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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yufei Song Ziqi Zhou Minghui Li Xianlong Wang Hangtao Zhang Menghao Deng Wei Wan Shengshan Hu Leo Yu Zhang Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Computer Science and Technology Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the rapid advancement of deep learning, the model robustness has become a significant research hotspot, i.e., adversarial attacks on deep neural networks. Existing works primarily focus on image classification tasks, aiming to alter the model’s predicted labels. Due to the output complexity and deeper network architectures, research on adversarial examples for segmentation models is still limited, particularly for universal adversarial perturbations. In this paper, we propose a novel universal adversarial attack method designed for segmentation models, which includes dual feature separation and low-frequency scattering modules. The two modules guide the training of adversarial examples in the pixel and frequency space, respectively. Experiments demonstrate that our method achieves high attack success rates surpassing the state-of-the-art methods, and exhibits strong transferability across different models.

关键词： Training Image segmentation Perturbation methods Semantics Scattering Signal processing Predictive models Network architecture Robustness Speech processing

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科学研究的第五范式——以智能驱动的材料设计为例

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Engineering 2023年第5期24卷 126-137,I0003,I0004页

作者： Can Leng Zhuo Tang Yi-Ge Zhou Zean Tian Wei-Qing Huang Jie Liu Keqin Li Kenli Li Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China National Supercomputing Center in Changsha Changsha 410082China College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China Institute of Chemical Biology and Nanomedicine State Key Laboratory of Chemo/Biosensing and ChemometricsCollege of Chemistry and Chemical EngineeringHunan UniversityChangsha 410082China Department of Applied Physics School of Physics and ElectronicsHunan UniversityChangsha 410082China Department of Computer Science State University of New YorkNew PaltzNY 12561USA

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地... 详细信息

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地阐明了第五范式的本质,旨在促进第五范式在其他领域的培养。第五范式平台主要包括模型自动构建(原始数据提取)、指纹自动构建(神经网络特征选择)以及跨学科知识串联的重复迭代(“火山图”)。与分解一起进行的是对迭代中实现的体系结构的性能评估。通过讨论,第五范式的智能驱动平台可以极大地简化和改进研究中极其繁琐和具有挑战性的工作,并通过补偿机器学习中缺少样本和替代一些由于计算资源不足而导致的数值计算来实现数值计算与机器学习之间的相互反馈,从而加速探索过程。在数据驱动的学科中,跨学科专家的协同作用和对动态数据需求的急剧增长仍然是一个挑战。我们相信,对第五范式平台的一瞥可以为其在其他领域的应用铺平道路。

关键词：机器学习自动构建天河一号数据驱动神经网络动态数据知识整合科学研究

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Research on Assistant Diagnostic Method of TCM Based on BERT 11

Research on Assistant Diagnostic Method of TCM Based on BERT

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11th International Conference on Information Technology in Medicine and Education, ITME 2021

作者： Xu, Chuanjie Yuan, Feng Chen, Shouqiang Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China Shandong Management University School of Information Engineering Jinan China Ctr. of Hear of the Second Affil. Hospital of Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (纸本)9781665406796

Traditional Chinese medicine (TCM) auxiliary diagnosis is a systematic diagnosis platform that uses computer modeling technology to assist TCM doctors in recording diseases, providing on time diagnoses, writing prescriptions, performing tele-medicine, and supporting medical teaching. This study proposes a Bidirectional Encoder Representations from Transformers TCM auxiliary diagnosis model using 20,000 items of TCM records. These records were collected from the outpatient clinic of the Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine. Specifically, our model aims to predict final diagnosis while taking TCM symptoms as inputs;for example, when we input relief of chest tightness but persistent tiredness and sluggishness, the model provides a diagnosis of chest paralysis. An experiment was conducted on these real-world Chinese medical data. Results show that our model achieves state-of-the-art performance. Hence, our proposed model can effectively use the information from the four diagnostic procedures in the TCM text. © 2021 IEEE.

关键词： Diagnosis

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Adaptive Intervention for Crowd Negative Emotional Contagion

Adaptive Intervention for Crowd Negative Emotional Contagion

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International Conference on computer Supported Cooperative Work in Design

作者： Yepeng Shi Guijuan Zhang Dianjie Lu Lei Lv Hong Liu Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

ISBN: (纸本)9781728165981

Negative emotion will spread among the crowd, and make people irrational. Intervention can reduce crowd panic by isolating the source of transmission or spreading positive emotions, which is an effective measure to ensure the safety of the crowd. However, it is still a very challenging problem that how to use appropriate intervention to maximize the influence to infected individuals while minimizing the cost of the intervention. To this end, we present an intervention optimization method for crowd emotional contagion (IO-CEC) to adaptively intervene in negative emotional contagion. First, we build a stochastic events based emotional contagion model (SEEC) to capture the dynamic evolution characteristics of crowd emotional contagion. In this model, we combine the SIS infection model and the Markov stochastic process to study the dynamic change of the number of infected individuals. Second, we propose a Hawkes process based intervention model (HIM) to describe the intensity of intervention. By using the Hawkes stochastic point process, two intervention intensity functions are defined to quantify the infection rate and recovery rate. Then, in order to improve the efficiency of intervention, we formulate the intervention optimization as a problem of maximizing the utility and use a heuristic algorithm to solve it. Finally, we implement a crowd emotional contagion simulation system to visualize the results of emotional intervention. Experimental results show that our method is effective and has great significance for guiding realistic emotional intervention.

关键词： Adaptation models Visualization Computational modeling Heuristic algorithms Stochastic processes Optimization methods Manuals

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Violence Detection In Videos via Motion-Guided Global and Local Views

Violence Detection In Videos via Motion-Guided Global and Lo...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Ning Su Lijuan Sun Yutong Gao Jingchen Wu Xu Wu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security BUPT School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China School of Computer Science (National Pilot Software Engineering School) BUPT

Video violence detection aims to locate the time window in which violent behavior occurs. Most methods focus on utilizing RGB features directly or only fusing RGB and audio features, ignoring the effective exploitation of motion information carried in optical flow. This lack of emphasis on motion information may impact the overall accuracy of violence detection. Moreover, we observe that videos contain strong local correlations, so it is insufficient to analyze only from a holistic perspective without capturing finer details. Therefore, in this paper, we design a novel Global-and-Local Cross-Modal Network (GL-CMN) for violence detection, which effectively integrates motion information and multi-granularity features from target videos. Specifically, we first propose a Motion-Guided Attention Module (MGAM) to obtain enhanced visual features by calibrating RGB features through optical flow features. Secondly, The enhanced features are simultaneously fed into two parallel branches of the network. The global branch fuses the visual and audio features into holistic representations. The local branch extracts multi-scale temporal dependencies through dilated convolutions. Experiments demonstrate that our method exhibits significant improvement compared to previous state-of-the-art methods on the XD-Violence dataset.

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RBCP: A Reputation-based Blockchain Consensus Protocol for Internet of Things

RBCP: A Reputation-based Blockchain Consensus Protocol for I...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Jiaze Shang Tianbo Lu Yingjie Cai Shuang Luo Zhaoxin Jin School of Computer Science (National Pilot Software Enginerring School) Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

The application of blockchain in the Internet of Things (IoT) solves the problem of centralization, enables data interaction of IoT nodes in untrusted environments. However, most of the blockchain consensus protocols are computationally expensive and poorly scalable, which cannot meet the current demand for low power consumption and low latency of IoT devices. Meanwhile, IoT nodes suffer from many malicious attacks. An effective reputation evaluation scheme is valuable for establishing a trusted, secure IoT environment with certain applications. In this paper, we propose RBCP, a novel reputation-based blockchain consensus protocol, and apply it to the IoT. The protocol divides IoT nodes into multiple regions, and consensus nodes in each region are selected based on real-time updated reputation values, which are used to participate in light PoW consensus and finally propose blocks. Then, we model the deposits and profits of the nodes as Stackelberg game and analyze the relationship between them. To evaluate our proposed protocol, we compare it with existing consensus protocols through several experiments. Our experimental results show that RBCP has advantages in terms of TPS, consensus time, and communication consumption, proving that it has low latency and less consensus time under the premise of sufficient security.

关键词： Time-frequency analysis Smart cities Scalability Games Real-time systems Consensus protocol Behavioral sciences

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Gt-Simnet: Improving Code Automatic Summarization Via Multi-Modal Similarity Networks

SSRN

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SSRN 2021年

作者： Gao, Xuejian Jiang, Xue Wu, Qiong Wang, Xiao Lyu, Chen Lyu, Lei Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology School of Information Science and Engineering Shandong Normal University Jinan250014 China

Code summarization aims to generate high-quality functional summaries of code snippets to improve the efficiency of program development and maintenance. It is a pressing challenge for code summarization models to capture more comprehensive code knowledge by integrating the feature correlations between the semantics and syntax of the code. In this paper, we propose a multi-modal similarity network based code summarization method: GT-SimNet. It proposes a novel code semantic modelling method based on a local API dependency graph (local-ADG), which exhibits an excellent ability to mask irrelevant semantics outside the current code snippet. For code feature fusion, GT-SimNet uses the SimNet network to calculate the correlation coefficients between local-ADG and AST nodes and perform fusion under the influence of the correlation coefficients. Finally, it completes the prediction of the target summary by the code generator. We conduct extensive experiments to evaluate the performance of GT-SimNet on two Java datasets. The results shows that GT-SimNet achieved BLEU scores of 38.73% and 39.22% on the two datasets, respectively, 1.58%~2.23% higher than the best existing baseline. Importantly, GT-SimNet reduces the BLEU scores by 20% after removing local-ADG. This indicates that local-ADG is effective for the semantic representation of the code. © 2021, The Authors. All rights reserved.

关键词： Semantics

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Reflections and Countermeasures on the Development of Secondary Vocational Teachers' Teaching Ability Based on AI and Students' Characteristics

Reflections and Countermeasures on the Development of Second...

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2020 International Conference on Artificial Intelligence and Education, ICAIE 2020

作者： Liu, Lining Zhang, Yongsheng Zhang, Penglong Sun, Wenqian Shandong Normal University School of Information Science and Engineering Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China

ISBN: (纸本)9781728166599

The arrival of the era of artificial intelligence has a great impact on the produce of relationship and mode, resulting in changes in the demand for talents. As an important place to train professional and technical talents, secondary vocational schools need teachers to make changes. Teachers should improve their own educational and teaching ability, and cultivate talents of high-quality, innovative and in line with the needs of social development. By analyzing the development status of artificial intelligence in the field of education and the current students' problems facing teachers (students' characteristics), this paper gives some ideals and suggestions on the development of professional ability of secondary vocational teachers in the era of artificial intelligence. © 2020 IEEE.

关键词： Students

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Research on Staged Practice Teaching System Based on Systems Engineering

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计算机教育 2021年第12期 51-55页

作者： Wanjiang Han Jincui Yang Tao Qi Pengfei Sun Xin Jin Jinpeng Chen Yongping Zhang School of Computer Science(National Pilot Software Engineering School)Beijing University of Posts and Telecommunication Key Laboratory of Trustworthy Distributed Computing and Service(BUPT)Ministry of EducationBeijing100876China

The practical teaching management system is an important factor to ensure the quality of *** training of systematic thinking needs a systematic teaching system,and a systematic process is a gradual process and a standardized *** paper discusses the process of establishing a standardized practice teaching system step by step through a progressive *** the same time,taking test practice as an example,it describes how the practice teaching system enables students to gradually adapt to and benefit from the management of the *** project practice,students not only enhance their practical ability,but also learn the standardized practice *** skills can help students adapt to the work pace of the enterprise faster,and make students’practical ability meet the requirements of the enterprise.

关键词： Practice Teaching System Test Practice Staged Practical Process Standardization System Practice Base

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Review of Semi-Structured Document Information Extraction Techniques Based on Deep Learning

Review of Semi-Structured Document Information Extraction Te...

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Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM), International Conference on

作者： Yangchun Li Wei Jiang Shouyou Song Chinese Academy of Cybersapce Studies School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Chinese Academy of Cybersapce Studies Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Chinese Academy of Cybersapce Studies Ministry of Education Beijing University of Posts and Telecommunications Beijing China

With the advent of global digital transformation, using an intelligent method based on deep learning to extract crucial information from semi-structured documents, as represented by various types of receipts and invoices, has emerged as an imperative measure to ensure business stability, data security, and improved work efficiency. This paper provides a detailed review on deep learning-based techniques for information extraction, with systematic introduction, hierarchical analysis, method comparison, and summary with expectations for future development. The review begins with a comprehensive explication of the defining characteristics of semi-structured documents, along with a detailed introduction to the research background, application areas, and technical challenges related to information extraction from semi-structured documents. Then the review extends to an overview of two developmental stages, i.e. the shift from traditional information extraction to deep learning-based information extraction, followed by discussion about technical architecture and method classification, which elaborates on key technologies in terms of typical datasets, detection and recognition, and information reduction. Lastly, paper summarizes the prospects and development in the field. Future research will focus on strengthening algorithm universal and lightweight, as well as improving information protection capabilities and the diversity of datasets.

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