Miracle-dz/UniFAR-FTUs huggingface dataset:
Using ontology in electronic evaluation for personalization of e-Learning systems

The testing or the evaluation is a key element of the process learning. The modern techniques, the computers and the Internet, brought new tendencies in learning and evaluation. The distance is no more a problem so the relationship teacher-student is changed more and more. As a new signification of evaluation, we mention here about the self-evaluation, which can be achieved periodic or on the semester end of the discipline. The teacher can choose a type of evaluation and then creates a frame in which the student to be evaluated. In the paper is presented a solution for electronic evaluation, which can be easily adjusted to achieve the self-evaluation. Learning is a cognitive activity that differs from student to student. Most of the eLearning systems do not take into account individual aspects of students, ignoring the different needs that are specific to existing cognitive profiles. Students can have several cognitive styles [12], which makes the efficiency and efficacy of an eLearning system different with distinct students. We have developed an approach based on a dynamic student model (which comprises the student knowledge data) and ontology in order to personalize an eLearning system. The eLearning system will behave differently according to the student characteristics and performance, depending also on the concepts that the student knows.
Inferential Modeling and Independent Component Analysis for Redundant Sensor Validation

The calibration of redundant safety critical sensors in nuclear power plants is a manual task that consumes valuable time and resources. Automated, data-driven techniques, to monitor the calibration of redundant sensors have been developed over the last two decades, but have not been fully implemented. Parity space methods such as the Instrumentation and Calibration Monitoring Program (ICMP) method developed by Electric Power Research Institute and other empirical based inferential modeling techniques have been developed but have not become viable options. Existing solutions to the redundant sensor validation problem have several major flaws that restrict their applications. Parity space method, such as ICMP, are not robust for low redundancy conditions and their operation becomes invalid when there are only two redundant sensors. Empirical based inferential modeling is only valid when intrinsic correlations between predictor variables and response variables remain static during the model training and testing phase. They also commonly produce high variance results and are not the optimal solution to the problem. This dissertation develops and implements independent component analysis (ICA) for redundant sensor validation. Performance of the ICA algorithm produces sufficiently low residual variance parameter estimates when compared to simple averaging, ICMP, and principal component regression (PCR) techniques. For stationary signals, it can detect and isolate sensor drifts for as few as two redundant sensors. It is fast and can be embedded into a real-time system. This is demonstrated on a water level control system.
The Strategic Decision-Making Process in Organizations

This paper intends to describe the evolution of the strategic decision-making processes in organizations and propose an integrate view to these processes. This paper seeks to make a number of contributions to the literature on strategy development processes. It explores a multidimensional conceptualization of strategy development and inquires specific process characteristics, such as creativity, development of resources and rationality. Further, the paper provides a critical evalua- tion of the nature of the relationship between decision-making and performance. Finally, the au- thor emphasizes the need for identifying the different levels of analysis, the need for a continuous temporal analysis, the need for evaluating the events with their natural complexity In summary, the author argues that what is needed in strategy field are "process" models which are more environment-focused. In doing this, the paper highlights promising directions for the development of process models by drawing upon lessons learned in organization-focused strat- egy process research, taking as an example the development of the "decision-making school" as it moved from a content focus to a process focus.
An approach on software interface selection

The aim of this paper is to show one way of the most convenient software interfaces selection for the implementation of special sampled distributed applications. The different types of software interfaces were described (functional profiles and middleware) as well as the attributes of computer networks that influence on target software interface selection. The best solution of the software interface selection was realized as combination of the attributes impact to the software interface and their significance to the distributed applications. The final result shows how some of software interface is convenient for realization of distributed applications.
PERMAINAN MONOPOLI SEBAGAI MEDIA PEMBELAJARAN SUB MATERI SEL PADA SISWA SMA KELAS XI IPA

Penelitian ini bertujuan mengembangkan media permainan monopoli, dan mendeskripsikan kelayakan media permainan monopoli sebagai media pembelajaran biologi SMA kelas XI IPA semester 1 pada materi Sel. Jenis penelitian ini merupakan penelitian pengembangan menggunakan model pengembangan 4 D model sampai tahap pengembangan ( Develope ). Sasaran penelitian ini yaitu media pembelajaran Sel yang dikemas dalam bentuk permainan monopoli biologi dan perangkat pembelajaran. Uji coba secara terbatas diterapkan pada siswa kelas XI IPA semester 1 SMA Al Azhar Menganti, Gresik tahun ajaran 2011-2012. Hasil penelitian ini berupa media permainan monopoli yang mendapatkan validitas secara teoritis dengan kelayakan aspek format media 90%, aspek visual 94%, aspek fungsi/kualitas media 92,86%, dan aspek kejelasan media dalam penyajian konsep 88,33%. Dari deskripsi tersebut, media permainan monopoli layak dan dapat diimplementasikan untuk kegiatan belajar mengajar pada materi struktur dan fungsi sel. Kata kunci: media pembelajaran, permainan monopoli materi sel.
The Statistical Features of eLSe Learning Environment

This paper describes the statistical features of eLSe learning environment. The platform is developed in Department of Computing at University of Rousse. It is entirely based on open source components and is used in 14 Bulgarian Universities and also in educational institutions in Germany, Hungary and Russia. A comparison between the use of eLSe in three countries - Bulgaria, Germany and Russia is given.
Introducing Research Methods to Computer Science Honours Students

Research skills are important for any academic and can be of great benefit to any professional person. These skills are, however, difficult to teach and to learn. In the Department of Computer Science at the University of the Witwatersrand we have for a number of years included the completion of a research report as part of our Honours programme. This paper is a case study of how we have implemented an introductory research methods Honours course to increase our students’ exposure to research and to help them cope better with their research reports. This course provides an effective way of introducing research to a relatively large class of Honours students. It is now being given for the second time. Our experience with the course is positive and we believe it has made a major impact on the way research is done by Honours students. Many of the problems we encountered with the Honours research reports prior to the introduction of the research methods course have been alleviated.
TDL Specification and Report

This report defines the syntax and semantics of the timing definition language TDL, which has been developed as part of project MoDECS at the Paris Lodron University of Salzburg (Austria). TDL allows us to specify the timing behavior of a hard real time control application in a descriptive way and separates the timing aspect of such applications from the functionality, which must be provided separately using an imperative programming language such as Java, C or C++. TDL is conceptually based on Giotto, but provides extended features, a more convenient syntax, and an improved set of programming tools.
Leader-following consensus protocols for formation control of multi-agent network

Two protocols are presented, which can make agents reach consensus while achieving and preserving the desired for- mation in fixed topology with and without communication time- delay for multi-agent network. First, the protocol without consid- ering the communication time-delay is presented, and by using Lyapunov stability theory, the sufficient condition of stability for this multi-agent system is presented. Further, considering the com- munication time-delay, the effectiveness of the protocol based on Lyapunov-Krasovskii function is demonstrated. The main contri- bution of the proposed protocols is that, as well as the velocity consensus is considered, the formation control is concerned for multi-agent systems described as the second-order equations. Finally, numerical examples are presented to illustrate the effec- tiveness of the proposed protocols.
Adaptable technique for recovering lost internet audio packets

With rapid progress in both computers and networks, real-time multimedia applications are now possible on the Internet. Since the Internet was designed to support traditional applications, multimedia applications on the Internet often suffer from unacceptable delay, jitter and data loss. Among these, data loss has the largest impact on quality. When the retransmission technique is used to recover lost audio packets, more obstacles, like the delay time resulted from lost packets retransmission, network traffics overloading, and buffer management, are faced. All above demonstrated problems are due to the real-time communication features. In this paper, we demonstrate a new technique that studies the audio packet's priority and it's effect on the transmission process. Hence, our technique arranges the audio packets depending on there importance. So, before deciding to retransmit a packet, we should determine if this packet could be recovered by other suitable technique or not. Hence, the number of retransmitted packets is decreased. Therefore, all above problems may be calmed. Some additional parameters, like a buffer management and a packet position are taken in the consideration. These parameters help us in selecting an ideal recovery technique for lost audio packets. Finally, NS2 is used to build a simulated environment for testing our technique.
SHORTEST PATH METHODS IN TRANSPORTATION MODELS

A survey of shortest path methods is presented, focusing on their use in transportation models. All the algorithms described are shown to derive from one single prototype method, the difference between them depending only on the particular data structure used in their implementation. This analysis suggests a new classification of the shortest path algorithms. The single shortest path tree problem and the all pairs shortest path problem are both considered, the latter being particularly important in transportation models. Experimental results are illustrated to assess the relative efficiency of the algorithms. (TRRL)
Grammar Instruction in the Learner-Celltered EFL Classroom

Today, the topic of grammar seems to be once again at the forefront of discussions in teaching and learning English as a foreign language (EFL) in Japan. This paper aims to reconsider English grammar instruction in the leamer-centered EFL classroom. I discussed deductive and inductive learning, task-supported/based teaching, grammar and authenticity, and grammatical consciousness-raising. Acquisition of language form may be better facilitated by the leamer's working through grammatical processes than by working at assembling grammatical constructs. Japanese teachers of English should teach grammar in a principled manner and pay more attention to how they should set about doing so.
Composite objective mirror descent

We present a new method for regularized convex optimization and analyze it under both online and stochastic optimization settings. In addition to unifying previously known firstorder algorithms, such as the projected gradient method, mirror descent, and forwardbackward splitting, our method yields new analysis and algorithms. We also derive specific instantiations of our method for commonly used regularization functions, such as l1, mixed norm, and trace-norm.
Using component metadata to regression test component-based software: Research Articles

Increasingly, modern-day software systems are being built by combining externally-developed software components with application-specific code. For such systems, existing program-analysis-based software engineering techniques may not directly apply, due to lack of information about components. To address this problem, the use of component metadata has been proposed. Component metadata are metadata and metamethods provided with components, that retrieve or calculate information about those components. In particular, two component-metadata-based approaches for regression test selection are described: one using code-based component metadata and the other using specification-based component metadata. The results of empirical studies that illustrate the potential of these techniques to provide savings in re-testing effort are provided. Copyright © 2006 John Wiley & Sons, Ltd. Based in part on ‘Using component metacontents to support the regression testing of component-based software’ by A. Orso, M. J. Harrold, D. Rosenblum, G. Rothermel, M. L. Soffa and H. Do, which appeared in Proceedings of the International Conference on Software Maintenance, Florence, Italy, November 2001, pp. 716–725 [1]. © 2001 IEEE. This revised and expanded version appears here with the permission of the IEEE.
Tools on R for Dose-response curves analysis

In experimental pharmacology, studies on drug-receptor interactions commonly use dose-response curves (DRC) established under repeated measurements designs. The best approach to analyse a doseresponse relationship is to use non linear mixed effects models (nlme) (Davidian and Giltinian, 1995), but specific softwares dedicated to analyse pharmacological data have not yet developped nlme procedures. The aim of this work was to provide accurate and easy-to-use tools on R to assist pharmacologists with using nlme modelling to fit DRC. Five functions using available R packages have been built. The Est.Pop function, using nlme function in nlme package (Pinheiro and Bates, 2000), gives an estimation of the different parameters included in the predicted function and a qqplot of the residuals. The IC.par function provides a confidence interval for each parameter of the predicted function for the confidence level asked by users. The Graph.curves function displays a graph showing the individual fitted curves and the population fitted curve which illustrate the individual effect on physiological response. Nevertheless, nlme procedures are very susceptible to outliers points in the data sets and the convergence of the iterative calculus is not always achieved. In those situations and when the residuals seem not to be normally distributed the Est.Boot function is more accurate to give an estimation of the predicted function parameters by a non parametric bootstrap method using the bootstrap package (Huet et al, 2004). Depending on the bioassay and the relative asymmetry of the curves, four predictive functions (Hill equation, Richards, Gompertz, Hill modified functions) can be tested (Giraldo et al, 2002) with those tools; the Comp.Mod function is dedicated to compare established models and to detect the best one.
On the Subjectivity of Human Authored Summaries

We address the issue of human subjectivity when authoring summaries, aiming at a simple, robust evaluation of machine generated summaries. Applying a cross comprehension test on human authored short summaries from broadcast news, the level of subjectivity is gauged among four authors. The instruction set is simple, thus there is enough room for subjectivity. However the approach is robust because the test does not use the absolute score, relying instead on relative comparison, effectively alleviating the subjectivity. Finally we illustrate the application of the above scheme when evaluating the informativeness of machine generated summaries.
Approaches to image retrieval based on compressed data for multimedia database systems

This dissertation develops and implements image retrieval approaches which can be applied to compressed image representations for multimedia database systems. The main image representations we deal with are fractal image coding and wavelet transform coding. The experiments we conducted in this research demonstrate the effectiveness of our approaches. We first investigate the image representation through fractal image coding, which exploits self-similarity within an image. We build an image data model called nona-tree to facilitate image retrieval based on matching of fractal codes between the query image and database images. It ensures a query can be carried out to retrieve images based on the query image not only as a whole example image, but also as a subimage. We then extend the work of fractal coding to joint fractal coding, a technique which can be utilized to explore similarities between images. An effective similarity metric has been set up for measuring degree of similarity between the query image and a database image. Joint fractal coding is proved to be a powerful clustering approach for large image databases. We believe that the clustering of images into categories on the basis of content is a prerequisite to the effective and efficient execution of such queries. Once the query is narrowed to a specific category, image retrieval can then proceed efficiently. We also describe the implementation of an image retrieval approach based on measuring the feature vectors constructed by subband energies obtained from wavelet transform. We compare the effectiveness of image retrieval from the fractal coding approach and the wavelet approach. Finally we describe the design and implementation of ImageSeek, a Graphical User Interface developed by X Windows/Motif widget sets, for interactively performing image retrieval. We demonstrate the functions we have for the interface, and discuss future integration into a more comprehensive multimedia retrieving tool. The results presented in this dissertation collectively offer a viable and practical approach to the design and implementation of effective image clustering and image retrieval in a large image database environment. We believe that the research conducted is well suited for incorporation into many digital library applications to support effective and efficient visual data retrieval.
Relational query interface to the LDS Hospital patient database

Since 1970, the HELP hospital information system has been collecting data into a common integrated database at the LDS Hospital. With the vast amount of stored information, there is a critical need for data extraction tools to use and verify these collected data. The purpose of this thesis is to increase and improve the access to the HELP patient database so that physicians can utilize the wealth of stored information in establishing new medical standards or verifying existing ones. In order to increase the access to the patient database, an information retrieval system will be created to acquire the data stored on the LDS Hospital TANDEM computer system and allow the transfer of that data to a variety of personal computers. To improve access to the patient database, a new query interface or editor will generate and execute PAL (PTXT APPLICATION LANGUAGE) queries to extract information from the
A Multimodal Hierarchial Approach to Robot Learning by Imitation

In this paper we propose an approach to robot learning by imitation that uses the multimodal inputs of language, vision and motor. In our approach a student robot learns from a teacher robot how to perform three separate behaviours based on these inputs. We considered two neural architectures for performing this robot learning. First, a one-step hierarchial architecture trained with two different learning approaches either based on Kohonen's self-organising map or based on the Helmholtz machine turns out to be inefficient or not capable of performing differentiated behavior. In response we produced a hierarchial architecture that combines both learning approaches to overcome these problems. In doing so the proposed robot system models specific aspects of learning using concepts of the mirror neuron system (Rizzolatti and Arbib, 1998) with regards to demonstration learning.
Media Synchronization and Causality Control for Distributed Multimedia Applications

SUMMARY This paper proposes a media synchronization scheme with causality control for distributed multimedia applications in which the temporal and causal relationships exist among media streams such as computer data, voice, and video. In the scheme, the ∆-causality control is performed for causality, and the Virtual-Time Rendering (VTR) algorithm, which the authors previously proposed, is used for media synchronization. The paper deals with a networked shooting game as an example of such applications and demonstrates the eﬀectiveness of the scheme by experiment.
Sense-based text classification by semantic hierarchy representation

Automatic classification of web pages is an effective way to facilitate the process of retrieving information from the Internet. Currently, two major classification methods are used in this area: keyword-based classification and sense-based classification. For keyword-based classification, keywords often have different semantic meanings, and the correct keyword matching is largely based on using exactly the same keywords. Thus, the classification results of keyword-based classification are not always satisfying. Many sense-based classification algorithms and systems have been presented, but they pay little attention to the relationship between senses. In this dissertation, we present a method to automatically classify documents based on the meanings of words and the relationships between groups of meanings or concepts. The classification algorithm builds on the word sense structures provided by a lexical database, which not only arranges words into groups of synonyms, but also arranges these groups of synonyms into hierarchies that represent the relationships between concepts. Another problem with current classification systems is that most of them ignore the conflict between the fixed number of categories and the growing number of documents being added to the system. To address this problem, a category-based clustering method is developed to automatically extract a new category from a category that needs to be split. A category must be divided when the number of documents in the category is larger than a predefined size. Experimental results show that the semantic hierarchy classification algorithm increases the classification accuracy by 13% compared to existing sense-based classification algorithms. The category-based clustering algorithm achieves a higher quality cluster than other existing methods that do not use category information. Combining the automatic classification based on word meanings and the dynamic addition of new categories based on clustering, we develop a new system to meet the current and future needs of a growing Internet.