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SSRN 2023年

作者： He, Xing Peng, Changgen Wang, Lin Tan, Weijie State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University PR Guiyang550025 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University PR Guiyang550025 China Guizhou Big Data Academy Guizhou University PR Guiyang550025 China Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University PR Guiyang550025 China

Deep learning techniques usually require large amounts of expensive labeled data to train networks, and the extracted deep representations are usually mixed with multiple attributes having uninterpretability, which limits the application and development of deep learning techniques. Therefore, it is crucial to investigate the stability of training models by unsupervised learning methods and to learn feature decoupled representations. Although the method based on depth mutual information maximization(Deep InfoMax or DIM) has achieved good results in the field of computer vision, it is extremely prone to "gradient explosion" or "gradient disappearance", which makes the training model unstable. At the same time, it is difficult to decouple the features learned by the encoder. In order to solve these problems, we stable and feature decoupled of deep mutual information maximization based on Wasserstein distance, which we call the method called WDIM. The method first addresses the instability of the model trained by the DIM method and proposes to measure the distance between the encoder output and the prior distribution based on the Wasserstein distance, which is a good guide to the stability of the model training. Second, to address the problem that the features learned by the encoder are difficult to be decoupled, the mutual information between the features learned by the hidden layer of the encoder and the intermediate layer is required to be minimized during the training of the encoder so that the features learned by each filter are as uncorrelated as possible to achieve feature decoupling. Finally, the WDIM method is validated on the CIFAR-10, CIFAR-100, STL-10, and FashionMNIST datasets. Experiments show that our proposed WDIM method is more stable for model training, faster model convergence, and more significant feature decoupling. © 2023, The Authors. All rights reserved.

关键词： Network coding

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Improvement of the DPoS Consensus Mechanism in Blockchain Based on PLTS 7

Improvement of the DPoS Consensus Mechanism in Blockchain Ba...

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7th IEEE International Conference on Big data security on Cloud, 7th IEEE International Conference on High Performance and Smart Computing, and 6th IEEE International Conference on intelligent data and security, Bigdatasecurity/HPSC/IDS 2021

作者： Chen, Shuyu Xie, Mingyue Liu, Jun Zhang, Yunan School of Big Data and Software Engineering Chongqing University Chongqing China College of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

ISBN: (纸本)9781665439275

Contemporarily, DPoS is the fastest, most effective, decentralized and flexible consensus mechanism among all consensus mechanisms. Similar to the vote of the board, the holders cast a certain number of delegates to perform verification and accounting on their behalf in DPoS. In order to select the most appropriate delegates to implement the operations in blockchain, the DPoS consensus mechanism needs to be improved. Therefore, an improved DPoS consensus mechanism based on Probabilistic Linguistic Term Set (PLTS) is proposed in this paper. Just like human elections, this method allows each node to vote 'support' and 'oppose' for the delegate node. The efficient and flexible method reduces the probability of malicious nodes becoming delegates and guarantees the security of block generation in the blockchain. The effectiveness of the improved DPoS was verified by examples. © 2021 IEEE.

关键词： Blockchain

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion

Multi-Label Text Classification for Judicial Texts via Dual ...

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Smart World Congress (SWC), IEEE

作者： Qiliang Gu Qin Lu Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to ${2. 2 8 \%}$ and a minimum of ${1. 2 9 \%}$ across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model.

关键词： Attention mechanisms Correlation Law Graph convolutional networks Fuses Text categorization Semantics Predictive models Multitasking Internet

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Robust Containment Control of Heterogeneous Uncertain Multi-Agent systems under Markovian Switching Topologies

Robust Containment Control of Heterogeneous Uncertain Multi-...

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Fully Actuated system Theory and Applications (CFASTA), Conference on

作者： Haihua Guo Cong Bi Xiang Xu Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen China Hong Kong Institute for Data Science City University of Hong Kong Hong Kong China College of Artificial Intelligence Nankai University Tianjin China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

This study addresses the robust output containment control problem of heterogeneous uncertain multi-agent systems under Markovian switching topologies. A novel distributed output feedback controller is proposed. Based on the internal model approach, the main result shows that the output of each follower agent converges to the convex hull formed by the outputs of the leader agents under the proposed distributed controller. An illustrative example is given to validate the effectiveness of the proposed controller.

关键词： Switches Control systems Formation control Topology Output feedback Multi-agent systems

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Ru Li Aimin Li Rong Fei Xie Guo Fang-Xiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions.

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Decoupling Learning and Remembering: a Bilevel Memory Framework with Knowledge Projection for Task-Incremental Learning

Decoupling Learning and Remembering: a Bilevel Memory Framew...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wenju Sun Qingyong Li Jing Zhang Wen Wang Yangli-Ao Geng Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University

The dilemma between plasticity and stability arises as a common challenge for incremental learning. In contrast, the human memory system is able to remedy this dilemma owing to its multilevel memory structure, which motivates us to propose a Bilevel Memory system with Knowledge Projection (BMKP) for incremental learning. BMKP decouples the functions of learning and remembering via a bilevel-memory design: a working memory responsible for adaptively model learning, to ensure plasticity; a long-term memory in charge of enduringly storing the knowledge incorporated within the learned model, to guarantee stability. However, an emerging issue is how to extract the learned knowledge from the working memory and assimilate it into the long-term memory. To approach this issue, we reveal that the parameters learned by the working memory are actually residing in a redundant high-dimensional space, and the knowledge incorporated in the model can have a quite compact representation under a group of pattern basis shared by all incremental learning tasks. Therefore, we propose a knowledge projection process to adaptively maintain the shared basis, with which the loosely organized model knowledge of working memory is projected into the compact representation to be remembered in the long-term memory. We evaluate BMKP on CIFAR-10, CIFAR-100, and Tiny-ImageNet. The experimental results show that BMKP achieves state-of-the-art performance with lower memory usage 1 1 The code is available at https://***/SunWenJu123/BMKP.

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Question generation based on chat-response conversion

Question generation based on chat-response conversion

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作者： Zhong, Sheng-Hua Peng, Jianfeng Liu, Peiqi College of Computer Science and Software Engineering Shenzhen University Shenzhen China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

Today, thanks to the major breakthrough of sequences to sequences model in the field of natural language, most of the dialogue generation tasks are focused on generating more effective responses. However, the responses proposed by the chat-bot are only a passive answer or assentation, which does not arouse the desire of people to continue communicating. How to transform the chat robot from a passive reply to an active questioner has become an urgent problem. In this paper, a question generalization method with four types of question proposing schemes are designed, implemented, and tested to automate question generation process. The proposed system is controlled by a probability-triggered multiple conversion mechanism to actively propose different types of questions. We embed our methods in the mainstream dialogue generation model and demonstrate its effectiveness in dialogue response generalization on a standard dataset. In addition, it achieves good performance in subjective conversational assessment. © 2019 John Wiley & Sons, Ltd.

关键词： dialogue response generalization system probability-triggered question generation

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Robust Imagined Speech Production Using AI-Generated Content Network for Patients with Language Impairments

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IEEE Transactions on Consumer Electronics 2024年

作者： Xu, Xu Fu, Chong Dalian University of Technology School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110004 China

Imagined speech production is critical for brain-computer interface systems. It is able to provide the communication ability for patients with language impairments. Nowadays, many studies have developed algorithms for this purpose. However, they often struggle with an issue that is the noise in physiological signals from various devices. This makes current methods have low robustness when performing speech generation. To this end, we propose a framework to robustly producing imagined speech for patients with language impairments. It is based on artificial intelligent-generated content (AIGC). In specific, this work develops a diffusion model to remove noise from brain signals, which are then converted and normalized into log-mel spectrograms. Next, the product feeds into a transformer-based model to extract local and global features. Finally, these enhanced features are inputted into full connected layers to generate the imagined speech. The effectiveness of our model is validated using the high-quality, i.e., single-word-production-Dutch-iBIDS dataset. It achieves Pearson correlation scores above 0.8 and standard deviations below 0.2 across different volunteers. Experimental results well demonstrate that our model is robust and has advantages over peer methods. © 2024 IEEE.

关键词： Deep learning

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Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency

Detecting Backdoors During the Inference Stage Based on Corr...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xiaogeng Liu Minghui Li Haoyu Wang Shengshan Hu Dengpan Ye Hai Jin Libing Wu Chaowei Xiao School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Software Engineering Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan University School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab Arizona State University

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo) 1 1 https://***/CGCL-codes/TeCo, a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability.

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Achieving Network Resilience through Graph Neural Network-enabled Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Li, Xuzeng Zhang, Tao Wang, Jian Han, Zhen Liu, Jiqiang Kang, Jiawen Niyato, Dusit Jamalipour, Abbas The School of Cyberspace Science and Technology Beijing Jiaotong University China The Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China The Technology School of Automation Guangdong University of Technology China The College of Computing and Data Science Nanyang Technological University Singapore The University of Sydney SydneyNSW2006 Australia

Deep reinforcement learning (DRL) has been widely used in many important tasks of communication networks. In order to improve the perception ability of DRL on the network, some studies have combined graph neural networks (GNNs) with DRL, which use the GNNs to extract unstructured features of the network. However, as networks continue to evolve and become increasingly complex, existing GNN-DRL methods still face challenges in terms of scalability and robustness. Moreover, these methods are inadequate for addressing network security issues. From the perspective of security and robustness, this paper explores the solution of combining GNNs with DRL to build a resilient network. This article starts with a brief tutorial of GNNs and DRL, and introduces their existing applications in networks. Furthermore, we introduce the network security methods that can be strengthened by GNN-DRL approaches. Then, we designed a framework based on GNN-DRL to defend against attacks and enhance network resilience. Additionally, we conduct a case study using an encrypted traffic dataset collected from real IoT environments, and the results demonstrated the effectiveness and superiority of our framework. Finally, we highlight key open challenges and opportunities for enhancing network resilience with GNN-DRL. © 2025, CC BY.

关键词： Deep reinforcement learning

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