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IEEE Conference on Vehicular Technology (VTC)

作者： Abdul Rehman Piergiuseppe Di Marco Roberto Valentini Fortunato Santucci Department of Information Engineering Computer Science and Mathematics and Center of Excellence EX-EMERGE University of L’Aquila L’Aquila Italy

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Intelligent Reflective Surfaces (IRSs) enable controlling the radio propagation environment to enhance wireless communications and are particularly relevant for vehicle-to-everything (V2X) applications. This paper explores the integration of IRSs for virtual line-of-sight (VLOS) propagation in urban vehicle platoons that operate with New Radio (NR)-V2X mode 2 sidelink communications and proposes a semi-analytical framework to assess the performance of such systems. The framework considers the availability of resources, the number of interference vehicles, and their relative positions. A Nakagami-Lognormal composite channel model is assumed to describe the propagation environment. This approach provides a cost-effective and accurate estimate of packet success rates, facilitating optimal configuration and parameter adjustments for vehicle platoons. We demonstrate that under typical inter-vehicle distances and with large numbers of vehicles, VLOS communication via IRS can even surpass the performance of conventional Line-of-Sight (LOS) scenarios.

关键词： Wireless communication Vehicular and wireless technologies Urban areas Line-of-sight propagation Sidelink Interference Channel models Resource management Vehicle-to-everything Testing

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A Systematic Literature Review of Decentralized Applications in Web3: Identifying Challenges and Opportunities for Blockchain Developers

A Systematic Literature Review of Decentralized Applications...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Hossain Faruk, Md Jobair Raya, Pratusha Siam, Md Kamrul Cheng, Jerry Q. Shahriar, Hossain Cuzzocrea, Alfredo Bringas, Pablo Garcia New York Institute of Technology Department of Computer Science United States Kennesaw State University Department of Software Engineering United States University of West Florida Center for Cybersecurity United States University of Deusto D4K Group Bilbao Spain University of Calabria IDEA Lab Rende Italy University of Paris City Department of Computer Science Paris France

ISBN: (纸本)9798350362480

The Internet has opened the floor to stakeholders by redefining the way of organizing, communicating, and collaborating that was initiated by the Web's development. The advancement of the World Wide Web is an outright phenomenon and significant and we witnessed the evolution of the Web. As decentralized technologies continue to gain traction, Web3, or the decentralized internet, has emerged as a promising approach to enable a more secure, transparent, and privacy-preserved digital landscape. In this paper, we thoroughly conduct a systematic study to explore the challenges and opportunities encountered by blockchain developers in the context of decentralized applications (dApps) in Web3. We analyze a set of peer-reviewed research articles, whitepapers, and technical reports and present an in-depth understanding of the current state of Web3 development and its implications. Our finding indicates the opportunities that Web3 can facilitate, such as expanded use cases, enhanced security and privacy, decentralized infrastructure, and the potential for enabling inclusive development resources for blockchain developers. Additionally, we highlight various challenges that blockchain developers deal with including scalability, security, privacy, interoperability, and the need for standardized tools and frameworks along with various challenges in the software development lifecycle (SDLC). While there are significant challenges to overcome, the potential benefits of Web3 are substantial and could lead to a more inclusive, secure, and transparent digital ecosystem. Furthermore, we emphasize the importance of continued research, collaboration, and innovation among stakeholders to address the identified challenges and capitalize on Web3's opportunities. © 2024 IEEE.

关键词： Decentralized finance

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MABPD: Mobile Agent-Based Prevention and Black Hole Attack Detection in Wireless Sensor Networks 2

MABPD: Mobile Agent-Based Prevention and Black Hole Attack D...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ashraf, Humaira Khan, Fizza Ihsan, Uswa Al-Quayed, Fatima Jhanjhi, N.Z. Humayun, Mamoona International Islamic University Islamabad Department of Computer Science and Software Engineering Islamabad Pakistan Jouf University College of Computer and Information Sciences Department of Computer Science Sakakah Saudi Arabia Malaysia Applied Science Private University Applied Science Research Center Amman11937 Jordan Jouf University College of Computer and Information Sciences Department of Information Systems Sakakah Saudi Arabia

ISBN: (纸本)9798350335644

With the advancement and availability of the internet in the present age, everything is being wireless. Be it our home appliances or high defined monitoring systems, Wireless sensor networks play an important role. But with advancement comes a high probability of getting attacked. One of these attacks is the Blackhole attack (BHA), in which a node drains all the traffic towards itself and discards the data traffic without sending it towards the receiver. There exist multiple approaches that help to protect a network against BHA, but existing approaches have some drawbacks. Many proposed schemes make sure that the given technique uses less energy but failed to reduce the packet drop ratio. In the detection of BHA, the proposed scheme should not only detect defected nodes but also manages the efficiency. The proposed methodology used mobile agents with authentication of nodes and trust values to detect black hole nodes. The methodology is tested using different evaluation measures such as energy consumption, latency, network life, and packet delivery ratio. The proposed method uses a detection algorithm that increases energy consumption and hence networks lifetime. Packet delivery rate is increased by 19.51%, the energy consumption is reduced by 53.3%, and network life is increased by 43.3% as compared to the previous technique. © 2023 IEEE.

关键词： Energy utilization

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3D Object Detection with Attention:Shell-Based Modeling

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computer Systems Science & engineering 2023年第7期46卷 537-550页

作者： Xiaorui Zhang Ziquan Zhao Wei Sun Qi Cui School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Department of Electrical and Computer Engineering University of WindsorWindsorN9B 3P4Canada

LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box(BBox).However,under the three-dimensional space of autonomous driving scenes,the previous object detection methods,due to the pre-processing of the original LIDAR point cloud into voxels or pillars,lose the coordinate information of the original point cloud,slow detection speed,and gain inaccurate bounding box *** address the issues above,this study proposes a new two-stage network structure to extract point cloud features directly by PointNet++,which effectively preserves the original point cloud coordinate *** improve the detection accuracy,a shell-based modeling method is *** roughly determines which spherical shell the coordinates belong ***,the results are refined to ground truth,thereby narrowing the localization range and improving the detection *** improve the recall of 3D object detection with bounding boxes,this paper designs a self-attention module for 3D object detection with a skip connection *** of these features are highlighted by weighting them on the feature *** training,it makes the feature weights that are favorable for object detection get ***,the extracted features are more adapted to the object detection *** comparison experiments and ablation experiments conducted on the KITTI dataset verify the effectiveness of our proposed method in improving recall and precision.

关键词： 3D object detection autonomous driving point cloud shell-based modeling self-attention mechanism

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S3GCL: spectral, swift, spatial graph contrastive learning 24

S3GCL: spectral, swift, spatial graph contrastive learning

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Proceedings of the 41st International Conference on Machine Learning

作者： Guancheng Wan Yijun Tian Wenke Huang Nitesh V Chawla Mang Ye National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Department of Computer Science University of Notre Dame National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China and Taikang Center for Life and Medical Sciences Wuhan University Wuhan China

Graph Contrastive Learning (GCL) has emerged as a highly effective self-supervised approach in graph representation learning. However, prevailing GCL methods confront two primary challenges: 1) They predominantly operate under homophily assumptions, focusing on low-frequency signals in node features while neglecting heterophilic edges that connect nodes with dissimilar features. 2) Their reliance on neighborhood aggregation for inference leads to scalability challenges and hinders deployment in real-time applications. In this paper, we introduce S3GCL, an innovative framework designed to tackle these challenges. Inspired by spectral GNNs, we initially demonstrate the correlation between frequency and homophily levels. Then, we propose a novel cosine-parameterized Chebyshev polynomial as low/high-pass filters to generate biased graph views. To resolve the inference dilemma, we incorporate an MLP encoder and enhance its awareness of graph context by introducing structurally and semantically neighboring nodes as positive pairs in the spatial domain. Finally, we formulate a cross-pass GCL objective between fullpass MLP and biased-pass GNN filtered features, eliminating the need for augmentation. Extensive experiments on real-world tasks validate S3GCL proficiency in generalization to diverse homophily levels and its superior inference efficiency.

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Using Structural Equation Modeling to Enhance Decision-Making in Wine Tourism in the Federal District and RIDE 11

Using Structural Equation Modeling to Enhance Decision-Makin...

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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Mariano, Ari Melo Ramos Alves, Vitor João Souza, Carolina de Moraes Palhares, Carolina Menezes dos Santos, Alessandra Santos Felix Souza, João Carlos Longen Zindel, Marcia Terezinha Department of Production Engineering University of Brasília Brazil Department of Computer Science University of Brasilia Brazil DataLab-Laborório de Dados University of Brasilia Brazil CET- Center of Excellence in Tourism University of Brasilia Brazil Instituto de Pesquisa Econômica Aplicada - DF IPEA Brazil

The Federal District and RIDE region is emerging as a new wine tourism destination in Brazil. Wine tourism offers opportunities for modernization at economic, social, and cultural levels. It is important to understand the most relevant experiences and the level of satisfaction among wine tourists to plan and structure the sector effectively in the region. This research aimed to present a roadmap to enhance the satisfaction and experience of wine tourists. Researchers conducted an exploratory study using structural equations with a quantitative approach involving 67 wine tourists. The findings indicate that quality of service accounts for 43.82% of tourist satisfaction, making it the most critical aspect of the wine tourism experience. Consequently, the study produced a roadmap based on the importance and performance matrix, outlining strategies to enhance service quality perceptions through high-quality wines during tastings, memorable tour experiences, and staff friendliness. © 2024 The Authors.

关键词： Wine

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An Enhanced Authentication Protocol Suitable for Constrained RFID Systems

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IEEE Access 2024年 12卷 61610-61628页

作者： Hosseinzadeh, Mehdi Servati, Mohammad Reza Rahmani, Amir Masoud Safkhani, Masoumeh Lansky, Jan Janoscova, Renata Ahmed, Omed Hassan Tanveer, Jawad Lee, Sang-Woong Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Duy Tan University School of Medicine and Pharmacy Da Nang550000 Viet Nam Shahid Rajaee Teacher Training University Department of Computer Engineering Tehran16788-15811 Iran National Yunlin University of Science and Technology Future Technology Research Center Douliou Yunlin64002 Taiwan School of Computer Science Tehran19395-5746 Iran University of Finance and Administration Faculty of Economic Studies Department of Computer Science and Mathematics Prague101 00 Czech Republic University of Human Development Department of Information Technology Sulaymaniyah0778-6 Iraq Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of Gachon University Pattern Recognition and Machine Learning Laboratory Department of Ai Software Seongnam-si13557 Korea Republic of

RFID technology offers an affordable and user-friendly solution for contactless identification of objects and individuals. However, the widespread adoption of RFID systems raises concerns regarding security and privacy. Vulnerabilities such as message tampering, interception, and eavesdropping pose significant risks to the integrity of the system. This study examines the effectiveness of two recently proposed ultra-lightweight RFID authentication protocols, URASP and KUAJB, in addressing these security challenges. Surprisingly, our findings reveal that both URASP and KUAJB protocols are susceptible to secret disclosure attacks, despite their claims of providing robust security. Consequently, the development of a truly ultra-lightweight protocol that ensures adequate security becomes a formidable task. As a potential solution, the enhancement of the URASP protocol through the integration of a simple cryptographic primitive is suggested to bolster its security measures. © 2013 IEEE.

关键词： Hash functions

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Agroecology: Intelligent Implementation of Digital Farming Using machine Learning & Deep Learning

Agroecology: Intelligent Implementation of Digital Farming U...

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Communication & Computing (WCONF), World Conference on

作者： Preeti Dhoke Neha Purohit Prasad P. Lokulwar Department of Computer Science and Engineering G H Raisoni College of Engineering Nagpur India Department of Computer Science and Engineering G H Raisoni College of Engineering In charge of Center of Excellence (Digital Technology) Nagpur India

Agroecology is one of the revered and illustrious professions in India. Agribusiness occupies more than 60% of the country’s land, which feeds 1.428 billion people. The cultivation of plants and animals is known as agriculture. Farmers face a lot of hardship while using conventional farming techniques and also face heavy losses every year. Digital farming helps increase productivity by precisely determining the steps that need to be practiced at the appropriate time. It can be done in a laboratory, but it consumes a lot of time. That’s why nowadays researchers are focusing on digital farming techniques because it takes less time and give better results. Till date, many studies have been conducted to improve agricultural planning. In this research, we present an intelligent system called Digital Farming, which intends to assist the farmers in making an informed decision about which crop to grow, the fertilizer needed to improve the productivity of soil, and the water supply needed according to the soil. Not only the soil type and crop type play an important role in crop yield, but pests and weeds present in the agricultural fields also lead to heavy losses to the farmers, so the proposed model also detects the pests and weeds present in the field and also recommends pesticides. The work uses Convolution Neural Network (CNN) for soil classification, crop recommendation and fertilizer recommendation, for pest detection and pesticides recommendation it uses K Nearest Neighbour (KNN) for weed detection it uses Support Vector Machine (SVM). CNN shows 91% accuracy SVM shows 71% accuracy and KNN shows 83% accuracy.

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Two-dimensional superlattices intercalated with chiral molecules for spin selectivity

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Science China Materials 2022年第11期65卷 3184-3185页

作者： Wen Su Fanglong Yuan CAS Center for Excellence in Nanoscience National Center for Nanoscience and TechnologyBeijing 100190China Department of Physics Chemistryand Biology(IFM)Linköping UniversityLinköping 58183Sweden Department of Electrical and Computer Engineering University of TorontoTorontoM5S 2E8 OntarioCanada

Chirality,one of the most fundamental characteristics of nature,plays an important role in different fields ranging from physics to astronomy,chemistry and biology[1].Materials with certain chirality usually show unique optical and photophysical *** chiral-induced spin selectivity(CISS)effect,which was first reported in 1999,is considered as one of the most important spin-related properties of chiral *** electron transportation through chiral materials depends on the electron *** is,the electrons with a certain spin state such as spin-up state can pass through the chiral materials more easily in one direction,while the transportation of electrons in the spin-down state will be ***,the distinct advantage of CISS effect opens up the possibility to control and manipulate the spin orientation of electrons without using external magnetic fields,which is promising for the fabrication of new spintronic devices[2].

关键词： properties materials chiral

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CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

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International Journal of computer Vision 2025年 1-20页

作者： Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Wenzhou-Kean University Wenzhou325060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore639798 Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215400 China Jarvis Research Center Tencent YouTu Lab Shenzhen518703 China

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross Language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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