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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Sobana, P. Yaiaman, Gummagatta Vybhavi Kumar, A. Pradeep Karthi, S. Almusawi, Muntather Rani, Hema Sethu Institute of Technology Department of Computer Science Engineering Pulloor Tamil Nadu Kariapatti626115 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Cse Telangana Hyderabad India Cmr College of Engineering & Technology Department of Ece Telanagana Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil nadu Chennai India College of technical engineering The Islamic University Department of computers Techniques engineering Najaf Iraq Centre for Managament studies Gitarattan International Business School Delhi India

ISBN: (纸本)9798331527495

It has been established for a long time and has evolved correspondingly that actuators and sensor systems may be connected to the internet for the purpose of continuously monitoring and controlling biological phenomena. The term 'Internet of Things (IoT)' was first used to combine these methods into a coherent framework a little more than a decade earlier. The IoT based idea has evolved along with science, adopting new vocabulary that is suited for technical advancements and many applications sectors. The IoT has undergone modifications since its inception, and this study examines studies on how technical advancements have transformed it as well and encouraged the emergence of modified terms appropriate for certain fields. A three separate study of the literature was done using games companies and classification data warehouses. First step involved looking for proposed changes on the Iot devices principle & evaluating them to compute the similarity, disparities, and functionality that enable us to generate a number of links its progression. The 1st step involved identifying and briefly assessing the much more common names handed to it IoT for multiple areas. As underpinning innovation continues to advance and software update areas are put out, the research supports the assertion that a consensus on the IoT concept is still not established. With information and information solutions playing an important role in the panorama, recent developments have been somewhat restrained and its variants on possible applications are distinct. © 2025 IEEE.

关键词： Labeled data

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Multi-scale Context Intertwining for Panoramic Renal Pathology Segmentation

Multi-scale Context Intertwining for Panoramic Renal Patholo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ye Zhang Xianchao Guan Hengrui Li Xiangming Yan Ziyue Wang Yongbing Zhang School of Computer Science and Technology Harbin Institute of Technology Shenzhen Leibniz-Institut für Analytische Wissenschaften ISAS - e.V. Pengcheng Laboratory Shenzhen Faculty of Computing Harbin Institute of Technology Department of Electronic and Computer Engineering National University of Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Panoramic segmentation of renal pathological tissues plays a crucial role in diagnosing renal carcinoma and other kidney-related diseases. The multi-scale nature of kidney tissues, which requires different magnification levels for accurate analysis, presents a significant challenge for segmentation models. In this work, we propose a Multi-scale Context Intertwining Network (MCINet) to address this issue. Our approach utilizes an auxiliary interaction network to enhance feature interaction between different scales and generate pseudo-labels for unannotated structures. By incorporating exponential moving average strategies, we ensure seamless feature integration across scales. Extensive experiments demonstrate that MCINet outperforms state-of-the-art models in key metrics such as Dice and Hausdorff Distance, proving its efficacy in renal tissue segmentation tasks.

关键词： Measurement Image segmentation Pathology Analytical models Signal processing Acoustics Speech processing Optimization Kidney Diseases

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Data-Driven Power Flow Estimation for MVDC Distribution Systems Based on Physics-Embedded FCN

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IEEE Transactions on Smart Grid 2025年

作者： Sun, Pingyang Wu, Rongcheng Shen, Zhiwei Wang, Hongyi Li, Gen Khalid, Muhammad Konstantinou, Georgios School of Electrical Engineering and Telecommunications UNSW Sydney Australia University of Technology Sydney Data Science Institute Australia Aalborg University Department of AAU energy Denmark Energy Technology and Computer Science Section Department of Engineering Technology Ballerup2750 Denmark King Fahd University of Petroleum and Minerals Electrical Engineering Department Interdisciplinary Research Center for Sustainable Energy Systems Dhahran31261 Saudi Arabia

Maintaining high prediction accuracy with varying grid topologies poses a significant challenge to adopting neural network (NN)-based approaches for power flow (PF) estimation in medium-voltage direct current (MVDC) distribution systems. This paper proposes a physics-embedded fully convolutional network (PEFCN) to improve the accuracy. Physics-embedded techniques are incorporated in the proposed PEFCN through 1) architecture reconstruction, and 2) loss function reformulation. Architecture is reconstructed by input channel conversion calculation in a new physics operation layer. This process offers physical connections among the three input matrix channels (voltage, current, and line conductance). Three new physics loss terms are included in the loss function to restrict the outliers violating the limits of converter power ratings and terminal MVDC voltages. The two operations enable the FCN to achieve improved prediction accuracy and strong generalization capabilities. Five MVDC distribution systems, characterized by different dc voltage levels and topology configurations, serve to validate the superiority of the proposed PEFCN over other NNs in the PF estimation for scenarios involving both fixed and varying system structures. Moreover, a modified IEEE 69-bus distribution system is further used to demonstrate the applicability of the proposed PEFCN for larger systems. © 2010-2012 IEEE.

关键词： DC distribution systems

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EAPDS: Efficient Auditable and Privacy-preservation Data Sharing Scheme Based on Attribute-Based Encryption for IoMT

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IEEE Internet of Things Journal 2025年

作者： Wang, Hui Xie, Yong Luo, Min Liu, Yining Shirazi, Syed Hamad Qinghai University Department of Information Technology Xining810016 China Qinghai Institute of Technology School of Computer and Information Science Xining810016 China Qinghai Provincial Key Laboratory of Big Data in Finance and Artificial Intelligence Application Technology Xining810016 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325000 China Hazara University Department of Information Technology Mansehra21300 Pakistan

Data sharing schemes based on the Internet of Medical Things (IoMT) have emerged as a more convenient way to monitor and manage individuals’ health. However, this scenario faces challenges such as privacy preservation, effectiveness, and practicality, which hinder its further development. To the best of our knowledge, there is no agreed-upon data-sharing method that addresses all of these problems. In this paper, we make a step ahead by designing an Efficient and Auditable Privacy-preservation Data Sharing scheme (EAPDS) based on multi-authority attribute-based encryption. EAPDS designs an auditable anonymous authentication mechanism to realize identity privacy protection, as well as an efficient multi-authority attribute-based encryption mechanism to achieve the efficiency and practicability of data-sharing. Formal security analysis demonstrates EAPDS can resist replayable chosen-ciphertext attack. Many performance evaluation experiments and functional analyses show that EAPDS not only performs better than existing medical data-sharing schemes in terms of efficiency, but also in terms of privacy protection and feasibility. Consequently, our EAPDS scheme holds promising application prospects. © 2014 IEEE.

关键词： Data consistency

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Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation

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IEEE Transactions on Multimedia 2025年

作者： Huang, Xinyang Zhu, Chuang Ren, Ruiying Liu, Shengjie Huang, Tiejun Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing China Peking University BAAI and Institute for Artificial Intelligence Beijing China Peking University National Engineering Research Center of Visual Technology School of Computer Science China

Semi-supervised domain adaptation (SSDA) has been extensively researched due to its ability to improve classification performance and generalization ability of models by using a small amount of labeled data on the target domain. However, existing methods cannot effectively adapt to the target domain due to difficulty in fully learning rich and complex target semantic information and relationships. In this paper, we propose a novel SSDA learning framework called semantic regularization learning (SERL), which captures the target semantic information from multiple perspectives of regularization learning to achieve adaptive fine-tuning of the source pre-trained model on the target domain. SERL includes three robust semantic regularization techniques. Firstly, semantic probability contrastive regularization (SPCR) helps the model learn more discriminative feature representations from a probabilistic perspective, using semantic information on the target domain to understand the similarities and differences between samples. Additionally, adaptive weights in SPCR can help the model learn the semantic distribution correctly through the probabilities of different samples. To further comprehensively understand the target semantic distribution, we introduce hard-sample mixup regularization (HMR), which uses easy samples as guidance to mine the latent target knowledge contained in hard samples, thereby learning more complete and complex target semantic knowledge. Finally, target prediction regularization (TPR) regularizes the target predictions of the model by maximizing the correlation between the current prediction and the past learned objective, thereby mitigating the misleading of semantic information caused by erroneous pseudo-labels. Extensive experiments on three benchmark datasets demonstrate that our SERL method achieves state-of-the-art performance. The code for our method will be available at https://***/bupt-ai-cz/SERL. © 2025 IEEE.

关键词： Latent semantic analysis

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Pedestrian Trajectory Prediction Based on Social Interactions Learning with Random Weights

arXiv

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arXiv 2025年

作者： Xie, Jiajia Zhang, Sheng Xia, Beihao Xiao, Zhu Jiang, Hongbo Zhou, Siwang Qin, Zheng Chen, Hongyang College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Shenzhen Research Institute Hunan University Shenzhen518055 China School of Computer Science and Technology Zhejiang University Hangzhou310000 China School of Electronic Information and Communication Huazhong University of Science and Technology Wu Han430074 China Zhejiang Lab Hangzhou311121 China

Pedestrian trajectory prediction is a critical technology in the evolution of self-driving cars toward complete artificial intelligence. Over recent years, focusing on the trajectories of pedestrians to model their social interactions has surged with great interest in more accurate trajectory predictions. However, existing methods for modeling pedestrian social interactions rely on pre-defined rules, struggling to capture non-explicit social interactions. In this work, we propose a novel framework named DTGAN, which extends the application of Generative Adversarial Networks (GANs) to graph sequence data, with the primary objective of automatically capturing implicit social interactions and achieving precise predictions of pedestrian trajectory. DTGAN innovatively incorporates random weights within each graph to eliminate the need for pre-defined interaction rules. We further enhance the performance of DTGAN by exploring diverse task loss functions during adversarial training, which yields improvements of 16.7% and 39.3% on metrics ADE and FDE, respectively. The effectiveness and accuracy of our framework are verified on two public datasets. The experimental results show that our proposed DTGAN achieves superior performance and is well able to understand pedestrians’ intentions. © 2025, CC BY.

关键词： Generative adversarial networks

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SWIPTNet: A Unified Deep Learning Framework for SWIPT based on GNN and Transfer Learning

arXiv

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arXiv 2025年

作者： Han, Hong Lu, Yang Song, Zihan Zhang, Ruichen Chen, Wei Ai, Bo Niyato, Dusit Kim, Dong In School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

This paper investigates the deep learning based approaches for simultaneous wireless information and power transfer (SWIPT). The quality-of-service (QoS) constrained sum-rate maximization problems are, respectively, formulated for power-splitting (PS) receivers and time-switching (TS) receivers and solved by a unified graph neural network (GNN) based model termed SWIPT net (SWIPTNet). To improve the performance of SWIPTNet, we first propose a single-type output method to reduce the learning complexity and facilitate the satisfaction of QoS constraints, and then, utilize the Laplace transform to enhance input features with the structural information. Besides, we adopt the multi-head attention and layer connection to enhance feature extracting. Furthermore, we present the implementation of transfer learning to the SWIPTNet between PS and TS receivers. Ablation studies show the effectiveness of key components in the SWIPTNet. Numerical results also demonstrate the capability of SWIPTNet in achieving near-optimal performance with millisecond-level inference speed which is much faster than the traditional optimization algorithms. We also show the effectiveness of transfer learning via fast convergence and expressive capability improvement. © 2025, CC BY.

关键词： Contrastive Learning

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Artificial intelligence tools: a potential for error-free scholarly communication in Nigerian universities

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AI and Ethics 2025年 1-12页

作者： Okafor, Peter-Ebuka Udosen, Abasiafak Ndifreke Igboanugo, Benson Ikechukwu MOE Key Laboratory of Thermo-Fluid Science and Engineering School of Energy and Power Engineering Xi’an Jiaotong University Xi’an China Department of Computer and Information Technology Purdue University West Lafayette United States Department of Science Education Alex Ekwueme Federal University Ndufu-Alike Abakaliki Nigeria

Recently, efforts have been intensified in Nigerian universities to address errors and ethical issues in scholarly communications. However, despite significant efforts by the government and university administrators to resolve these challenges, most researchers still lack the expertise to effectively utilize advanced artificial intelligence (AI) tools that are essential for producing ethically sound and error-free scholarly articles. AI has speedily emerged as one of the most significant and transformative innovations of our time, with wide-ranging applications in almost every field of knowledge. Among these applications, research has arguably witnessed the fastest growth and adoption of AI-driven tools in developed nations. This study provides an in-depth review of the present applications of AI-driven tools in higher education in Nigeria, with a focus on universities, and explores the factors affecting innovative research output in universities in Nigeria. The findings from the articles reveal that several factors contribute to this, such as poor research sponsorship, limited access to online scholarly articles, unethical practices, unintentional errors, and brain drain. Benefits, implications, and strategies for improvement, along with a roadmap outlining the way forward, are suggested.

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GraphThought: Graph Combinatorial Optimization with Thought Generation

arXiv

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arXiv 2025年

作者： Huang, Zixiao Guo, Lifeng Sheng, Junjie Chen, Haosheng Li, Wenhao Jin, Bo Lu, Changhong Wang, Xiangfeng School of Computer Science and Technology East China Normal University China School of Mathematical Sciences East China Normal University China School of Computer Science and Technology Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University China Key Laboratory of Mathematics and Engineering Applications MoE East China Normal University China

Large language models (LLMs) have demonstrated remarkable capabilities across various domains, especially in text processing and generative tasks. Recent advancements in the reasoning capabilities of state-of-the-art LLMs, such as OpenAI-o1, have significantly broadened their applicability, particularly in complex problem-solving and logical inference. However, most existing LLMs struggle with notable limitations in handling graph combinatorial optimization (GCO) problems. To bridge this gap, we formally define the Optimal Thoughts Design (OTD) problem, including its state and action thought space. We then introduce a novel framework, GraphThought, designed to generate high-quality thought datasets for GCO problems. Leveraging these datasets, we fine-tune the Llama-3-8B-Instruct model to develop Llama-GT. Notably, despite its compact 8B-parameter architecture, Llama-GT matches the performance of state-of-the-art LLMs on the GraphArena benchmark. Experimental results show that our approach outperforms both proprietary and open-source models, even rivaling specialized models like o1-mini. This work sets a new state-of-the-art benchmark while challenging the prevailing notion that model scale is the primary driver of reasoning capability. © 2025, CC BY.

关键词： Combinatorial optimization

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BBDAS: Blockchain-Assisted Blinded Data Auditing Scheme for Cloud-Edge Systems 6th

BBDAS: Blockchain-Assisted Blinded Data Auditing Scheme for...

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6th International Conference on Blockchain and Trustworthy Systems, BlockSys 2024

作者： Wang, Chen Cui, Yuxin Yang, Bing Shen, Wei Tong, Wei School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China School of Computer Science Nanjing University of Information Science Technology Jiangsu210044 China Zhejiang Sci-Tech University Hangzhou310018 China Hangzhou Institute of Technology Xidian University Hangzhou311200 China

ISBN: (纸本)9789819614134

In distributed large-scale intelligent computing, efficient collaboration between the cloud and edge devices is essential to provide rapid data access for a vast number of users, which is crucial for optimal data storage services. The edge servers are utilized to meet the data access requirements of adjacent group users. Numerous data auditing schemes for cloud computing have been proposed to protect user data integrity and avoid the threat of data leakage, loss, or tampering. However, secure cloud-edge storage of group users remains to be further studied. This paper proposes a novel blockchain-assisted blinded data auditing scheme for cloud-edge systems. Firstly, blockchain technology is utilized to monitor the activities of a semi-trusted third-party auditor (TPA). Secondly, user identifiers are utilized to trace the malicious behaviors in the systems. In addition, a data blinding algorithm is utilized to ensure that the raw data is unavailable to a curious TPA. Both security analysis and performance assessment demonstrate that this scheme conducts data auditing for cloud-edge systems with reliability and efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data integrity

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