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2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Li, Yangding Fu, Shaobin Feng, Hao Zeng, Yangyang Wang, Jinghao Jiang, Zhihao Zhang, Lvyun Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University School of Information Science and Engineering Changsha China Shenzhen Lazada Software Technology Company Limited Shenzhen China Hechi University School of Big Data and Computer Science Guangxi China

ISBN: (纸本)9798350312492

Existing methods for modeling recommendation systems based on knowledge graphs include embedding-based, pathbased, and propagation-based methods. The embedding-based approach is flexible but more suitable for intra-graph applications, the path-based approach can model complex relationships but has a high computational cost, and the propagation-based approach considers global information but may introduce noise. This study proposed a simple and efficient model, called SEKGAT, which comprehensive the ideology of path-based and propagation approach to personalized recommendation by aggregating the user preferences through graph attention mechanism and fusing multiple feature representations on the knowledge graph into item features through pooling aggregators. Experimental results for the CTR prediction and Top-K recommendation tasks on three datasets of real-world scenarios show that our model approach is competitive. © 2023 IEEE.

关键词： Recommender systems

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ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling 41

ESM All-Atom: Multi-scale Protein Language Model for Unified...

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41st International Conference on Machine Learning, ICML 2024

作者： Zheng, Kangjie Long, Siyu Lu, Tianyu Yang, Junwei Dai, Xinyu Zhang, Ming Nie, Zaiqing Ma, Wei-Ying Zhou, Hao School of Computer Science National Key Laboratory for Multimedia Information Processing Peking University Anker Embodied AI Lab Peking University Beijing100871 China School of Artificial Intelligence National Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science Tsinghua University China Tsinghua University China PharMolix Inc. China

Protein language models have demonstrated significant potential in the field of protein ***, current protein language models primarily operate at the residue scale, which limits their ability to provide information at the atom *** limitation prevents us from fully exploiting the capabilities of protein language models for applications involving both proteins and small *** this paper, we propose ESM-AA (ESM All-Atom), a novel approach that enables atom-scale and residue-scale unified molecular ***-AA achieves this by pretraining on multi-scale code-switch protein sequences and utilizing a multi-scale position encoding to capture relationships among residues and *** results indicate that ESM-AA surpasses previous methods in protein-molecule tasks, demonstrating the full utilization of protein language *** investigations reveal that through unified molecular modeling, ESM-AA not only gains molecular knowledge but also retains its understanding of proteins. Copyright 2024 by the author(s)

关键词： Molecular docking

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Federated Learning with Common Representation Learning Criterion and Personalized Predictor

Federated Learning with Common Representation Learning Crite...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Wenzhong Xie, Zaipeng Yu, Bingzhe Qu, Zhihao Zhang, Yufeng Cao, Hongli Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China College of Computer and Information Hohai University Nanjing China Southeast University Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Nanjing China

ISBN: (纸本)9798350337020

Federated learning (FL) enables model training on decentralized devices while preserving data privacy. However, data heterogeneity poses a significant challenge to FL, and various approaches have been proposed to address it. Existing research has mainly focused on either enhancing global models or customizing personalized models for clients. This paper proposes a novel approach, FedCRC, that decouples the machine learning model into a representation extractor and predictor. This enables us to enhance both generalization and personalization, thereby addressing the challenge of data heterogeneity in FL. The approach employs a stable global predictor to unify the representation learning criterion during the training of the representation extractor. Additionally, a personalized predictor is trained for each client to achieve a personalized model tailored to the local data distribution. Our FedCRC algorithm was evaluated on multiple benchmark datasets with varying distributions, covering diverse settings. Extensive experimental results demonstrate the effectiveness of our method. © 2023 IEEE.

关键词： Data Heterogeneity Federated Learning Personalized Predictor Representation Learning

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PC-BNA: Parallel Convolution Binary Neural Network Accelerator 24

PC-BNA: Parallel Convolution Binary Neural Network Accelerat...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Liu, Haihui Zhang, Yuang Zheng, Xiangwei Yu, Mengxin An, Baoxu Liu, Xiaojie School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (纸本)9798350319934

Binary neural network (BNN) is widely used in speech recognition, image processing and other fields to save memory and speed up computing. However, the accuracy of the existing binarization scheme in the realistic dataset is obviously low, and the input layer uses 32-bit floating point to avoid excessive precision loss, which requires additional computing units and increases the computational burden. Therefore, it is very important to improve the input layer, save computing resources and reduce precision loss. In this paper, we propose a parallel convolution binary neural network accelerator architecture (PC-BNA). Based on our proposed BNN model, we design an efficient field programmable gate array (FPGA) based accelerator. The input of the first layer is binarized, the traditional binary convolution layer is replaced by a parallel binary convolution, and the network building blocks are improved. The experimental results show that the proposed PC-BNA has higher accuracy and better performance on CIFAR-10. The image recognition accuracy is as high as 91.4%, which is superior to the state-of-art of the BNN accelerator. Compared to the state-of-the-art BNN model, when using the same model size, the use of look-up-table (LUT) saves 9.08%, and the digital signal processor (DSP) saves 27.7%. The results suggest that it is promising for PC-BNA in future high performance mobile artificial intelligence applications. © 2022 IEEE.

关键词： Convolution

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Iteratively Refined Behavior Regularization for Offline Reinforcement Learning 38

Iteratively Refined Behavior Regularization for Offline Rein...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Ma, Yi Hao, Jianye Hu, Xiaohan Zheng, Yan Xiao, Chenjun School of Computer and Information Technology Shanxi University China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education China College of Intelligence and Computing Tianjin University China Noah's Ark Lab Huawei Canada The Chinese University of Hong Kong Shenzhen China

One of the fundamental challenges for offline reinforcement learning (RL) is ensuring robustness to data distribution. Whether the data originates from a near-optimal policy or not, we anticipate that an algorithm should demonstrate its ability to learn an effective control policy that seamlessly aligns with the inherent distribution of offline data. Unfortunately, behavior regularization, a simple yet effective offline RL algorithm, tends to struggle in this regard. In this paper, we propose a new algorithm that substantially enhances behavior-regularization based on conservative policy iteration. Our key observation is that by iteratively refining the reference policy used for behavior regularization, conservative policy update guarantees gradually improvement, while also implicitly avoiding querying out-of-sample actions to prevent catastrophic learning failures. We prove that in the tabular setting this algorithm is capable of learning the optimal policy covered by the offline dataset, commonly referred to as the in-sample optimal policy. We then explore several implementation details of the algorithm when function approximations are applied. The resulting algorithm is easy to implement, requiring only a few lines of code modification to existing methods. Experimental results on the D4RL benchmark indicate that our method outperforms previous state-of-the-art baselines in most tasks, clearly demonstrate its superiority over behavior regularization. © 2024 Neural information processing systems foundation. All rights reserved.

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GC-SALM: Multi-Task Runoff Prediction Using Spatial-Temporal Attention Graph Convolution Networks

GC-SALM: Multi-Task Runoff Prediction Using Spatial-Temporal...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Lu, Jin Xie, Zaipeng Chen, Jiayu Li, Maohua Xu, Chenghong Cao, Hongli Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China College of Computer and Information Hohai University Nanjing China Southeast University Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Nanjing China

ISBN: (纸本)9798350337020

Runoff prediction is essential for flood forecasting, irrigation planning, and sustainable water resource management. However, accurate predictions can be challenging due to the involvement of multiple variables. This paper presents a novel Graph Convolution-based Spatial-temporal Attention LSTM Multi-Task learning (GC-SALM) model for accurate runoff predictions. Our approach combines a multilayer neural network and an attention mechanism for enhanced generalization performance. The GC-SALM model employs spatial attention and graph convolutional networks to discern local and global spatial patterns, while temporal attention and LSTM are utilized to capture temporal characteristics within extended sequences. Experimental results reveal that the proposed model outperforms six state-of-the-art methods in runoff prediction and flow calibration, emphasizing its potential for real-world hydrological applications. © 2023 IEEE.

关键词： Graph convolutional networks Multi-Task Learning Runoff prediction Spatial and temporal modeling

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Koebe Type Theorems and Pre-Schwarzian of Harmonic K-quasiconformal Mappings,and Their Applications

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Acta Mathematica Sinica,English Series 2022年第11期38卷 1965-1980页

作者： Shao Lin CHEN Saminathan PONNUSAMY College of Mathematics and Statistics Hengyang Normal UniversityHengyang 421002P.R.China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang 421002P.R.China Department of Mathematics Indian Institute of Technology MadrasChennai-600036India

In this article,we first establish an asymptotically sharp Koebe type covering theorem for harmonic K-quasiconformal *** we use it to obtain an asymptotically Koebe type distortion theorem,a coefficients estimate,a Lipschitz characteristic and a linear measure distortion theorem of harmonic K-quasiconformal *** last,we give some characterizations of the radial John disks with the help of pre-Schwarzian of harmonic mappings.

关键词： Harmonic K-quasiconformal mapping Koebe type covering theorem Koebe type distortion theorem Radial John disk

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A New Diagnosis and Treatment Platform for Autism Spectrum Disorder Based on Artificial Intelligence 2

A New Diagnosis and Treatment Platform for Autism Spectrum D...

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2nd International Conference on Digital Society and Intelligent Systems, DSInS 2022

作者： Zhang, Yuhuan Chen, Mo Lin, Qiwen Song, Yipin Wang, Miaomiao Xie, Yao Sun, Cuimin School of computer Electronics and Information Guangxi University Nanning China Guangxi Colleges Universities Key Laboratory of Multimedia Communications and Information Processing Nanning China School of Information Science and Technology University of Science and Technology of China Hefei China Anhui Tongling Bionic Technology Co. Ltd Hefei China

ISBN: (纸本)9781510663244

This paper aims to develop a new diagnosis and treatment platform for autism spectrum disorder based on artificial intelligence in view of the difficulties in the autism diagnosis and treatment industry in the market, such as a mix of institutions, shortage of talents and high treatment costs, as well as the shortcomings of related diagnosis and treatment digital platforms with single functions and low differentiation. The platform is mainly presented in the form of websites, apps and small programs. It integrates the functions of autism diagnosis, autism intervention, prognosis monitoring and other functions. It quantifies and visualizes the diagnosis data of autism through artificial intelligence technology, and provides an intelligent "stethoscope" for autism diagnosis, with a view to further advancing the diagnosis age of children. To achieve early detection, intervention and treatment of autism, so that children with autism have a better prognosis. Families will be connected with professional resources through artificial intelligence, and services between people will be connected through science and technology, allowing professional intervention from organizations to enter families. © 2023 SPIE.

关键词： Artificial intelligence

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Quality-Oriented Task Assignment for Heterogeneous Users in Mobile Crowdsensing 26

Quality-Oriented Task Assignment for Heterogeneous Users in ...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Chen, Kang Li, Peng Huang, Weiyi Nie, Lei Bao, Haizhou Liu, Qin Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China Wuhan University School of Cyber Science and Engineering China

ISBN: (纸本)9798350331684

Mobile crowdsensing (MCS) is a potential technology for large-scale data collection. This technology requires the platform to recruit users to complete tasks in specific areas. A vital issue in MCS is task assignment, and most existing task assignment efforts consider only a single user type, which is not reasonable in real scenarios. Task assignment becomes more complicated when the platform tries to assign tasks to heterogeneous users with a limited budget in the platform. In this paper, we consider a quality-oriented task assignment for heterogeneous users problem. Professional users have a high sensing quality with a high cost, and normal users have a low sensing quality with a low cost First, we model the sensing capabilities and the costs of different users and formulate the quality-oriented task assignment for heterogeneous users problem. Then, we design the integer linear programming form and prove that the problem is NP-hard. By verifying the submodularity of the objective function, we present a greedy algorithm. Considering the inefficiency of the algorithm, we design two genetic algorithms to improve the total sensing quality. Finally, we evaluate the proposed algorithms under different cost cases based on a real dataset The results show that our proposed algorithm performs well under different cost distribution scenarios. © 2023 IEEE.

关键词： Genetic algorithms

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An Efficient Threshold Ring Signature from G+G Identification Protocol 2nd

An Efficient Threshold Ring Signature from G+G Identificati...

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2nd International Conference on Data Security and Privacy Protection, DSPP 2024

作者： Wu, Chunhui Jiang, Siwei Zhang, Fangguo Du, Yusong Lin, Qiping School of Internet Finance and Information Engineering Guangdong University of Finance Guangzhou510521 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Provincial Key Laboratory of Information Security Technology Guangzhou510006 China Xingtang Telecommunication Technology Co. Ltd. Beijing100191 China

ISBN: (纸本)9789819785391

Threshold ring signature confirms that t signers are participating in the signature and keep them anonymous. It is a generalization of ring signature, and has many important applications such as whistleblower, e-voting and blockchain et al. In this paper, an improved lattice-based ring signature and threshold ring signature scheme from Fiat-Shamir heuristic are proposed. The schemes eliminte the dependence on Stern-like identification protocols and have much shorter signature sizes. We also use the Gaussian convolution technique (G+G) presented by Devevey et al. in Asiacrypt 2023 to remove the reject-sampling in BLISS. This allows the threshold ring signature to overcome the proof issue caused by "abort" and achieve higher computation efficiency than previous ones. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication Protocol

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