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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Xiao, Mingjun Xu, Yin Zhou, Jinrui Wu, Jie Zhang, Sheng Zheng, Jun University of Science and Technology of China School of Computer Science and Technology China Suzhou Institute for Advanced Research University of Science and Technology of China China Temple Universityy Department of Computer and Information Sciences United States Nanjing University State Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798350334142

Mobile CrowdSensing (MCS) is a mobile computing paradigm, through which a platform can coordinate a crowd of workers to accomplish large-scale data collection tasks using their mobile devices. Information freshness has attracted much focus on MCS research worldwide. In this paper, we investigate the incentive mechanism design in MCS systems that take the freshness of collected data and social benefits into concerns. First, we introduce the Age of Information (AoI) metric to measure the freshness of data. Then, we model the incentive mechanism design with AoI guarantees as a novel incomplete information two-stage Stackelberg game with multiple constraints. Next, we derive the optimal strategies of this game so as to determine the optimal reward paid by the platform and the optimal data update frequency for each worker. Moreover, we prove that these optimal strategies form a unique Stackelberg equilibrium. Based on the optimal strategies, we propose an AoI-Aware Incentive (AIAI) mechanism for the MCS system, whereby the platform and all workers can maximize their utilities simultaneously. Meanwhile, the system can ensure that the AoI values of all data uploaded to the platform are not larger than a given threshold to achieve high data freshness. Extensive simulations on real-world traces are conducted to demonstrate the significant performance of AIAI. © 2023 IEEE.

关键词： Optimal systems

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AutoManual: Constructing Instruction Manuals by LLM Agents via Interactive Environmental Learning 38

AutoManual: Constructing Instruction Manuals by LLM Agents v...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Minghao Li, Yihang Yang, Yanting Yu, Shiyu Lin, Binbin He, Xiaofei School of Computer Science Hangzhou Dianzi University China State Key Lab of CAD&CG Zhejiang University China School of Software Technology Zhejiang University China Fullong Inc. China NingBo Port Group China

Large Language Models (LLM) based agents have shown promise in autonomously completing tasks across various domains, e.g., robotics, games, and web navigation. However, these agents typically require elaborate design and expert prompts to solve tasks in specific domains, which limits their adaptability. We introduce AutoManual, a framework enabling LLM agents to autonomously build their understanding through interaction and adapt to new environments. AutoManual categorizes environmental knowledge into diverse rules and optimizes them in an online fashion by two agents: 1) The Planner codes actionable plans based on current rules for interacting with the environment. 2) The Builder updates the rules through a well-structured rule system that facilitates online rule management and essential detail retention. To mitigate hallucinations in managing rules, we introduce a case-conditioned prompting strategy for the Builder. Finally, the Formulator agent compiles these rules into a comprehensive manual. The self-generated manual can not only improve the adaptability but also guide the planning of smaller LLMs while being human-readable. Given only one simple demonstration, AutoManual significantly improves task success rates, achieving 97.4% with GPT-4-turbo and 86.2% with GPT-3.5-turbo on ALFWorld benchmark tasks. The code is available at https://***/minghchen/automanual. © 2024 Neural information processing systems foundation. All rights reserved.

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DreamBooth++: Boosting Subject-Driven Generation via Region-Level References Packing 24

DreamBooth++: Boosting Subject-Driven Generation via Region-...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Fan, Zhongyi Yin, Zixin Li, Gang Zhan, Yibing Zheng, Heliang Beijing Institute of Technology Beijing China State Key Lab of Software Development Environment Beihang University Beijing China University of the Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences Beijing China JD Explore Academy Beijing China University of Science and Technology of China Anhui Hefei China

ISBN: (纸本)9798400706868

DreamBooth has demonstrated significant potential in subject-driven text-to-image generation, especially in scenarios requiring precise preservation of a subject's appearance. However, it still suffers from inefficiency and requires extensive iterative training to customize concepts using a small set of reference images. To address these issues, we introduce DreamBooth++, a region-level training strategy designed to significantly improve the efficiency and effectiveness of learning specific subjects. In particular, our approach employs a region-level data re-formulation technique that packs a set of reference images into a single sample, significantly reducing computational costs. Moreover, we adapt convolution and self-attention layers to ensure their processings are restricted within individual regions. Thus their operational scope (i.e., receptive field) can be preserved within a single subject, avoiding generating multiple sub-images within a single image. Last but not least, we design a text-guided prior regularization between our model and the pretrained one to preserve the original semantic generation ability. Comprehensive experiments demonstrate that our training strategy not only accelerates the subject-learning process but also significantly boosts fidelity to both subject and prompts in subject-driven generation. © 2024 ACM.

关键词： Image segmentation

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DasAtom: A Divide-and-Shuttle Atom Approach to Quantum Circuit Transformation

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Huang, Yunqi Gao, Dingchao Ying, Shenggang Li, Sanjiang Faculty of Engineering and Information Technology SydneyNSW2007 Australia Institute of Software Chinese Academy of Sciences Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science China

Neutral atom (NA) quantum systems are emerging as a leading platform for quantum computation, offering superior or competitive qubit count and gate fidelity compared to superconducting circuits and ion traps. However, the unique features of NA devices, such as long-range interactions, long qubit coherence time, and the ability to physically move qubits, present distinct challenges for quantum circuit compilation. In this paper, we introduce DasAtom, a novel divide-and-shuttle atom approach designed to optimise quantum circuit transformation for NA devices by leveraging these capabilities. DasAtom partitions circuits into subcircuits, each associated with a qubit mapping that allows all gates within the subcircuit to be directly executed. The algorithm then shuttles atoms to transition seamlessly from one mapping to the next, enhancing both execution efficiency and overall fidelity. For a 30-qubit Quantum Fourier Transform (QFT), DasAtom achieves a 415.8x improvement in fidelity over the move-based algorithm Enola and a 10.6x improvement over the SWAP-based algorithm Tetris. Notably, this improvement is expected to increase exponentially with the number of qubits, positioning DasAtom as a highly promising solution for scaling quantum computation on NA platforms. © 1982-2012 IEEE.

关键词： Qubits

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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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The Power of Age-based Reward in Fresh Information Acquisition 42

The Power of Age-based Reward in Fresh Information Acquisiti...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Wang, Zhiyuan Meng, Qingkai Zhang, Shan Luo, Hongbin Beihang University School of Computer Science and Engineering Beijing China Beihang University State Key Laboratory of Virtual Reality Technology and Systems China Beihang University Institute of Artificial Intelligence Beijing China State Key Laboratory of Software Development Environment China

ISBN: (纸本)9798350334142

Many Internet platforms collect fresh information of various points of interest (PoIs) relying on users who happen to be nearby the PoIs. The platform will offer reward to incentivize users and compensate their costs incurred from information acquisition. In practice, the user cost (and its distribution) is hidden to the platform, thus it is challenging to determine the optimal reward. In this paper, we investigate how the platform dynamically rewards the users, aiming to jointly reduce the age of information (AoI) and the operational expenditure (OpEx). Due to the hidden cost distribution, this is an online non-convex learning problem with partial feedback. To overcome the challenge, we first design an age-based rewarding scheme, which decouples the OpEx from the unknown cost distribution and enables the platform to accurately control its OpEx. We then take advantage of the age-based rewarding scheme and propose an exponentially discretizing and learning (EDAL) policy for platform operation. We prove that the EDAL policy performs asymptotically as well as the optimal decision (derived based on the cost distribution). Simulation results show that the age-based rewarding scheme protects the platform's OpEx from the influence of the user characteristics, and verify the asymptotic optimality of the EDAL policy. © 2023 IEEE.

关键词： Simulation platform

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Pattern Shifting or Knowledge Losing? A Forgetting Perspective for Understanding the Effect of Instruction Fine-Tuning 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Zhang, Chunkang Cao, Boxi Lu, Yaojie Lin, Hongyu Cao, Liu Zeng, Ke Wan, Guanglu Cai, Xunliang Han, Xianpei Sun, Le Chinese Information Processing Laboratory Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Meituan Beijing China

ISBN: (纸本)9780000000002

Instruction Fine-Tuning(IFT) emerges as an essential step of training large language models to robustly carry out tasks of interest. However, there lacks a systematic investigation about the underlying mechanisms of instruction fine-tuning, particularly on the forgetting phenomenon after IFT, known as alignment tax. Therefore, to understand the mechanism of IFT from the forgetting perspective, we investigate the alternation of the text pattern and knowledge within models throughout the entire IFT process. Specifically, we restore fine-tuned models to their base version by training them on the data sharing a similar distribution with the pre-training corpus and compare their results Our experiment indicates that there is a stage transition of forgetting during IFT process: (1) Pseudo Forgetting: in this stage, models mainly shift their familiar text pattern away from pre-training data format while the world knowledge is preserved. Consequently, models will recover to their original performance when they are restored to the base version. (2) Actual Forgetting: in this stage, models forget the acquired knowledge as well. Therefore, they fail to reach the original performance even if they are restored to the base version. © 2024 China National Conference on Computational Linguistics.

关键词： Restoration

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Make It Easy: Action Quality Assessment of Cyborg Animals Based on Spatial-Temporal Pose Inference 10

Make It Easy: Action Quality Assessment of Cyborg Animals Ba...

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10th IEEE Smart World Congress, SWC 2024

作者： Li, Qiqi Han, Le Wang, Pengfei Zheng, Nenggan Zhejiang University Qiushi Academy for Advanced Studies College of Computer Science and Technology Hangzhou China Zhejiang University School of Software Technology Hangzhou China Zhejiang University Qiushi Academy for Advanced Studies The State Key Lab of Brain-Machine Intelligence College of Computer Science and Technology Hangzhou China Bengbu University Bengbu China

ISBN: (纸本)9798331520861

Assessing the action quality of cyborg animals helps to adjust control strategies, guide the development of control algorithm, and enhance the efficiency of navigation and military applications. However, existing research is limited in control algorithm and ignores the assessment of movement smoothness. In this paper, we first propose EASY, an effective assessment framework, which is able to (i) evaluate the action quality of cyborg animals of various sizes and motion patterns;(ii) obtain assessment results directly based on keypoints. Specifically, EASY first captures keypoints sequences through a small object pose estimation module. It employs the convolutional block attention module (CBAM) and depthwise over parameterized convolution (DOConv), allowing more accurate feature recognition with fewer parameters. Then, EASY employs multi-field fusion strategy that merges commonalities and differences, enabling a comprehensive and accurate assessment of fluency. We tested EASY on both CyborgRat-MFA dataset and CyborgBee-MFA dataset. Extensive experiments demonstrate that the assessment results are consistent with human expert scores, which prove that our proposed EASY is reasonable, effective, and interpretable. © 2024 IEEE.

关键词： action quality assessment cyborg animals electrical stimulation control motion fluency

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Adaptive information fusion network for multi-modal personality recognition

Adaptive information fusion network for multi-modal personal...

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作者： Bao, Yongtang Liu, Xiang Qi, Yue Liu, Ruijun Li, Haojie School of Computer Science and Engineering Shandong University of Science and Technology Qingdao China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Virtual Reality Research Institute Beihang University Qingdao Research Institute Qingdao China School of Software Beihang University Beijing China

Personality recognition is of great significance in deepening the understanding of social relations. While personality recognition methods have made significant strides in recent years, the challenge of heterogeneity between modalities during feature fusion still needs to be solved. This paper introduces an adaptive multi-modal information fusion network (AMIF-Net) capable of concurrently processing video, audio, and text data. First, utilizing the AMIF-Net encoder, we process the extracted audio and video features separately, effectively capturing long-term data relationships. Then, adding adaptive elements in the fusion network can alleviate the problem of heterogeneity between modes. Lastly, we concatenate audio-video and text features into a regression network to obtain Big Five personality trait scores. Furthermore, we introduce a novel loss function to address the problem of training inaccuracies, taking advantage of its unique property of exhibiting a peak at the critical mean. Our tests on the ChaLearn First Impressions V2 multi-modal dataset show partial performance surpassing state-of-the-art networks. © 2024 John Wiley & Sons Ltd.

关键词： Information fusion

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computer-Vision-Based Non-Contact Paste Concentration Measurement*

Computer-Vision-Based Non-Contact Paste Concentration Measur...

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2024 International Conference on Automation in Manufacturing, Transportation and Logistics, ICaMaL 2024

作者： Liang, Tailin Yuan, Zhaolin School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Beijing China Research Institute of Advanced Manufacturing Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350378658

For monitoring the paste concentration, existing techniques, such as ultrasonic concentration meters and neutron meters, suffer from radiation hazards and low precision in high concentrations. This paper proposes a novel non-contact concentration measurement method based on deep learning. With the dataset collected by preparing the paste with different concentrations and taking photographs from the paste surface, a convolutional neural network is trained to extract the features from images and predict the concentration. The experiments indicate that the measurement accuracy is close to 88.79 % for the manual stirring paste dataset and 91.42 % for the automatic stirring paste dataset, which are sufficient in industrial applications. As a substitution of traditional concentration measurement instruments, the proposed vision-based concentration measurement method is non-contact and its accuracy is sufficient in industrial applications. © 2024 IEEE.

关键词： Radiation hazards

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