Learning Technology in Scheduling Based on the Mixed Graphs

We propose the adaptive algorithm for solving a set of similar scheduling problems using learning technology. It is devised to combine the merits of an exact algorithm based on the mixed graph model and heuristics oriented on the real-world scheduling problems. The former may ensure high quality of the solution by means of an implicit exhausting enumeration of the feasible schedules. The latter may be developed for certain type of problems using their peculiarities. The main idea of the learning technology is to produce effective (in performance measure) and efficient (in computational time) heuristics by adapting local decisions for the scheduling problems under consideration. Adaptation is realized at the stage of learning while solving a set of sample scheduling problems using a branch-and-bound algorithm and structuring knowledge using pattern recognition apparatus.

Parallelization of Logical Inference for Confluent Rule-based System

* This paper was made according to the program ��� 14 of fundamental scientific research of the Presidium of the Russian Academy of Sciences, the project 06-I-��14-052

A rule-based approach to implicit emotion detection in text

Most research in the area of emotion detection in written text focused on detecting explicit expressions of emotions in text. In this paper, we present a rule-based pipeline approach for detecting implicit emotions in written text without emotion-bearing words based on the OCC Model. We have evaluated our approach on three different datasets with five emotion categories. Our results show that the proposed approach outperforms the lexicon matching method consistently across all the three datasets by a large margin of 17���30% in F-measure and gives competitive performance compared to a supervised classifier. In particular, when dealing with formal text which follows grammatical rules strictly, our approach gives an average F-measure of 82.7% on ���Happy���, ���Angry-Disgust��� and ���Sad���, even outperforming the supervised baseline by nearly 17% in F-measure. Our preliminary results show the feasibility of the approach for the task of implicit emotion detection in written text.

A k��lts��gvet��si politika fenntarthat��s��ga ��s a szab��lyalap�� gazdas��gpolitika (The sustainability of fiscal policy and a rule-based economic policy)

A k��lts��gvet��si p��nz��gyek irodalm��ban a fenntarthat��s��g koncepci��ja csak az elm��lt k��t-h��rom ��vtizedben ker��lt ��jra a vizsg��l��d��s f��kusz��ba. Ennek oka kett��s. Az 1960-as ��vek v��g��ig a fegyelmezett fisk��lis politikai gyakorlat nem ig��nyelte annak ��lland�� napirenden tart��s��t. Csak az olajv��ls��gok idej��re es�� ��s azut��n ��lland��sulni l��tsz�� k��lts��gvet��si hi��nyok ��s a n��vekv�� ��llamad��ss��g-��llom��nyok, illetve az ezek ok��n er��s��d�� ad��ss��gkock��zat ir��ny��totta ��jra a figyelmet a k��lts��gvet��si fegyelem fenntart��s��nak fontoss��g��ra. Ezt a v��ltoz��st a k��zgazdas��gtudom��nyi elm��lett��rt��netben be��llott gy��keres v��ltoz��s k��s��rte. Az akt��v keresletmenedzsment b��r��latak��nt megfogalmaz��d�� monetarista kritika, illetve annak radik��lisabb ��jklasszikus v��ltozata, a politikai d��nt��shoz��kr��l (��s ��gy a diszkrecion��lis k��lts��gvet��si politika hat��soss��g��r��l) les��jt�� v��lem��nyt fogalmazott meg, ami azut��n az akt��v int��zked��sek korl��toz��s��nak ir��ny��ba terelte a gazdas��gpolitika alak��t��it is. A k��vetkez��kben e kett��s ��� a fisk��lis politikai gyakorlat ��s a k��zgazdas��gi elm��letek ter��let��n bek��vetkezett ���fordulat bemutat��s��ra v��llalkozunk az Akad��miai Kiad��n��l megjelen�� K��lts��gvet��si p��nz��gyek ��� Hi��ny, ��llamad��ss��g, fenntarthat��s��g c��m�� k��tet��nk bizonyos r��szeinek felhaszn��l��s��val.

Comparing novice designer performance using a rule -based approach versus a pattern -based approach in conceptual data modeling tasks

Conceptual database design is an unusually difficult and error-prone task for novice designers. This study examined how two training approaches---rule-based and pattern-based---might improve performance on database design tasks. A rule-based approach prescribes a sequence of rules for modeling conceptual constructs, and the action to be taken at various stages while developing a conceptual model. A pattern-based approach presents data modeling structures that occur frequently in practice, and prescribes guidelines on how to recognize and use these structures. This study describes the conceptual framework, experimental design, and results of a laboratory experiment that employed novice designers to compare the effectiveness of the two training approaches (between-subjects) at three levels of task complexity (within subjects). Results indicate an interaction effect between treatment and task complexity. The rule-based approach was significantly better in the low-complexity and the high-complexity cases; there was no statistical difference in the medium-complexity case. Designer performance fell significantly as complexity increased. Overall, though the rule-based approach was not significantly superior to the pattern-based approach in all instances, it out-performed the pattern-based approach at two out of three complexity levels. The primary contributions of the study are (1) the operationalization of the complexity construct to a degree not addressed in previous studies; (2) the development of a pattern-based instructional approach to database design; and (3) the finding that the effectiveness of a particular training approach may depend on the complexity of the task.

Rule-based and Resource-bounded: A New Look at Epistemic Logic

Syntactic logics do not suffer from the problems of logical omniscience but are often thought to lack interesting properties relating to epistemic notions. By focusing on the case of rule-based agents, I develop a framework for modelling resource-bounded agents and show that the resulting models have a number of interesting properties.

An Artificial Immune System Based Recommender

The immune system is a complex biological system with a highly distributed, adaptive and self-organising nature. This paper presents an artificial immune system (AIS) that exploits some of these characteristics and is applied to the task of film recommendation by collaborative filtering (CF). Natural evolution and in particular the immune system have not been designed for classical optimisation. However, for this problem, we are not interested in finding a single optimum. Rather we intend to identify a sub-set of good matches on which recommendations can be based. It is our hypothesis that an AIS built on two central aspects of the biological immune system will be an ideal candidate to achieve this: Antigen - antibody interaction for matching and antibody - antibody interaction for diversity. Computational results are presented in support of this conjecture and compared to those found by other CF techniques. Notes: Uwe Aickelin, University of the West of England, Coldharbour Lane, Bristol, BS16 1QY, UK

A Belief rule-based environmental responsibility assessment system for small and medium-sized enterprises

Various environmental management systems, standards and tools are being created to assist companies to become more environmental friendly. However, not all the enterprises have adopted environmental policies in the same scale and range. Additionally, there is no existing guide to help them determine their level of environmental responsibility and subsequently, provide support to enable them to move forward towards environmental responsibility excellence. This research proposes the use of a Belief Rule-Based approach to assess an enterprise���s level commitment to environmental issues. The Environmental Responsibility BRB assessment system has been developed for this research. Participating companies will have to complete a structured questionnaire. An automated analysis of their responses (using the Belief Rule-Based approach) will determine their environmental responsibility level. This is followed by a recommendation on how to progress to the next level. The recommended best practices will help promote understanding, increase awareness, and make the organization greener. BRB systems consist of two parts: Knowledge Base and Inference Engine. The knowledge base in this research is constructed after an in-depth literature review, critical analyses of existing environmental performance assessment models and primarily guided by the EU Draft Background Report on "Best Environmental Management Practice in the Telecommunications and ICT Services Sector". The reasoning algorithm of a selected Drools JBoss BRB inference engine is forward chaining, where an inference starts iteratively searching for a pattern-match of the input and if-then clause. However, the forward chaining mechanism is not equipped with uncertainty handling. Therefore, a decision is made to deploy an evidential reasoning and forward chaining with a hybrid knowledge representation inference scheme to accommodate imprecision, ambiguity and fuzzy types of uncertainties. It is believed that such a system generates well balanced, sensible and Green ICT readiness adapted results, to help enterprises focus on making improvements on more sustainable business operations.

The interaction factor as a challenge for foreign language learning in the technology based education: a pedagogical reflection

The author carries out a pedagogical reflection on how the technology driven distance learning repeatedly neglects the scientific achievements of Second Language Acquisition and Language Pedagogy. Seeing communicative competence as a major goal of a language classroom, she presents the main challenges that the communicative approach poses to distance learning. To this end, a general distance learning theory by Moore is adapted to the needs of language education, through a distinction between three aspects of learner interaction ��� with the teacher, with other learners and with content. In this three-dimensional paradigm the learner is seen as the main actor of the process, the teacher as a facilitator, the text as a main source of communicative data and the learner autonomy as the fundament of the process.

Development of an innovative non rule-based energy management strategy for a Hybrid Electric Vehicle, structured for predictive driving controls based on external information knowledge

Recently, the interest of the automotive market for hybrid vehicles has increased due to the more restrictive pollutants emissions legislation and to the necessity of decreasing the fossil fuel consumption, since such solution allows a consistent improvement of the vehicle global efficiency. The term hybridization regards the energy flow in the powertrain of a vehicle: a standard vehicle has, usually, only one energy source and one energy tank; instead, a hybrid vehicle has at least two energy sources. In most cases, the prime mover is an internal combustion engine (ICE) while the auxiliary energy source can be mechanical, electrical, pneumatic or hydraulic. It is expected from the control unit of a hybrid vehicle the use of the ICE in high efficiency working zones and to shut it down when it is more convenient, while using the EMG at partial loads and as a fast torque response during transients. However, the battery state of charge may represent a limitation for such a strategy. That���s the reason why, in most cases, energy management strategies are based on the State Of Charge, or SOC, control. Several studies have been conducted on this topic and many different approaches have been illustrated. The purpose of this dissertation is to develop an online (usable on-board) control strategy in which the operating modes are defined using an instantaneous optimization method that minimizes the equivalent fuel consumption of a hybrid electric vehicle. The equivalent fuel consumption is calculated by taking into account the total energy used by the hybrid powertrain during the propulsion phases. The first section presents the hybrid vehicles characteristics. The second chapter describes the global model, with a particular focus on the energy management strategies usable for the supervisory control of such a powertrain. The third chapter shows the performance of the implemented controller on a NEDC cycle compared with the one obtained with the original control strategy.

Web2.0, knowledge sharing and privacy in E-learning

Quand le E-learning a ��merg�� il ya 20 ans, cela consistait simplement en un texte affich�� sur un ��cran d'ordinateur, comme un livre. Avec les changements et les progr��s dans la technologie, le E-learning a parcouru un long chemin, maintenant offrant un mat��riel ��ducatif personnalis��, interactif et riche en contenu. Aujourd'hui, le E-learning se transforme de nouveau. En effet, avec la prolif��ration des syst��mes d'apprentissage ��lectronique et des outils d'��dition de contenu ��ducatif, ainsi que les normes ��tablies, c���est devenu plus facile de partager et de r��utiliser le contenu d'apprentissage. En outre, avec le passage �� des m��thodes d'enseignement centr��es sur l'apprenant, en plus de l'effet des techniques et technologies Web2.0, les apprenants ne sont plus seulement les r��cipiendaires du contenu d'apprentissage, mais peuvent jouer un r��le plus actif dans l'enrichissement de ce contenu. Par ailleurs, avec la quantit�� d'informations que les syst��mes E-learning peuvent accumuler sur les apprenants, et l'impact que cela peut avoir sur leur vie priv��e, des pr��occupations sont soulev��es afin de prot��ger la vie priv��e des apprenants. Au meilleur de nos connaissances, il n'existe pas de solutions existantes qui prennent en charge les diff��rents probl��mes soulev��s par ces changements. Dans ce travail, nous abordons ces questions en pr��sentant Cadmus, SHAREK, et le E-learning pr��servant la vie priv��e. Plus pr��cis��ment, Cadmus est une plateforme web, conforme au standard IMS QTI, offrant un cadre et des outils ad��quats pour permettre �� des tuteurs de cr��er et partager des questions de tests et des examens. Plus pr��cis��ment, Cadmus fournit des modules telles que EQRS (Exam Question Recommender System) pour aider les tuteurs �� localiser des questions appropri��es pour leur examens, ICE (Identification of Conflits in Exams) pour aider �� r��soudre les conflits entre les questions contenu dans un m��me examen, et le Topic Tree, con��u pour aider les tuteurs �� mieux organiser leurs questions d'examen et �� assurer facilement la couverture des diff��rent sujets contenus dans les examens. D'autre part, SHAREK (Sharing REsources and Knowledge) fournit un cadre pour pouvoir profiter du meilleur des deux mondes : la solidit�� des syst��mes E-learning et la flexibilit�� de PLE (Personal Learning Environment) tout en permettant aux apprenants d'enrichir le contenu d'apprentissage, et les aider �� localiser nouvelles ressources d'apprentissage. Plus pr��cis��ment, SHAREK combine un syst��me recommandation multicrit��res, ainsi que des techniques et des technologies Web2.0, tels que le RSS et le web social, pour promouvoir de nouvelles ressources d'apprentissage et aider les apprenants �� localiser du contenu adapt��. Finalement, afin de r��pondre aux divers besoins de la vie priv��e dans le E-learning, nous proposons un cadre avec quatre niveaux de vie priv��e, ainsi que quatre niveaux de tra��abilit��. De plus, nous pr��sentons ACES (Anonymous Credentials for E-learning Systems), un ensemble de protocoles, bas��s sur des techniques cryptographiques bien ��tablies, afin d'aider les apprenants �� atteindre leur niveau de vie priv��e d��sir��.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

eRules: a modular adaptive classification rule learning algorithm for data streams

Advances in hardware and software in the past decade allow to capture, record and process fast data streams at a large scale. The research area of data stream mining has emerged as a consequence from these advances in order to cope with the real time analysis of potentially large and changing data streams. Examples of data streams include Google searches, credit card transactions, telemetric data and data of continuous chemical production processes. In some cases the data can be processed in batches by traditional data mining approaches. However, in some applications it is required to analyse the data in real time as soon as it is being captured. Such cases are for example if the data stream is infinite, fast changing, or simply too large in size to be stored. One of the most important data mining techniques on data streams is classification. This involves training the classifier on the data stream in real time and adapting it to concept drifts. Most data stream classifiers are based on decision trees. However, it is well known in the data mining community that there is no single optimal algorithm. An algorithm may work well on one or several datasets but badly on others. This paper introduces eRules, a new rule based adaptive classifier for data streams, based on an evolving set of Rules. eRules induces a set of rules that is constantly evaluated and adapted to changes in the data stream by adding new and removing old rules. It is different from the more popular decision tree based classifiers as it tends to leave data instances rather unclassified than forcing a classification that could be wrong. The ongoing development of eRules aims to improve its accuracy further through dynamic parameter setting which will also address the problem of changing feature domain values.

Teaching and learning with social software in higher education: content management systems integrated with Web-based applications as a key factor for success

The change of paradigm imposed by the Bologna process, in which the student will be responsible for their own learning, and the presence of a new generation of students with higher technological skills, represent a huge challenge for higher education institutions. The use of new Web Social concepts in teaching process, supported by applications commonly called Web 2.0, with which these new students feel at ease, can bring benefits in terms of motivation and the frequency and quality of students' involvement in academic activities. An e-learning platform with web-based applications as a complement can significantly contribute to the development of different skills in higher education students, covering areas which are usually in deficit.