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A data representation method using distance correlation

Frontiers of Computer science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO Keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

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Efficient Plug-and-Play Module for Transformer-Based 3D Human Pose Estimation 5

Efficient Plug-and-Play Module for Transformer-Based 3D Huma...

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5th International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Qiu, Yaru Zhang, Jianfang Wu, Guoxia Sun, Yuanyuan School of Data Science Qingdao University of Science and Technology Qingdao China School of Information Science and Technology Qingdao University of Science and Technology Qingdao China

ISBN: (纸本)9798331528911

Video pose transformers (VPTs) have demonstrated remarkable performance in 3D human pose prediction. However, transformer-based architectures are often computationally intensive, leading to prolonged training times. To address this issue, we propose an efficient and plug-and-play module named the Adaptive Token Management (ATM), to prune and recover pose tokens, enabling high efficiency and accurate predictions within the VPTs framework. ATM consists of two key modules: the Dynamic Token Pruning (DTP) and the Lightweight Token Recovery (LTR). The DTP is presented for dynamic tokens pruning, where it determines the optimal number of representative tokens based on feature distribution and selects the representative tokens through a scoring mechanism. The LTR module, an optional lightweight module, is designed for tasks requiring the recovery of full-length tokens. It selects an appropriate full-length token as a learnable initialization token, utilizing attention mechanisms to restore spatio-temporal information. We conducted extensive experiments on the Human3.6M and MPI-INF-3DHP datasets, demonstrating that our approach can enhance the efficiency of mainstream VPTs while maintaining prediction accuracy. The results show that, when using MixSTE as the underlying framework on the Human3.6M dataset, ATM accelerated training time per epoch, reduced FLOPs by over 40%, and decreased prediction error by 0.2. © 2024 IEEE.

关键词： Distribution transformers

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WheatNGP: Neural Radiation Field Optimization for Wheat Flocks Under Boundless Free Trajectories 5

WheatNGP: Neural Radiation Field Optimization for Wheat Floc...

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5th International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Tian, Liming Qiu, Yaru Wu, Guoxia Sun, Yuanyuan School of Data Science Qingdao University of Science and Technology Qingdao China School of Information Science and Technology Qingdao University of Science and Technology Qingdao China

ISBN: (纸本)9798331528911

Accurate NeRF synthesis can help plant breeders to effectively analyze and identify plant traits to improve crop yield. Most of the existing mesh-based NeRF new perspective synthesis methods are targeted at bounded scenes, such as Instant-NGP, Plenoxels, DVGO, and tensorf, but wheat is usually grown in outdoor field environments, which belongs to non-boundary scenes. At present, the widely used method is to rely on space warping, but it can only deal with forward trajectory and 360° center surround trajectory, which is a special trajectory, and can't deal with arbitrary trajectory, but there are more free trajectories in the daily shooting process. In order to solve this problem, we propose a new warping function called Variable warping. In this study, we use cell phone to shoot wheat experimental field data, and use the improved Variable warping algorithm to synthesize the new perspective of wheat under free trajectory. The experimental results show that it allows us to reconstruct the new perspective of wheat with high quality under arbitrary camera trajectory. The experimental results show that allows us to perform high-quality reconstruction of wheat under arbitrary camera trajectories with new perspective synthesis. © 2024 IEEE.

关键词： Borderless component Free Trajectories Novel view synthesis of wheat

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Knowledge Graph Reasoning Neural Network Based on Dynamic Programming 4

Knowledge Graph Reasoning Neural Network Based on Dynamic Pr...

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4th International Symposium on Computer technology and information science, ISCTIS 2024

作者： Mei, Tong Zhang, Jiashuo Xu, Zipeng Chu, Shuqi Hu, Zili Qingdao University of Science and Technology School of Information Science and Technology Qingdao China Qingdao University of Science and Technology School of Data Science Qingdao China

ISBN: (纸本)9798350354560

Knowledge graph is a semantic network that abstracts data into points and edges, which contains logical and structural information. In order to mine the potential facts, the reasoning algorithm of the knowledge graph came into being. However, current knowledge graph reasoning algorithms usually use path or graph neural networks for reasoning. The former will cause the problem of time and space complexity explosion, while the latter will cause the problem of lack of interpretability, so it cannot be well applied to real life. In order to solve these two problems, we proposed a knowledge graph reasoning network model based on dynamic programming, which uses a recursive method to record path information, reduces the time and space complexity from exponential level to linear level, and the reasoning path is preserved when passing messages, so it is interpretable while keeping time and space complexity low. In addition, we use PNA aggregation to replace the aggregation method of traditional graph neural network, making the model performance even better. Experiments have proven that the network model we proposed not only surpasses the state-of-the-art performance on the WN18RR and NELL-995 data sets, but also has a spatio-temporal complexity far lower than other reasoning algorithms. © 2024 IEEE.

关键词： Knowledge graph

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Toward Secure Trajectory Similarity Range Query Under Multiuser Setting

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IEEE Internet of Things Journal 2025年第11期12卷 16336-16348页

作者： Cui, Ningning Sun, Xun Pei, Lili Wang, Mengxiang Wang, Dong Li, Jianxin Jin, Hulin Cui, Jie Zhong, Hong Anhui University School of Computer Science and Technology Hefei230093 China Chang’an University School of Data Science and Artificial Intelligence Xi’an710064 China China National Institute of Standardization Beijing China Deakin University School of Information Technology GeelongVIC3220 Australia Anhui University School of Big Data Hefei China

The widespread availability of similarity queries over trajectory data has led to numerous real-world applications, such as traffic management and path planning. With the proliferation of trajectory data, data owners often outsource storage and computation tasks to the cloud due to limited computing and storage resources. However, this scenario raises sharp security concerns, where it is critical to ensure both the integrity of query results and privacy during query processing. Furthermore, most existing works assume a single-user setting where all query users share the same key, which may lead to query privacy leakage. Therefore, in this article, we take the first step in studying the issue of multiuser and secure trajectory similarity range query (MSRQ). Specifically, inspired by the M-tree, we propose a secure index based on a distributed two-trapdoor public-key cryptosystem (DT-PKC), called M*-tree, and devise secure protocols to support multiuser query processing. We also carefully design a filtering strategy and verification scheme to ensure fast search and integrity guarantees. Finally, we theoretically analyze the security and complexity and empirically evaluate the performance and feasibility of our proposed approach. © 2014 IEEE.

关键词： data integrity

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Guest editorial preface information and communication technology

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Informatica (Slovenia) 2023年第3期47卷 301-302页

作者： Binh, Huynh Thi Thanh Ide, Ichiro School of Information and Communication Technology Hanoi University of Science and Technology Viet Nam Nagoya University School of Informatics Mathematical and Data Science Center Japan

The symposium covered four major areas of research including Artificial Intelligence and Big data, information Networks and Communication Systems, Human-Computer Interaction, and Software Engineering and Applied Computing. The first paper, "Lightweight Multi-Objective and Many-Objective Problem Formulations for Evolutionary Neural Architecture Search with the Training-Free Performance Metric Synaptic Flow" by An Vo, Tan Ngoc Pham, Van Bich Nguyen and Ngoc Hoang Luong employed a widely-used multi-objective evolutionary algorithm, i.e., the non-dominated sorting genetic algorithm II (NSGA-II), to approximate the optimal Pareto-optimal fronts for Neural Architecture Search problem formulations. The third paper, "Motion Embedded Images: An Approach to Capture Spatial and Temporal Features for Action Recognition", by Tri Le, Nham Huynh-Duc, Chung Thai Nguyen and Minh-Triet Tran, investigated the use of motion-embedded images in a variant of two-stream Convolutional Neural Network architecture, in which one stream captures motion using combined batches of frames, while another stream employs a normal image classification ConvNet to classify static appearance.

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Revealing Security Flaws in Cross-Modal Retrieval Models through Video Poisoning

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 6184-6194页

作者： Jin, Ming Hu, Wenbo Hong, Richang Zhu, Lei Hefei University of Technology School of Computer and Information Hefei China Data Space Research Institute Hefei Comprehensive National Science Center China Tongji University School of Electronic and Information Engineering Shanghai China

Video-text cross-modal retrieval is widely studied to improve retrieval accuracy. However, the security of video-text cross-modal retrieval models receives little attention. If attackers exploit the security vulnerabilities in these models, it poses a significant threat to the retrieval models. Thus, identifying security flaws in video-text cross-modal retrieval models becomes the focus of our research. We are the first to design a video poisoning model to uncover security vulnerabilities in retrieval models. Existing poisoning models have certain limitations when it comes to exploiting vulnerabilities in retrieval models. These include failing to comprehensively embed malicious information into the original video and being unable to maintain visual consistency between the original and poisoned videos. These limitations can result in unsuccessful attacks on retrieval models and an inability to effectively identify security flaws within them. To address these shortcomings, we design an efficient poisoning model that embeds malicious information thoroughly into the original clean data to attack video-text cross-modal retrieval models. We are the first to use a poisoning model to attack retrieval models, thereby uncovering their security vulnerabilities. Second, we introduce a bi-level poisoning module to ensure that malicious information is thoroughly embedded into the original video, thereby enhancing the attack capability of the poisoning model. Finally, we design an adversarial module to improve visual consistency between the original and poisoned videos, thus enhancing the concealment of malicious information within the training data of retrieval models. Our poisoning model can identify security flaws in video-text cross-modal retrieval models, providing insights into improving the security of retrieval models. The effectiveness of our model is validated on the MSR-VTT, LSMDC, and MSVD datasets. © 1991-2012 IEEE.

关键词： Content based retrieval

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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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Integrated Sensing-Communication-Computation for Edge Artificial Intelligence

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IEEE Internet of Things Magazine 2024年第4期7卷 14-20页

作者： Wen, Dingzhu Li, Xiaoyang Zhou, Yong Shi, Yuanming Wu, Sheng Jiang, Chunxiao School of Information Science and Technology ShanghaiTech University China Shenzhen Research Institute of Big Data China Beijing University of Posts and Telecommunications China Tsinghua University Beijing National Research Center for Information Science and Technology China

Edge artificial intelligence (AI) has been a promising solution towards 6G to empower a series of advanced techniques such as digital twins, holographic projection, semantic communications, and auto-driving, for achieving intelligence of everything. The performance of edge AI tasks, including edge learning and edge AI inference, depends on the quality of three highly coupled processes, i.e., sensing for data acquisition, computation for information extraction, and communication for information transmission. However, these three modules need to compete for network resources for enhancing their own quality-of-services. To this end, integrated sensing-communication-computation (ISCC) is of paramount significance for improving resource utilization as well as achieving the customized goals of edge AI tasks. By investigating the interplay among the three modules, this article presents various kinds of ISCC schemes for federated edge learning tasks and edge AI inference tasks in both application and physical layers. © 2018 IEEE.

关键词： Artificial intelligence

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Few-shot image semantic segmentation based on meta-learning: A review

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Journal of Intelligent and Fuzzy Systems 2024年第5-6期47卷 351-367页

作者： Liu, Yu Zhu, Ye Chong, Haoze Yu, Ming School of Electronic and Information Engineering Hebei University of Technology Tianjin China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China College of Computer and Information Engineering Tianjin Agricultural University Tianjin China

Deep learning-based image semantic segmentation approaches heavily rely on large-scale training datasets with dense annotations and often suffer from scarce semantic labels for unseen categories. This limitation has spurred a research trend in Few-shot image Semantic Segmentation (FSS), which makes it possible to segment objects of new categories using only a few labeled samples. Although more and more FSS methods are emerging and gradually integrated into practical applications, a deep understanding of its achievements and issues is still missing. In this survey, we focus on the recent developments of FSS, specifically on FSS methods based on meta-learning. According to different network architectures, we summarize the related research into three classes, that are Convolutional Neural Network-based (CNN-based) models, Graph Neural Network-based (GNN-based) models, and Transformer-based models. Then, we explore the specific implementations of these models, including parameter-based methods, metric-based methods, attention-based methods, and optimization-based methods. Furthermore, we illustrate datasets and analyze the experimental results of various kinds of methods. Toward the end of the paper, we discuss the limitations of FSS and present its applications and challenges to provide further research directions. © 2024 - IOS Press. All rights reserved.

关键词： Semantic Segmentation

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