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arXiv 2023年

作者： Tao, Linwei Dong, Minjing Liu, Daochang Sun, Changming Xu, Chang School of Computer Science Faculty of Engineering University of Sydney Australia CSIRO's Data61 Australia

Confidence calibration - the process to calibrate the output probability distribution of neural networks - is essential for safety-critical applications of such networks. Recent works verify the link between mis-calibration and overfitting. However, early stopping, as a well-known technique to mitigate overfitting, fails to calibrate networks. In this work, we study the limitions of early stopping and comprehensively analyze the overfitting problem of a network considering each individual block. We then propose a novel regularization method, predecessor combination search (PCS), to improve calibration by searching a combination of best-fitting block predecessors, where block predecessors are the corresponding network blocks with weight parameters from earlier training stages. PCS achieves the state-of-the-art calibration performance on multiple datasets and architectures. In addition, PCS improves model robustness under dataset distribution shift. Supplementary material and code are available at https://***/Linwei94/PCS. © 2023, CC BY.

关键词： Calibration

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Exploring Advanced Algorithms in Multi-Dimensional Spatial data Modeling for Enhanced Geospatial Analysis

Exploring Advanced Algorithms in Multi-Dimensional Spatial D...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Asha V S Sreekanth Ginni Nijhawan Vipashi Kansal R.P Ram Kumar Tamam Ali Abd Ulabbas Abedi Department of Computer Applications N.H.C.E Bangalore India Computer Science and Engineering(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India LPU Phagwara India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of CSBS GRIET Bachupally Hyderabad Telangana India College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

This study examines map analysis with the goal of improving current methods by looking at cutting-edge algorithms for modeling multidimensional spatial data. Tensor Decomposition (TD), Convolutional Neural Networks (CNNs), and Spatiotemporal Regression (STR) are all parts of the suggested method that work together to solve the complex problems that come up when representing spatial data. The abstract provides a concise summary of the primary objectives and methodologies of the study, while also elucidating the rationale behind its execution. In our work, we use new developments in deep learning and machine learning to go beyond common ways of looking at space, such as graph-based spatial analysis, geostatistical methods, and space partitioning strategies. When you combine convolutional neural networks with tensor decomposition, you can get hierarchical features out of the data and figure out what the complex spatial patterns mean. Using spatial-temporal regression also helps us understand spatial events better by adding temporal changes to the analysis framework. Our study's goal is to push the boundaries of geographic research by combining the newest techniques. This will make it possible to create more precise and all-encompassing methods that can correctly understand the complex nature of location data in many dimensions.

关键词： Analytical models Adaptation models Tensors Computational modeling Urban planning Spatial databases data models Spatiotemporal phenomena Geospatial analysis Convolutional neural networks

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Relay-Assisted Connectivity Management for Underwater High Speed Communication Networks

Relay-Assisted Connectivity Management for Underwater High S...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Sahoo, Girija Shankar Renuka, G. Shinde, Swapnil K. Batri, Krishnan Lone, Zeeshan Ahmad Faiz, Ayaan Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350329773

Relay-assisted connectivity control for underwater high-pace verbal exchange networks improves verbal exchange velocity and reliability by connecting Wi-Fi networks to relays. By allowing communication among unique networks, it enables higher insurance, higher air interface, higher interference immunity, and higher network resilience. With underwater excessive pace verbal exchange networks, relays are used to ensure reliable operation to increase the networks' transmission range. Such relay-assisted verbal exchange networks can offer higher records costs than a single factor-to-factor link should provide. They can help to reduce the effect of interference that affects the speed of conversation over lengthy distances. This generation offers some benefits: extended latency, stepped forward bandwidth usage, accelerated community topology, improved fault tolerance and robustness, better statistics synchronization, and quicker handover in multi-radio networks. Additionally, with the help of relay-assisted verbal exchange networks, such networks' deployment, and operation costs can be decreased substantially. © 2023 IEEE.

关键词： Wi-Fi

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UAV-Assisted Joint Mobile Edge Computing and data Collection via Matching-Enabled Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年

作者： Wang, Boxiong Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues. Different from existing works that consider MEC and DC scenarios separately, this paper investigates a multi-UAV-assisted joint MEC-DC system. Specifically, we formulate a joint optimization problem to minimize the MEC latency and maximize the collected data volume. This problem can be classified as a non-convex mixed integer programming problem that exhibits long-term optimization and dynamics. Thus, we propose a deep reinforcement learning-based approach that jointly optimizes the UAV movement, user transmit power, and user association in real time to solve the problem efficiently. Specifically, we reformulate the optimization problem into an action space-reduced Markov decision process (MDP) and optimize the user association by using a two-phase matching-based association (TMA) strategy. Subsequently, we propose a soft actor-critic (SAC)-based approach that integrates the proposed TMA strategy (SAC-TMA) to solve the formulated joint optimization problem collaboratively. Simulation results demonstrate that the proposed SAC-TMA is able to coordinate the two subsystems and can effectively reduce the system latency and improve the data collection volume compared with other benchmark algorithms. © 2025 IEEE. All rights reserved,

关键词： Unmanned aerial vehicles (UAV)

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Improving Event Coreference Resolution Using Prompt Learning and U-shaped Module

Improving Event Coreference Resolution Using Prompt Learning...

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computer science and Blockchain (CCSB), International Conference on

作者： Mengyao Zhang Jun Long Wenti Huang School of Software Xinjiang University Urumqi China Institute of Big Data Central South University Changsha China School of Computer Science and Engineering Hunan University of Science and Technology Changsha China

ISBN: (数字)9798350356502

ISBN: (纸本)9798350356519

Event Coreference Resolution (ECR) focuses on clustering event mentions that allude to identical actual events. Previous research primarily focuses on encoding event mentions without incorporating human interpretations, making it difficult to integrate human-summarized rules for guiding the ECR task. Furthermore, the semantic relations between event mentions have not been thoroughly explored, which impacts coreference judgment. To address these issues, we first utilize the powerful Longformer to acquire event representations at the document level. In addition, we introduce a prompt learning template to better emphasize event types and parameters, enhancing the interaction between event modeling and coreference judgment. Ultimately, we create matching vectors for event mentions using tensor matching and incorporate a U-shaped module to effectively identify the semantic relationships among these mentions, which enhances coreference discrimination. DPTU surpasses other ECR models on the KBP2017 dataset in cause of some experiments.

关键词： computer science Tensors Computational modeling Semantics Vectors Encoding Blockchains

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MHRNet: A Multi-stage Image Deblurring Approach with High-Resolution Representation Learning

MHRNet: A Multi-stage Image Deblurring Approach with High-Re...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wenfu Liu Junjie Peng Haochen Yuan Luming Zhang Zesu Cai School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China School of Computer Science and Technology Harbin Institute of Technology Harbin China

Image deblurring is a classic task in computer vision that aims to recover clean images from blurred images and is widely used in surveillance, medical imaging and so on. Existing methods typically apply a multi-stage encoder-decoder architecture to learn features at different scales, and they have achieved remarkable results. However, these methods usually map the input to a low-resolution (LR) image to expand its receptive field and then gradually reverse this image to the original resolution. Although these approaches obtain rich semantic information via spatial resolution reduction, they lose a large amount of spatial information, which is essential for image deblurring and extremely difficult to recover. To solve this problem, we propose a novel multi-stage model with high-resolution (HR) representation learning (MHRNet). In this model, HR representations are always preserved to reduce the loss of spatial information, and the features across all the scales at each resolution are fused to obtain spatially accurate and semantically rich features. Extensive experiments conducted on the GoPro, HIDE and RealBlur datasets demonstrate that MHRNet outperforms the state-of-the-art (SOTA) methods and reaches a 33.99-dB peak signal-to-noise ratio (PSNR) on the GoPro dataset.

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Machine Learning Approach for Mitigating Security Threats in IoT Environment 5

Machine Learning Approach for Mitigating Security Threats in...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Saini, Harnit Bala, Saroj Ida, S.Jhansi Kumar, K. Suresh Swetha, Shankerpally Divyabarathi, P. Ajay Kumar Garg Engineering College Department of Cse Ghaziabad India Ajay Kumar Garg Engineering College Department of Mca Ghaziabad India R.M.K Engineering College Department of Computer Science and Business Systems Kavaraipettai India Kongu Engineering College Department of Cse Erode India Cvr College of Engineering Department of Information Technology Telangana India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India

ISBN: (纸本)9798350321425

In today's world, a huge amount of data and information is transferred through wireless networks. The Internet of Things (IoT) is a complex network that consists of diverse sensors, devices, and things, which often have limited resources and are susceptible to various security threats, including Distributed Denial of Service (DDoS) attacks. These networks are vulnerable to a variety of cyber threats and privacy issues. Intrusions are harmful activities that can damage a network. IoT networks are particularly vulnerable to security threats. The Bi-Layer Intrusion Detection Model (BIDM) identifies intrusions based on the optimal features selected in the Optimal Feature Vector Selection (OFVS) phase. This scheme not only prevents attacks, but also serves as a defense against legitimate threats. The proposed technique was evaluated using the KDD CUP 99 dataset, a well-known Intrusion Detection System (IDS) benchmark. The performance of the proposed scheme was further analyzed using the NSL-KDD and CICIDS-2017 datasets. The performance of OFVS was also investigated using the relatively new IoT Network Intrusion dataset. The implementation was carried out in Jupyter Notebook using the Python programming language. Matplotlib, Scikit-learn, pandas, and NumPy were some of the packages used in the implementation. The proposed framework achieved high accuracy rates, with NB at 97.4%, SVM at 96.1%, and DT at 98.1%. By utilizing this approach, the security of IoT networks can be significantly enhanced, and the risks associated with DDoS attacks can be mitigated. © 2023 IEEE.

关键词： Denial-of-service attack

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IEEE Internet of Things Journal 2025年第12期12卷 22443-22462页

作者： Li, Yangning Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Wang, Jiacheng Zhao, Changyuan Niyato, Dusit Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

An expansion of Internet of Things (IoTs) has led to significant challenges in wireless data harvesting, dissemination, and energy management due to the massive volumes of data generated by IoT devices. These challenges are exacerbated by data redundancy arising from spatial and temporal correlations. To address these issues, this paper proposes a novel data-driven collaborative beamforming (CB)-based communication framework for IoT networks. Specifically, the framework integrates CB with an overlap-based multi-hop routing protocol (OMRP) to enhance data transmission efficiency while mitigating energy consumption and addressing hot spot issues in remotely deployed IoT networks. Based on the data aggregation to a specific node by OMRP, we formulate a node selection problem for the CB stage, with the objective of optimizing uplink transmission energy consumption. Given the complexity of the problem, we introduce a softmax-based proximal policy optimization with long-short-term memory (SoftPPO-LSTM) algorithm to intelligently select CB nodes for improving transmission efficiency. Simulation results show that the proposed OMRP improves network lifetime by 17% compared to benchmark routing protocols, while the SoftPPO-LSTM method for CB node selection achieves an 8.3% increase in throughput over benchmark algorithms. The results also reveal that the combined OMRP with the SoftPPO-LSTM method effectively mitigates hot spot problems and offers superior performance compared to traditional strategies. © 2014 IEEE.

关键词： Deep reinforcement learning

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BMDP:Blockchain-Based Multi-Cloud Storage data Provenance 26

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Feiyu Zhou, Jian-Tao Guo, Xu Inner Mongolia University College of Computer Science Inner Mongolia Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software China

ISBN: (纸本)9798350331684

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme's safety and do simulation experiments to show that the scheme has practicality. © 2023 IEEE.

关键词： Smart contract

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A Multi-Agent Deep Reinforcement Learning Framework for Detecting and Mitigating DDoS Attacks in IoT Networks 4

A Multi-Agent Deep Reinforcement Learning Framework for Dete...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Shaharkar, Bhushan Mittal, Tanusha Sunkara, Krishna Chaitanya Shukla, Vishal Desai, Ishan Mookherjee, Ujal Walchand Institute of Technology Department of Information Technology Solapur India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Oracle Cloud Engineering Data Center Design & Modeling Raleigh United States Ramdeobaba University Department of Mechanical Engineering Nagpur India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electrical Engineering Post Limda India Faculty of Law Pune India

ISBN: (纸本)9798350352931

The rise of Internet of Things (IoT) networks has introduced new opportunities for innovation but also increased vulnerability to cyberattacks, particularly Distributed Denial-of-Service (DDoS) attacks. Traditional detection and mitigation mechanisms often struggle to handle the dynamic and distributed nature of IoT systems. In this study, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) framework designed to detect and mitigate DDoS attacks in IoT networks in real-time. The framework consists of multiple collaborative agents, each deployed at different network layers or nodes, trained using deep reinforcement learning to identify attack patterns and take immediate mitigation actions. Through cooperative learning, these agents adapt to evolving attack strategies and optimize defense policies. Experimental evaluations demonstrate that the proposed MADRL framework outperforms traditional techniques by achieving higher detection accuracy, faster response times, and reduced false positive rates. Our results indicate that this framework can effectively secure IoT networks against complex DDoS attacks, maintaining network stability and availability. © 2024 IEEE.

关键词： Deep reinforcement learning

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