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arXiv 2023年

作者： Barmpalias, George Zhang, Xiaoyan Zhan, Bohua State Key Lab of Computer Science Inst. of Software Chinese Acad. of Sciences Beijing China

We study the compressibility of enumerations, and its role in the relative Kolmogorov complexity of computably enumerable sets, with respect to density. With respect to a strong and a weak form of compression, we examine the gain: the amount of auxiliary information embedded in the compressed enumeration. Strong compression and weak gainless compression is shown for any computably enumerable set, and a positional game is studied toward understanding strong gainless compression. Copyright © 2023, The Authors. All rights reserved.

关键词： Computability and decidability

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Pay Self-Attention to Audio-Visual Navigation 33

Pay Self-Attention to Audio-Visual Navigation

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33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Yu, Yinfeng Cao, Lele Sun, Fuchun Liu, Xiaohong Wang, Liejun Department of Computer Science and Technology State Key Lab on Intelligent Technology and Systems Tsinghua University Beijing China Motherbrain EQT Stockholm Sweden College of Information Science and Engineering Xinjiang University Urumqi China

Audio-visual embodied navigation, as a hot research topic, aims training a robot to reach an audio target using egocentric visual (from the sensors mounted on the robot) and audio (emitted from the target) input. The audio-visual information fusion strategy is naturally important to the navigation performance, but the state-of-the-art methods still simply concatenate the visual and audio features, potentially ignoring the direct impact of context. Moreover, the existing approaches requires either phase-wise training or additional aid (e.g. topology graph and sound semantics). Up till this date, the work that deals with the more challenging setup with moving target(s) is still rare. As a result, we propose an end-to-end framework FSAAVN (feature self-attention audio-visual navigation) to learn chasing after a moving audio target using a context-aware audio-visual fusion strategy implemented as a self-attention module. Our thorough experiments validate the superior performance (both quantitatively and qualitatively) of FSAAVN in comparison with the state-of-the-arts, and also provide unique insights about the choice of visual modalities, visual/audio encoder backbones and fusion patterns. © 2022. The copyright of this document resides with its authors.

关键词： Robots

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Talk With Human-like Agents: Empathetic Dialogue Through Perceptible Acoustic Reception and Reaction

arXiv

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arXiv 2024年

作者： Yan, Haoqiu Zhu, Yongxin Zheng, Kai Liu, Bing Cao, Haoyu Jiang, Deqiang Xu, Linli School of Computer Science and Technology University of Science and Technology China School of Data Science University of Science and Technology China State Key Laboratory of Cognitive Intelligence China Tencent Youtu Lab China

Large Language Model (LLM)-enhanced agents become increasingly prevalent in Human-AI communication, offering vast potential from entertainment to professional domains. However, current multi-modal dialogue systems overlook the acoustic information present in speech, which is crucial for understanding human communication nuances. This oversight can lead to misinterpretations of speakers' intentions, resulting in inconsistent or even contradictory responses within dialogues. To bridge this gap, in this paper, we propose PerceptiveAgent, an empathetic multi-modal dialogue system designed to discern deeper or more subtle meanings beyond the literal interpretations of words through the integration of speech modality perception. Employing LLMs as a cognitive core, PerceptiveAgent perceives acoustic information from input speech and generates empathetic responses based on speaking styles described in natural language. Experimental results indicate that PerceptiveAgent excels in contextual understanding by accurately discerning the speakers' true intentions in scenarios where the linguistic meaning is either contrary to or inconsistent with the speaker's true feelings, producing more nuanced and expressive spoken dialogues. Code is publicly available at: https://***/Haoqiu-Yan/PerceptiveAgent. Copyright © 2024, The Authors. All rights reserved.

关键词： Speech communication

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Compression of Enumerations and Gain

SSRN

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SSRN 2023年

作者： Barmpalias, George Zhang, Xiaoyan Zhan, Bohua State Key Lab of Computer Science Inst. of Software Chinese Acad. of Sciences Beijing China

We study the compressibility of enumerations, and its role in the relative Kolmogorov complexity of computably enumerable sets, with respect to density. With repspect to a strong and a weak form of compression, we examine the gain: the amount of auxiliary information embedded in the compressed enumeration. Strong compression and weak gainless compression is shown for any computably enumerable set, and a positional game is studied toward understanding strong gainless compression. © 2023, The Authors. All rights reserved.

关键词： Computability and decidability

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A Blockchain Wallet Scheme with Multi-Factor Authentication Based on Distributed System

A Blockchain Wallet Scheme with Multi-Factor Authentication ...

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Blockchain Technology and Information Security (ICBCTIS), International Conference on

作者： Qianwen Gao Yichi Tu TCA Lab of State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331528324

ISBN: (纸本)9798331528331

Blockchain wallet is a technology focusing on generating, storing and utilizing private keys to manage digital assets securely. As a service, blockchain wallets require user authentication. Traditional blockchain wallets employ mnemonic phrases to assist users in storing and managing private keys. However, the complex operation procedures and the totally different identity authentication mechanism of traditional wallets undoubtedly hinder their development. What's worse, it's inconvenient for secure storage, and doesn't support account recovery, which affects the security and usability of traditional wallets. To address this issue, this paper provides a solution for identity authentication and private key management of blockchain digital wallets in distributed scenarios. It proposes an architecture for building a distributed multi-factor authentication digital wallet for blockchain. We design and implement this blockchain digital wallet based on a distributed multi-factor authentication system. By employing a secret sharing scheme, the encrypted private key shares are stored on distributed servers. When a user wants to use his private key, the wallet conducts a private key recovery operation, which significantly reducing the risk of storing the private key locally. This mitigates security issues such as single-point failure and weak password attacks. What's more, Users are only required to operate the hardware token once during the authentication and private key recovery operation, ensuring the balance between the security and usability of blockchain digital wallets.

关键词： Multi-factor authentication Buildings Information security Focusing Passwords Hardware Blockchains Servers Usability Secure storage

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Anomize: Better Open Vocabulary Video Anomaly Detection

arXiv

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arXiv 2025年

作者： Li, Fei Liu, Wenxuan Chen, Jingjing Zhang, Ruixu Wang, Yuran Zhong, Xian Wang, Zheng National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China State Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology China

Open Vocabulary Video Anomaly Detection (OVVAD) seeks to detect and classify both base and novel anomalies. However, existing methods face two specific challenges related to novel anomalies. The first challenge is detection ambiguity, where the model struggles to assign accurate anomaly scores to unfamiliar anomalies. The second challenge is categorization confusion, where novel anomalies are often misclassified as visually similar base instances. To address these challenges, we explore supplementary information from multiple sources to mitigate detection ambiguity by leveraging multiple levels of visual data alongside matching textual information. Furthermore, we propose incorporating label relations to guide the encoding of new labels, thereby improving alignment between novel videos and their corresponding labels, which helps reduce categorization confusion. The resulting Anomize framework effectively tackles these issues, achieving superior performance on UCF-CRIME and XD-VIOLENCE datasets, demonstrating its effectiveness in OVVAD. Copyright © 2025, The Authors. All rights reserved.

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Semi-Cervixseg: A Multi-Stage Training Strategy for Semi-Supervised Cervical Segmentation

Semi-Cervixseg: A Multi-Stage Training Strategy for Semi-Sup...

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IEEE International Symposium on Biomedical Imaging

作者： Juntao Jiang Yali Bi Chunlin Zhou Yong Liu Jiangning Zhang College of Control Science and Engineering Zhejiang University College of Computer and Information Science Southwest University State Key Laboratory of Industrial Control Technology Zhejiang University Youtu Lab Tencent

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Image segmentation plays a critical role in computer-aided diagnosis and treatment planning for cervical cancer. Obtaining a large number of labeled images for supervised cervical segmentation is often labor-intensive and time-consuming. In this paper, we propose a multi-stage semi-supervised learning framework (Semi-CervixSeg) to address the cervical seg-mentation task in ultrasound images for Fetal Ultrasound Grand Challenge: Semi-Supervised Cervical Segmentation in ISBI 2025. Specifically, in the initial stage, we utilize unlabeled data through a multi-view random augmentation strategy, using consistency constraints and a contrastive learning method. Subsequently, a progressive multi-stage training strategy is adopted to generate and optimize pseudo-labels, further improving segmentation results. Experimental re-sults demonstrate that the proposed method significantly im-proves segmentation performance compared with supervised methods. As a technical report for the challenge, this paper elaborates on our methodology and experimental findings in detail. The code can be accessed at h t t p s: / / g i t hub. com/juntaoJianggavin/Semi-CervixSeg.

关键词： Training Image segmentation Ultrasonic imaging Codes Contrastive learning Semisupervised learning Writing Data models Planning computer aided diagnosis

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Identification of Microservices through Processed Dynamic Traces and Static Calls 3

Identification of Microservices through Processed Dynamic Tr...

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3rd International Conference on computer science and Management Technology, ICCSMT 2022

作者： Wu, Sijie Zhang, Cheng Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University China Anhui University School of Computer Science and Technology China Nanjing University State Key Lab for Novel Software Technology Hefei China

ISBN: (纸本)9798350320404

As the complexity of software systems continues to increase, traditional monolithic software architectures will become difficult to scale and maintain. Microservices as an emerging software architecture it advocates decentralized management, service autonomy, and distinct module boundaries. These features of microservices make software systems easy to maintain, deploy, and modify. However, microservice identification is still not well addressed due to the lack of an effective approach. The existing work on microservice identification mainly starts from the system functionality perspective and does not take into account the actual business rules, which leads to microservice candidates not conforming to the concept of microservices. To address this challenge, we propose an automated approach to microservice identification that combines dynamic and static analysis and takes into account the actual business. Our proposed approach starts with a dynamic and static analysis of the monolithic system. Once the behavioral data of the system is collected, the behavioral data will be further processed according to our proposed guidelines. Then, the proposed approach calculates the degree of the correlation between classes in a monolithic system. Finally, we propose an improved clustering algorithm based on K-means to classify the classes in a monolithic system into different microservices. The proposed approach is validated by comparison with other approaches on open source projects. © 2022 IEEE.

关键词： K-means clustering

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Multi-objective software test case selection based on density analysis 22

Multi-objective software test case selection based on densit...

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5th International Conference on computer science and Software Engineering, CSSE 2022

作者： Jia, Huihui Zhang, Cheng Wu, Sijie Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University China School of Computer Science and Technology Anhui University China State Key Lab for Novel Software Technology Nanjing University Hefei China

ISBN: (纸本)9781450397780

Software test case selection is committed to select the fewest test cases from test suites to perform a complete test at the least cost. Machine learning and multi-objective optimization techniques have developed rapidly in recent years, and they have been successfully applied to test case selection. In this paper, we present a method called DB-NSGA2, which uses the density clustering algorithm in machine learning combined with the non-dominated ranking algorithm (NSGA2) for test case selection, which can better select the test cases required for testing. In particular, we apply some of the clustering results generated by the clustering algorithm to the crossover and mutation operations of the NSGA2 to improve diversity progeny populations and ensure the transmission of good individuals. Extensive experiments show that the test cases selected by our method can produce a better set of Pareto solutions and can detect more faults at a lower cost than other methods. © 2022 ACM.

关键词： Clustering algorithms

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OneActor: consistent subject generation via cluster-conditioned guidance 24

OneActor: consistent subject generation via cluster-conditio...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jiahao Wang Caixia Yan Haonan Lin Weizhan Zhang Mengmeng Wang Tieliang Gong Guang Dai Hao Sun School of Computer Science and Technology MOEKLINNS Xi'an Jiaotong University and State Key Laboratory of Communication Content Cognition School of Computer Science and Technology MOEKLINNS Xi'an Jiaotong University College of Computer Science and Technology Zhejiang University of Technology and SGIT AI Lab State Grid Corporation of China SGIT AI Lab State Grid Corporation of China China Telecom Artificial Intelligence Technology Co.Ltd

ISBN: (纸本)9798331314385

Text-to-image diffusion models benefit artists with high-quality image generation. Yet their stochastic nature hinders artists from creating consistent images of the same subject. Existing methods try to tackle this challenge and generate consistent content in various ways. However, they either depend on external restricted data or require expensive tuning of the diffusion model. For this issue, we propose a novel one-shot tuning paradigm, termed OneActor. It efficiently performs consistent subject generation solely driven by prompts via a learned semantic guidance to bypass the laborious backbone tuning. We lead the way to formalize the objective of consistent subject generation from a clustering perspective, and thus design a cluster-conditioned model. To mitigate the overfitting challenge shared by one-shot tuning pipelines, we augment the tuning with auxiliary samples and devise two inference strategies: semantic interpolation and cluster guidance. These techniques are later verified to significantly improve the generation quality. Comprehensive experiments show that our method outperforms a variety of baselines with satisfactory subject consistency, superior prompt conformity as well as high image quality. Our method is capable of multi-subject generation and compatible with popular diffusion extensions. Besides, we achieve a 4× faster tuning speed than tuning-based baselines and, if desired, avoid increasing the inference time. Furthermore, our method can be naturally utilized to pre-train a consistent subject generation network from scratch, which will implement this research task into more practical applications. Project page: https://***/OneActor-webpage/.

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