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A Novel Community Detection Algorithm Based on Node Propagation and Attraction

IEEE Access 2025年 13卷 69161-69174页

作者： Fu, Lidong Xie, Xinxing Dou, Zengfa Bai, Yun Zhao, Xi Xi'an University of Science and Technology College of Computer Science and Technology Xi'an710600 China Qinghai Institute of Technology School of Computer and Information Science Xining810016 China Xi'an University School of Information Engineering Xi'an710065 China Xi'an Big Data Service Center Xi'an710007 China

High-quality community detection in complex networks typically relies on the similarity between nodes to select communities. However, when a node exhibits the same similarity with different communities, it becomes ambiguous to determining the optimal community assignment for that node, potentially leading to incorrect predictions and affecting the accuracy of community assignments for other nodes. To address this problem, this paper proposes a novel community detection algorithm based on node propagation and attraction. The algorithm first introduces a node propagation strategy, which identifies high-propagation nodes based on their connectivity and core attributes. These high-propagation nodes form the initial backbone and framework of communities, minimizing the interference of low-propagation nodes on community structure. Subsequently, an attraction definition is introduced, which starts from high-propagation nodes to select partitioning methods based on their relationships with neighboring nodes, using the attraction among these nodes facilitate community organization and propagation, aiming to enhance community quality. Finally, in the community optimization phase, the algorithm effectively improves the accuracy of partitions that result from similar attractions by considering the association degree between nodes and communities. Experimental results demonstrate that, on both synthetic and real networks, the proposed algorithm outperforms various existing community detection algorithms. © 2013 IEEE.

关键词： Optimization algorithms

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Knowledge-Centered Dual-Process Reasoning for Math Word Problems With Large Language Models

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IEEE Transactions on Knowledge and data Engineering 2025年第6期37卷 3457-3471页

作者： Liu, Jiayu Huang, Zhenya Liu, Qi Ma, Zhiyuan Zhai, Chengxiang Chen, Enhong University of Science and Technology of China School of Data Science State Key Laboratory of Cognitive Intelligence Hefei230000 China University of Science and Technology of China School of Computer Science and Technology State Key Laboratory of Cognitive Intelligence Hefei230000 China University of Illinois at Urbana-Champaign Department of Computer Science ChampaignIL61820 United States

Math word problem (MWP) serves as a critical milestone for assessing the text mining ability and knowledge mastery level of models. Recent advancements have witnessed large language models (LLMs) showcasing remarkable performance on MWP. However, current LLMs still frequently exhibit logical errors, which highlights their inability to fully grasp the knowledge required for genuine step-by-step mathematical reasoning. To this end, in this paper, we propose a novel Knowledge-guided Solver (KNOS) framework that empowers LLMs to simulate human mathematical reasoning, whose core idea is to Invoke-Verify-Inject necessary knowledge to solve MWP. We draw inspiration from the dual-process theory to construct two cooperative systems: a Knowledge System and an Inference System. Specifically, the Knowledge System employs LLMs as the knowledge base and develops a novel knowledge invoker that can elicit their relevant knowledge to support the strict step-level mathematical reasoning. In the Inference System, we propose a knowledge verifier and a knowledge injector to evaluate the knowledge rationality and further guide the step-wise symbolic deduction in an interpretable manner based on human cognitive mechanism, respectively. Moreover, to tackle the potential scarcity issue of mathematics-specific knowledge in LLMs, we consider an open-book exam scenario and propose an improved version of KNOS called EKNOS. In EKNOS, we meticulously design knowledge selectors to extract the most relevant commonsense and math formulas from external knowledge sources for each reasoning step. This knowledge is utilized to assist the knowledge invoker in better stimulating LLMs' reasoning abilities. Both KNOS and EKNOS are flexible to empower different LLMs. Our experiments with GPT3, ChatGPT, and GPT4 not only demonstrate their reasoning accuracy improvement but also show how they bring the strict step-wise interpretability of mathematical thinking. © 2025 IEEE.

关键词： Inference engines

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Harnessing Syntax GCN and Multi-view Interaction for Conversational Aspect-Based Quadruple Sentiment Analysis

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IEEE Access 2025年 13卷 73332-73341页

作者： Kang, Houwei Wu, Chunling Chongqing Normal University School of Computer and Information Science Chongqing400000 China Chongqing Polytechnic University of Electronic Technology School of Artificial Intelligence and Big Data Chongqing400000 China

Conversational Aspect-Based Sentiment Analysis (DiaASQ) aims to extract fine-grained sentiment quadruples {target, aspect, opinion, polarity} from multiple segments of dialogue. The composition of these quadruples is often not limited to a single utterance but may span across the entire conversation. Therefore, the analysis needs to consider both the syntactic structure of individual utterances and the interactions between different views. However, previous studies often lack modeling of dialogue features and overlook the interdependence between utterances. To address this, this paper introduces the Syntax and Consecutive Multi-View Network. This model captures the syntactic dependencies among utterances using Graph Convolutional Networks (GCN) and employs multi-view interactions along with three consecutive multi-head attention modules to construct contextual connections within the dialogue. Finally, a triple scorer is used to decode the quadruples, thereby enhancing the weakly labeled relationships within the quadruples. Extensive experiments on standard datasets demonstrate that the proposed model significantly outperforms baseline methods across multiple dimensions. © 2013 IEEE.

关键词： Emotion Recognition

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Crack-DETR: Complex Pavement Crack Detection by Multifrequency Feature Extraction and Fusion

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IEEE Sensors Journal 2025年第9期25卷 16349-16360页

作者： Liu, Guoxi Liu, Guozhi Chai, Xinzhuo Li, Lecheng Dai, Fei Huang, Bi Southwest Forestry University School of Big Data and Intelligent Engineering Kunming650233 China South China University of Technology School of Computer Science and Engineering Guangdong Guangzhou510006 China

Automated detection of pavement cracks plays a crucial role in road maintenance and traffic safety. However, pavement crack detection under noisy conditions is challenging due to the complex expression forms of pavement cracks and the presence of multitype background noise. In this article, we propose Crack-DETR, an efficient pavement crack detection deep learning network, that can detect complex pavement cracks from complex background noise. In this network, we first propose a multifrequency feature extraction and fusion (MFEF) block to enhance pavement crack features and suppress background noise from both high-frequency and low-frequency perspectives in two stages: multifrequency feature extraction and multifrequency feature fusion. In the multifrequency feature extraction stage, we design a high-frequency attention mechanism to extract high-frequency features from high-level features. Then, we propose a low-frequency attention mechanism to capture low-frequency features of high-level features. In the multifrequency feature fusion stage, we fuse the high- and low-frequency features through intrafrequency feature fusion and cross-frequency feature fusion. Second, we propose an evaluating metric, diagonal intersection over union (DIoU), to enhance the loss function of Crack-DETR, improving the learning effectiveness of the model. Finally, we conduct extensive experiments on three real-world datasets with different scenarios, and the experimental results show that our method can improve the frames per second (FPS) by about 33.4% and mAP@50 by about 6.78% on average compared with baseline model in the YN dataset. Consequently, Crack-DETR achieves higher accuracy while reducing computational complexity, paving the way for the development of sensor-based real-time pavement crack detection. © 2001-2012 IEEE.

关键词： Feature extraction

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An Experimental Study of Passive UAV Tracking With Digital Arrays and Cellular Downlink Signals

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 3779-3794页

作者： Sun, Yifei Yu, Chao Luo, Yan Xiao Han, Tony Tan, Haisheng Wang, Rui Lau, Francis C. M. Southern University of Science and Technology Department of Electrical and Electronic Engineering Shenzhen518055 China The University of Hong Kong School of Computing and Data Science Department of Computer Science Hong Kong Huawei Technologies Company Ltd. Wireless Technology Laboratory Shenzhen518063 China University of Science and Technology of China School of Artificial Intelligence and Data Science Hefei230027 China

Given the prospects of the low-altitude economy (LAE) and the popularity of autonomous aerial vehicles (UAVs), there are increasing demands on monitoring flying objects at low altitude in wide urban areas. In this work, the widely deployed long-term evolution (LTE) base station (BS) is exploited to illuminate UAVs in bistatic trajectory tracking. Specifically, a passive sensing receiver with two digital antenna arrays is proposed and developed to capture both the line-of-sight (LoS) signal and the scattered signal off a target UAV. From their cross ambiguity function, the bistatic range, Doppler shift and angle-of-arrival (AoA) of the target UAV can be detected in a sequence of time slots. In order to address missed detections and false alarms of passive sensing, a target tracking framework is adopted to track the trajectory of the target UAV. It is demonstrated by experiments that the proposed UAV tracking system can achieve a meter-level accuracy. © 2020 IEEE.

关键词： Long Term Evolution (LTE)

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Dynamic and Verifiable Fuzzy Keyword Search With Forward Security in Cloud Environments

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IEEE Internet of Things Journal 2025年第9期12卷 11482-11494页

作者： Cui, Ningning Wang, Zhihao Pei, Lili Wang, Mengxiang Wang, Dong Cui, Jie Zhong, Hong Anhui University School of Computer Science Hefei230093 China Chang’an University School of Data Science and Artificial Intelligence Xi’an710064 China China National Institute of Standardization Division of Service Standardization Beijing100181 China

Dynamic searchable symmetric encryption (DSSE) ensures that outsourced data can be searched and updated without compromising data availability. Recent efforts on DSSE have mainly focused on exact keyword retrieval, but considering that misspellings are common and practical, it is necessary to support the functionality of fuzzy keyword search. However, most existing fuzzy keyword search schemes do not consider malicious cloud servers and forward-privacy guarantees. The former may lead to the implementation of a fraction of search operations or forge the results and the latter may reveal the association between the newly updated data and previous search tokens. Therefore, in this article, we investigate the issue of dynamic and verifiable fuzzy keyword search with forward security (DVFKF). Specifically, DVFKF first uses locality sensitive hash to map approximate keywords into the same hash bucket, then links the hash buckets to obtain the bucket strings. Next, to accelerate the performance and support forward privacy, we propose a chain index for each bucket string through counters. Further, to guarantee the integrity of the results, we integrate the Merkle hash tree and chain index to verify the correctness and completeness of the results. Finally, we conduct formal security analysis and empirical evaluations to demonstrate the feasibility and practicality of our scheme on real datasets. © 2014 IEEE.

关键词： Differential privacy

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Enhancing Situational Awareness: Anomaly Detection using Real-Time Video Across Multiple Domains

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IEEE Access 2025年 13卷 73680-73696页

作者： Singh, Rajan Pal, Amrit Mishra, Shruti Chowdhury, Abishi Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai600127 India Vellore Institute of Technology Centre for Advanced Data Science Tamil Nadu Chennai600127 India

Though it is challenging, anomaly detection in real-time video data has significant implications in many disciplines like public safety, education, and agriculture. This paper proposes a novel approach to handle the complexity of dynamic real-world anomaly detection scenarios using three state of the art machine learning models: Convolutional Neural Networks (CNN), Region-based Convolutional Neural Network (R-CNN), and You Only Look Once (YOLO). The proposed system is precisely crafted to fast inform authorities of irregularities, so improving situational awareness and security procedures. Using trained models, applying the most effective one for real-time detection, and extensively investigating video data are part of the study method suggested here. The key objectives are to guarantee flexibility to match numerous circumstances and reduce false positives. First findings show the system’s good performance in numerous settings, thereby offering the basis for additional development and study. Especially, following rigorous training and testing, every model displayed varying degrees of accuracy. Emphasizing its effectiveness in focused detection tasks, the CNN model shown a remarkable accuracy of 73% in spotting anomalies inside specific areas of interest. R-CNN, on the other hand, shown greater performance in demanding environments and an accuracy of 80%. Especially appropriate for quick anomaly identification, YOLO, renowned for its speed and accuracy in real-time object detection, shone out with an amazing 91%. This work greatly helps the field of surveillance technologies by providing specific techniques to increase security and safety in useful surroundings. Emphasizing the transformational possibilities of novel anomaly detection systems, this work underlines the importance of constant research and deployment of innovative concepts to boost security and safety measures. © 2013 IEEE.

关键词： Convolutional neural networks

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FlexIM: Efficient and Verifiable Index Management in Blockchain

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IEEE Transactions on Knowledge and data Engineering 2025年第6期37卷 3399-3412页

作者： Li, Binhong Lin, Licheng Zhang, Shijie Xu, Jianliang Xiao, Jiang Li, Bo Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Laboratory The Cluster and Grid Computing Laboratory School of Computer Science and Technology Wuhan430074 China Hong Kong Baptist University School of Computer Science Hong Kong Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong999077 Hong Kong

Blockchain-based query with its traceability and data provenance has become increasingly popular and widely adopted in numerous applications. Yet existing index-based query approaches are only efficient under static blockchain query workloads where the query attribute or type must be fixed. It turns out to be particularly challenging to construct an efficient index for dynamic workloads due to prohibitively long construction time and excessive storage consumption. In this paper, we present FlexIM, the first efficient and verifiable index management system for blockchain dynamic queries. The key innovation in FlexIM is to uncover the inherent characteristics of blockchain, i.e., data distribution and block access frequency, and then to optimally choose the index by utilizing reinforcement learning technique under varying workloads. In addition, we enhance and facilitate verifiability with low storage overhead by leveraging Root Merkle Tree (RMT) and Bloom Filter Merkle Tree (BMT). Our comprehensive evaluations demonstrate that FlexIM outperforms the state-of-the-art blockchain query mechanism, vChain+, by achieving a 26.5% speedup while consuming 94.2% less storage, on average, over real-world Bitcoin datasets. © 1989-2012 IEEE.

关键词： Structured Query Language

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Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

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DkvSSO: Delegatable Keyed-Verification Credentials for Efficient Anonymous Single Sign-On

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4196-4211页

作者： Xue, Wenyi Yang, Yang Huang, Minming Li, Yingjiu Pang, Hwee Hwa Deng, Robert H. Fuzhou University College of Computer and Data Science Fuzhou350116 China Singapore Management University School of Computing and Information Systems Bras Basah 188065 Singapore University of Oregon Department of Computer and Information Science EugeneOR97403 United States

Anonymous single sign-on (ASSO) is an anonymous multi-service authentication method for end users. However, existing ASSO schemes suffer from heavy ticket requesting and verifying overheads, limiting their applications in large-scale settings. To address this problem, we propose a novel concept called keyed-verification anonymous credentials with disposable delegation (KVAC-DD) in the multi-verifier setting. Next, we extend KVAC-DD to build an efficient ASSO system, dubbed DkvSSO. The construction of DkvSSO can be instantiated in efficient prime-order groups, avoiding costly operations required in previous ASSO systems. We formally prove the security of our proposed constructions. Extensive experiments show that DkvSSO is significantly more efficient than existing ASSO schemes, making it suitable to be deployed in large-scale settings. © 2005-2012 IEEE.

关键词： Anonymity

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