Learning Colours and Shapes by Using Mobile Devices
In recent years, it has become evident that the exploitation of mobile devices has much to offer to the learning process. This research paper is looking into the extent to which an educational application on mobile devices could enhance pupils’ performance in identifying colours and shapes in pre-school and first year of primary school. In this scope, we developed a suitable application which is presented together with some remarkable observations that arouse in the research process. Furthermore, we experimented in order to ascertain whether educational benefits exist and determine what these are exactly. The results of our experimentation are clear, particularly positive and encourage further research and utilization of our application.
Induction of formal concepts by lattice computing techniques for tunable classification
This work proposes an enhancement of Formal Concept Analysis (FCA) by Lattice Computing (LC) techniques. More specifically, a novel Galois connection is introduced toward defining tunable metric distances as well as tunable inclusion measure functions between formal concepts induced from hybrid (i.e., nominal and numerical) data. An induction of formal concepts is pursued here by a novel extension of the Karnaugh map, or K-map for short, technique from digital electronics. In conclusion, granular classification can be pursued. The capacity of a classifier based on formal concepts is demonstrated here with promising results. The formal concepts are interpreted as descriptive decision- making knowledge (rules) induced from the training data.
Fuzzy lattice reasoning (FLR) type neural computation for weighted graph partitioning
The fuzzy lattice reasoning (FLR) neural network was introduced lately based on an inclusion measure function. This work presents a novel FLR extension, namely agglomerative similarity measure FLR, or asmFLR for short, for clustering based on a similarity measure function, the latter (function) may also be based on a metric. We demonstrate application in a metric space emerging from a weighted graph towards partitioning it. The asmFLR compares favorably with four alternative graph-clustering algorithms from the literature in a series of computational experiments on arti?cial data. In addition, our work introduces a novel index for the quality of clustering, which (index) compares favorably with two popular indices from the literature.
Guidelines for evaluating e-learning environments
The evolution of Internet Technology has influenced the basis of education by introducing new methodologies in to teaching and giving a new dimension to distance learning. On the other hand, there is an emerging need on the users part to define some standards that will be used for judging the quality and effectiveness of the educational Websites. In this project, a presentation of evaluation methodology of Virtual Learning Environments (VLEs) is presented, along with basic guidelines that must be followed when evaluating a VLE. This report emphasizes the importance of evalua- tion in the educational practice and presents a review of the current literature in terms of helping the VLE practitioner to find his own way concerning the evaluation process.
A comparative theoretical and benchmarking evaluation of modern operating system kernels
The paper compares core kernel architecture and functionality of four modern operating systems. The subsystems examined are process / thread architecture, scheduling and interrupt handling. Linux, Solaris and FreeBSD have a lot of similarities, owning UNIX roots, but also have some notable differences. However, Windows is significantly different, being a radical non-UNIX design. The paper compares some aspects of the UNIX-like approaches of Linux/Solaris/FreeBSD with Windows, emphasising the consequences of their different design decisions, and presents some comparative performance results.
A Comparative Evaluation of Core Kernel Features of the Recent Linux, FreeBSD, Solaris and Windows Operating Systems
The paper compares core kernel architecture and functionality of four modern operating systems. The subsystems examined are process / thread architecture, scheduling and interrupt handling. Linux, Solaris and FreeBSD have a lot of similarities, owning Unix roots, but also have some notable differences. However, Windows is significantly different, being a radical non-Unix design. The paper compares some aspects of the Unix-like approaches of Linux/Solaris/FreeBSD with Windows, emphasizing the consequences of their different design decisions, and presents some comparative performance results, using Java benchmarks.
Granular Graph Clustering in the Web
We investigate the partition of a weighted graph, representing traffic, to a number of subgraphs such that both inter(external)-subgraph traffic is minimized and intra(internal)-subgraph traffic is maximized. The long-term objective is the development of a Web-navigation support system. We pursue a solution by applying an agglomerative clustering algorithm, or ACA for short, to a metric space emerging from a weighted graph. An enabling technology is inspired from mathematical lattice theory. The proposed techniques compare favorably with alternative techniques in an application to a graph stemming from a University Web-site.
A comparative evaluation of core kernel features of the recent Linux,FreeBSD, Solaris and Windows operating systems.
The paper compares core kernel archi- tecture and functionality of four modern operating systems. The subsystems examined are process / thread architecture, scheduling and interrupt handling. Linux, Solaris and FreeBSD have a lot of similari- ties, owning Unix roots, but also have some notable differences. However, Windows is significantly dif- ferent, being a radical non-Unix design. The paper compares some aspects of the Unix-like approaches of Linux/Solaris/FreeBSD with Windows, emphasizing the consequences of their different design decisions, and presents some comparative performance results, using Java benchmarks.
Developing Effective Educational Videos for Blended Learning
Video is widely used in the context of blended learning as an effective media for delivering educational content. Modern technology allows the rapid and economic development of educational videos using software tools without the need of cameras or other expensive resources. We call such a video, Video as Software. In this paper, we propose a framework for the effective development of Educational Video as Software (EVS). The proposed framework consists of a methodology and a set of design guidelines, both oriented towards the achievement of the learning objectives related to an EVS. In addition, we include a concise presentation of important contemporary software technologies that could be utilized in EVS development. Experimentally, we compare videos produced following the proposed methodology with videos produced based on their author’s creativity, exclusively. Preliminary experimental results are positive and encourages us for further exploration.
PDetect: A Clustering Approach for Detecting Plagiarism in Source Code Datasets
Efficient detection of plagiarism in programming assignments of students is of a great importance to the educational procedure. This paper presents a clustering oriented approach for facing the problem of source code plagiarism. The implemented software, called PDetect, accepts as input a set of program sources and extracts subsets (the clusters of plagiarism) such that each program within a particular subset has been derived from the same original. PDetect proposes the use of an appropriate measure for evaluating plagiarism detection performance and supports the idea of combining different plagiarism detection schemes. Furthermore, a cluster analysis is performed in order to provide information beneficial to the plagiarism detection process. PDetect is designed such that it may be easily adapted over any keyword-based programming language and it is quite beneficial when compared with earlier (state-of-the-art) plagiarism detection approaches.
A framework for the development of educational video
Lately, the video is widely used as an effective media for delivering varied educational content. The enormous expansion of educational video is due to its effectiveness and to the spectacular evolution of video construction technology. Modern technology allows the rapid and economic development of educational videos as Software Systems. Such Videos may be totally developed using software tools without the need for cameras or other expensive resources, e.g. actors. In this paper, we propose a framework for the effective development of Educational Video. The proposed framework consists of a methodology and a set of design guidelines, both oriented towards the achievement of the learning objectives related to an Educational Video. Experimentally, we compare videos produced following the proposed framework with videos produced following a well-known alternative methodology. Experimental results give rise to the success of our approach and encourage us for further exploration.
Διαδικτυακή υπηρεσία δημιουργικής απασχόλησης παιδιών με ειδικές ανάγκες
Υπάρχει ευρεία συμφωνία σχετικά με την σημαντική συμβολή του εκπαιδευτικού λογισμικού στην δημιουργική απασχόληση παιδιών με ειδικές ανάγκες. Ωστόσο, το σχετικό λογισμικό, συχνά έχει υψηλό κόστος ή απαιτεί πολύπλοκες διαδικασίες εγκατάστασης. Επιπλέον, παρατηρείται ένας κατακερματισμός, δηλαδή υπάρχουν πολλά λογισμικά που το καθένα απαιτεί ξεχωριστή εγκατάσταση, συντήρηση και διαχείριση. Συχνά, οι παράγοντες αυτοί δυσχεραίνουν σημαντικά την αξιοποίησή του, ειδικά από παιδιά με ειδικές ανάγκες. Σε αυτήν την εργασία κατασκευάζουμε και αξιολογούμε κατάλληλη διαδικτυακή υπηρεσία που προσφέρει πλήθος εναλλακτικών επιλογών για την δημιουργική απασχόληση των παιδιών με ειδικές ανάγκες. η αρχική αξιολόγηση την υπηρεσίας δείχνει μεγάλη αποδοχή από τα παιδιά με ειδικές ανάγκες και μας ενθαρρύνει για την περαιτέρω ανάπτυξή της.
An XML-Based Framework for the Development of Adaptive Educational Software
Large-scale adaptive educational software requires advanced technological infrastructures in order to support various cooperative educational organizations. This paper proposes a framework for heterogeneity of current collaborative educational technologies. The proposed framework is based on XML, it is of distributive and cooperative nature since it is characterized by the following:
1) Each educational organization may plan its development to meet its own needs.
2) It is possible for each organization to concurrently reuse the software that has already been developed by any other organization.
3) Each organization can combine its own software production, with the production of other cooperative organizations, in various ways.
Adaptive Technological Education Delivery and Student Examination Based on Machine-Learning Tools
The on-going expansion of high technology education in Greece is followed by rapid increases in the numbers of enrolled students. There is an urgent need to deal effectively with both a large number of students in a class and with an ever-updated curriculum. A pilot study is currently under way in the Department of Industrial Informatics at the Technological Educational Institution of Kavala to meet the aforementioned need using novel tutoring- and examination- software tools supported by machine learning techniques. The software will be driven by parametric models applicable in a (mathematical) lattice data domain so as to be able to accommodate both numeric and non-numeric data; furthermore, the model parameters can be estimated/learned adaptively “on line”. This work reports preliminary results with emphasis on examination software.
Εκμάθηση χρωμάτων και σχημάτων με αξιοποίηση κινητών συσκευών
Τα τελευταία χρόνια γίνεται σταδιακά σαφές πως η αξιοποίηση κινητών συσκευών έχει πολλά να προσφέρει στην διαδικασία της μάθησης. Στην εργασία αυτή διερευνούμε κατά πόσο μια εκπαιδευτική εφαρμογή σε κινητή συσκευή μπορεί να βελτιώσει την επίδοση στην αναγνώριση χρωμάτων και σχημάτων στην προσχολική και πρώτη σχολική ηλικία. Στο πλαίσιο αυτό αναπτύξαμε κατάλληλη εφαρμογή την οποία και παρουσιάζουμε μαζί με μια σειρά από αξιοσημείωτες παρατηρήσεις που προέκυψαν από την εμπειρία ανάπτυξης. Επιπλέον, πειραματιστήκαμε με σκοπό να εξακριβώσουμε εάν υπάρχουν εκπαιδευτικά οφέλη και ποια ακριβώς είναι αυτά. Τα αποτελέσματα των πειραμάτων μας είναι σαφή, ιδιαίτερα θετικά και ενθαρρύνουν την περαιτέρω διερεύνηση και αξιοποίηση της εφαρμογής μας.
Clustering dense graphs: A web site graph paradigm
Typically graph-clustering approaches assume that a cluster is a vertex subset such that for all of its vertices, the number of links connecting a vertex to its cluster is higher than the number of links connecting the vertex to the remaining graph. We consider a cluster such that for all of its vertices, the number of links connecting a vertex to its cluster is higher than the number of links connecting the vertex to any other cluster. Based on this funda- mental view, we propose a graph-clustering algorithm that identi?es clusters even if they contain vertices more strongly connected outside than inside their cluster; hence, the pro- posed algorithm is proved exceptionally ef?cient in clustering densely interconnected graphs. Extensive experimentation with arti?cial and real datasets shows that our approach outperforms earlier alternate clustering techniques.
Discovering Clusters of Plagiarism in Students’ Source Codes
Plagiarism in students’ source codes constitutes an important drawback for the educational process. In addition, plagiarism detection in source codes is time consuming and tiresome task. Therefore, many approaches for plagiarism detection have been proposed. Most of the aforementioned approaches receive as input a set of source files and calculate a similarity between each pair of the input set. However, the tutor often needs to detect the clusters of plagiarism, i.e. clusters of students’ assignments such as all assignments in a cluster derive from a common original. In this paper, we propose a novel plagiarism detection algorithm that receives as input a set of source codes and calculates the clusters of plagiarism. Experimental results show the efficiency of our approach and encourage us to further research.
Mac OS vs FreeBSD: a comparative evaluation
FreeBSD and Apple’s Mac OS both implement similar BSD UNIX functionality, using radically different approaches. FreeBSD implements a traditional compact monolithic UNIX ker- nel but Mac OS builds the BSD UNIX functionality on top of the Mach microkernel. The paper aims to an in-depth technical investigation of these approaches. The discussion provides some theoretical insights along with supportive performance benchmarks that will enlighten somehow the relative advantages and disadvantages of each approach. The theoretical and experimental analysis attribute to the Mach’s ports as the major responsible for the rather poor performance of Mac OS on some operations. The study also highlights some directions for improvements of the OSes. Concerning Mac OS, these are performance related.
Benchmark graphs for the evaluation of Clustering Algorithms
Artificial graphs are commonly used for the evaluation of community mining and clustering algorithms. Each artificial graph is assigned a pre-specified clustering, which is compared to clustering solutions obtained by the algorithms
under evaluation. Hence, the pre-specified clustering should comply with specifications that are assumed to delimit a good clustering. However, existing construction processes for artificial graphs do not set explicit specifications for the pre-specified clustering. We call these graphs, randomly clustered graphs. Here, we introduce a new class of benchmark graphs which are clustered according to explicit specifications. We call them optimally clustered graphs. We present the basic properties of optimally clustered graphs and propose algorithms for their construction. Experimentally, we compare two community mining algorithms using both randomly and optimally clustered graphs. Results of this evaluation reveal interesting insights both for the algorithms and the artificial graphs.
Το εκπαιδευτικό βίντεο στην εκπαίδευση από απόσταση ενήλικων
Τα τελευταία χρόνια η εκπαίδευση από απόσταση έχει καταστεί ένας από τους βασικούς τρόπους επιμόρφωσης ενηλίκων, ενώ η εξέλιξη της Τεχνολογίας, στην οποία έχει προστεθεί και το εκπαιδευτικό βίντεο, δημιουργεί προηγμένα ηλεκτρονικά περιβάλλοντα που προσαρμόζονται σε διαφορετικούς τρόπους μάθησης. Στο πλαίσιο αυτό, η παρούσα εργασία παρουσιάζει τα θετικά αποτελέσματα που είχε η χρήση του εκπαιδευτικού βίντεο σε σεμινάριο από απόσταση με θέμα <<Τα εργαλεία Web 2.0 και το εκπαιδευτικό βίντεο: εφαρμογές στην Περιβαλλοντική Εκπαίδευση>>. Το σεμινάριο υλοποιήθηκε με χρήση της πλατφόρμας Moodle και είχε σαν στόχο την επιμόρφωση εργαζομένων ενηλίκων. Έγινε ποιοτική ανάλυση των αναρτήσεων που δημοσιεύτηκαν στην πλατφόρμα, σε συνδυασμό με τις απαντήσεις που δόθηκαν από τους συμμετέχοντες σε ηλεκτρονικό ερωτηματολόγιο. Τα συμπεράσματα δείχνουν τη θετική επίδραση του βίντεο στην κατανόηση του διδακτικού αντικειμένου και στη δημιουργία κινήτρων μάθησης σε ενήλικες εργαζόμενους μαθητές.
Combining Scala with C++ for Efficient Scientific Computation in the Context of ScalaLab
ScalaLab is a MATLAB-like environment for the Java Virtual Machine (JVM).ScalaLab is based on the Scala programming language. It utilizes an extensive set of Java and Scala scientific libraries and also has access to many native C/C++ scientific libraries by using mainly the Java Native Interface (JNI). The performance of the JVM platform is continuously improved at a fast pace. Today JVM can effectively support demanding high-performance computing and scales well on multicore platforms. However, sometimes optimized native C/C++ code can yield even better performance. That code can exploit the peculiarities of the hardware architecture and of special parallel hardware, as for example Graphical Processing Units. The present work reports some of the experience that we gained with experiments with both JITed JVM code and native code. We compare some aspects of Scala and C++ that concern the requirements of scientific computing. The article describes how ScalaLab tries to combine the best features of the Java Virtual Machine with those of the C/C++ technology, in order to implement an effective scientific computing environment.
Αναγνώριση κοινοτήτων τόπων του Παγκόσμιου Ιστού
Clustering is an important issue in the analysis and exploration of data. There is a wide area of clustering applications including information retrieval, image segmentation, character recognition, VLSI design, computer graphics and gene analysis. In particular, applications of graph-clustering algorithms include: monitoring computer networks for administration purposes, visualizing knowledge bases to support human understanding of complex data structures, clustering metric data, clustering of web data and identification of web communities. In addition, there is a growing interest in network analysis. In this context, graph-clustering is also known as community mining. Community mining algorithms have been applied in the study of several networks, including networks of email messages as well as social, metabolic, gene networks, etc. Therefore, the problem of graph-clustering or community mining is well studied and a variety of related algorithms is presented in the literature.Many graph-clustering or community mining algorithms are based on the intuitive notion of intra-cluster (within clusters) density “versus” the inter-cluster (between clusters) sparsity. More precisely, they assume that a community (cluster) is a vertex subset such that for all of its vertices, the number of links connecting a vertex to its community is higher than the number of links connecting the vertex to the remaining graph.
FCknn: A Granular knn Classifier Based on Formal Concepts
Recent work has proposed an enhancement of Formal Concept Analysis (FCA) in a tunable, hybrid formal context including both numerical and nominal data . This work introduces FCknn, that is a granular knn classifier based on formal concepts, whose effectiveness is demonstrated on two benchmark datasets including both numerical and nominal data. Preliminary experimental results compare well with the results by alternative classifiers. Descriptive decision-making knowledge, namely rules, is also induced from the data.
Mining the Community Structure of a Web Site
Most approaches for mining the community structure of a graph are based on the assumption that each member of a community has more links within than outside its community. We argue that this delimitation of a community is not appropriate for graphs representing web sites; hence, we propose a more detailed delimitation for the community structure of web sites. Moreover, we propose a novel graph clustering algorithm for mining communities from web sites. Experimentation on real web sites shows that our approach compares favourably to alternative, well known, community mining algorithms.