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information Technology (ACIT)

作者： Majdi Maabreh Omar Darwish Ola Karajeh Yahya Tashtoush Department of Computer Information Systems The Hashemite University Zarqa Jordan Information Security and Applied Computing Department Eastern Michigan University MI USA Department of Computer Science Virginia Polytechnic Institute and State University VA USA Department of Computer Science Jordan University of Science and Technology Irbid Jordan

ISBN: (数字)9798350320244

ISBN: (纸本)9798350320251

Deep learning (DL) has demonstrated several successes in a variety of fields, particularly in the era of big data. The process of training a deep learning model entails selecting the ideal learning parameters such as the number of hidden layers and neurons. Particle Swarm Optimization (PSO) is one useful nature-inspired algorithm to set those two influential learning parameters. In this study, two different datasets are used to study and evaluate the particle swarm optimizer with deep learning on datasets of different concentrations of poisoning attack, where some adversarial samples were crafted by attackers to ruin the learning process. The results showed that particle swarm optimization could find settings for deep learning with the existence of poisoned data that maximizes the model accuracy on the unseen testing dataset, and could also offer better recommendations compared to those recommended on all benign samples. This may introduce a concern that optimizers might conceal the existence of data poisoning, which may lead to unreliable learning in the advanced stages of upgrading the model on updated datasets.

关键词： Deep learning Training Neurons Big Data Data models Safety Particle swarm optimization

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Multi-Channel Hypergraph Convolution Group Recommendation with Member information Enhancement 25

Multi-Channel Hypergraph Convolution Group Recommendation wi...

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25th IEEE International Conferences on High Performance computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Chen, Tianhao Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach. © 2023 IEEE.

关键词： Recommender systems

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Social-enhanced recommendation using graph-based contrastive learning 25

Social-enhanced recommendation using graph-based contrastive...

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作者： Xue, Peng Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

The social network-based recommendation model use social network information to mitigate data sparsity issues and improve the accuracy of recommendation models. However, In most social network-based recommendation algorithms, the social neighbors' contributions are difficult to distinguish from the central user's, neglecting hidden correlations in social infor-mation. In order to solve this problem, this paper presents a graph-based contrastive learning framework for social-enhanced recommendation (SoGCLR) that utilizes implicit social information captured by a new social relation attention mechanism, en-rich user representations, and improve model robustness through graph-based contrastive learning. Specifically, the paper captures the degree of contribution of each neighbor in the social graph to the central user through a social relation attention layer, thus obtaining hidden correlations in social information, and further integrates this with user information in the user-item interaction graph to enrich user representations. In addition, the paper incorporates graph-based contrastive learning into the recommendation task using cross-layer contrastive learning, calculating contrastive loss and mapping nodes with similar but different exposure rates to nearby regions to mitigate exposure bias issues. Results from Ciao and Epinions demonstrate that SoGCLR reduces RMSE and MAE by 1.33% to 1.84% compared with baseline models. © 2023 IEEE.

关键词： Recommender systems

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Deep Generative Network for Image Inpainting with Gradient Semantics and Spatial-Smooth Attention

SSRN

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SSRN 2023年

作者： Sheng, Ziqi Xu, Wenbo Lin, Cong Lu, Wei 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 Guangzhou510006 China

As a powerful means of tampering in image content security area, image inpainting based on deep generative networks can yield visually appealing outputs but often produces ambiguous artifacts, particularly 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 the spatial structural relations and the surrounding information in the hole region respectively. © 2023, The Authors. All rights reserved.

关键词： Semantics

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IoT-based Smart Parking System using Android Application

IoT-based Smart Parking System using Android Application

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2020 International Conference on Emerging Technology in computing, Communication and Electronics, ETCCE 2020

作者： Warif, Nor Bakiah Abd Azman, Mohd Izzat Syahmi Saiful Ismail, Nor-Syahidatul N Remli, Muhammad Akmal Faculty of Computer Science Information Technology Universiti Tun Hussien Onn Malaysia Department of Information Security and Web Technology Batu Pahat Johor Malaysia Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pekan Pahang Malaysia Kota Bharu Kelantan Malaysia

ISBN: (纸本)9781665419628

Since all people used vehicles as a necessity, the growing number of vehicles is increasing day by day. With the increasing number of vehicles on the road, the requirement of parking lot will also be restricted. Thus, it become a problem for the people to find a free parking space in the busy building especially in shopping complex. Internet of Things (IoT) is an interesting technology that can be applied in this paper because this technology has a capability to transfer the data over a network without requiring human intervention in almost all applications in today's society. In this paper, a user-friendly mobile application, named as Android-based Car Parking Monitoring System (ACPMS) is built to aid in locating a particular parking place. ACPMS can provide a user with the ability to check vacant parking space and locate the nearest parking lot. ACPMS obtained the parking location from the current user's position with the sensor located in the shopping complex's parking lot. ACPMS is testing in a realistic environment for the purpose of movement detection and location service to notify user using the mobile application. By considering seven test case scenarios, the combination of the ACPMS mobile application with a parking prototype kit shows that the proposed work able to solve the parking problem. © 2020 IEEE.

关键词： Internet of things

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Hierarchical certification of nonclassical network correlations

arXiv

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arXiv 2023年

作者： Luo, Ming-Xing Yang, Xue Pozas-Kerstjens, Alejandro Information Security and National Computing Grid Laboratory Southwest Jiaotong University Chengdu610031 China CAS Center for Excellence in Quantum Information and Quantum Physics Hefei230026 China Group of Applied Physics University of Geneva Geneva 41211 Switzerland Constructor University Geneva Switzerland Instituto de Ciencias Matemáticas CSIC UAM UC3M UCM Madrid28049 Spain

With the increased availability of quantum technological devices, it becomes more important to have tools to guarantee their correct nonclassical behavior. This is especially important for quantum networks, which constitute the platforms where multipartite cryptographic protocols will be implemented, and where guarantees of nonclassicality translate into security proofs. We derive linear and nonlinear Bell-like inequalities for networks, whose violation certifies the absence of a minimum number of classical sources in them. We do so, first, without assuming that nature is ultimately governed by quantum mechanics, providing a hierarchy interpolating between network nonlocality and full network nonlocality. Second we insert this assumption, which leads to results more amenable to certification in experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum cryptography

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The differential spectrum and boomerang spectrum of a class of locally-APN functions

arXiv

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arXiv 2022年

作者： Hu, Zhao Li, Nian Xu, Linjie Zeng, Xiangyong Tang, Xiaohu The Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan430062 China The WuHan Marine Communication Research Institute Wuhan430000 China The Information Security and National Computing Grid Laboratory Southwest Jiaotong University Chengdu610031 China

In this paper, we study the boomerang spectrum of the power mapping F(x) = xk(q−1) over Fq2, where q = pm, p is a prime, m is a positive integer and gcd(k, q + 1) = 1. We first determine the differential spectrum of F (x) and show that F (x) is locally-APN. This extends a result of [IEEE Trans. Inf. Theory 57(12):8127-8137, 2011] from (p, k) = (2, 1) to general (p, k). We then determine the boomerang spectrum of F (x) by making use of its differential spectrum, which shows that the boomerang uniformity of F (x) is 4 if p = 2 and m is odd and otherwise it is 2. Our results not only generalize the results in [Des. Codes Cryptogr. 89:2627-2636, 2021] and [Adv. Math. Commun., doi: 10.3934/amc.2022046] but also extend the example x45 over F28 in [Des. Codes Cryptogr. 89:2627-2636, 2021] into an infinite class of power mappings with boomerang uniformity *** Codes 12E20, 11T06, 94A60 Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping

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A Classifier to Detect Stream Applications Based on Network Traffic Statistical Metrics

A Classifier to Detect Stream Applications Based on Network ...

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information Technology (ACIT)

作者： Omar Darwish Sarah Herzallah Majdi Maabreh Shouroq Al-Eidi Mahmoud Al-Maani Yahya Tashtoush Information Security and Applied Computing Eastern Michigan University Ypsilanti Michigan Electrical and Computer Engineering Eastern Michigan University Ypsilanti Michigan Department of Information Technology The Hashemite University Zarqa Jordan Computer Science Department Tafila Technical University Tafila Jordan Department of Computer Science Jordan University of Science and Technology Irbid Jordan

ISBN: (数字)9798350320244

ISBN: (纸本)9798350320251

One of the most crucial considerations, when considering security vulnerabilities, is network traffic. There is still potential for more research on the inter-arrival time side, even though some studies concentrate on network traffic from the perspective of the packet fields such as packet length and packet number. Inter-arrival timings are crucial to investigate because there are numerous attacks, such as Covert Timing Channels attacks, that heavily rely on them. In this article, we conduct a statistical analysis of the TCP inter-arrival times of two major key streaming programs (Zoom and Skype), which are frequently used, particularly during and following the COVID-19 pandemic. Using two internet-connected devices and the statistical measures of TCP, a dataset of 18,371 instances is created for this use. Five machine learning algorithms are evaluated on balanced and imbalanced forms of the dataset. The results revealed that the traffic of Zoom and Skype calls can be identified by machine learning algorithms with an accuracy of up to 99% by random forest.

关键词： Measurement COVID-19 Machine learning algorithms Statistical analysis Pandemics Telecommunication traffic Timing

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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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