318,692 research outputs found

    Pattern recognition in software engineering trend adapting

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    Whether and when to adapt to certain software engineering trends are difficult questions to be answered by many decision-makers. The main reasons are due to the fact that evolution of software engineering trends itself is determined by various factors, many of which come from the fields outside of the software technology, thus hard to predict. So it is even harder to estimate the cost and benefit when adapting to certain trends. This paper is intended to study ways to decrease the risk involved in such decision making processes, by developing a pattern from past software engineering trends. While the pattern cannot answer all the questions by itself, it can relief the decision makers in a large extent by providing the most important information relevant to the software engineering trends. The pattern recognition is achieved by using neural networks. Our result seems to be very encouraging, which begins to prove that there does exist pattern between the input data that we can observe and the output data that we need to know. Although more trends need to be observed and analyzed before we can reach a more concrete conclusion, it does show that neural network may be a valid approach in future research

    Kaleidoscope JEIRP on Learning Patterns for the Design and Deployment of Mathematical Games: Final Report

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    Project deliverable (D40.05.01-F)Over the last few years have witnessed a growing recognition of the educational potential of computer games. However, it is generally agreed that the process of designing and deploying TEL resources generally and games for mathematical learning specifically is a difficult task. The Kaleidoscope project, "Learning patterns for the design and deployment of mathematical games", aims to investigate this problem. We work from the premise that designing and deploying games for mathematical learning requires the assimilation and integration of deep knowledge from diverse domains of expertise including mathematics, games development, software engineering, learning and teaching. We promote the use of a design patterns approach to address this problem. This deliverable reports on the project by presenting both a connected account of the prior deliverables and also a detailed description of the methodology involved in producing those deliverables. In terms of conducting the future work which this report envisages, the setting out of our methodology is seen by us as very significant. The central deliverable includes reference to a large set of learning patterns for use by educators, researchers, practitioners, designers and software developers when designing and deploying TEL-based mathematical games. Our pattern language is suggested as an enabling tool for good practice, by facilitating pattern-specific communication and knowledge sharing between participants. We provide a set of trails as a "way-in" to using the learning pattern language. We report in this methodology how the project has enabled the synergistic collaboration of what started out as two distinct strands: design and deployment, even to the extent that it is now difficult to identify those strands within the processes and deliverables of the project. The tools and outcomes from the project can be found at: http://lp.noe-kaleidoscope.org

    Methods to Localize Shorts Between Power and Ground Circuits

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    In the competitive world of microprocessor design and manufacturing, rapid advancements can be facilitated by learning from the components made by one’s closest rivals. To make this possible, Orisar Inc. (formerly Semiconductor Insights) provides reverse engineering services to integrated circuit (IC) manufacturers. The process produces a circuit diagram from a chip and allows the manufacturer to learn about a competitor’s product. These services are also used to determine if any intellectual property infringements have been committed by their competitor. Reverse engineering of integrated circuit is made difficult by the shrinking form factor and increasing transistor density. To perform this complex task Orisar Inc. employs sophisticated techniques to capture the design of an IC. Electron microscope photography captures a detailed image of an IC layer. Because a typical IC contains more than one layer, each layer is photographed and physically removed from the IC to expose the next layer. A noise removal algorithm is then applied to the pictures, which are then passed to pattern recognition software in order to transfer the layer design into a polygonal representation of the circuit. At the last step a knowledgeable human expert looks at the polygonal representation and inputs the design into a standard electronic schematic with a CAD package. This process us currently very time consuming. We propose a method which can be easily automated thereby saving valuable worker time and accelerating the process of reverse engineering

    Rule groupings in expert systems using nearest neighbour decision rules, and convex hulls

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    Expert System shells are lacking in many areas of software engineering. Large rule based systems are not semantically comprehensible, difficult to debug, and impossible to modify or validate. Partitioning a set of rules found in CLIPS (C Language Integrated Production System) into groups of rules which reflect the underlying semantic subdomains of the problem, will address adequately the concerns stated above. Techniques are introduced to structure a CLIPS rule base into groups of rules that inherently have common semantic information. The concepts involved are imported from the field of A.I., Pattern Recognition, and Statistical Inference. Techniques focus on the areas of feature selection, classification, and a criteria of how 'good' the classification technique is, based on Bayesian Decision Theory. A variety of distance metrics are discussed for measuring the 'closeness' of CLIPS rules and various Nearest Neighbor classification algorithms are described based on the above metric

    Formal Verification, Quantitative Analysis and Automated Detection of Design Patterns

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    Present-day software engineering concepts emphasize on developing software based on design patterns. Design patterns form the basis of generic solution to a recurring design problem. Software requirement analysis and design methodologies based on different Unified Modelling Language (UML) diagrams need to be strengthened by the use of a number of design patterns. In this study, an attempt has been made for automated verification of the design patterns. A grammar has been developed for verification and recognition of selected design patterns. ANTLR (ANother Tool for Language Recognition) tool has been used for verification of developed grammar. After proper verification and validation of design patterns, there comes a need to quantitatively determine the quality of design patterns. Hence, we have provided a methodology to compare the quality attributes of a system having design pattern solution with a system having non-pattern solution, both the system intending to provide same functionalities. Using Quality Model for Object-Oriented Design (QMOOD) approach, the cut-off points are calculated in order to provide the exact size of the system in terms of the number of classes, for which the solution adopted using design pattern, provides more quality parameters. Again Design Pattern Detection (DPD) has also considered as an emerging field of Software Reverse Engineering. An attempt has been made to present a noble approach for design pattern detection with the help of Graph Isomorphism and Normalized Cross Correlation (NCC) techniques. Eclipse Plugin i.e., ObjectAid is used to extract UML class diagrams as well as the eXtensible Markup Language (XML) files from the Software System and Design Pattern. An algorithm is proposed to extract relevant information from the XML files, and Graph Isomorphism technique is used to find the pattern subgraph. Use of NCC provides the percentage existence of the pattern in the system

    CoCalc as a Learning Tool for Neural Network Simulation in the Special Course "Foundations of Mathematic Informatics"

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    The role of neural network modeling in the learning content of the special course "Foundations of Mathematical Informatics" was discussed. The course was developed for the students of technical universities - future IT-specialists and directed to breaking the gap between theoretic computer science and it's applied applications: software, system and computing engineering. CoCalc was justified as a learning tool of mathematical informatics in general and neural network modeling in particular. The elements of technique of using CoCalc at studying topic "Neural network and pattern recognition" of the special course "Foundations of Mathematic Informatics" are shown. The program code was presented in a CoffeeScript language, which implements the basic components of artificial neural network: neurons, synaptic connections, functions of activations (tangential, sigmoid, stepped) and their derivatives, methods of calculating the network's weights, etc. The features of the Kolmogorov-Arnold representation theorem application were discussed for determination the architecture of multilayer neural networks. The implementation of the disjunctive logical element and approximation of an arbitrary function using a three-layer neural network were given as an examples. According to the simulation results, a conclusion was made as for the limits of the use of constructed networks, in which they retain their adequacy. The framework topics of individual research of the artificial neural networks is proposed.Comment: 16 pages, 3 figures, Proceedings of the 13th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer (ICTERI, 2018
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