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arXiv 2024年

作者： Yuan, Weiwen Ren, Jinke Wang, Chongjie Zhang, Ruichen Wei, Jun Kim, Dong In Cui, Shuguang FNii-Shenzhen China SSE China Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

Semantic communication has emerged as a promising technology for enhancing communication efficiency. However, most existing research emphasizes single-task reconstruction, neglecting model adaptability and generalization across multi-task systems. In this paper, we propose a novel generative semantic communication system that supports both image reconstruction and segmentation tasks. Our approach builds upon semantic knowledge bases (KBs) at both the transmitter and receiver, with each semantic KB comprising a source KB and a task KB. The source KB at the transmitter leverages a hierarchical Swin-Transformer, a generative AI scheme, to extract multi-level features from the input image. Concurrently, the counterpart source KB at the receiver utilizes hierarchical residual blocks to generate task-specific knowledge. Furthermore, the task KBs adopt a semantic similarity model to map different task requirements into pre-defined task instructions, thereby facilitating the feature selection of the source KBs. Additionally, we develop a unified residual block-based joint source and channel (JSCC) encoder and two task-specific JSCC decoders to achieve the two image tasks. In particular, a generative diffusion model is adopted to construct the JSCC decoder for the image reconstruction task. Experimental results show that our multi-task generative semantic communication system outperforms previous single-task communication systems in terms of peak signal-to-noise ratio and segmentation accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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An efficient multi-attribute combinatorial auction for 4PL transportation service procurement

An efficient multi-attribute combinatorial auction for 4PL t...

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第35届中国控制与决策会议

作者： Na Yuan Haiming Liang Min Huang Yanfeng Zhang College of Information Science and Engineering Northeastern University Center for Network Big Data and Decision-Making Business School Sichuan University College of Computer Science and Engineering Northeastern University

In this paper,we study the mechanism design problem of combinatorial reverse auction for 4 PL transportation service procurement(TSP) considering price and multiple nonprice *** order to achieve efficient winner determination and transportation request allocation,we develop a 0-1 programing model to minimize social cost,where social cost includes real transportation cost of 3 PLs and the costs incurred by non-price attributes.A multi-attribute VCG(MA-VCG) reverse auction mechanism is proposed,which satisfies efficiency,incentive compatibility,individual rationality,and budget ***,a numerical example is designed to demonstrate the effectiveness and applicability of the proposed ***,sensitivity analysis is implemented to illustrate the influences of attribute preference,cost coefficient and other parameters on auction results.

关键词： Transportation service procurement Mechanism design Combinatorial reverse auction Multi-attribute auction

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GIDCL: A Graph-Enhanced Interpretable data Cleaning Framework with Large Language Models

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Proceedings of the ACM on Management of data 2024年第6期2卷 1-29页

作者： Mengyi Yan Yaoshu Wang Yue Wang Xiaoye Miao Jianxin Li School of Computer Science and Engineering Beihang University Beijing CN Shenzhen Institute of Computing Sciences Shenzhen University Shenzhen Guangdong CN Shenzhen Institute of Computing Sciences Shenzhen Guangdong CN Center for Data Science Zhejiang University Hangzhou Zhejiang CN

data quality is critical across many applications. The utility of data is undermined by various errors, making rigorous data cleaning a necessity. Traditional data cleaning systems depend heavily on predefined rules and constraints, which necessitate significant domain knowledge and manual effort. Moreover, while configuration-free approaches and deep learning methods have been explored, they struggle with complex error patterns, lacking interpretability, requiring extensive feature engineering or labeled data. This paper introduces GIDCL (Graph-enhanced Interpretable data Cleaning with Large language models), a pioneering framework that harnesses the capabilities of Large Language Models (LLMs) alongside Graph Neural Network (GNN) to address the challenges of traditional and machine learning-based data cleaning methods. By converting relational tables into graph structures, GIDCL utilizes GNN to effectively capture and leverage structural correlations among data, enhancing the model's ability to understand and rectify complex dependencies and errors. The framework's creator-critic workflow innovatively employs LLMs to automatically generate interpretable data cleaning rules and tailor feature engineering with minimal labeled data. This process includes the iterative refinement of error detection and correction models through few-shot learning, significantly reducing the need for extensive manual configuration. GIDCL not only improves the precision and efficiency of data cleaning but also enhances its interpretability, making it accessible and practical for non-expert users. Our extensive experiments demonstrate that GIDCL significantly outperforms existing methods, improving F1-scores by 10% on average while requiring only 20 labeled tuples.

关键词： data quality graph neural network interpretable large language models

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COMET: NFT Price Prediction with Wallet Profiling

arXiv

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arXiv 2024年

作者： Wang, Tianfu Deng, Liwei Wang, Chao Lian, Jianxun Yan, Yue Yuan, Nicholas Jing Zhang, Qi Xiong, Hui School of Computer Science and Technology University of Science and Technology of China Hefei China Microsoft Inc Suzhou China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei China Microsoft Research Asia Beijing China Microsoft Inc Beijing China Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

As the non-fungible token (NFT) market flourishes, price prediction emerges as a pivotal direction for investors gaining valuable insight to maximize returns. However, existing works suffer from a lack of practical definitions and standardized evaluations, limiting their practical application. Moreover, the influence of users' multi-behaviour transactions that are publicly accessible on NFT price is still not explored and exhibits challenges. In this paper, we address these gaps by presenting a practical and hierarchical problem definition. This approach unifies both collection-level and token-level task and evaluation methods, which cater to varied practical requirements of investors. To further understand the impact of user behaviours on the variation of NFT price, we propose a general wallet profiling framework and develop a COmmunity enhanced Multi-bEhavior Transaction graph model, named COMET. COMET profiles wallets with a comprehensive view and considers the impact of diverse relations and interactions within the NFT ecosystem on NFT price variations, thereby improving prediction performance. Extensive experiments conducted in our deployed system demonstrate the superiority of COMET, underscoring its potential in the insight toolkit for NFT investors. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph theory

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Blockchain-Based Identity Management: Enhancing Privacy and Security in Digital Identity System

Blockchain-Based Identity Management: Enhancing Privacy and ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： S Saranya Monika K Manikandan Jangam Nagaraju S. Nagendiran B. T. Geetha Computer Science and Engineering Dr. N.G.P. Institute of Technology Coimbatore Mangalmay Institute of Management and Technology Greater Noida India School of Computer Science & Engineering (SCOPE) Vellore Institute of Technology (VIT) Vellore Tamilnadu India Department of Data Science MLR Institute of Technology Dundigal Hyderabad Department of Artificial Intelligence and Data Science R. M. K. Engineering College Department of ECE Saveetha School of Engineering (SIMATS) Saveetha University Tamil Nadu India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Privacy and security are major concerns when it comes to digital identities, particularly in centralized systems where data can be easily hacked or where people engage in identity fraud. These issues can be addressed using blockchain technology since it allows for the creation of a decentralized digital identity that is both secure and tamper-proof. This study employs Kaggle dataset to document the transactions and the devices involved to make the distinctions on how blockchain can boost privacy and security. Predominantly, various machine learning algorithms are used to analyze blockchain applications, and the results emphasize the fact blockchain systems greatly enhance transaction viability and diminish fraudulent actions. Therefore, the study argues that blockchain-based identity management systems offset the shortcomings of the centralized model by enhancing data ownership and combating fraud. Subsequent analysis expanded on the discussion of elaborating the scalability, interoperability, and application of algorithms to blockchains as well as how it can fundamentally change the way that certain industries, particularly identity management, would work with consumers at the center of reliable and secure blockchain solutions.

关键词： Machine learning algorithms Protocols Scalability Instruments Machine learning Blockchains Fraud Security Reliability Interoperability

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Synergizing Vision and Language in Remote Sensing: A Multimodal Approach for Enhanced Disaster Classification in Emergency Response Systems

Synergizing Vision and Language in Remote Sensing: A Multimo...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Shubham Gupta Nandini Saini Suman Kundu Chiranjoy Chattopadhyay Debasis Das Computer Science & Engineering Indian Institute of Technology Jodhpur Rajasthan India School of Computing and Data Sciences FLAME University Pune Maharashtra India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

As remote sensing capabilities continue to advance, there is a growing interest in leveraging computer vision and natural language processing for enhanced interpretation of remote sensing scenes. This paper explores the integration of textual information with images to augment traditional disaster classification methods. Our approach utilizes a predefined vision-language model to generate descriptive captions for images, fostering a more nuanced understanding of the remote sensing data. Next, we seamlessly integrate the generated textual information with image data through multimodal training, employing a multimodal deep learning method for disaster classification. The system categorizes input data into predefined disaster categories, presenting a comprehensive and accurate approach to emergency response system development. Experimental evaluations conducted on the AIDER dataset (Aerial Image database for Emergency Response applications) showcase the efficacy of our approach, demonstrating improved accuracy compare to unimodal approach and reliability in disaster classification. This research contributes to the advancement of intelligent emergency response systems by harnessing the synergy between vision and language in the context of remote sensing.

关键词： Training Accuracy Social networking (online) Image databases Disasters Emergency services Real-time systems

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Secrecy Performance Analysis of Artificial Noise Assisted Secure Transmission for MISO systems with Multiple Eavesdroppers

Secrecy Performance Analysis of Artificial Noise Assisted Se...

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2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023

作者： Xu, Panpan Tan, Weijie Niu, Kun Li, Zhen Peng, Changgen School of Mathematics and Statistics Guizhou University Guiyang China Key Laboratory of Advanced Manufacturing Technology of Ministry of Education Guizhou China Guizhou Big Data Academy Guizhou University Guiyang China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China College of Big Data and Information Engineering Guizhou University Guiyang China

ISBN: (纸本)9798350316728

In the paper, we investigate the secure communication of multiple-input single-output (MISO) systems with multiple eavesdroppers. We jointly design the beamforming (BF) and the artificial noise (AN) in MISO systems with multiple eavesdroppers to enhance the secrecy rate. Furthermore, under the secrecy interrupt constraint, we obtain the calculation formulas of the secrecy interrupt probability and the optimal secrecy rate. The simulation results demonstrate that AN with a higher dimension can effectively obstruct eavesdroppers and enhance the system's optimal secrecy rate. In addition, it is proved that the 1/4 full-dimension AN's secrecy rate close to that of the full-dimension AN in the system with multiple eavesdroppers, allowing the trade-off between system secrecy performance and artificial noise design complexity to be realized. © 2023 IEEE.

关键词： Economic and social effects

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Improving Zero-Shot Coordination with Diversely Rewarded Partner Agents

Improving Zero-Shot Coordination with Diversely Rewarded Par...

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International Joint Conference on Neural Networks (IJCNN)

作者： Peilin Wu Zhenhua Yang Peng Yang Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Statistics and Data Science Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Zero-shot coordination studies the training of well-generalizing human-AI coordination agents in the scenario where human data is unavailable. To obtain a coordination agent generalize to unseen humans, prevailing methods generate a population of partner agents as proxy models of human partners and then train a coordination agent with these partner agents. Constructed partner agents are expected to be as diverse as possible to cover a wide range of human behaviors, preventing a distribution shift between training and testing stages. Recent works concentrate on studying effective methods of creating a group of high-reward while diverse partner agents to model unseen human partners. However, the resulting high-reward partner agents do not accurately reflect real-world situations, considering that human decisions are not always optimal and may sometimes even hinder the progression of coordination. Therefore, these studies still struggle to capture the potential characteristics of human partners. In this work, reinforcement learning (RL) and supervised learning (SL) are integrated to train a reward-conditioned policy. By conditioned on different desired rewards, a reward-conditioned policy simulates both low-reward and high-reward partners. Additionally, a reward-bucketed replay buffer and curriculum learning are applied to enhance reward diversity and boost the training of coordination agents. Experiments demonstrate that the proposed reward-conditioned policy is capable of generating agents with different rewards. Moreover, the zero-shot coordination performance of agents trained with these partners surpasses previous methods in the majority of scenarios within the Overcooked human-AI coordination benchmark.

关键词： Training Impedance matching Supervised learning Neural networks Reinforcement learning Benchmark testing Trajectory

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A Unified Framework to Assess Market Implications of Institutional Investments

A Unified Framework to Assess Market Implications of Institu...

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2022 IEEE International Conference on Big data, Big data 2022

作者： Seth, Taruna Tiu, Cristian Chaudhary, Vipin University at Buffalo Department of Computer Science and Engineering Buffalo United States University at Buffalo Department of Finance Buffalo United States Case Western Reserve University Department of Computer and Data Sciences Cleveland United States

ISBN: (纸本)9781665480451

US financial markets are influenced by complex interactions of diverse entities like institutional investors, which control a considerable portion of all US financial assets. Despite significant increase in the institutional ownership over the last several years, detection of causal associations between institutional investments and equity markets remains elusive due to inherent intricacies of the investment behavior. In this paper, we propose a novel solution to establish linkages between the institutional investments and market dynamics. We accomplish this task by deploying a multi-stage methodology that includes evaluation of heterogeneous data from disparate sources, an integrated framework comprising of tools to facilitate unstructured- and structured- data modeling, data integration, unsupervised learning, and an evaluation approach to uncover discernible patterns capturing market fragility. Our results on the real data confirm the efficacy of the proposed solution by establishing linkages between the investment behavior and market movements. For instance, the results show that the co-ownership and selling of large capitalization stocks held by institutional investors drive market returns co-movements. Our results confirm the efficacy of the presented framework. The proposed solution can assist the economists and policy makers detect fraud and proactively prepare against disruptive market movements thereby minimizing the risk to the economy. © 2022 IEEE.

关键词： Investments

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Mandarin Speech Recognition Based on Chinese Syllable Similarity for Children 7

Mandarin Speech Recognition Based on Chinese Syllable Simila...

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7th International Conference on Signal and Image Processing, ICSIP 2022

作者： Xu, Anchong Hu, Wenxin Yang, Jing Zheng, Jun East China Normal University School of Data Science Engineering 3663 N. Zhongshan Rd. Shanghai China Institute of Brain and Education Innovation School of Data Science Engineering East China Normal University 3663 N. Zhongshan Rd. Shanghai China East China Normal University Computer Science and Technology 3663 N. Zhongshan Rd. Shanghai China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

Deep learning has facilitated the advancement and wide application of speech recognition technology. While mainstream speech recognition models are usually trained with adult standard speech data, which do not take into account the particularities of special groups, which is not suitable for all groups. Compared with adult speech, child speech recognition is more challenging because there are greater differences in acoustic and linguistic features between speakers. Furthermore, children have their own unique pronunciation habits and pronunciation methods and it's hard for them to clear the difference between such as level and oblique tones, front nasal and back nasal. In addition, the lack of adequate and appropriate children's voice resources increases the challenge of designing robust end-to-end neural structures. Therefore, we propose a novel model for Chinese children's speech recognition, the Chinese children syllable similarity recognition model, which is used to help distinguish and identify children's ambiguous pronunciation, in order to achieve a better recognition effect. Since the speech recognition of young children is usually short words and phrase recognition, the existing recognition evaluation metrics are not suitable. In the paper, we design a sliding window matching judgment method, which greatly improves the accuracy of children's speech recognition and achieves very good recognition effect. Our model achieves a judgment accuracy of 91.7% on our dataset, which is higher than other models. © 2022 IEEE.

关键词： Speech recognition

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