In this issue of CIT. Journal of Computing and Information Technology we bring five papers from diverse fields of computing. Three of them deal with various aspects of networks, while the other two address issues in optimization and semantic text processing

Vlado Glavinić

English

The present issue of CIT\u27s Vol.25 brings five papers dealing mostly with applications of computing and information technology. The papers address topics in stability analysis for interconnected systems, steganography, use of Semantic Web technologies to support data interoperability, query processing for XML documents on the Web and application of machine learning in medicine

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EditorialiCIT. Journal of Computing and Information Technology, Vol. 25, No. 3, September 2017, i–iidoi:  10.20532/cit.2017.1003800The present issue of CIT's Vol.25 brings five papers dealing mostly with applications of computing and information technology. The papers address topics in stability analysis for interconnected sys-tems, use of Semantic Web technologies to support data interoperability, query processing for XML documents on the Web and application of machine learning in medicine. The first paper in this issue, Decentralized H∞ Control of Interconnected Systems with Time-Vary-ing Delays by Amal Zouhri and Ismail Boumhidi, focuses on the problem of delay dependent stability/stabilization of interconnected systems with time-varying delays, an issue usually met in practical systems like engineering physics, neural networks, communications networks and tele-communications. The authors' approach, which is based on a new Lyapunov-Krasovskii functional, basically consists of two steps. Firstly a decentralized delay-dependent stability analysis is per-formed which assumes that every local subsystem of the linear interconnected delay system is asymptotically stable. Then a decentralized state-feedback stabilization scheme is designed, for which the family of closed-loop feedback subsystems shows delay-dependent asymptotic stability for each subsystem. This approach has been assessed on a representative example and compared with some other recent ones. Weiguo Ma, Xia Xu and Hairong Zhu tackle issues in control systems caused by lost packets in both feedback and forward channels in the paper Networked Non-fragile H∞ Control for Lipschitz Nonlinear System with Quantization and Packet Dropout in Both Feedback and Forward Chan-nels. They model the transmission in both channels (sensor to controller, and controller to actuator) as Markov chains, and design the controller basing on the H∞ control theory so as to behave sat-isfactorily in the presence of packet dropout. They prove the effectiveness and superiority of the proposed method both mathematically, and by simulation through a numerical example. In their paper LinkED: A Novel Methodology for Publishing Linked Enterprise Data, Shreyas Suresh Rao and Ashalatha Nayak address the important task of Linked Open Data (LOD) publi-cation in enterprise contexts. While current LOD technologies focus on expert search, creation of unified information space, and augmentation of core data from an enterprise, they suffer from a lack of uniform representation across domains, which severely limits their widespread adoption. The authors propose a novel, cross-domain methodology called LinkED, which streamlines the publishing process and addresses the key challenges pertaining to data consistency and replica-bility. The authors demonstrate the soundness of their approach in a case study where they apply LinkED to data from the education domain. The paper New Path Based Index Structure for Processing CAS Queries Over XML Database by Dhanalekshmi Gopinathan, Krishna Asawa deals with query processing of nested data, which are characteristic for retrieval of information on the Web, that typically consists of XML documents. As opposed to existing approaches, mostly focusing on the structure alone, the authors propose to combine siblings of terminal nodes into one path hence efficiently processing twig queries with a smaller number of lookups and joins. In fact, their approach builds on the construction of an index consisting of two indices, namely path and path combined index, in such a way efficiently answer-ing both XML path queries and twig queries with or without value predicates with less expensive joins, and in single lookups. Padmapriya Nammalwar, Venkateswaran Narasimhan, Toshitha Kannan and SindhuMadhuri Morapakala explore ocular infrared thermography that can be used for non-invasive detection of iiglaucoma. In their paper titled Non-invasive Glaucoma Screening Using Ocular Thermal Image Classification, they propose a machine learning method which is based on a combination of ap-proaches: extraction of region of interest (ROI) area attribute based on Euclidean distance; se-lection of four features from Gray Level Co-occurrence Matrix (GLCM) using correlation based feature selection; and mean value of temperature mapping histogram. Five support vector machines (SVM) classifiers, one for each combination of two attributes that include ROI, were trained on a set of 100 images (50 with glaucoma, 50 without) and tested on a set of 16 images. The authors report a 96% accuracy for sensitivity, specificity and accuracy using ROI and GLCM homogeneity features and linear kernel SVM. The results indicate potential use of the method for early screening of glaucoma.Vlado Glavinić Editor-in-Chief 

Editorial for Vol. 25 No. 3

The present issue of CIT's Vol.25 brings five papers dealing mostly with applications of computing and information technology. The papers address topics in stability analysis for interconnected systems, steganography, use of Semantic Web technologies to support data interoperability, query processing for XML documents on the Web and application of machine learning in medicine

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Journal of Computing and Information Technology

EditorialiCIT. Journal of Computing and Information Technology, Vol. 25, No. 2, June 2017, i–iidoi:  10.20532/cit.2017.1003691In this issue of CIT. Journal of Computing and Information Technology we bring five papers from diverse fields of computing. Three of them deal with various aspects of networks, while the other two address issues in optimization and semantic text processing.In the first paper of this issue, Hai Liu, Yong Feng, Qian Qian and Bin Zhang consider the ques-tion of constructing virtual backbones in Wireless Sensor Networks (WSN). Namely, in real WSN implementations, nodes are connected to others with a certain probability, contrary to the exist-ing deterministic network model (DNM). Hence it makes sense to consider probabilistic network models for planning algorithms in WSNs. In this respect, the paper Constructing Reliable Virtual Backbones in Probabilistic Wireless Sensor Networks brings the design of RVBP-CDS, a new algo-rithm for constructing a reliable virtual backbone in a probabilistic WSN, using Effective Degree of Delivery Probability (EDDP) as the key parameter to determine network connectivity by selecting, for each node, those neighboring nodes with the highest EDDP. Simulation results provided prove that RVBP-CDS shows higher performance in terms of network lifetime and network delay when compared to other existing algorithms (RMCDS-GA and LBVBP-MOGA).The second paper, Design, Implementation and Performance Estimation of mtd64-ng, a New Tiny DNS64 Proxy by Gábor Lencse and Dániel Bakai, tackles an issue regarding intercommunication of IPv6 and IPv4 networks, specifically the communication of IPv6-only clients with IPv4-only servers. Within such a context, the authors focus on high performance and computation efficient multi-threaded DNS64 server implementations able to utilize novel multi-core CPU designs. In this respect, the paper describes the redesign of their previous DNS64 server implementation named MTD64, which explicitly addresses this latter's identified shortcomings regarding memory leaking and potential vulnerability to DoS attacks. This new server, named mtd64-ng, was designed and implemented using modern programming concepts of object oriented decomposition and RAII (Resource Acquisition Is Initialization) in order to ensure readable code and avoid critical security vulnerabilities and memory leakage issues. Measurements provided show that mtd64-ng outper-forms both the standard BIND server as well as MTD64 in the number of served ''AAAA'' record requests per second. The next paper, Evaluation of Correlation Measures for Computationally-Light vs. Computation-ally-Heavy Centrality Metrics on Real-World Graphs, addresses a problem within the emerging discipline of network science, an academic field which studies complex networks in a number of application areas from a graph theoretic perspective. Specifically, the author Natarajan Meghana-than investigates data clustering and ranking in real-world networks. He identifies three levels of correlation to be assessed between centrality metrics for real-world networks, and uses Kendall's concordance-based correlation measure to quantitatively assess the relative ordering of two verti-ces within "computationally-light" and "computationally-heavy" centrality metrics. Among other things, the paper provides a comprehensive review of several centrality metrics, which are used as tools for ranking of nodes in a network. Performance of "computationally-light" vs. "computation-ally-heavy" centrality metrics is evaluated on a set of 50 real-world networks.In their paper titled Hybrid Feature Selection Approach Based on GRASP for Cancer Microarray Data, the authors Arpita Nagpal and Deepti Gaur present a computational study of complex mi-croarray data containing a large number of genes with the objective to extract some useful medical information by adopting known heuristic methods. In particular, in order to improve the computa-tional efficiency of the learning model they developed two improved algorithms by modifying with iispecific local search procedures the Greedy Randomized Adaptive Search Procedure (GRASP) for high dimensional microarray data sets. The first algorithm (Tabu based Greedy Randomized Adaptive Search Procedure, TGRASP) results from the application of the tabu search algorithm, while the other (Firefly Greedy Randomized Adaptive Search Procedure, FFGRASP) results from the firefly optimization algorithm. Experimental results show that both introduced algorithms out-perform the existing ones with respect to three performance parameters viz. accuracy, run time and number of selected subsets of features.The last paper in this issue, Mix Multiple Features to Evaluate the Content and the Linguistic Qual-ity of Text Summaries by Samira Ellouze, Mahar Jaoua and Lamia Hadrich Belguith, addresses evaluation of document summaries produced by automatic summarization systems. The authors propose a supervised machine learning-based approach to predict the overall content and linguis-tic quality scores of a summary, as assigned by human judges, which is in contrast to the existing work on summary evaluation, which mostly relies on metrics that capture specific content- or lin-guistic-oriented aspects of a summary. Thus, they consider a number of machine learning models that combine scores produced by individual metrics, alongside a number of linguistic features, including coherence and redundancy features. The experimental evaluation provided in the paper demonstrates that models combining content and linguistic features considerably outperform indi-vidual metrics in terms of correlation against human-assigned scores for both content and linguistic quality, with linguistic- and content-based features jointly contributing to these improvements.Vlado Glavinić Editor-in-Chief 

Editorial for Vol. 25 No. 2

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