Learning from ordered sets and applications in collaborative ranking

Ranking over sets arise when users choose between groups of items. For example, a group may be of those movies deemed 5 stars to them, or a customized tour package. It turns out, to model this data type properly, we need to investigate the general combinatorics problem of partitioning a set and ordering the subsets. Here we construct a probabilistic log-linear model over a set of ordered subsets. Inference in this combinatorial space is highly challenging: The space size approaches (N!/2)6.93145N+1 as N approaches infinity. We propose a split-and-merge Metropolis-Hastings procedure that can explore the state-space efficiently. For discovering hidden aspects in the data, we enrich the model with latent binary variables so that the posteriors can be efficiently evaluated. Finally, we evaluate the proposed model on large-scale collaborative filtering tasks and demonstrate that it is competitive against state-of-the-art methods.

An evaluation of the impact of using authentic design and build industry projects in project-based learning

BACKGROUNDChisholm’s ‘first year experience’ is a significant feature of the new industry focused Bachelor of Engineering Technology program delivered in association with the South East Melbourne Manufacturers’ Alliance (SEMMA). This conceive-design-implement-operate (CDIO Initiative) program commenced as a full time program in first semester 2012. Whereas it is common for CDIO Initiative programs to have a first year experience program containing a project typical of the type of industry project they would complete as a graduate engineer or engineering technologist, this goes further by using real industry projects provided by SEMMA members.This design-and-build industry project runs across both semesters supporting project-based learning in three first year subjects. A concern is that the industry involvement of the projects adds substantially to an already heavy student workload. This has been further increased by the addition of two additional first year initiatives: writing workshops, and training in, and substantial use of, student oral presentations. It is recognised that an excessive workload could lead students to adopt surface learning approaches in other subjects.PURPOSEThe goal of the project is to evaluate student perceptions of the value and work load impact of the industry project and the other new first year initiatives.DESIGN/METHODCentral to this project is a student survey-based evaluation of the industry project based learning that is the core of the ‘first year experience’. The participants were limited to the small group of students who, in a single year, completed all three subjects that comprise the ‘first year experience’. To avoid compromising the results the survey was administered by Chisholm Institute’s Department of Strategy and Planning with no engineering technology degree program staff present. The survey included questions to enable responses to be linked with specific student demographics without identifying any of the respondents.RESULTSThe study showed the industry project-based learning had worthwhile outcomes but placed considerable time pressures on most respondents. For some, this also impacted on their other subjects. A first year oral presentation program was also shown to have worthwhile outcomes. However no conclusions could be reliably drawn on the third initiative – writing workshops.CONCLUSIONSThe results confirm that the authentic industry project is considered a worthwhile initiative but contributes significantly to student overload. This applies also – to a lesser extent – to the first year oral presentation program. Both also require new approaches to delivery as student numbers increase. Strategies to address these issues are discussed.

Fuzzy system with tabu search learning for classification of motor imagery data

This paper introduces an approach to classify EEG signals using wavelet transform and a fuzzy standard additive model (FSAM) with tabu search learning mechanism. Wavelet coefficients are ranked based on statistics of the Wilcoxon test. The most informative coefficients are assembled to form a feature set that serves as inputs to the tabu-FSAM. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II are employed for the experiments. Classification performance is evaluated using accuracy, mutual information, Gini coefficient and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The proposed tabu-FSAM method considerably dominates the competitive classifiers, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II.

Challenges to providing work integrated learning to international business students at Australian universities

Purpose – The purpose of this paper is to report on a three-year Australian study of international business and accounting students and the transition to employment. For international students seeking to differentiate themselves in a highly competitive global labour market, foreign work experience is now an integral part of the overseas study “package”. Work-integrated learning (WIL) is seen to provide critical “employability” knowledge and skills, however, international students have low participation rates. The high value placed on WIL among international students poses challenges for Australia as well as opportunities. Understanding the issues surrounding international students and WIL is closely linked to Australia’s continued success in the international education sector which has broad, long-term, social and economic implications.Design/methodology/approach – This paper draws on 59 interviews with a range of stakeholders including international students, universities, government, employers and professional bodies. Central to the paper is an in-depth case study of WIL in the business and accounting discipline at one Australian university.Findings – Providing international students with access to discipline-related work experience has emerged as a critical issue for Australian universities. The study finds that enhancing the employability skills of internationals students via integrated career education, a focus on English language proficiency and “soft skills” development are central to success in WIL. Meeting the growing demand for WIL among international students requires a multipronged approach which hinges on cooperation between international students, universities, employers and government.Originality/value – This project aims to fill a critical knowledge gap by advancing theories in relation to international students and WIL. While there is a significant body of research in the fields of international education and WIL, there is an absence of research exploring the intersection between the two fields. The study will contribute to the advancement of knowledge in both fields by exploring the emerging issue of WIL and international students.

Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)

Electronic medical record (EMR) offers promises for novel analytics. However, manual feature engineering from EMR is labor intensive because EMR is complex - it contains temporal, mixed-type and multimodal data packed in irregular episodes. We present a computational framework to harness EMR with minimal human supervision via restricted Boltzmann machine (RBM). The framework derives a new representation of medical objects by embedding them in a low-dimensional vector space. This new representation facilitates algebraic and statistical manipulations such as projection onto 2D plane (thereby offering intuitive visualization), object grouping (hence enabling automated phenotyping), and risk stratification. To enhance model interpretability, we introduced two constraints into model parameters: (a) nonnegative coefficients, and (b) structural smoothness. These result in a novel model called eNRBM (EMR-driven nonnegative RBM). We demonstrate the capability of the eNRBM on a cohort of 7578 mental health patients under suicide risk assessment. The derived representation not only shows clinically meaningful feature grouping but also facilitates short-term risk stratification. The F-scores, 0.21 for moderate-risk and 0.36 for high-risk, are significantly higher than those obtained by clinicians and competitive with the results obtained by support vector machines.

Particle competition and cooperation to prevent error propagation from mislabeled data in semi-supervised learning

Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.

Foreign investment and competitive development in Latin America and the Caribbean

Includes bibliography

Combined active and semi-supervised learning using particle walking temporal dynamicscombined active and semi-supervised learning using particle walking temporal dynamics

Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to visit. The particles, which have both competitive and cooperative behavior, are created on the network as the result of label queries. They may be created as the algorithm executes and only nodes affected by the new particles have to be updated. Therefore, it saves execution time compared to traditional active learning frameworks, in which the learning algorithm has to be executed several times. The data items to be queried are select based on information extracted from the nodes and particles temporal dynamics. Two different rules for queries are explored in this paper, one of them is based on querying by uncertainty approaches and the other is based on data and labeled nodes distribution. Each of them may perform better than the other according to some data sets peculiarities. Experimental results on some real-world data sets are provided, and the proposed method outperforms the semi-supervised learning method, from which it is derived, in all of them.

Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning

Active machine learning algorithms are used when large numbers of unlabeled examples are available and getting labels for them is costly (e.g. requiring consulting a human expert). Many conventional active learning algorithms focus on refining the decision boundary, at the expense of exploring new regions that the current hypothesis misclassifies. We propose a new active learning algorithm that balances such exploration with refining of the decision boundary by dynamically adjusting the probability to explore at each step. Our experimental results demonstrate improved performance on data sets that require extensive exploration while remaining competitive on data sets that do not. Our algorithm also shows significant tolerance of noise.

A policy-driven architecture for supporting joint degrees through e-learning services

En un mercado de educación superior cada vez más competitivo, la colaboración entre universidades es una efectiva estrategia para acceder al mercado global. El desarrollo de titulaciones conjuntas es un importante mecanismo para fortalecer las colaboraciones académicas y diversificar los conocimientos. Las titulaciones conjuntas están siendo cada vez más implementadas en las universidades de todo el mundo. En Europa, el proceso de Bolonia y el programa Erasmus, están fomentado el reconocimiento de titulaciones conjuntas y dobles y promoviendo la colaboración entre las instituciones académicas. En el imparable proceso de la globalización y convergencia educativa, el uso de sistemas de e-learning para soportar cursos tanto semipresencial como online es una tendencia en crecimiento. Dado que los sistemas de e-learning soportan una amplia variedad de cursos, es necesario encontrar una solución adecuada que permita a las universidades soportar y gestionar las titulaciones conjuntas a través de sus sistemas de e-learning en conformidad con los acuerdos de colaboración establecidos por las universidades participantes. Esta tesis doctoral abordará las siguientes preguntas de investigación: 1. ¿Qué factores deben tenerse en cuenta en la implementación y gestión de titulaciones conjuntas? 2. ¿Cómo pueden los sistemas actuales de e-learning soportar el desarrollo de titulaciones conjuntas? 3. ¿Qué otros servicios y sistemas necesitan ser adaptados por las universidades interesadas en participar en una titulación conjunta a través de sus sistemas de e-learning? La implementación de titulaciones conjuntas a través de sistemas de e-learning es compleja e implica retos técnicos, administrativos, culturales, financieros, jurídicos y de seguridad. Esta tesis doctoral propone una serie de contribuciones que pueden ayudar a resolver algunos de los retos identificados. En primer lugar se ha elaborado un modelo conceptual que incluye la información del contexto de las titulaciones conjuntas que es relevante para la implementación de estas titulaciones en los sistemas de e-learning. Después de definir el modelo conceptual, se ha propuesto una arquitectura basada en políticas para la implementación de titulaciones interinstitucionales a través de sistemas de e-learning de acuerdo a los términos estipulados en los acuerdos de colaboración que son firmados por las universidades participantes. El autor se ha centrado en el componente de gestión de flujos de trabajo de esta arquitectura. Por último y con el fin de permitir la interoperabilidad de repositorios de objetos educativos, los componentes básicos a implementar han sido identificados y validados. El uso de servicios multimedia en educación es una tendencia creciente, proporcionando servicios de e-learning que permiten mejorar la comunicación y la interacción entre profesores y alumnos. Dentro de estos servicios, nos hemos centrado en el uso de la videoconferencia y la grabación de clases como servicios adecuados para el desarrollo de cursos impartidos en escenarios de educación colaborativos. Las contribuciones han sido validadas en proyectos de investigación de ámbito nacional y europeo en los que el autor ha participado. Abstract In an increasingly competitive higher education market, collaboration between universities is an effective strategy for gaining access to the global market. The development of joint degrees is an important mechanism for strengthening academic research collaborations and diversifying knowledge. Joint degrees are becoming increasingly implemented in universities around the world. In Europe, the Bologna process and the Erasmus programme have encouraged both the global recognition of joint and double degrees and promoted close collaboration between academic institutions. In the unstoppable process of globalization and educational convergence, the use of e-learning systems for supporting both blended and online courses is becoming a growing trend. Since e-learning systems covers a wide range of courses, it becomes necessary to find a suitable solution that enables universities to support and manage joint degrees through their e-learning systems in accordance with the collaboration agreements established by the universities involved. This dissertation will address the following research questions: 1. What factors need to be considered in the implementation and management of joint degrees? 2. How can the current e-learning systems support the development of joint degrees? 3. What other services and systems need to be adapted by universities interested in participating in a joint degree through their e-learning systems? The implementation of joint degrees using e-learning systems is complex and involves technical, administrative, security, cultural, financial and legal challenges. This dissertation proposes a series of contributions to help solve some of the identified challenges. One of the cornerstones of this proposal is a conceptual model of all the relevant issues related to the support of joint degrees by means of e-learning systems. After defining the conceptual model, this dissertation proposes a policy-driven architecture for implementing inter-institutional degree collaborations through e-learning systems as stipulated by a collaboration agreement signed by two universities. The author has focused on the workflow management component of this architecture. Finally, the building blocks for achieving interoperability of learning object repositories have been identified and validated. The use of multimedia services in education is a growing trend, providing rich e-learning services that improve the communication and interaction between teachers and students. Within these e-learning services, we have focused on the use of videoconferencing and lecture recording as the best-suited services to support collaborative learning scenarios. The contributions have been validated within national and European research projects that the author has been involved in.

Co-evolutionary and Reinforcement Learning Techniques Applied to Computer Go players

The objective of this thesis is model some processes from the nature as evolution and co-evolution, and proposing some techniques that can ensure that these learning process really happens and useful to solve some complex problems as Go game. The Go game is ancient and very complex game with simple rules which still is a challenge for the Artificial Intelligence. This dissertation cover some approaches that were applied to solve this problem, proposing solve this problem using competitive and cooperative co-evolutionary learning methods and other techniques proposed by the author. To study, implement and prove these methods were used some neural networks structures, a framework free available and coded many programs. The techniques proposed were coded by the author, performed many experiments to find the best configuration to ensure that co-evolution is progressing and discussed the results. Using co-evolutionary learning processes can be observed some pathologies which could impact co-evolution progress. In this dissertation is introduced some techniques to solve pathologies as loss of gradients, cycling dynamics and forgetting. According to some authors, one solution to solve these co-evolution pathologies is introduce more diversity in populations that are evolving. In this thesis is proposed some techniques to introduce more diversity and some diversity measurements for neural networks structures to monitor diversity during co-evolution. The genotype diversity evolved were analyzed in terms of its impact to global fitness of the strategies evolved and their generalization. Additionally, it was introduced a memory mechanism in the network neural structures to reinforce some strategies in the genes of the neurons evolved with the intention that some good strategies learned are not forgotten. In this dissertation is presented some works from other authors in which cooperative and competitive co-evolution has been applied. The Go board size used in this thesis was 9x9, but can be easily escalated to more bigger boards.The author believe that programs coded and techniques introduced in this dissertation can be used for other domains.

Regularized model learning in EDAs for continuous and multi-objective optimization

Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods.

Response-reinforcement learning is dependent on N-methyl-d-aspartate receptor activation in the nucleus accumbens core

The nucleus accumbens, a site within the ventral striatum, is best known for its prominent role in mediating the reinforcing effects of drugs of abuse such as cocaine, alcohol, and nicotine. Indeed, it is generally believed that this structure subserves motivated behaviors, such as feeding, drinking, sexual behavior, and exploratory locomotion, which are elicited by natural rewards or incentive stimuli. A basic rule of positive reinforcement is that motor responses will increase in magnitude and vigor if followed by a rewarding event. It is likely, therefore, that the nucleus accumbens may serve as a substrate for reinforcement learning. However, there is surprisingly little information concerning the neural mechanisms by which appetitive responses are learned. In the present study, we report that treatment of the nucleus accumbens core with the selective competitive N-methyl-d-aspartate (NMDA) antagonist 2-amino-5-phosphonopentanoic acid (AP-5; 5 nmol/0.5 μl bilaterally) impairs response-reinforcement learning in the acquisition of a simple lever-press task to obtain food. Once the rats learned the task, AP-5 had no effect, demonstrating the requirement of NMDA receptor-dependent plasticity in the early stages of learning. Infusion of AP-5 into the accumbens shell produced a much smaller impairment of learning. Additional experiments showed that AP-5 core-treated rats had normal feeding and locomotor responses and were capable of acquiring stimulus-reward associations. We hypothesize that stimulation of NMDA receptors within the accumbens core is a key process through which motor responses become established in response to reinforcing stimuli. Further, this mechanism, may also play a critical role in the motivational and addictive properties of drugs of abuse.

The aid of machine learning to overcome the classification of real health discharge reports written in Spanish

Hospitals attached to the Spanish Ministry of Health are currently using the International Classification of Diseases 9 Clinical Modification (ICD9-CM) to classify health discharge records. Nowadays, this work is manually done by experts. This paper tackles the automatic classification of real Discharge Records in Spanish following the ICD9-CM standard. The challenge is that the Discharge Records are written in spontaneous language. We explore several machine learning techniques to deal with the classification problem. Random Forest resulted in the most competitive one, achieving an F-measure of 0.876.

Exploring the characteristics of the market-driven firms and antecedents to sustained competitive advantage

During the last few decades, identifying and examining the characteristics of market-driven firms have been a dominant theme in strategic marketing research. It has been argued that market-driven firms are superior in their market sensing and customer linking capabilities, enabling market-driven firms to outperform their competitors. This paper reports the findings of a study that examines the role market-focused learning capability and marketing capability in innovation-based competitive strategy on sustainable competitive advantage. The findings indicate that entrepreneurship is an important factor in sustained competitive advantage (SCA) and while market-focused learning capability leads to higher degrees of innovation, marketing capability enables SCA. (C) 2003 Elsevier Inc. All rights reserved.