Parallel strategy for optimal learning in perceptrons

We developed a parallel strategy for learning optimally specific realizable rules by perceptrons, in an online learning scenario. Our result is a generalization of the Caticha–Kinouchi (CK) algorithm developed for learning a perceptron with a synaptic vector drawn from a uniform distribution over the N-dimensional sphere, so called the typical case. Our method outperforms the CK algorithm in almost all possible situations, failing only in a denumerable set of cases. The algorithm is optimal in the sense that it saturates Bayesian bounds when it succeeds.

Communities and emerging semantics in semantic link network:discovery and learning

The World Wide Web provides plentiful contents for Web-based learning, but its hyperlink-based architecture connects Web resources for browsing freely rather than for effective learning. To support effective learning, an e-learning system should be able to discover and make use of the semantic communities and the emerging semantic relations in a dynamic complex network of learning resources. Previous graph-based community discovery approaches are limited in ability to discover semantic communities. This paper first suggests the Semantic Link Network (SLN), a loosely coupled semantic data model that can semantically link resources and derive out implicit semantic links according to a set of relational reasoning rules. By studying the intrinsic relationship between semantic communities and the semantic space of SLN, approaches to discovering reasoning-constraint, rule-constraint, and classification-constraint semantic communities are proposed. Further, the approaches, principles, and strategies for discovering emerging semantics in dynamic SLNs are studied. The basic laws of the semantic link network motion are revealed for the first time. An e-learning environment incorporating the proposed approaches, principles, and strategies to support effective discovery and learning is suggested.

Running a professional academic-related team in an academic environment

This paper considers the position of a large full-range business school and ways in which it can improve its efficiency and effectiveness, and enhance students' learning environment by the strategic use of academic-related staff within key roles in the School. Some of these roles have traditionally been undertaken by academic staff, but the increased complexity of the Business School environment makes it impossible for academic staff to undertake all roles if the School wants to be innovative and successful in a highly changing external environment. The investigation is carried out via a series of semi-structure interviews, conducted with academic and academic related staff across the School. This is compared with a review of recent literature in the subject. The paper concludes that both the efficient running of the School and the learning environment of students are improved via the partnership of academics and support staff. The findings reveal, however, that the use of academic-related staff must be done sensitively, to ensure that institutions do not become over bureaucratic or academics alienated in the drive to focus on the student experience.

Can diversity amongst learners improve online object tracking?

We present a novel analysis of the state of the art in object tracking with respect to diversity found in its main component, an ensemble classifier that is updated in an online manner. We employ established measures for diversity and performance from the rich literature on ensemble classification and online learning, and present a detailed evaluation of diversity and performance on benchmark sequences in order to gain an insight into how the tracking performance can be improved. © Springer-Verlag 2013.

Learning to be different:heterogeneity and efficiency in distributed smart camera networks

In this paper we study the self-organising behaviour of smart camera networks which use market-based handover of object tracking responsibilities to achieve an efficient allocation of objects to cameras. Specifically, we compare previously known homogeneous configurations, when all cameras use the same marketing strategy, with heterogeneous configurations, when each camera makes use of its own, possibly different marketing strategy. Our first contribution is to establish that such heterogeneity of marketing strategies can lead to system wide outcomes which are Pareto superior when compared to those possible in homogeneous configurations. However, since the particular configuration required to lead to Pareto efficiency in a given scenario will not be known in advance, our second contribution is to show how online learning of marketing strategies at the individual camera level can lead to high performing heterogeneous configurations from the system point of view, extending the Pareto front when compared to the homogeneous case. Our third contribution is to show that in many cases, the dynamic behaviour resulting from online learning leads to global outcomes which extend the Pareto front even when compared to static heterogeneous configurations. Our evaluation considers results obtained from an open source simulation package as well as data from a network of real cameras. © 2013 IEEE.

Incremental tensor biased discriminant analysis:a new color-based visual tracking method

Most existing color-based tracking algorithms utilize the statistical color information of the object as the tracking clues, without maintaining the spatial structure within a single chromatic image. Recently, the researches on the multilinear algebra provide the possibility to hold the spatial structural relationship in a representation of the image ensembles. In this paper, a third-order color tensor is constructed to represent the object to be tracked. Considering the influence of the environment changing on the tracking, the biased discriminant analysis (BDA) is extended to the tensor biased discriminant analysis (TBDA) for distinguishing the object from the background. At the same time, an incremental scheme for the TBDA is developed for the tensor biased discriminant subspace online learning, which can be used to adapt to the appearance variant of both the object and background. The experimental results show that the proposed method can track objects precisely undergoing large pose, scale and lighting changes, as well as partial occlusion. © 2009 Elsevier B.V.

Towards e+Learning or How to Increase the Learning into e-Learning

It is discussed some changes in the traditional e-learning notion on the point of view of R. Koper’s question 'where is the learning in e-learning?’. We put a focus on the conception of learning as a management process and present the project Bulgarian Educational Site (BEST) – a possible answer to Koper’s question. The BEST is a virtual learning environment, based on the following principles: learning is a goal-directed and didactics-managed process; learners may define their own learning objectives, monitor and regulate the learning process; collaborative e-learning is more effective; etc. The BEST is based on two famous e-learning systems (Moodle, LAMS) and Plovdiv e-University (versions 1.0 and 2.0). The paper brings up a mater about the new ‘electronic’ pedagogy and proposes an approach for pedagogical modeling and interpretation of e-learning applied in the BEST.

About Massachusetts Colleges Online June 2006 Sharing Best Practices Conference

This paper summarizes trends and issues in online learning in the United States of America as reflected in the presentations of the Massachusetts Colleges Online “Sharing Best Practices in E-Learning” Conference held on June 13-14, 2006.

Collaborative Learning and Authoring in the Frame of e-Projects

It is presented a research on the application of a collaborative learning and authoring during all delivery phases of e-learning programmes or e-courses offered by educational institutions. The possibilities for modelling of an e-project as a specific management process based on planned, dynamically changing or accidentally arising sequences of learning activities, is discussed. New approaches for project-based and collaborative learning and authoring are presented. Special types of test questions are introduced which allow test generation and authoring based on learners’ answers accumulated in the frame of given e-course. Experiments are carried out in an e-learning environment, named BEST.

Self-organising zooms for decentralised redundancy management in visual sensor networks

When visual sensor networks are composed of cameras which can adjust the zoom factor of their own lens, one must determine the optimal zoom levels for the cameras, for a given task. This gives rise to an important trade-off between the overlap of the different cameras’ fields of view, providing redundancy, and image quality. In an object tracking task, having multiple cameras observe the same area allows for quicker recovery, when a camera fails. In contrast having narrow zooms allow for a higher pixel count on regions of interest, leading to increased tracking confidence. In this paper we propose an approach for the self-organisation of redundancy in a distributed visual sensor network, based on decentralised multi-objective online learning using only local information to approximate the global state. We explore the impact of different zoom levels on these trade-offs, when tasking omnidirectional cameras, having perfect 360-degree view, with keeping track of a varying number of moving objects. We further show how employing decentralised reinforcement learning enables zoom configurations to be achieved dynamically at runtime according to an operator’s preference for maximising either the proportion of objects tracked, confidence associated with tracking, or redundancy in expectation of camera failure. We show that explicitly taking account of the level of overlap, even based only on local knowledge, improves resilience when cameras fail. Our results illustrate the trade-off between maintaining high confidence and object coverage, and maintaining redundancy, in anticipation of future failure. Our approach provides a fully tunable decentralised method for the self-organisation of redundancy in a changing environment, according to an operator’s preferences.

Applying Data Mining Tools in E-Learning

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

Students' recollections of participating in a first year residential learning community experience

The purpose of this qualitative study was to gain an understanding of what participation in a first year residential learning community meant to students 2–3 years after their involvement in the program. Various theories including environmental, student involvement, psychosocial and intellectual, were used as a framework for this case study. Each of the ten participants was a junior or senior level student at the time of the study, but had previously participated in a first year residential learning community at Florida International University. The researcher held two semi-structured interviews with each participant, and collected data sheets from each. ^ The narrative data produced from the interviews were transcribed, coded and analyzed to gain insights into the experiences and perspectives of the participants. Member checking was used after the interview process. A peer reviewer offered feedback during the data analysis. The resulting data was coded into categories, with a final selection of four themes and 15 sub-themes, which captured the essence of the participants' experiences. The four major themes included: (a) community, (b) involvement, (c) identity, and (d) academics. The community theme is used to describe how students perceived the environment to be. The involvement theme is used to describe the students' participation in campus life and their interaction with other members of the university community. The identity theme is used to describe the students' process of development, and the personal growth they underwent as a result of their experiences. The academics theme refers to the intellectual development of students and their interaction around academic issues. ^ The results of this study showed that the participants valued greatly their involvement in the First Year Residents Succeeding Together program (FYRST) and can articulate how it helped them succeed as students. In describing their experience, they most recall the sense of community that existed, the personal growth they experienced, the academic development process they went through, and their involvement, both with other people and with activities in their community. Recommendations are provided for practice and research, including several related to enhancing the academic culture, integrating faculty, utilizing peer influence and providing further opportunities to create a seamless learning environment. ^

Self-other rating discrepancy and personality antecedents

This study explored individual difference factors to help explain the discrepancy that has been found to exist between self and other ratings in prior research. Particularly, personality characteristics of the self-rater were researched in the current study as a potential antecedent for self-other rating agreement. Self, peer, and supervisor ratings were provided for global performance as well as five competencies specific to the organization being examined. Four rating tendency categories, over-raters, under-raters, in-agreement (good), and in-agreement (poor), established in research by Atwater and Yammarino were used as the basis of the current research. The sample for rating comparisons within the current study consisted of 283 self and supervisor dyads and 275 for self and peer dyads from a large financial organization. Measures included a custom multi-rater performance instrument and the personality survey instrument, ASSESS, which measures 20 specific personality characteristics. MANCOVAs were then performed on this data to examine if specific personality characteristics significantly distinguished the four rating tendency groups. Examination of all personality dimensions and overall performance uncovered significant findings among rating groups for self-supervisor rating comparisons but not for self-peer rating comparisons. Examination of specific personality dimensions for self-supervisory ratings group comparisons and overall performance showed Detail Interest to be an important characteristic among the hypothesized variables. For self-supervisor rating comparisons and specific competencies, support was found for the hypothesized personality dimension of Fact-based Thinking which distinguished the four rating groups for the competency, Builds Relationships. For both self-supervisor and self-peer rating comparisons, the competencies, Builds Relationships and Leads in a Learning Environment, were found to have significant relationship with several personality characteristics, however, these relationships were not consistent with the hypotheses in the current study. Several unhypothesized personality dimensions were also found to distinguish rating groups for both self-supervisor and self-peer comparisons on overall performance and various competencies. Results of the current study hold implications for the training and development session that occur after a 360-degree evaluation process. Particularly, it is suggested that feedback sessions may be designed according to particular rating tendencies to maximize the interpretation, acceptance and use of evaluation information. ^

Classroom Reform with CMPLE: A Cogenerative Mediation Process for Learning Environments

We have designed a classroom goal setting process whereby students and instructors rank, discuss, and combine their learning preferences and then rate their classroom with respect to those preferences. All participants have the opportunity to be collectively engaged in building a preferred learning environment.

The Integrated Process of Engagement in Adult Learning

This paper explores the role of engagement in adult learning based on Illeris’ three dimensional model of learning and Yang’s holistic theory of knowledge and learning. Engagement and learning are integrated processes by which adult learners gain a deeper understanding and make meaning of the activities he or she is exposed to in a given learning environment.