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arXiv 2023年

作者： Hou, Danyang Pang, Liang Lan, Yanyan Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology CAS Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Institute for AI Industry Research Tsinghua University Beijing China

Video corpus moment retrieval (VCMR) is the task of retrieving a relevant video moment from a large corpus of untrimmed videos via a natural language query. State-of-the-art work for VCMR is based on two-stage method. In this paper, we focus on improving two problems of two-stage method: (1) Moment prediction bias: The predicted moments for most queries come from the top retrieved videos, ignoring the possibility that the target moment is in the bottom retrieved videos, which is caused by the inconsistency of Shared Normalization during training and inference. (2) Latent key content: Different modalities of video have different key information for moment localization. To this end, we propose a two-stage model MultI-video raNking with mUlTimodal cluE (MINUTE). MINUTE uses Shared Normalization during both training and inference to rank candidate moments from multiple videos to solve moment predict bias, making it more efficient to predict target moment. In addition, Mutilmdaol Clue Mining (MCM) of MINUTE can discover key content of different modalities in video to localize moment more accurately. MINUTE outperforms the baselines on TVR and DiDeMo datasets, achieving a new state-of-the-art of VCMR. Our code will be available at GitHub. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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A Secure Lightweight RFID Mutual Authentication Protocol Without Explicit Challenge-Response Pairs 2nd

A Secure Lightweight RFID Mutual Authentication Protocol Wit...

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2nd EAI International Conference on Applied Cryptography in Computer and Communications, AC3 2022

作者： Huang, Keke Lin, Changlu Liu, Yali College of Computer and Cyber Security Fujian Normal University Fujian Fuzhou350117 China Fujian Provincial Key Lab of Network Security and Cryptology Fujian Fuzhou350007 China School of Mathematics and Statistics Fujian Normal University Fujian Fuzhou350117 China College of Computer Science and Technology Jiangsu Normal University Jiangsu Xuzhou221116 China

ISBN: (纸本)9783031170805

Radio Frequency Identification (RFID) has been widely deployed to various scenarios, but its security and privacy issues need to be concerned due to the tag’s limited computing and storage resources. While benefiting from the great convenience and advantages of RFID systems, security is still a considerable threat to their applications, such as desynchronization attacks and cloning attacks. In this paper, we propose a secure lightweight mutual authentication protocol based on the configurable tristate physical unclonable functions and the cryptographic primitive of verifiable secret sharing to solve these issues. More specifically, the tag equipped with the configurable tristate physical unclonable functions structure can enhance the tag’s security and effectively resist machine learning modeling attacks. Verifiable secret sharing plays the role of decentralized storage of secrets, and ensures the verifiability of the correctness of the each shares. The verifiability provided by verifiable secret sharing is effective against tag impersonation attacks, and the validity of the each share provided by the tag has to be verified before it is adopted. Finally, the correctness of our protocol is analyzed formally using BAN-logic and the its security is verified informally by the Scyther. In addition, we analyze security properties including data integrity, data confidentiality, anonymity, mutual authentication, forward security and resistance to various malicious attacks. The results show that the proposed protocol satisfies various security properties and resistance to diverse malicious attacks. © 2022, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Digital storage

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HL-DPoS: An Enhanced Anti-Long-Range Attack DPoS Algorithm

arXiv

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arXiv 2023年

作者： Li, Yang Xia, Chunhe Li, Chunyan Zhao, Yuan Chen, Chen Wang, Tianbo School of Computer Science and Engineering Beihang University Beijing China Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China

The consensus algorithm is crucial in blockchain for ensuring the validity and security of transactions across the decentralized network. However, achieving consensus among nodes and packaging blocks in blockchain networks is a complex task that requires efficient and secure consensus algorithms. The DPoS consensus algorithm has emerged as a popular choice due to its fast transaction processing and high throughput. Despite these advantages, the algorithm still suffers from weaknesses such as centralization and vulnerability to long-range attacks, which can compromise the integrity of the blockchain network. To combat these problems, we developed an Enhanced Anti-Long-Range Attack DPoS algorithm (HL-DPoS). First, we split nodes into pieces to reduce centralization issues while giving witness nodes the power to report and benefit from malicious node's reports, maintaining high efficiency and high security. Second, we propose a validation method in HL-DPoS that compares consensuses transactions with the longest chain to detect long-range attacks. Algorithm analysis and simulation experiment results demonstrate that our HL-DPoS consensus algorithm improves security while achieving better consensus performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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A Cooperative Iterated Greedy Algorithm for the Distributed Flowshop Group Robust Scheduling Problem with Uncertain Processing Times

SSRN

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SSRN 2023年

作者： Wang, Zhi-Yuan Pan, Quan-Ke Gao, Liang Jing, Xue-Lei Sun, Qing School of Mechanical and Electrical Engineering and Automation Shanghai University Shanghai200444 China School of Computer Science Liaocheng University Liaocheng252000 China State Key Lab of Digital Manufacturing Equipment & Technology Huazhong University of Science & Technology Wuhan430074 China Network Information Center Liaocheng University Liaocheng252000 China

This paper studies a distributed flowshop group robust scheduling problem with uncertain processing times, which has great significance in actual production activities. A robust mathematical model and an efficacious cooperative iterated greedy (CIG) algorithm are proposed to address the studied problem. A heuristic method with a novel sequence rule is presented to obtain an initial solution. A dummy scenario method is developed to reduce the complexity of the multi-scenario environment and raise the convergence speed. Two iterated greedy processes are utilized to optimize the family scheduling sub-problem and the job scheduling sub-problem respectively. A cooperation mechanism is designed elaborately to emphasize the coupling relationship between sub-problems. A comparative and comprehensive evaluation is conducted by comparing CIG with six high-performing algorithms. The results indicate that CIG significantly outperforms the other competitors from the average relative deviation index. © 2023, The Authors. All rights reserved.

关键词： Heuristic methods

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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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AIDE: Attack Inference Based on Heterogeneous Dependency Graphs with MITRE ATT&CK

AIDE: Attack Inference Based on Heterogeneous Dependency Gra...

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

作者： Weidong Zhou Chunhe Xia Nan Feng Xinyi Pan Tianbo Wang Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Software & Microelectronics Peking University Beijing China School of Cyber Science and Technology Beihang University Beijing China School of Computer Science and Engineering Guangxi Normal University Guilin China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

The MITRE ATT&CK Framework provides a rich and actionable repository of adversarial tactics, techniques, and procedures (TTPs). By leveraging the associations among these techniques, we can infer unobserved techniques based on those have been observed. Current machine learning inference methods based on small-scale data face the following problems: "logical dependencies missing" and "complex associations missing." The former means that current association methods rely solely on the statistical dependencies between attack techniques while ignoring the logical dependencies involved in executing those techniques. The latter indicates that current inference methods simplify the complex associations between technologies based on statistical assumptions, failing to adequately capture the long-range and intricate real-world relationships between techniques. To address these problems, we propose an Attack Inference model based on heterogeneous DEpendency graphs (AIDE). First, we extract three types of logical dependencies: CVE, Pattern, and Conditional, which allows us to construct a heterogeneous dependency graph among the techniques, thus addressing the "logical dependencies missing" problem. Second, we employ Relation Graph Convolution Neural networks (RGCN) to aggregate heterogeneous dependencies into representation vectors of the techniques. Finally, we utilize the sequence inference model Decoder, which excels at capturing implicit contextual associations, to infer unknown attacks, thus addressing the "complex associations missing" problem. Experiments conducted on real datasets demonstrate that our model significantly outperforms state-of-the-art baseline models in both hits@k and map@k metrics.

关键词： Adaptation models Aggregates Refining Machine learning Vectors Threat assessment Real-time systems Decoding Security Context modeling

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RobustPFL: Robust Personalized Federated Learning

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Chen, Guorong Wang, Wei Wu, Yufang Li, Chao Xu, Guangquan Ji, Shouling Li, Tao Shen, Meng Han, Yufei Xi'an Jiaotong University Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an China Beijing Jiaotong University 3 Shangyuancun Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Tianjin University Tianjin Key Laboratory of Advanced Networking College of Intelligence and Computing Tianjin300350 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China ITAI Haihe Lab Tianjin China Nankai University College of Computer Science Tianjin300350 China Beijing Institute of Technology School of Cyberspace Security Beijing100081 China Peng Cheng Laboratory Cyberspace Security Research Center Shenzhen518066 China INRIA Bretagne Rennes35042 France

Conventional federated learning (FL) coordinated by a central server focuses on training a global model and protecting the privacy of clients' training data by storing it locally. However, the statistical heterogeneity hinders the global model from adapting to the non-IID distributions among clients. Moreover, untrusted and unreliable central servers and malicious clients may compromise model integrity and availability, thus degrading the robustness of FL. To address these challenges, we present RobustPFL, a decentralized personalized federated learning (PFL) approach that combines α-based Layer-position Normalized Similarity (α-LNS) and local collaborative training to improve personalized performance while utilizing a blockchain-based committee mechanism to coordinate the aggregation process, thereby achieving high personalized accuracy and robustness. Extensive experiments show that our RobustPFL approach outperforms multiple algorithms, including Local training, FedAvg, FedReptile, Per-FedAvg, FedBN, and SPFL, on MNIST, CIFAR10, EMNIST, and N-BaIoT datasets in four non-IID settings. We also evaluate RobustPFL's effectiveness against attacks - poisoning attacks and free-riding attacks. Particularly, for three prevalent poisoning attacks (backdoor, label flipping, and model poisoning attacks), we compare non-defensive (FedAvg) and defensive (Krum, trimmed mean, Bulyan, FedBN, FLAME, and FangTrmean) methods with our proposed RobustPFL. The results show that our approach achieves significant defensive effects. © 2004-2012 IEEE.

关键词： Federated learning

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MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures

arXiv

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arXiv 2023年

作者： Wang, Zhuoyuan Mi, Jiacong Lu, Shan He, Jieyue School of Computer Science and Engineering Key Lab of Computer Network and Information Integration MOE Southeast University Jiangsu Nanjing210018 China Nanjing Fenghuo Tiandi Communication Technology Co. Ltd. Jiangsu Nanjing211161 China

The quest for accurate prediction of drug molecule properties poses a fundamental challenge in the realm of Artificial Intelligence Drug Discovery (AIDD). An effective representation of drug molecules emerges as a pivotal component in this pursuit. Contemporary leading-edge research predominantly resorts to self-supervised learning (SSL) techniques to extract meaningful structural representations from large-scale, unlabeled molecular data, subsequently fine-tuning these representations for an array of downstream tasks. However, an inherent shortcoming of these studies lies in their singular reliance on one modality of molecular information, such as molecule image or SMILES representations, thus neglecting the potential complementarity of various molecular modalities. In response to this limitation, we propose MolIG, a novel MultiModaL molecular pre-training framework for predicting molecular properties based on Image and Graph structures. MolIG model innovatively leverages the coherence and correlation between molecule graph and molecule image to execute self-supervised tasks, effectively amalgamating the strengths of both molecular representation forms. This holistic approach allows for the capture of pivotal molecular structural characteristics and high-level semantic information. Upon completion of pre-training, Graph Neural network (GNN) Encoder is used for the prediction of downstream tasks. In comparison to advanced baseline models, MolIG exhibits enhanced performance in downstream tasks pertaining to molecular property prediction within benchmark groups such as MoleculeNet Benchmark Group and ADMET Benchmark Group. © 2023, CC BY.

关键词： Molecules

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Nonlinear Optical Performance and Mechanism in Mos2/Ws2 Van Der Waals Heterostructures

SSRN

引用

SSRN 2023年

作者： Xu, Yanmin Yan, Lihe Wang, Anyi Tian, Feng Huang, Xiaojun Liu, Jin Xi'an Key Laboratory of Network Convergence Communication School of Communication and Information Engineering Xi'an University of Science and Technology Xi'An710054 China Key Laboratory for Physical Electronics and Devices of the Ministry of Education Shaanxi Key Lab of Information Photonic Technique School of Electronics & Information Engineering Xi'an Jiaotong University Xi'An710049 China

Two-dimensional transition metal dichalcogenides (TMDs) have been widely used due to their excellent nonlinear optical properties. To enhance the optical nonlinearity of TMDs, some van der Waals heterostructures have been designed and the carrier dynamics plays a key role in the nonlinear response. In this paper, the optical nonlinearity of molybdenum disulfide/tungsten disulfide (MoS2/WS2) heterostructures were investigated by a nanosecond laser Z-scan technology, and an enhanced saturable absorption (SA) behavior was founded in heterostructures. Using the femtosecond time-resolved transient absorption measurements, the carrier dynamics in the nonlinear response, as well as the enhancement mechanism of the heterostructures were further clarified. © 2023, The Authors. All rights reserved.

关键词： Nonlinear optics

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SLIM: Let LLM Learn More and Forget Less with Soft LoRA and Identity Mixture

arXiv

引用

arXiv 2024年

作者： Han, Jiayi Du, Liang Du, Hongwei Zhou, Xiangguo Wu, Yiwen Zhang, Yuanfang Zheng, Weibo Han, Donghong Inspur Genersoft Co. Ltd. Inspur Group Co. Ltd China Shandong Key Lab of Automated Complex Network Software Construction China Interactive Entertainment Group Tencent Inc China Nanjing University of Information Science and Technology China Northeastern University China

Despite the recent efforts from the NLP community, balancing the training budget, downstream performance, and general capabilities of large language models (LLM) remains a challenge in many applications. Training the entire model for downstream tasks is expensive, and could easily result in catastrophic forgetting. Parameter-efficient fine-tuning (PEFT) could reduce the training cost, but it still suffers from forgetting, and limits the learning on the downstream tasks. To address the aforementioned issues, we propose a novel mixture of experts (MoE) framework based on Soft LoRA and Identity Mixture (SLIM). SLIM allows dynamic routing between LoRA adapters and identity layers, thus enabling the bypass of LoRA adapters to suppress forgetting of general capacity. We adopt weight yielding with sliding clustering for better out-of-domain distinguish to enhance the routing. We also convert the mixture of LoRA adapters to the model merging formulation and introduce dynamic merging with its fast implementation for LoRA adapters to keep the general capabilities. Extensive experiments demonstrate that the proposed SLIM is comparable to the state-of-the-art PEFT approaches on the downstream tasks while achieving the leading performance in mitigating catastrophic forgetting. © 2024, CC BY.

关键词： Budget control

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