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arXiv 2023年

作者： Li, Shuangli Zhou, Jingbo Liu, Ji Xu, Tong Chen, Enhong Xiong, Hui School of Computer Science and Technology University of Science and Technology of China Baidu Research China Business Intelligence Lab Baidu Research China Big Data Lab Baidu Research China School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology China

Finding multiple temporal relationships among locations can benefit a bunch of urban applications, such as dynamic offline advertising and smart public transport planning. While some efforts have been made on finding static relationships among locations, little attention is focused on studying time-aware location relationships. Indeed, abundant location-based human activities are time-varying and the availability of these data enables a new paradigm for understanding the dynamic relationships in a period among connective locations. To this end, we propose to study a new problem, namely multi-Temporal relationship inference among locations (Trial for short), where the major challenge is how to integrate dynamic and geographical influence under the relationship sparsity constraint. Specifically, we propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) with two collaborative components: spatially evolving graph convolution module (SEConv) and spatially evolving self-supervised learning strategy (SE-SSL). SEConv performs the intra-time aggregation and inter-time propagation to capture the multifaceted spatially evolving contexts from the view of location message passing. In addition, SE-SSL designs time-aware self-supervised learning tasks in a global-local manner with additional evolving constraint to enhance the location representation © 2023, CC BY.

关键词： Location

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A review of federated learning technology and its research progress in healthcare applications

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Applied Intelligence 2025年第10期55卷 1-30页

作者： Ma, Zezhong Ruhaiyem, Nur Intan Raihana Zhang, Meng Musa, Kamarul Imran Hanis, Tengku Muhammad Xiao, Tianyun Hua, Dianbo Li, Hao School of Computer Sciences Universiti Sains Malaysia Gelugor Malaysia Beijing Sitairui Cancer Data Analysis Joint Laboratory Beijing China School of Cyberspace Security (School of Cryptology) Hainan University Haikou China School of Medical Sciences Universiti Sains Malaysia Kelantan Malaysia Department of Community Medicine Faculty of Medicine Universiti Sultan Zainal Abidin Medical Campus Kuala Terengganu Malaysia Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Tangshan China College of Science North China University of Science and Technology Tangshan China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Tangshan China

data-driven medical applications, powered by big data and artificial intelligence, generate scalable models from extensive datasets. This innovation attracts both academia and industry, significantly enhancing healthcare quality while posing integration challenges. Federated learning emerges as a transformative approach in healthcare, facilitating collaborative machine learning while preserving data privacy. This article reviews research on federated learning in healthcare, utilizing the "Web of Science" database to examine development trends and application progress. Initially, the federated learning system is dissected, and its application methods are analyzed, summarizing 18 federated learning frameworks and 9 privacy-preserving methods suitable for federated learning, and exploring its limitations in healthcare applications. Subsequently, recent literature on federated learning in four major areas—disease diagnosis and risk assessment, medical image analysis, drug discovery and development, and disease management—is reviewed, summarizing the general process of applying Federated Learning in healthcare. Finally, the challenges faced by federated learning in the medical field and the solutions currently being explored by scholars are summarized. This article aims to comprehensively summarize the research progress and application trends of federated learning technology in the medical field, analyze its limitations and challenges, and anticipate its future development to further promote sustainable advancement in the healthcare industry.

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Neural Network Algorithm of Multi-team Game and Its application in Parallel-Link Communication Networks Flow Control 12th

Neural Network Algorithm of Multi-team Game and Its Applicat...

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12th EAI International Conference on Simulation Tools and Techniques, SIMUtools 2020

作者： Liu, Zixin Yang, Huawei Xiong, Lianglin School of Mathematics and Statistics Guizhou University of Finance and Economics Guiyang China Guizhou Key Laboratory of Big Data Statistics Analysis Guizhou University of Finance and Economics Guiyang China School of Big Data Application and Economics Guizhou University of Finance and Economics Guiyang China School of Mathematics and Computer Science Yunnan Minzu University Kunming China

ISBN: (纸本)9783030727918

This paper investigates the approximate calculation problem of noninferior Nash equilibrium (NNE) in multi-team game. Combined with variational inequalities theory, Nash equilibrium theory, and dynamic system theory, a projection neural network (PNN) algorithm for computing NNE of multi-team game with smooth payoff functions is derived. Utilizing stable theory, stability criteria of NNE in multi-team game are further given. As an application, a flow control model of parallel-link communication networks based on multi-team game and neural network algorithm is elaborated. Finally, a simulation result for two teams, two communication links, and two users in each team parallel-linkcommunication network is also given to illustrate the effectiveness of the PNN algorithm proposed in this paper. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2021.

关键词： Flow control

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Backdoor Defense via Deconfounded Representation Learning

arXiv

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arXiv 2023年

作者： Zhang, Zaixi Liu, Qi Wang, Zhicai Lu, Zepu Hu, Qingyong Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Hong Kong University of Science and Technology Hong Kong University of Science and Technology of China China

Deep neural networks (DNNs) are recently shown to be vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by injecting a few poisoned examples into the training dataset. While extensive efforts have been made to detect and remove backdoors from backdoored DNNs, it is still not clear whether a backdoor-free clean model can be directly obtained from poisoned datasets. In this paper, we first construct a causal graph to model the generation process of poisoned data and find that the backdoor attack acts as the confounder, which brings spurious associations between the input images and target labels, making the model predictions less reliable. Inspired by the causal understanding, we propose the Causality-inspired Backdoor Defense (CBD), to learn deconfounded representations for reliable classification. Specifically, a backdoored model is intentionally trained to capture the confounding effects. The other clean model dedicates to capturing the desired causal effects by minimizing the mutual information with the confounding representations from the backdoored model and employing a sample-wise re-weighting scheme. Extensive experiments on multiple benchmark datasets against 6 state-of-the-art attacks verify that our proposed defense method is effective in reducing backdoor threats while maintaining high accuracy in predicting benign samples. Further analysis shows that CBD can also resist potential adaptive attacks. The code is available at https://***/zaixizhang/CBD. © 2023, CC BY-NC-SA.

关键词： Deep neural networks

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Finding Simplex Items in data Streams

Finding Simplex Items in Data Streams

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International Conference on data Engineering

作者： Zhuochen Fan Jiarui Guo Xiaodong Li Tong Yang Yikai Zhao Yuhan Wu Bin Cui Yanwei Xu Steve Uhlig Gong Zhang School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University Beijing China Peng Cheng Laboratory Shenzhen China Theory Lab Central Research Institute 2012 Labs Huawei Technologies Co. Ltd. Hong Kong SAR China School of Electronic Engineering and Computer Science Queen Mary University of London London UK

In this paper, we propose a new type of item in data streams, called simplex items. Simplex items have frequencies in consecutive p windows that can be approximated by a polynomial of degree at most k, where k = 0, 1, 2. These low-order representable simplex items have a wide range of potential applications. For example, when k = 1, we can leverage these items whose frequency has obvious linear increase or decrease to speed up the running time of a class of machine learning models and detect network attacks such as distributed denial-of-service (DDoS), etc. To find k-degree simplex items in real time, we propose a novel sketch, namely X-Sketch, to accurately record simplex items in a compact space. The key idea of X-Sketch is to effectively filter out non-simplex items with less memory overhead, and then monitor the remaining potential simplex items and keep those items with more consecutive windows. We conduct extensive experiments, and the experimental results show that the F1 Score of X-Sketch is on average 68.6%, 57.9%, and 42.2% higher than the baseline solution for k = 0, 1, 2, respectively. Finally, we also provide a case study that applies X-Sketch to "accelerate" the two machine learning models through end-to-end experiments. We have released our source code at GitHub.

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Semi-supervised Segmentation and Quantization Algorithm for Infarct Size Measurement of Rat Heart Sections

Semi-supervised Segmentation and Quantization Algorithm for ...

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International Conference on computer and Communications (ICCC)

作者： Xiaoke Zhu Yulong Cai Xiaopan Chen Qi Hu Lingyun Dong Caihong Yuan School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

Segmentation of rat heart section images and infarct area quantization are essential components of infarct size measurement in processing data from animal experiments on ischemic heart disease. Previously, the task of measuring the infarct size of rat heart sections was performed manually by experienced researchers, which was very costly and time-consuming. In this paper, we propose a two-stage semi-supervised approach to solve the task of ischemic heart infarct size calculation in images of rat section images. The approach uses a semi-supervised semantic segmentation model and a semi-automated quantization algorithm to separate the differently stained regions of rat heart sections. We demonstrate the results of the approach on a rat heart section dataset, and experiments on this dataset emphasize the effectiveness of our approach compared to other methods.

关键词： Heart Training Quantization (signal) Semantic segmentation Manuals Rats Size measurement

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Modeling Offline Knowledge Evolution Effect for Online Knowledge Tracing

Modeling Offline Knowledge Evolution Effect for Online Knowl...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Xiaonan Zeng Shuanghong Shen Zhenya Huang Enhong Chen Yu Su Shijin Wang Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence Hefei China Hefei Comprehensive National Science Center School of Computer Science and Technology Hefei Normal University & Institute of Artificial Intelligence Hefei China State Key Laboratory of Cognitive Intelligence & iFLYTEK AI Research (Central China) iFLYTEK Co. Ltd Hefei China

Knowledge Tracing (KT) focuses on tracing stu-dents' evolving knowledge state in the process of question-answering, which has been widely applied to online learning systems. Existing KT methods leverage students' online question-answering logs to infer their knowledge state and predict their subsequent performance. However, these methods only focus on students' online behaviors without considering the fact that students' knowledge also evolves offline. Specifically, students have plenty of offline learning actions that also make changes in their knowledge state, which is called the offline knowledge evolution effect in this paper. However, these offline actions are not recorded by online learning systems, therefore leading to unreliable knowledge tracing results. To alleviate this problem, we propose a novel model named offline-aware knowledge tracing (OKT) that considers students' offline knowledge evolution for KT. In O KT, we introduce a novel concept named offline trait to represent student's offline learning action preference. Based on the preference, the model evaluates offline knowledge evolution, and apply the evolution to generate a new knowledge state for students' future performance prediction. To the end, we conduct experiments on three real-world benchmark datasets and demonstrate that OKT achieves state-of-the-art performance. Besides, the experimental results indicate that considering offline actions can help interpret the KT results.

关键词： Learning systems Predictive models Benchmark testing Behavioral sciences

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Point-Level and Set-Level Deep Representation Learning for Cross-Modal Person Re-identification

Point-Level and Set-Level Deep Representation Learning for C...

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International Conference on computer and Communications (ICCC)

作者： Jihui Hu Pengfei Ye Danyang Li Lingyun Dong Xiaopan Chen Xiaoke Zhu Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University

In practice, significant modality differences usually exist between visible and infrared images, which makes visible-infrared Person Re-Identification (VI-ReID) a challenging research task. Due to the existing influence of pose variation, background changes, and occlusion, there are often outlier samples within the set of images from the same person. These outlier samples can adversely affect the process of learning the cross-modal matching model. The existing VI-ReID methods mainly focus on learning cross-modal feature representation by using image-level discriminant constraints, i.e., the distance between the truly-matching cross-modal images should be smaller than that between wrong-matching cross-modal images. However, most of these methods ignore the adverse influence caused by outliers. To solve the above problems, we proposed a Point-level and Set-level Deep Representation Learning (PSDRL) approach for VI-ReID in this paper. By using the set-level constraint in the process of deep representation learning, the discrepancy between visible and infrared modalities can be decreased, and the adverse effect of outliers can be weakened. By employing the image-level constraint, the discriminability of the obtained deep feature representations can be improved. Extensive experiments are conducted on the publicly available cross-modal Person Re-Identification datasets, including SYSU-MM01 and RegDB. Experimental results demonstrate the effectiveness of the proposed approach.

关键词： Representation learning Training Image color analysis Computational modeling Task analysis

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Lightweight Transformer Network and Self-supervised Task for Kinship Verification

Lightweight Transformer Network and Self-supervised Task for...

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International Conference on computer and Communications (ICCC)

作者： Xiaoke Zhu Yunwei Li Danyang Li Lingyun Dong Xiaopan Chen School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

Kinship verification is one of the interesting and critical problems in computer vision research, with significant progress in the past decades. Meanwhile, Vision Transformer (VIT) has recently achieved impressive success in many domains, including object detection, image recognition, and semantic segmentation, among others. Most of the previous work on kinship verification are based on convolutional or recurrent neural networks. Compared with the local processing of images like convolutions, transformers can effectively understand and process images globally. However, due to overuse, there are many Transformer layers of fully connected layers, and VIT speed is still an issue. Therefore, in this paper, inspired by the recent success of Transformer models in vision tasks, we propose a Transformer-based kinship verification for training and optimizing kinship verification models. We first train the basic vision transformer (VIT-B) with 12 transformer layers, then we reduce the transformer layers to 6 layers, namely VIT-S (Small Vit) and 4 layers, namely VIT-T (Tiny Vit), to make a tradeoff between optimization accuracy and efficiency. As the first attempt to apply Transformer to the kinship verification task, it provides a feasible strategy for kinship research topics and verifies the effectiveness of the method in terms of the accuracy of the experimental results.

关键词： Training Knowledge engineering Recurrent neural networks Semantic segmentation Self-supervised learning Object detection Transformers

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Leveraging proficiency and preference for online Karaoke recommendation

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Frontiers of computer Science 2020年第2期14卷 273-290页

作者： Ming HE Hao GUO Guangyi LV Le WU Yong GE Enhong CHEN Haiping MA Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230027China School of Computer and Information Hefei University of TechnologyHefei 230026China Eller College of Management The University of ArizonaArizona 85721-0108USA iFlyTek Research Hefei 230026China

Recently,many online Karaoke(KTV)platforms have been released,where music lovers sing songs on these *** the meantime,the system automatically evaluates user proficiency according to their singing *** approximate songs to users can initialize singers5 participation and improve users,loyalty to these ***,this is not an easy task due to the unique characteristics of these ***,since users may be not achieving high scores evaluated by the system on their favorite songs,how to balance user preferences with user proficiency on singing for song recommendation is still ***,the sparsity of the user-song interaction behavior may greatly impact the recommendation *** solve the above two challenges,in this paper,we propose an informationfused song recommendation model by considering the unique characteristics of the singing ***,we first devise a pseudo-rating matrix by combing users’singing behavior and the system evaluations,thus users'preferences and proficiency are *** we mitigate the data sparsity problem by fusing users*and songs'rich information in the matrix factorization process of the pseudo-rating ***,extensive experimental results on a real-world dataset show the effectiveness of our proposed model.

关键词： KTV matrix factorization recommendation system

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