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2022 IEEE International Conference on Blockchain, Smart Healthcare and Emerging Technologies, SmartBlock4Health 2022

作者： Al-Asadi, Hanan Hussein Goga, Maria Ali, Haider Abdullah Popa, Ramona Cristina Oludele, Awodele University POLITEHNICA of Bucharest Faculty of Automatic Control and Computers Bucharest Romania University of Technology-Iraq Baghdad Iraq Technical University of Civil Engineering of Bucharest UTCB Department for Training of Teaching Personnel Bucharest Romania Madenat Alelem University College Baghdad Iraq Babcock University Computer Science Department Ogun State Nigeria

ISBN: (纸本)9781665460149

The use of Agriculture Intelligent Systems (AISs) in Iraq has become popular among farmers. The greenhouse is a solution for plant growth, which uses an effective microclimate to simulate seasons and to use alternative energy sources like solar, thermal, and photovoltaic systems for microclimate control. Greenhouses are considered a successful solution with developments and achievements in the technology of this field. However, problems like high costs, difficulties in installation and maintenance, or the need for some procedures should be considered. Because Iraq is close to the equator (3669 km), it receives sufficient solar power. It also has large areas available for the building of solar greenhouse systems. The farmers need an affordable and functional smart greenhouse to enable and encourage them to use the environment efficiently. We intend to develop low-cost, accurate, and easy-to-use greenhouse systems controlled by artificial intelligence algorithms. Farmers will be able to control the greenhouse by using simple commands. Machine learning algorithms provide real-time and effective analysis that could support farmers in decision-making and provide relevant data for researchers and policymakers. AISs algorithms can lead to a sustainable and productive agriculture system. The system can be used in Iraq with sufficient government support and subsidy. © 2022 IEEE.

关键词： Intelligent systems

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Optimizing User Data Privacy and Confidentiality in Cloud Storage Systems Through Advanced Obfuscrypt Encryption Methods for Enhanced Security and Efficient Data Protection

Optimizing User Data Privacy and Confidentiality in Cloud St...

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Recent Advances in Science and engineering Technology (ICRASET), International Conference on

作者： P.S. Ramesh I. Sudha J. Jagannathan Rajkumar Pandiarajan A. Ponmalar Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India Department of Information and Technology School of Computing Kalasalingam Academy Of Research and Education Krishnankoil Virudhunagar India R.M.K. Engineering College Chennai Tamilnadu India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

With a steady increase in the adoption of cloud storage systems, the client’s data are at risk of being leaked. There are several methods of encrypting data which are quite useful but they lack some of the features of today’s sophisticated threats. The methodology used in this research propose an encryption framework known as Obfuscrypt to improve the issues of data privacy as well as confidentiality of data stored in cloud storage systems. Obfuscrypt uses conventional encryption strategies incorporated with obfuscation and post-quantum cryptographic algorithms to provide a complex security plan immune to classic and quantum approaches. Some of the proposed other innovations include the decentralised key management with multi-factor authentication and RBAC-ABE as the fine-grained access control techniques. These features make it possible for only the authorized personnel to be in a position to be in a position of decrypting and retrieving the stored data thereby minimizing the possibility of the data being accessed by unauthorized people. The work also considers the issue of performance efficiency of the cloud storage systems. Similar to other symmetric encryption programs, Obfuscrypt uses light encryption algorithms and brings in to play the hardware acceleration so that it may infuse lesser computational overhead in the process of encryption and decryption. As can be clearly seen from the results, the actual work proves that the proposed framework not only offers a higher level of security but does not affect the system performance to a great extent. Also, the study addresses the post-quantum cryptographic algorithm for making the system resistant to an attack that can be posed by quantum computing systems. With the help of these improved algorithms, Obfuscrypt guarantees the permanent data security in the world of constantly emerging cyber threats.

关键词： Resistance Cloud computing Technological innovation Multi-factor authentication Quantum computing System performance Memory management software performance Encryption Protection

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On the Scheduling of Industrial IoT Tasks in a Fog Computing Environment 4th

On the Scheduling of Industrial IoT Tasks in a Fog Computing...

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4th Computational Methods in Systems and software, CoMeSySo 2020

作者： Badidi, Elarbi Department of Computer Science and Software Engineering College of Information Technology UAE University Al-Ain United Arab Emirates

ISBN: (纸本)9783030633219

In the industrial sector, a growing number of companies have an ongoing smart factory initiative. In such initiative, previously disparate systems and equipment become connected so the data streams they generate can be turned into actionable insights. Industrial IoT (IIoT) data originate from various sensors and Internet of Things devices deployed in industrial equipment and facilities. The vast volume of generated data need to be leveraged to improve robots’ operation, optimize processes, and help industry stakeholders and applications make faster and more informed decisions. Many existing industrial applications use the power of the cloud for data processing. However, time-sensitive industrial applications cannot tolerate sending IIoT data to the cloud for processing due to unacceptable network bandwidth requirements and high latency. The operation and maintenance staff of industrial facilities need the ability to efficiently stream data and process data in real-time at the edge. Smart factory operations are typically executed as workflows of dependent tasks. This paper investigates the performance of some scheduling stategies for the exection of workflow tasks in a smarty factory fog environment. Simulation results show that the MinMin, GA, and PSO scheduling algorithms offered the best results in terms of execution time. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Industry 4.0

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A Novel Deep Learning-Based Method for Real-Time Face Spoof Detection

SSRN

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SSRN 2023年

作者： Malik, Muhammad Amir Mazhar, Tehseen Haq, Inayatul Shahzad, Tariq Ghadi, Yazeed Yaseen Mallek, Fatma Hamam, Habib Department of Computer Science and Software Engineering International Islamic Unversity Islamabad44000 Pakistan Department of Computer Science Virtual University of Pakistan Lahore55150 Pakistan School of Information Engineering Zhengzhou University Zhengzhou450001 China Department of Computer Sciences COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering Al Ain University Abu Dhabi15322 United Arab Emirates Faculty of Engineering University of de Moncton MonctonNBE1A3E9 Canada National College Of Business Administration & Economics Canada

This research aims to improve the detection accuracy of face spoof recognition systems by employing a hybrid model of machine learning and computer vision-based approaches. Differences, including Decision Tree, Nave Bayes, K-nearest Neighbor, Support Vector Machine, Convolutional Neural Network (CCN), and Recurrent Neural Networks are used for face spoof detection. For face spoof detection, the proposed model is a hybrid variant of the CNN-based classifier used in the proposed face spoof detection model. This study improves real-time face fake detection using machine learning and computer vision. The proposed system is based on a CNN-based classification approach with optimized hyper parameters that detect real-time face spoofing attacks using print, video, and repeat attacks, improving detection accuracy. IDIAP, USSA & and MSFD datasets are used in the simulation;the proposed model has achieved a maximum accuracy of 87.5%. a high sensitivity score of 92.45%,. © 2023, The Authors. All rights reserved.

关键词： Decision trees

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Disentangled Hypergraph Collaborative Filtering for Social Recommendation

Disentangled Hypergraph Collaborative Filtering for Social R...

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IEEE International Conference on Web Services (ICWS)

作者： Xiao Liu Shunmei Meng Qianmu Li Xiaolong Xu Lianyong Qi Wanchun Dou Jing Zhang Xuyun Zhang Nanjing University of Science and Technology China State Key Lab. for Novel Software Technology Nanjing University China School of Computer and Software Nanjing University of Information Science and Technology China College of Computer Science and Technology China University of Petroleum (East China) China School of Cyber Science and Engineering Southeast University China School of Computing Macquarie University Australia

In the current era of information overload, service recommendations have emerged as a valuable tool for enhancing the user experience. Among them, social recommendation models have shown promising results by incorporating social relationships to improve representation learning. However, most of these models lack fine-grained modeling of social user behavior, leading to a unified representation of users and a loss of expressiveness in user representations. To address this issue, we propose DisenHGCF, a new social recommendation approach based on disentangled hypergraph collaborative filtering, to disentangle the representations of users and items at the granularity of social users’ intents. This approach aims to provide a more nuanced understanding of the user, leading to more accurate and personalized recommendations. To be specific, DisenHGCF leverages hypergraphs to represent the complex relationships among users, friends, and items. By using the hypergraph disentangling module based on attention, it is able to disentangle the user’s intents and generate users representations in cooperating their intents for recommendation tasks. Additionally, a contrastive learning task based on intent-weight perturbation is designed to enhance representational learning. The experimental results obtained from the BeiBei and Beidian datasets demonstrate the superiority of our proposed approach in comparison to previous baseline methods, as evidenced by higher Recall and NDCG scores.

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Preface

SpringerBriefs in Computer Science

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SpringerBriefs in computer Science 2023年 v-vi页

作者： Yao, Junmei Wu, Kaishun College of Computer Science and Software Engineering Shenzhen University Shenzhen China

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Expert Phishing Detection System

Expert Phishing Detection System

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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Alsarhan, Ayoub Al-Aiash, Issa Al-Fraihat, Dimah Aljaidi, Mohammad Abu Laila, Dena Abd Al Hakem Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa13133 Jordan Department of Cybersecurity Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa13133 Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman11622 Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa13110 Jordan Department of Cyber Security Faculty of Information Technology Zarqa Technical Intermediate College Zarqa Jordan

ISBN: (纸本)9798350353464

Phishing attacks are a threat that continuously emphasizes the need for effective detection methods. This paper presents a machine learning detection method that employs a random forest classifier, which is an adaptable algorithm that can handle a variety of feature sets. The dataset used in this work offers a range of features extracted from various URLs, addressing elements such as URL length, domain structure, and keyword presence. We deployed a computational approach for feature engineering. The random forest classifier is employed for both prediction and to assess feature importance, guiding an automated feature selection process. By doing this, the model’s performance is optimized, and the most important features are utilized. The findings of this study show the effectiveness of our method with an impressive accuracy of 98.45% on a test set. The classifier exhibits balanced precision and recall for both phishing and legitimate instances. Moreover, the automated feature selection process improves the interpretation of the model, emphasizing key indicators of phishing attacks. The proposed approach not only produces outstanding outcomes but also offers a flexible and scalable approach to address the dynamically evolving threat landscape of phishing attempts. ©2024 IEEE.

关键词： Cyber-security Phishing attacks Phishing URLs

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Soft Masked Transformer for Point Cloud Processing with Skip Attention-Based Upsampling

arXiv

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arXiv 2024年

作者： He, Yong Yu, Hongshan Ibrahim, Muhammad Liu, Xiaoyan Chen, Tongjia Ulhaq, Anwaar Mian, Ajmal The National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University Lushan South Rd. Yuelu Dist. Changsha410082 China The Department of Computer Science The University of Western Australia WA6009 Australia Central Queensland University Sydney Campus Australia

Point cloud processing methods leverage local and global point features to cater to downstream tasks, yet they often overlook the task-level context inherent in point clouds during the encoding stage. We argue that integrating task-level information into the encoding stage significantly enhances performance. To that end, we propose SMTransformer which incorporates task-level information into a vector-based transformer by utilizing a soft mask generated from task-level queries and keys to learn the attention weights. Additionally, to facilitate effective communication between features from the encoding and decoding layers in high-level tasks such as segmentation, we introduce a skip-attention-based up-sampling block. This block dynamically fuses features from various resolution points across the encoding and decoding layers. To mitigate the increase in network parameters and training time resulting from the complexity of the aforementioned blocks, we propose a novel shared position encoding strategy. This strategy allows various transformer blocks to share the same position information over the same resolution points, thereby reducing network parameters and training time without compromising accuracy. Experimental comparisons with existing methods on multiple datasets demonstrate the efficacy of SMTransformer and skip-attention-based up-sampling for point cloud processing tasks, including semantic segmentation and classification. In particular, we achieve state-of-the-art semantic segmentation results of 73.4% mIoU on S3DIS Area 5 and 62.4% mIoU on SWAN dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

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Semantic Communication-Empowered Physical-layer Network Coding

Semantic Communication-Empowered Physical-layer Network Codi...

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IEEE Conference on Wireless Communications and Networking

作者： Shuai Yang Haoyuan Pan Tse-Tin Chan Zhaorui Wang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong SAR China School of Science and Engineering Future Network of Intelligence Institute The Chinese University of Hong Kong (Shenzhen) Shenzhen China

In a two-way relay channel (TWRC), physical-layer network coding (PNC) doubles the system throughput by turning superimposed signals transmitted simultaneously by different end nodes into useful network-coded information (known as PNC de-coding). Prior works indicated that the PNC decoding performance is affected by the relative phase offset between the received signals from different nodes. In particular, some "bad" relative phase offsets could lead to huge performance degradation. Previous solutions to mitigate the relative phase offset effect were limited to the conventional bit-oriented communication paradigm, aiming at delivering a given information stream as quickly and reliably as possible. In contrast, this paper puts forth the first semantic communication-empowered PNC-enabled TWRC to address the relative phase offset issue, referred to as SC-PNC. Despite the bad relative phase offsets, SC-PNC directly extracts the semantic meaning of transmitted messages rather than ensuring accurate bit stream transmission. We jointly design deep neural network (DNN)-based transceivers at the end nodes and propose a semantic PNC decoder at the relay. Taking image delivery as an example, experimental results show that the SC-PNC TWRC achieves high and stable image reconstruction quality under different channel conditions and relative phase offsets, compared with the conventional bit-oriented counterparts.

关键词： Degradation Semantics Streaming media Network coding Turning Throughput Reliability engineering

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MOEA/D based UAV swarm deployment for wireless coverage

MOEA/D based UAV swarm deployment for wireless coverage

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Shanshan Lu Xiao Zhang Yu Zhou Shilong Sun College of Computer Science South-Central University for Nationalities Wuhan China College of Computer Science and Software Engineering Shenzhen University School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen

ISBN: (纸本)9781665412544;9781665429467

In recent years, unmanned aerial vehicles (UAVs) have been widely used as flying-based stations to provide wireless coverage services to ground users. Owing to the UAV's limited battery capacity and coverage range, its energy consumption or coverage have been explored by researchers. However, the existing research largely overlooks the tradeoff involved in optimizing UAV swarm deployment for wireless coverage over a ground area. This study considers homogeneous UAV deployment in a 3D space to provide sustainable wireless services as a multi-objective problem. We introduce three objectives: 1) minimize the total energy consumption while deploying a UAV to UAVs on duty, 2) minimize the number of UAVs, and 3) maximize the coverage rate of the target area. With the aim of achieving a better trade-off between these objectives, we adopt the framework of MOEA/D, which allows search progress cooperating with neighboring subproblems each other. Particularly, we introduce a single-tuple encoding scheme and genetic operators (i.e., selection, crossover, and mutation) to generate feasible optimal solutions. The simulations demonstrate that the proposed algorithm is effective and surpasses the improved SPEA II and NSGA II, which indicates that the approach is dependable in solving the proposed multi-objective optimization for UAV deployment.

关键词： Wireless communication Energy consumption Three-dimensional displays Computational modeling Search problems Genetics Unmanned aerial vehicles

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