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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： He, Chao Wei, Hongxi Inner Mongolia University School of Computer Science Hohhot China Provincial Key Laboratory of Mongolian Information Processing Technology Hohhot China National and Local Joint Engineering Research Center of Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9781728163277

The deep image hashing aims to map the input image into simply binary hash codes via deep neural networks. Motivated by the recent advancements of Vision Transformers (ViT), many deep hashing methods based on ViT have been proposed. Nevertheless, the ViT has enormous number of model parameters and high computational complexity. Moreover, the last layer of the ViT outputs only the classification tokens as image feature vectors, while the rest of the vectors are discarded. This results in the inefficiency of the model computation and the neglect of useful image information. Therefore, this paper proposes a Transformer-based deep hashing method for multi-scale feature fusion (TDH). Specifically, we use a hierarchical Transformer backbone to capture both global and local features of images. The hierarchical Transformer utilizes a local self-attention mechanism to process image blocks in parallel, which reduces computational complexity and promotes computational efficiency. Multi-scale feature fusion module captures all image feature vectors of the hierarchical Transformer output to obtain more enriched image feature information. We perform comprehensive experiments on three widely-studied datasets: CIFAR-10, NUS-WIDE and IMAGENET. The experimental results demonstrate that the proposed method in this paper indicates superior results compared to the existing state-of-the-art work. Source code is available https://***/shuaichaochao/TDH. © 2023 IEEE.

关键词： deep hashing hash code image retrieval multi-scale feature fusion Transformer

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Efficient vulnerability detection based on an optimized rule-checking static analysis technique

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Frontiers of information technology & Electronic Engineering 2017年第3期18卷 332-345页

作者： Deng CHEN Yan-duo ZHANG Wei WEI Shi-xun WANG Ru-bing HUANG Xiao-lin LI Bin-bin QU Sheng JIANG Hubei Provincial Key Laboratory of Intelligent Robot Wuhan Institute of TechnologyWuhan 430205China Industrial Robot Engineering Center Wuhan Institute of TechnologyWuhan 430205China School of Computer and Information Engineering Henan Normal UniversityXinxiang 453007China School of Computer Science and Telecommunication Engineering Jiangsu UniversityZhenjiang 212013China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Static analysis is an efficient approach for software assurance. It is indicated that its most effective usage is to perform analysis in an interactive way through the software development process, which has a high performance requirement. This paper concentrates on rule-based static analysis tools and proposes an optimized rule-checking algorithm. Our technique improves the performance of static analysis tools by filtering vulnerability rules in terms of characteristic objects before checking source files. Since a source file always contains vulnerabilities of a small part of rules rather than all, our approach may achieve better performance. To investigate our technique's feasibility and effectiveness, we implemented it in an open source static analysis tool called PMD and used it to conduct experiments. Experimental results show that our approach can obtain an average performance promotion of 28.7% compared with the original PMD. While our approach is effective and precise in detecting vulnerabilities, there is no side effect.

关键词： Rule-based static analysis Software quality Software validation Performance improvement

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Rough knowledge mining algorithm based on extension decision rule lattice for electronic patient record

Rough knowledge mining algorithm based on extension decision...

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International Conference on Artificial Intelligence and Computational Intelligence

作者： Ding, Weiping Wang, Jiandong Guan, Zhijing Zhu, Hao Shi, Quan College of Information Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Computer Science and Technology Nantong University Nantong China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

ISBN: (纸本)9780769542256

Electronic patient record mining deals with the implicit and useful medical information stored in the electronic patient record *** this technology the useful knowledge is extracted and the scientific and auxiliary decision-making is proved for the diagnosis and treatment of disease. In this paper,a rough knowledge mining algorithm (named RKMA) based on extension decision rule lattice is presented. Firstly the electronic patient records are converted into different recordings in order to be further reduced and classified in the medical database. Secondly both variable rough thresholding and concept lattice are relatethed.A novel extension decision rule lattice is *** extension lattice can rough reduce the mutative knowledge attributions and well mine all disease diagnosis rules without more redundancy by avoiding finding frequent itemsets. The experimental results show this RKMA algorithm well can mine consistent decision rules,and it is better robustness than other algorithms on the capability of attribution reduction and rule mining for electronic patient records. © 2010 IEEE.

关键词： Decision making

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A New Approach of Intrusion Detection with Command Sequence-To-Sequence Model 6th

A New Approach of Intrusion Detection with Command Sequence-...

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6th Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2019

作者： Liu, Wei Mao, Yu Ci, Linlin Zhang, Fuquan School of Computer Science and Technology Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350117 China

ISBN: (纸本)9789811650352

Traditionally, researchers have focused on network level intrusion detection and program level intrusion detection to improve computer security. However, neither approach is foolproof. We argue that the internal and external security of a computer system are equally important. Typically, a successful attacker manifests in the form of the attacker becoming a user on the host either with elevated or normal user privileges. At this point, user-level intrusion detection attempts to deter and curtail an attacker even after the system has been compromised. In this work, we introduce a new approach of intrusion detection based on recurrent neural networks (RNNs) to solve the long sequential problem. We build a robust user command sequence-to-sequence model by semantic modeling command. Our model implements the prediction of user command sequence and the prophesying of user behaviors. The experimental results on data sets of Purdue University, SEA and self-collected data show that an accurate, effective and efficient detection can be achieved by using the proposed approach. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recurrent neural networks

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An Improved Q-RRT^(*) Algorithm Based on Virtual Light

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computer Systems Science & Engineering 2021年第10期39卷 107-119页

作者： Chengchen Zhuge Qun Wang Jiayin Liu Lingxiang Yao Department of Computer Information and Cyber Security Jiangsu Police InsituteNanjing210031China Jiangsu Electronic Data Forensics and Analysis Engineering Research Center Jiangsu Police InsituteNanjing210031China Jiangsu Provincial Public Security Department Key Laboratory of Digital Forensics Jiangsu Police InsituteNanjing210031China University of Technology Sydney Sydney2007Australia

The Rapidly-exploring Random Tree(RRT)algorithm is an efficient path-planning algorithm based on random *** RRT^(*)algorithm is a variant of the RRT algorithm that can achieve convergence to the optimal ***,it has been proven to take an infinite time to do *** improved Quick-RRT^(*)(Q-RRT^(*))algorithm based on a virtual light source is proposed in this paper to overcome this *** virtual light-based Q-RRT^(*)(LQRRT^(*))takes advantage of the heuristic information generated by the virtual light on the *** this way,the tree can find the initial solution ***,the LQRRT^(*)algorithm combines the heuristic information with the optimization capability of the Q-RRT^(*)algorithm to find the approximate optimal ***^(*)further optimizes the sampling space compared with the Q-RRT^(*)algorithm and improves the sampling *** efficiency of the algorithm is verified by comparison experiments in different simulation *** results show that the proposed algorithm can converge to the approximate optimal solution in less time and with lower memory consumption.

关键词： Path planning RRT^(*) Q-RRT^(*) LQ-RRT^(*) virtual light

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A decoupled hybrid structure for active noise control with uncorrelatednarrowband disturbances 43

A decoupled hybrid structure for active noise control with u...

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43rd International Congress on Noise Control Engineering: Improving the World Through Noise Control, INTERNOISE 2014

作者： Wu, Lifu Qiu, Xiaojun Burnett, Ian S. Cheng, Eva Guo, Yecai Royal Melbourne Institute of Technology School of Electrical and Computer Engineering Australia Jiangsu Key Laboratory of Meteorological Observation and Information Processing Nanjing University of Information Science and Technology 210044 China

ISBN: (纸本)9780909882037

In real active noise control (ANC)applications,the following situations frequently occur, one isthat disturbances only present at the error sensor and havelowcorrelation with reference signal, the other is thatthere is no enough space or ideal position for locating the reference sensor to satisfy causality condition. Thusthe residual noise after feedforward control can be seen as uncorrelated narrowband disturbancesin these situationsand ahybrid adaptive feedforward and feedback structure is often utilized to cope with this *** efforts have been paid to improve the performance of the hybrid ANC system, nevertheless, few interests are concerned about the combination method between the feedforward and feedback structure. After investigating the conventional combination method of hybrid feedforward and feedback system, this paper introduces analternate combination method for hybrid ANC systemwhich featuresthat itavoidsthe coupling between the feedforward and feedback structures and both structures are concatenated to attenuate the ambient noise. Simulations are carried out to validatethe effectiveness of the introduced methodfor ANCwith uncorrelated narrowband disturbances.

关键词： Hybrid systems

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Reliable Multi-Modal Object Re-Identification via Modality-Aware Graph Reasoning

arXiv

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arXiv 2025年

作者： Wan, Xixi Zheng, Aihua Wang, Zi Jiang, Bo Tang, Jin Ma, Jixin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei230601 China School of Biomedical Engineering Anhui Medical University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China School of Computing and Mathematical Sciences University of Greenwich LondonSE10 9LS United Kingdom

Multi-modal data provides abundant and diverse object information, crucial for effective modal interactions in Re-Identification (ReID) tasks. However, existing approaches often overlook the quality variations in local features and fail to fully leverage the complementary information across modalities, particularly in the case of low-quality features. In this paper, we propose to address this issue by leveraging a novel graph reasoning model, termed the Modality-aware Graph Reasoning Network (MGRNet). Specifically, we first construct modality-aware graphs to enhance the extraction of fine-grained local details by effectively capturing and modeling the relationships between patches. Subsequently, the selective graph nodes swap operation is employed to alleviate the adverse effects of low-quality local features by considering both local and global information, enhancing the representation of discriminative information. Finally, the swapped modality-aware graphs are fed into the local-aware graph reasoning module, which propagates multimodal information to yield a reliable feature representation. Another advantage of the proposed graph reasoning approach is its ability to reconstruct missing modal information by exploiting inherent structural relationships, thereby minimizing disparities between different modalities. Experimental results on four benchmarks (RGBNT201, Market1501-MM, RGBNT100, MSVR310) indicate that the proposed method achieves state-of-the-art performance in multi-modal object ReID. The code for our method will be available upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Modal analysis

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Rotation-DPeak: Improving Density Peaks Selection for Imbalanced Data 1

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8th China computer Federation International Conference on Big Data, CCF BIGDATA 2020

作者： Hu, Xiaoliang Yan, Ming Chen, Yewang Yang, Lijie Du, Jixiang The College of Computer Science and Technology Huaqiao University Xiamen China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China

ISBN: (数字)9789811607059

ISBN: (纸本)9789811607042

Density Peak (DPeak) is an effective clustering algorithm. It maps arbitrary dimensional data onto a 2-dimensional space, which yields cluster centers and outliers automatically distribute on upper right and upper left corner, respectively. However, DPeak is not suitable for imbalanced data set with large difference in density, where sparse clusters are usually not identified. Hence, an improved DPeak, namely Rotation-DPeak, is proposed to overcome this drawback according to an simple idea: the higher density of a point p, the larger δ it should have such that p can be picked as a density peak, where δ is the distance from p to its nearest neighbor with higher density. Then, we use a quadratic curve to select points with the largest decision gap as density peaks, instead of choosing points with the largest γ, where γ= ρ× δ. Experiments shows that the proposed algorithm obtains better performance on imbalanced data set, which proves that it is promising. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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A unified semi-supervised framework for author disambiguation in academic social network

A unified semi-supervised framework for author disambiguatio...

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25th International Conference on Database and Expert Systems Applications, DEXA 2014

作者： Wang, Peng Zhao, Jianyu Huang, Kai Xu, Baowen School of Computer Science and Engineering Southeast University China State Key Laboratory for Novel Software Technology Nanjing University China State Key Laboratory of Software Engineering Wuhan University China Provincial Key Laboratory for Computer Information Processing Technology Soochow University China

ISBN: (纸本)9783319100845

This paper addresses the author disambiguation problem in academic social network, namely, resolves the phenomenon of synonym problem "multiple names refer to one person" and polysemy problem "one name refers to multiple persons". A unified semi-supervised framework is proposed to deal with both the synonym and polysemy problems. First, the framework uses semi-supervised approach to solve the cold-start problem in author disambiguation. Second, robust training data generating method based on multi-aspect similarity indicator is used and a way based on support vector machine is employed to model different kinds of feature combinations. Third, a self-taught procedure is proposed to solve ambiguity in coauthor information to boost the performances from other models. The proposed framework is verified on a large-scale real-world dataset, and obtains promising results. © 2014 Springer International Publishing Switzerland.

关键词： Semantics

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On the parameterized complexity of minimum/maximum degree vertex deletion on several special graphs

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Frontiers of computer Science 2023年第4期17卷 97-107页

作者： Jia LI Wenjun LI Yongjie YANG Xueying YANG School of Information Engineering Hunan Industry PolytechnicChangsha 410036China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha 410015China Chair of Economic Theory Saarland UniversitySaarbrücken 66123Germany

In the minimum degree vertex deletion problem,we are given a graph,a distinguished vertex in the graph,and an integer κ,and the question is whether we can delete at most κ vertices from the graph so that the distinguished vertex has the unique minimum *** maximum degree vertex deletion problem is defined analogously but here we want the distinguished vertex to have the unique maximum *** is known that both problems areΨ-hard and fixed-parameter intractable with respect to some natural *** this paper,we study the(parameterized)complexity of these two problems restricted to split graphs,p-degenerate graphs,and planar *** study provides a comprehensive complexity landscape of the two problems restricted to these special graphs.

关键词： minimum degree maximum degree vertex deletion split graphs planar graphs parameterized complexity

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