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Image Processing and Capsule Networks (ICIPCN), International Conference on

作者： S. Ananthi R. Venkateswaran G. Vijaya J. Arun R. Sathya Advanced Research Centre for Future Networks and Digital Twin Sri Eshwar College of Engineering Coimbatore India Faculty-Cyber and Information Security College of Computing & Information SciencesUniversity of Technology and Applied Sciences Salalah Oman Dept. of CSE Sri Krishna College of Engineering and Tech Coimbatore India Dept. of ECE Sri Eshwar College of Engineering Coimbatore India Department of IT Kongunadu College of Engg and Tech Namakkal India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

information Retrieval system facilitates the retrieval efficiency of the user’s requirement from a collection of information resources. In this research work, Genetic Algorithm (GA) with various fitness functions are implemented for retrieving the relevant document for the given textual query. To increase the efficiency of the retrieval system extended keywords are added to the retrieval system using query reformulation method. Furthermore, the selection procedure namely fitness proportionate selection is used to identify the best fit individual among the chosen population. This selection algorithm assumes that, for the chosen population, the fittest individuals have a greater chance of endurance than weaker ones. The performance of the proposed system is evaluated with three different datasets solely consists of 20,322 documents are analyzed using various matching functions and their results are examined.

关键词： Text mining Accuracy information resources Text recognition Image processing Data preprocessing information retrieval

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Multiobjective Forecasting Model Based on the Interval Discrete Type-2 Fuzzy Sets and Genetic Algorithm

Multiobjective Forecasting Model Based on the Interval Discr...

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International Conference on information Technologies (InfoTech)

作者： Liliya Anatolievna Demidova Maksim Anatolievich Stepanov Department of Intelligent Systems of Information Security Institute of Integrated Safety and Special Instrumentation Russian Technological University – MIREA Vernadsky Avenue78 Moscow Russia Department of Computational and Applied Mathematics Faculty of Computing Engineering Ryazan State Radio Engineering University named after V.F. Utkin Gagarin Str. 59/1 Ryazan Russia

ISBN: (数字)9781728169149

ISBN: (纸本)9781728169156

The forecasting task for the short time series has been investigated. The main problem of this task is the availability of a small amount of relevant information. Such tasks can be considered as the tasks in the conditions of uncertainty, fuzziness of the initial data. We suggest to develop the multiobjective forecasting model based on the interval discrete type-2 fuzzy sets. As the criteria which should be minimized, it is proposed to use the average relative forecasting error and the tendency mismatch indicator. The search for the optimal parameters values of the forecasting model is implemented using the genetic algorithm. The examples showing the possibilities of the proposed forecasting model have been discussed.

关键词： Forecasting Predictive models Genetic algorithms Fuzzy sets Task analysis Uncertainty Optimization

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Reappraising Business Resumption

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EDPACS 2006年第11期29卷 5-16页

作者： Belden Menkus The editor of EDPACS and the consulting editor of the Auerbach Information Management Service on EDP Auditing. He is a principal in Menkus Associates consultants in IT auditing security and quality of Manchester Tennessee. Menkus co-developed the basic computing disaster recovery response methodology in 1964 under contract to what was at the time a component of the U.S. Department of Defense. This methodology has been applied in many different settings in subsequent years. Menkus can be reached by e-mail at bmenkus@***.

The September 2001 attacks on New York City's World Trade Center and the Pentagon in Arlington, Virginia, call for a reap-praisal of the assumptions and practices of business resump-tion efforts (sometimes referred to as disaster recovery). The author of this article has more than 50 years of experience in continuing analysis of business resumption efforts and how and why they tend to succeed or fail.

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Blockchain Solution For SMEs Cybersecurity Threats In E-Commerce

Blockchain Solution For SMEs Cybersecurity Threats In E-Comm...

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Computer Science and Emerging Technologies (CSET), International Conference on

作者： Firas H. Zawaideh Waheeb Abu-Ulbeh Salameh A. Mjlae Yousef A. Baker El-Ebiary Yazeed Al Moaiad Sunanda Das Department of Cyber Security Faculty of Science and Informaiton Technology Irbid National University Irbid Jordan Cybersecurity Department Faculty of Administrative sciences and Informatics Al-Istiqlal University Palestine CS Department Prince Abdullah bin Ghazi Faculty of Information Technology Al-Balqa Applied University (BAU) Jordan Faculty of Informatics and Computing UniSZA Malaysia Faculty of Computer and Information Technology MEDIU Malaysia School of Computer Science and Engineering Jain University Bangalore India

The rapid growth of electronic commerce (e-commerce) has revolutionized the way Small and Medium-sized Enterprises (SMEs) conduct business, enabling them to reach global markets and unlock new growth opportunities. However, this digital transformation has also exposed SMEs to an escalating array of cybersecurity threats that could compromise their data, financial integrity, and reputation. Blockchain technology, known for its decentralized and immutable nature, has emerged as a potential solution to enhance the security and resilience of e-commerce operations for SMEs. This research paper aims to investigate the current state of cybersecurity preparedness among SMEs engaged in e-commerce and identify the prevalent threats they face in the digital landscape. To achieve this goal, the study incorporates systematic literature review (SLR) method to examine the best technological solution to address the cybersecurity issues faced by e-commerce organizations in this paper of SMEs operating in e-commerce to gather data on their security practices, incident history, and perceptions of cybersecurity risks. In the context of this research, blockchain technology will be explored as a potential mechanism to enhance the security of e-commerce operations for SMEs. The inherent features of blockchain, such as decentralization, transparency, and immutability, could help protect sensitive data and thwart cyberattacks. By exploring the integration of blockchain into e-commerce systems, the research aims to shed light on the potential benefits and challenges associated with its adoption by SMEs. The research findings will not only shed light on the prevailing cybersecurity practices within SMEs and highlight their vulnerabilities but will also explore how blockchain technology can be leveraged to address these challenges. By understanding these patterns and exploring the feasibility of blockchain integration, the research aims to provide comprehensive recommendations for enhancing cy

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Deep generative network for image inpainting with gradient semantics and spatial-smooth attention

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Journal of Visual Communication and Image Representation 2024年 98卷

作者： Sheng, Ziqi Xu, Wenbo Lin, Cong Lu, Wei Ye, Long School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China Applied Laboratory of Dig Data and Education Statistics School of Statistics and Mathematics Guangdong University of Finance and Economics Guangzhou510320 China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China

As a powerful forgery operation in the image content security area, image inpainting based on deep generative networks can yield visually appealing outputs but often produces ambiguous artifacts, especially in boundary and high semantic areas. To address this issue, a novel end-to-end network with gradient semantics and spatial-smooth attention (GS-SSA) is proposed, which combines a gradient learning network and an image inpainting network. The gradient learning network is meant to properly anticipate the gradient semantics in the hole region and get a complete gradient semantic map. The image inpainting network utilizes the complete gradient semantic map to better repair the missing pixels and obtain the final inpainting results. Moreover, spatial-smooth attention is introduced into the image inpainting network, considering both the spatial structural relations and the surrounding information in the hole region. Experimental results on public datasets show the superiority of the proposed method, especially in large-hole region tasks. © 2023 Elsevier Inc.

关键词： Semantics

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Distributed Pinning Set Stabilization of Large-Scale Boolean Networks

arXiv

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arXiv 2022年

作者： Zhu, Shiyong Lu, Jianquan Sun, Liangjie Cao, Jinde The Department of Systems Science School of Mathematics Southeast University Nanjing210096 China The Advanced Modeling and Applied Computing Laboratory Department of Mathematics The University of Hong Kong Hong Kong The School of Mathematics Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211111 China The Yonsei Frontier Lab Yonsei University Seoul03722 Korea Republic of

In this article, we design the distributed pinning controllers to globally stabilize a Boolean network (BN), specially a sparsely connected large-scale one, towards a preassigned subset of state space through the node-to-node message exchange. Given an appointed state set, system nodes are partitioned into two disjoint parts, which respectively gather the nodes whose states are fixed or arbitrary with respect to the given state set. With such node division, three parts of pinned nodes are selected and the state feedback controllers are accordingly designed such that the resulting BN satisfies three conditions: the states of the other nodes cannot affect the nodal dynamics of fixed-state nodes, the subgraph of network structure induced by the fixed-state nodes is acyclic, and the steady state of the subnetwork induced by the fixed-state nodes lies in the state set given beforehand. If the BN after control is acyclic, the stabilizing time is revealed to be no more than the length of the longest path in the current network structure plus one. This enables us to further design the pinning controllers with the constraint of stabilizing time. Noting that the overall procedure runs in an exponentially increasing time with respect to the largest number of functional variables in the dynamics of pinned nodes, the sparsely-connected large-scale BNs can be well addressed in a reasonable amount of time. Finally, we demonstrate the applications of our theoretical results in a T-LGL survival signal network with 29 nodes and T-cell receptor signaling network with 90 nodes. Copyright © 2022, The Authors. All rights reserved.

关键词： Controllers

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Cutting-Edge Predictive Methods for Enhancing Short-Term Load Prediction

Cutting-Edge Predictive Methods for Enhancing Short-Term Loa...

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Electrical and Computer Engineering Researches (ICECER), International Conference on

作者： Taher M. Ghazal Jamshaid Iqbal Janjua Walid Abushiba Munir Ahmad Tahir Abbas Karamath Ateeq College of Arts & Science Applied Science University Manama Kingdom of Bahrain Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Al-Khawarizimi Institute of Computer Science (KICS) University of Engineering & Technology (UET) Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul Republic of Korea Department of Computer Science TIMES Institute Multan Pakistan School of Computing Skyline University College University City Sharjah Sharjah UAE

ISBN: (数字)9798331539733

ISBN: (纸本)9798331539740

Efficient management and cost-related factors in the power sector call for accurate short-term load forecasting as it enables better planning with the electric grid and achieving stability within it. This paper looks into the state-of-the-art forecasting strategies that enhance load forecasts made in shorter time frames. Such techniques include but are not limited to machine learning applications, effective data models, and multi-dimensional modeling leveraging historical, meteorological, and economic data. Their review is carried out in detail with their advantages and disadvantages and their application in practice. Furthermore, this study strives to understand the barriers that need to be overcome in the quest to increase the accuracy of the forecasting models especially concerning renewable energy sources. We illustrate how new trends relate to future developments of predictive models about supporting the decision-making in operating power systems and improving the reliability and sustainability of energy systems.

关键词： Renewable energy sources Accuracy Load forecasting Machine learning Predictive models Power system stability Data models Forecasting Sustainable development Load modeling

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FEST: Feature Enhancement Swin Transformer for Remote Sensing Image Semantic Segmentation

FEST: Feature Enhancement Swin Transformer for Remote Sensin...

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International Conference on Computer Supported Cooperative Work in Design

作者： Ronghuan Zhang Jing Zhao Ming Li Qingzhi Zou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The global context is crucial for the precise segmentation of remote sensing images. However, the large volumes and high spatial resolutions of remote sensing images make efficient analysis of the entire scene challenging for most convolutional neural network (CNN)-based methods. To address this issue, we propose to design an innovative framework for semantic segmentation of remote sensing images called Feature Enhancement Swin Transformer (FEST). Firstly, we utilize the Swin Transformer as the encoder and incorporates a Global information Enhancement Model (GIEM) within each Swin Transformer block to reduce information loss and enable encoding of more accurate spatial information. Secondly, we introduce an enhanced decoding structure called Enhanced Feature Fusion Module (EFFM) with added enhanced channel and spatial attention modules to retain localized information while obtaining extensive contextual information. Finally, for loss calculation, we utilize the dice and cross-entropy loss to jointly supervise the model, aiming to achieve a competitive performance. We comprehensively evaluated FEST on the ISPRS-Vaihingen and Potsdam datasets. The results indicate that our approach has achieved significant improvements in semantic segmentation tasks compared to existing methods.

关键词： Deep learning Federated learning Semantic segmentation Semantics Transformers information retrieval Encoding

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Hybrid Deep Relation Matrix Bidirectional Approach for Relational Triple Extraction

Hybrid Deep Relation Matrix Bidirectional Approach for Relat...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yushuai Hu Jing Zhao Ming Li Lin Yuan Qingzhi Zou Ronghuan Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Relation extraction is a crucial task within information extraction, and numerous models have demonstrated impressive results. However, most of the tagging-based relation triple extraction methods employ unidirectional approaches to extract subjects, objects, and relations, which may overlook crucial information. In this paper, we introduce a novel deep matrix-based bidirectional relation extraction model. Firstly, we extract forward and backward entity pairs. During the bidirectional extraction process,there may be some redundant relationships,so we use a shared encoder to connect and enhance the extraction process. Secondly, we design a low-complexity relation extraction matrix to allocate all possible relations. We assess our model using diverse benchmark datasets, and comprehensive experiments show that our approach effectively addresses subsequent triple extraction issues stemming from entity extraction failures.

关键词： Learning systems Federated learning Computational modeling Benchmark testing information retrieval Data mining Task analysis

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Topic-based Multi-layer Knowledge Filtering for Emotion Recognition in Conversation

Topic-based Multi-layer Knowledge Filtering for Emotion Reco...

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International Conference on Computer Supported Cooperative Work in Design

作者： Xiaoyang Li Zhenyu Yang Yiwen Li Zhijun Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Emotion recognition in conversation (ERC) is a prominent research topic in natural language processing, widely applicable in various scenarios. However, accessing external commonsense knowledge and related dialogue topics, along with the complexity of fusing utterances, presents significant challenges. In this paper, we introduce a Topic-based Multilayer Knowledge Filtering (TMKF) model to enhance dialog emotion recognition accuracy. TMKF acquires commonsense knowledge for each utterance and employs two Variational Autoencoder (VAE) modules to extract global and speaker-level dialogue topics. We utilize Global Knowledge Filtering (GKF) and Local Knowledge Filtering (LKF) to obtain topic-specific knowledge representations after filtering commonsense information through global and local topic hierarchies. Subsequently, we leverage relational graphs to convolve commonsense knowledge with utterances, resulting in knowledge-constrained utterance representations for classification. Our proposed model is evaluated through extensive experiments on four widely used benchmark datasets for conversational emotion recognition. The results underscore the effectiveness of TMKF, which significantly outperforms other methods in standard metric evaluations.

关键词： Measurement Emotion recognition Filtering Convolution Oral communication Benchmark testing Nonhomogeneous media

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