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2024 International Conference on Cloud Computing and Big data, ICCBD 2024

作者： Zhang, Zidan Xu, Hua Yang, Chongyang School of Artificial Intelligence and Computer Science Jiangnan University China

ISBN: (纸本)9798400710223

Density Peaks Clustering (DPC) algorithm identifies cluster centers based on local density and relative distance, disregarding the effect of the sample's environment on its density. This makes it challenging to identify cluster centers in low-density regions. Moreover, the single-step allocation strategy of DPC algorithm lacks fault tolerance. If a sample point is incorrectly allocated, it can trigger a cascade of subsequent allocation error. To solve the aforementioned issue, a density peak clustering algorithm based on natural neighbors and diffusion allocation (NNDA-DPC) is proposed. Firstly, the concept of natural neighbors is introduced, and the local density of sample points is redefined by considering the density of sample points and the environment in which the sample points are located. This is done to reduce the influence of the sparsity of class clusters on the selection of class cluster centers. The clustering centers are automatically determined by computing the decision value. Secondly, the natural neighbor points of the selected clustering centers are assigned to the corresponding clustering centers to form the initial microclusters. Finally, the initial microclusters are formed based on the remaining sample points and the initial clusters. The final clustering result is obtained by assigning the sample points based on the correlation between the remaining sample points and the initial clusters, which helps prevent the cascading effect of assignment errors. We compared the NNDA-DPC algorithm with DPC and its enhanced versions on artificial and UCI datasets. The experimental results demonstrate that the NNDA-DPC algorithm can efficiently cluster the datasets. © 2024 ACM.

关键词： Consensus algorithm

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Fake News Detection Based on Attention Mechanism 6

Fake News Detection Based on Attention Mechanism

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6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Wang, Zihao Zhao, Tongzhou Wang, Jiageng Li, Shiya School of Computer Science and Engineering & Artificial Intelligence Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331527426

Currently, most multi-modal fake news detection methods rely on fusing multi-modal features using a single criterion, overlooking the potential contribution of original multi-modal information in fake news detection and classification. This paper proposes an attention-based multi-criteria evaluation model that strengthens text feature representation on the basis of using multi-criteria. The model utilizes ResNet for extracting image feature representations and weights the output of BERT hidden layers to obtain richer text features. Subsequently, text and image features are inputted into a multi-modal feature fusion module, and the resulting output is concatenated with pooled text and image features before being passed to the final classification module, which then outputs experimental results. Experimental results demonstrate that, compared to other baseline models, the proposed model achieves better performance on Weibo and Twitter datasets. © 2024 IEEE.

关键词： attention mechanism component fake news detection multi-criteria evaluation

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An optimization method for enterprise user anomaly detection based on FCM 3

An optimization method for enterprise user anomaly detection...

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3rd International Conference on Electronic Information Engineering and data Processing, EIEDP 2024

作者： Huang, Yifei Li, Xin Zou, Chengming School of Computer Science and Artificial Intelligence Wuhan University of Technology Hubei Wuhan China

ISBN: (纸本)9781510680531

As businesses continue to expand and digitize, there is a surge in both internal and load data. However, internal data, which is one of the core assets of a company, is increasingly under severe security threats. More covert attacks characterized by long cycles, low frequency, and high stealth are bypassing traditional security detection methods, causing extensive damage to data. To address this, we propose an approach that integrates various types of data reflecting the baseline of user behavior, based on the correlation of users, entities, and behaviors. We extract several fundamental features that most effectively indicate user anomalies and combine the XGBoost feature selection strategy with the FCM clustering algorithm. By scoring anomalies, we can identify the users posing the highest risk of anomalies. The results demonstrate that the newly proposed anomaly detection algorithm achieves an accuracy rate of over 83.5% and a recall rate of over 86.7%. © 2024 SPIE.

关键词： Anomaly detection

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SymbTQL: A Symbolic Temporal Logic Framework for Enhancing LLM-based TKGQA 22

SymbTQL: A Symbolic Temporal Logic Framework for Enhancing L...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhu, ShuaiChen Yuan, Yang Shi, Lin Xu, Shoukun Changzhou University School of Computer Science and Artificial Intelligence China

ISBN: (纸本)9798331509712

Temporal Knowledge Graph Question Answering (TKGQA) deals with the intricate task of processing and understanding time-evolving facts, and responding to natural language queries that involve sophisticated temporal constraints. Current Large Language Models (LLM) exhibit notable limitations in converting natural language queries into temporal reasoning tasks within complex TKGQA contexts. To mitigate these limitations, this paper proposes a pioneering approach named Symbolic Temporal Query for LLM TKGQA (SymbTQL). This novel framework bolsters LLM capacity to tackle complex temporal challenges through a tripartite process: initially, LLM is fine-tuned with a limited set of annotated data to heighten its comprehension of temporal constraints within TKGQA tasks;this is followed by employing a symbolic logic chain reasoning framework that breaks down intricate natural language queries into a sequence of simpler sub-questions;ultimately, the accuracy of the queries is ensured by aligning the generated query statements with actual data using sentence similarity encoding. Experimental evidence indicates that SymbTQL outperforms numerous baselines by 5.1% and 3.5% on the MultiTQ and CronQuestions datasets, respectively, and excels by 15.4% in managing multi-step complex temporal challenges, with ablation studies confirming its strong generalizability and logical coherence. © 2024 IEEE.

关键词： Spatio-temporal data

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Household Waste Classification Algorithms Based on Various Convolutional Neural Networks: A Review of Performance Optimization 21

Household Waste Classification Algorithms Based on Various C...

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Renxiang, Huang Jia, Chen Jianping, Li School of Artificial Intelligence and Big Data Sichuan University of Arts and Science Chengdu635002 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

This paper reviews the research progress of deep learning-based household waste classification algorithms. It first introduces the importance of household waste classification and the application value of deep learning in this field. Next, it outlines the theoretical foundations of deep learning technology in waste classification, including the principles of convolutional neural networks and their applications in image recognition. The article provides a detailed analysis of the application of different deep learning models in waste classification, such as MobileNetV2, ShuffleNetV1, EfficientNetV1, and YOLOV5, as well as their optimizations and innovations in practical applications. Furthermore, it discusses optimization and innovation methods for deep learning models, including learning rate adjustment, optimizer selection, attention mechanisms, data augmentation, and lightweight network design. Finally, it summarizes the current research shortcomings and challenges, and looks forward to future research directions. This review provides a systematic perspective for a deeper understanding of the application of deep learning in waste classification and offers directions for future research. © 2024 IEEE.

关键词： Convolutional neural networks

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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science China(Information sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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A unified user behavior model for trajectory-based tasks with different types of path constraints

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science China(Information sciences) 2023年第10期66卷 316-318页

作者： Hao ZHANG Jin HUANG Huawei TU Feng TIAN Guozhong DAI Hongan WANG Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Department of Computer Science and Information Technology La Trobe University

An important research branch of human-computer interaction(HCI) is to develop predictive models for human performance in fundamental interactions [1]. On today's graphical user interface(GUI), users often implicitly perform various trajectory-based interactions, such as navigating through menus [2], entering the boundary of a button,

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Brain tumor segmentation algorithm based on pathology topological merging

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Multimedia Tools and Applications 2024年第40期83卷 88019-88037页

作者： Liu, Deshan Zhang, Yanchao Wang, Xin Jiang, Yumeng Wang, Hongkai Fang, Lingling School of Computer Science and Artificial Intelligence Liaoning Normal University Liaoning Dalian China Medical Department Dalian University of Technology Liaoning Dalian China

Automatically segmenting the lesion of clinical data can aid doctors in diagnosis. The key issues with clinical brain tumor segmentation are partial volume effect, bias field, and noise interference. This paper proposes a novel segmentation algorithm based on pathology topological merging to solve the above problems. Here, an improved superpixel technique is used to group the pathology topological blocks, and the vector distance is refined to avoiding the problem of grouping pixels with small similarity near the tumor contour into the same region. Furthermore, meaningful pathology topological blocks are formed, and the entire brain tumor is segmented based on the pathology topological relationship and weight between pathology topological blocks. The proposed method is validated on the BraTS 2015 dataset and 123 patient images with brain tumors from a local hospital, and the mean Dice, Jaccard, Precision, and Recall values are 0.91, 0.92, 0.90, and 0.91, respectively, indicating that the proposed method can efficiently and accurately distinguish brain tumors from other tissues (such as edema). The method can overcome some defects (e.g. partial volume effect, bias field, and noise interference) in medical brain images while having practical clinical application prospects. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Merging

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Dual-Recommendation Disentanglement Network for View Fuzz in Action Recognition

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IEEE Transactions on Image Processing 2023年 32卷 2719-2733页

作者： Liu, Wenxuan Zhong, Xian Zhou, Zhuo Jiang, Kui Wang, Zheng Lin, Chia-Wen Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan430070 China Wuhan University of Technology Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan430070 China Peking University School of Electronics Engineering and Computer Science Beijing100091 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University School of Computer Science Wuhan430072 China National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Multi-view action recognition aims to identify action categories from given clues. Existing studies ignore the negative influences of fuzzy views between view and action in disentangling, commonly arising the mistaken recognition results. To this end, we regard the observed image as the composition of the view and action components, and give full play to the advantages of multiple views via the adaptive cooperative representation among these two components, forming a Dual-Recommendation Disentanglement Network (DRDN) for multi-view action recognition. Specifically, 1) For the action, we leverage a multi-level Specific Information Recommendation (SIR) to enhance the interaction among intricate activities and views. SIR offers a more comprehensive representation of activities, measuring the trade-off between global and local information. 2) For the view, we utilize a Pyramid Dynamic Recommendation (PDR) to learn a complete and detailed global representation by transferring features from different views. It is explicitly restricted to resist the fuzzy noise influence, focusing on positive knowledge from other views. Our DRDN aims for complete action and view representation, where PDR directly guides action to disentangle with view features and SIR considers mutual exclusivity of view and action clues. Extensive experiments have indicated that the multi-view action recognition method DRDN we proposed achieves state-of-the-art performance over powerful competitors on several standard benchmarks. The code will be available at https://***/51cloud/DRDN. © 1992-2012 IEEE.

关键词： Economic and social effects

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Enhancing Fairness in Meta-learned User Modeling via Adaptive Sampling 24

Enhancing Fairness in Meta-learned User Modeling via Adaptiv...

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33rd ACM Web Conference, WWW 2024

作者： Zhang, Zheng Liu, Qi Hu, Zirui Zhan, Yi Huang, Zhenya Gao, Weibo Mao, Qingyang School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China School of Computer Science and Technology University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center State Key Laboratory of Cognitive Intelligence Hefei China

ISBN: (纸本)9798400701719

Meta-learning has been widely employed to tackle the cold-start problem in user modeling. Similar to a guidebook for a new traveler, meta-learning significantly affects decision-making for new users in crucial scenarios, such as career recommendations. Consequently, the issue of fairness in meta-learning has gained paramount importance. Several methods have been proposed to mitigate unfairness in meta-learning and have shown promising results. However, a fundamental question remains unexplored: What is the critical factor leading to unfairness in meta-learned user modeling? Through the theoretical analysis that integrates the meta-learning paradigm with group fairness metrics, we identify group proportion imbalance as a critical factor. Subsequently, in order to mitigate the impact of this factor, we introduce a novel Fairness-aware Adaptive Sampling framework for meTa-learning, abbreviated as FAST. Its core concept involves adaptively adjusting the sampling distribution for different user groups during the interleaved training process of meta-learning. Furthermore, we provide theoretical guarantees demonstrating the convergence of FAST. Finally, empirical experiments conducted on three datasets reveal that FAST effectively enhances fairness while maintaining high accuracy. The code for FAST is available at https://***/zhengz99/FAST. © 2024 Owner/Author.

关键词： Decision making

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