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Towards formal specification and generation of autonomic policies

EUC 2005 Workshops: 2nd International Symposium on Ubiquitous Intelligence and Smart Worlds, UISW 2005, IFIP International Symposium on Network-Centric Ubiquitous Systems, NCUS 2005, 1st International Workshop on Security in Ubiquitous Computing Systems, SecUbiq 2005, 1st International Workshop on RFID and Ubiquitous Sensor Networks, USN 2005 and International Workshop on Trusted and Autonomic Ubiquitous and Embedded Systems, TAUES 2005 held in conjunction with the IFIP International Conference on Embedded and Ubiquitous Computing, 2005

作者： Sterritt, Roy Hinchey, Michael G. Rash, James L. Truszkowski, Walt Rouff, Christopher A. Gracanin, Denis University of Ulster Ireland NASA Software Engineering Laboratory NASA Goddard Space Flight Center GreenbeltMD20771 United States Advanced Architectures and Automation Branch NASA Goddard Space Flight Center GreenbeltMD20771 United States Advanced Concepts Business Unit Science Applications International Corp McLeanVA22102 United States Virginia Tech Department of Computer Science BlacksburgVA United States

ISBN: (纸本)3540308032

Autonomic Computing (AC), self-management based on high level guidance from humans, is increasingly gaining momentum as the way forward in designing reliable systems to hide complexity and conquer IT management costs. Effectively, AC may be viewed as Policy-Based Self-Management. In this paper we look at the motivation for utilizing NASA requirements-based programming technologies for mechanically transforming policies (expressed in restricted natural language, or appropriate graphical notations) into a provably equivalent formal model that can be used as the basis for code generation and other transformations, with the goal of self-generation of provable autonomic policies. © IFIP International Federation for Information Processing 2005.

关键词： Formal specification

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Objective Full-Reference Image Assessment Metrics for Estimating the Quality of Remote Sensing Images

Objective Full-Reference Image Assessment Metrics for Estima...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： R. Maruthi P. Anusha P. Sankar K. Thiyagaragan Department of Computer Applications Hindustan Institute of Technology and Science Chennai Tamil Nadu India Department of Software Engineering Periyar Maniammai Institute of Science and Technology Thanjavur India Department of Electronics and Communication Engg Hindustan Institute of Technology and Science Chennai Tamil Nadu India

Image Quality (IQ) assessment is a very complex task and it is extremely important to evaluate the images with the metrics. The metrics applied can be a full reference, partial reference or no-reference metric and it depends on the application and availability of the ground truth. Most of the IQ metrics are developed by considering the Visual System (VS) of humans. The assessment methods studied in this paper focuses on some of the Full-Reference (FR) measures and it is used to estimate the remote sensing noisy images. The effectiveness of the measures demonstrates a considerable outcome and demonstrates how well the noisy remote sensing images are being quantified.

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Resource Optimization using Virtual Machine Swapping

Resource Optimization using Virtual Machine Swapping

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International Conference on Circuits, Power and Computing Technologies

作者： R Suchithra N. Rajkumar Department of Computer Applications SET Jain University Bangalore India Department of Software Engineering Ramakrishna College of Engineering Coimbatore India

ISBN: (纸本)9781467349215

While Virtualization has gained popularity in recent years;data centers reap the benefits of resource utilization through virtual machine migration. We present a virtual machine placement algorithm in a resource constrained environment .The first part of the algorithm deals with virtual machine migration. In the second part we implement a virtual machine swapping model that improves the complexity of finding the right neighborhood for swapping virtual machines that are running between two servers. The aim of our approach is to increase the resource utilization and reduce the resource wastage. We have evaluated the applicability of our models through experimentation and proved that our algorithm leads to efficient resource allocation.

关键词： Cloud Computing Resource Allocation Swapping Virtualization Virtual Machine Migration

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A Toolbox of Feminist Wonder: Theories and methods that can make a difference 26

A Toolbox of Feminist Wonder: Theories and methods that can ...

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26th ACM Conference on computer-Supported Cooperative Work and Social Computing, CSCW 2023

作者： Hansson, Karin Bardzell, Shaowen Bhandari, Aparajita Boulicault, Marion Doyle, Dylan Thomas Erete, Sheena Cerratto Pargman, Teresa Lazem, Shaimaa Muller, Michael Normark, Maria Petterson, Adrian Rosén, Anton Poikolainen Taylor, Alex S. Thomas, Jakita O. Watson, Julia Södertörn University The School of Natural Sciences Technology and Environmental Studies Sweden Georgia Institute of Technology The School of Interactive Computing United States Cornell University Department of Communication United States University of Edinburgh Department of Philosophy United States University of Colorado Boulder Department of Information Science United States University of Maryland College of Information United States Stockholm University The Department of Computer and Systems Sciences Sweden City for Scientific Research and Technological Applications The Informatics Research Institute Egypt Ibm Research Human Centered Ai United States Uppsala University Department of Informatics and Media Sweden University of Toronto Faculty of Information Canada Aalto University The School of Arts Design and Architecture Finland City University of London The Centre for Human Computer Interaction Design United Kingdom Auburn University Department of Computer Science and Software Engineering United States University of Toronto Department of Computer Science Canada

ISBN: (纸本)9798400701290

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to;1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists;2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy. © 2023 Owner/Author.

关键词： Computation theory

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Two-Stage Guided Constraint Differential Evolution Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1109-1133页

作者： Sung, Tien-Wen Liang, Qiaoxin Hong, Chuanbo Huang, Zeming Li, Wei Nguyen, Trinh-Dong College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China School of Electronic Electrical Engineering and Physics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Department of Software Engineering University of Information Technology Ho Chi Minh City Viet Nam

The development of population intelligence has shown a great trend of a linear surge in recent years, and a large number of intelligent algorithms inspired by biology have been studied. Among them, differential evolution (DE) algorithms have attracted the attention of many researchers because of their simplicity, high efficiency, powerful search capability, etc. DE has strong exploitability and can approach the global optimum faster, but it cannot avoid the phenomenon of search stagnation and premature convergence. To address the drawbacks of DE, the two-stage bootstrap constrained differential evolution algorithm (TSGCDE) is proposed in this paper. In TSGCDE, instead of the traditional one population, the population is divided into two as a way to increase the diversity of the population. Divided into two stages, each stage searches for optimal solutions in different directions in different populations, and the optimal solutions and the bootstrap solutions generated by the populations in the two stages are bounded by each other’s bootstrap, which makes the DE have a more powerful optimal-seeking ability and easier to jump out of the local optimum. To validate the performance of TSGCDE, we conducted experiments on CEC2013 and CEC2014 benchmarks with some well-known algorithm variants, and the experimental results show that our proposed algorithm is competitive. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Swarm intelligence

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A survey of hybrid human-artificial intelligence for social computing

arXiv

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arXiv 2021年

作者： Wang, Wenxi Ning, Huansheng Shi, Feifei Dhelim, Sahraoui Zhang, Weishan Chen, Liming School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083 China Department of Software Engineering China University of Petroleum Qingdao266580 China School of Computing Ulster University United Kingdom

Along with the development of modern computing technology and social sciences, both theoretical research and practical applications of social computing have been continuously extended. In particular with the boom of artificial intelligence (AI), social computing is significantly influenced by AI. However, the conventional technologies of AI have drawbacks in dealing with more complicated and dynamic problems. Such deficiency can be rectified by hybrid human-artificial intelligence (H-AI) which integrates both human intelligence and AI into one unity, forming a new enhanced intelligence. H-AI in dealing with social problems shows the advantages that AI can’t surpass. This paper firstly introduces the concepts of H-AI. AI is the intelligence in the transition stage of H-AI, so the latest research progresses of AI in social computing are reviewed. Secondly, it summarizes typical challenges faced by AI in social computing, and makes it possible to introduce H-AI to solve these challenges. Finally, the paper proposes a holistic framework of social computing combining with H-AI, which consists of four layers: object layer, base layer, analysis layer, and application layer. It represents HAI has significant advantages over AI in solving social problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Social sciences computing

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Expert knowledge correlated intrusion detection system evaluation framework for heterogeneous IoT

TechRxiv

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TechRxiv 2021年

作者： Nitish, A. Hanumanthappa, J. Prakash, Shiva S.P. Krinkin, Kirill Department of Studies in Computer Science University of Mysore Manasagangothri Karnataka Mysuru570006 India Department of Information Science and Engineering JSS Science and Technology University JSS Technical Institutions Campus Karnataka Mysuru570006 India Department of Software Engineering and Computer Applications St. Petersburg Electrotechnical University "LETI" Ulitsa Professora Popova 5 St. Petersburg197022 Russia

The dynamic contexts of heterogeneous Internet of Things (HetIoT) adversely affect the performance of learning-based network intrusion detection systems (NIDS) resulting in increased misclassification rates—necessitating an expert knowledge correlated evaluation framework. The proposed generalizable framework includes intrusion root cause analysis, correlation model, and correlated classification metrics that can be generalized over any NID dataset, corresponding expert knowledge, detection technique, and learning-based algorithm to facilitate context-awareness in reducing false alerts. To achieve this, we perform experimentations on the Bot-IoT dataset—with generalized traffic behaviors from multiple existing NID datasets—employing the Support Vector Machine (SVM) machine learning and Multilayer Perceptron (MLP) shallow neural network classifiers, demonstrating the generalizability, robustness, and improved performance of the propounded framework compared to the existing literature. Furthermore, the proposed framework offers minimal processing overhead on the classifier algorithms. © 2021, CC BY-NC-SA.

关键词： Internet of things

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An Intelligent Method for Detecting Jute Pests Using Deep Convolutional Neural Networks

An Intelligent Method for Detecting Jute Pests Using Deep Co...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： S. Vignesh S. Prabhu R. Sampathkumar M. Thillai Rani J. Rejina Parvin G. Uma Maheswari R. Saravanakumar Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Mechatronics Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Sri Krishna College of Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Pest attacks pose a serious threat to the production of jute and other significant crops. Jute farmers often utilize their visual sense and hands-on expertise to distinguish between multiple diseases that seem to be identical. The intelligent model we built for the identification of jute pests was based on a deep convolutional neural network (DCNN). This practical difficulty led to the development of this concept. The proposed DCNN model can automatically identify jute pest concerns with high speed and accuracy based on image recognition. In addition, images of the four most common jute bugs are included in an organized image graphics collection. Our model yields a DCNN accuracy is 96.72% for the four most significant jute pest kinds. Further proof of the model's effectiveness is provided by the accuracy, recall, F1-score, and confusion matrix metrics.

关键词： Performance evaluation Visualization Accuracy Operating systems Production Pest control Feature extraction software Convolutional neural networks Overfitting

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A framework for performance optimisation at software architecture level

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International Journal of Wireless and Mobile Computing 2016年第1期10卷 42-47页

作者： Ye, Peng Ni, You-Cong Du, Xin Li, Song Xie, Da-Tong Xiao, Ru-Liang College of Mathematics and Computer Wuhan Textile University WuhanHubei China Faculty of Software Fujian Normal University FuzhouFujian China Centre of Excellence for Research in Computational Intelligence and Applications School of Computer Science University of Birmingham EdgbastonBirmingham United Kingdom Department of Information Management Engineering Fujian Commercial College FuzhouFujian China

Architecture-based software performance optimisation can significantly not only save time but also reduce cost. A few rule-based performance optimisation approaches at software architecture level have been proposed in recent years. However, the count and the order of each rule usage in the optimisation process have not been fully considered in these approaches. Consequently, the search space for performance improvement is limited so that the optimal solution is hard to find out. Aiming at this problem, firstly, rule-based software performance optimisation is abstracted into a mathematical model called RPOM. The mathematical relation between usage of rules and optimal solution in performance improvement space is precisely characterised in the RPOM. Furthermore, we design a rule sequence execution framework named RSEF which can solve RPOM model. The result of this paper will help those rule-based software performance optimisation approaches to search the larger space in order to improve the quality of optimisation. © Copyright 2016 Inderscience Enterprises Ltd.

关键词： software architecture

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A Machine Learning Approach for Predicting Air Quality Index in Smart Cities

A Machine Learning Approach for Predicting Air Quality Index...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： S. Swamynathan N. Sneha S. P. Ramesh R. Niranjana D. David Neels Ponkumar R. Saravanakumar Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering School of Computer Science & Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

One essential natural resource, the air quality index has been deteriorated by economic activities. Many studies have been conducted about the forecasting of periods of unsafe atmospheric conditions; however, the majority of these studies are limited in their capacity to include seasonal and other factors due to insufficient longitudinal data. India noise pollution has compiled a 6-year dataset that has been used to build several prediction models. There are encouraging outcomes when AQI levels are predicted using machine learning methodologies such as support vector machines (SVMs) artificial neural networks (ANNs), and Naive Bayes (NB). However, in several tests involving datasets from three different locations, NB, ANN, and SVM performed well to maximize prediction performance. When it comes to mean absolute error (MAE), R-squared (R2), and Root-mean-square deviation (RMSE), the combined performance of stacks is unsurpassed. In the related work section, summarize the limitations of existing models, such as inadequate data preprocessing, low spatialtemporal resolution, limited feature selection, or overfitting in complex models. Highlight gaps in scalability, real-time adaptability, and generalization across regions, emphasizing the need for robust, efficient models tailored to air quality prediction.

关键词： Support vector machines Adaptation models Atmospheric modeling Biological system modeling Computational modeling Artificial neural networks Predictive models Air quality Data models Indexes

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