching in e-learning environment is based on paying attention and giving support to various learning styles. More effective, user friendly thus better quality education can be achieved through such an environment. Learning can be influenced by many factors. In the paper we deal with such factors as student’s personality and qualities – particularly learning style and motivation. In addition we want to prepare study materials and study environment which respects students’ differences. Adaptive e-learning means an automated way of teaching which adapts to different qualities of students which are characteristic for their learning styles. In the last few years we can see a gradual individualization of study not only in distance forms of study but also with full-time study students. Instructional supports, namely those of e-learning, should take this trend into account and adapt the educational processes to individual students’ qualities. The present learning management systems (LMS offers this possibility only to a very limited extent. This paper deals with a design of intelligent virtual tutor behavior, which would adapt its learning ability to both static and dynamically changing student’s qualities. Virtual tutor, in order to manage all that, has to have a sufficiently rich supply of different styles and forms of teaching, with enough information about styles of learning, kinds of memory and other student’s qualities. This paper describes a draft adaptive education model and the results of the first part of the solution – definition of learning styles, pilot testing on students and an outline of further research.

  20. Learn-and-Adapt Stochastic Dual Gradients for Network Resource Allocation

    OpenAIRE

    Chen, Tianyi; Ling, Qing; Giannakis, Georgios B.

    2017-01-01

    Network resource allocation shows revived popularity in the era of data deluge and information explosion. Existing stochastic optimization approaches fall short in attaining a desirable cost-delay tradeoff. Recognizing the central role of Lagrange multipliers in network resource allocation, a novel learn-and-adapt stochastic dual gradient (LA-SDG) method is developed in this paper to learn the sample-optimal Lagrange multiplier from historical data, and accordingly adapt the upcoming resource...

  1. What Can We Learn from a Well-Adapted Enterprise System? A Case Study Approach

    DEFF Research Database (Denmark)

    Svejvig, Per; Jensen, Tina Blegind

    how the system was highly integrated, accepted by its users, and well-aligned to the work processes. This leads to the research question: Why is the enterprise system so well-adapted in SCANDI and what can we learn from this case study? Building on the structural model of technology to investigate...... as a long-term institutionalization and legitimization course of events leading to secondary socialization as the key lessons learned in achieving successful ES adaptations....

  2. Service-Learning and Interior Design: A Case Study

    Science.gov (United States)

    Sterling, Mary

    2007-01-01

    The case study approach was used to analyze experiential learning through its three components: knowledge, action, and reflection. Two interior design courses were integrated through a university service-learning project. The restoration/adaptive reuse of a 95-year-old library building was to serve as a prototype for future off-campus…

  3. An Ontology for Learning Services on the Shop Floor

    Science.gov (United States)

    Ullrich, Carsten

    2016-01-01

    An ontology expresses a common understanding of a domain that serves as a basis of communication between people or systems, and enables knowledge sharing, reuse of domain knowledge, reasoning and thus problem solving. In Technology-Enhanced Learning, especially in Intelligent Tutoring Systems and Adaptive Learning Environments, ontologies serve as…

  4. Intelligent Adaptation and Personalization Techniques in Computer-Supported Collaborative Learning

    CERN Document Server

    Demetriadis, Stavros; Xhafa, Fatos

    2012-01-01

    Adaptation and personalization have been extensively studied in CSCL research community aiming to design intelligent systems that adaptively support eLearning processes and collaboration. Yet, with the fast development in Internet technologies, especially with the emergence of new data technologies and the mobile technologies, new opportunities and perspectives are opened for advanced adaptive and personalized systems. Adaptation and personalization are posing new research and development challenges to nowadays CSCL systems. In particular, adaptation should be focused in a multi-dimensional way (cognitive, technological, context-aware and personal). Moreover, it should address the particularities of both individual learners and group collaboration. As a consequence, the aim of this book is twofold. On the one hand, it discusses the latest advances and findings in the area of intelligent adaptive and personalized learning systems. On the other hand it analyzes the new implementation perspectives for intelligen...

  5. Stimulating the cerebellum affects visuomotor adaptation but not intermanual transfer of learning.

    Science.gov (United States)

    Block, Hannah; Celnik, Pablo

    2013-12-01

    When systematic movement errors occur, the brain responds with a systematic change in motor behavior. This type of adaptive motor learning can transfer intermanually; adaptation of movements of the right hand in response to training with a perturbed visual signal (visuomotor adaptation) may carry over to the left hand. While visuomotor adaptation has been studied extensively, it is unclear whether the cerebellum, a structure involved in adaptation, is important for intermanual transfer as well. We addressed this question with three experiments in which subjects reached with their right hands as a 30° visuomotor rotation was introduced. Subjects received anodal or sham transcranial direct current stimulation on the trained (experiment 1) or untrained (experiment 2) hemisphere of the cerebellum, or, for comparison, motor cortex (M1). After the training period, subjects reached with their left hand, without visual feedback, to assess intermanual transfer of learning aftereffects. Stimulation of the right cerebellum caused faster adaptation, but none of the stimulation sites affected transfer. To ascertain whether cerebellar stimulation would increase transfer if subjects learned faster as well as a larger amount, in experiment 3 anodal and sham cerebellar groups experienced a shortened training block such that the anodal group learned more than sham. Despite the difference in adaptation magnitude, transfer was similar across these groups, although smaller than in experiment 1. Our results suggest that intermanual transfer of visuomotor learning does not depend on cerebellar activity and that the number of movements performed at plateau is an important predictor of transfer.

  6. A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition

    Science.gov (United States)

    Huang, Qi; Yang, Dapeng; Jiang, Li; Zhang, Huajie; Liu, Hong; Kotani, Kiyoshi

    2017-01-01

    Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle). PMID:28608824

  7. A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Qi Huang

    2017-06-01

    Full Text Available Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC, by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC. We compared PAC performance with incremental support vector classifier (ISVC and non-adapting SVC (NSVC in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05 and ISVC (13.38% ± 2.62%, p = 0.001, and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle.

  8. Toward a Theory of Adaptive Transfer: Expanding Disciplinary Discussions of "Transfer" in Second-Language Writing and Composition Studies

    Science.gov (United States)

    DePalma, Michael-John; Ringer, Jeffrey M.

    2011-01-01

    In this paper, we argue that discussions of transfer in L2 writing and composition studies have focused primarily on the reuse of past learning and thus have not adequately accounted for the adaptation of learned writing knowledge in unfamiliar situations. In an effort to expand disciplinary discussions of transfer in L2 writing and composition…

  9. Designing an Adaptive Web-Based Learning System Based on Students' Cognitive Styles Identified Online

    Science.gov (United States)

    Lo, Jia-Jiunn; Chan, Ya-Chen; Yeh, Shiou-Wen

    2012-01-01

    This study developed an adaptive web-based learning system focusing on students' cognitive styles. The system is composed of a student model and an adaptation model. It collected students' browsing behaviors to update the student model for unobtrusively identifying student cognitive styles through a multi-layer feed-forward neural network (MLFF).…

  10. Evaluation Framework Based on Fuzzy Measured Method in Adaptive Learning Systems

    Science.gov (United States)

    Ounaies, Houda Zouari; Jamoussi, Yassine; Ben Ghezala, Henda Hajjami

    2008-01-01

    Currently, e-learning systems are mainly web-based applications and tackle a wide range of users all over the world. Fitting learners' needs is considered as a key issue to guaranty the success of these systems. Many researches work on providing adaptive systems. Nevertheless, evaluation of the adaptivity is still in an exploratory phase.…

  11. Professional Learning Communities Assessment: Adaptation, Internal Validity, and Multidimensional Model Testing in Turkish Context

    Science.gov (United States)

    Dogan, Selçuk; Tatik, R. Samil; Yurtseven, Nihal

    2017-01-01

    The main purpose of this study is to adapt and validate the Professional Learning Communities Assessment Revised (PLCA-R) by Olivier, Hipp, and Huffman within the context of Turkish schools. The instrument was translated and adapted to administer to teachers in Turkey. Internal structure of the Turkish version of PLCA-R was investigated by using…

  12. The Optimization by Using the Learning Styles in the Adaptive Hypermedia Applications

    Science.gov (United States)

    Hamza, Lamia; Tlili, Guiassa Yamina

    2018-01-01

    This article addresses the learning style as a criterion for optimization of adaptive content in hypermedia applications. First, the authors present the different optimization approaches proposed in the area of adaptive hypermedia systems whose goal is to define the optimization problem in this type of system. Then, they present the architecture…

  13. Learning Motivation and Adaptive Video Caption Filtering for EFL Learners Using Handheld Devices

    Science.gov (United States)

    Hsu, Ching-Kun

    2015-01-01

    The aim of this study was to provide adaptive assistance to improve the listening comprehension of eleventh grade students. This study developed a video-based language learning system for handheld devices, using three levels of caption filtering adapted to student needs. Elementary level captioning excluded 220 English sight words (see Section 1…

  14. Low-Back Pain Patients Learn to Adapt Motor Behavior with Adverse Secondary Consequences

    NARCIS (Netherlands)

    van Dieën, Jaap H.; Flor, Herta; Hodges, Paul W.

    2017-01-01

    ABSTRACT: We hypothesize that changes in motor behavior in individuals with low-back pain are adaptations aimed at minimizing the real or perceived risk of further pain. Through reinforcement learning, pain and subsequent adaptions result in less dynamic motor behavior, leading to increased loading

  15. On the problem of first year students adaptation to the learning ...

    African Journals Online (AJOL)

    The relevance of the studied problem is the fact that successful adaptation of a first year student to life and academic activity in a university is the key to the further development of each student as a personality ... Keywords: Adaptation, students, learning process, means of physical education, sports ans mass sports events.

  16. Learning to push and learning to move: The adaptive control of contact forces

    Directory of Open Access Journals (Sweden)

    Maura eCasadio

    2015-11-01

    Full Text Available To be successful at manipulating objects one needs to apply simultaneously well controlled movements and contact forces. We present a computational theory of how the brain may successfully generate a vast spectrum of interactive behaviors by combining two independent processes. One process is competent to control movements in free space and the other is competent to control contact forces against rigid constraints. Free space and rigid constraints are singularities at the boundaries of a continuum of mechanical impedance. Within this continuum, forces and motions occur in compatible pairs connected by the equations of Newtonian dynamics. The force applied to an object determines its motion. Conversely, inverse dynamics determine a unique force trajectory from a movement trajectory. In this perspective, we describe motor learning as a process leading to the discovery of compatible force/motion pairs. The learned compatible pairs constitute a local representation of the environment's mechanics. Experiments on force field adaptation have already provided us with evidence that the brain is able to predict and compensate the forces encountered when one is attempting to generate a motion. Here, we tested the theory in the dual case, i.e. when one attempts at applying a desired contact force against a simulated rigid surface. If the surface becomes unexpectedly compliant, the contact point moves as a function of the applied force and this causes the applied force to deviate from its desired value. We found that, through repeated attempts at generating the desired contact force, subjects discovered the unique compatible hand motion. When, after learning, the rigid contact was unexpectedly restored, subjects displayed after effects of learning, consistent with the concurrent operation of a motion control system and a force control system. Together, theory and experiment support a new and broader view of modularity in the coordinated control of forces and

  17. Innovations in Lifelong Learning: Capitalising on ADAPT. CEDEFOP Panorama Series.

    Science.gov (United States)

    Janssens, Jos

    A community initiative (called ADAPT) was intended to help the workforce in European Union countries to adapt to industrial change and prepare for the information society, as well as to promote growth, employment, and the competitiveness of companies in the countries. Between 1995 and 1999, 4,000 projects were funded to transform the ways in which…

  18. Adaptive Semantic and Social Web-based learning and assessment environment for the STEM

    Science.gov (United States)

    Babaie, Hassan; Atchison, Chris; Sunderraman, Rajshekhar

    2014-05-01

    We are building a cloud- and Semantic Web-based personalized, adaptive learning environment for the STEM fields that integrates and leverages Social Web technologies to allow instructors and authors of learning material to collaborate in semi-automatic development and update of their common domain and task ontologies and building their learning resources. The semi-automatic ontology learning and development minimize issues related to the design and maintenance of domain ontologies by knowledge engineers who do not have any knowledge of the domain. The social web component of the personal adaptive system will allow individual and group learners to interact with each other and discuss their own learning experience and understanding of course material, and resolve issues related to their class assignments. The adaptive system will be capable of representing key knowledge concepts in different ways and difficulty levels based on learners' differences, and lead to different understanding of the same STEM content by different learners. It will adapt specific pedagogical strategies to individual learners based on their characteristics, cognition, and preferences, allow authors to assemble remotely accessed learning material into courses, and provide facilities for instructors to assess (in real time) the perception of students of course material, monitor their progress in the learning process, and generate timely feedback based on their understanding or misconceptions. The system applies a set of ontologies that structure the learning process, with multiple user friendly Web interfaces. These include the learning ontology (models learning objects, educational resources, and learning goal); context ontology (supports adaptive strategy by detecting student situation), domain ontology (structures concepts and context), learner ontology (models student profile, preferences, and behavior), task ontologies, technological ontology (defines devices and places that surround the

  19. Impact of Nursing Learning Environments on Adaptive Competency Development in Baccalaureate Nursing Students.

    Science.gov (United States)

    Laschinger, Heather K. Spence

    1992-01-01

    Kolb's experiential learning theory was used as a framework to study 179 generic baccalaureate students' perceptions of the different types of learning environments and adaptive competencies. Clinical experience and preceptorships contributed more to competency development than did nursing or nonnursing classes. (JOW)

  20. Towards Increased Relevance: Context-Adapted Models of the Learning Organization

    Science.gov (United States)

    Örtenblad, Anders

    2015-01-01

    Purpose: The purposes of this paper are to take a closer look at the relevance of the idea of the learning organization for organizations in different generalized organizational contexts; to open up for the existence of multiple, context-adapted models of the learning organization; and to suggest a number of such models.…

  1. Fast But Fleeting: Adaptive Motor Learning Processes Associated with Aging and Cognitive Decline

    Science.gov (United States)

    Trewartha, Kevin M.; Garcia, Angeles; Wolpert, Daniel M.

    2014-01-01

    Motor learning has been shown to depend on multiple interacting learning processes. For example, learning to adapt when moving grasped objects with novel dynamics involves a fast process that adapts and decays quickly—and that has been linked to explicit memory—and a slower process that adapts and decays more gradually. Each process is characterized by a learning rate that controls how strongly motor memory is updated based on experienced errors and a retention factor determining the movement-to-movement decay in motor memory. Here we examined whether fast and slow motor learning processes involved in learning novel dynamics differ between younger and older adults. In addition, we investigated how age-related decline in explicit memory performance influences learning and retention parameters. Although the groups adapted equally well, they did so with markedly different underlying processes. Whereas the groups had similar fast processes, they had different slow processes. Specifically, the older adults exhibited decreased retention in their slow process compared with younger adults. Within the older group, who exhibited considerable variation in explicit memory performance, we found that poor explicit memory was associated with reduced retention in the fast process, as well as the slow process. These findings suggest that explicit memory resources are a determining factor in impairments in the both the fast and slow processes for motor learning but that aging effects on the slow process are independent of explicit memory declines. PMID:25274819

  2. Educational Multimedia Profiling Recommendations for Device-Aware Adaptive Mobile Learning

    Science.gov (United States)

    Moldovan, Arghir-Nicolae; Ghergulescu, Ioana; Muntean, Cristina Hava

    2014-01-01

    Mobile learning is seeing a fast adoption with the increasing availability and affordability of mobile devices such as smartphones and tablets. As the creation and consumption of educational multimedia content on mobile devices is also increasing fast, educators and mobile learning providers are faced with the challenge to adapt multimedia type…

  3. An adaptive deep Q-learning strategy for handwritten digit recognition.

    Science.gov (United States)

    Qiao, Junfei; Wang, Gongming; Li, Wenjing; Chen, Min

    2018-02-22

    Handwritten digits recognition is a challenging problem in recent years. Although many deep learning-based classification algorithms are studied for handwritten digits recognition, the recognition accuracy and running time still need to be further improved. In this paper, an adaptive deep Q-learning strategy is proposed to improve accuracy and shorten running time for handwritten digit recognition. The adaptive deep Q-learning strategy combines the feature-extracting capability of deep learning and the decision-making of reinforcement learning to form an adaptive Q-learning deep belief network (Q-ADBN). First, Q-ADBN extracts the features of original images using an adaptive deep auto-encoder (ADAE), and the extracted features are considered as the current states of Q-learning algorithm. Second, Q-ADBN receives Q-function (reward signal) during recognition of the current states, and the final handwritten digits recognition is implemented by maximizing the Q-function using Q-learning algorithm. Finally, experimental results from the well-known MNIST dataset show that the proposed Q-ADBN has a superiority to other similar methods in terms of accuracy and running time. Copyright © 2018 Elsevier Ltd. All rights reserved.

  4. Fast but fleeting: adaptive motor learning processes associated with aging and cognitive decline.

    Science.gov (United States)

    Trewartha, Kevin M; Garcia, Angeles; Wolpert, Daniel M; Flanagan, J Randall

    2014-10-01

    Motor learning has been shown to depend on multiple interacting learning processes. For example, learning to adapt when moving grasped objects with novel dynamics involves a fast process that adapts and decays quickly-and that has been linked to explicit memory-and a slower process that adapts and decays more gradually. Each process is characterized by a learning rate that controls how strongly motor memory is updated based on experienced errors and a retention factor determining the movement-to-movement decay in motor memory. Here we examined whether fast and slow motor learning processes involved in learning novel dynamics differ between younger and older adults. In addition, we investigated how age-related decline in explicit memory performance influences learning and retention parameters. Although the groups adapted equally well, they did so with markedly different underlying processes. Whereas the groups had similar fast processes, they had different slow processes. Specifically, the older adults exhibited decreased retention in their slow process compared with younger adults. Within the older group, who exhibited considerable variation in explicit memory performance, we found that poor explicit memory was associated with reduced retention in the fast process, as well as the slow process. These findings suggest that explicit memory resources are a determining factor in impairments in the both the fast and slow processes for motor learning but that aging effects on the slow process are independent of explicit memory declines. Copyright © 2014 the authors 0270-6474/14/3413411-11$15.00/0.

  5. Rich media content adaptation in e-learning systems

    OpenAIRE

    Mirri, Silvia

    2007-01-01

    The wide use of e-technologies represents a great opportunity for underserved segments of the population, especially with the aim of reintegrating excluded individuals back into society through education. This is particularly true for people with different types of disabilities who may have difficulties while attending traditional on-site learning programs that are typically based on printed learning resources. The creation and provision of accessible e-learning contents may therefore become ...

  6. A service based adaptive U-learning system using UX.

    Science.gov (United States)

    Jeong, Hwa-Young; Yi, Gangman

    2014-01-01

    In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users' tailored materials according to their learning style. That is, we analyzed the user's data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques.

  7. A Service Based Adaptive U-Learning System Using UX

    Directory of Open Access Journals (Sweden)

    Hwa-Young Jeong

    2014-01-01

    Full Text Available In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users’ tailored materials according to their learning style. That is, we analyzed the user’s data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques.

  8. Children and adolescents with migratory experience at risk in language learning and psychosocial adaptation contexts.

    OpenAIRE

    Figueiredo, Sandra; Silva, Carlos Fernandes da; Monteiro, Sara

    2007-01-01

    A compelling body of evidence shows a strong association between psychological, affective and learning variables, related also with the age and gender factors, which are involved in the language learning development process. Children and adolescents with migratory experience (direct/indirect) can develop behaviours at risk in their academic learning and psychosocial adaptation, according to several stressors as anxiety, low motivation, negative attitudes, within a stressed internal l...

  9. Learning to bridge the gap between adaptive management and organisational culture

    Directory of Open Access Journals (Sweden)

    Richard J. Stirzaker

    2011-05-01

    Full Text Available Adaptive management is the problem-solving approach of choice proposed for complex and multistakeholder environments, which are, at best, only partly predictable. We discuss the implications of this approach as applicable to scientists, who have to overcome certain entrained behaviour patterns in order to participate effectively in an adaptive management process. The challenge does not end there. Scientists and managers soon discover that an adaptive management approach does not only challenge conventional scientific and management behaviour but also clashes with contemporary organisational culture. We explore the shortcomings and requirements of organisations with regard to enabling adaptive management. Our overall conclusion relates to whether organisations are learning-centred or not. Do we continue to filter out unfamiliar information which does not fit our world view and avoid situations where we might fail, or do we use new and challenging situations to reframe the question and prepare ourselves for continued learning? Conservation implications: For an organisation to effectively embrace adaptive management, its mangers and scientists may first have to adapt their own beliefs regarding their respective roles. Instead of seeking certainty for guiding decisions, managers and scientists should acknowledge a degree of uncertainty inherent to complex social and ecological systems and seek to learn from the patterns emerging from every decision and action. The required organisational culture is one of ongoing and purposeful learning with all relevant stakeholders. Such a learning culture is often talked about but rarely practised in the organisational environment.

  10. Using Ontology to Drive an Adaptive Learning Interface

    Directory of Open Access Journals (Sweden)

    Andrew Crapo

    2004-10-01

    Full Text Available Intelligent, adaptive interfaces are a pre-requisite to elevating computer-based applications to the realm of collaborative decision support in complex, relatively open-ended domains such as logistics and planning. This is because the composition and effective presentation of even the most useful information must be tailored to constantly changing circumstances. Our objective is to not only achieve an adaptive human-machine interface, but to imbue the software with a significant portion of the responsibility for effectively controlling the adaptation, freeing the user from unnecessary distraction and making the human-machine relationship more collaborative in nature. The foundational concepts of interface adaptation are discussed and a specific logistics application is described as an example.

  11. Adaptative Peer to Peer Data Sharing for Technology Enhanced Learning

    Science.gov (United States)

    Angelaccio, Michele; Buttarazzi, Berta

    Starting from the hypothesis that P2P Data Sharing in a direct teaching scenario (e.g.: a classroom lesson) may lead to relevant benefits, this paper explores the features of EduSHARE a Collaborative Learning System useful for Enhanced Learning Process.

  12. Psychosocial and Adaptive Deficits Associated with Learning Disability Subtypes

    Science.gov (United States)

    Backenson, Erica M.; Holland, Sara C.; Kubas, Hanna A.; Fitzer, Kim R.; Wilcox, Gabrielle; Carmichael, Jessica A.; Fraccaro, Rebecca L.; Smith, Amanda D.; Macoun, Sarah J.; Harrison, Gina L.; Hale, James B.

    2015-01-01

    Children with specific learning disabilities (SLD) have deficits in the basic psychological processes that interfere with learning and academic achievement, and for some SLD subtypes, these deficits can also lead to emotional and/or behavior problems. This study examined psychosocial functioning in 123 students, aged 6 to 11, who underwent…

  13. Complex Mobile Learning That Adapts to Learners' Cognitive Load

    Science.gov (United States)

    Deegan, Robin

    2015-01-01

    Mobile learning is cognitively demanding and frequently the ubiquitous nature of mobile computing means that mobile devices are used in cognitively demanding environments. This paper examines the use of mobile devices from a Learning, Usability and Cognitive Load Theory perspective. It suggests scenarios where these fields interact and presents an…

  14. Adaptation of elaborated feedback in e-learning

    NARCIS (Netherlands)

    Vasilyeva, E.; Pechenizkiy, M.; De Bra, P.M.E.; Nejdl, W.; Kay, J.; Pu, P.; Herder, E.

    2008-01-01

    Design of feedback is a critical issue of online assessment development within Web-based Learning Systems (WBLSs). In our work we demonstrate the possibilities of tailoring the feedback to the students’ learning style (LS), certitude in response and its correctness. We observe in the experimental

  15. Using a social learning configuration to increase Vietnamese smallholder farmers’ adaptive capacity to respond to climate change

    NARCIS (Netherlands)

    Phuong, Le Thi Hong; Wals, Arjen; Sen, Le Thi Hoa; Hoa, Nguyen Quoc; Lu, Van Phan; Biesbroek, Robbert

    2018-01-01

    Social learning is crucial for local smallholder farmers in developing countries to improve their adaptive capacity and to adapt to the current and projected impacts of climate change. While it is widely acknowledged that social learning is a necessary condition for adaptation, few studies have

  16. A neural learning classifier system with self-adaptive constructivism for mobile robot control.

    Science.gov (United States)

    Hurst, Jacob; Bull, Larry

    2006-01-01

    For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.

  17. Specialized hybrid learners resolve Rogers' paradox about the adaptive value of social learning.

    Science.gov (United States)

    Kharratzadeh, Milad; Montrey, Marcel; Metz, Alex; Shultz, Thomas R

    2017-02-07

    Culture is considered an evolutionary adaptation that enhances reproductive fitness. A common explanation is that social learning, the learning mechanism underlying cultural transmission, enhances mean fitness by avoiding the costs of individual learning. This explanation was famously contradicted by Rogers (1988), who used a simple mathematical model to show that cheap social learning can invade a population without raising its mean fitness. He concluded that some crucial factor remained unaccounted for, which would reverse this surprising result. Here we extend this model to include a more complex environment and limited resources, where individuals cannot reliably learn everything about the environment on their own. Under such conditions, cheap social learning evolves and enhances mean fitness, via hybrid learners capable of specializing their individual learning. We then show that while spatial or social constraints hinder the evolution of hybrid learners, a novel social learning strategy, complementary copying, can mitigate these effects. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Prototype water reuse system

    Science.gov (United States)

    Lucchetti, G.; Gray, G.A.

    1988-01-01

    A small-scale water reuse system (150 L/min) was developed to create an environment for observing fish under a variety of temperature regimes. Key concerns of disease control, water quality, temperature control, and efficiency and case of operation were addressed. Northern squawfish (Ptychocheilus oregonensis) were held at loading densities ranging from 0.11 to 0.97 kg/L per minute and at temperatures from 10 to 20°C for 6 months with no disease problems or degradation ofwater quality in the system. The system required little maintenance during 2 years of operation.

  19. Adapted Lethality: What We Can Learn from Guinea Pig-Adapted Ebola Virus Infection Model.

    Science.gov (United States)

    Cheresiz, S V; Semenova, E A; Chepurnov, A A

    2016-01-01

    Establishment of small animal models of Ebola virus (EBOV) infection is important both for the study of genetic determinants involved in the complex pathology of EBOV disease and for the preliminary screening of antivirals, production of therapeutic heterologic immunoglobulins, and experimental vaccine development. Since the wild-type EBOV is avirulent in rodents, the adaptation series of passages in these animals are required for the virulence/lethality to emerge in these models. Here, we provide an overview of our several adaptation series in guinea pigs, which resulted in the establishment of guinea pig-adapted EBOV (GPA-EBOV) variants different in their characteristics, while uniformly lethal for the infected animals, and compare the virologic, genetic, pathomorphologic, and immunologic findings with those obtained in the adaptation experiments of the other research groups.

  20. Adapted Lethality: What We Can Learn from Guinea Pig-Adapted Ebola Virus Infection Model

    Directory of Open Access Journals (Sweden)

    S. V. Cheresiz

    2016-01-01

    Full Text Available Establishment of small animal models of Ebola virus (EBOV infection is important both for the study of genetic determinants involved in the complex pathology of EBOV disease and for the preliminary screening of antivirals, production of therapeutic heterologic immunoglobulins, and experimental vaccine development. Since the wild-type EBOV is avirulent in rodents, the adaptation series of passages in these animals are required for the virulence/lethality to emerge in these models. Here, we provide an overview of our several adaptation series in guinea pigs, which resulted in the establishment of guinea pig-adapted EBOV (GPA-EBOV variants different in their characteristics, while uniformly lethal for the infected animals, and compare the virologic, genetic, pathomorphologic, and immunologic findings with those obtained in the adaptation experiments of the other research groups.

  1. Lessons Learned from the First Decade of Adaptive Management in Comprehensive Everglades Restoration

    Directory of Open Access Journals (Sweden)

    Andrew J. LoSchiavo

    2013-12-01

    Full Text Available Although few successful examples of large-scale adaptive management applications are available to ecosystem restoration scientists and managers, examining where and how the components of an adaptive management program have been successfully implemented yields insight into what approaches have and have not worked. We document five key lessons learned during the decade-long development and implementation of the Comprehensive Everglades Restoration Plan (CERP Collaborative Adaptive Management Program that might be useful to other adaptive management practitioners. First, legislative and regulatory authorities that require the development of an adaptive management program are necessary to maintain funding and support to set up and implement adaptive management. Second, integration of adaptive management activities into existing institutional processes, and development of technical guidance, helps to ensure that adaptive management activities are understood and roles and responsibilities are clearly articulated so that adaptive management activities are implemented successfully. Third, a strong applied science framework is critical for establishing a prerestoration ecosystem reference condition and understanding of how the system works, as well as for providing a conduit for incorporating new scientific information into the decision-making process. Fourth, clear identification of uncertainties that pose risks to meeting restoration goals helps with the development of hypothesis-driven strategies to inform restoration planning and implementation. Tools such as management options matrices can provide a coherent way to link hypotheses to specific monitoring efforts and options to adjust implementation if performance goals are not achieved. Fifth, independent external peer review of an adaptive management program provides important feedback critical to maintaining and improving adaptive management implementation for ecosystem restoration. These lessons

  2. Adapting Parameterized Motions using Iterative Learning and Online Collision Detection

    DEFF Research Database (Denmark)

    Laursen, Johan Sund; Sørensen, Lars Carøe; Schultz, Ulrik Pagh

    2018-01-01

    utilizing Gaussian Process learning. This allows for motion parameters to be optimized using real world trials which incorporate all uncertainties inherent in the assembly process without requiring advanced robot and sensor setups. The result is a simple and straightforward system which helps the user...... automatically find robust and uncertainty-tolerant motions. We present experiments for an assembly case showing both detection and learning in the real world and how these combine to a robust robot system....

  3. Robust Visual Knowledge Transfer via Extreme Learning Machine Based Domain Adaptation.

    Science.gov (United States)

    Zhang, Lei; Zhang, David

    2016-08-10

    We address the problem of visual knowledge adaptation by leveraging labeled patterns from source domain and a very limited number of labeled instances in target domain to learn a robust classifier for visual categorization. This paper proposes a new extreme learning machine based cross-domain network learning framework, that is called Extreme Learning Machine (ELM) based Domain Adaptation (EDA). It allows us to learn a category transformation and an ELM classifier with random projection by minimizing the -norm of the network output weights and the learning error simultaneously. The unlabeled target data, as useful knowledge, is also integrated as a fidelity term to guarantee the stability during cross domain learning. It minimizes the matching error between the learned classifier and a base classifier, such that many existing classifiers can be readily incorporated as base classifiers. The network output weights cannot only be analytically determined, but also transferrable. Additionally, a manifold regularization with Laplacian graph is incorporated, such that it is beneficial to semi-supervised learning. Extensively, we also propose a model of multiple views, referred as MvEDA. Experiments on benchmark visual datasets for video event recognition and object recognition, demonstrate that our EDA methods outperform existing cross-domain learning methods.

  4. Iterative learning-based decentralized adaptive tracker for large-scale systems: a digital redesign approach.

    Science.gov (United States)

    Tsai, Jason Sheng-Hong; Du, Yan-Yi; Huang, Pei-Hsiang; Guo, Shu-Mei; Shieh, Leang-San; Chen, Yuhua

    2011-07-01

    In this paper, a digital redesign methodology of the iterative learning-based decentralized adaptive tracker is proposed to improve the dynamic performance of sampled-data linear large-scale control systems consisting of N interconnected multi-input multi-output subsystems, so that the system output will follow any trajectory which may not be presented by the analytic reference model initially. To overcome the interference of each sub-system and simplify the controller design, the proposed model reference decentralized adaptive control scheme constructs a decoupled well-designed reference model first. Then, according to the well-designed model, this paper develops a digital decentralized adaptive tracker based on the optimal analog control and prediction-based digital redesign technique for the sampled-data large-scale coupling system. In order to enhance the tracking performance of the digital tracker at specified sampling instants, we apply the iterative learning control (ILC) to train the control input via continual learning. As a result, the proposed iterative learning-based decentralized adaptive tracker not only has robust closed-loop decoupled property but also possesses good tracking performance at both transient and steady state. Besides, evolutionary programming is applied to search for a good learning gain to speed up the learning process of ILC. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  5. Synthesizing Learning on Adaptation to Climate Change | IDRC ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Climate Change Adaptation in Africa (CCAA), a program is supported by IDRC and the United Kingdom's Department for International Development (DFID), supports three kinds of activity: research, capacity building and networking. Since 2006, CCAA has supported more than 30 participatory action research projects.

  6. Adaptive WTA with an analog VLSI neuromorphic learning chip.

    Science.gov (United States)

    Häfliger, Philipp

    2007-03-01

    In this paper, we demonstrate how a particular spike-based learning rule (where exact temporal relations between input and output spikes of a spiking model neuron determine the changes of the synaptic weights) can be tuned to express rate-based classical Hebbian learning behavior (where the average input and output spike rates are sufficient to describe the synaptic changes). This shift in behavior is controlled by the input statistic and by a single time constant. The learning rule has been implemented in a neuromorphic very large scale integration (VLSI) chip as part of a neurally inspired spike signal image processing system. The latter is the result of the European Union research project Convolution AER Vision Architecture for Real-Time (CAVIAR). Since it is implemented as a spike-based learning rule (which is most convenient in the overall spike-based system), even if it is tuned to show rate behavior, no explicit long-term average signals are computed on the chip. We show the rule's rate-based Hebbian learning ability in a classification task in both simulation and chip experiment, first with artificial stimuli and then with sensor input from the CAVIAR system.

  7. Water reuse systems: A review of the principal components

    Science.gov (United States)

    Lucchetti, G.; Gray, G.A.

    1988-01-01

    Principal components of water reuse systems include ammonia removal, disease control, temperature control, aeration, and particulate filtration. Effective ammonia removal techniques include air stripping, ion exchange, and biofiltration. Selection of a particular technique largely depends on site-specific requirements (e.g., space, existing water quality, and fish densities). Disease control, although often overlooked, is a major problem in reuse systems. Pathogens can be controlled most effectively with ultraviolet radiation, ozone, or chlorine. Simple and inexpensive methods are available to increase oxygen concentration and eliminate gas supersaturation, these include commercial aerators, air injectors, and packed columns. Temperature control is a major advantage of reuse systems, but the equipment required can be expensive, particularly if water temperature must be rigidly controlled and ambient air temperature fluctuates. Filtration can be readily accomplished with a hydrocyclone or sand filter that increases overall system efficiency. Based on criteria of adaptability, efficiency, and reasonable cost, we recommend components for a small water reuse system.

  8. Learning and adaptation in the management of waterfowl harvests

    Science.gov (United States)

    Johnson, Fred A.

    2011-01-01

    A formal framework for the adaptive management of waterfowl harvests was adopted by the U.S. Fish and Wildlife Service in 1995. The process admits competing models of waterfowl population dynamics and harvest impacts, and relies on model averaging to compute optimal strategies for regulating harvest. Model weights, reflecting the relative ability of the alternative models to predict changes in population size, are used in the model averaging and are updated each year based on a comparison of model predictions and observations of population size. Since its inception the adaptive harvest program has focused principally on mallards (Anas platyrhynchos), which constitute a large portion of the U.S. waterfowl harvest. Four competing models, derived from a combination of two survival and two reproductive hypotheses, were originally assigned equal weights. In the last year of available information (2007), model weights favored the weakly density-dependent reproductive hypothesis over the strongly density-dependent one, and the additive mortality hypothesis over the compensatory one. The change in model weights led to a more conservative harvesting policy than what was in effect in the early years of the program. Adaptive harvest management has been successful in many ways, but nonetheless has exposed the difficulties in defining management objectives, in predicting and regulating harvests, and in coping with the tradeoffs inherent in managing multiple waterfowl stocks exposed to a common harvest. The key challenge now facing managers is whether adaptive harvest management as an institution can be sufficiently adaptive, and whether the knowledge and experience gained from the process can be reflected in higher-level policy decisions.

  9. Adaptive memory: animacy effects persist in paired-associate learning.

    Science.gov (United States)

    VanArsdall, Joshua E; Nairne, James S; Pandeirada, Josefa N S; Cogdill, Mindi

    2015-01-01

    Recent evidence suggests that animate stimuli are remembered better than matched inanimate stimuli. Two experiments tested whether this animacy effect persists in paired-associate learning of foreign words. Experiment 1 randomly paired Swahili words with matched animate and inanimate English words. Participants were told simply to learn the English "translations" for a later test. Replicating earlier findings using free recall, a strong animacy advantage was found in this cued-recall task. Concerned that the effect might be due to enhanced accessibility of the individual responses (e.g., animates represent a more accessible category), Experiment 2 selected animate and inanimate English words from two more constrained categories (four-legged animals and furniture). Once again, an advantage was found for pairs using animate targets. These results argue against organisational accounts of the animacy effect and potentially have implications for foreign language vocabulary learning.

  10. Performing Verification and Validation in Reuse-Based Software Engineering

    Science.gov (United States)

    Addy, Edward A.

    1999-01-01

    The implementation of reuse-based software engineering not only introduces new activities to the software development process, such as domain analysis and domain modeling, it also impacts other aspects of software engineering. Other areas of software engineering that are affected include Configuration Management, Testing, Quality Control, and Verification and Validation (V&V). Activities in each of these areas must be adapted to address the entire domain or product line rather than a specific application system. This paper discusses changes and enhancements to the V&V process, in order to adapt V&V to reuse-based software engineering.

  11. Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

    Science.gov (United States)

    Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J

    2014-01-01

    This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.

  12. Intelligent Adaptable e-Assessment for Inclusive e-Learning

    Science.gov (United States)

    Nacheva-Skopalik, Lilyana; Green, Steve

    2016-01-01

    Access to education is one of the main human rights. Everyone should have access to education and be capable of benefiting from it. However there are a number who are excluded, not because of a lack of ability but simply because they have a disability or specific need which current education systems do not address. A learning system in which…

  13. The utility of adaptive eLearning in cervical cytopathology education.

    Science.gov (United States)

    Samulski, T Danielle; Taylor, Laura A; La, Teresa; Mehr, Chelsea R; McGrath, Cindy M; Wu, Roseann I

    2018-02-01

    Adaptive eLearning allows students to experience a self-paced, individualized curriculum based on prior knowledge and learning ability. The authors investigated the effectiveness of adaptive online modules in teaching cervical cytopathology. eLearning modules were created that covered basic concepts in cervical cytopathology, including artifacts and infections, squamous lesions (SL), and glandular lesions (GL). The modules used student responses to individualize the educational curriculum and provide real-time feedback. Pathology trainees and faculty from the authors' institution were randomized into 2 groups (SL or GL), and identical pre-tests and post-tests were used to compare the efficacy of eLearning modules versus traditional study methods (textbooks and slide sets). User experience was assessed with a Likert scale and free-text responses. Sixteen of 17 participants completed the SL module, and 19 of 19 completed the GL module. Participants in both groups had improved post-test scores for content in the adaptive eLearning module. Users indicated that the module was effective in presenting content and concepts (Likert scale [from 1 to 5], 4.3 of 5.0), was an efficient and convenient way to review the material (Likert scale, 4.4 of 5.0), and was more engaging than lectures and texts (Likert scale, 4.6 of 5.0). Users favored the immediate feedback and interactivity of the module. Limitations included the inability to review prior content and slow upload time for images. Learners demonstrated improvement in their knowledge after the use of adaptive eLearning modules compared with traditional methods. Overall, the modules were viewed positively by participants. Adaptive eLearning modules can provide an engaging and effective adjunct to traditional teaching methods in cervical cytopathology. Cancer Cytopathol 2018;126:129-35. © 2017 American Cancer Society. © 2017 American Cancer Society.

  14. Environmental benefits from reusing clothes

    DEFF Research Database (Denmark)

    Farrant, Laura; Olsen, Stig Irving; Wangel, Arne

    2010-01-01

    and Estonia, it was assumed that over 100 collected items 60 would be reused, 30 recycled in other ways and 10 go to final disposal Using these inputs, the LCA showed that the collection, processing and transport of second-hand clothing has insignificant impacts on the environment in comparison to the savings...... of establishing the net benefits from introducing clothes reuse. Indeed, it enables to take into consideration all the activities connected to reusing clothes, including, for instance, recycling and disposal of the collected clothes not suitable for reuse. In addition, the routes followed by the collected clothes....... Conclusions The results of the study show that clothes reuse can significantly contribute to reducing the environmental burden of clothing. Recommendations and perspectives It would be beneficial to apply other methods for estimating the avoided production of new clothes in order to check the validity...

  15. Becoming a Coach in Developmental Adaptive Sailing: A Lifelong Learning Perspective.

    Science.gov (United States)

    Duarte, Tiago; Culver, Diane M

    2014-10-02

    Life-story methodology and innovative methods were used to explore the process of becoming a developmental adaptive sailing coach. Jarvis's (2009) lifelong learning theory framed the thematic analysis. The findings revealed that the coach, Jenny, was exposed from a young age to collaborative environments. Social interactions with others such as mentors, colleagues, and athletes made major contributions to her coaching knowledge. As Jenny was exposed to a mixture of challenges and learning situations, she advanced from recreational para-swimming instructor to developmental adaptive sailing coach. The conclusions inform future research in disability sport coaching, coach education, and applied sport psychology.

  16. Lost in Translation: Adapting a Face-to-Face Course Into an Online Learning Experience.

    Science.gov (United States)

    Kenzig, Melissa J

    2015-09-01

    Online education has grown dramatically over the past decade. Instructors who teach face-to-face courses are being called on to adapt their courses to the online environment. Many instructors do not have sufficient training to be able to effectively move courses to an online format. This commentary discusses the growth of online learning, common challenges faced by instructors adapting courses from face-to-face to online, and best practices for translating face-to-face courses into online learning opportunities. © 2015 Society for Public Health Education.

  17. Modeling the behavioral substrates of associate learning and memory - Adaptive neural models

    Science.gov (United States)

    Lee, Chuen-Chien

    1991-01-01

    Three adaptive single-neuron models based on neural analogies of behavior modification episodes are proposed, which attempt to bridge the gap between psychology and neurophysiology. The proposed models capture the predictive nature of Pavlovian conditioning, which is essential to the theory of adaptive/learning systems. The models learn to anticipate the occurrence of a conditioned response before the presence of a reinforcing stimulus when training is complete. Furthermore, each model can find the most nonredundant and earliest predictor of reinforcement. The behavior of the models accounts for several aspects of basic animal learning phenomena in Pavlovian conditioning beyond previous related models. Computer simulations show how well the models fit empirical data from various animal learning paradigms.

  18. Designing and Developing a Novel Hybrid Adaptive Learning Path Recommendation System (ALPRS) for Gamification Mathematics Geometry Course

    Science.gov (United States)

    Su, Chung-Ho

    2017-01-01

    Since recommendation systems possess the advantage of adaptive recommendation, they have gradually been applied to e-learning systems to recommend subsequent learning content for learners. However, problems exist in current learning recommender systems available to students in that they are often general learning content and unable to offer…

  19. Adaptation Provisioning with Respect to Learning Styles in a Web-Based Educational System: An Experimental Study

    Science.gov (United States)

    Popescu, E.

    2010-01-01

    Personalized instruction is seen as a desideratum of today's e-learning systems. The focus of this paper is on those platforms that use learning styles as personalization criterion called learning style-based adaptive educational systems. The paper presents an innovative approach based on an integrative set of learning preferences that alleviates…

  20. A framework for adaptive e-learning for continuum mechanics and structural analysis

    OpenAIRE

    Mosquera Feijoo, Juan Carlos; Plaza Beltrán, Luis Francisco; González Rodrigo, Beatriz

    2015-01-01

    This paper presents a project for providing the students of Structural Engineering with the flexibility to learn outside classroom schedules. The goal is a framework for adaptive E-learning based on a repository of open educational courseware with a set of basic Structural Engineering concepts and fundamentals. These are paramount for students to expand their technical knowledge and skills in structural analysis and design of tall buildings, arch-type structures as well as bridges. Thus, conc...

  1. Adaptive Learning Rule for Hardware-based Deep Neural Networks Using Electronic Synapse Devices

    OpenAIRE

    Lim, Suhwan; Bae, Jong-Ho; Eum, Jai-Ho; Lee, Sungtae; Kim, Chul-Heung; Kwon, Dongseok; Park, Byung-Gook; Lee, Jong-Ho

    2017-01-01

    In this paper, we propose a learning rule based on a back-propagation (BP) algorithm that can be applied to a hardware-based deep neural network (HW-DNN) using electronic devices that exhibit discrete and limited conductance characteristics. This adaptive learning rule, which enables forward, backward propagation, as well as weight updates in hardware, is helpful during the implementation of power-efficient and high-speed deep neural networks. In simulations using a three-layer perceptron net...

  2. Managing Cognitive Load in Adaptive ICT-Based Learning

    Directory of Open Access Journals (Sweden)

    Slava Kalyuga

    2009-10-01

    Full Text Available The history of technological innovations in education has many examples of failed high expectations. To avoid becoming another one, current multimedia ICT tools need to be designed in accordance with how the human mind works. There are well established characteristics of its architecture that should be taken into account when evaluating, selecting, and using educational technology. This paper starts with a review of the most important features of human cognitive architecture and their implications for ICT-based learning. Expertise reversal effect relates to the interactions between levels of learner prior knowledge and effectiveness of different instructional techniques and procedures. Designs and techniques that are effective with low-knowledge learners can lose their effectiveness and even have negative consequences for more proficient learners. The paper describes recent empirical findings associated with the expertise reversal effect in multimedia and hypermedia learning environments, their interpretation within a cognitive load framework, and implications for the design of learner-tailored multimedia.

  3. Adaptive Sampling for Nonlinear Dimensionality Reduction Based on Manifold Learning

    DEFF Research Database (Denmark)

    Franz, Thomas; Zimmermann, Ralf; Goertz, Stefan

    2017-01-01

    We make use of the non-intrusive dimensionality reduction method Isomap in order to emulate nonlinear parametric flow problems that are governed by the Reynolds-averaged Navier-Stokes equations. Isomap is a manifold learning approach that provides a low-dimensional embedding space that is approxi...... to detect and fill up gaps in the sampling in the embedding space. The performance of the proposed manifold filling method will be illustrated by numerical experiments, where we consider nonlinear parameter-dependent steady-state Navier-Stokes flows in the transonic regime.......We make use of the non-intrusive dimensionality reduction method Isomap in order to emulate nonlinear parametric flow problems that are governed by the Reynolds-averaged Navier-Stokes equations. Isomap is a manifold learning approach that provides a low-dimensional embedding space...

  4. Thai nursing students' adaption to problem-based learning: a qualitative study.

    Science.gov (United States)

    Klunklin, Areewan; Subpaiboongid, Pornpun; Keitlertnapha, Pongsri; Viseskul, Nongkran; Turale, Sue

    2011-11-01

    Student-centred forms of learning have gained favour internationally over the last few decades including problem based learning, an approach now incorporated in medicine, nursing and other disciplines' education in many countries. However, it is still new in Thailand and being piloted to try to offset traditional forms of didactic, teacher-centred forms of teaching. In this qualitative study, 25 undergraduate nursing students in northern Thailand were interviewed about their experiences with problem-based learning in a health promotion subject. Content analysis was used to interrogate interview data, which revealed four categories: adapting, seeking assistance, self-development, and thinking process development. Initially participants had mixed emotions of confusion, negativity or boredom in the adaption process, but expressed satisfaction with creativity in learning, group work, and leadership development. They described increased abilities to problem solve and think critically, but struggled to develop questioning behaviours in learning. Socio-culturally in Thai education, students have great respect for teachers, but rarely question or challenge them or their learning. We conclude that problem-based learning has great potential in Thai nursing education, but educators and systems need to systematically prepare appropriate learning environments, their staff and students, to incorporate this within curricula. Copyright © 2011 Elsevier Ltd. All rights reserved.

  5. Multiple Kernel Learning for adaptive graph regularized nonnegative matrix factorization

    KAUST Repository

    Wang, Jim Jing-Yan; AbdulJabbar, Mustafa Abdulmajeed

    2012-01-01

    Nonnegative Matrix Factorization (NMF) has been continuously evolving in several areas like pattern recognition and information retrieval methods. It factorizes a matrix into a product of 2 low-rank non-negative matrices that will define parts-based, and linear representation of non-negative data. Recently, Graph regularized NMF (GrNMF) is proposed to find a compact representation, which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure. In GNMF, an affinity graph is constructed from the original data space to encode the geometrical information. In this paper, we propose a novel idea which engages a Multiple Kernel Learning approach into refining the graph structure that reflects the factorization of the matrix and the new data space. The GrNMF is improved by utilizing the graph refined by the kernel learning, and then a novel kernel learning method is introduced under the GrNMF framework. Our approach shows encouraging results of the proposed algorithm in comparison to the state-of-the-art clustering algorithms like NMF, GrNMF, SVD etc.

  6. Evidence of Cross-boundary Use and Reuse of Digital Educational Resources

    Directory of Open Access Journals (Sweden)

    Riina Vuorikari

    2009-12-01

    Full Text Available In this study an investigation using log-files of teachers’ Collections of educational resources in two different platforms was conducted. The goal was to find empirical evidence from the field that teachers use and reuse learning resources that are in a language other than their mother tongue and originate from different countries than they do, for this, the term cross-boundary use of learning resources is used. In both contexts behavioural evidence was found that cross-boundary use and reuse takes place, and it was shown that it correlates with the general use and reuse trends. Moreover, it was found that cross-boundary reuse, when compared to 20% of general reuse, was notably less (37% to 55% of it. The motivation to study cross-boundary use and reuse is to set a baseline for future studies, and to understand how it can be supported and enhanced in the future.

  7. Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems.

    Science.gov (United States)

    Gao, Weinan; Jiang, Zhong-Ping; Weinan Gao; Zhong-Ping Jiang; Gao, Weinan; Jiang, Zhong-Ping

    2018-06-01

    This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. Adaptive dynamic programming (ADP) and nonlinear output regulation theories are integrated for the first time to compute an adaptive near-optimal tracker without any a priori knowledge of the system dynamics. Fundamentally different from adaptive optimal stabilization problems, the solution to a Hamilton-Jacobi-Bellman (HJB) equation, not necessarily a positive definite function, cannot be approximated through the existing iterative methods. This paper proposes a novel policy iteration technique for solving positive semidefinite HJB equations with rigorous convergence analysis. A two-phase data-driven learning method is developed and implemented online by ADP. The efficacy of the proposed adaptive optimal tracking control methodology is demonstrated via a Van der Pol oscillator with time-varying exogenous signals.

  8. The Study of Reinforcement Learning for Traffic Self-Adaptive Control under Multiagent Markov Game Environment

    Directory of Open Access Journals (Sweden)

    Lun-Hui Xu

    2013-01-01

    Full Text Available Urban traffic self-adaptive control problem is dynamic and uncertain, so the states of traffic environment are hard to be observed. Efficient agent which controls a single intersection can be discovered automatically via multiagent reinforcement learning. However, in the majority of the previous works on this approach, each agent needed perfect observed information when interacting with the environment and learned individually with less efficient coordination. This study casts traffic self-adaptive control as a multiagent Markov game problem. The design employs traffic signal control agent (TSCA for each signalized intersection that coordinates with neighboring TSCAs. A mathematical model for TSCAs’ interaction is built based on nonzero-sum markov game which has been applied to let TSCAs learn how to cooperate. A multiagent Markov game reinforcement learning approach is constructed on the basis of single-agent Q-learning. This method lets each TSCA learn to update its Q-values under the joint actions and imperfect information. The convergence of the proposed algorithm is analyzed theoretically. The simulation results show that the proposed method is convergent and effective in realistic traffic self-adaptive control setting.

  9. AN ADAPTIVE ACO-DRIVEN SCHEME FOR LEARNING AIM ORIENTED PERSONALIZED E-LEARNING

    Directory of Open Access Journals (Sweden)

    Sushma Hans

    2014-10-01

    Full Text Available The e-learning paradigm is now a well-established vehicle of modern education. It caters to a wide spectrum of students with diverse backgrounds who enroll with their own learning aims. A core challenge under this scenario is to generate personalized learning paths so that each student can achieve her learning aim most effectively. Prior works used static attributes such as prior knowledge level, learning ability, browsing preferences, learning style etc. to generate personalized learning paths. In this paper, we take an entirely new route by taking into account the continuous improvement of a learner in the light of her own learning aim, to redefine her learning path at each level of the course. We introduce the concept of personalized examination system that systematically evaluates the dynamic learning ability of every student according to her pre-set goals. The proposed intelligent e-learning system uses Ant Colony Optimization to iteratively optimize the forward learning paths. Experimental results reveal that the system is able to tap a student’s improved learning ability to choose more difficult paths that contribute highly towards her own aims. We demonstrate that the overall learning success of weaker students doubles as compared to statically generated paths while there is considerable improvement of 50% in the learning success for average students as well. This clearly indicates that our approach gives realistic benefits to initially weak students who gradually evolve as the course progresses.

  10. SU-D-BRB-05: Quantum Learning for Knowledge-Based Response-Adaptive Radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    El Naqa, I; Ten, R [Haken University of Michigan, Ann Arbor, MI (United States)

    2016-06-15

    Purpose: There is tremendous excitement in radiotherapy about applying data-driven methods to develop personalized clinical decisions for real-time response-based adaptation. However, classical statistical learning methods lack in terms of efficiency and ability to predict outcomes under conditions of uncertainty and incomplete information. Therefore, we are investigating physics-inspired machine learning approaches by utilizing quantum principles for developing a robust framework to dynamically adapt treatments to individual patient’s characteristics and optimize outcomes. Methods: We studied 88 liver SBRT patients with 35 on non-adaptive and 53 on adaptive protocols. Adaptation was based on liver function using a split-course of 3+2 fractions with a month break. The radiotherapy environment was modeled as a Markov decision process (MDP) of baseline and one month into treatment states. The patient environment was modeled by a 5-variable state represented by patient’s clinical and dosimetric covariates. For comparison of classical and quantum learning methods, decision-making to adapt at one month was considered. The MDP objective was defined by the complication-free tumor control (P{sup +}=TCPx(1-NTCP)). A simple regression model represented state-action mapping. Single bit in classical MDP and a qubit of 2-superimposed states in quantum MDP represented the decision actions. Classical decision selection was done using reinforcement Q-learning and quantum searching was performed using Grover’s algorithm, which applies uniform superposition over possible states and yields quadratic speed-up. Results: Classical/quantum MDPs suggested adaptation (probability amplitude ≥0.5) 79% of the time for splitcourses and 100% for continuous-courses. However, the classical MDP had an average adaptation probability of 0.5±0.22 while the quantum algorithm reached 0.76±0.28. In cases where adaptation failed, classical MDP yielded 0.31±0.26 average amplitude while the

  11. Adaptation, postpartum concerns, and learning needs in the first two weeks after caesarean birth.

    Science.gov (United States)

    Weiss, Marianne; Fawcett, Jacqueline; Aber, Cynthia

    2009-11-01

    The purpose of this Roy Adaptation Model-based study was to describe women's physical, emotional, functional and social adaptation; postpartum concerns; and learning needs during the first two weeks following caesarean birth and identify relevant nursing interventions. Studies of caesarean-delivered women indicated a trend toward normalisation of the caesarean birth experience. Escalating caesarean birth rates mandate continued study of contemporary caesarean-delivered women. Mixed methods (qualitative and quantitative) descriptive research design. Nursing students collected data from 233 culturally diverse caesarean-delivered women in urban areas of the Midwestern and Northeastern USA between 2002-2004. The focal stimulus was the planned or unplanned caesarean birth; contextual stimuli were cultural identity and parity. Adaptation was measured by open-ended interview questions, fixed choice questionnaires about postpartum concerns and learning needs and nurse assessment of post-discharge problems. Potential interventions were identified using the Omaha System Intervention Scheme. More positive than negative responses were reported for functional and social adaptation than for physical and emotional adaptation. Women with unplanned caesarean births and primiparous women reported less favourable adaptation than planned caesarean mothers and multiparas. Black women reported lower social adaptation, Hispanic women had more role function concerns and Black and Hispanic women had more learning needs than White women. Post-discharge nursing assessments revealed that actual problems accounted for 40% of identified actual or potential problems or needs. Health teaching was the most commonly recommended postpartum intervention strategy followed by case management, treatment and surveillance interventions. Caesarean-delivered women continue to experience some problems with adapting to childbirth. Recommended intervention strategies reflect the importance of health teaching

  12. A Case Study of Horizontal Reuse in a Project-Driven Organisation

    DEFF Research Database (Denmark)

    Christensen, Henrik Bærbak; Røn, Henrik

    2000-01-01

    This experience paper presents observations, lessons learned, and recommendations based on a case study of reuse. The case study is concerned with the development, maturation, and reuse of a business domain independent software component (horizontal reuse) in a project-driven organisation that has...... knowledge is transferred within an organisation; (c) design patterns can be as risky as they can be beneficial; and (d) there is more to architectural mismatch than “merely ” packaging mismatch....

  13. An adaptive deep learning approach for PPG-based identification.

    Science.gov (United States)

    Jindal, V; Birjandtalab, J; Pouyan, M Baran; Nourani, M

    2016-08-01

    Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models. The approach is tested on TROIKA dataset using 10-fold cross validation and achieved an accuracy of 96.1%.

  14. Impact of Adapted Hypermedia on Undergraduate Students' Learning of Astronomy in an Elearning Environment

    Science.gov (United States)

    Zuel, Brian

    The purpose of this dissertation was to examine the effectiveness of matching learners' optimal learning styles to their overall knowledge retention. The study attempted to determine if learners who are placed in an online learning environment that matches their optimal learning styles will retain the information at a higher rate than those learners who are not in an adapted learning environment. There were 56 participants that took one of two lessons; the first lesson was textual based, had no hypertext, and was not influenced heavily by the coherence principle, while the second lesson was multimedia based utilizing hypermedia guided by the coherence principle. Each participant took Felder and Soloman's (1991, 2000) Index of Learning Styles (ILS) questionnaire and was classified using the Felder-Silverman Learning Style Model (FSLSM; 1998) into four individual categories. Groups were separated using the Visual/Verbal section of the FSLSM with 55% (n = 31) of participants going to the adapted group, and 45% (n =25) of participants going to the non-adapted group. Each participant completed an immediate posttest directly after the lesson and a retention posttest a week later. Several repeated measures MANOVA tests were conducted to measure the significance of differences in the tests between groups and within groups. Repeated measures MANOVA tests were conducted to determine if significance existed between the immediate posttest results and the retention posttest results. Also, participants were asked their perspectives if the lesson type they received was beneficial to their perceived learning of the material. Of the 56 students who took part in this study, 31 students were placed in the adapted group and 25 in the non-adapted group based on outcomes of the ILS and the FLSSM. No significant differences were found between groups taking the multimedia lesson and the textual lesson in the immediate posttest. No significant differences were found between the adapted and

  15. Challenges in reusing transactional data for daily documentation in neonatal intensive care.

    Science.gov (United States)

    Kim, G R; Lawson, E E; Lehmann, C U

    2008-11-06

    The reuse of transactional data for clinical documentation requires navigation of computational, institutional and adaptive barriers. We describe organizational and technical issues in developing and deploying a daily progress note tool in a tertiary neonatal intensive care unit that reuses and aggregates data from a commercial integrated clinical information system.

  16. Adaptive learning compressive tracking based on Markov location prediction

    Science.gov (United States)

    Zhou, Xingyu; Fu, Dongmei; Yang, Tao; Shi, Yanan

    2017-03-01

    Object tracking is an interdisciplinary research topic in image processing, pattern recognition, and computer vision which has theoretical and practical application value in video surveillance, virtual reality, and automatic navigation. Compressive tracking (CT) has many advantages, such as efficiency and accuracy. However, when there are object occlusion, abrupt motion and blur, similar objects, and scale changing, the CT has the problem of tracking drift. We propose the Markov object location prediction to get the initial position of the object. Then CT is used to locate the object accurately, and the classifier parameter adaptive updating strategy is given based on the confidence map. At the same time according to the object location, extract the scale features, which is able to deal with object scale variations effectively. Experimental results show that the proposed algorithm has better tracking accuracy and robustness than current advanced algorithms and achieves real-time performance.

  17. Neuromorphic adaptive plastic scalable electronics: analog learning systems.

    Science.gov (United States)

    Srinivasa, Narayan; Cruz-Albrecht, Jose

    2012-01-01

    Decades of research to build programmable intelligent machines have demonstrated limited utility in complex, real-world environments. Comparing their performance with biological systems, these machines are less efficient by a factor of 1 million1 billion in complex, real-world environments. The Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) program is a multifaceted Defense Advanced Research Projects Agency (DARPA) project that seeks to break the programmable machine paradigm and define a new path for creating useful, intelligent machines. Since real-world systems exhibit infinite combinatorial complexity, electronic neuromorphic machine technology would be preferable in a host of applications, but useful and practical implementations still do not exist. HRL Laboratories LLC has embarked on addressing these challenges, and, in this article, we provide an overview of our project and progress made thus far.

  18. Strategies for Adapting WebQuests for Students with Learning Disabilities

    Science.gov (United States)

    Skylar, Ashley A.; Higgins, Kyle; Boone, Randall

    2007-01-01

    WebQuests are gaining popularity as teachers explore using the Internet for guided learning activities. A WebQuest involves students working on a task that is broken down into clearly defined steps. Students often work in groups to actively conduct the research. This article suggests a variety of methods for adapting WebQuests for students with…

  19. Adaptations for Culturally and Linguistically Diverse Families of English Language Learning Students with Autisim Spectrum Disorders

    Science.gov (United States)

    Mitchell, Deborah J.

    2012-01-01

    The purpose of this qualitative, grounded theory study was to describe adaptations for culturally and linguistically diverse families of English language learning students with autism spectrum disorders. Each family's parent was interviewed three separate times to gather information to understand the needs and experiences regarding their…

  20. Adapting the Survivor Game to Create a Group Learning Term Project in Business Finance

    Science.gov (United States)

    Campbell, Robert D.

    2017-01-01

    A large and growing body of research supports the view that the small-group learning structure can be an effective tool to enhance student performance and encourage innovative problem solving. This paper explains in detail how the framework of the popular television reality show Survivor has been adapted to form a vehicle for a college level group…

  1. LEARNING STYLES BASED ADAPTIVE INTELLIGENT TUTORING SYSTEMS: DOCUMENT ANALYSIS OF ARTICLES PUBLISHED BETWEEN 2001. AND 2016.

    Directory of Open Access Journals (Sweden)

    Amit Kumar

    2017-12-01

    Full Text Available Actualizing instructional intercessions to suit learner contrasts has gotten extensive consideration. Among these individual contrast factors, the observational confirmation in regards to the academic benefit of learning styles has been addressed, yet the examination on the issue proceeds. Late improvements in web-based executions have driven researchers to re-examine the learning styles in adaptive tutoring frameworks. Adaptivity in intelligent tutoring systems is strongly influenced by the learning style of a learner. This study involved extensive document analysis of adaptive tutoring systems based on learning styles. Seventy-eight studies in literature from 2001 to 2016 were collected and classified under select parameters such as main focus, purpose, research types, methods, types and levels of participants, field/area of application, learner modelling, data gathering tools used and research findings. The current studies reveal that majority of the studies defined a framework or architecture of adaptive intelligent tutoring system (AITS while others focused on impact of AITS on learner satisfaction and academic outcomes. Currents trends, gaps in literature and ications were discussed.

  2. Adapting and Evaluating a Tree of Life Group for Women with Learning Disabilities

    Science.gov (United States)

    Randle-Phillips, Cathy; Farquhar, Sarah; Thomas, Sally

    2016-01-01

    Background: This study describes how a specific narrative therapy approach called 'the tree of life' was adapted to run a group for women with learning disabilities. The group consisted of four participants and ran for five consecutive weeks. Materials and Methods: Participants each constructed a tree to represent their lives and presented their…

  3. Japanese English Education and Learning: A History of Adapting Foreign Cultures

    Science.gov (United States)

    Shimizu, Minoru

    2010-01-01

    This essay is a history that relates the Japanese tradition of accepting and adapting aspects of foreign culture, especially as it applies to the learning of foreign languages. In particular, the essay describes the history of English education in Japan by investigating its developments after the Meiji era. The author addresses the issues from the…

  4. Quality Assessment of Adaptive Bitrate Videos using Image Metrics and Machine Learning

    DEFF Research Database (Denmark)

    Søgaard, Jacob; Forchhammer, Søren; Brunnström, Kjell

    2015-01-01

    Adaptive bitrate (ABR) streaming is widely used for distribution of videos over the internet. In this work, we investigate how well we can predict the quality of such videos using well-known image metrics, information about the bitrate levels, and a relatively simple machine learning method...

  5. The Dynamic Interplay among EFL Learners' Ambiguity Tolerance, Adaptability, Cultural Intelligence, Learning Approach, and Language Achievement

    Science.gov (United States)

    Alahdadi, Shadi; Ghanizadeh, Afsaneh

    2017-01-01

    A key objective of education is to prepare individuals to be fully-functioning learners. This entails developing the cognitive, metacognitive, motivational, cultural, and emotional competencies. The present study aimed to examine the interrelationships among adaptability, tolerance of ambiguity, cultural intelligence, learning approach, and…

  6. Bridging Scientific Reasoning and Conceptual Change through Adaptive Web-Based Learning

    Science.gov (United States)

    She, Hsiao-Ching; Liao, Ya-Wen

    2010-01-01

    This study reports an adaptive digital learning project, Scientific Concept Construction and Reconstruction (SCCR), and examines its effects on 108 8th grade students' scientific reasoning and conceptual change through mixed methods. A one-group pre-, post-, and retention quasi-experimental design was used in the study. All students received tests…

  7. Co-operative learning and adaptive instruction in a mathematics curriculum

    NARCIS (Netherlands)

    Terwel, J.; Herfs, P.G.P.; Mertens, E.H.M.; Perrenet, J.Chr.

    1994-01-01

    The AGO 12 to 16 Project (the acronym AGO stands for the Dutch equivalent of 'Adaptive Instruction and Co-operative Learning') seeks to develop and evaluate a mathematics curriculum which is suitable for mixed-ability groups in secondary education. The research questions we will address here are,

  8. Negotiating Service Learning through Community Engagement: Adaptive Leadership, Knowledge, Dialogue and Power

    Science.gov (United States)

    Preece, Julia

    2016-01-01

    This article builds on two recent publications (Preece 2013; 2013a) concerning the application of asset-based community development and adaptive leadership theories when negotiating university service learning placements with community organisations in one South African province. The first publication introduced the concept of 'adaptive…

  9. A Hybrid Approach for Supporting Adaptivity in E-Learning Environments

    Science.gov (United States)

    Al-Omari, Mohammad; Carter, Jenny; Chiclana, Francisco

    2016-01-01

    Purpose: The purpose of this paper is to identify a framework to support adaptivity in e-learning environments. The framework reflects a novel hybrid approach incorporating the concept of the event-condition-action (ECA) model and intelligent agents. Moreover, a system prototype is developed reflecting the hybrid approach to supporting adaptivity…

  10. Adaptation to Altitude as a Vehicle for Experiential Learning of Physiology by University Undergraduates

    Science.gov (United States)

    Weigle, David S.; Buben, Amelia; Burke, Caitlin C.; Carroll, Nels D.; Cook, Brett M.; Davis, Benjamin S.; Dubowitz, Gerald; Fisher, Rian E.; Freeman, Timothy C.; Gibbons, Stephen M.; Hansen, Hale A.; Heys, Kimberly A.; Hopkins, Brittany; Jordan, Brittany L.; McElwain, Katherine L.; Powell, Frank L.; Reinhart, Katherine E.; Robbins, Charles D.; Summers, Cameron C.; Walker, Jennifer D.; Weber, Steven S.; Weinheimer, Caroline J.

    2007-01-01

    In this article, an experiential learning activity is described in which 19 university undergraduates made experimental observations on each other to explore physiological adaptations to high altitude. Following 2 wk of didactic sessions and baseline data collection at sea level, the group ascended to a research station at 12,500-ft elevation.…

  11. A Factor-Analytic Study of Adaptive Behavior and Intellectual Functioning in Learning Disabled Children.

    Science.gov (United States)

    Yeargan, Dollye R.

    The factorial structure of intellectual functioning and adaptive behavior was examined in 160 learning disabled students (6 to 16 years old). Ss were administered the Wechsler Intelligence Scale for Children-Revised (WISC-R) and the Coping Inventory (CI). Factor analysis of WISC-R scores revealed three factors: verbal comprehenson, perceptual…

  12. Development of an Advanced, Automatic, Ultrasonic NDE Imaging System via Adaptive Learning Network Signal Processing Techniques

    Science.gov (United States)

    1981-03-13

    UNCLASSIFIED SECURITY CLAS,:FtfC ’i OF TH*!’ AGC W~ct P- A* 7~9r1) 0. ABSTRACT (continued) onuing in concert with a sophisticated detector has...and New York, 1969. Whalen, M.F., L.J. O’Brien, and A.N. Mucciardi, "Application of Adaptive Learning Netowrks for the Characterization of Two

  13. A Framework for Creating Semantically Adaptive Collaborative E-learning Environments

    Directory of Open Access Journals (Sweden)

    Marija Cubric

    2009-09-01

    Full Text Available In this paper we present a framework that can be used to generate web-based, semantically adaptive, e-learning and computer-assisted assessment (CAA tools for any given knowledge domain, based upon dynamic ontological modeling. We accomplish this by generating “learning ontologies” for a given knowledge domain. The generated learning ontologies are built upon our previous work on a domain “Glossary” ontology and augmented with additional conceptual relations from the WordNet 3.0 lexical database, using Text2Onto, an open source ontology extraction tool. The main novelty of this work is in “on the fly” generation of computer assisted assessments based on the underlying ontology and pre-defined question templates that are founded on the Bloom’s taxonomy of educational objectives. The main deployment scenario for the framework is a web-service providing collaborative e- learning and knowledge management capabilities to various learning communities. The framework can be extended to provide collection and exploitation of the users’ learning behaviour metrics, in order to further adapt the generated e-learning environment to the learners’ needs.

  14. A New Approach to Teaching Biomechanics Through Active, Adaptive, and Experiential Learning.

    Science.gov (United States)

    Singh, Anita

    2017-07-01

    Demand of biomedical engineers continues to rise to meet the needs of healthcare industry. Current training of bioengineers follows the traditional and dominant model of theory-focused curricula. However, the unmet needs of the healthcare industry warrant newer skill sets in these engineers. Translational training strategies such as solving real world problems through active, adaptive, and experiential learning hold promise. In this paper, we report our findings of adding a real-world 4-week problem-based learning unit into a biomechanics capstone course for engineering students. Surveys assessed student perceptions of the activity and learning experience. While students, across three cohorts, felt challenged to solve a real-world problem identified during the simulation lab visit, they felt more confident in utilizing knowledge learned in the biomechanics course and self-directed research. Instructor evaluations indicated that the active and experiential learning approach fostered their technical knowledge and life-long learning skills while exposing them to the components of adaptive learning and innovation.

  15. Sleep benefits consolidation of visuo-motor adaptation learning in older adults.

    Science.gov (United States)

    Mantua, Janna; Baran, Bengi; Spencer, Rebecca M C

    2016-02-01

    Sleep is beneficial for performance across a range of memory tasks in young adults, but whether memories are similarly consolidated in older adults is less clear. Performance benefits have been observed following sleep in older adults for declarative learning tasks, but this benefit may be reduced for non-declarative, motor skill learning tasks. To date, studies of sleep-dependent consolidation of motor learning in older adults are limited to motor sequence tasks. To examine whether reduced sleep-dependent consolidation in older adults is generalizable to other forms of motor skill learning, we examined performance changes over intervals of sleep and wake in young (n = 62) and older adults (n = 61) using a mirror-tracing task, which assesses visuo-motor adaptation learning. Participants learned the task either in the morning or in evening, and performance was assessed following a 12-h interval containing overnight sleep or daytime wake. Contrary to our prediction, both young adults and older adults exhibited sleep-dependent gains in visuo-motor adaptation. There was a correlation between performance improvement over sleep and percent of the night in non-REM stage 2 sleep. These results indicate that motor skill consolidation remains intact with increasing age although this relationship may be limited to specific forms of motor skill learning.

  16. Alignment Condition-Based Robust Adaptive Iterative Learning Control of Uncertain Robot System

    Directory of Open Access Journals (Sweden)

    Guofeng Tong

    2014-04-01

    Full Text Available This paper proposes an adaptive iterative learning control strategy integrated with saturation-based robust control for uncertain robot system in presence of modelling uncertainties, unknown parameter, and external disturbance under alignment condition. An important merit is that it achieves adaptive switching of gain matrix both in conventional PD-type feedforward control and robust adaptive control in the iteration domain simultaneously. The analysis of convergence of proposed control law is based on Lyapunov's direct method under alignment initial condition. Simulation results demonstrate the faster learning rate and better robust performance with proposed algorithm by comparing with other existing robust controllers. The actual experiment on three-DOF robot manipulator shows its better practical effectiveness.

  17. The Adaptation of Contents for the Creation of Foreign Language Learning Exams for Mobile Devices

    Directory of Open Access Journals (Sweden)

    Gimenez López Jose Luis

    2009-07-01

    Full Text Available This article describes the process of adaptation of online digital contents for the realization of foreign language learning tests through mobile devices. Taking into account the need detected in relation to the quick development of mobile technologies, the development and adaptation of existing online exams for mobile devices will be studied. We will do that by considering the possible navigation limits when using multiplatforms, and the aspects related to the formal and technical conditions which the audiovisual contents shown by the device must fulfil. The existing online language learning tests can be adapted to mobile devices through the programming XHTML language. But, the limitations of navigability in relation to contents and the handling of interaction devices available for users to do the tests must also be considered.

  18. Adaptive rival penalized competitive learning and combined linear predictor model for financial forecast and investment.

    Science.gov (United States)

    Cheung, Y M; Leung, W M; Xu, L

    1997-01-01

    We propose a prediction model called Rival Penalized Competitive Learning (RPCL) and Combined Linear Predictor method (CLP), which involves a set of local linear predictors such that a prediction is made by the combination of some activated predictors through a gating network (Xu et al., 1994). Furthermore, we present its improved variant named Adaptive RPCL-CLP that includes an adaptive learning mechanism as well as a data pre-and-post processing scheme. We compare them with some existing models by demonstrating their performance on two real-world financial time series--a China stock price and an exchange-rate series of US Dollar (USD) versus Deutschmark (DEM). Experiments have shown that Adaptive RPCL-CLP not only outperforms the other approaches with the smallest prediction error and training costs, but also brings in considerable high profits in the trading simulation of foreign exchange market.

  19. Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

    Science.gov (United States)

    Stclair, D. C.; Sabharwal, C. L.; Bond, W. E.; Hacke, Keith

    1988-01-01

    Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base.

  20. Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization

    International Nuclear Information System (INIS)

    Yu, Kunjie; Chen, Xu; Wang, Xin; Wang, Zhenlei

    2017-01-01

    Highlights: • SATLBO is proposed to identify the PV model parameters efficiently. • In SATLBO, the learners self-adaptively select different learning phases. • An elite learning is developed in teacher phase to perform local searching. • A diversity learning is proposed in learner phase to maintain population diversity. • SATLBO achieves the first in ranking on overall performance among nine algorithms. - Abstract: Parameters identification of photovoltaic (PV) model based on measured current-voltage characteristic curves plays an important role in the simulation and evaluation of PV systems. To accurately and reliably identify the PV model parameters, a self-adaptive teaching-learning-based optimization (SATLBO) is proposed in this paper. In SATLBO, the learners can self-adaptively select different learning phases based on their knowledge level. The better learners are more likely to choose the learner phase for improving the population diversity, while the worse learners tend to choose the teacher phase to enhance the convergence rate. Thus, learners at different levels focus on different searching abilities to efficiently enhance the performance of algorithm. In addition, to improve the searching ability of different learning phases, an elite learning strategy and a diversity learning method are introduced into the teacher phase and learner phase, respectively. The performance of SATLBO is firstly evaluated on 34 benchmark functions, and experimental results show that SATLBO achieves the first in ranking on the overall performance among nine algorithms. Then, SATLBO is employed to identify parameters of different PV models, i.e., single diode, double diode, and PV module. Experimental results indicate that SATLBO exhibits high accuracy and reliability compared with other parameter extraction methods.

  1. Adaptive learning in agents behaviour: A framework for electricity markets simulation

    DEFF Research Database (Denmark)

    Pinto, Tiago; Vale, Zita; Sousa, Tiago M.

    2014-01-01

    decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology...... that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management...... allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides...

  2. Cooperative Learning Groups and the Evolution of Human Adaptability : (Another Reason) Why Hermits Are Rare in Tonga and Elsewhere.

    Science.gov (United States)

    Bell, Adrian Viliami; Hernandez, Daniel

    2017-03-01

    Understanding the prevalence of adaptive culture in part requires understanding the dynamics of learning. Here we explore the adaptive value of social learning in groups and how formal social groups function as effective mediums of information exchange. We discuss the education literature on Cooperative Learning Groups (CLGs), which outlines the potential of group learning for enhancing learning outcomes. Four qualities appear essential for CLGs to enhance learning: (1) extended conversations, (2) regular interactions, (3) gathering of experts, and (4) incentives for sharing knowledge. We analyze these four qualities within the context of a small-scale agricultural society using data we collected in 2010 and 2012. Through an analysis of surveys, interviews, and observations in the Tongan islands, we describe the role CLGs likely plays in facilitating individuals' learning of adaptive information. Our analysis of group affiliation, membership, and topics of conversation suggest that the first three CLG qualities reflect conditions for adaptive learning in groups. We utilize ethnographic anecdotes to suggest the fourth quality is also conducive to adaptive group learning. Using an evolutionary model, we further explore the scope for CLGs outside the Tongan socioecological context. Model analysis shows that environmental volatility and migration rates among human groups mediate the scope for CLGs. We call for wider attention to how group structure facilitates learning in informal settings, which may be key to assessing the contribution of groups to the evolution of complex, adaptive culture.

  3. Adaptation.

    Science.gov (United States)

    Broom, Donald M

    2006-01-01

    The term adaptation is used in biology in three different ways. It may refer to changes which occur at the cell and organ level, or at the individual level, or at the level of gene action and evolutionary processes. Adaptation by cells, especially nerve cells helps in: communication within the body, the distinguishing of stimuli, the avoidance of overload and the conservation of energy. The time course and complexity of these mechanisms varies. Adaptive characters of organisms, including adaptive behaviours, increase fitness so this adaptation is evolutionary. The major part of this paper concerns adaptation by individuals and its relationships to welfare. In complex animals, feed forward control is widely used. Individuals predict problems and adapt by acting before the environmental effect is substantial. Much of adaptation involves brain control and animals have a set of needs, located in the brain and acting largely via motivational mechanisms, to regulate life. Needs may be for resources but are also for actions and stimuli which are part of the mechanism which has evolved to obtain the resources. Hence pigs do not just need food but need to be able to carry out actions like rooting in earth or manipulating materials which are part of foraging behaviour. The welfare of an individual is its state as regards its attempts to cope with its environment. This state includes various adaptive mechanisms including feelings and those which cope with disease. The part of welfare which is concerned with coping with pathology is health. Disease, which implies some significant effect of pathology, always results in poor welfare. Welfare varies over a range from very good, when adaptation is effective and there are feelings of pleasure or contentment, to very poor. A key point concerning the concept of individual adaptation in relation to welfare is that welfare may be good or poor while adaptation is occurring. Some adaptation is very easy and energetically cheap and

  4. Anatomy of Student Models in Adaptive Learning Systems: A Systematic Literature Review of Individual Differences from 2001 to 2013

    Science.gov (United States)

    Nakic, Jelena; Granic, Andrina; Glavinic, Vlado

    2015-01-01

    This study brings an evidence-based review of user individual characteristics employed as sources of adaptation in recent adaptive learning systems. Twenty-two user individual characteristics were explored in a systematically designed search procedure, while 17 of them were identified as sources of adaptation in final selection. The content…

  5. A Case-Study for Life-Long Learning and Adaptation in Cooperative Robot Teams

    International Nuclear Information System (INIS)

    Parker, L.E.

    1999-01-01

    While considerable progress has been made in recent years toward the development of multi-robot teams, much work remains to be done before these teams are used widely in real-world applications. Two particular needs toward this end are the development of mechanisms that enable robot teams to generate cooperative behaviors on their own, and the development of techniques that allow these teams to autonomously adapt their behavior over time as the environment or the robot team changes. This paper proposes the use of the Cooperative Multi-Robot Observation of Multiple Moving Targets (CMOMMT) application as a rich domain for studying the issues of multi-robot learning and adaptation. After discussing the need for learning and adaptation in multi-robot teams, this paper describes the CMOMMT application and its relevance to multi-robot learning. We discuss the results of the previously- developed, hand-generated algorithm for CMOMMT and the potential for learning that was discovered from the hand-generated approach. We then describe the early work that has been done (by us and others) to generate multi- robot learning techniques for the CMOMMT application, as well as our ongoing research to develop approaches that give performance as good, or better, than the hand-generated approach. The ultimate goal of this research is to develop techniques for multi-robot learning and adaptation in the CMOMMT application domain that will generalize to cooperative robot applications in other domains, thus making the practical use of multi-robot teams in a wide variety of real-world applications much closer to reality

  6. Studying citizen science through adaptive management and learning feedbacks as mechanisms for improving conservation.

    Science.gov (United States)

    Jordan, Rebecca; Gray, Steven; Sorensen, Amanda; Newman, Greg; Mellor, David; Newman, Greg; Hmelo-Silver, Cindy; LaDeau, Shannon; Biehler, Dawn; Crall, Alycia

    2016-06-01

    Citizen science has generated a growing interest among scientists and community groups, and citizen science programs have been created specifically for conservation. We examined collaborative science, a highly interactive form of citizen science, which we developed within a theoretically informed framework. In this essay, we focused on 2 aspects of our framework: social learning and adaptive management. Social learning, in contrast to individual-based learning, stresses collaborative and generative insight making and is well-suited for adaptive management. Adaptive-management integrates feedback loops that are informed by what is learned and is guided by iterative decision making. Participants engaged in citizen science are able to add to what they are learning through primary data collection, which can result in the real-time information that is often necessary for conservation. Our work is particularly timely because research publications consistently report a lack of established frameworks and evaluation plans to address the extent of conservation outcomes in citizen science. To illustrate how our framework supports conservation through citizen science, we examined how 2 programs enacted our collaborative science framework. Further, we inspected preliminary conservation outcomes of our case-study programs. These programs, despite their recent implementation, are demonstrating promise with regard to positive conservation outcomes. To date, they are independently earning funds to support research, earning buy-in from local partners to engage in experimentation, and, in the absence of leading scientists, are collecting data to test ideas. We argue that this success is due to citizen scientists being organized around local issues and engaging in iterative, collaborative, and adaptive learning. © 2016 Society for Conservation Biology.

  7. MRSA model of learning and adaptation: a qualitative study among the general public

    Science.gov (United States)

    2012-01-01

    Background More people in the US now die from Methicillin Resistant Staphylococcus aureus (MRSA) infections than from HIV/AIDS. Often acquired in healthcare facilities or during healthcare procedures, the extremely high incidence of MRSA infections and the dangerously low levels of literacy regarding antibiotic resistance in the general public are on a collision course. Traditional medical approaches to infection control and the conventional attitude healthcare practitioners adopt toward public education are no longer adequate to avoid this collision. This study helps us understand how people acquire and process new information and then adapt behaviours based on learning. Methods Using constructivist theory, semi-structured face-to-face and phone interviews were conducted to gather pertinent data. This allowed participants to tell their stories so their experiences could deepen our understanding of this crucial health issue. Interview transcripts were analysed using grounded theory and sensitizing concepts. Results Our findings were classified into two main categories, each of which in turn included three subthemes. First, in the category of Learning, we identified how individuals used their Experiences with MRSA, to answer the questions: What was learned? and, How did learning occur? The second category, Adaptation gave us insights into Self-reliance, Reliance on others, and Reflections on the MRSA journey. Conclusions This study underscores the critical importance of educational programs for patients, and improved continuing education for healthcare providers. Five specific results of this study can reduce the vacuum that currently exists between the knowledge and information available to healthcare professionals, and how that information is conveyed to the public. These points include: 1) a common model of MRSA learning and adaptation; 2) the self-directed nature of adult learning; 3) the focus on general MRSA information, care and prevention, and antibiotic

  8. MRSA model of learning and adaptation: a qualitative study among the general public

    Directory of Open Access Journals (Sweden)

    Rohde Rodney E

    2012-04-01

    Full Text Available Abstract Background More people in the US now die from Methicillin Resistant Staphylococcus aureus (MRSA infections than from HIV/AIDS. Often acquired in healthcare facilities or during healthcare procedures, the extremely high incidence of MRSA infections and the dangerously low levels of literacy regarding antibiotic resistance in the general public are on a collision course. Traditional medical approaches to infection control and the conventional attitude healthcare practitioners adopt toward public education are no longer adequate to avoid this collision. This study helps us understand how people acquire and process new information and then adapt behaviours based on learning. Methods Using constructivist theory, semi-structured face-to-face and phone interviews were conducted to gather pertinent data. This allowed participants to tell their stories so their experiences could deepen our understanding of this crucial health issue. Interview transcripts were analysed using grounded theory and sensitizing concepts. Results Our findings were classified into two main categories, each of which in turn included three subthemes. First, in the category of Learning, we identified how individuals used their Experiences with MRSA, to answer the questions: What was learned? and, How did learning occur? The second category, Adaptation gave us insights into Self-reliance, Reliance on others, and Reflections on the MRSA journey. Conclusions This study underscores the critical importance of educational programs for patients, and improved continuing education for healthcare providers. Five specific results of this study can reduce the vacuum that currently exists between the knowledge and information available to healthcare professionals, and how that information is conveyed to the public. These points include: 1 a common model of MRSA learning and adaptation; 2 the self-directed nature of adult learning; 3 the focus on general MRSA information, care and

  9. Preventing KPI Violations in Business Processes based on Decision Tree Learning and Proactive Runtime Adaptation

    Directory of Open Access Journals (Sweden)

    Dimka Karastoyanova

    2012-01-01

    Full Text Available The performance of business processes is measured and monitored in terms of Key Performance Indicators (KPIs. If the monitoring results show that the KPI targets are violated, the underlying reasons have to be identified and the process should be adapted accordingly to address the violations. In this paper we propose an integrated monitoring, prediction and adaptation approach for preventing KPI violations of business process instances. KPIs are monitored continuously while the process is executed. Additionally, based on KPI measurements of historical process instances we use decision tree learning to construct classification models which are then used to predict the KPI value of an instance while it is still running. If a KPI violation is predicted, we identify adaptation requirements and adaptation strategies in order to prevent the violation.

  10. Towards more efficient e-learning, intelligence and adapted teaching material

    Directory of Open Access Journals (Sweden)

    Damir Kalpić

    2010-12-01

    Full Text Available This article presents results of a research project in which we attempted to determine the relationship between efficient E-learning and teaching materials adapted based on students’ structure of intelligence. The project was conducted on approximately 500 students, 23 classes, nine elementary schools, with ten teachers of history, informatics and several licensed psychologists. E-teaching material was prepared for the subject of History for eight-grade students of elementary school. Students were tested for the structure of intelligence, and based on their most prominent component, they were divided into groups, using teaching materials adapted to their most prominent intelligence component. The results have shown that use of the adapted teaching materials achieved 6-12% better results than E-materials not adapted to students’ structure of intelligence.

  11. Adaptation

    International Development Research Centre (IDRC) Digital Library (Canada)

    building skills, knowledge or networks on adaptation, ... the African partners leading the AfricaAdapt network, together with the UK-based Institute of Development Studies; and ... UNCCD Secretariat, Regional Coordination Unit for Africa, Tunis, Tunisia .... 26 Rural–urban Cooperation on Water Management in the Context of.

  12. Towards Individualized Online Learning: The Design and Development of an Adaptive Web Based Learning Environment

    Science.gov (United States)

    Inan, Fethi A.; Flores, Raymond; Ari, Fatih; Arslan-Ari, Ismahan

    2011-01-01

    The purpose of this study was to document the design and development of an adaptive system which individualizes instruction such as content, interfaces, instructional strategies, and resources dependent on two factors, namely student motivation and prior knowledge levels. Combining adaptive hypermedia methods with strategies proposed by…

  13. Older adults learn less, but still reduce metabolic cost, during motor adaptation

    Science.gov (United States)

    Huang, Helen J.

    2013-01-01

    The ability to learn new movements and dynamics is important for maintaining independence with advancing age. Age-related sensorimotor changes and increased muscle coactivation likely alter the trial-and-error-based process of adapting to new movement demands (motor adaptation). Here, we asked, to what extent is motor adaptation to novel dynamics maintained in older adults (≥65 yr)? We hypothesized that older adults would adapt to the novel dynamics less well than young adults. Because older adults often use muscle coactivation, we expected older adults to use greater muscle coactivation during motor adaptation than young adults. Nevertheless, we predicted that older adults would reduce muscle activity and metabolic cost with motor adaptation, similar to young adults. Seated older (n = 11, 73.8 ± 5.6 yr) and young (n = 15, 23.8 ± 4.7 yr) adults made targeted reaching movements while grasping a robotic arm. We measured their metabolic rate continuously via expired gas analysis. A force field was used to add novel dynamics. Older adults had greater movement deviations and compensated for just 65% of the novel dynamics compared with 84% in young adults. As expected, older adults used greater muscle coactivation than young adults. Last, older adults reduced muscle activity with motor adaptation and had consistent reductions in metabolic cost later during motor adaptation, similar to young adults. These results suggest that despite increased muscle coactivation, older adults can adapt to the novel dynamics, albeit less accurately. These results also suggest that reductions in metabolic cost may be a fundamental feature of motor adaptation. PMID:24133222

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

    DEFF Research Database (Denmark)

    Dolog, Peter; Kravcik, Milos; Cristea, Alexandra

    2007-01-01

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

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

    Science.gov (United States)

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

    2017-10-01

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

  16. Basal ganglia-dependent processes in recalling learned visual-motor adaptations.

    Science.gov (United States)

    Bédard, Patrick; Sanes, Jerome N

    2011-03-01

    Humans learn and remember motor skills to permit adaptation to a changing environment. During adaptation, the brain develops new sensory-motor relationships that become stored in an internal model (IM) that may be retained for extended periods. How the brain learns new IMs and transforms them into long-term memory remains incompletely understood since prior work has mostly focused on the learning process. A current model suggests that basal ganglia, cerebellum, and their neocortical targets actively participate in forming new IMs but that a cerebellar cortical network would mediate automatization. However, a recent study (Marinelli et al. 2009) reported that patients with Parkinson's disease (PD), who have basal ganglia dysfunction, had similar adaptation rates as controls but demonstrated no savings at recall tests (24 and 48 h). Here, we assessed whether a longer training session, a feature known to increase long-term retention of IM in healthy individuals, could allow PD patients to demonstrate savings. We recruited PD patients and age-matched healthy adults and used a visual-motor adaptation paradigm similar to the study by Marinelli et al. (2009), doubling the number of training trials and assessed recall after a short and a 24-h delay. We hypothesized that a longer training session would allow PD patients to develop an enhanced representation of the IM as demonstrated by savings at the recall tests. Our results showed that PD patients had similar adaptation rates as controls but did not demonstrate savings at both recall tests. We interpret these results as evidence that fronto-striatal networks have involvement in the early to late phase of motor memory formation, but not during initial learning.

  17. Evaluation of an Adaptive Learning Technology in a First-year Extended Curriculum Programme Physics course

    Directory of Open Access Journals (Sweden)

    Moses Mushe Basitere

    2017-12-01

    Full Text Available Personalised, adaptive online learning platforms that form part of web-based proficiency tests play a major role in the improvement of the quality of learning in physics and assist learners in building proficiency, preparing for tests and using their time more effectively. In this study, the effectiveness of an adaptive learning platform, Wiley Plus ORION, was evaluated using proficiency test scores compared to paper-based test scores in a first-year introductory engineering physics course. Learners’ performance activities on the adaptive learning platform as well as their performance on the proficiency tests and their impact on the paper-based midterm averaged test were investigated using both qualitative and quantitative methods of data collection. A comparison between learners’ performance on the proficiency tests and a paper-based midterm test was done to evaluate whether there was a correlation between their performance on the proficiency tests and the midterm test. Focus group interviews were carried out with three categories of learners to elicit their experiences. Results showed that there was a positive relationship between high-performing learners’ proficiency score in the midterm averaged test and that the proficiency test enhanced learners’ performance in the paper-based midterm averaged test.

  18. Investigating the Impact of Formal Reflective Activities on Skill Adaptation in a Work-Related Instrumental Learning Setting

    Science.gov (United States)

    Roessger, Kevin M.

    2013-01-01

    In work-related, instrumental learning contexts the role of reflective activities is unclear. Kolb's (1985) experiential learning theory and Mezirow's transformative learning theory (2000) predict skill-adaptation as a possible outcome. This prediction was experimentally explored by manipulating reflective activities and assessing participants'…

  19. A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics.

    Science.gov (United States)

    Ingram, James N; Howard, Ian S; Flanagan, J Randall; Wolpert, Daniel M

    2011-09-01

    Motor learning has been extensively studied using dynamic (force-field) perturbations. These induce movement errors that result in adaptive changes to the motor commands. Several state-space models have been developed to explain how trial-by-trial errors drive the progressive adaptation observed in such studies. These models have been applied to adaptation involving novel dynamics, which typically occurs over tens to hundreds of trials, and which appears to be mediated by a dual-rate adaptation process. In contrast, when manipulating objects with familiar dynamics, subjects adapt rapidly within a few trials. Here, we apply state-space models to familiar dynamics, asking whether adaptation is mediated by a single-rate or dual-rate process. Previously, we reported a task in which subjects rotate an object with known dynamics. By presenting the object at different visual orientations, adaptation was shown to be context-specific, with limited generalization to novel orientations. Here we show that a multiple-context state-space model, with a generalization function tuned to visual object orientation, can reproduce the time-course of adaptation and de-adaptation as well as the observed context-dependent behavior. In contrast to the dual-rate process associated with novel dynamics, we show that a single-rate process mediates adaptation to familiar object dynamics. The model predicts that during exposure to the object across multiple orientations, there will be a degree of independence for adaptation and de-adaptation within each context, and that the states associated with all contexts will slowly de-adapt during exposure in one particular context. We confirm these predictions in two new experiments. Results of the current study thus highlight similarities and differences in the processes engaged during exposure to novel versus familiar dynamics. In both cases, adaptation is mediated by multiple context-specific representations. In the case of familiar object dynamics

  20. A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics.

    Directory of Open Access Journals (Sweden)

    James N Ingram

    2011-09-01

    Full Text Available Motor learning has been extensively studied using dynamic (force-field perturbations. These induce movement errors that result in adaptive changes to the motor commands. Several state-space models have been developed to explain how trial-by-trial errors drive the progressive adaptation observed in such studies. These models have been applied to adaptation involving novel dynamics, which typically occurs over tens to hundreds of trials, and which appears to be mediated by a dual-rate adaptation process. In contrast, when manipulating objects with familiar dynamics, subjects adapt rapidly within a few trials. Here, we apply state-space models to familiar dynamics, asking whether adaptation is mediated by a single-rate or dual-rate process. Previously, we reported a task in which subjects rotate an object with known dynamics. By presenting the object at different visual orientations, adaptation was shown to be context-specific, with limited generalization to novel orientations. Here we show that a multiple-context state-space model, with a generalization function tuned to visual object orientation, can reproduce the time-course of adaptation and de-adaptation as well as the observed context-dependent behavior. In contrast to the dual-rate process associated with novel dynamics, we show that a single-rate process mediates adaptation to familiar object dynamics. The model predicts that during exposure to the object across multiple orientations, there will be a degree of independence for adaptation and de-adaptation within each context, and that the states associated with all contexts will slowly de-adapt during exposure in one particular context. We confirm these predictions in two new experiments. Results of the current study thus highlight similarities and differences in the processes engaged during exposure to novel versus familiar dynamics. In both cases, adaptation is mediated by multiple context-specific representations. In the case of familiar

  1. Active and Adaptive Learning from Biased Data with Applications in Astronomy

    DEFF Research Database (Denmark)

    Kremer, Jan

    This thesis addresses the problem of machine learning from biased datasets in the context of astronomical applications. In astronomy there are many cases in which the training sample does not follow the true distribution. The thesis examines different types of biases and proposes algorithms...... set. Against this background, the thesis begins with a survey of active learning algorithms for the support vector machine. If the cost of additional labeling is prohibitive, unlabeled data can often be utilized instead and the sample selection bias can be overcome through domain adaptation, that is...... to handle them. During learning and when applying the predictive model, active learning enables algorithms to select training examples from a pool of unlabeled data and to request the labels. This allows for selecting examples that maximize the algorithm's accuracy despite an initial bias in the training...

  2. Peer Pressure and Adaptive Behavior Learning: A Study of Adolescents in Gujrat City

    OpenAIRE

    Asma Yunus; Shahzad Khaver Mushtaq; Sobia Qaiser

    2012-01-01

    The study aims at discovering the influences of Peer Pressure on adaptive behavior learning in the adolescents. For the purpose two scales, Adaptive behavior scale (ABS) and Peer Pressure Scale (PPS) were developed to measure both variables. The Sample of the study was purposive in nature and comprised of late adolescents (n=120) i.e. 60 males and 60 females, from Gujrat city. Cronbach alpha was calculated and found to be significant for Peer Pressure Scale(PPS) and its subscales i.e. Belongi...

  3. The cerebellum does more than sensory prediction error-based learning in sensorimotor adaptation tasks.

    Science.gov (United States)

    Butcher, Peter A; Ivry, Richard B; Kuo, Sheng-Han; Rydz, David; Krakauer, John W; Taylor, Jordan A

    2017-09-01

    Individuals with damage to the cerebellum perform poorly in sensorimotor adaptation paradigms. This deficit has been attributed to impairment in sensory prediction error-based updating of an internal forward model, a form of implicit learning. These individuals can, however, successfully counter a perturbation when instructed with an explicit aiming strategy. This successful use of an instructed aiming strategy presents a paradox: In adaptation tasks, why do individuals with cerebellar damage not come up with an aiming solution on their own to compensate for their implicit learning deficit? To explore this question, we employed a variant of a visuomotor rotation task in which, before executing a movement on each trial, the participants verbally reported their intended aiming location. Compared with healthy control participants, participants with spinocerebellar ataxia displayed impairments in both implicit learning and aiming. This was observed when the visuomotor rotation was introduced abruptly ( experiment 1 ) or gradually ( experiment 2 ). This dual deficit does not appear to be related to the increased movement variance associated with ataxia: Healthy undergraduates showed little change in implicit learning or aiming when their movement feedback was artificially manipulated to produce similar levels of variability ( experiment 3 ). Taken together the results indicate that a consequence of cerebellar dysfunction is not only impaired sensory prediction error-based learning but also a difficulty in developing and/or maintaining an aiming solution in response to a visuomotor perturbation. We suggest that this dual deficit can be explained by the cerebellum forming part of a network that learns and maintains action-outcome associations across trials. NEW & NOTEWORTHY Individuals with cerebellar pathology are impaired in sensorimotor adaptation. This deficit has been attributed to an impairment in error-based learning, specifically, from a deficit in using sensory

  4. Learning from Learning: the experiences with implementing Adaptive Collaborative Forest Management in Zimbabwe.

    NARCIS (Netherlands)

    Mutimukuru, T.; Almekinders, C.J.M.

    2011-01-01

    Convinced that participatory resource management is the way forward in the conservation of natural resources, despite the increasing criticism of participatory approaches, the Centre for International Forestry Research (CIFOR) initiated a multi-country adaptive collaborative management (ACM)

  5. Organising for Learning - Adaptive and Innovative Learning in Customer-Supplier Relationships

    DEFF Research Database (Denmark)

    Christensen, Poul Rind; Damgaard, Torben; Munksgaard, Kristin B.

    2004-01-01

    Based on studies of supplier associations, the concepts of adaptived and innovative learning in an interoganisational setting are defined and discussed.......Based on studies of supplier associations, the concepts of adaptived and innovative learning in an interoganisational setting are defined and discussed....

  6. A Fuzzy Logic-Based Personalized Learning System for Supporting Adaptive English Learning

    Science.gov (United States)

    Hsieh, Tung-Cheng; Wang, Tzone-I; Su, Chien-Yuan; Lee, Ming-Che

    2012-01-01

    As a nearly global language, English as a Foreign Language (EFL) programs are essential for people wishing to learn English. Researchers have noted that extensive reading is an effective way to improve a person's command of English. Choosing suitable articles in accordance with a learner's needs, interests and ability using an e-learning system…

  7. Teaching and Learning Hand in Hand: Adaptive Teaching and Self-Regulated Learning

    Science.gov (United States)

    Randi, Judi

    2017-01-01

    This article presents case studies of two novice teachers and their mentors who, without formal knowledge of self-regulation theory, established a classroom environment that promoted self-regulated learning. This case was drawn from a larger descriptive study of novice teachers learning to integrate a student-centered visual literacy instructional…

  8. Adaptive enhancement of learning protocol in hippocampal cultured networks grown on multielectrode arrays

    Science.gov (United States)

    Pimashkin, Alexey; Gladkov, Arseniy; Mukhina, Irina; Kazantsev, Victor

    2013-01-01

    Learning in neuronal networks can be investigated using dissociated cultures on multielectrode arrays supplied with appropriate closed-loop stimulation. It was shown in previous studies that weakly respondent neurons on the electrodes can be trained to increase their evoked spiking rate within a predefined time window after the stimulus. Such neurons can be associated with weak synaptic connections in nearby culture network. The stimulation leads to the increase in the connectivity and in the response. However, it was not possible to perform the learning protocol for the neurons on electrodes with relatively strong synaptic inputs and responding at higher rates. We proposed an adaptive closed-loop stimulation protocol capable to achieve learning even for the highly respondent electrodes. It means that the culture network can reorganize appropriately its synaptic connectivity to generate a desired response. We introduced an adaptive reinforcement condition accounting for the response variability in control stimulation. It significantly enhanced the learning protocol to a large number of responding electrodes independently on its base response level. We also found that learning effect preserved after 4–6 h after training. PMID:23745105

  9. Adaptive learning in a compartmental model of visual cortex—how feedback enables stable category learning and refinement

    Science.gov (United States)

    Layher, Georg; Schrodt, Fabian; Butz, Martin V.; Neumann, Heiko

    2014-01-01

    The categorization of real world objects is often reflected in the similarity of their visual appearances. Such categories of objects do not necessarily form disjunct sets of objects, neither semantically nor visually. The relationship between categories can often be described in terms of a hierarchical structure. For instance, tigers and leopards build two separate mammalian categories, both of which are subcategories of the category Felidae. In the last decades, the unsupervised learning of categories of visual input stimuli has been addressed by numerous approaches in machine learning as well as in computational neuroscience. However, the question of what kind of mechanisms might be involved in the process of subcategory learning, or category refinement, remains a topic of active investigation. We propose a recurrent computational network architecture for the unsupervised learning of categorial and subcategorial visual input representations. During learning, the connection strengths of bottom-up weights from input to higher-level category representations are adapted according to the input activity distribution. In a similar manner, top-down weights learn to encode the characteristics of a specific stimulus category. Feedforward and feedback learning in combination realize an associative memory mechanism, enabling the selective top-down propagation of a category's feedback weight distribution. We suggest that the difference between the expected input encoded in the projective field of a category node and the current input pattern controls the amplification of feedforward-driven representations. Large enough differences trigger the recruitment of new representational resources and the establishment of additional (sub-) category representations. We demonstrate the temporal evolution of such learning and show how the proposed combination of an associative memory with a modulatory feedback integration successfully establishes category and subcategory representations

  10. Adaptive learning in a compartmental model of visual cortex - how feedback enables stable category learning and refinement

    Directory of Open Access Journals (Sweden)

    Georg eLayher

    2014-12-01

    Full Text Available The categorization of real world objects is often reflected in the similarity of their visual appearances. Such categories of objects do not necessarily form disjunct sets of objects, neither semantically nor visually. The relationship between categories can often be described in terms of a hierarchical structure. For instance, tigers and leopards build two separate mammalian categories, but both belong to the category of felines. In other words, tigers and leopards are subcategories of the category Felidae. In the last decades, the unsupervised learning of categories of visual input stimuli has been addressed by numerous approaches in machine learning as well as in the computational neurosciences. However, the question of what kind of mechanisms might be involved in the process of subcategory learning, or category refinement, remains a topic of active investigation. We propose a recurrent computational network architecture for the unsupervised learning of categorial and subcategorial visual input representations. During learning, the connection strengths of bottom-up weights from input to higher-level category representations are adapted according to the input activity distribution. In a similar manner, top-down weights learn to encode the characteristics of a specific stimulus category. Feedforward and feedback learning in combination realize an associative memory mechanism, enabling the selective top-down propagation of a category's feedback weight distribution. We suggest that the difference between the expected input encoded in the projective field of a category node and the current input pattern controls the amplification of feedforward-driven representations. Large enough differences trigger the recruitment of new representational resources and the establishment of (sub- category representations. We demonstrate the temporal evolution of such learning and show how the approach successully establishes category and subcategory

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

    Directory of Open Access Journals (Sweden)

    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.

  12. Error-Induced Learning as a Resource-Adaptive Process in Young and Elderly Individuals

    Science.gov (United States)

    Ferdinand, Nicola K.; Weiten, Anja; Mecklinger, Axel; Kray, Jutta

    Thorndike described in his law of effect [44] that actions followed by positive events are more likely to be repeated in the future, whereas actions that are followed by negative outcomes are less likely to be repeated. This implies that behavior is evaluated in the light of its potential consequences, and non-reward events (i.e., errors) must be detected for reinforcement learning to take place. In short, humans have to monitor their performance in order to detect and correct errors, and this allows them to successfully adapt their behavior to changing environmental demands and acquire new behavior, i.e., to learn.

  13. AN INDUCTIVE, INTERACTIVE AND ADAPTIVE HYBRID PROBLEM-BASED LEARNING METHODOLOGY: APPLICATION TO STATISTICS

    Directory of Open Access Journals (Sweden)

    ADA ZHENG

    2011-10-01

    Full Text Available We have developed an innovative hybrid problem-based learning (PBL methodology. The methodology has the following distinctive features: i Each complex question was decomposed into a set of coherent finer subquestions by following the carefully designed criteria to maintain a delicate balance between guiding the students and inspiring them to think independently. This learning methodology enabled the students to solve the complex questions progressively in an inductive context. ii Facilitated by the utilization of our web-based learning systems, the teacher was able to interact with the students intensively and could allocate more teaching time to provide tailor-made feedback for individual student. The students were actively engaged in the learning activities, stimulated by the intensive interaction. iii The answers submitted by the students could be automatically consolidated in the report of the Moodle system in real-time. The teacher could adjust the teaching schedule and focus of the class to adapt to the learning progress of the students by analysing the automatically generated report and log files of the web-based learning system. As a result, the attendance rate of the students increased from about 50% to more than 90%, and the students’ learning motivation have been significantly enhanced.

  14. Adaptive eLearning modules for cytopathology education: A review and approach.

    Science.gov (United States)

    Samulski, T Danielle; La, Teresa; Wu, Roseann I

    2016-11-01

    Clinical training imposes time and resource constraints on educators and learners, making it difficult to provide and absorb meaningful instruction. Additionally, innovative and personalized education has become an expectation of adult learners. Fortunately, the development of web-based educational tools provides a possible solution to these challenges. Within this review, we introduce the utility of adaptive eLearning platforms in pathology education. In addition to a review of the current literature, we provide the reader with a suggested approach for module creation, as well as a critical assessment of an available platform, based on our experience in creating adaptive eLearning modules for teaching basic concepts in gynecologic cytopathology. Diagn. Cytopathol. 2016;44:944-951. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  15. Adaptive and Energy Efficient Walking in a Hexapod Robot under Neuromechanical Control and Sensorimotor Learning

    DEFF Research Database (Denmark)

    Xiong, Xiaofeng; Wörgötter, Florentin; Manoonpong, Poramate

    2016-01-01

    The control of multilegged animal walking is a neuromechanical process, and to achieve this in an adaptive and energy efficient way is a difficult and challenging problem. This is due to the fact that this process needs in real time: 1) to coordinate very many degrees of freedom of jointed legs; 2......) to generate the proper leg stiffness (i.e., compliance); and 3) to determine joint angles that give rise to particular positions at the endpoints of the legs. To tackle this problem for a robotic application, here we present a neuromechanical controller coupled with sensorimotor learning. The controller...... energy efficient walking, compared to other small legged robots. In addition, this paper also shows that the tight combination of neural control with tunable muscle-like functions, guided by sensory feedback and coupled with sensorimotor learning, is a way forward to better understand and solve adaptive...

  16. Distance learning education for mitigation/adaptation policy: a case study

    Science.gov (United States)

    Slini, T.; Giama, E.; Papadopoulou, Ch.-O.

    2016-02-01

    The efficient training of young environmental scientists has proven to be a challenging goal over the last years, while several dynamic initiatives have been developed aiming to provide complete and consistent education. A successful example is the e-learning course for participants mainly coming from emerging economy countries 'Development of mitigation/adaptation policy portfolios' organised in the frame of the project Promitheas4: Knowledge transfer and research needs for preparing mitigation/adaptation policy portfolios, aiming to provide knowledge transfer, enhance new skills and competencies, using modern didactic approaches and learning technologies. The present paper addresses the experience and the results of these actions, which seem promising and encouraging and were broadly welcomed by the participants.

  17. Water reuse practices in the United States and abroad

    Energy Technology Data Exchange (ETDEWEB)

    Sheikh, B.

    1998-07-01

    Reuse of water reclaimed from waste takes various adaptations in different parts of the globe to accommodate the economic forces underlying water supply constraints and local public health and sanitation conditions. The more developed regions have adopted and enforced the most rigorous water reuse regulations. The strong environmental safeguards adopted and the immense investments made in these countries in wastewater treatment provide for a very high quality of discharged effluent. Unfortunately, high (even adequate) levels of investment in sanitation and environmental protection have been lacking in most of the rest of the world. In the developing nations of the world a de facto brand of water reuse is practiced, generally without the benefit of protective standards of acceptable public health practice. Between the extremes of high standards of public health protection on the one hand, and the unsanitary use of raw sewage on the other, there are wide varieties of uses and treatment levels dictated by and evolved to accommodate the local economy.

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

    OpenAIRE

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

    2015-01-01

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

  19. Perceptions of Preservice Teachers about Adaptive Learning Programs in K-8 Mathematics Education

    OpenAIRE

    Smith, Kevin

    2018-01-01

    Adaptivelearning programs are frequently used in the K-8 mathematics classroom. Theseprograms provide instruction to students at the appropriate level of difficultyby presenting content, providing feedback, and allowing students to masterskills before progressing. The purpose of the study was to seek to interprethow preservice teachers’ experiences influence their perceptions and plans tointegrate adaptive learning programs in their future K-8 mathematics classroom.This was a qualitative stud...

  20. Adapt

    Science.gov (United States)

    Bargatze, L. F.

    2015-12-01

    Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted

  1. A Case Study of Framework Design for Horizontal Reuse

    DEFF Research Database (Denmark)

    Christensen, Henrik Bærbak; Røn, Henrik

    2000-01-01

    through the application of design patterns. We outline the reuse process and analyse and classify the problems encountered during the first-instance framework reuse. The major lessons learned are: (1) that, while design patterns are well-known for providing decoupling solutions at the code level, the lack...... of similar decoupling techniques at the non-code level may give rise to technical mismatch problems between the framework and the client systems; (2) that such technical mismatch problems can be costly; and (3) that a reusable framework may beneficially provide a solution template when it cannot provide...

  2. A planning quality evaluation tool for prostate adaptive IMRT based on machine learning

    International Nuclear Information System (INIS)

    Zhu Xiaofeng; Ge Yaorong; Li Taoran; Thongphiew, Danthai; Yin Fangfang; Wu, Q Jackie

    2011-01-01

    Purpose: To ensure plan quality for adaptive IMRT of the prostate, we developed a quantitative evaluation tool using a machine learning approach. This tool generates dose volume histograms (DVHs) of organs-at-risk (OARs) based on prior plans as a reference, to be compared with the adaptive plan derived from fluence map deformation. Methods: Under the same configuration using seven-field 15 MV photon beams, DVHs of OARs (bladder and rectum) were estimated based on anatomical information of the patient and a model learned from a database of high quality prior plans. In this study, the anatomical information was characterized by the organ volumes and distance-to-target histogram (DTH). The database consists of 198 high quality prostate plans and was validated with 14 cases outside the training pool. Principal component analysis (PCA) was applied to DVHs and DTHs to quantify their salient features. Then, support vector regression (SVR) was implemented to establish the correlation between the features of the DVH and the anatomical information. Results: DVH/DTH curves could be characterized sufficiently just using only two or three truncated principal components, thus, patient anatomical information was quantified with reduced numbers of variables. The evaluation of the model using the test data set demonstrated its accuracy ∼80% in prediction and effectiveness in improving ART planning quality. Conclusions: An adaptive IMRT plan quality evaluation tool based on machine learning has been developed, which estimates OAR sparing and provides reference in evaluating ART.

  3. Providing QoS through machine-learning-driven adaptive multimedia applications.

    Science.gov (United States)

    Ruiz, Pedro M; Botía, Juan A; Gómez-Skarmeta, Antonio

    2004-06-01

    We investigate the optimization of the quality of service (QoS) offered by real-time multimedia adaptive applications through machine learning algorithms. These applications are able to adapt in real time their internal settings (i.e., video sizes, audio and video codecs, among others) to the unpredictably changing capacity of the network. Traditional adaptive applications just select a set of settings to consume less than the available bandwidth. We propose a novel approach in which the selected set of settings is the one which offers a better user-perceived QoS among all those combinations which satisfy the bandwidth restrictions. We use a genetic algorithm to decide when to trigger the adaptation process depending on the network conditions (i.e., loss-rate, jitter, etc.). Additionally, the selection of the new set of settings is done according to a set of rules which model the user-perceived QoS. These rules are learned using the SLIPPER rule induction algorithm over a set of examples extracted from scores provided by real users. We will demonstrate that the proposed approach guarantees a good user-perceived QoS even when the network conditions are constantly changing.

  4. The dynamic interplay among EFL learners’ ambiguity tolerance, adaptability, cultural intelligence, learning approach, and language achievement

    Directory of Open Access Journals (Sweden)

    Shadi Alahdadi

    2017-01-01

    Full Text Available A key objective of education is to prepare individuals to be fully-functioning learners. This entails developing the cognitive, metacognitive, motivational, cultural, and emotional competencies. The present study aimed to examine the interrelationships among adaptability, tolerance of ambiguity, cultural intelligence, learning approach, and language achievement as manifestations of the above competencies within a single model. The participants comprised one hundred eighty BA and MA Iranian university students studying English language teaching and translation. The instruments used in this study consisted of the translated versions of four questionnaires: second language tolerance of ambiguity scale, adaptability taken from emotional intelligence inventory, cultural intelligence (CQ inventory, and the revised study process questionnaire measuring surface and deep learning. The results estimated via structural equation modeling (SEM revealed that the proposed model containing the variables under study had a good fit with the data. It was found that all the variables except adaptability directly influenced language achievement with deep approach having the highest impact and ambiguity tolerance having the lowest influence. In addition, ambiguity tolerance was a positive and significant predictor of deep approach. CQ was found to be under the influence of both ambiguity tolerance and adaptability. The findings were discussed in the light of the yielded results.

  5. 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.

  6. Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

    OpenAIRE

    Kidziński, Łukasz; Mohanty, Sharada Prasanna; Ong, Carmichael; Huang, Zhewei; Zhou, Shuchang; Pechenko, Anton; Stelmaszczyk, Adam; Jarosik, Piotr; Pavlov, Mikhail; Kolesnikov, Sergey; Plis, Sergey; Chen, Zhibo; Zhang, Zhizheng; Chen, Jiale; Shi, Jun

    2018-01-01

    In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement learning approaches, based on algorithms such as Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region Policy Optimization. Many solutions use similar ...

  7. L1-norm locally linear representation regularization multi-source adaptation learning.

    Science.gov (United States)

    Tao, Jianwen; Wen, Shiting; Hu, Wenjun

    2015-09-01

    In most supervised domain adaptation learning (DAL) tasks, one has access only to a small number of labeled examples from target domain. Therefore the success of supervised DAL in this "small sample" regime needs the effective utilization of the large amounts of unlabeled data to extract information that is useful for generalization. Toward this end, we here use the geometric intuition of manifold assumption to extend the established frameworks in existing model-based DAL methods for function learning by incorporating additional information about the target geometric structure of the marginal distribution. We would like to ensure that the solution is smooth with respect to both the ambient space and the target marginal distribution. In doing this, we propose a novel L1-norm locally linear representation regularization multi-source adaptation learning framework which exploits the geometry of the probability distribution, which has two techniques. Firstly, an L1-norm locally linear representation method is presented for robust graph construction by replacing the L2-norm reconstruction measure in LLE with L1-norm one, which is termed as L1-LLR for short. Secondly, considering the robust graph regularization, we replace traditional graph Laplacian regularization with our new L1-LLR graph Laplacian regularization and therefore construct new graph-based semi-supervised learning framework with multi-source adaptation constraint, which is coined as L1-MSAL method. Moreover, to deal with the nonlinear learning problem, we also generalize the L1-MSAL method by mapping the input data points from the input space to a high-dimensional reproducing kernel Hilbert space (RKHS) via a nonlinear mapping. Promising experimental results have been obtained on several real-world datasets such as face, visual video and object. Copyright © 2015 Elsevier Ltd. All rights reserved.

  8. Theoretical information reuse and integration

    CERN Document Server

    Rubin, Stuart

    2016-01-01

    Information Reuse and Integration addresses the efficient extension and creation of knowledge through the exploitation of Kolmogorov complexity in the extraction and application of domain symmetry. Knowledge, which seems to be novel, can more often than not be recast as the image of a sequence of transformations, which yield symmetric knowledge. When the size of those transformations and/or the length of that sequence of transforms exceeds the size of the image, then that image is said to be novel or random. It may also be that the new knowledge is random in that no such sequence of transforms, which produces it exists, or is at least known. The nine chapters comprising this volume incorporate symmetry, reuse, and integration as overt operational procedures or as operations built into the formal representations of data and operators employed. Either way, the aforementioned theoretical underpinnings of information reuse and integration are supported.

  9. NETWORKED LEARNING AS A PROCESS OF IDENTIFICATION IN THE INTERSECTION OF COLLABORATIVE KNOWLEDGE BUILDING - FOSTERING CREATIVITY, AWARENESS AND RE-USE OF OER

    DEFF Research Database (Denmark)

    Østergaard, Rina; Sorensen, Elsebeth Korsgaard

    2011-01-01

    goals. In relation to objectives of competencies, attention is given to creative, innovative and action-oriented types. This paper addresses the role of OER in design of innovative, networked learning processes in diverse educational contexts of higher education, continuing education and in relation......Within professional education a recent shift has taken place. Professional education has moved from specialized education and update of professional knowledge, over competence-based education, to, recently, education with goals such as creativity, innovation, entrepreneur- and entrepreneurship....... OECDs Centre for Educational Research and Innovation (CERI) reveals this tendency. The core idea here is that education, in a very goal-directed way, supports initiatives, which – in turn – results in added-value to society. As such, the educational shift may be interpreted as related to societal change...

  10. Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-09-20

    Nonnegative matrix factorization (NMF), a popular part-based representation technique, does not capture the intrinsic local geometric structure of the data space. Graph regularized NMF (GNMF) was recently proposed to avoid this limitation by regularizing NMF with a nearest neighbor graph constructed from the input data set. However, GNMF has two main bottlenecks. First, using the original feature space directly to construct the graph is not necessarily optimal because of the noisy and irrelevant features and nonlinear distributions of data samples. Second, one possible way to handle the nonlinear distribution of data samples is by kernel embedding. However, it is often difficult to choose the most suitable kernel. To solve these bottlenecks, we propose two novel graph-regularized NMF methods, AGNMFFS and AGNMFMK, by introducing feature selection and multiple-kernel learning to the graph regularized NMF, respectively. Instead of using a fixed graph as in GNMF, the two proposed methods learn the nearest neighbor graph that is adaptive to the selected features and learned multiple kernels, respectively. For each method, we propose a unified objective function to conduct feature selection/multi-kernel learning, NMF and adaptive graph regularization simultaneously. We further develop two iterative algorithms to solve the two optimization problems. Experimental results on two challenging pattern classification tasks demonstrate that the proposed methods significantly outperform state-of-the-art data representation methods.

  11. EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation

    Directory of Open Access Journals (Sweden)

    Suwicha Jirayucharoensak

    2014-01-01

    Full Text Available Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers.

  12. Adoption, adaptation, and abandonment: Appropriation of science education professional development learning

    Science.gov (United States)

    Longhurst, Max L.

    Understanding factors that impact teacher utilization of learning from professional development is critical in order maximize the educational and financial investment in teacher professional learning. This study used a multicase mixed quantitative and qualitative methodology to investigate the factors that influence teacher adoption, adaption, or abandonment of learning from science teacher professional development. The theoretical framework of activity theory was identified as a useful way to investigate the phenomenon of teacher appropriation of pedagogical practices from professional development. This framework has the capacity to account for a multitude of elements in the context of a learning experience. In this study educational appropriation is understood through a continuum of how an educator acquires and implements both practical and conceptual aspects of learning from professional development within localized context. The variability associated with instructional changes made from professional development drives this inquiry to search for better understandings of the appropriation of pedagogical practices. Purposeful sampling was used to identify two participants from a group of eighth-grade science teachers engaged in professional development designed to investigate how cyber-enabled technologies might enhance instruction and learning in integrated science classrooms. The data from this investigation add to the literature of appropriation of instructional practices by connecting eight factors that influence conceptual and practical tools with the development of ownership of pedagogical practices in the appropriation hierarchy. Recommendations are shared with professional development developers, providers, and participants in anticipation that future science teaching experiences might be informed by findings from this study.

  13. Adaptive structured dictionary learning for image fusion based on group-sparse-representation

    Science.gov (United States)

    Yang, Jiajie; Sun, Bin; Luo, Chengwei; Wu, Yuzhong; Xu, Limei

    2018-04-01

    Dictionary learning is the key process of sparse representation which is one of the most widely used image representation theories in image fusion. The existing dictionary learning method does not use the group structure information and the sparse coefficients well. In this paper, we propose a new adaptive structured dictionary learning algorithm and a l1-norm maximum fusion rule that innovatively utilizes grouped sparse coefficients to merge the images. In the dictionary learning algorithm, we do not need prior knowledge about any group structure of the dictionary. By using the characteristics of the dictionary in expressing the signal, our algorithm can automatically find the desired potential structure information that hidden in the dictionary. The fusion rule takes the physical meaning of the group structure dictionary, and makes activity-level judgement on the structure information when the images are being merged. Therefore, the fused image can retain more significant information. Comparisons have been made with several state-of-the-art dictionary learning methods and fusion rules. The experimental results demonstrate that, the dictionary learning algorithm and the fusion rule both outperform others in terms of several objective evaluation metrics.

  14. Adaptive Load Balancing of Parallel Applications with Multi-Agent Reinforcement Learning on Heterogeneous Systems

    Directory of Open Access Journals (Sweden)

    Johan Parent

    2004-01-01

    Full Text Available We report on the improvements that can be achieved by applying machine learning techniques, in particular reinforcement learning, for the dynamic load balancing of parallel applications. The applications being considered in this paper are coarse grain data intensive applications. Such applications put high pressure on the interconnect of the hardware. Synchronization and load balancing in complex, heterogeneous networks need fast, flexible, adaptive load balancing algorithms. Viewing a parallel application as a one-state coordination game in the framework of multi-agent reinforcement learning, and by using a recently introduced multi-agent exploration technique, we are able to improve upon the classic job farming approach. The improvements are achieved with limited computation and communication overhead.

  15. QUALITY ASSURANCE IN RWANDAN HIGHER LEARNING EDUCATION: IS THE SYSTEM ADAPTIVE OR COMPLEX?

    Directory of Open Access Journals (Sweden)

    Nathan Kanuma Taremwa

    2014-01-01

    Full Text Available Developing knowledge infrastructure by massive investments in education and training are taken as a benchmark in facilitating the acceleration and possible increases in skills, capacities and competences of Rwandan people has become apriority issue in the recent years. This notion is relevant to vision 2020 where human resource development and building of a knowledge based economy are fundamental pillars. In the past years, several policy reforms have taken place in education sector. However, the overarching question is if such reforms are becoming adaptive or complex and if such reforms will not compromise the quality of education in higher learning education in Rwanda? The main objective of the study was to investigate the impact of changes in Higher Learning Institutions on the quality of education in Rwanda. This research had three hypotheses, namely; there is an impact of changes in Higher Learning Institutions on quality of education in Rwanda; the current complexity in Rwandan education system is affecting the quality of education in HLIs; Tailoring education system to the regional reforms and implementation strategies is affecting the quality of education in Rwanda. This study was carried out in 10 higher learning institutions (5 public, 5 private and 2 Ministry of Education directorates (HEC and REB. Key informants were the senior management/head of institutions, experienced academic staff, and students. The parameters considered included; the learning methods, assessment styles, workloads, language of instruction, merging of public HLIs, curriculum, and the transformation of some private higher learning institutions into company forms. Main research instruments were questionnaires and interview guides. Both qualitative and quantitative research was collected. Analyses were done using SPSS and excel packages. Major findings indicate that the system is still in transition with indicative gaps. Ample time would therefore be necessary for

  16. Self-organizing adaptive map: autonomous learning of curves and surfaces from point samples.

    Science.gov (United States)

    Piastra, Marco

    2013-05-01

    Competitive Hebbian Learning (CHL) (Martinetz, 1993) is a simple and elegant method for estimating the topology of a manifold from point samples. The method has been adopted in a number of self-organizing networks described in the literature and has given rise to related studies in the fields of geometry and computational topology. Recent results from these fields have shown that a faithful reconstruction can be obtained using the CHL method only for curves and surfaces. Within these limitations, these findings constitute a basis for defining a CHL-based, growing self-organizing network that produces a faithful reconstruction of an input manifold. The SOAM (Self-Organizing Adaptive Map) algorithm adapts its local structure autonomously in such a way that it can match the features of the manifold being learned. The adaptation process is driven by the defects arising when the network structure is inadequate, which cause a growth in the density of units. Regions of the network undergo a phase transition and change their behavior whenever a simple, local condition of topological regularity is met. The phase transition is eventually completed across the entire structure and the adaptation process terminates. In specific conditions, the structure thus obtained is homeomorphic to the input manifold. During the adaptation process, the network also has the capability to focus on the acquisition of input point samples in critical regions, with a substantial increase in efficiency. The behavior of the network has been assessed experimentally with typical data sets for surface reconstruction, including suboptimal conditions, e.g. with undersampling and noise. Copyright © 2012 Elsevier Ltd. All rights reserved.

  17. Water Reuse and Pathogen Tracking

    Science.gov (United States)

    Building product water. By designing our buildings to collect and treat water generated on-site, can be and reused for flushing our toilets and irrigating our landscaping. Several water sources are generated with-in a building including: rainwater, stormwater, graywater, blackwa...

  18. Third Age Learning: Adapting the Idea to a Thailand Context of Lifelong Learning

    Science.gov (United States)

    Ratana-Ubol, Archanya; Richards, Cameron

    2016-01-01

    The concept of the university of the third age (U3A) is well established overseas and a key international focus for emerging global networks of senior citizen (i.e. seniors) lifelong learning. However it is yet to become so in Thailand although it too is in the process of becoming an ageing society. Moreover, this is despite the extent to which…

  19. The local enhancement conundrum: in search of the adaptive value of a social learning mechanism.

    Science.gov (United States)

    Arbilly, Michal; Laland, Kevin N

    2014-02-01

    Social learning mechanisms are widely thought to vary in their degree of complexity as well as in their prevalence in the natural world. While learning the properties of a stimulus that generalize to similar stimuli at other locations (stimulus enhancement) prima facie appears more useful to an animal than learning about a specific stimulus at a specific location (local enhancement), empirical evidence suggests that the latter is much more widespread in nature. Simulating populations engaged in a producer-scrounger game, we sought to deploy mathematical models to identify the adaptive benefits of reliance on local enhancement and/or stimulus enhancement, and the alternative conditions favoring their evolution. Surprisingly, we found that while stimulus enhancement readily evolves, local enhancement is advantageous only under highly restricted conditions: when generalization of information was made unreliable or when error in social learning was high. Our results generate a conundrum over how seemingly conflicting empirical and theoretical findings can be reconciled. Perhaps the prevalence of local enhancement in nature is due to stimulus enhancement costs independent of the learning task itself (e.g. predation risk), perhaps natural habitats are often characterized by unreliable yet highly rewarding payoffs, or perhaps local enhancement occurs less frequently, and stimulus enhancement more frequently, than widely believed. Copyright © 2013 Elsevier Inc. All rights reserved.

  20. Using concept similarity in cross ontology for adaptive e-Learning systems

    Directory of Open Access Journals (Sweden)

    B. Saleena

    2015-01-01

    Full Text Available e-Learning is one of the most preferred media of learning by the learners. The learners search the web to gather knowledge about a particular topic from the information in the repositories. Retrieval of relevant materials from a domain can be easily implemented if the information is organized and related in some way. Ontologies are a key concept that helps us to relate information for providing the more relevant lessons to the learner. This paper proposes an adaptive e-Learning system, which generates a user specific e-Learning content by comparing the concepts with more than one system using similarity measures. A cross ontology measure is defined, which consists of fuzzy domain ontology as the primary ontology and the domain expert’s ontology as the secondary ontology, for the comparison process. A personalized document is provided to the user with a user profile, which includes the data obtained from the processing of the proposed method under a User score, which is obtained through the user evaluation. The results of the proposed e-Learning system under the designed cross ontology similarity measure show a significant increase in performance and accuracy under different conditions. The assessment of the comparative analysis, showed the difference in performance of our proposed method over other methods. Based on the assessment results it is proved that the proposed approach is effective over other methods.

  1. DYNAMIC AND INCREMENTAL EXPLORATION STRATEGY IN FUSION ADAPTIVE RESONANCE THEORY FOR ONLINE REINFORCEMENT LEARNING

    Directory of Open Access Journals (Sweden)

    Budhitama Subagdja

    2016-06-01

    Full Text Available One of the fundamental challenges in reinforcement learning is to setup a proper balance between exploration and exploitation to obtain the maximum cummulative reward in the long run. Most protocols for exploration bound the overall values to a convergent level of performance. If new knowledge is inserted or the environment is suddenly changed, the issue becomes more intricate as the exploration must compromise the pre-existing knowledge. This paper presents a type of multi-channel adaptive resonance theory (ART neural network model called fusion ART which serves as a fuzzy approximator for reinforcement learning with inherent features that can regulate the exploration strategy. This intrinsic regulation is driven by the condition of the knowledge learnt so far by the agent. The model offers a stable but incremental reinforcement learning that can involve prior rules as bootstrap knowledge for guiding the agent to select the right action. Experiments in obstacle avoidance and navigation tasks demonstrate that in the configuration of learning wherein the agent learns from scratch, the inherent exploration model in fusion ART model is comparable to the basic E-greedy policy. On the other hand, the model is demonstrated to deal with prior knowledge and strike a balance between exploration and exploitation.

  2. A Text Mining Approach for Extracting Lessons Learned from Project Documentation: An Illustrative Case Study

    Directory of Open Access Journals (Sweden)

    Benjamin Matthies

    2017-12-01

    Full Text Available Lessons learned are important building blocks for continuous learning in project-based organisations. Nonetheless, the practical reality is that lessons learned are often not consistently reused for organisational learning. Two problems are commonly described in this context: the information overload and the lack of procedures and methods for the assessment and implementation of lessons learned. This paper addresses these problems, and appropriate solutions are combined in a systematic lesson learned process. Latent Dirichlet Allocation is presented to solve the first problem. Regarding the second problem, established risk management methods are adapted. The entire lessons learned process will be demonstrated in a practical case study

  3. An Adaptive Learning Based Network Selection Approach for 5G Dynamic Environments

    Directory of Open Access Journals (Sweden)

    Xiaohong Li

    2018-03-01

    Full Text Available Networks will continue to become increasingly heterogeneous as we move toward 5G. Meanwhile, the intelligent programming of the core network makes the available radio resource be more changeable rather than static. In such a dynamic and heterogeneous network environment, how to help terminal users select optimal networks to access is challenging. Prior implementations of network selection are usually applicable for the environment with static radio resources, while they cannot handle the unpredictable dynamics in 5G network environments. To this end, this paper considers both the fluctuation of radio resources and the variation of user demand. We model the access network selection scenario as a multiagent coordination problem, in which a bunch of rationally terminal users compete to maximize their benefits with incomplete information about the environment (no prior knowledge of network resource and other users’ choices. Then, an adaptive learning based strategy is proposed, which enables users to adaptively adjust their selections in response to the gradually or abruptly changing environment. The system is experimentally shown to converge to Nash equilibrium, which also turns out to be both Pareto optimal and socially optimal. Extensive simulation results show that our approach achieves significantly better performance compared with two learning and non-learning based approaches in terms of load balancing, user payoff and the overall bandwidth utilization efficiency. In addition, the system has a good robustness performance under the condition with non-compliant terminal users.

  4. Adaptive critic learning techniques for engine torque and air-fuel ratio control.

    Science.gov (United States)

    Liu, Derong; Javaherian, Hossein; Kovalenko, Olesia; Huang, Ting

    2008-08-01

    A new approach for engine calibration and control is proposed. In this paper, we present our research results on the implementation of adaptive critic designs for self-learning control of automotive engines. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in this research project. The goals of the present learning control design for automotive engines include improved performance, reduced emissions, and maintained optimum performance under various operating conditions. Using the data from a test vehicle with a V8 engine, we developed a neural network model of the engine and neural network controllers based on the idea of approximate dynamic programming to achieve optimal control. We have developed and simulated self-learning neural network controllers for both engine torque (TRQ) and exhaust air-fuel ratio (AFR) control. The goal of TRQ control and AFR control is to track the commanded values. For both control problems, excellent neural network controller transient performance has been achieved.

  5. Reinforcement learning design-based adaptive tracking control with less learning parameters for nonlinear discrete-time MIMO systems.

    Science.gov (United States)

    Liu, Yan-Jun; Tang, Li; Tong, Shaocheng; Chen, C L Philip; Li, Dong-Juan

    2015-01-01

    Based on the neural network (NN) approximator, an online reinforcement learning algorithm is proposed for a class of affine multiple input and multiple output (MIMO) nonlinear discrete-time systems with unknown functions and disturbances. In the design procedure, two networks are provided where one is an action network to generate an optimal control signal and the other is a critic network to approximate the cost function. An optimal control signal and adaptation laws can be generated based on two NNs. In the previous approaches, the weights of critic and action networks are updated based on the gradient descent rule and the estimations of optimal weight vectors are directly adjusted in the design. Consequently, compared with the existing results, the main contributions of this paper are: 1) only two parameters are needed to be adjusted, and thus the number of the adaptation laws is smaller than the previous results and 2) the updating parameters do not depend on the number of the subsystems for MIMO systems and the tuning rules are replaced by adjusting the norms on optimal weight vectors in both action and critic networks. It is proven that the tracking errors, the adaptation laws, and the control inputs are uniformly bounded using Lyapunov analysis method. The simulation examples are employed to illustrate the effectiveness of the proposed algorithm.

  6. The Link between Age, Career Goals, and Adaptive Development for Work-Related Learning among Local Government Employees

    Science.gov (United States)

    Tones, Megan; Pillay, Hitendra; Kelly, Kathy

    2011-01-01

    More recently, lifespan development psychology models of adaptive development have been applied to the workforce to investigate ageing worker and lifespan issues. The current study uses the Learning and Development Survey (LDS) to investigate employee selection and engagement of learning and development goals and opportunities and constraints for…

  7. The added value of a gaming context and intelligent adaptation for a mobile application for vocabulary learning

    NARCIS (Netherlands)

    Sandberg, J.; Maris, M.; Hoogendoorn, P.

    2014-01-01

    Two groups participated in a study on the added value of a gaming context and intelligent adaptation for a mobile learning application. The control group worked at home for a fortnight with the original Mobile English Learning application (MEL-original) developed in a previous project. The

  8. Implementation of an Automated Grading System with an Adaptive Learning Component to Affect Student Feedback and Response Time

    Science.gov (United States)

    Matthews, Kevin; Janicki, Thomas; He, Ling; Patterson, Laurie

    2012-01-01

    This research focuses on the development and implementation of an adaptive learning and grading system with a goal to increase the effectiveness and quality of feedback to students. By utilizing various concepts from established learning theories, the goal of this research is to improve the quantity, quality, and speed of feedback as it pertains…

  9. Attitudes and norms affecting scientists' data reuse.

    Directory of Open Access Journals (Sweden)

    Renata Gonçalves Curty

    Full Text Available The value of sharing scientific research data is widely appreciated, but factors that hinder or prompt the reuse of data remain poorly understood. Using the Theory of Reasoned Action, we test the relationship between the beliefs and attitudes of scientists towards data reuse, and their self-reported data reuse behaviour. To do so, we used existing responses to selected questions from a worldwide survey of scientists developed and administered by the DataONE Usability and Assessment Working Group (thus practicing data reuse ourselves. Results show that the perceived efficacy and efficiency of data reuse are strong predictors of reuse behaviour, and that the perceived importance of data reuse corresponds to greater reuse. Expressed lack of trust in existing data and perceived norms against data reuse were not found to be major impediments for reuse contrary to our expectations. We found that reported use of models and remotely-sensed data was associated with greater reuse. The results suggest that data reuse would be encouraged and normalized by demonstration of its value. We offer some theoretical and practical suggestions that could help to legitimize investment and policies in favor of data sharing.

  10. Chaos Synchronization Using Adaptive Dynamic Neural Network Controller with Variable Learning Rates

    Directory of Open Access Journals (Sweden)

    Chih-Hong Kao

    2011-01-01

    Full Text Available This paper addresses the synchronization of chaotic gyros with unknown parameters and external disturbance via an adaptive dynamic neural network control (ADNNC system. The proposed ADNNC system is composed of a neural controller and a smooth compensator. The neural controller uses a dynamic RBF (DRBF network to online approximate an ideal controller. The DRBF network can create new hidden neurons online if the input data falls outside the hidden layer and prune the insignificant hidden neurons online if the hidden neuron is inappropriate. The smooth compensator is designed to compensate for the approximation error between the neural controller and the ideal controller. Moreover, the variable learning rates of the parameter adaptation laws are derived based on a discrete-type Lyapunov function to speed up the convergence rate of the tracking error. Finally, the simulation results which verified the chaotic behavior of two nonlinear identical chaotic gyros can be synchronized using the proposed ADNNC scheme.

  11. Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

    Science.gov (United States)

    Luo, Guangchun; Qin, Ke; Wang, Nan; Niu, Weina

    2018-01-01

    Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy. PMID:29494543

  12. Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Zhiyuan Ma

    2018-03-01

    Full Text Available Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy.

  13. Collaborative Education in Climate Change Sciences and Adaptation through Interactive Learning

    Science.gov (United States)

    Ozbay, G.; Sriharan, S.; Fan, C.

    2014-12-01

    As a result of several funded climate change education grants, collaboration between VSU, DSU, and MSU, was established to provide the innovative and cohesive education and research opportunities to underrepresented groups in the climate related sciences. Prior to offering climate change and adaptation related topics to the students, faculty members of the three collaborating institutions participated at a number of faculty training and preparation workshops for teaching climate change sciences (i.e. AMS Diversity Project Workshop, NCAR Faculty-Student Team on Climate Change, NASA-NICE Program). In order to enhance the teaching and student learning on various issues in the Environmental Sciences Programs, Climatology, Climate Change Sciences and Adaptation or related courses were developed at Delaware State University and its partner institutions (Virginia State University and Morgan State University). These courses were prepared to deliver information on physical basis for the earth's climate system and current climate change instruction modules by AMS and historic climate information (NOAA Climate Services, U.S. and World Weather Data, NCAR and NASA Climate Models). By using Global Seminar as a Model, faculty members worked in teams to engage students in videoconferencing on climate change through Contemporary Global Studies and climate courses including Climate Change and Adaptation Science, Sustainable Agriculture, Introduction to Environmental Sciences, Climatology, and Ecology and Adaptation courses. All climate change courses have extensive hands-on practices and research integrated into the student learning experiences. Some of these students have presented their classroom projects during Earth Day, Student Climate Change Symposium, Undergraduate Summer Symposium, and other national conferences.

  14. Improvement of defect characterization in ultrasonic testing by adaptative learning network

    International Nuclear Information System (INIS)

    Bieth, M.; Adamonis, D.C.; Jusino, A.

    1982-01-01

    Numerous methods exist now for signal analysis in ultrasonic testing. These methods give more or less accurate information for defects characterization. In this paper is presented the development of a particular system based on a computer Signal processing: the Adaptative Learning Network (ALN) allowing the discrimination of defects in function of their nature. The ultrasonic signal is sampled and characterized by parameters amplitude-time and amplitude-frequency. The method was tested on stainless steel tubes welds showing fatigue cracks. The ALN model developed allows, under certain conditions, the discrimination of cracks from other defects [fr

  15. Adaptive Neuron Model: An architecture for the rapid learning of nonlinear topological transformations

    Science.gov (United States)

    Tawel, Raoul (Inventor)

    1994-01-01

    A method for the rapid learning of nonlinear mappings and topological transformations using a dynamically reconfigurable artificial neural network is presented. This fully-recurrent Adaptive Neuron Model (ANM) network was applied to the highly degenerate inverse kinematics problem in robotics, and its performance evaluation is bench-marked. Once trained, the resulting neuromorphic architecture was implemented in custom analog neural network hardware and the parameters capturing the functional transformation downloaded onto the system. This neuroprocessor, capable of 10(exp 9) ops/sec, was interfaced directly to a three degree of freedom Heathkit robotic manipulator. Calculation of the hardware feed-forward pass for this mapping was benchmarked at approximately 10 microsec.

  16. Machine learning for adaptive many-core machines a practical approach

    CERN Document Server

    Lopes, Noel

    2015-01-01

    The overwhelming data produced everyday and the increasing performance and cost requirements of applications?are transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data.This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind.

  17. Growing adaptive machines combining development and learning in artificial neural networks

    CERN Document Server

    Bredeche, Nicolas; Doursat, René

    2014-01-01

    The pursuit of artificial intelligence has been a highly active domain of research for decades, yielding exciting scientific insights and productive new technologies. In terms of generating intelligence, however, this pursuit has yielded only limited success. This book explores the hypothesis that adaptive growth is a means of moving forward. By emulating the biological process of development, we can incorporate desirable characteristics of natural neural systems into engineered designs, and thus move closer towards the creation of brain-like systems. The particular focus is on how to design artificial neural networks for engineering tasks. The book consists of contributions from 18 researchers, ranging from detailed reviews of recent domains by senior scientists, to exciting new contributions representing the state of the art in machine learning research. The book begins with broad overviews of artificial neurogenesis and bio-inspired machine learning, suitable both as an introduction to the domains and as a...

  18. Effectiveness of Adaptive Contextual Learning Model of Integrated Science by Integrating Digital Age Literacy on Grade VIII Students

    Science.gov (United States)

    Asrizal, A.; Amran, A.; Ananda, A.; Festiyed, F.

    2018-04-01

    Educational graduates should have good competencies to compete in the 21st century. Integrated learning is a good way to develop competence of students in this century. Besides that, literacy skills are very important for students to get success in their learning and daily life. For this reason, integrated science learning and literacy skills are important in 2013 curriculum. However, integrated science learning and integration of literacy in learning can’t be implemented well. Solution of this problem is to develop adaptive contextual learning model by integrating digital age literacy. The purpose of the research is to determine the effectiveness of adaptive contextual learning model to improve competence of grade VIII students in junior high school. This research is a part of the research and development or R&D. Research design which used in limited field testing was before and after treatment. The research instruments consist of three parts namely test sheet of learning outcome for assessing knowledge competence, observation sheet for assessing attitudes, and performance sheet for assessing skills of students. Data of student’s competence were analyzed by three kinds of analysis, namely descriptive statistics, normality test and homogeneity test, and paired comparison test. From the data analysis result, it can be stated that the implementation of adaptive contextual learning model of integrated science by integrating digital age literacy is effective to improve the knowledge, attitude, and literacy skills competences of grade VIII students in junior high school at 95% confidence level.

  19. An associative model of adaptive inference for learning word-referent mappings.

    Science.gov (United States)

    Kachergis, George; Yu, Chen; Shiffrin, Richard M

    2012-04-01

    People can learn word-referent pairs over a short series of individually ambiguous situations containing multiple words and referents (Yu & Smith, 2007, Cognition 106: 1558-1568). Cross-situational statistical learning relies on the repeated co-occurrence of words with their intended referents, but simple co-occurrence counts cannot explain the findings. Mutual exclusivity (ME: an assumption of one-to-one mappings) can reduce ambiguity by leveraging prior experience to restrict the number of word-referent pairings considered but can also block learning of non-one-to-one mappings. The present study first trained learners on one-to-one mappings with varying numbers of repetitions. In late training, a new set of word-referent pairs were introduced alongside pretrained pairs; each pretrained pair consistently appeared with a new pair. Results indicate that (1) learners quickly infer new pairs in late training on the basis of their knowledge of pretrained pairs, exhibiting ME; and (2) learners also adaptively relax the ME bias and learn two-to-two mappings involving both pretrained and new words and objects. We present an associative model that accounts for both results using competing familiarity and uncertainty biases.

  20. Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems.

    Science.gov (United States)

    Grisafi, Andrea; Wilkins, David M; Csányi, Gábor; Ceriotti, Michele

    2018-01-19

    Statistical learning methods show great promise in providing an accurate prediction of materials and molecular properties, while minimizing the need for computationally demanding electronic structure calculations. The accuracy and transferability of these models are increased significantly by encoding into the learning procedure the fundamental symmetries of rotational and permutational invariance of scalar properties. However, the prediction of tensorial properties requires that the model respects the appropriate geometric transformations, rather than invariance, when the reference frame is rotated. We introduce a formalism that extends existing schemes and makes it possible to perform machine learning of tensorial properties of arbitrary rank, and for general molecular geometries. To demonstrate it, we derive a tensor kernel adapted to rotational symmetry, which is the natural generalization of the smooth overlap of atomic positions kernel commonly used for the prediction of scalar properties at the atomic scale. The performance and generality of the approach is demonstrated by learning the instantaneous response to an external electric field of water oligomers of increasing complexity, from the isolated molecule to the condensed phase.

  1. Ionizing radiation and water reuse

    International Nuclear Information System (INIS)

    Borrely, Sueli Ivone; Sampa, Maria Helena de Oliveira; Oikawa, Hiroshi; Silveira, Carlos Gaia da; Duarte, Celina Lopes; Cherbakian, Eloisa Helena

    2002-01-01

    The aim of the present paper is to point out the possibility of including ionizing radiation for wastewater treatment and reuse. Radiation processing is an efficient technology which can be useful for water reuse once the process can reduce not only the biological contamination but also organic substances, promoting an important acute toxicity removal from aquatic resources. Final secondary effluents from three different wastewater treatment plant were submitted to electron beam radiation and the process efficacy was evaluated. Concerning disinfection, relatively low radiation doses (2,0 - 4,0 kGy) accounted for 4 to 6 cycle log reduction for total coliforms. When radiation was applied for general wastewater improvement related to the chemical contamination, radiation process reduced from 78% up to 100% the total acute toxicity, measured for crustaceans, D. similis, and for V. fiscehri bacteria. (author)

  2. Adaptation and validation of the instrument Clinical Learning Environment and Supervision for medical students in primary health care

    Directory of Open Access Journals (Sweden)

    Eva Öhman

    2016-12-01

    Full Text Available Abstract Background Clinical learning takes place in complex socio-cultural environments that are workplaces for the staff and learning places for the students. In the clinical context, the students learn by active participation and in interaction with the rest of the community at the workplace. Clinical learning occurs outside the university, therefore is it important for both the university and the student that the student is given opportunities to evaluate the clinical placements with an instrument that allows evaluation from many perspectives. The instrument Clinical Learning Environment and Supervision (CLES was originally developed for evaluation of nursing students’ clinical learning environment. The aim of this study was to adapt and validate the CLES instrument to measure medical students’ perceptions of their learning environment in primary health care. Methods In the adaptation process the face validity was tested by an expert panel of primary care physicians, who were also active clinical supervisors. The adapted CLES instrument with 25 items and six background questions was sent electronically to 1,256 medical students from one university. Answers from 394 students were eligible for inclusion. Exploratory factor analysis based on principal component methods followed by oblique rotation was used to confirm the adequate number of factors in the data. Construct validity was assessed by factor analysis. Confirmatory factor analysis was used to confirm the dimensions of CLES instrument. Results The construct validity showed a clearly indicated four-factor model. The cumulative variance explanation was 0.65, and the overall Cronbach’s alpha was 0.95. All items loaded similarly with the dimensions in the non-adapted CLES except for one item that loaded to another dimension. The CLES instrument in its adapted form had high construct validity and high reliability and internal consistency. Conclusion CLES, in its adapted form, appears

  3. Improving preschooler conduct adaptation by using a social learning program based on motion games

    Directory of Open Access Journals (Sweden)

    Zsuzsa Szilárda

    2017-03-01

    Full Text Available Being aware of the changes which occur under the influence of environmental conditions, education, culture and social roles upon the child is indispensable with a view to build up a conduct adapted to the social environment. For any preschooler child, entering kindergarten is an important social event and getting adapted to the new situation is not easy. Broadening the relational framework with objects, other individuals, with one’s own self, results in disciplining preschooler conducts and increasing the number of socially desirable conducts. Relying upon the above statements, this study is aimed at working out a social learning programme made up of motion games involving socialization/cooperation elements intended for inducing amelioration in terms of the child’s conduct during the process of adaptation to the kindergarten environment. The experiment was conducted using a sample of “little group” preschoolers (children 3-4 years of age. As research methods, the following have been used: studying the reference literature, the method of pedagogical observation, the method of experiment and the method of playing. Further to the practical application of the programme worked out with a view to enhance the adaptation conduct in the said subjects, the experimental group proved to have undergone a significant positive evolution and each subject showed improvements considering the conduct of adaptation to kindergarten conditions, as highlighted by the change i.e. higher values in terms of the individual scores achieved at the final test. Preschool education is meant to provide all possible ways and means to enable any child’s integration into groups of children of a peer age, to develop sociability in children and to create favorable conditions for building out inter-children networks.

  4. Formalisms for reuse and systems integration

    CERN Document Server

    Rubin, Stuart

    2015-01-01

    Reuse and integration are defined as synergistic concepts, where reuse addresses how to minimize redundancy in the creation of components; while, integration focuses on component composition. Integration supports reuse and vice versa. These related concepts support the design of software and systems for maximizing performance while minimizing cost. Knowledge, like data, is subject to reuse; and, each can be interpreted as the other. This means that inherent complexity, a measure of the potential utility of a system, is directly proportional to the extent to which it maximizes reuse and integration. Formal methods can provide an appropriate context for the rigorous handling of these synergistic concepts. Furthermore, formal languages allow for non ambiguous model specification; and, formal verification techniques provide support for insuring the validity of reuse and integration mechanisms.   This edited book includes 12 high quality research papers written by experts in formal aspects of reuse and integratio...

  5. Adapting a Technology-Based Implementation Support Tool for Community Mental Health: Challenges and Lessons Learned.

    Science.gov (United States)

    Livet, Melanie; Fixsen, Amanda

    2018-01-01

    With mental health services shifting to community-based settings, community mental health (CMH) organizations are under increasing pressure to deliver effective services. Despite availability of evidence-based interventions, there is a gap between effective mental health practices and the care that is routinely delivered. Bridging this gap requires availability of easily tailorable implementation support tools to assist providers in implementing evidence-based intervention with quality, thereby increasing the likelihood of achieving the desired client outcomes. This study documents the process and lessons learned from exploring the feasibility of adapting such a technology-based tool, Centervention, as the example innovation, for use in CMH settings. Mixed-methods data on core features, innovation-provider fit, and organizational capacity were collected from 44 CMH providers. Lessons learned included the need to augment delivery through technology with more personal interactions, the importance of customizing and integrating the tool with existing technologies, and the need to incorporate a number of strategies to assist with adoption and use of Centervention-like tools in CMH contexts. This study adds to the current body of literature on the adaptation process for technology-based tools and provides information that can guide additional innovations for CMH settings.

  6. One-Shot Learning of Human Activity With an MAP Adapted GMM and Simplex-HMM.

    Science.gov (United States)

    Rodriguez, Mario; Orrite, Carlos; Medrano, Carlos; Makris, Dimitrios

    2016-05-10

    This paper presents a novel activity class representation using a single sequence for training. The contribution of this representation lays on the ability to train an one-shot learning recognition system, useful in new scenarios where capturing and labeling sequences is expensive or impractical. The method uses a universal background model of local descriptors obtained from source databases available on-line and adapts it to a new sequence in the target scenario through a maximum a posteriori adaptation. Each activity sample is encoded in a sequence of normalized bag of features and modeled by a new hidden Markov model formulation, where the expectation-maximization algorithm for training is modified to deal with observations consisting in vectors in a unit simplex. Extensive experiments in recognition have been performed using one-shot learning over the public datasets Weizmann, KTH, and IXMAS. These experiments demonstrate the discriminative properties of the representation and the validity of application in recognition systems, achieving state-of-the-art results.

  7. An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning

    Directory of Open Access Journals (Sweden)

    Jingsha He

    2017-03-01

    Full Text Available In recent years, smart home technologies have started to be widely used, bringing a great deal of convenience to people’s daily lives. At the same time, privacy issues have become particularly prominent. Traditional encryption methods can no longer meet the needs of privacy protection in smart home applications, since attacks can be launched even without the need for access to the cipher. Rather, attacks can be successfully realized through analyzing the frequency of radio signals, as well as the timestamp series, so that the daily activities of the residents in the smart home can be learnt. Such types of attacks can achieve a very high success rate, making them a great threat to users’ privacy. In this paper, we propose an adaptive method based on sample data analysis and supervised learning (SDASL, to hide the patterns of daily routines of residents that would adapt to dynamically changing network loads. Compared to some existing solutions, our proposed method exhibits advantages such as low energy consumption, low latency, strong adaptability, and effective privacy protection.

  8. Interacting orientations and instrumentalities to adapt a learning tool for health professionals

    Directory of Open Access Journals (Sweden)

    Kathrine L. Nygård

    2015-09-01

    Full Text Available Web-based instructional software offers new opportunities for collaborative, task-oriented in-service training. Planning and negotiation of content to adapt a web-based learning resource for nursing is the topic of this paper. We draw from Cultural Historical Activity Theory to elaborate the dialectical relationship of changing and stabilizing organizational practice. Local adaptation to create a domain-specific resource plays out as interactions of orientations and instrumentalities. Our analysis traces how orientations, i.e., in situ selection of knowledge and mobilization of experiences, and instrumentality, i.e., interpreted affordances of available cultural tools, interact. The adaptation processes are mediated by a set of new and current tools that interact with multiple orientations to ensure stability and promote change. Practice and project are introduced as intermediate, analytic concepts to assess tensions in the observed activity. Our analysis shows three central tensions, how they are introduced, addressed and subsequently resolved. Considering the opportunities help understand how engagement with technology can lead to new representations for introduction to a local knowledge domain.

  9. Data mining methods application in reflexive adaptation realization in e-learning systems

    Directory of Open Access Journals (Sweden)

    A. S. Bozhday

    2017-01-01

    Full Text Available In recent years, e-learning technologies are rapidly gaining momentum in their evolution. In this regard, issues related to improving the quality of software for virtual educational systems are becoming topical: increasing the period of exploitation of programs, increasing their reliability and flexibility. The above characteristics directly depend on the ability of the software system to adapt to changes in the domain, environment and user characteristics. In some cases, this ability is reduced to the timely optimization of the program’s own interfaces and data structure. At present, several approaches to creating mechanisms for self-optimization of software systems are known, but all of them have an insufficient degree of formalization and, as a consequence, weak universality. The purpose of this work is to develop the basics of the technology of self-optimization of software systems in the structure of e-learning. The proposed technology is based on the formulated and formalized principle of reflexive adaptation of software, applicable to a wide class of software systems and based on the discovery of new knowledge in the behavioral products of the system.To solve this problem, methods of data mining were applied. Data mining allows finding regularities in the functioning of software systems, which may not be obvious at the stage of their development. Finding such regularities and their subsequent analysis will make it possible to reorganize the structure of the system in a more optimal way and without human intervention, which will prolong the life cycle of the software and reduce the costs of its maintenance. Achieving this effect is important for e-learning systems, since they are quite expensive.The main results of the work include: the proposed classification of software adaptation mechanisms, taking into account the latest trends in the IT field in general and in the field of e-learning in particular; Formulation and formalization of

  10. Statistical Learning Framework with Adaptive Retraining for Condition-Based Maintenance

    International Nuclear Information System (INIS)

    An, Sang Ha; Chang, Soon Heung; Heo, Gyun Young; Seo, Ho Joon; Kim, Su Young

    2009-01-01

    As systems become more complex and more critical in our daily lives, the need for the maintenance based on the reliable monitoring and diagnosis has become more apparent. However, in reality, the general opinion has been that 'maintenance is a necessary evil' or 'nothing can be done to improve maintenance costs'. Perhaps these were true statements twenty years ago when many of the diagnostic technologies were not fully developed. The developments of microprocessor or computer based instrumentation that can be used to monitor the operating condition of plant equipment, machinery and systems have provided the means to manage the maintenance operation. They have provided the means to reduce or eliminate unnecessary repairs, prevent catastrophic machine failures and reduce the negative impact of the maintenance operation on the profitability of manufacturing and production plants. Condition-based maintenance (CBM) techniques help determine the condition of in-service equipment in order to predict when maintenance should be performed. Most of the statistical learning techniques are only valid as long as the physics of a system does not change. If any significant change such as the replacement of a component or equipment occurs in the system, the statistical learning model should be re-trained or re-developed to adapt the new system. In this research, authors will propose a statistical learning framework which can be applicable for various CBMs, and the concept of the adaptive retraining technique will be described to support the execution of the framework so that the monitoring system does not need to be re-developed or re-trained even though there are any significant changes in the system or component

  11. Modelling energy technology dynamics: methodology for adaptive expectations models with learning by doing and learning by searching

    International Nuclear Information System (INIS)

    Kouvaritakis, N.; Soria, A.; Isoard, S.

    2000-01-01

    This paper presents a module endogenising technical change which is capable of being attached to large scale energy models that follow an adaptive-expectations. The formulation includes, apart from the more classical learning by doing effects, quantitative relationships between technology performance and R and D expenditure. It even attempts to go further by partially endogenising the latter by incorporating an optimisation module describing private equipment manufacturers' R and D budget allocation in a context of risk and expectation. Having presented this module in abstract, the paper proceeds to describe how an operational version of it has been constructed and implemented inside a large-scale partial equilibrium world energy model (the POLES model). Concerning learning functions problems associated with the data are alluded to, the hybrid econometric methods used to estimate them are presented as well as the adjustments which had to be effected to ensure a smooth incorporation into the large model. In the final sections is explained the use of the model itself to generate partial foresight parameters for the determination of return expectations particularly in view of CO 2 constraints and associated carbon values. (orig.)

  12. Value-based management of design reuse

    Science.gov (United States)

    Carballo, Juan Antonio; Cohn, David L.; Belluomini, Wendy; Montoye, Robert K.

    2003-06-01

    Effective design reuse in electronic products has the potential to provide very large cost savings, substantial time-to-market reduction, and extra sources of revenue. Unfortunately, critical reuse opportunities are often missed because, although they provide clear value to the corporation, they may not benefit the business performance of an internal organization. It is therefore crucial to provide tools to help reuse partners participate in a reuse transaction when the transaction provides value to the corporation as a whole. Value-based Reuse Management (VRM) addresses this challenge by (a) ensuring that all parties can quickly assess the business performance impact of a reuse opportunity, and (b) encouraging high-value reuse opportunities by supplying value-based rewards to potential parties. In this paper we introduce the Value-Based Reuse Management approach and we describe key results on electronic designs that demonstrate its advantages. Our results indicate that Value-Based Reuse Management has the potential to significantly increase the success probability of high-value electronic design reuse.

  13. Learning from neural control.

    Science.gov (United States)

    Wang, Cong; Hill, David J

    2006-01-01

    One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.

  14. Usability of clinical decision support system as a facilitator for learning the assistive technology adaptation process.

    Science.gov (United States)

    Danial-Saad, Alexandra; Kuflik, Tsvi; Weiss, Patrice L Tamar; Schreuer, Naomi

    2016-01-01

    The aim of this study was to evaluate the usability of Ontology Supported Computerized Assistive Technology Recommender (OSCAR), a Clinical Decision Support System (CDSS) for the assistive technology adaptation process, its impact on learning the matching process, and to determine the relationship between its usability and learnability. Two groups of expert and novice clinicians (total, n = 26) took part in this study. Each group filled out system usability scale (SUS) to evaluate OSCAR's usability. The novice group completed a learning questionnaire to assess OSCAR's effect on their ability to learn the matching process. Both groups rated OSCAR's usability as "very good", (M [SUS] = 80.7, SD = 11.6, median = 83.7) by the novices, and (M [SUS] = 81.2, SD = 6.8, median = 81.2) by the experts. The Mann-Whitney results indicated that no significant differences were found between the expert and novice groups in terms of OSCAR's usability. A significant positive correlation existed between the usability of OSCAR and the ability to learn the adaptation process (rs = 0.46, p = 0.04). Usability is an important factor in the acceptance of a system. The successful application of user-centered design principles during the development of OSCAR may serve as a case study that models the significant elements to be considered, theoretically and practically in developing other systems. Implications for Rehabilitation Creating a CDSS with a focus on its usability is an important factor for its acceptance by its users. Successful usability outcomes can impact the learning process of the subject matter in general, and the AT prescription process in particular. The successful application of User-Centered Design principles during the development of OSCAR may serve as a case study that models the significant elements to be considered, theoretically and practically. The study emphasizes the importance of close collaboration between the developers and

  15. Multilevel learning in the adaptive management of waterfowl harvests: 20 years and counting

    Science.gov (United States)

    Johnson, Fred A.; Boomer, G. Scott; Williams, Byron K.; Nichols, James D.; Case, David J.

    2015-01-01

    In 1995, the U.S. Fish and Wildlife Service implemented an adaptive harvest management program (AHM) for the sport harvest of midcontinent mallards (Anas platyrhynchos). The program has been successful in reducing long-standing contentiousness in the regulatory process, while integrating science and policy in a coherent, rigorous, and transparent fashion. After 20 years, much has been learned about the relationship among waterfowl populations, their environment, and hunting regulations, with each increment of learning contributing to better management decisions. At the same time, however, much has been changing in the social, institutional, and environmental arenas that provide context for the AHM process. Declines in hunter numbers, competition from more pressing conservation issues, and global-change processes are increasingly challenging waterfowl managers to faithfully reflect the needs and desires of stakeholders, to account for an increasing number of institutional constraints, and to (probabilistically) predict the consequences of regulatory policy in a changing environment. We review the lessons learned from the AHM process so far, and describe emerging challenges and ways in which they may be addressed. We conclude that the practice of AHM has greatly increased an awareness of the roles of social values, trade-offs, and attitudes toward risk in regulatory decision-making. Nevertheless, going forward the waterfowl management community will need to focus not only on the relationships among habitat, harvest, and waterfowl populations, but on the ways in which society values waterfowl and how those values can change over time. 

  16. Online learning control using adaptive critic designs with sparse kernel machines.

    Science.gov (United States)

    Xu, Xin; Hou, Zhongsheng; Lian, Chuanqiang; He, Haibo

    2013-05-01

    In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming (HDP), dual heuristic programming (DHP), and their action-dependent ones, have been widely studied to realize online learning control of dynamical systems. However, because neural networks with manually designed features are commonly used to deal with continuous state and action spaces, the generalization capability and learning efficiency of previous ACDs still need to be improved. In this paper, a novel framework of ACDs with sparse kernel machines is presented by integrating kernel methods into the critic of ACDs. To improve the generalization capability as well as the computational efficiency of kernel machines, a sparsification method based on the approximately linear dependence analysis is used. Using the sparse kernel machines, two kernel-based ACD algorithms, that is, kernel HDP (KHDP) and kernel DHP (KDHP), are proposed and their performance is analyzed both theoretically and empirically. Because of the representation learning and generalization capability of sparse kernel machines, KHDP and KDHP can obtain much better performance than previous HDP and DHP with manually designed neural networks. Simulation and experimental results of two nonlinear control problems, that is, a continuous-action inverted pendulum problem and a ball and plate control problem, demonstrate the effectiveness of the proposed kernel ACD methods.

  17. System level mechanisms of adaptation, learning, memory formation and evolvability: the role of chaperone and other networks.

    Science.gov (United States)

    Gyurko, David M; Soti, Csaba; Stetak, Attila; Csermely, Peter

    2014-05-01

    During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here, we describe first the protein structure networks of molecular chaperones, then characterize chaperone containing sub-networks of interactomes called as chaperone-networks or chaperomes. We review the role of molecular chaperones in short-term adaptation of cellular networks in response to stress, and in long-term adaptation discussing their putative functions in the regulation of evolvability. We provide a general overview of possible network mechanisms of adaptation, learning and memory formation. We propose that changes of network rigidity play a key role in learning and memory formation processes. Flexible network topology provides ' learning-competent' state. Here, networks may have much less modular boundaries than locally rigid, highly modular networks, where the learnt information has already been consolidated in a memory formation process. Since modular boundaries are efficient filters of information, in the 'learning-competent' state information filtering may be much smaller, than after memory formation. This mechanism restricts high information transfer to the 'learning competent' state. After memory formation, modular boundary-induced segregation and information filtering protect the stored information. The flexible networks of young organisms are generally in a 'learning competent' state. On the contrary, locally rigid networks of old organisms have lost their 'learning competent' state, but store and protect their learnt information efficiently. We anticipate that the above mechanism may operate at the level of both protein-protein interaction and neuronal networks.

  18. Comparison of the Psychological Characteristics of Adaptation in Orphan Students of Initial Learning Stage to Adaptation Potential in Students Brought up in Families

    Directory of Open Access Journals (Sweden)

    Zamorueva V.V.,

    2014-08-01

    Full Text Available We present a study of psychological characteristics of preadult orphans, their psychological adaptation to the conditions of learning in high school compared to the norm population (students living in family. We assumed that the level of adaptation of the orphan students is significantly smaller than in other students, because of their special life circumstances (maternal deprivation, living in residential care institutions, sometimes bad heredity, lack of life skills in everyday issues, personal problems. The results of the survey of 49 orphan students (26 girls and 23 boys and 49 first-year students brought up by parents (28 girls and 21 boys, confirmed this hypothesis and allow us to tell that orphan students need special psychological help in the learning process in high school to grow at a personal and professional level.

  19. ICT reuse in socio-economic enterprises

    International Nuclear Information System (INIS)

    Ongondo, F.O.; Williams, I.D.; Dietrich, J.; Carroll, C.

    2013-01-01

    Highlights: • We analyse ICT equipment reuse operations of socio-economic enterprises. • Most common ICT products dealt with are computers and related equipment. • In the UK in 2010, ∼143,750 appliances were reused. • Marketing and legislative difficulties are the common hurdles to reuse activities. • Socio-economic enterprises can significantly contribute to resource efficiency. - Abstract: In Europe, socio-economic enterprises such as charities, voluntary organisations and not-for-profit companies are involved in the repair, refurbishment and reuse of various products. This paper characterises and analyses the operations of socio-economic enterprises that are involved in the reuse of Information and Communication Technology (ICT) equipment. Using findings from a survey, the paper specifically analyses the reuse activities of socio-economic enterprises in the UK from which Europe-wide conclusions are drawn. The amount of ICT products handled by the reuse organisations is quantified and potential barriers and opportunities to their operations are analysed. By-products from reuse activities are discussed and recommendations to improve reuse activities are provided. The most common ICT products dealt with by socio-economic enterprises are computers and related equipment. In the UK in 2010, an estimated 143,750 appliances were reused. However, due to limitations in data, it is difficult to compare this number to the amount of new appliances that entered the UK market or the amount of waste electrical and electronic equipment generated in the same period. Difficulties in marketing products and numerous legislative requirements are the most common barriers to reuse operations. Despite various constraints, it is clear that organisations involved in reuse of ICT could contribute significantly to resource efficiency and a circular economy. It is suggested that clustering of their operations into “reuse parks” would enhance both their profile and their

  20. ICT reuse in socio-economic enterprises

    Energy Technology Data Exchange (ETDEWEB)

    Ongondo, F.O., E-mail: f.ongondo@soton.ac.uk [Centre for Environmental Sciences, Faculty of Engineering and the Environment, Lanchester Building, University of Southampton, University Rd., Highfield, Southampton, Hampshire SO17 1BJ (United Kingdom); Williams, I.D. [Centre for Environmental Sciences, Faculty of Engineering and the Environment, Lanchester Building, University of Southampton, University Rd., Highfield, Southampton, Hampshire SO17 1BJ (United Kingdom); Dietrich, J. [Technische Universität Berlin, Centre for Scientific Continuing Education and Cooperation, Cooperation and Consulting for Environmental Questions (kubus) FH10-1, Fraunhoferstraße 33-36, 10587 Berlin (Germany); Carroll, C. [Centre for Environmental Sciences, Faculty of Engineering and the Environment, Lanchester Building, University of Southampton, University Rd., Highfield, Southampton, Hampshire SO17 1BJ (United Kingdom)

    2013-12-15

    Highlights: • We analyse ICT equipment reuse operations of socio-economic enterprises. • Most common ICT products dealt with are computers and related equipment. • In the UK in 2010, ∼143,750 appliances were reused. • Marketing and legislative difficulties are the common hurdles to reuse activities. • Socio-economic enterprises can significantly contribute to resource efficiency. - Abstract: In Europe, socio-economic enterprises such as charities, voluntary organisations and not-for-profit companies are involved in the repair, refurbishment and reuse of various products. This paper characterises and analyses the operations of socio-economic enterprises that are involved in the reuse of Information and Communication Technology (ICT) equipment. Using findings from a survey, the paper specifically analyses the reuse activities of socio-economic enterprises in the UK from which Europe-wide conclusions are drawn. The amount of ICT products handled by the reuse organisations is quantified and potential barriers and opportunities to their operations are analysed. By-products from reuse activities are discussed and recommendations to improve reuse activities are provided. The most common ICT products dealt with by socio-economic enterprises are computers and related equipment. In the UK in 2010, an estimated 143,750 appliances were reused. However, due to limitations in data, it is difficult to compare this number to the amount of new appliances that entered the UK market or the amount of waste electrical and electronic equipment generated in the same period. Difficulties in marketing products and numerous legislative requirements are the most common barriers to reuse operations. Despite various constraints, it is clear that organisations involved in reuse of ICT could contribute significantly to resource efficiency and a circular economy. It is suggested that clustering of their operations into “reuse parks” would enhance both their profile and their

  1. Factors affecting reuse of wastewater

    Energy Technology Data Exchange (ETDEWEB)

    Haraszti, L

    1981-01-01

    Changing the quality of circulating water, raising the effectiveness of sedimentation, examples of biological treatment of wastewater are presented. The necessity of continuing the studies on biological treatment of wastewater is demonstrated. It is considered useful to define the importance of KhPK and BP5 in each case. During biological treatment in ponds, to define the relation BPK5:N:P, research on conditions for nutrient removal must be done. To do this, as well as decrease the significance of KhPK, a mathematical model for defining the effectiveness of biological treatment of wastewater and consequently their reuse must be developed.

  2. Increasing productivity through Total Reuse Management (TRM)

    Science.gov (United States)

    Schuler, M. P.

    1991-01-01

    Total Reuse Management (TRM) is a new concept currently being promoted by the NASA Langley Software Engineering and Ada Lab (SEAL). It uses concepts similar to those promoted in Total Quality Management (TQM). Both technical and management personnel are continually encouraged to think in terms of reuse. Reuse is not something that is aimed for after a product is completed, but rather it is built into the product from inception through development. Lowering software development costs, reducing risk, and increasing code reliability are the more prominent goals of TRM. Procedures and methods used to adopt and apply TRM are described. Reuse is frequently thought of as only being applicable to code. However, reuse can apply to all products and all phases of the software life cycle. These products include management and quality assurance plans, designs, and testing procedures. Specific examples of successfully reused products are given and future goals are discussed.

  3. Adaptive Education.

    Science.gov (United States)

    Anderson, Lorin W.

    1979-01-01

    Schools have devised several ways to adapt instruction to a wide variety of student abilities and needs. Judged by criteria for what adaptive education should be, most learning for mastery programs look good. (Author/JM)

  4. MO-G-17A-05: PET Image Deblurring Using Adaptive Dictionary Learning

    International Nuclear Information System (INIS)

    Valiollahzadeh, S; Clark, J; Mawlawi, O

    2014-01-01

    Purpose: The aim of this work is to deblur PET images while suppressing Poisson noise effects using adaptive dictionary learning (DL) techniques. Methods: The model that relates a blurred and noisy PET image to the desired image is described as a linear transform y=Hm+n where m is the desired image, H is a blur kernel, n is Poisson noise and y is the blurred image. The approach we follow to recover m involves the sparse representation of y over a learned dictionary, since the image has lots of repeated patterns, edges, textures and smooth regions. The recovery is based on an optimization of a cost function having four major terms: adaptive dictionary learning term, sparsity term, regularization term, and MLEM Poisson noise estimation term. The optimization is solved by a variable splitting method that introduces additional variables. We simulated a 128×128 Hoffman brain PET image (baseline) with varying kernel types and sizes (Gaussian 9×9, σ=5.4mm; Uniform 5×5, σ=2.9mm) with additive Poisson noise (Blurred). Image recovery was performed once when the kernel type was included in the model optimization and once with the model blinded to kernel type. The recovered image was compared to the baseline as well as another recovery algorithm PIDSPLIT+ (Setzer et. al.) by calculating PSNR (Peak SNR) and normalized average differences in pixel intensities (NADPI) of line profiles across the images. Results: For known kernel types, the PSNR of the Gaussian (Uniform) was 28.73 (25.1) and 25.18 (23.4) for DL and PIDSPLIT+ respectively. For blinded deblurring the PSNRs were 25.32 and 22.86 for DL and PIDSPLIT+ respectively. NADPI between baseline and DL, and baseline and blurred for the Gaussian kernel was 2.5 and 10.8 respectively. Conclusion: PET image deblurring using dictionary learning seems to be a good approach to restore image resolution in presence of Poisson noise. GE Health Care

  5. MO-G-17A-05: PET Image Deblurring Using Adaptive Dictionary Learning

    Energy Technology Data Exchange (ETDEWEB)

    Valiollahzadeh, S [RICE University, Houston, Tx (United States); Clark, J [MD Anderson Cancer Ctr., Houston, TX (United States); Mawlawi, O

    2014-06-15

    Purpose: The aim of this work is to deblur PET images while suppressing Poisson noise effects using adaptive dictionary learning (DL) techniques. Methods: The model that relates a blurred and noisy PET image to the desired image is described as a linear transform y=Hm+n where m is the desired image, H is a blur kernel, n is Poisson noise and y is the blurred image. The approach we follow to recover m involves the sparse representation of y over a learned dictionary, since the image has lots of repeated patterns, edges, textures and smooth regions. The recovery is based on an optimization of a cost function having four major terms: adaptive dictionary learning term, sparsity term, regularization term, and MLEM Poisson noise estimation term. The optimization is solved by a variable splitting method that introduces additional variables. We simulated a 128×128 Hoffman brain PET image (baseline) with varying kernel types and sizes (Gaussian 9×9, σ=5.4mm; Uniform 5×5, σ=2.9mm) with additive Poisson noise (Blurred). Image recovery was performed once when the kernel type was included in the model optimization and once with the model blinded to kernel type. The recovered image was compared to the baseline as well as another recovery algorithm PIDSPLIT+ (Setzer et. al.) by calculating PSNR (Peak SNR) and normalized average differences in pixel intensities (NADPI) of line profiles across the images. Results: For known kernel types, the PSNR of the Gaussian (Uniform) was 28.73 (25.1) and 25.18 (23.4) for DL and PIDSPLIT+ respectively. For blinded deblurring the PSNRs were 25.32 and 22.86 for DL and PIDSPLIT+ respectively. NADPI between baseline and DL, and baseline and blurred for the Gaussian kernel was 2.5 and 10.8 respectively. Conclusion: PET image deblurring using dictionary learning seems to be a good approach to restore image resolution in presence of Poisson noise. GE Health Care.

  6. Software reuse example and challenges at NSIDC

    Science.gov (United States)

    Billingsley, B. W.; Brodzik, M.; Collins, J. A.

    2009-12-01

    NSIDC has created a new data discovery and access system, Searchlight, to provide users with the data they want in the format they want. NSIDC Searchlight supports discovery and access to disparate data types with on-the-fly reprojection, regridding and reformatting. Architected to both reuse open source systems and be reused itself, Searchlight reuses GDAL and Proj4 for manipulating data and format conversions, the netCDF Java library for creating netCDF output, MapServer and OpenLayers for defining spatial criteria and the JTS Topology Suite (JTS) in conjunction with Hibernate Spatial for database interaction and rich OGC-compliant spatial objects. The application reuses popular Java and Java Script libraries including Struts 2, Spring, JPA (Hibernate), Sitemesh, JFreeChart, JQuery, DOJO and a PostGIS PostgreSQL database. Future reuse of Searchlight components is supported at varying architecture levels, ranging from the database and model components to web services. We present the tools, libraries and programs that Searchlight has reused. We describe the architecture of Searchlight and explain the strategies deployed for reusing existing software and how Searchlight is built for reuse. We will discuss NSIDC reuse of the Searchlight components to support rapid development of new data delivery systems.

  7. Moving Past Curricula and Strategies: Language and the Development of Adaptive Pedagogy for Immersive Learning Environments

    Science.gov (United States)

    Hand, Brian; Cavagnetto, Andy; Chen, Ying-Chih; Park, Soonhye

    2016-04-01

    Given current concerns internationally about student performance in science and the need to shift how science is being learnt in schools, as a community, we need to shift how we approach the issue of learning and teaching in science. In the future, we are going to have to close the gap between how students construct and engage with knowledge in a media-rich environment, and how school classroom environments engage them. This is going to require a shift to immersive environments where attention is paid to the knowledge bases and resources students bring into the classroom. Teachers will have to adopt adaptive pedagogical approaches that are framed around a more nuanced understanding of epistemological orientation, language and the nature of prosocial environments.

  8. Populists as Chameleons? An Adaptive Learning Approach to the Rise of Populist Politicians

    Directory of Open Access Journals (Sweden)

    Jasper Muis

    2015-04-01

    Full Text Available This paper envisions populism as a vote- and attention-maximizing strategy. It applies an adaptive learning approach to understand successes of populist party leaders. I assume that populists are ideologically flexible and continually search for a more beneficial policy position, in terms of both electoral support and media attention, by retaining political claims that yield positive feedback and discard those that encounter negative feedback. This idea is empirically tested by analyzing the Dutch populist leader Pim Fortuyn and the development of his stance about immigration and integration issues. In contrast to the conventional wisdom, the results do not show any empirical support for the claim that Fortuyn was ideologically driven by the opinion polls or by media publicity during the 2002 Dutch parliamentary election campaign. The findings thus suggest that populist parties are perhaps less distinctive in their strategies from mainstream parties than often claimed.

  9. Mild-moderate TBI: clinical recommendations to optimize neurobehavioral functioning, learning, and adaptation.

    Science.gov (United States)

    Chen, Anthony J-W; Loya, Fred

    2014-11-01

    Traumatic brain injury (TBI) can result in functional deficits that persist long after acute injury. The authors present a case study of an individual who experienced some of the most common debilitating problems that characterize the chronic phase of mild-to-moderate TBI-difficulties with neurobehavioral functions that manifest via complaints of distractibility, poor memory, disorganization, poor frustration tolerance, and feeling easily overwhelmed. They present a rational strategy for management that addresses important domain-general targets likely to have far-ranging benefits. This integrated, longitudinal, and multifaceted approach first addresses approachable targets and provides an important foundation to enhance the success of other, more specific interventions requiring specialty intervention. The overall approach places an emphasis on accomplishing two major categories of clinical objectives: optimizing current functioning and enhancing learning and adaptation to support improvement of functioning in the long-term for individuals living with brain injury. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

  10. Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

    Science.gov (United States)

    Yang, Xiong; Liu, Derong; Wang, Ding

    2014-03-01

    In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

  11. The influence of demographics and work related goals on adaptive development for work related learning amongst private hospital employees.

    Science.gov (United States)

    Tones, Megan; Pillay, Hitendra; Fraser, Jennifer

    2010-01-01

    Contemporary lifespan development models of adaptive development have been applied to the workforce to examine characteristics of the ageing employee. Few studies have examined adaptive development in terms of worker perceptions of workplace, or their learning and development issues. This study used the recently developed Revised Learning and Development Survey to investigate employee selection and engagement of learning and development goals, opportunities for learning and development at work, and constraints to learning and development at work. Demographic and career goal variables were tested amongst a sample of private hospital employees, almost all of whom were nurses. Workers under 45 years of age perceived greater opportunities for training and development than more mature aged workers. Age and physical demands interacted such that physical demands of work were associated with lower engagement in learning and development goals in mature aged workers. The opposite was observed amongst younger workers. Engagement in learning and development goals at work predicted goals associated with an intention to decrease work hours or change jobs to a different industry when opportunities to learn via work tasks were limited. At the same time limited opportunities for training and development and perceptions of constraints to development at work predicted the intention to change jobs. Results indicate consideration must be paid to employee perceptions in the workplace in relation to goals. They may be important factors in designing strategies to retain workers.

  12. Reuse of hydroponic waste solution.

    Science.gov (United States)

    Kumar, Ramasamy Rajesh; Cho, Jae Young

    2014-01-01

    Attaining sustainable agriculture is a key goal in many parts of the world. The increased environmental awareness and the ongoing attempts to execute agricultural practices that are economically feasible and environmentally safe promote the use of hydroponic cultivation. Hydroponics is a technology for growing plants in nutrient solutions with or without the use of artificial medium to provide mechanical support. Major problems for hydroponic cultivation are higher operational cost and the causing of pollution due to discharge of waste nutrient solution. The nutrient effluent released into the environment can have negative impacts on the surrounding ecosystems as well as the potential to contaminate the groundwater utilized by humans for drinking purposes. The reuse of non-recycled, nutrient-rich hydroponic waste solution for growing plants in greenhouses is the possible way to control environmental pollution. Many researchers have successfully grown several plant species in hydroponic waste solution with high yield. Hence, this review addresses the problems associated with the release of hydroponic waste solution into the environment and possible reuse of hydroponic waste solution as an alternative resource for agriculture development and to control environmental pollution.

  13. Adaptive neural network/expert system that learns fault diagnosis for different structures

    Science.gov (United States)

    Simon, Solomon H.

    1992-08-01

    Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

  14. Bio-inspired adaptive feedback error learning architecture for motor control.

    Science.gov (United States)

    Tolu, Silvia; Vanegas, Mauricio; Luque, Niceto R; Garrido, Jesús A; Ros, Eduardo

    2012-10-01

    This study proposes an adaptive control architecture based on an accurate regression method called Locally Weighted Projection Regression (LWPR) and on a bio-inspired module, such as a cerebellar-like engine. This hybrid architecture takes full advantage of the machine learning module (LWPR kernel) to abstract an optimized representation of the sensorimotor space while the cerebellar component integrates this to generate corrective terms in the framework of a control task. Furthermore, we illustrate how the use of a simple adaptive error feedback term allows to use the proposed architecture even in the absence of an accurate analytic reference model. The presented approach achieves an accurate control with low gain corrective terms (for compliant control schemes). We evaluate the contribution of the different components of the proposed scheme comparing the obtained performance with alternative approaches. Then, we show that the presented architecture can be used for accurate manipulation of different objects when their physical properties are not directly known by the controller. We evaluate how the scheme scales for simulated plants of high Degrees of Freedom (7-DOFs).

  15. Linear hypergeneralization of learned dynamics across movement speeds reveals anisotropic, gain-encoding primitives for motor adaptation.

    Science.gov (United States)

    Joiner, Wilsaan M; Ajayi, Obafunso; Sing, Gary C; Smith, Maurice A

    2011-01-01

    The ability to generalize learned motor actions to new contexts is a key feature of the motor system. For example, the ability to ride a bicycle or swing a racket is often first developed at lower speeds and later applied to faster velocities. A number of previous studies have examined the generalization of motor adaptation across movement directions and found that the learned adaptation decays in a pattern consistent with the existence of motor primitives that display narrow Gaussian tuning. However, few studies have examined the generalization of motor adaptation across movement speeds. Following adaptation to linear velocity-dependent dynamics during point-to-point reaching arm movements at one speed, we tested the ability of subjects to transfer this adaptation to short-duration higher-speed movements aimed at the same target. We found near-perfect linear extrapolation of the trained adaptation with respect to both the magnitude and the time course of the velocity profiles associated with the high-speed movements: a 69% increase in movement speed corresponded to a 74% extrapolation of the trained adaptation. The close match between the increase in movement speed and the corresponding increase in adaptation beyond what was trained indicates linear hypergeneralization. Computational modeling shows that this pattern of linear hypergeneralization across movement speeds is not compatible with previous models of adaptation in which motor primitives display isotropic Gaussian tuning of motor output around their preferred velocities. Instead, we show that this generalization pattern indicates that the primitives involved in the adaptation to viscous dynamics display anisotropic tuning in velocity space and encode the gain between motor output and motion state rather than motor output itself.

  16. Adaptive learning can result in a failure to profit from good conditions: implications for understanding depression.

    Science.gov (United States)

    Trimmer, Pete C; Higginson, Andrew D; Fawcett, Tim W; McNamara, John M; Houston, Alasdair I

    2015-04-26

    Depression is a major medical problem diagnosed in an increasing proportion of people and for which commonly prescribed psychoactive drugs are frequently ineffective. Development of treatment options may be facilitated by an evolutionary perspective; several adaptive reasons for proneness to depression have been proposed. A common feature of many explanations is that depressive behaviour is a way to avoid costly effort where benefits are small and/or unlikely. However, this viewpoint fails to explain why low mood persists when the situation improves. We investigate whether a behavioural rule that is adapted to a stochastically changing world can cause inactivity which appears similar to the effect of depression, in that it persists after the situation has improved. We develop an adaptive learning model in which an individual has repeated choices of whether to invest costly effort that may result in a net benefit. Investing effort also provides information about the current conditions and rates of change of the conditions. An individual following the optimal behavioural strategy may sometimes remain inactive when conditions are favourable (i.e. when it would be better to invest effort) when it is poorly informed about the current environmental state. Initially benign conditions can predispose an individual to inactivity after a relatively brief period of negative experiences. Our approach suggests that the antecedent factors causing depressed behaviour could go much further back in an individual s history than is currently appreciated. The insights from our approach have implications for the ongoing debate about best treatment options for patients with depressive symptoms. © The Author(s) 2015. Published by Oxford University Press on behalf of the Foundation for Evolution, Medicine, and Public Health.

  17. Adaptive template generation for amyloid PET using a deep learning approach.

    Science.gov (United States)

    Kang, Seung Kwan; Seo, Seongho; Shin, Seong A; Byun, Min Soo; Lee, Dong Young; Kim, Yu Kyeong; Lee, Dong Soo; Lee, Jae Sung

    2018-05-11

    Accurate spatial normalization (SN) of amyloid positron emission tomography (PET) images for Alzheimer's disease assessment without coregistered anatomical magnetic resonance imaging (MRI) of the same individual is technically challenging. In this study, we applied deep neural networks to generate individually adaptive PET templates for robust and accurate SN of amyloid PET without using matched 3D MR images. Using 681 pairs of simultaneously acquired 11 C-PIB PET and T1-weighted 3D MRI scans of AD, MCI, and cognitively normal subjects, we trained and tested two deep neural networks [convolutional auto-encoder (CAE) and generative adversarial network (GAN)] that produce adaptive best PET templates. More specifically, the networks were trained using 685,100 pieces of augmented data generated by rotating 527 randomly selected datasets and validated using 154 datasets. The input to the supervised neural networks was the 3D PET volume in native space and the label was the spatially normalized 3D PET image using the transformation parameters obtained from MRI-based SN. The proposed deep learning approach significantly enhanced the quantitative accuracy of MRI-less amyloid PET assessment by reducing the SN error observed when an average amyloid PET template is used. Given an input image, the trained deep neural networks rapidly provide individually adaptive 3D PET templates without any discontinuity between the slices (in 0.02 s). As the proposed method does not require 3D MRI for the SN of PET images, it has great potential for use in routine analysis of amyloid PET images in clinical practice and research. © 2018 Wiley Periodicals, Inc.

  18. Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

    Directory of Open Access Journals (Sweden)

    Min Wang

    2017-01-01

    Full Text Available A dynamic learning method is developed for an uncertain n-link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function (RBF neural network (NN approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steady-state control process, RBF NNs are verified to satisfy the partial persistent excitation (PE condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme.

  19. Envisioning the future of wildlife in a changing climate: Collaborative learning for adaptation planning

    Science.gov (United States)

    LeDee, Olivia E.; Karasov, W.H.; Martin, Karl J.; Meyer, Michael W.; Ribic, Christine; Van Deelen, Timothy R.

    2011-01-01

    Natural resource managers are tasked with assessing the impacts of climate change on conservation targets and developing adaptation strategies to meet agency goals. The complex, transboundary nature of climate change demands the collaboration of scientists, managers, and stakeholders in this effort. To share, integrate, and apply knowledge from these diverse perspectives, we must engage in social learning. In 2009, we initiated a process to engage university researchers and agency scientists and managers in collaborative learning to assess the impacts of climate change on terrestrial fauna in the state of Wisconsin, USA. We constructed conceptual Bayesian networks to depict the influence of climate change, key biotic and abiotic factors, and existing stressors on the distribution and abundance of 3 species: greater prairie-chicken (Tympanuchus cupido), wood frog (Lithobates sylvaticus), and Karner blue butterfly (Plebejus melissa samuelis). For each species, we completed a 2-stage expert review that elicited dialogue on information gaps, management opportunities, and research priorities. From our experience, collaborative network modeling proved to be a powerful tool to develop a common vision of the potential impacts of climate change on conservation targets.

  20. Automated Detection of Microaneurysms Using Scale-Adapted Blob Analysis and Semi-Supervised Learning

    Energy Technology Data Exchange (ETDEWEB)

    Adal, Kedir M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Sidebe, Desire [Univ. of Burgundy, Dijon (France); Ali, Sharib [Univ. of Burgundy, Dijon (France); Chaum, Edward [Univ. of Tennessee, Knoxville, TN (United States); Karnowski, Thomas Paul [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Meriaudeau, Fabrice [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2014-01-07

    Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are then introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier to detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images.

  1. Learning-Based Adaptive Imputation Methodwith kNN Algorithm for Missing Power Data

    Directory of Open Access Journals (Sweden)

    Minkyung Kim

    2017-10-01

    Full Text Available This paper proposes a learning-based adaptive imputation method (LAI for imputing missing power data in an energy system. This method estimates the missing power data by using the pattern that appears in the collected data. Here, in order to capture the patterns from past power data, we newly model a feature vector by using past data and its variations. The proposed LAI then learns the optimal length of the feature vector and the optimal historical length, which are significant hyper parameters of the proposed method, by utilizing intentional missing data. Based on a weighted distance between feature vectors representing a missing situation and past situation, missing power data are estimated by referring to the k most similar past situations in the optimal historical length. We further extend the proposed LAI to alleviate the effect of unexpected variation in power data and refer to this new approach as the extended LAI method (eLAI. The eLAI selects a method between linear interpolation (LI and the proposed LAI to improve accuracy under unexpected variations. Finally, from a simulation under various energy consumption profiles, we verify that the proposed eLAI achieves about a 74% reduction of the average imputation error in an energy system, compared to the existing imputation methods.

  2. Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning.

    Science.gov (United States)

    Adal, Kedir M; Sidibé, Désiré; Ali, Sharib; Chaum, Edward; Karnowski, Thomas P; Mériaudeau, Fabrice

    2014-04-01

    Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier which can detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  3. Learning effects of interactive decision-making processes for climate change adaptation

    NARCIS (Netherlands)

    Baird, J.; Plummer, R.; Haug, C.C.; Huitema, D.

    2014-01-01

    Learning is gaining attention in relation to governance processes for contemporary environmental challenges; however, scholarship at the nexus of learning and environmental governance lacks clarity and understanding about how to define and measure learning, and the linkages between learning, social

  4. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

    Energy Technology Data Exchange (ETDEWEB)

    Park, Sang Hyun [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong, E-mail: yzgao@cs.unc.edu [Department of Computer Science, Department of Radiology, and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Shi, Yinghuan, E-mail: syh@nju.edu.cn [State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

    2014-11-01

    Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to

  5. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

    International Nuclear Information System (INIS)

    Park, Sang Hyun; Gao, Yaozong; Shi, Yinghuan; Shen, Dinggang

    2014-01-01

    Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to

  6. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection.

    Science.gov (United States)

    Park, Sang Hyun; Gao, Yaozong; Shi, Yinghuan; Shen, Dinggang

    2014-11-01

    Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to evaluate both the efficiency

  7. Effects of Adaptive Training on Working Memory and Academic Achievement of Children with Learning Disabilities: A School-Based Study

    Science.gov (United States)

    Cunningham, Rhonda Phillips

    2013-01-01

    Research has suggested many children with learning disabilities (LD) have deficits in working memory (WM) that hinder their academic achievement. Cogmed RM, a computerized intervention, uses adaptive training over 25 sessions and has shown efficacy in improving WM in children with attention deficit hyperactivity disorder (ADHD) and a variety of…

  8. Psychosocial Adjustment over a Two-Year Period in Children Referred for Learning Problems: Risk, Resilience, and Adaptation.

    Science.gov (United States)

    Sorensen, Lisa G.; Forbes, Peter W.; Bernstein, Jane H.; Weiler, Michael D.; Mitchell, William M.; Waber, Deborah P.

    2003-01-01

    A 2-year study evaluated the relationship among psychosocial adjustment, changes in academic skills, and contextual factors in 100 children (ages 7-11) with learning problems. Contextual variables were significantly associated with psychosocial adaptation, including the effectiveness of the clinical assessment, extent of academic support, and the…

  9. A Model of Successful Adaptation to Online Learning for College-Bound Native American High School Students

    Science.gov (United States)

    Kaler, Collier Butler

    2012-01-01

    Purpose: The purpose of this paper is to examine the conditions for Native American high school students that result in successful adaptation to an online learning environment. Design/methodology/approach: In total, eight Native American students attending high schools located on Montana Indian reservations, and one urban city, were interviewed.…

  10. Methods of Psychological and Pedagogical Accompaniment of First-Year Students in Process of Adapting to Learning at University

    Science.gov (United States)

    Maralova, Tatyana P.; Filipenkova, Olesya G.; Galitskikh, Elena O.; Shulga, Tatiana I.; Sidyacheva, Natalya V.; Ovsyanik, Olga A.

    2016-01-01

    The relevance of the study is conditioned by the complexity of students' adaptation to learning at University due to the change of social environment, an alarming feelings about the precise self-determination, lack of knowledge in opportunities for self-expression in art, science, sport and public life. The purpose of the paper is to identify…

  11. Impacts of Organizational Knowledge Sharing Practices on Employees' Job Satisfaction: Mediating Roles of Learning Commitment and Interpersonal Adaptability

    Science.gov (United States)

    Malik, Muhammad Shaukat; Kanwal, Maria

    2018-01-01

    Purpose: The purpose of this paper is to investigate empirically impacts of organizational knowledge-sharing practices (KSP) on employees' job satisfaction (JS), interpersonal adaptability (IA) and learning commitment (LC). Indirect effects of KSP on JS are also confirmed through mediating factors (LC and IA). Design/methodology/approach:…

  12. Theta synchronization between medial prefrontal cortex and cerebellum is associated with adaptive performance of associative learning behavior

    Science.gov (United States)

    Chen, Hao; Wang, Yi-jie; Yang, Li; Sui, Jian-feng; Hu, Zhi-an; Hu, Bo

    2016-01-01

    Associative learning is thought to require coordinated activities among distributed brain regions. For example, to direct behavior appropriately, the medial prefrontal cortex (mPFC) must encode and maintain sensory information and then interact with the cerebellum during trace eyeblink conditioning (TEBC), a commonly-used associative learning model. However, the mechanisms by which these two distant areas interact remain elusive. By simultaneously recording local field potential (LFP) signals from the mPFC and the cerebellum in guinea pigs undergoing TEBC, we found that theta-frequency (5.0–12.0 Hz) oscillations in the mPFC and the cerebellum became strongly synchronized following presentation of auditory conditioned stimulus. Intriguingly, the conditioned eyeblink response (CR) with adaptive timing occurred preferentially in the trials where mPFC-cerebellum theta coherence was stronger. Moreover, both the mPFC-cerebellum theta coherence and the adaptive CR performance were impaired after the disruption of endogenous orexins in the cerebellum. Finally, association of the mPFC -cerebellum theta coherence with adaptive CR performance was time-limited occurring in the early stage of associative learning. These findings suggest that the mPFC and the cerebellum may act together to contribute to the adaptive performance of associative learning behavior by means of theta synchronization. PMID:26879632

  13. Discriminating Children with Autism from Children with Learning Difficulties with an Adaptation of the Short Sensory Profile

    Science.gov (United States)

    O'Brien, Justin; Tsermentseli, Stella; Cummins, Omar; Happe, Francesca; Heaton, Pamela; Spencer, Janine

    2009-01-01

    In this article, we examine the extent to which children with autism and children with learning difficulties can be discriminated from their responses to different patterns of sensory stimuli. Using an adapted version of the Short Sensory Profile (SSP), sensory processing was compared in 34 children with autism to 33 children with typical…

  14. Learning-based adaptive prescribed performance control of postcapture space robot-target combination without inertia identifications

    Science.gov (United States)

    Wei, Caisheng; Luo, Jianjun; Dai, Honghua; Bian, Zilin; Yuan, Jianping

    2018-05-01

    In this paper, a novel learning-based adaptive attitude takeover control method is investigated for the postcapture space robot-target combination with guaranteed prescribed performance in the presence of unknown inertial properties and external disturbance. First, a new static prescribed performance controller is developed to guarantee that all the involved attitude tracking errors are uniformly ultimately bounded by quantitatively characterizing the transient and steady-state performance of the combination. Then, a learning-based supplementary adaptive strategy based on adaptive dynamic programming is introduced to improve the tracking performance of static controller in terms of robustness and adaptiveness only utilizing the input/output data of the combination. Compared with the existing works, the prominent advantage is that the unknown inertial properties are not required to identify in the development of learning-based adaptive control law, which dramatically decreases the complexity and difficulty of the relevant controller design. Moreover, the transient and steady-state performance is guaranteed a priori by designer-specialized performance functions without resorting to repeated regulations of the controller parameters. Finally, the three groups of illustrative examples are employed to verify the effectiveness of the proposed control method.

  15. Regulation of Emotions in Socially Challenging Learning Situations: An Instrument to Measure the Adaptive and Social Nature of the Regulation Process

    Science.gov (United States)

    Jarvenoja, Hanna; Volet, Simone; Jarvela, Sanna

    2013-01-01

    Self-regulated learning (SRL) research has conventionally relied on measures, which treat SRL as an aptitude. To study self-regulation and motivation in learning contexts as an ongoing adaptive process, situation-specific methods are needed in addition to static measures. This article presents an "Adaptive Instrument for Regulation of Emotions"…

  16. Seamless Integration of Desktop and Mobile Learning Experience through an Ontology-Based Adaptation Engine: Report of a Pilot-Project

    Science.gov (United States)

    Mercurio, Marco; Torre, Ilaria; Torsani, Simone

    2014-01-01

    The paper describes a module within the distance language learning environment of the Language Centre at the Genoa University which adapts, through an ontology, learning activities to the device in use. Adaptation means not simply resizing a page but also the ability to transform the nature of a task so that it fits the device with the smallest…

  17. Using an adapted form of the picture exchange communication system to increase independent requesting in deafblind adults with learning disabilities.

    Science.gov (United States)

    Bracken, Maeve; Rohrer, Nicole

    2014-02-01

    The current study assessed the effectiveness of an adapted form of the Picture Exchange Communication System (PECS) in increasing independent requesting in deafblind adults with learning disabilities. PECS cards were created to accommodate individual needs, including adaptations such as enlarging photographs and using swelled images which consisted of images created on raised line drawing paper. Training included up to Phase III of PECS and procedures ensuring generalizations across individuals and contexts were included. The effects of the intervention were evaluated using a multiple baseline design across participants. Results demonstrated an increase in independent requesting with each of the participants reaching mastery criterion. These results suggest that PECS, in combination with some minor adaptations, may be an effective communicative alternative for individuals who are deafblind and have learning impairments. Copyright © 2013 Elsevier Ltd. All rights reserved.

  18. SEL Ada reuse analysis and representations

    Science.gov (United States)

    Kester, Rush

    1990-01-01

    Overall, it was revealed that the pattern of Ada reuse has evolved from initial reuse of utility components into reuse of generalized application architectures. Utility components were both domain-independent utilities, such as queues and stacks, and domain-specific utilities, such as those that implement spacecraft orbit and attitude mathematical functions and physics or astronomical models. The level of reuse was significantly increased with the development of a generalized telemetry simulator architecture. The use of Ada generics significantly increased the level of verbatum reuse, which is due to the ability, using Ada generics, to parameterize the aspects of design that are configurable during reuse. A key factor in implementing generalized architectures was the ability to use generic subprogram parameters to tailor parts of the algorithm embedded within the architecture. The use of object oriented design (in which objects model real world entities) significantly improved the modularity for reuse. Encapsulating into packages the data and operations associated with common real world entities creates natural building blocks for reuse.

  19. Adaptive online monitoring for ICU patients by combining just-in-time learning and principal component analysis.

    Science.gov (United States)

    Li, Xuejian; Wang, Youqing

    2016-12-01

    Offline general-type models are widely used for patients' monitoring in intensive care units (ICUs), which are developed by using past collected datasets consisting of thousands of patients. However, these models may fail to adapt to the changing states of ICU patients. Thus, to be more robust and effective, the monitoring models should be adaptable to individual patients. A novel combination of just-in-time learning (JITL) and principal component analysis (PCA), referred to learning-type PCA (L-PCA), was proposed for adaptive online monitoring of patients in ICUs. JITL was used to gather the most relevant data samples for adaptive modeling of complex physiological processes. PCA was used to build an online individual-type model and calculate monitoring statistics, and then to judge whether the patient's status is normal or not. The adaptability of L-PCA lies in the usage of individual data and the continuous updating of the training dataset. Twelve subjects were selected from the Physiobank's Multi-parameter Intelligent Monitoring for Intensive Care II (MIMIC II) database, and five vital signs of each subject were chosen. The proposed method was compared with the traditional PCA and fast moving-window PCA (Fast MWPCA). The experimental results demonstrated that the fault detection rates respectively increased by 20 % and 47 % compared with PCA and Fast MWPCA. L-PCA is first introduced into ICU patients monitoring and achieves the best monitoring performance in terms of adaptability to changes in patient status and sensitivity for abnormality detection.

  20. ICT reuse in socio-economic enterprises.

    Science.gov (United States)

    Ongondo, F O; Williams, I D; Dietrich, J; Carroll, C

    2013-12-01

    In Europe, socio-economic enterprises such as charities, voluntary organisations and not-for-profit companies are involved in the repair, refurbishment and reuse of various products. This paper characterises and analyses the operations of socio-economic enterprises that are involved in the reuse of Information and Communication Technology (ICT) equipment. Using findings from a survey, the paper specifically analyses the reuse activities of socio-economic enterprises in the U.K. from which Europe-wide conclusions are drawn. The amount of ICT products handled by the reuse organisations is quantified and potential barriers and opportunities to their operations are analysed. By-products from reuse activities are discussed and recommendations to improve reuse activities are provided. The most common ICT products dealt with by socio-economic enterprises are computers and related equipment. In the U.K. in 2010, an estimated 143,750 appliances were reused. However, due to limitations in data, it is difficult to compare this number to the amount of new appliances that entered the U.K. market or the amount of waste electrical and electronic equipment generated in the same period. Difficulties in marketing products and numerous legislative requirements are the most common barriers to reuse operations. Despite various constraints, it is clear that organisations involved in reuse of ICT could contribute significantly to resource efficiency and a circular economy. It is suggested that clustering of their operations into "reuse parks" would enhance both their profile and their products. Reuse parks would also improve consumer confidence in and subsequently sales of the products. Further, it is advocated that industrial networking opportunities for the exchange of by-products resulting from the organisations' activities should be investigated. The findings make two significant contributions to the current literature. One, they provide a detailed insight into the reuse operations

  1. Re-use of disposable coil dialysers

    International Nuclear Information System (INIS)

    Abbud Filho, M.

    1980-01-01

    Re-use of disposable dialysers has been in practice for over 16 years throughout the world but it still is a polemical subject. The main justification for it is the reduction of costs in the hemodialytic treatment. We evaluated the technique of re-use that we adopt by studying 33 patients who should re-utilize coil dialysers for 8 consecutive hemodialysis sessions. We investigated: 1) small and middle molecules clearances trough a radioisotopic method; 2) the integrity of the system regarding bacterial invasion; 3) the frequency of anti-N antibodies; 4) aspects of scanning electron microscopy (SEM) of dialysis membrane after re-use. We observed no changes in the dialysers performance during re-use. We conclude that the re-use of dialyzers is feasible, without risks for the patients, allowing marked reduction of costs, thus making possible to offer treatment to a larger number of uremic patients. (author)

  2. Adaptation Challenges in Complex River Basins: Lessons Learned and Unlearned for the Colorado

    Science.gov (United States)

    Pulwarty, R. S.

    2008-12-01

    management (e.g. through metering and pricing), and institutional changes that improve the tradability of water rights. The co-evolution of climate history and adaptation did not start with the release of IPCC scenarios. The development of the Colorado River Basin was itself influenced by water resources planners from around the world (including the Middle East) in the late 1800s. As such lessons identified, but not always learned, abound. These hold considerable promise for water savings and the reallocation of water to highly valued uses. Supply-side strategies generally involve increases in storage capacity, abstraction from watercourses, and water transfers. Integrated water resources management provides an important governance framework to achieve adaptation measures across socio-economic, environmental and administrative systems. However, several paradoxes in water management and governance mitigate against the effectiveness of scientific information for meeting short term needs in the context of reducing longer-term vulnerabilities and for providing water to meet environmental needs. Consequently a complete analysis of the effects of climate change on human water uses would consider cross-sector interactions, including the impacts of changes in water use efficiency and intentional transfers of the use of water from one sector to another.

  3. In-Depth Case Studies of Superfund Reuse

    Science.gov (United States)

    SRI’s in-depth case studies explore Superfund reuse stories from start to finish. Their purpose is to see what redevelopment strategies worked, acknowledge reuse barriers and understand how communities overcame the barriers to create new reuse outcomes.

  4. Neuromodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control.

    Science.gov (United States)

    Dasgupta, Sakyasingha; Wörgötter, Florentin; Manoonpong, Poramate

    2014-01-01

    Goal-directed decision making in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). A number of computational and experimental studies have well established the role of the basal ganglia in reward-based learning, where as the cerebellum plays an important role in developing specific conditioned responses. Although viewed as distinct learning systems, recent animal experiments point toward their complementary role in behavioral learning, and also show the existence of substantial two-way communication between these two brain structures. Based on this notion of co-operative learning, in this paper we hypothesize that the basal ganglia and cerebellar learning systems work in parallel and interact with each other. We envision that such an interaction is influenced by reward modulated heterosynaptic plasticity (RMHP) rule at the thalamus, guiding the overall goal directed behavior. Using a recurrent neural network actor-critic model of the basal ganglia and a feed-forward correlation-based learning model of the cerebellum, we demonstrate that the RMHP rule can effectively balance the outcomes of the two learning systems. This is tested using simulated environments of increasing complexity with a four-wheeled robot in a foraging task in both static and dynamic configurations. Although modeled with a simplified level of biological abstraction, we clearly demonstrate that such a RMHP induced combinatorial learning mechanism, leads to stabler and faster learning of goal-directed behaviors, in comparison to the individual systems. Thus, in this paper we provide a computational model for adaptive combination of the basal ganglia and cerebellum learning systems by way of neuromodulated plasticity for goal-directed decision making in biological and bio-mimetic organisms.

  5. Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering.

    Science.gov (United States)

    Kneissler, Jan; Drugowitsch, Jan; Friston, Karl; Butz, Martin V

    2015-01-01

    Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF). PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than 10-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.

  6. Simultaneous Learning and Filtering without Delusions: A Bayes-Optimal Derivation of Combining Predictive Inference and AdaptiveFiltering

    Directory of Open Access Journals (Sweden)

    Jan eKneissler

    2015-04-01

    Full Text Available Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF. PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than ten-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.

  7. Adaptive Kalman filtering for histogram-based appearance learning in infrared imagery.

    Science.gov (United States)

    Venkataraman, Vijay; Fan, Guoliang; Havlicek, Joseph P; Fan, Xin; Zhai, Yan; Yeary, Mark B

    2012-11-01

    Targets of interest in video acquired from imaging infrared sensors often exhibit profound appearance variations due to a variety of factors, including complex target maneuvers, ego-motion of the sensor platform, background clutter, etc., making it difficult to maintain a reliable detection process and track lock over extended time periods. Two key issues in overcoming this problem are how to represent the target and how to learn its appearance online. In this paper, we adopt a recent appearance model that estimates the pixel intensity histograms as well as the distribution of local standard deviations in both the foreground and background regions for robust target representation. Appearance learning is then cast as an adaptive Kalman filtering problem where the process and measurement noise variances are both unknown. We formulate this problem using both covariance matching and, for the first time in a visual tracking application, the recent autocovariance least-squares (ALS) method. Although convergence of the ALS algorithm is guaranteed only for the case of globally wide sense stationary process and measurement noises, we demonstrate for the first time that the technique can often be applied with great effectiveness under the much weaker assumption of piecewise stationarity. The performance advantages of the ALS method relative to the classical covariance matching are illustrated by means of simulated stationary and nonstationary systems. Against real data, our results show that the ALS-based algorithm outperforms the covariance matching as well as the traditional histogram similarity-based methods, achieving sub-pixel tracking accuracy against the well-known AMCOM closure sequences and the recent SENSIAC automatic target recognition dataset.

  8. Adaptive high learning rate probabilistic disruption predictors from scratch for the next generation of tokamaks

    International Nuclear Information System (INIS)

    Vega, J.; Moreno, R.; Pereira, A.; Acero, A.; Murari, A.; Dormido-Canto, S.

    2014-01-01

    The development of accurate real-time disruption predictors is a pre-requisite to any mitigation action. Present theoretical models of disruptions do not reliably cope with the disruption issues. This article deals with data-driven predictors and a review of existing machine learning techniques, from both physics and engineering points of view, is provided. All these methods need large training datasets to develop successful predictors. However, ITER or DEMO cannot wait for hundreds of disruptions to have a reliable predictor. So far, the attempts to extrapolate predictors between different tokamaks have not shown satisfactory results. In addition, it is not clear how valid this approach can be between present devices and ITER/DEMO, due to the differences in their respective scales and possibly underlying physics. Therefore, this article analyses the requirements to create adaptive predictors from scratch to learn from the data of an individual machine from the beginning of operation. A particular algorithm based on probabilistic classifiers has been developed and it has been applied to the database of the three first ITER-like wall campaigns of JET (1036 non-disruptive and 201 disruptive discharges). The predictions start from the first disruption and only 12 re-trainings have been necessary as a consequence of missing 12 disruptions only. Almost 10 000 different predictors have been developed (they differ in their features) and after the chronological analysis of the 1237 discharges, the predictors recognize 94% of all disruptions with an average warning time (AWT) of 654 ms. This percentage corresponds to the sum of tardy detections (11%), valid alarms (76%) and premature alarms (7%). The false alarm rate is 4%. If only valid alarms are considered, the AWT is 244 ms and the standard deviation is 205 ms. The average probability interval about the reliability and accuracy of all the individual predictions is 0.811 ± 0.189. (paper)

  9. Adaptive high learning rate probabilistic disruption predictors from scratch for the next generation of tokamaks

    Science.gov (United States)

    Vega, J.; Murari, A.; Dormido-Canto, S.; Moreno, R.; Pereira, A.; Acero, A.; Contributors, JET-EFDA

    2014-12-01

    The development of accurate real-time disruption predictors is a pre-requisite to any mitigation action. Present theoretical models of disruptions do not reliably cope with the disruption issues. This article deals with data-driven predictors and a review of existing machine learning techniques, from both physics and engineering points of view, is provided. All these methods need large training datasets to develop successful predictors. However, ITER or DEMO cannot wait for hundreds of disruptions to have a reliable predictor. So far, the attempts to extrapolate predictors between different tokamaks have not shown satisfactory results. In addition, it is not clear how valid this approach can be between present devices and ITER/DEMO, due to the differences in their respective scales and possibly underlying physics. Therefore, this article analyses the requirements to create adaptive predictors from scratch to learn from the data of an individual machine from the beginning of operation. A particular algorithm based on probabilistic classifiers has been developed and it has been applied to the database of the three first ITER-like wall campaigns of JET (1036 non-disruptive and 201 disruptive discharges). The predictions start from the first disruption and only 12 re-trainings have been necessary as a consequence of missing 12 disruptions only. Almost 10 000 different predictors have been developed (they differ in their features) and after the chronological analysis of the 1237 discharges, the predictors recognize 94% of all disruptions with an average warning time (AWT) of 654 ms. This percentage corresponds to the sum of tardy detections (11%), valid alarms (76%) and premature alarms (7%). The false alarm rate is 4%. If only valid alarms are considered, the AWT is 244 ms and the standard deviation is 205 ms. The average probability interval about the reliability and accuracy of all the individual predictions is 0.811 ± 0.189.

  10. A learning heuristic for space mapping and searching self-organizing systems using adaptive mesh refinement

    Science.gov (United States)

    Phillips, Carolyn L.

    2014-09-01

    In a complex self-organizing system, small changes in the interactions between the system's components can result in different emergent macrostructures or macrobehavior. In chemical engineering and material science, such spontaneously self-assembling systems, using polymers, nanoscale or colloidal-scale particles, DNA, or other precursors, are an attractive way to create materials that are precisely engineered at a fine scale. Changes to the interactions can often be described by a set of parameters. Different contiguous regions in this parameter space correspond to different ordered states. Since these ordered states are emergent, often experiment, not analysis, is necessary to create a diagram of ordered states over the parameter space. By issuing queries to points in the parameter space (e.g., performing a computational or physical experiment), ordered states can be discovered and mapped. Queries can be costly in terms of resources or time, however. In general, one would like to learn the most information using the fewest queries. Here we introduce a learning heuristic for issuing queries to map and search a two-dimensional parameter space. Using a method inspired by adaptive mesh refinement, the heuristic iteratively issues batches of queries to be executed in parallel based on past information. By adjusting the search criteria, different types of searches (for example, a uniform search, exploring boundaries, sampling all regions equally) can be flexibly implemented. We show that this method will densely search the space, while preferentially targeting certain features. Using numerical examples, including a study simulating the self-assembly of complex crystals, we show how this heuristic can discover new regions and map boundaries more accurately than a uniformly distributed set of queries.

  11. Lessons Learned in Designing and Implementing a Computer-Adaptive Test for English

    Directory of Open Access Journals (Sweden)

    Jack Burston

    2014-09-01

    Full Text Available This paper describes the lessons learned in designing and implementing a computer-adaptive test (CAT for English. The early identification of students with weak L2 English proficiency is of critical importance in university settings that have compulsory English language course graduation requirements. The most efficient means of diagnosing the L2 English ability of incoming students is by means of a computer-based test since such evaluation can be administered quickly, automatically corrected, and the outcome known as soon as the test is completed. While the option of using a commercial CAT is available to institutions with the ability to pay substantial annual fees, or the means of passing these expenses on to their students, language instructors without these resources can only avail themselves of the advantages of CAT evaluation by creating their own tests.  As is demonstrated by the E-CAT project described in this paper, this is a viable alternative even for those lacking any computer programing expertise.  However, language teaching experience and testing expertise are critical to such an undertaking, which requires considerable effort and, above all, collaborative teamwork to succeed. A number of practical skills are also required. Firstly, the operation of a CAT authoring programme must be learned. Once this is done, test makers must master the art of creating a question database and assigning difficulty levels to test items. Lastly, if multimedia resources are to be exploited in a CAT, test creators need to be able to locate suitable copyright-free resources and re-edit them as needed.

  12. Arousal regulation and affective adaptation to human responsiveness by a robot that explores and learns a novel environment.

    Science.gov (United States)

    Hiolle, Antoine; Lewis, Matthew; Cañamero, Lola

    2014-01-01

    In the context of our work in developmental robotics regarding robot-human caregiver interactions, in this paper we investigate how a "baby" robot that explores and learns novel environments can adapt its affective regulatory behavior of soliciting help from a "caregiver" to the preferences shown by the caregiver in terms of varying responsiveness. We build on two strands of previous work that assessed independently (a) the differences between two "idealized" robot profiles-a "needy" and an "independent" robot-in terms of their use of a caregiver as a means to regulate the "stress" (arousal) produced by the exploration and learning of a novel environment, and (b) the effects on the robot behaviors of two caregiving profiles varying in their responsiveness-"responsive" and "non-responsive"-to the regulatory requests of the robot. Going beyond previous work, in this paper we (a) assess the effects that the varying regulatory behavior of the two robot profiles has on the exploratory and learning patterns of the robots; (b) bring together the two strands previously investigated in isolation and take a step further by endowing the robot with the capability to adapt its regulatory behavior along the "needy" and "independent" axis as a function of the varying responsiveness of the caregiver; and (c) analyze the effects that the varying regulatory behavior has on the exploratory and learning patterns of the adaptive robot.

  13. Adaptive e-learning to improve dietary behaviour: a systematic review and cost-effectiveness analysis.

    Science.gov (United States)

    Harris, J; Felix, L; Miners, A; Murray, E; Michie, S; Ferguson, E; Free, C; Lock, K; Landon, J; Edwards, P

    2011-10-01

    UK public health policy strongly advocates dietary change for the improvement of population health and emphasises the importance of individual empowerment to improve health. A new and evolving area in the promotion of dietary behavioural change is 'e-learning', the use of interactive electronic media to facilitate teaching and learning on a range of issues including health. The high level of accessibility, combined with emerging advances in computer processing power, data transmission and data storage, makes interactive e-learning a potentially powerful and cost-effective medium for improving dietary behaviour. This review aims to assess the effectiveness and cost-effectiveness of adaptive e-learning interventions for dietary behaviour change, and also to explore potential psychological mechanisms of action and components of effective interventions. Electronic bibliographic databases (Cumulative Index to Nursing and Allied Health Literature, The Cochrane Library, Dissertation Abstracts, EMBASE, Education Resources Information Center, Global Health, Health Economic Evaluations Database, Health Management Information Consortium, MEDLINE, PsycINFO and Web of Science) were searched for the period January 1990 to November 2009. Reference lists of included studies and previous reviews were also screened; authors were contacted and trial registers were searched. Studies were included if they were randomised controlled trials, involving participants aged ≥ 13 years, which evaluated the effectiveness of interactive software programs for improving dietary behaviour. Primary outcomes were measures of dietary behaviours, including estimated intakes or changes in intake of energy, nutrients, dietary fibre, foods or food groups. Secondary outcome measures were clinical outcomes such as anthropometry or blood biochemistry. Psychological mediators of dietary behaviour change were also investigated. Two review authors independently screened results and extracted data from

  14. Conflict Adaptation and Cue Competition during Learning in an Eriksen Flanker Task

    Science.gov (United States)

    Ghinescu, Rodica; Ramsey, Ashley K.; Gratton, Gabriele; Fabiani, Monica

    2016-01-01

    Two experiments investigated competition between cues that predicted the correct target response to a target stimulus in a response conflict procedure using a flanker task. Subjects received trials with five-character arrays with a central target character and distractor flanker characters that matched (compatible) or did not match (incompatible) the central target. Subjects’ expectancies for compatible and incompatible trials were manipulated by presenting pre-trial cues that signaled the occurrence of compatible or incompatible trials. On some trials, a single cue predicted the target stimulus and the required target response. On other trials, a second redundant, predictive cue was also present on such trials. The results showed an effect of competition between cues for control over strategic responding to the target stimuli, a finding that is predicted by associative learning theories. The finding of competition between pre-trial cues that predict incompatible trials, but not cues that predict compatible trials, suggests that different strategic processes may occur during adaptation to conflict when different kinds of trials are expected. PMID:27941977

  15. Adaptive metric learning with deep neural networks for video-based facial expression recognition

    Science.gov (United States)

    Liu, Xiaofeng; Ge, Yubin; Yang, Chao; Jia, Ping

    2018-01-01

    Video-based facial expression recognition has become increasingly important for plenty of applications in the real world. Despite that numerous efforts have been made for the single sequence, how to balance the complex distribution of intra- and interclass variations well between sequences has remained a great difficulty in this area. We propose the adaptive (N+M)-tuplet clusters loss function and optimize it with the softmax loss simultaneously in the training phrase. The variations introduced by personal attributes are alleviated using the similarity measurements of multiple samples in the feature space with many fewer comparison times as conventional deep metric learning approaches, which enables the metric calculations for large data applications (e.g., videos). Both the spatial and temporal relations are well explored by a unified framework that consists of an Inception-ResNet network with long short term memory and the two fully connected layer branches structure. Our proposed method has been evaluated with three well-known databases, and the experimental results show that our method outperforms many state-of-the-art approaches.

  16. Perceptron learning rule derived from spike-frequency adaptation and spike-time-dependent plasticity.

    Science.gov (United States)

    D'Souza, Prashanth; Liu, Shih-Chii; Hahnloser, Richard H R

    2010-03-09

    It is widely believed that sensory and motor processing in the brain is based on simple computational primitives rooted in cellular and synaptic physiology. However, many gaps remain in our understanding of the connections between neural computations and biophysical properties of neurons. Here, we show that synaptic spike-time-dependent plasticity (STDP) combined with spike-frequency adaptation (SFA) in a single neuron together approximate the well-known perceptron learning rule. Our calculations and integrate-and-fire simulations reveal that delayed inputs to a neuron endowed with STDP and SFA precisely instruct neural responses to earlier arriving inputs. We demonstrate this mechanism on a developmental example of auditory map formation guided by visual inputs, as observed in the external nucleus of the inferior colliculus (ICX) of barn owls. The interplay of SFA and STDP in model ICX neurons precisely transfers the tuning curve from the visual modality onto the auditory modality, demonstrating a useful computation for multimodal and sensory-guided processing.

  17. An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning

    Directory of Open Access Journals (Sweden)

    Xiaohui Yan

    2012-01-01

    Full Text Available Bacterial Foraging Algorithm (BFO is a recently proposed swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. However, its optimization ability is not so good compared with other classic algorithms as it has several shortages. This paper presents an improved BFO Algorithm. In the new algorithm, a lifecycle model of bacteria is founded. The bacteria could split, die, or migrate dynamically in the foraging processes, and population size varies as the algorithm runs. Social learning is also introduced so that the bacteria will tumble towards better directions in the chemotactic steps. Besides, adaptive step lengths are employed in chemotaxis. The new algorithm is named BFOLS and it is tested on a set of benchmark functions with dimensions of 2 and 20. Canonical BFO, PSO, and GA algorithms are employed for comparison. Experiment results and statistic analysis show that the BFOLS algorithm offers significant improvements than original BFO algorithm. Particulary with dimension of 20, it has the best performance among the four algorithms.

  18. Water Reuse in Brazilian Manufacturing Firms

    OpenAIRE

    José Féres; Arnaud Reynaud; Alban Thomas

    2015-01-01

    This paper examines the factors influencing water reuse in manufacturing firms and analyzes whether the structure of intake water demand differs between firms that adopt water reuse practices and those which do not. To this purpose, we estimate a two-stage econometric model based on a sample of 447 industrial facilities located in the Paraíba do Sul river basin. The first stage applies a probit model for the water reuse decision and the second stage employs an endogenous switching regression ...

  19. The software-cycle model for re-engineering and reuse

    Science.gov (United States)

    Bailey, John W.; Basili, Victor R.

    1992-01-01

    This paper reports on the progress of a study which will contribute to our ability to perform high-level, component-based programming by describing means to obtain useful components, methods for the configuration and integration of those components, and an underlying economic model of the costs and benefits associated with this approach to reuse. One goal of the study is to develop and demonstrate methods to recover reusable components from domain-specific software through a combination of tools, to perform the identification, extraction, and re-engineering of components, and domain experts, to direct the applications of those tools. A second goal of the study is to enable the reuse of those components by identifying techniques for configuring and recombining the re-engineered software. This component-recovery or software-cycle model addresses not only the selection and re-engineering of components, but also their recombination into new programs. Once a model of reuse activities has been developed, the quantification of the costs and benefits of various reuse options will enable the development of an adaptable economic model of reuse, which is the principal goal of the overall study. This paper reports on the conception of the software-cycle model and on several supporting techniques of software recovery, measurement, and reuse which will lead to the development of the desired economic model.

  20. Sparing of descending axons rescues interneuron plasticity in the lumbar cord to allow adaptive learning after thoracic spinal cord injury

    Directory of Open Access Journals (Sweden)

    Christopher Nelson Hansen

    2016-03-01

    Full Text Available This study evaluated the role of spared axons on structural and behavioral neuroplasticity in the lumbar enlargement after a thoracic spinal cord injury (SCI. Previous work has demonstrated that recovery in the presence of spared axons after an incomplete lesion increases behavioral output after a subsequent complete spinal cord transection (TX. This suggests that spared axons direct adaptive changes in below-level neuronal networks of the lumbar cord. In response to spared fibers, we postulate that lumbar neuron networks support behavioral gains by preventing aberrant plasticity. As such, the present study measured histological and functional changes in the isolated lumbar cord after complete TX or incomplete contusion (SCI. To measure functional plasticity in the lumbar cord, we used an established instrumental learning paradigm. In this paradigm, neural circuits within isolated lumbar segments demonstrate learning by an increase in flexion duration that reduces exposure to a noxious leg shock. We employed this model using a proof-of-principle design to evaluate the role of sparing on lumbar learning and plasticity early (7 days or late (42 days after midthoracic SCI in a rodent model. Early after SCI or TX at 7d, spinal learning was unattainable regardless of whether the animal recovered with or without axonal substrate. Failed learning occurred alongside measures of cell soma atrophy and aberrant dendritic spine expression within interneuron populations responsible for sensorimotor integration and learning. Alternatively, exposure of the lumbar cord to a small amount of spared axons for 6 weeks produced near-normal learning late after SCI. This coincided with greater cell soma volume and fewer aberrant dendritic spines on interneurons. Thus, an opportunity to influence activity-based learning in locomotor networks depends on spared axons limiting maladaptive plasticity. Together, this work identifies a time dependent interaction between

  1. An empirical analysis of ontology reuse in BioPortal.

    Science.gov (United States)

    Ochs, Christopher; Perl, Yehoshua; Geller, James; Arabandi, Sivaram; Tudorache, Tania; Musen, Mark A

    2017-07-01

    Biomedical ontologies often reuse content (i.e., classes and properties) from other ontologies. Content reuse enables a consistent representation of a domain and reusing content can save an ontology author significant time and effort. Prior studies have investigated the existence of reused terms among the ontologies in the NCBO BioPortal, but as of yet there has not been a study investigating how the ontologies in BioPortal utilize reused content in the modeling of their own content. In this study we investigate how 355 ontologies hosted in the NCBO BioPortal reuse content from other ontologies for the purposes of creating new ontology content. We identified 197 ontologies that reuse content. Among these ontologies, 108 utilize reused classes in the modeling of their own classes and 116 utilize reused properties in class restrictions. Current utilization of reuse and quality issues related to reuse are discussed. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. Reuse of Hydrotreating Spent Catalyst

    International Nuclear Information System (INIS)

    Habib, A.M.; Menoufy, M.F.; Amhed, S.H.

    2004-01-01

    All hydro treating catalysts used in petroleum refining processes gradually lose activity through coking, poisoning by metal, sulfur or halides or lose surface area from sintering at high process temperatures. Waste hydrotreating catalyst, which have been used in re-refining of waste lube oil at Alexandria Petroleum Company (after 5 years lifetime) compared with the same fresh catalyst were used in the present work. Studies are conducted on partial extraction of the active metals of spent catalyst (Mo and Ni) using three leaching solvents,4% oxidized oxalic acid, 10% aqueous sodium hydroxide and 10% citric acid. The leaching experiments are conducting on the de coked extrude [un crushed] spent catalyst samples. These steps are carried out in order to rejuvenate the spent catalyst to be reused in other reactions. The results indicated that 4% oxidized oxalic acid leaching solution gave total metal removal 45.6 for de coked catalyst samples while NaOH gave 35% and citric acid gave 31.9 % The oxidized leaching agent was the most efficient leaching solvent to facilitate the metal removal, and the rejuvenated catalyst was characterized by the unchanged crystalline phase The rejuvenated catalyst was applied for hydrodesulfurization (HDS) of vacuum gas oil as a feedstock, under different hydrogen pressure 20-80 bar in order to compare its HDS activity

  3. DECONTAMINATION TECHNOLOGIES FOR FACILITY REUSE

    International Nuclear Information System (INIS)

    Bossart, Steven J.; Blair, Danielle M.

    2003-01-01

    As nuclear research and production facilities across the U.S. Department of Energy (DOE) nuclear weapons complex are slated for deactivation and decommissioning (D and D), there is a need to decontaminate some facilities for reuse for another mission or continued use for the same mission. Improved technologies available in the commercial sector and tested by the DOE can help solve the DOE's decontamination problems. Decontamination technologies include mechanical methods, such as shaving, scabbling, and blasting; application of chemicals; biological methods; and electrochemical techniques. Materials to be decontaminated are primarily concrete or metal. Concrete materials include walls, floors, ceilings, bio-shields, and fuel pools. Metallic materials include structural steel, valves, pipes, gloveboxes, reactors, and other equipment. Porous materials such as concrete can be contaminated throughout their structure, although contamination in concrete normally resides in the top quarter-inch below the surface. Metals are normally only contaminated on the surface. Contamination includes a variety of alpha, beta, and gamma-emitting radionuclides and can sometimes include heavy metals and organic contamination regulated by the Resource Conservation and Recovery Act (RCRA). This paper describes several advanced mechanical, chemical, and other methods to decontaminate structures, equipment, and materials

  4. A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system.

    Science.gov (United States)

    Chrysafiadi, Konstantina; Virvou, Maria

    2013-12-01

    In this paper a knowledge representation approach of an adaptive and/or personalized tutoring system is presented. The domain knowledge should be represented in a more realistic way in order to allow the adaptive and/or personalized tutoring system to deliver the learning material to each individual learner dynamically taking into account her/his learning needs and her/his different learning pace. To succeed this, the domain knowledge representation has to depict the possible increase or decrease of the learner's knowledge. Considering that the domain concepts that constitute the learning material are not independent from each other, the knowledge representation approach has to allow the system to recognize either the domain concepts that are already partly or completely known for a learner, or the domain concepts that s/he has forgotten, taking into account the learner's knowledge level of the related concepts. In other words, the system should be informed about the knowledge dependencies that exist among the domain concepts of the learning material, as well as the strength on impact of each domain concept on others. Fuzzy Cognitive Maps (FCMs) seem to be an ideal way for representing graphically this kind of information. The suggested knowledge representation approach has been implemented in an e-learning adaptive system for teaching computer programming. The particular system was used by the students of a postgraduate program in the field of Informatics in the University of Piraeus and was compared with a corresponding system, in which the domain knowledge was represented using the most common used technique of network of concepts. The results of the evaluation were very encouraging.

  5. Towards a comprehensive framework for reuse: A reuse-enabling software evolution environment

    Science.gov (United States)

    Basili, V. R.; Rombach, H. D.

    1988-01-01

    Reuse of products, processes and knowledge will be the key to enable the software industry to achieve the dramatic improvement in productivity and quality required to satisfy the anticipated growing demand. Although experience shows that certain kinds of reuse can be successful, general success has been elusive. A software life-cycle technology which allows broad and extensive reuse could provide the means to achieving the desired order-of-magnitude improvements. The scope of a comprehensive framework for understanding, planning, evaluating and motivating reuse practices and the necessary research activities is outlined. As a first step towards such a framework, a reuse-enabling software evolution environment model is introduced which provides a basis for the effective recording of experience, the generalization and tailoring of experience, the formalization of experience, and the (re-)use of experience.

  6. Applying Adaptive Swarm Intelligence Technology with Structuration in Web-Based Collaborative Learning

    Science.gov (United States)

    Huang, Yueh-Min; Liu, Chien-Hung

    2009-01-01

    One of the key challenges in the promotion of web-based learning is the development of effective collaborative learning environments. We posit that the structuration process strongly influences the effectiveness of technology used in web-based collaborative learning activities. In this paper, we propose an ant swarm collaborative learning (ASCL)…

  7. Water brief-WDM & wastewater reuse

    International Development Research Centre (IDRC) Digital Library (Canada)

    aalfouns

    Wastewater Reuse for Water Demand Management in the Middle East and ... Among the substantial WDM tools in MENA is the use of wastewater to reduce the pressure on scarce freshwater .... recycled water to irrigate crops with associated ...

  8. the greywater reuse case of Jordan

    International Development Research Centre (IDRC) Digital Library (Canada)

    Fox Run Craftsmen

    2003-03-06

    Mar 6, 2003 ... new and creative methods and systems of dealing with wastewater reuse. .... and attended by 35 individuals representing 8 different agencies. .... Water and Irrigation calls for covering the operation and maintenance costs for.

  9. Treated Wastewater Reuse on Potato (Solanum Tuberosum)

    DEFF Research Database (Denmark)

    Battilani, A; Plauborg, Finn; Andersen, Mathias Neumann

    2014-01-01

    A field experiment was carried out in Northern Italy (Po Valley), within the frame of the EU project SAFIR, to asses the impact of treated wastewater reuse on potato yield, quality and hygiene. The potato crop was drip irrigated and fertigated. Wastewater produced by small communities (≤2000 EI......) was treated by Membrane Bio Reactor (MBR) technology and gravel filter (FTS) during three cropping seasons. Treated wastewater, soil and tubers were analysed for the faecal indicator bacterium E. coli and heavy metals contents. Potato total yield was similar for tap and reused water, while the marketable...... production has been found higher with the latter. The tuber dry matter content as well as reducing sugars were not affected by reused water. Total sugars content was higher with MBR and FTS water. Water use efficiency (WUE) was significantly higher with reused water. Compared to tap water, crop gross margin...

  10. Public opinion on water reuse options

    International Nuclear Information System (INIS)

    Bruvold, W.H.

    1988-01-01

    Public policy on waste water reuse options must be informed by public opinion because it is the public who must pay the cost of developing the option and who will be served by the option in the future. For public policy on reuse, guidance for innovative reuse is not as simple as first believed. It seems that public opinion regarding actual community reuse options is affected by the linkage of several factors, including water conservation, health protection, treatment and distribution costs, and environmental enhancement. Probability sampling was used in 7 studies to select respondents who were queried regarding their opinions on various reclaimed water uses such as ranging from cooling tower water to full domestic use. These 7 are briefly reviewed

  11. Decontamination and reuse of ORGDP aluminum scrap

    International Nuclear Information System (INIS)

    Compere, A.L.; Griffith, W.L.; Hayden, H.W.; Wilson, D.F.

    1996-12-01

    The Gaseous Diffusion Plants, or GDPs, have significant amounts of a number of metals, including nickel, aluminum, copper, and steel. Aluminum was used extensively throughout the GDPs because of its excellent strength to weight ratios and good resistance to corrosion by UF 6 . This report is concerned with the recycle of aluminum stator and rotor blades from axial compressors. Most of the stator and rotor blades were made from 214-X aluminum casting alloy. Used compressor blades were contaminated with uranium both as a result of surface contamination and as an accumulation held in surface-connected voids inside of the blades. A variety of GDP studies were performed to evaluate the amounts of uranium retained in the blades; the volume, area, and location of voids in the blades; and connections between surface defects and voids. Based on experimental data on deposition, uranium content of the blades is 0.3%, or roughly 200 times the value expected from blade surface area. However, this value does correlate with estimated internal surface area and with lengthy deposition times. Based on a literature search, it appears that gaseous decontamination or melt refining using fluxes specific for uranium removal have the potential for removing internal contamination from aluminum blades. A melt refining process was used to recycle blades during the 1950s and 1960s. The process removed roughly one-third of the uranium from the blades. Blade cast from recycled aluminum appeared to perform as well as blades from virgin material. New melt refining and gaseous decontamination processes have been shown to provide substantially better decontamination of pure aluminum. If these techniques can be successfully adapted to treat aluminum 214-X alloy, internal and, possibly, external reuse of aluminum alloys may be possible

  12. Revisiting Reuse in Main Memory Database Systems

    OpenAIRE

    Dursun, Kayhan; Binnig, Carsten; Cetintemel, Ugur; Kraska, Tim

    2016-01-01

    Reusing intermediates in databases to speed-up analytical query processing has been studied in the past. Existing solutions typically require intermediate results of individual operators to be materialized into temporary tables to be considered for reuse in subsequent queries. However, these approaches are fundamentally ill-suited for use in modern main memory databases. The reason is that modern main memory DBMSs are typically limited by the bandwidth of the memory bus, thus query execution ...

  13. Beneficial Reuse of Produced and Flowback Water

    Science.gov (United States)

    Water reuse and recycling is a significant issue in the development of oil and gas shale plays in the United StatesDrilling operations – 60,000 to 650,000 gallons per wellHydraulic fracturing operations – 3 million to 5 million gallons per wellDefinition of produced water and flowback waterInteractions of water quality constituents as they relate to water reuse and recyclingTesting criteria in the laboratory and field operations

  14. Controlling the chaotic discrete-Hénon system using a feedforward neural network with an adaptive learning rate

    OpenAIRE

    GÖKCE, Kürşad; UYAROĞLU, Yılmaz

    2013-01-01

    This paper proposes a feedforward neural network-based control scheme to control the chaotic trajectories of a discrete-Hénon map in order to stay within an acceptable distance from the stable fixed point. An adaptive learning back propagation algorithm with online training is employed to improve the effectiveness of the proposed method. The simulation study carried in the discrete-Hénon system verifies the validity of the proposed control system.

  15. Individual Learner and Team Modeling for Adaptive Training and Education in Support of the US Army Learning Model: Research Outline

    Science.gov (United States)

    2015-09-01

    system to include the learner, domain, and pedagogical models needed to deliver this training via an ITS. 4 3.1 Self-Regulated Learning and the US...elements and also to highlight their relationships : Adaptive Tutoring: also known as intelligent tutoring; tailored instructional methods to...asserts that through the use of case study examples, instruction can provide the pedagogical foundation for decision-making under uncertainty

  16. Collaborative adaptations in social work intervention research in real-world settings: lessons learned from the field.

    Science.gov (United States)

    Blank Wilson, Amy; Farkas, Kathleen

    2014-01-01

    Social work research has identified the crucial role that service practitioners play in the implementation of evidence-based practices. This has led some researchers to suggest that intervention research needs to incorporate collaborative adaptation strategies in the design and implementation of studies focused on adapting evidence-based practices to real-world practice settings. This article describes a collaborative approach to service adaptations that was used in an intervention study that integrated evidence-based mental health and correctional services in a jail reentry program for people with serious mental illness. This description includes a discussion of the nature of the collaboration engaged in this study, the implementation strategies that were used to support this collaboration, and the lessons that the research team has learned about engaging a collaborative approach to implementing interventions in research projects being conducted in real-world social service delivery settings.

  17. Recent practices on wastewater reuse in Turkey.

    Science.gov (United States)

    Tanik, A; Ekdal, A; Germirli Babuna, F; Orhon, D

    2005-01-01

    Reuse of wastewater for irrigational purposes in agriculture has been a widely applied practice all around the world compared to such applications in industries. In most of the developing countries, high costs of wastewater treatment stimulate the direct reuse of raw or partly treated effluent in irrigation despite the socio-cultural objections in some countries regarding religious rituals towards consuming wastewater. In Turkey, reuse applications in agriculture have been in use by indirect application by means of withdrawing water from the downstream end of treatment plants. Such practices affected the deterioration of surface water resources due to the lack of water quality monitoring and control. However, more conscious and planned reuse activities in agriculture have recently started by the operation of urban wastewater treatment plants. Turkey does not face any severe water scarcity problems for the time being, but as the water resources show the signs of water quality deterioration it seems to be one of the priority issues in the near future. The industrial reuse activities are only at the research stage especially in industries consuming high amounts of water. In-plant control implementation is the preferred effort of minimizing water consumption in such industries. The current reuse activities are outlined in the article forming an example from a developing country.

  18. Fast adaptation of the internal model of gravity for manual interceptions: evidence for event-dependent learning.

    Science.gov (United States)

    Zago, Myrka; Bosco, Gianfranco; Maffei, Vincenzo; Iosa, Marco; Ivanenko, Yuri P; Lacquaniti, Francesco

    2005-02-01

    We studied how subjects learn to deal with two conflicting sensory environments as a function of the probability of each environment and the temporal distance between repeated events. Subjects were asked to intercept a visual target moving downward on a screen with randomized laws of motion. We compared five protocols that differed in the probability of constant speed (0g) targets and accelerated (1g) targets. Probability ranged from 9 to 100%, and the time interval between consecutive repetitions of the same target ranged from about 1 to 20 min. We found that subjects systematically timed their responses consistent with the assumption of gravity effects, for both 1 and 0g trials. With training, subjects rapidly adapted to 0g targets by shifting the time of motor activation. Surprisingly, the adaptation rate was independent of both the probability of 0g targets and their temporal distance. Very few 0g trials sporadically interspersed as catch trials during immersive practice with 1g trials were sufficient for learning and consolidation in long-term memory, as verified by retesting after 24 h. We argue that the memory store for adapted states of the internal gravity model is triggered by individual events and can be sustained for prolonged periods of time separating sporadic repetitions. This form of event-related learning could depend on multiple-stage memory, with exponential rise and decay in the initial stages followed by a sample-and-hold module.

  19. Effects of the amount and schedule of varied practice after constant practice on the adaptive process of motor learning

    Directory of Open Access Journals (Sweden)

    Umberto Cesar Corrêa

    2014-12-01

    Full Text Available This study investigated the effects of different amounts and schedules of varied practice, after constant practice, on the adaptive process of motor learning. Participants were one hundred and seven children with a mean age of 11.1 ± 0.9 years. Three experiments were carried out using a complex anticipatory timing task manipulating the following components in the varied practice: visual stimulus speed (experiment 1; sequential response pattern (experiment 2; and visual stimulus speed plus sequential response pattern (experiment 3. In all experiments the design involved three amounts (18, 36, and 63 trials, and two schedules (random and blocked of varied practice. The experiments also involved two learning phases: stabilization and adaptation. The dependent variables were the absolute, variable, and constant errors related to the task goal, and the relative timing of the sequential response. Results showed that all groups worsened the performances in the adaptation phase, and no difference was observed between them. Altogether, the results of the three experiments allow the conclusion that the amounts of trials manipulated in the random and blocked practices did not promote the diversification of the skill since no adaptation was observed.

  20. Software Reuse Within the Earth Science Community

    Science.gov (United States)

    Marshall, James J.; Olding, Steve; Wolfe, Robert E.; Delnore, Victor E.

    2006-01-01

    Scientific missions in the Earth sciences frequently require cost-effective, highly reliable, and easy-to-use software, which can be a challenge for software developers to provide. The NASA Earth Science Enterprise (ESE) spends a significant amount of resources developing software components and other software development artifacts that may also be of value if reused in other projects requiring similar functionality. In general, software reuse is often defined as utilizing existing software artifacts. Software reuse can improve productivity and quality while decreasing the cost of software development, as documented by case studies in the literature. Since large software systems are often the results of the integration of many smaller and sometimes reusable components, ensuring reusability of such software components becomes a necessity. Indeed, designing software components with reusability as a requirement can increase the software reuse potential within a community such as the NASA ESE community. The NASA Earth Science Data Systems (ESDS) Software Reuse Working Group is chartered to oversee the development of a process that will maximize the reuse potential of existing software components while recommending strategies for maximizing the reusability potential of yet-to-be-designed components. As part of this work, two surveys of the Earth science community were conducted. The first was performed in 2004 and distributed among government employees and contractors. A follow-up survey was performed in 2005 and distributed among a wider community, to include members of industry and academia. The surveys were designed to collect information on subjects such as the current software reuse practices of Earth science software developers, why they choose to reuse software, and what perceived barriers prevent them from reusing software. In this paper, we compare the results of these surveys, summarize the observed trends, and discuss the findings. The results are very

  1. Adaptive social learning strategies in temporally and spatially varying environments : how temporal vs. spatial variation, number of cultural traits, and costs of learning influence the evolution of conformist-biased transmission, payoff-biased transmission, and individual learning.

    Science.gov (United States)

    Nakahashi, Wataru; Wakano, Joe Yuichiro; Henrich, Joseph

    2012-12-01

    Long before the origins of agriculture human ancestors had expanded across the globe into an immense variety of environments, from Australian deserts to Siberian tundra. Survival in these environments did not principally depend on genetic adaptations, but instead on evolved learning strategies that permitted the assembly of locally adaptive behavioral repertoires. To develop hypotheses about these learning strategies, we have modeled the evolution of learning strategies to assess what conditions and constraints favor which kinds of strategies. To build on prior work, we focus on clarifying how spatial variability, temporal variability, and the number of cultural traits influence the evolution of four types of strategies: (1) individual learning, (2) unbiased social learning, (3) payoff-biased social learning, and (4) conformist transmission. Using a combination of analytic and simulation methods, we show that spatial-but not temporal-variation strongly favors the emergence of conformist transmission. This effect intensifies when migration rates are relatively high and individual learning is costly. We also show that increasing the number of cultural traits above two favors the evolution of conformist transmission, which suggests that the assumption of only two traits in many models has been conservative. We close by discussing how (1) spatial variability represents only one way of introducing the low-level, nonadaptive phenotypic trait variation that so favors conformist transmission, the other obvious way being learning errors, and (2) our findings apply to the evolution of conformist transmission in social interactions. Throughout we emphasize how our models generate empirical predictions suitable for laboratory testing.

  2. An adaptive multi-agent memetic system for personalizing e-learning experiences

    NARCIS (Netherlands)

    Acampora, G.; Gaeta, M.; Munoz, E.; Vitiello, A.

    2011-01-01

    The rapid changes in modern knowledge, due to exponential growth of information sources, are complicating learners' activity. For this reason, novel approaches are necessary to obtain suitable learning solutions able to generate efficient, personalized and flexible learning experiences. From this

  3. Revisiting the Blended Learning Literature: Using a Complex Adaptive Systems Framework

    Science.gov (United States)

    Wang, Yuping; Han, Xibin; Yang, Juan

    2015-01-01

    This research has two aims: (1) to bridge a gap in blended learning research--the lack of a systems approach to the understanding of blended learning research and practice, and (2) to promote a more comprehensive understanding of what has been achieved and what needs to be achieved in blended learning research and practice. To achieve these aims,…

  4. QoS-based experience-aware adaptation in multimedia e-learning - A learner, is a learner, is a user, is a customer

    OpenAIRE

    Moebs, Sabine

    2011-01-01

    One of the challenges for the future of technology-enhanced learning is the retention of learners. On-line learning environments should engage learners and provide an appropriate “Quality of Experience” (QoE). For more than a decade, adaptive hypermedia systems have been used to adapt content and instruction to individual knowledge, goals and preferences in an effort to engage learners. However, even if the content is highly engaging it can be very difficult to achieve good Quality ...

  5. Transfer of memory trace of cerebellum-dependent motor learning in human prism adaptation: a model study.

    Science.gov (United States)

    Nagao, Soichi; Honda, Takeru; Yamazaki, Tadashi

    2013-11-01

    Accumulating experimental evidence suggests that the memory trace of ocular reflex adaptation is initially encoded in the cerebellar cortex, and later transferred to the cerebellar nuclei for consolidation through repetitions of training. However, the memory transfer is not well characterized in the learning of voluntary movement. Here, we implement our model of memory transfer to interpret the data of prism adaptation (Martin, Keating, Goodkin, Bastian, & Thach, 1996a, 1996b), assuming that the cerebellar nuclear memory formed by memory transfer is used for normal throwing. When the subject was trained to throw darts wearing prisms in 30-40 trials, the short-term memory for recalibrating the throwing direction by gaze would be formed in the cerebellar cortex, which was extinguished by throwing with normal vision in a similar number of trials. After weeks of repetitions of short-term prism adaptation, the long-term memory would be formed in the cerebellar nuclei through memory transfer, which enabled one to throw darts to the center wearing prisms without any training. These two long-term memories, one for throwing with normal vision and the other for throwing wearing prisms, are assumed to be utilized automatically under volitional control. Moreover, when the prisms were changed to new prisms, a new memory for adapting to the new prisms would be formed in the cerebellar cortex, just to counterbalance the nuclear memory of long-term adaptation to the original prisms in a similar number of trials. These results suggest that memory transfer may occur in the learning of voluntary movements. Copyright © 2013 Elsevier Ltd. All rights reserved.

  6. Adaptive Reuse: Alternative to Vacant Schools. Kalamazoo: Bread and Roses.

    Science.gov (United States)

    Rapley, Frank E.

    1984-01-01

    A Michigan law allows school districts to make energy conservation improvements and pay for them with savings generated by lowered energy consumption. A 71-year-old school, now the Community Education Center of Kalamazoo, Michigan, also serves as the base for a centralized energy management system for the school district. (MLF)

  7. Cerebellar and prefrontal cortex contributions to adaptation, strategies, and reinforcement learning.

    Science.gov (United States)

    Taylor, Jordan A; Ivry, Richard B

    2014-01-01

    Traditionally, motor learning has been studied as an implicit learning process, one in which movement errors are used to improve performance in a continuous, gradual manner. The cerebellum figures prominently in this literature given well-established ideas about the role of this system in error-based learning and the production of automatized skills. Recent developments have brought into focus the relevance of multiple learning mechanisms for sensorimotor learning. These include processes involving repetition, reinforcement learning, and strategy utilization. We examine these developments, considering their implications for understanding cerebellar function and how this structure interacts with other neural systems to support motor learning. Converging lines of evidence from behavioral, computational, and neuropsychological studies suggest a fundamental distinction between processes that use error information to improve action execution or action selection. While the cerebellum is clearly linked to the former, its role in the latter remains an open question. © 2014 Elsevier B.V. All rights reserved.

  8. Innovations in advanced technology for learning : authoring for adaptive educational hypermedia

    NARCIS (Netherlands)

    Cristea, A.I.

    2004-01-01

    Invited editor's note. - Why should we look at the authoring process in adaptive educational hypermedia design? How does detecting authoring patterns help the process? Why do we need to consider cognitive styles in adaptive hypermedia? What do these seemingly unrelated topics have in common? These

  9. ADAPTATION OF THE STUDENTS' MOTIVATION TOWARDS SCIENCE LEARNING QUESTIONNAIRE TO MEASURE GREEK STUDENTS’ MOTIVATION TOWARDS BIOLOGY LEARNING

    OpenAIRE

    Andressa, Helen; Mavrikaki, Evangelia; Dermitzaki, Irini

    2015-01-01

    The purpose of this study was to investigate students’ motivation towards biology learning and to determine the factors that are related to it: students’ gender and their parents’ occupation (relevant with biology or not) were investigated. The sample of the study consisted of 360 Greek high school students of the 10th grade (178 boys and 182 girls). The data were collected through Students’ Motivation Toward Science Learning (SMTSL) questionnaire. It was found that it was a valid and reliabl...

  10. A cerebellar learning model of vestibulo-ocular reflex adaptation in wild-type and mutant mice.

    Science.gov (United States)

    Clopath, Claudia; Badura, Aleksandra; De Zeeuw, Chris I; Brunel, Nicolas

    2014-05-21

    Mechanisms of cerebellar motor learning are still poorly understood. The standard Marr-Albus-Ito theory posits that learning involves plasticity at the parallel fiber to Purkinje cell synapses under control of the climbing fiber input, which provides an error signal as in classical supervised learning paradigms. However, a growing body of evidence challenges this theory, in that additional sites of plasticity appear to contribute to motor adaptation. Here, we consider phase-reversal training of the vestibulo-ocular reflex (VOR), a simple form of motor learning for which a large body of experimental data is available in wild-type and mutant mice, in which the excitability of granule cells or inhibition of Purkinje cells was affected in a cell-specific fashion. We present novel electrophysiological recordings of Purkinje cell activity measured in naive wild-type mice subjected to this VOR adaptation task. We then introduce a minimal model that consists of learning at the parallel fibers to Purkinje cells with the help of the climbing fibers. Although the minimal model reproduces the behavior of the wild-type animals and is analytically tractable, it fails at reproducing the behavior of mutant mice and the electrophysiology data. Therefore, we build a detailed model involving plasticity at the parallel fibers to Purkinje cells' synapse guided by climbing fibers, feedforward inhibition of Purkinje cells, and plasticity at the mossy fiber to vestibular nuclei neuron synapse. The detailed model reproduces both the behavioral and electrophysiological data of both the wild-type and mutant mice and allows for experimentally testable predictions. Copyright © 2014 the authors 0270-6474/14/347203-13$15.00/0.

  11. CO2 Capture and Reuse

    International Nuclear Information System (INIS)

    Thambimuthu, K.; Gupta, M.; Davison, J.

    2003-01-01

    CO2 capture and storage including its utilization or reuse presents an opportunity to achieve deep reductions in greenhouse gas emissions from fossil energy use. The development and deployment of this option could significantly assist in meeting a future goal of achieving stabilization of the presently rising atmospheric concentration of greenhouse gases. CO2 capture from process streams is an established concept that has achieved industrial practice. Examples of current applications include the use of primarily, solvent based capture technologies for the recovery of pure CO2 streams for chemical synthesis, for utilization as a food additive, for use as a miscible agent in enhanced oil recovery operations and removal of CO2 as an undesired contaminant from gaseous process streams for the production of fuel gases such as hydrogen and methane. In these applications, the technologies deployed for CO2 capture have focused on gas separation from high purity, high pressure streams and in reducing (or oxygen deficient) environments, where the energy penalties and cost for capture are moderately low. However, application of the same capture technologies for large scale abatement of greenhouse gas emissions from fossil fuel use poses significant challenges in achieving (at comparably low energy penalty and cost) gas separation in large volume, dilute concentration and/or low pressure flue gas streams. This paper will focus on a review of existing commercial methods of CO2 capture and the technology stretch, process integration and energy system pathways needed for their large scale deployment in fossil fueled processes. The assessment of potential capture technologies for the latter purpose will also be based on published literature data that are both 'transparent' and 'systematic' in their evaluation of the overall cost and energy penalties of CO2 capture. In view of the of the fact that many of the existing commercial processes for CO2 capture have seen applications in

  12. Developing the learning physical science curriculum: Adapting a small enrollment, laboratory and discussion based physical science course for large enrollments

    Science.gov (United States)

    Goldberg, Fred; Price, Edward; Robinson, Stephen; Boyd-Harlow, Danielle; McKean, Michael

    2012-06-01

    We report on the adaptation of the small enrollment, lab and discussion based physical science course, Physical Science and Everyday Thinking (PSET), for a large-enrollment, lecture-style setting. Like PSET, the new Learning Physical Science (LEPS) curriculum was designed around specific principles based on research on learning to meet the needs of nonscience students, especially prospective and practicing elementary and middle school teachers. We describe the structure of the two curricula and the adaptation process, including a detailed comparison of similar activities from the two curricula and a case study of a LEPS classroom implementation. In LEPS, short instructor-guided lessons replace lengthier small group activities, and movies, rather than hands-on investigations, provide the evidence used to support and test ideas. LEPS promotes student peer interaction as an important part of sense making via “clicker” questions, rather than small group and whole class discussions typical of PSET. Examples of student dialog indicate that this format is capable of generating substantive student discussion and successfully enacting the design principles. Field-test data show similar student content learning gains with the two curricula. Nevertheless, because of classroom constraints, some important practices of science that were an integral part of PSET were not included in LEPS.

  13. Developing the learning physical science curriculum: Adapting a small enrollment, laboratory and discussion based physical science course for large enrollments

    Directory of Open Access Journals (Sweden)

    Fred Goldberg1

    2012-05-01

    Full Text Available We report on the adaptation of the small enrollment, lab and discussion based physical science course, Physical Science and Everyday Thinking (PSET, for a large-enrollment, lecture-style setting. Like PSET, the new Learning Physical Science (LEPS curriculum was designed around specific principles based on research on learning to meet the needs of nonscience students, especially prospective and practicing elementary and middle school teachers. We describe the structure of the two curricula and the adaptation process, including a detailed comparison of similar activities from the two curricula and a case study of a LEPS classroom implementation. In LEPS, short instructor-guided lessons replace lengthier small group activities, and movies, rather than hands-on investigations, provide the evidence used to support and test ideas. LEPS promotes student peer interaction as an important part of sense making via “clicker” questions, rather than small group and whole class discussions typical of PSET. Examples of student dialog indicate that this format is capable of generating substantive student discussion and successfully enacting the design principles. Field-test data show similar student content learning gains with the two curricula. Nevertheless, because of classroom constraints, some important practices of science that were an integral part of PSET were not included in LEPS.

  14. Disposal and re-use of TENORM - legal limitations and obstacles

    International Nuclear Information System (INIS)

    Weiss, D.; Ettenhuber, E.

    2004-01-01

    While implementing EURATOM guideline 96/29 in the German legislation, in June of 2001 an essential pre-condition was created for re-use or disposal of TENORM. An essential progress has been achieved allowing to re-use TENORM or to dump it together with other residues and waste, if the specific activity does not exceed the limits defined in the radiation protection ordinance (StrlSchV). Otherwise, if the specified limits in terms of concentration or radiation dose are exceeded, than these materials must remain under radiological protection. A practical application of the new German regulation turns out to be difficult especially for disposal together with other waste and for re-use as backfilling material in mines taking into account problems arising from adaptation of the respective legislation on radiation protection, soil protection, waste management and shipment of dangerous goods. The report tackles obstacles for re-use and disposal of TENORM together with garbage and toxic waste arising from the new legislation. Otherwise, proposals will be given how obstacles of selected options for re-use and disposal can be overcome. (orig.)

  15. Adaptation Stories

    International Development Research Centre (IDRC) Digital Library (Canada)

    By Reg'

    adaptation to climate change from various regions of the Sahel. Their .... This simple system, whose cost and maintenance were financially sustainable, brought ... method that enables him to learn from experience and save time, which he ...

  16. Adaptations and accommodations: The use of the WAIS III with people with a Learning Disability

    OpenAIRE

    McKenzie, Karen; Murray, George; Wright, Jenny

    2004-01-01

    Evidence of significant impairment in cognitive functioning has always been one of the main criteria of a learning disability (Pulsifer, 1996) and intellectual assessment is, therefore, one of the tasks of clinical psychologists working within learning disability services. Such assessments are commonly used to help establish of an individual’s cognitive strengths and weaknesses, support needs and more specifically, to help determine if an individual falls within the remit of learning disabili...

  17. Facilitating peer based learning through summative assessment - An adaptation of the Objective Structured Clinical Assessment tool for the blended learning environment.

    Science.gov (United States)

    Wikander, Lolita; Bouchoucha, Stéphane L

    2018-01-01

    Adapting a course from face to face to blended delivery necessitates that assessments are modified accordingly. In Australia the Objective Structured Clinical Assessment tool, as a derivative from the Objective Structured Clinical Examination, has been used in the face-to-face delivery mode as a formative or summative assessment tool in medicine and nursing since 1990. The Objective Structured Clinical Assessment has been used at Charles Darwin University to assess nursing students' simulated clinical skills prior to the commencement of their clinical placements since 2008. Although the majority of the course is delivered online, students attend a one-week intensive clinical simulation block yearly, prior to attending clinical placements. Initially, the Objective Structured Clinical Assessment was introduced as a lecturer assessed summative assessment, over time it was adapted to better suit the blended learning environment. The modification of the tool from an academic to peer assessed assessment tool, was based on the empirical literature, student feedback and a cross-sectional, qualitative study exploring academics' perceptions of the Objective Structured Clinical Assessment (Bouchoucha et al., 2013a, b). This paper presents an overview of the process leading to the successful adaptation of the Objective Structured Clinical Assessment to suit the requirements of a preregistration nursing course delivered through blended learning. This is significant as many universities are moving their curriculum to fully online or blended delivery, yet little attention has been paid to adapting the assessment of simulated clinical skills. The aim is to identify the benefits and drawbacks of using the peer assessed Objective Structured Clinical Assessment and share recommendations for successful implementation. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Closed-loop adaptation of neurofeedback based on mental effort facilitates reinforcement learning of brain self-regulation.

    Science.gov (United States)

    Bauer, Robert; Fels, Meike; Royter, Vladislav; Raco, Valerio; Gharabaghi, Alireza

    2016-09-01

    Considering self-rated mental effort during neurofeedback may improve training of brain self-regulation. Twenty-one healthy, right-handed subjects performed kinesthetic motor imagery of opening their left hand, while threshold-based classification of beta-band desynchronization resulted in proprioceptive robotic feedback. The experiment consisted of two blocks in a cross-over design. The participants rated their perceived mental effort nine times per block. In the adaptive block, the threshold was adjusted on the basis of these ratings whereas adjustments were carried out at random in the other block. Electroencephalography was used to examine the cortical activation patterns during the training sessions. The perceived mental effort was correlated with the difficulty threshold of neurofeedback training. Adaptive threshold-setting reduced mental effort and increased the classification accuracy and positive predictive value. This was paralleled by an inter-hemispheric cortical activation pattern in low frequency bands connecting the right frontal and left parietal areas. Optimal balance of mental effort was achieved at thresholds significantly higher than maximum classification accuracy. Rating of mental effort is a feasible approach for effective threshold-adaptation during neurofeedback training. Closed-loop adaptation of the neurofeedback difficulty level facilitates reinforcement learning of brain self-regulation. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  19. Teacher Effectiveness in Adapting Instruction to the Needs of Pupils With Learning Difficulties in Regular Primary Schools in Ghana

    Directory of Open Access Journals (Sweden)

    Abdul-Razak Kuyini Alhassan

    2014-01-01

    Full Text Available Ghana education system has failed to effectively address the needs of pupils with learning difficulties (LDs in regular classrooms. Underachievement, school dropout, streetism, and antisocial behaviors are the consequences. Teachers’ lack of adequate competence in adaptive instruction is one of the fundamental reasons responsible for this anomaly. This study aims to examine teachers’ competence in adapting instructions to teach pupils with LDs in the regular classroom in Ghana. The data were gathered from 387 sampled teachers in a cross-sectional survey using questionnaires and structured observation methods. We analyzed the data using descriptive statistic, chi-square test, correlation, t test, and ANOVA. The results show that (a teachers have limited to moderate competence in adaptive instruction, (b adaptive teaching is strongly associated with teachers’ competence in teaching pupils with LDs in the regular classroom, and (c apart from gender and class size, teachers’ background variables such as school location and teaching experience differ significantly. The study has serious implications for Ghana’s inclusive education policy and teaching practice.

  20. Technique adaptation, strategic replanning, and team learning during implementation of MR-guided brachytherapy for cervical cancer.

    Science.gov (United States)

    Skliarenko, Julia; Carlone, Marco; Tanderup, Kari; Han, Kathy; Beiki-Ardakani, Akbar; Borg, Jette; Chan, Kitty; Croke, Jennifer; Rink, Alexandra; Simeonov, Anna; Ujaimi, Reem; Xie, Jason; Fyles, Anthony; Milosevic, Michael

    MR-guided brachytherapy (MRgBT) with interstitial needles is associated with improved outcomes in cervical cancer patients. However, there are implementation barriers, including magnetic resonance (MR) access, practitioner familiarity/comfort, and efficiency. This study explores a graded MRgBT implementation strategy that included the adaptive use of needles, strategic use of MR imaging/planning, and team learning. Twenty patients with cervical cancer were treated with high-dose-rate MRgBT (28 Gy in four fractions, two insertions, daily MR imaging/planning). A tandem/ring applicator alone was used for the first insertion in most patients. Needles were added for the second insertion based on evaluation of the initial dosimetry. An interdisciplinary expert team reviewed and discussed the MR images and treatment plans. Dosimetry-trigger technique adaptation with the addition of needles for the second insertion improved target coverage in all patients with suboptimal dosimetry initially without compromising organ-at-risk (OAR) sparing. Target and OAR planning objectives were achieved in most patients. There were small or no systematic differences in tumor or OAR dosimetry between imaging/planning once per insertion vs. daily and only small random variations. Peer review and discussion of images, contours, and plans promoted learning and process development. Technique adaptation based on the initial dosimetry is an efficient approach to implementing MRgBT while gaining comfort with the use of needles. MR imaging and planning once per insertion is safe in most patients as long as applicator shifts, and large anatomical changes are excluded. Team learning is essential to building individual and programmatic competencies. Copyright © 2017 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.