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6th IEEE International Conference on Intelligent computing and Signal Processing, ICSP 2021

作者： Zhang, Qi Liu, Yan Cai, Yuheng Zhong Yuan Network Security Research Institute Zhengzhou University Zhengzhou China Pla Strategic Support Force Information Engineering University State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9780738143705

The network group detection has a large number of application researches and has achieved certain results in network security, IP entity location and load balancing. However, due to the large scale of the network topology graph and the large number of terminal nodes, most of the existing overlapping group detection algorithms are relatively complex, and the data size that can be processed is limited, and cannot be directly applied to the network topology. This paper proposes a network topology overlapping group detection algorithm based on h-core pruning. This algorithm decomposes the network into smaller strong connected components, reduces the interference of noisy nodes to overlapping group discovery, and more efficiently detect the largest k-plex group on the target network. A large number of experiments show that the method in this paper is effective. Compared with the full-enum algorithm, our method increases the network size that can be handled by the largest k-plex group by two orders of magnitude, and can detect overlapping groups in the network topology more efficiently. © 2021 IEEE.

关键词： Network topology

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MBF-DTI: A Fused Multi-dimensional Biochemical Feature-Based Drug Target Prediction Method Based on Heterogeneous Graph Attention Networks 10th

MBF-DTI: A Fused Multi-dimensional Biochemical Feature-Based...

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10th China Health Information Processing Conference, CHIP 2024

作者： Zhao, Haixue Yao, Kui Liu, Yunjiong Che, Chao Tang, Lin Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian116622 China School of Software Engineering Dalian University Dalian116622 China

ISBN: (纸本)9789819637515

Drug-target interaction prediction help reduce the cost and time of drug development. However, existing research often overlooks the complexity of biological interactions. To address this issue, this paper proposes a DTI prediction model that integrates multi-dimensional biochemical features. Specifically, the model utilizes a heterogeneous graph attention network to capture the topological relationships among biological entities from diverse data, providing a deep understanding of the interactions among drugs, proteins, diseases, and side effects. A molecular attention Transformer network and a CBiNet module are used to extract the key structural features of drugs and targets. By automatically optimizing the weight distribution between drugs and targets, the model enhances the information transfer during network training, significantly improving model performance. Experimental results on real-world datasets demonstrate that the proposed model outperforms the current state-of-the-art methods in the field. Among the top 50 novel COVID-19 therapeutic drugs predicted by our model, 30 have been supported by clinical trials or scientific literature, further demonstrating the effectiveness of our proposed method in drug repurposing. Experimental data and supplementary materials are available online at: https://***/Eadog/MBF-DTI. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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iManip: Skill-Incremental Learning for Robotic Manipulation

arXiv

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arXiv 2025年

作者： Zheng, Zexin Cai, Jia-Feng Wu, Xiao-Ming Wei, Yi-Lin Tang, Yu-Ming Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Pengcheng Lab China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

The development of a generalist agent with adaptive multiple manipulation skills has been a long-standing goal in the robotics community. In this paper, we explore a crucial task, skill-incremental learning, in robotic manipulation, which is to endow the robots with the ability to learn new manipulation skills based on the previous learned knowledge without re-training. First, we build a skill-incremental environment based on the RLBench benchmark, and explore how traditional incremental methods perform in this setting. We find that they suffer from severe catastrophic forgetting due to the previous methods on classification overlooking the characteristics of temporality and action complexity in robotic manipulation tasks. Towards this end, we propose an incremental Manipulation framework, termed iManip, to mitigate the above issues. We firstly design a temporal replay strategy to maintain the integrity of old skills when learning new skill. Moreover, we propose the extendable PerceiverIO, consisting of an action prompt with extendable weight to adapt to new action primitives in new skill. Extensive experiments show that our framework performs well in Skill-Incremental Learning. Codes of the skill-incremental environment with our framework will be open-source. Copyright © 2025, The Authors. All rights reserved.

关键词： Robot learning

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H∞Asynchronous Control for Discrete-Time Semi-Markov Jump Systems 16th

H∞Asynchronous Control for Discrete-Time Semi-Markov Jump S...

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16th IFAC Symposium on Large Scale Complex Systems: Theory and Applications LSS 2022

作者： Jin, Liqiang Yin, Yanyan Liu, Yanqing Teo, Kok Lay Liu, Fei National Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Institute of Automation Jiangnan University Wuxi214122 China School of Electrical Engineering Computing and Mathematical Sciences Curtin University PerthWA6102 Australia School of Mathematical Sciences Sunway University Kuala Lumpur47500 Malaysia Tianjin University of Finance and Economics Tianjin China

This paper investigates the problem of H∞asynchronous control for discrete-time semi-Markov jump systems. In practice, it is difficult to ensure that controller modes and system modes are updated synchronously. Therefore, a hidden semi-Markov model is introduced to characterize such an asynchronous phenomenon between system modes and controller modes. Meanwhile, the switching signal of the controller is generated by a given conditional probability related to the system mode. Based on the mode-dependent and elapsed-time-dependent Lyapunov function, together with the semi-Markov kernel (SMK) method, sufficient conditions constructed by linear matrix inequalities (LMIs) are derived to ensure the existence of an asynchronous controller such that the resulting system is σ-error mean square stable and satisfies an H∞performance index. Finally, a numerical example is given to illustrate the effectiveness and feasibility of the method proposed. © 2022 Elsevier B.V.. All rights reserved.

关键词： Controllers

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On the Sharing-Based Model of Steganography 19th

On the Sharing-Based Model of Steganography

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19th International Workshop on Digital Forensics and Watermarking, IWDW 2020

作者： Zhao, Xianfeng Yang, Chunfang Liu, Fenlin State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100195 China School of Cyber Security University of Chinese Academy of Sciences Beijing100093 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9783030694487

Steganography has long been considered as a way of hiding the fact of secret communication. However, the fact that a message sender and a receiver communicated with each other is seldom protected. It can be dangerous because they often have very secret relationship. Noticeably, in recent two years some researchers respectively proposed robust and secure steganographic schemes which realize dependable steganography in the lossy channel of social media without degrading the security and capacity, and more relevant technologies, such as robust steganographic coding and fast steganography, were also proposed. In this review, the sharing-based model of steganography implied by social media and promoted by both the emerging advanced robust steganography and fast steganography is formally presented and compared with the dominating prisoner-warden model. It is shown that designing steganography under the sharing-based model can protect the fact of communication and the sender-receiver relationship instead of merely the fact of secret communication, improving the overall security of steganographic systems. © 2021, Springer Nature Switzerland AG.

关键词： Steganography

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An Economic Framework for 6-DoF Grasp Detection

arXiv

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arXiv 2024年

作者： Wu, Xiao-Ming Cai, Jia-Feng Jiang, Jian-Jian Zheng, Dian Wei, Yi-Lin Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Peng Cheng Laboratory Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Robotic grasping in clutters is a fundamental task in robotic manipulation. In this work, we propose an economic framework for 6-DoF grasp detection, aiming to economize the resource cost in training and meanwhile maintain effective grasp performance. To begin with, we discover that the dense supervision is the bottleneck of current SOTA methods that severely encumbers the entire training overload, meanwhile making the training difficult to converge. To solve the above problem, we first propose an economic supervision paradigm for efficient and effective grasping. This paradigm includes a well-designed supervision selection strategy, selecting key labels basically without ambiguity, and an economic pipeline to enable the training after selection. Furthermore, benefit from the economic supervision, we can focus on a specific grasp, and thus we devise a focal representation module, which comprises an interactive grasp head and a composite score estimation to generate the specific grasp more accurately. Combining all together, the EconomicGrasp framework is proposed. Our extensive experiments show that EconomicGrasp surpasses the SOTA grasp method by about 3AP on average, and with extremely low resource cost, for about 1/4 training time cost, 1/8 memory cost and 1/30 storage cost. Our code is available at https://***/iSEE-laboratory/EconomicGrasp. Copyright © 2024, The Authors. All rights reserved.

关键词： Robotics

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Solid-state stepwise temperature-programmable synthesis of bioinspired Fe-N-C oxygen reduction electrocatalyst featuring Fe-N_(5)configuration

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Nano Research 2025年第3期18卷 654-665页

作者： Wei Sang Somboon Chaemchuen Longyang Zhang Zechen Wang Xingchuan Li Cocoro Andrew Nagasaka Mo Xiong Naoki Ogiwara Cheng Chen Zhao Wang Jian Zhang Francis Verpoort Shichun Mu Zongkui Kou Tingting Wang State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070China Department of Basic Science School of Arts and SciencesThe University of TokyoKomabaMeguro-kuTokyo 153-8902Japan MOE Key Laboratory for Non-Equilibrium Synthesis and Modulation of Condensed Matter School of PhysicsXi’an Jiao tong UniversityXi’an 710049China Sanya Science and Education Innovation Park of Wuhan University of Technology Sanya 572000China Department of Chemical Engineering Faculty of EngineeringMahidol UniversityNakhon Pathom 73170Thailand School of Mathematical&Physical Sciences Wuhan Textile UniversityWuhan 430200China Joint Institute of Chemical Research(FFMiEN) Peoples Friendship University of Russia(RUDN University)6 Miklukho-Maklaya Str.117198 MoscowRussia National Research Tomsk Polytechnic University Lenin Avenue 30634050 TomskRussia Hubei Key Laboratory of Ferro &Piezo-electric Materials and DevicesFaculty of Physics and Electronic SciencesHubei UniversityWuhan 430062China School of Chemistry and Chemical Engineering Hainan UniversityHaikou 57022China

The bioinspired Fe-N-C features an asymmetric Fe-N_(5)configuration to produce active metal-oxygen intermediates by introducing axial N ligand into a symmetric Fe-N_(4) structure,enabling highly active oxygen reduction reaction(ORR).However,the artificial creation of active Fe-N_(5)configuration with a direct,facile and green method has been rarely developed yet,as current techniques involve complex processes and costly ***,we advance a novel solid-state stepwise temperature-programmable(SST)route to directly produce bioinspired Fe-N_(5)-*** then demonstrate that such a Fe-N_(5)-C exhibits a quite higher half-wave potential(0.92 V)with 22-fold faster ORR kinetics(15.6 mA·cm^(−2)@0.85 V)over that of the commercial Pt/C ***,we perform density functional theory(DFT)to find that the Fe is discharged with an extra 0.1 e^(−)through the axially coordinate N ligand,which significantly enhances the ability to activate O_(2) and enables an easier desorption of the crucial intermediate ^(*)OH on the Fe-N_(5)configuration over the conventional Fe-N_(4) structure.

关键词： single atom catalysts Fe-N_(5)coordination solvent-free synthesis oxygen reduction reaction(ORR) metal-air battery

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Dendritic Integration Inspired Artificial Neural Networks Capture Data Correlation 38

Dendritic Integration Inspired Artificial Neural Networks Ca...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Chongming Ma, Jingyang Li, Songting Zhou, Douglas School of Mathematical Sciences Shanghai Jiao Tong University Shanghai China Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China Ministry of Education Key Laboratory of Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai China Shanghai Frontier Science Center of Modern Analysis Shanghai Jiao Tong University Shanghai China

Incorporating biological neuronal properties into Artificial Neural Networks (ANNs) to enhance computational capabilities is under active investigation in the field of deep learning. Inspired by recent findings indicating that dendrites adhere to quadratic integration rule for synaptic inputs, this study explores the computational benefits of quadratic neurons. We theoretically demonstrate that quadratic neurons inherently capture correlation within structured data, a feature that grants them superior generalization abilities over traditional neurons. This is substantiated by few-shot learning experiments. Furthermore, we integrate the quadratic rule into Convolutional Neural Networks (CNNs) using a biologically plausible approach, resulting in innovative architectures-Dendritic integration inspired CNNs (Dit-CNNs). Our Dit-CNNs compete favorably with state-of-the-art models across multiple classification benchmarks, e.g., ImageNet-1K, while retaining the simplicity and efficiency of traditional CNNs. All source code are available at https://***/liuchongming1999/Dendritic-integration-inspired-CNN-NeurIPS-2024. © 2024 Neural information processing systems foundation. All rights reserved.

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A Vulnerability Detection Method for Intermediate Code Based on a Relational Dependency Graph

A Vulnerability Detection Method for Intermediate Code Based...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Bing Xia Chongjun Tang Wenbo Liu Shihao Chu Yu Dong Dept. ZhongYuan University of Technology Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Dept. ZhongYuan University of Technology Henan Key Laboratory on Public Opinion Intelligent Analysis Zhengzhou China Dept. ZhongYuan University of Technology Zhengzhou China Dept. ZhongYuan University of Technology

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the advancement of information technology, the rapid increase in software systems has led to a growing demand for research on vulnerability detection. In recent years, the fast-paced development of deep learning has introduced new approaches and directions for this field. Existing source code vulnerability detection methods often fail to explicitly preserve the semantic information related to vulnerabilities in the source code, leading to difficulties in extracting vulnerable code features and high false-positive rates in detection. To address these issues, this study proposes a vulnerability detection method for intermediate code based on a relational dependency graph (RDG). After converting the source code into an intermediate representation, it is mapped to an RDG, which accurately expresses the data and control flow dependencies within the program. This approach retains critical node features and refines the types of control flow and data flow edges. By leveraging this method, it becomes possible to trace program data and control dependencies, understand data flow directions, and analyze interactions between different parts of the program, enabling the precise identification and interpretation of potential security vulnerabilities. Drawing on concepts from code property graphs (CPG), this method enhances the understanding and capture of complex relationships between nodes in graph-structured data. By performing vulnerability detection on intermediate code, this approach avoids false positives and negatives caused by language or platform-specific differences, thereby enabling deeper identification and interpretation of complex vulnerability patterns in source code. Experimental results on the SARD dataset demonstrate that the proposed method achieves a high precision rate of 98.7% for detecting command injection vulnerabilities (CWE78), significantly outperforming traditional methods.

关键词： Deep learning Privacy Codes Source coding Semantics Feature extraction Software systems Graph neural networks Security Information technology

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Bridge Past and Future: Overcoming Information Asymmetry in Incremental Object Detection

arXiv

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arXiv 2024年

作者： Mo, Qijie Gao, Yipeng Fu, Shenghao Yan, Junkai Wu, Ancong Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Peng Cheng Laboratory Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

In incremental object detection, knowledge distillation has been proven to be an effective way to alleviate catastrophic forgetting. However, previous works focused on preserving the knowledge of old models, ignoring that images could simultaneously contain categories from past, present, and future stages. The co-occurrence of objects makes the optimization objectives inconsistent across different stages since the definition for foreground objects differs across various stages, which limits the model's performance greatly. To overcome this problem, we propose a method called "Bridge Past and Future" (BPF), which aligns models across stages, ensuring consistent optimization directions. In addition, we propose a novel Distillation with Future (DwF) loss, fully leveraging the background probability to mitigate the forgetting of old classes while ensuring a high level of adaptability in learning new classes. Extensive experiments are conducted on both Pascal VOC and MS COCO benchmarks. Without memory, BPF outperforms current state-of-the-art methods under various settings. The code is available at https://***/iSEE-laboratory/BPF. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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