Recent Patents on Computer Science - Volume 7, Issue 1, 2014

Hamid Mcheick is currently an associate professor in computer science department at the University of Quebec At Chicoutimi (UQAC), Quebec, Canada. He holds a PhD degree in software engineering and distributed system from Montreal University, Canada. He holds a master degree from the University of Quebec at Montreal, Canada in information retrieval system. Professor Mcheick is interested in distributed software architecture, mobiles applications and pervasive computing, as well as in separation of concerns (component, services, aspect, etc.). He works also in ubiquitous computing, service composition of business process. He has given many talks in congresses and conferences as a keynote or invited speaker. He is a chief in editor and guest editor of several international journals. His research is supported by many research grants he has received from the Canadian government, University of Montreal, CRIM (Centre de Recherche informatique de Montreal), University of UQAM, and University of Quebec At Chicoutimi, Canada. The missions of Bentham Journal volume aim to bring, for both academics as well as for industrial practitioners, a set of articles discussing and answering the recent patents on core topics of software computing and intelligence and improving the state-of-the-art of worldwide research in the areas of computer science issues by publishing high-quality articles in this area, and by exposing the readers to various methodologies, tools, technologies, algorithms, results, and types of articles. It is our pleasure to highlight in June 2014, the 7th volume of the Journal Recent Patents on Computer Science (CSENG) that includes a selected and reviewed research papers. This issue includes papers of Software Defined Networks for Data Center Optimization, Intelligent techniques and systems in credit risk analysis and forecasting, Cultivating Software Solutions Development in the Scientific Academia, Information Security and Threats in Mobile Appliances, Hybrid Swarm Intelligence and Artificial Neural Network for Mitigating Malware Effects, An Experimental Investigation of MLPNN and GRNN Classification Methods for Evaluation of Different sEMG-Extracted Features, Review on Patents in Mobile Augmented Reality, and Exploring various information in DVC side information generation. A challenge to leap forward for the contributing Authors of this issue was to present the critical underlying of many research topics, such as possibility of optimizing data management, various software developmental models, credit risk domain, information security in mobile devices, and many others. Acting as the Editor-in-Chief of Recent Patents on Computer Science issue, I wish to express our gratitude to the Authors, my particular thanks to Bentham Science for their on-going commitment to the highest quality science, to Elma Tabita Bashir and Sumaiya Azhar, Publication Managers, and the entire Bentham Editorial Staff that have done an exceptional job in insuring the highest standards of peer review. I would like to particularly acknowledge the untiring contributions of our distinguished cadre of Editorial Board Members who do the lion’s share of the reviews and are largely responsible for the exceptional quality of the current and past issues. Also, I would appreciate to thank our readers and encourage you to contact me for any suggestions, criticismsand ideas that can help us to all make “Recent Patents on Computer Science” meet the expectations that it is targeted to do.

The rapid development of new technologies in the field of hardware, software and telecommunications allowed the creation of a new mobile device generation (smartphones and tablets) characterized by interesting and attractive features such as touch or multi-touch displays and embedded Operating Systems (OSs). Mobile OSs offer several functional and playful features to end-users by means of mobile applications. Such characteristics led to a worldwide increase in smartphones’ sales, resulting in Android being the world’s best-selling mobile OS. However, due to its popularity, Android is also the first mobile open source OS with a large number of vulnerabilities, cyber-attacks and malicious apps. This paper aims at presenting a review of recent patents, and state of art solutions as well, in the field of information security in mobile devices. Finally, a discussion pointing out the current and future developments in this area is presented.

Credit risk evaluation and bankruptcy analysis is essential for various financial institutions which must minimize their possible loss, as well as banking sector, investors, governing authorities, as it helps to identify possible financial problems or even predict future financial crises. Various artificial intelligence, soft computing and machine learning techniques often prove to overcome limitations of previously applied techniques or tend to show competitive results in terms of accuracy or precision. These techniques are widely developed, researched and applied to solve problems in credit risk domain. Data retrieval, collection, preprocessing and feature selection play an important role in this field; thus proper implementation of these techniques is adequately important. This review is focused on available patents from credit risk domain which involve intelligent techniques, with both systematic and implementation (engineering) aspects, as well as identification of future trends in this field.

The constantly increasing amount of data generated from our everyday life requires Data Centers to store data. Data Centers need a lot of resources to operate, and Companies are working to find new solutions to improve efficiency and as well as effectiveness. After using sea water to cool off servers and studying alternative ways of generating power, the attention is now moving on alternative ways to manage data. Optimizing data management has two goals: power saving and efficiency increase. In this paper, we will discuss how Software-Defined procedures could be applied on Data Centers to optimize their operating modes, also reviewing some recent patents. Finally, we will discuss the importance of this technology along with future developments, highlighting pro and cons.

The classification accuracy in a myoelectric control system depends on choosing the optimal features that represent surface electromyographic (sEMG) signal, and selecting robust and fast classification algorithm. In this work, eight hand motions were classified using different extracted features from sEMG signals. The results of the experiment show that the classification rate of 97.41% was achieved using wavelet coefficients as feature vector and general regression neural network (GRNN) classifier. In addition, we found that the combination of sample entropy (SampEnt), root mean square (RMS), myopulse percentage rate (MYOP), and difference absolute standard deviation value (DASDV) achieved the highest classification rate of 95.68% using multilayer perceptron neural network (MLPNN) classifier.

Today networks are interconnected wired and wireless network. With the explosive growth and increasing complexity of network applications, malware attacks such as worm attack against network are critical. Although of the evolution of worm detection techniques, worms are still the most malware threats attacking computer systems. Early detection of unknown worms is still a problem. Swarm Intelligence (SI) in recent patents seeks inspiration in the behavior of swarms of insects or other animals such as ants. SI is applied in other fields with success. We used it in the field of worm detection. Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. This paper introduces a system for detecting unknown worms based on the collected information from local victim using Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN). This system can detect unknown worms effectively in both small and large size networks. In addition, this system produces prediction to the infection percentage in the network. This prediction mechanism supports the network administrator in decision-making process to respond quickly to worm propagation accurately.

Software design and its engineering is crucial for the scientific software impact. Successful, comprehensive, flexible, futuristic and on demand, software development can only be achieved, if the system is properly designed, following the software engineering principles, modelling approaches and implementation life cycles. This paper briefly identifies and discusses that how can we overcome the major issues turning into the deficiencies during the effective software development life cycle execution, human computer interaction design and in choosing the technologies, especially when we are developing agile prototype scientific solutions in academia, without much resources and informatics background. Most of the existing software modelling approaches are complex and suffer from design short comings though their impact depends on professional, user friendly, sustainable and high quality solution. Meeting the hypothetical goals of this research, paper discusses a newly proposed science oriented approach ‘Butterfly’; simple, accumulative way of combining different benefiting aspects of the various software developmental models together with the missing elements, towards the abridged way of acceptance for the betterment of modelling of the scientific software solutions in academia by targeting key developmental points: intuitive, graphical user interface design, stable methodical implementation and comprehensive output presentation. Furthermore in this paper a comparative, measurement based analysis of different software development approaches is performed and concluded results justifies the significance of Butterfly model, and different related patents are discussed.