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Artificial Intelligence and Autonomous Robot Systems (AIARS), International Conference on

作者： Dong Wang Pianhui Wu Big Data and Computing Institute Guangdong Baiyun University Guangzhou China School of Mechatornic Engineering Guangdong Polytechnic Normal University Guangzhou China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

In recent years, there has been a noticeable rise in traffic accidents attributed to driver fatigue, sparking increased interest among researchers. Current research on fatigue driving is broadly categorized into two approaches: active detection and passive detection. Passive detection involves the use of technological tools to unobtrusively monitor physical signs of fatigue. This approach is favored over active detection for several reasons, including its higher precision, enhanced dependability, and independence from the driver's personal judgment. Passive detection has become the predominant focus in the study of driver fatigue monitoring. This paper presents a comprehensive review of the latest advancements in passive driver fatigue detection technologies over the last few years. It highlights key passive detection techniques, thoughtfully assesses the merits and drawbacks of various fatigue monitoring strategies, and concludes by suggesting potential areas for future research in fatigue detection.

关键词： Reviews Fatigue Feature extraction Monitoring Autonomous robots Accidents

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TrustedBench: An Efficient and User-friendly Distributed Performance Testing Tool for Blockchain System

TrustedBench: An Efficient and User-friendly Distributed Per...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Yang Cheng Kai Wei Yihui Zhang Chunyu Jiang Weiwei Pang Qi Zhang Bin Liu Lifeng Zhang Tingting Liu Yinqian Wu Cloud. Computing and Big Data Research Institute China Academy of Information and Communications Technology Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Performance is a crucial indicator for the blockchain system evaluation. However, it is difficult to measure and comprehensively evaluate the performance of blockchain systems, particularly due to their complexity and diversity. Based on the comprehensive analysis of common blockchain systems and existing performance testing tools, this paper proposes a novel efficient and user-friendly performance testing tool for blockchain systems named TrustedBench. This tool supports all-round analysis and testing, and provides a powerful tool for understanding and analyzing the performance of blockchain systems. The architecture design, test execution process and important performance indicators of blockchain systems are introduced, and their calculation methods are further expounded. Finally, the overall results from the implementation of TrustedBench are presented and interpreted, revealing the operation and performance characteristics of some different blockchain products. With this tool, users can have a more comprehensive understanding of the performance of blockchain systems. Furthermore, TrustedBench effectively enhances the efficiency of the blockchain platform in many real-world scenarios.

关键词： Measurement Privacy System performance Computer architecture Blockchains Complexity theory Security

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A Cooperative Flow Simulator for Distributed computing Based on Full-Dimensional Definable Network 4

A Cooperative Flow Simulator for Distributed Computing Based...

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4th International Conference on data-Driven Optimization of Complex Systems, DOCS 2022

作者： Liang, Yuan Xiao, Geyang Yao, Shaofeng Wang, Hongsheng Yi, Xiaoyu Tu, Yonggang Intelligent Network Research Institute Zhejiang Lab Hangzhou China Intelligent Computing Institute Zhejiang Lab Hangzhou China Jiaxing Big Data Center Jiaxing Municipal Peoples Government Jiaxing China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

To improve the efficiency of cluster computing, the research of cooperative flow scheduling algorithms provides another idea for further optimization. Cluster computing frameworks such as Spark and Map-Reduce carry running cluster computing tasks and have distinct stage division and dependency characteristics. With limited network resources, the overall completion time of the job can be reduced by minimizing the wait time of the computing phase due to the bottleneck of transmission capacity. The introduction of an artificial intelligence algorithm can provide a solution to this NP-hard problem. The simulator designed in this paper can realize the overall process of cooperative traffic generation, scheduling policy configuration, and scheduling effect feedback closed-loop through packet sender, packet receiver, scheduler, and programmable switch components according to the dependency between cluster computing stages. © 2022 IEEE.

关键词： Cluster computing

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A Person-job Matching Method Based on BM25 and Pre-trained Language Model 23

A Person-job Matching Method Based on BM25 and Pre-trained L...

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Proceedings of the 2023 6th International Conference on Machine Learning and Natural Language Processing

作者： Jiaqi Tang Heyun Chen Zifan Chen Jiahua Pan Yijiang He Jie Zhao Zhen Li PKU-Changsha Institute for Computing and Digital Economy China Center for Data Science Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China

ISBN: (纸本)9798400709241

To address the inefficiency of traditional methods for person-job matching and the lack of interpretability of deep learning approaches, a novel approach for person-job matching based on BM25 and pre-trained language models is proposed. First, the BM25 algorithm is utilized to perform an initial screening of resumes based on job descriptions. By calculating the matching degree of keywords, it rapidly filters out candidate resumes that do not align with the job requirements. Subsequently, the pre-trained language model BERT is employed to perform fine-grained sorting of the resumes that passed the initial screening, identifying the candidates that best match with the job. By combining the screening capability of the BM25 algorithm with the semantic understanding ability of pre-trained language models, this method enhances the accuracy and interpretability of person-job matching results while maintaining matching speed. Furthermore, to improve model generalization, large-scale language models are employed for data augmentation. Experimental results indicate the significant potential for the proposed approach in practical applications, offering improved solutions for the challenges of talent recruitment across diverse job demands.

关键词： BM25 data augmentation large language model person-job matching pre-trained language model

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RAGRAPH: A General Retrieval-Augmented Graph Learning Framework 38

RAGRAPH: A General Retrieval-Augmented Graph Learning Framew...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jiang, Xinke Qiu, Rihong Xu, Yongxin Zhang, Wentao Zhu, Yichen Zhang, Ruizhe Fang, Yuchen Chu, Xu Zhao, Junfeng Wang, Yasha School of Computer Science Peking University China University of Electronic Science and Technology of China China Center on Frontiers of Computing Studies Peking University Beijing China Big Data Technology Research Center Nanhu Laboratory Jiaxing China Peking University Information Technology Institute Binhai Tianjin China

Graph Neural Networks (GNNs) have become essential in interpreting relational data across various domains, yet, they often struggle to generalize to unseen graph data that differs markedly from training instances. In this paper, we introduce a novel framework called General Retrieval-Augmented Graph Learning (RAGRAPH), which brings external graph data into the general graph foundation model to improve model generalization on unseen scenarios. On the top of our framework is a toy graph vector library that we established, which captures key attributes, such as features and task-specific label information. During inference, the RAGRAPH adeptly retrieves similar toy graphs based on key similarities in downstream tasks, integrating the retrieved data to enrich the learning context via the message-passing prompting mechanism. Our extensive experimental evaluations demonstrate that RAGRAPH significantly outperforms state-of-the-art graph learning methods in multiple tasks such as node classification, link prediction, and graph classification across both dynamic and static datasets. Furthermore, extensive testing confirms that RAGRAPH consistently maintains high performance without the need for task-specific fine-tuning, highlighting its adaptability, robustness, and broad applicability. © 2024 Neural information processing systems foundation. All rights reserved.

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Frequency Fitness Assignment: Optimization Without Bias for Good Solution Outperforms Randomized Local Search on the Quadratic Assignment Problem 16th

Frequency Fitness Assignment: Optimization Without Bias for ...

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16th International Joint Conference on Computational Intelligence, IJCCI 2024

作者： Chen, Jiayang Wu, Zhize Thomson, Sarah L. Weise, Thomas Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Jinxiu Dadao 99 Anhui Hefei230601 China School of Computing Engineering & the Built Environment Edinburgh Napier University 10 Colinton Road EdinburghEH10 5DT United Kingdom

ISBN: (纸本)9789897587214

The Quadratic Assignment Problem (QAP) is one of the classical N P-hard tasks from operations research with a history of more than 65 years. It is often approached with heuristic algorithms and over the years, a multitude of such methods has been applied. All of them have in common that they tend to prefer better solutions over worse ones. We approach the QAP with Frequency Fitness Assignment (FFA), an algorithm module that can be plugged into arbitrary iterative heuristics and that removes this bias. One would expect that a heuristic that does not care whether a new solution is better or worse compared to the current one should not perform very well. We plug FFA into a simple randomized local search (RLS) and yield the FRLS, which surprisingly outperforms RLS on the vast majority of the instances of the well-known QAPLIB benchmark set. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Heuristic algorithms

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A Novel Effectiveness Assessment Framework for Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer Based on Multi-modal Intelligence

A Novel Effectiveness Assessment Framework for Neoadjuvant C...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Tian, Xiao Sui, Dong Liu, Weifeng Guo, Maozu Luo, Gongning Wang, Kuanquan Beijing Key Laboratory for Intelligent Processing Methods of Architectural Big Data Beijing China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China Perceptual Computing Research Center Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350337488

Neoadjuvant chemoradiotherapy (nCRT) is the standard treatment for locally advanced rectal cancer (LARC). With the development of artificial intelligence, an increasing number of studies have begun to explore its application in cancer treatment prediction. However, the prior methods exhibit considerable variability even with slight modifications to the input data, which could potentially undermine the reliability of the results. In this paper, we proposed RP-Net, a novel multi-modal fusion-based framework that combines feature information from magnetic resonance imaging (MRI) and whole slide images (WSI), establishing a relationship to map the therapeutic effectiveness of nCRT for LARC. We investigated the relationship of the tumour region and its periphery tissues, and demonstrated the validity of the proposed framework that involving 11 different combinations of modalities. The experimental results revealed that it has achieved higher prediction accuracy compared to the four intra-categories single-modal combinations and outperformed the two intra-categories multi-modal combinations. When compared to the other four inter-categories multi-modal combinations, the fusion features get accuracy of 2% ∼ 6% improvement respectively. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Machine Learning and Synthetic Minority Oversampling Techniques for Imbalanced data: Improving Machine Failure Prediction

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Computers, Materials & Continua 2023年第6期75卷 4821-4841页

作者： Yap Bee Wah Azlan Ismail Nur Niswah Naslina Azid Jafreezal Jaafar Izzatdin Abdul Aziz Mohd Hilmi Hasan Jasni Mohamad Zain Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARA(UiTM)Shah Alam40450SelangorMalaysia School of Computing Sciences College of ComputingInformatics and MediaUniversiti Teknologi MARA(UiTM)40450Shah AlamSelangorMalaysia Mathematical Sciences Studies College of ComputingInformatics and MediaUniversiti Teknologi MARA(UiTM)Kelantan BranchMachang CampusBukit Ilmu18500MachangKelantan Darul NaimMalaysia Centre for Research in Data Science(CeRDaS) Department of Computer and Information Sciences(DCIS)Universiti Teknologi PETRONAS(UTP)Seri Iskandar32610PerakMalaysia UNITAR International University Jalan SS6/3SS6Petaling Jaya47301SelangorMalaysia

Prediction of machine failure is challenging as the dataset is often imbalanced with a low failure *** common approach to han-dle classification involving imbalanced data is to balance the data using a sampling approach such as random undersampling,random oversampling,or Synthetic Minority Oversampling Technique(SMOTE)*** paper compared the classification performance of three popular classifiers(Logistic Regression,Gaussian Naïve Bayes,and Support Vector Machine)in predicting machine failure in the Oil and Gas *** original machine failure dataset consists of 20,473 hourly data and is imbalanced with 19945(97%)‘non-failure’and 528(3%)‘failure data’.The three independent variables to predict machine failure were pressure indicator,flow indicator,and level *** accuracy of the classifiers is very high and close to 100%,but the sensitivity of all classifiers using the original dataset was close to *** performance of the three classifiers was then evaluated for data with different imbalance rates(10%to 50%)generated from the original data using SMOTE,SMOTE-Support Vector Machine(SMOTE-SVM)and SMOTE-Edited Nearest Neighbour(SMOTE-ENN).The classifiers were evaluated based on improvement in sensitivity and *** showed that the sensitivity of all classifiers increases as the imbalance rate *** with radial basis function(RBF)kernel has the highest sensitivity when data is balanced(50:50)using SMOTE(Sensitivitytest=0.5686,Ftest=0.6927)compared to Naïve Bayes(Sensitivitytest=0.4033,Ftest=0.6218)and Logistic Regression(Sensitivitytest=0.4194,Ftest=0.621).Overall,the Gaussian Naïve Bayes model consistently improves sensitivity and F-measure as the imbalance ratio increases,but the sensitivity is below 50%.The classifiers performed better when data was balanced using SMOTE-SVM compared to SMOTE and SMOTE-ENN.

关键词： Machine failure machine learning imbalanced data SMOTE classification

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Protein Structure Prediction:Challenges,Advances,and the Shift of Research Paradigms

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Genomics, Proteomics & Bioinformatics 2023年第5期21卷 913-925页

作者： Bin Huang Lupeng Kong Chao Wang Fusong Ju Qi Zhang Jianwei Zhu Tiansu Gong Haicang Zhang Chungong Yu Wei-Mou Zheng Dongbo Bu Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Changping Laboratory Beijing 102206China Microsoft Research AI4Science Beijing 100080China Huawei Noah’s Ark Lab Wuhan 430206China Zhongke Big Data Academy Zhengzhou 450046China Institute of Theoretical Physics Chinese Academy of SciencesBeijing 100190China

Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields,including biochemistry,medicine,physics,mathematics,and computer *** researchers adopt various research paradigms to attack the same structure prediction problem:biochemists and physicists attempt to reveal the principles governing protein folding;mathematicians,especially statisticians,usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure,while computer scientists formulate protein structure prediction as an optimization problem-finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native *** research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman,namely,data modeling and algorithmic ***,we have also witnessed the great success of deep learning in protein structure *** this review,we present a survey of the efforts for protein structure *** compare the research paradigms adopted by researchers from different fields,with an emphasis on the shift of research paradigms in the era of deep *** short,the algorithmic modeling techniques,especially deep neural networks,have considerably improved the accuracy of protein structure prediction;however,theories interpreting the neural networks and knowledge on protein folding are still highly desired.

关键词： Protein folding Protein structure prediction Deep learning Transformer Language model

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Proximity-induced magnetic order in topological insulator on ferromagnetic semiconductor

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Science China(Information Sciences) 2023年第12期66卷 267-274页

作者： Hangtian WANG Koichi MURATA Weiran XIE Jing LI Jie ZHANG Kang L.WANG Weisheng ZHAO Tianxiao NIE School of Integrated Circuit Science and Engineering and Advanced Innovation Center for Big Data and Brain Computing Beihang University Beihang-Goertek Joint Microelectronics Institute Qingdao Research InstituteBeihang University Department of Electrical Engineering University of California

Introducing magnetic order into topological insulator（TI） to break the time-reversal symmetry can yield numerous fascinating physical phenomena,which brings new hope for the emerging spintronic *** proximity effect is regarded as a promising strategy that could advance the step for realistic application by choosing a suitable ferromagnetic layer with the merits of high Curie temperature and high compatibility with mainstream semiconductor ***,we prepare a Bi2Se3thin film on Sicompatible ferromagnetic semiconductor（FMS） of MnxGe1-xby molecular beam *** integration,the nonmagnetic Bi2Se3exhibits an anomalous Hall signal and a clear weak localization cusp in magnetoresistance until 150 K,confirming that a high-temperature magnetism can be induced by the proximity *** investigation of the magnetoconductance quantitatively indicates that the Bi2Se3conductance suffers a transition from weak antilocalization to weak localization behavior after integrating with MnxGe1-x,and an 80 meV bandgap is predicted to be opened in the surface states in Bi2Se3layer due to the proximityinduced *** results prove that the proximity effect could be an important method to achieve topological magnetism at high temperatures,and reveals its potential for the manipulation of the topological surface states.

关键词： topological insulator ferromagnetic semiconductor proximity effect quantum interference

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