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International Conference on Secure software Integration and Reliability Improvement Companion (SSIRI-C)

作者： Xuesong Cai Junwei Wu East China Normal University Shanghai Development Center of Computer Software Technology Shanghai China Fudan University Shanghai Academy of Science & Technology Shanghai China

With the growing of hazardous chemicals transportation, supervision system for hazardous chemicals transportation is getting more and more attention. In China, supervisory system for hazardous chemicals is limited to tracking by Global Positioning System (GPS), positioning and simple data collection and judgments which cannot achieve a real sense of risk pre-warning. The paper develops a system architecture based on cyber-physical systems (CPS) for specific application requirements of vehicle rollover warning, and proposes a collaborative model for rollover pre-warning which considering both the conventional and the external environmental factors. The feasibility of the proposed CPS system architecture and collaborative model is then proved by designing and implementing a monitoring system for rollover warning of hazardous chemicals vehicle.

关键词： Vehicles Chemicals Real time systems Collaboration Roads Silicon compounds

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A dynamic hebbian learning algorithm for constructing e-learner communities

A dynamic hebbian learning algorithm for constructing e-lear...

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5th International Conference on Natural Computation, ICNC 2009

作者： Chen, Qinghua Jin, Jing Chen, Huaxi School of Electroincs and Information Technology City College Wenzhou University Wenzhou China Dept. Software Research and Development Microsoft Research Asia Beijing China Dept. Computer Science and Technology Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9780769537368

In order to solve the problem of "information overflow" in e-learning, an algorithm based on Hebbian learning law is proposed for constructing self-organized communities which can automatically group e-learners according to their learning interests. Unlike Altering methods, this algorithm takes into consideration of the distributed open environment of e-learning. This paper designed a peer-to-peer architecture and applied Hebbian learning law in constructing e-learner communities, avoiding the difficulty in calculating user similarity. Compared with traditional Hebbian learning based algorithm, this algorithm uses dynamic thresholds to address the problem of unilateral trust weight adjustment in extreme cases, and it also improves the trust weight adaptation and neighbor adjustment policies. Experimental results show that this algorithm achieves faster community construction speed and better scalability and stability. © 2009 IEEE.

关键词： Learning algorithms

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A study on high order discrete scheme for two-fluid model 17

A study on high order discrete scheme for two-fluid model

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17th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, NURETH 2017

作者： Hao, Zhang Yanhua, Yang State Nuclear Power Software Development Center National Energy Key Laboratory of Nuclear Power Software Shanghai Jiao Tong University South Park Beijing Future Science and Technology City Beijing102209 China

The two-fluid model is considered an advanced two-phase flow model, and it is used in many nuclear design and analysis codes. Most of these codes use upwind scheme to discretize the convection term, and it leads to strong numerical viscosity. The high order scheme, which is developed based on Godunov’s method, can effectively reduce this effect. The discrete scheme for convection flux can be obtained by using a reconstruction function, which is used to approximate the exact solution. In this paper, the piecewise linear function with a limiter, which is used to make the scheme keep TVD, is used as reconstruction function. The high order scheme is applied in two-fluid model, and the verification is carried out. Three numerical benchmark problems, which are faucet flow problem, one dimension sedimentation problem and two dimension sedimentation problem, are calculated. The phase volume fraction propagation is predicted, and the wave includes discontinue points and unsmooth points. The performance of upwind and high order schemes, which are van Leer, MC, min-mod and van Albada 2, is analyzed. The results show that high order schemes perform better than upwind, particularly near the discontinue points and unsmooth points, the numerical diffusion effect of high order scheme is less than upwind. However, in some cases, these schemes are easy to oscillating. © 2016 Association for Computing Machinery Inc. All Rights Reserved.

关键词： Two phase flow

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DeepTD: Diversity-Guided Deep Neural Network Test Generation 9th

DeepTD: Diversity-Guided Deep Neural Network Test Generation

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9th International Symposium on Dependable software Engineering: Theories, Tools and Applications, SETTA 2023

作者： Zhu, Jin Tao, Chuanqi Guo, Hongjing Ju, Yue College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing China State Key Laboratory for Novel Software Technology Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (纸本)9789819986637

Coverage-guided Fuzz Testing (CGF) techniques have been applied to deep neural network (DNN) testing in recent years, generating a significant number of test samples to uncover inherent defects in DNN models. However, the effectiveness of CGF techniques that utilize structured coverage metrics as coverage criteria is currently being questioned. A few unstructured coverage metrics, such as surprise adequacy, only take into account the diversity of the test samples against the training set, while ignoring the diversity of the test samples themselves. In addition to this, the existing surprise adequacy metrics have some limitations in their applications. Therefore, this paper proposes DeepTD, a diversity-guided deep neural networks test generation method. Firstly, DeepTD selects high-loss test samples from each class on average, ensuring these test seeds possess a strong ability to reveal model errors. Then, DeepTD transforms these test seeds to enhance the diversity of the generated samples. Finally, Cluster-based Surprise Adequacy is designed to guide the generation of test samples. To evaluate the effectiveness of DeepTD, six DNN models are selected as subjects, covering two well-known image datasets. Experimental results demonstrate that the Cluster-based Surprise Adequacy outperforms the two existing metrics not only in computational efficiency but also in discovering more model defects. What’s more, the test samples generated by DeepTD are on average 6.04% and 3.24% more effective for model retraining in MNIST and CIFAR10 compared to baseline methods, respectively. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Coverage-guided fuzz testing Deep neural network Test generation Test samples diversity

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CMCF-Net: An End-to-End Context Multiscale Cross-Fusion Network for Robust Copy-Move Forgery Detection

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IEEE Transactions on Multimedia 2024年 26卷 6090-6101页

作者： Xiong, Lizhi Xu, Jianhua Yang, Ching-Nung Zhang, Xinpeng Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Nanjing University of Information Science and Technology Software Engineering Nanjing210044 China National Dong Hwa University Department of Computer Science and Information Engineering Hualien974301 Taiwan Fudan University School of Computer Science Shanghai200433 China

Image copy-move forgery detection (CMFD) has become a challenging problem due to increasingly powerful editing software that makes forged images increasingly realistic. Existing algorithms that directly connect multiple scales of features in the encoder part may not effectively aggregate contextual information, resulting in poor performance. In this paper, an end-to-end context multiscale cross-fusion network (CMCF-Net) is proposed to detect image copy-move forgery. The proposed network consists of a multiscale feature extraction fusion (MSF) module and a multi-information fusion decoding (MFD) module. Multiscale information is efficiently extracted and fused in the MSF module utilizing stacked-scale feature fusion, which improves the network's forgery localization ability on objects of different scales. The MFD module employs contextual information combination and weighted fusion of multiscale information to guide the network in obtaining relevant clues from correlated information at multiple different scales. Experimental results and analysis have demonstrated that the proposed CMCF-Net achieves the best localization results with higher robustness. © 1999-2012 IEEE.

关键词： Feature extraction

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Genome assembly and transcriptome analysis provide insights into the antischistosome mechanism of Microtus fortis

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Journal of Genetics and Genomics 2020年第12期47卷 743-755页

作者： Hong Li Zhen Wang Shumei Chai Xiong Bai Guohui Ding Yuanyuan Li Junyi Li Qingyu Xiao Benpeng Miao Weili Lin Jie Feng Mingyue Huang Cheng Gao Bin Li Wei Hu Jiaojiao Lin Zhiqiang Fu Jianyun Xie Yixue Li Bio-Med Big Data Center CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China Shanghai Veterinary Research Institute Chinese Academy of Agricultural SciencesKey Laboratory of Animal ParasitologyMinistry of AgricultureShanghai 200241China Shanghai Laboratory Animal Research Center Shanghai 201203China Institute for Digital Health International Human Phenome Institutes(Shanghai)Shanghai 200433China Shanghai Center for Bioinformation Technology Shanghai Academy of Science and TechnologyShanghai 201203China School of Computer Science and Technology Harbin Institute of Technology(Shenzhen)ShenzhenGuangdong 518055China Shanghai Institute of Immunology Department of Immunology and MicrobiologyShanghai JiaoTong University School of MedicineShanghai 200025China State Key Laboratory of Genetic Engineering Ministry of Education Key Laboratory of Contemporary AnthropologyCollaborative Innovation Center for Genetics and DevelopmentSchool of Life SciencesFudan UniversityShanghai 200438China Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhouZhejiang 330106China

Microtus fortis is the only mammalian host that exhibits intrinsic resistance against Schistosoma japonicum ***,the underlying molecular mechanisms of this resistance are not yet ***,we perform the first de novo genome assembly of ***,comprehensive gene annotation analysis,and evolution ***,we compare the recovery rate of schistosomes,pathological changes,and liver transcriptomes between *** and mice at different time points after *** observe that the time and type of immune response in *** are different from those in *** activates immune and inflammatory responses on the 10th day post infection,such as leukocyte extravasation,antibody activation,Fc-gamma receptor-mediated phagocytosis,and the interferon signaling cascade,which play important roles in preventing the development of *** contrast,an intense immune response occurrs in mice at the late stages of infection and could not eliminate *** mice suffer severe pathological injury and continuous decreases in cell cycle,lipid metabolism,and other *** findings offer new insights into the intrinsic resistance mechanism of *** against schistosome *** genome sequence also provides the basis for future studies of other important traits in ***.

关键词： Genome assembly Microtus fortis Schistosome Immune Transcriptome

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The effective combination of IPD and CMM: IPD-CMM process

The effective combination of IPD and CMM: IPD-CMM process

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World Congress on software Engineering

作者： Jia, Yue Han, Rui Department of Computer Science and Technology Dalian Neusoft Institute of Information Dalian China Department of Software Development Rockwell Automation Dalian R and D Center Dalian China

Started with the overview of IPD and CMM, based on the detailed analysis of the relationship between them, the article discussed how the IPD and CMM could be combined effectively and be formatted into the IPD-CMM inte... 详细信息

ISBN: (纸本)9780769543031

关键词： CMM IPD IPD-CMM process

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Construction of a Cloud Scenario Knowledge Graph for Cloud Service Market

Construction of a Cloud Scenario Knowledge Graph for Cloud S...

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IEEE International Conference on software Engineering and Service Sciences (ICSESS)

作者： Shiqi Zhang Xiaoqiang Liu Bo Xu Lizhi Cai Yun Hu School of Computer Science and Technology Donghua University & Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Software Center Shanghai China School of Computer Science and Technology Donghua University Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Software Center Shanghai China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

Fast-growing cloud computing technology makes cloud application scenarios more diversified and difficult to choose. A knowledge recommending system is an effective way to reach automatic recommendation services. In this paper, we constructed a cloud scenario knowledge graph (CSKG) for cloud service market to assist cloud knowledge management and provided a web service interface for recommended applications. First a cloud scenario domain ontology was defined based on product knowledge of cloud suppliers and experience of domain experts. Then the BiLSTM-CRF model and Rule-based method were combined to automatically extract structured knowledge from multi-source and heterogeneous texts, where we proposed a tagging schema combined with entity extraction and relation classifier. Finally, a cloud scenario knowledge graph was constructed and visualized, several application examples based on the CSKG service were suggested.

关键词： Knowledge engineering Cloud computing Visualization Web services Tagging Ontologies software engineering

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CIXG: A Comprehensive Approach to Driver Gene Identification and Causal Interpretation

CIXG: A Comprehensive Approach to Driver Gene Identification...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Yawen Chen, Yiwen Nie, Shanling Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China National University of Singapore Center for Continuing and Lifelong Education Singapore University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350386226

With the ongoing advancements in science and technology and the increasing research focus on cancer-related issues, there has been a proliferation of omics-related resources for in-depth analysis and exploration. This burgeoning volume and complexity of biological data have fostered the integration of machine-learning techniques into biology. As a result, numerous machine-learning strategies have been established to identify driver mutations. Yet, many of these strategies produce complex models, complicating comprehension and thereby clouding the impact of input features on the resulting predictions. Our analysis presented the CIXG framework, which integrates a driver gene prediction module using XGBoost with a causality interpretation module anchored on CXPlain. This architecture enables quantifying each input feature's contribution to the prediction outcome and ensures precise predictions of driver genes. When benchmarked against the state-of-the-art (SOTA) method, CIXG demonstrated superior accuracy in pinpointing driver genes across pan-cancer studies and within the 32 specific cancer types. Importantly, our results underscored that mutation features chiefly influence CIXG's predictive prowess, with additional support from other omics features. © 2024 IEEE.

关键词： Adversarial machine learning

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Identification of Electrical Equipment Based on V-I Trajectory, Odd Harmonic Currents 8

Identification of Electrical Equipment Based on V-I Trajecto...

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8th International Conference on Smart Grid and Smart Cities, ICSGSC 2024

作者： Fu, Yan Chen, Shengze Zhao, Fang Shi, Yvhan Ye, Siwen Jiang, Jianfeng Wang, Lei Yang, Xijun Electric Power Science Research Institute Shanghai Electric Power Company Shanghai China Software Development Center Shanghai China Shanghai Jiao Tong University Dept. of Electrical Engineering Shanghai China Beijing Zhixiang Technology Co. Ltd Beijing China

ISBN: (纸本)9798350377408

Non-intrusive load monitoring (NILM) and identification are important to smart grid construction and safe operation. A method combining mixed feature matrix and convolutional neural network based on V-I trajectory and odd harmonic components is proposed and proved for non-intrusive electrical load monitoring technology. In the paper, the specific contents are to analyze the basic characteristics of electrical equipment, including steady-state and transient characteristics of input current. Six simulation circuits of single-phase AC power supply powered electrical equipment are established by means of MATLAB/Simulink using Electrical or Electronic blockset, including heater, hair dryers LED string, adapter, variable frequency air conditioner (air-CON), and variable frequency refrigerators, as the electrical equipment to be analyzed and identified. The grid side current waveforms and spectrums, V-I trajectories, and odd harmonic current components of these six load models are extracted as basic features. The mixed feature matrix using V-I trajectory and odd harmonic features, as well as convolutional neural network (CNN), are proposed for the analysis and identification of electrical equipment. After training and testing on the PLAID dataset, the system can effectively identify electrical equipment under steady-state operation. In addition, three different detection methods are compared and analyzed to verify the superiority of the proposed method for analyzing and identifying electrical equipment based on mixed feature matrix and convolutional neural network. © 2024 IEEE.

关键词： Smart power grids

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