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arXiv 2023年

作者： Wan, Wei Ning, Yuxuan Hu, Shengshan Xue, Lulu Li, Minghui Zhang, Leo Yu Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab China

Federated learning (FL) and split learning (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users' devices. The former excels in parallel execution capabilities, while the latter enjoys low dependence on edge computing resources and strong privacy protection. Split federated learning (SFL) combines the strengths of both FL and SL, making it one of the most popular distributed architectures. Furthermore, a recent study has claimed that SFL exhibits robustness against poisoning attacks, with a fivefold improvement compared to FL in terms of robustness. In this paper, we present a novel poisoning attack known as MISA. It poisons both the top and bottom models, causing a misalignment in the global model, ultimately leading to a drastic accuracy collapse. This attack unveils the vulnerabilities in SFL, challenging the conventional belief that SFL is robust against poisoning attacks. Extensive experiments demonstrate that our proposed MISA poses a significant threat to the availability of SFL, underscoring the imperative for academia and industry to accord this matter due attention. Copyright © 2023, The Authors. All rights reserved.

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A sensor data processing and storage method for wireless body area networks 10

A sensor data processing and storage method for wireless bod...

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10th International Conference on Information Technology in Medicine and Education, ITME 2019

作者： Han, Lu Xun, Lulu Liu, Jinfa Yin, Wenjun Zheng, Xiangwei Zheng, Zhuoran Shandong Normal University Ji'nan China Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Ji'nan China

ISBN: (纸本)9781728139173

This paper proposes an adaptive processing method and a dynamic storage method of pulsed wave signal based on wireless body area network. Firstly, the ZigBee star network model is adopted as the basic network architecture of the system. Secondly, an adaptive Gaussian filter model is designed to eliminate the influence of environmental and man-made factors. Finally, the dynamic storage algorithm is used to map the pulsed wave signal to a new stored sample, and the regression model is used to restore the new sample to the original signal. Simulation experiments show that: 1) The adaptive Gaussian model minimizes the signal noise and better maintains the characteristics of the pulsed wave signal. 2) On the premise of using ARIMA regression model of neural network, dynamic storage algorithm improves the regression accuracy compared with fixed storage algorithm. © 2019 IEEE.

关键词： Zigbee

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Research on education resource recommendation algorithm based on implicit feedback and pairwise 10

Research on education resource recommendation algorithm base...

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10th International Conference on Information Technology in Medicine and Education, ITME 2019

作者： Huang, Xiaoxiang Zhang, Yongsheng Zheng, Zhihua Zhang, Jie Duan, Jiaxi School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China

ISBN: (纸本)9781728139173

In view of the problem of how to improve the accuracy of education resources recommendation in Internet era, a recommendation algorithm based on implicit feedback and pairwise education resources are proposed. Firstly, the feature extraction of learner-education resource is carried out, and the implicit features of learner-education resource are extracted by implicit semantic model, and the prediction results of factor decomposition are used as input to sorting learning algorithm. Converting sorting problems to classification problems uses the theory of ordering learning. Based on the experiment comparison with the traditional recommendation algorithm, it is compared with the comparison algorithm, which has better effect on the recommendation of educational resources. The algorithm increases by nearly 30% on metrics MAP, NDGG. © 2019 IEEE.

关键词： Learning algorithms

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The study of searchable encryption mechanism

The study of searchable encryption mechanism

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International Conference on Intelligent Computing, Communication and Devices, ICCD 2017

作者： Cui, Ranran Zhang, Yongsheng Li, Nengneng School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China

ISBN: (纸本)9789811089435

With the development of information technology, more and more data information uploaded to the cloud server, cloud computing encryption storage technology for data security has become particularly important. This paper mainly studies and analyzes the searchable encryption technology in cloud computing. Firstly, the research status of domestic and foreign research is analyzed. Secondly, the classification of searchable encryption technology is introduced from different aspects, and the classification model of searchable encryption technology A detailed elaboration. Finally, some problems encountered in searchable confidentiality technology and the prospect of this technology are introduced. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Cryptography

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Research on college teachers teaching ability evaluation model based on DHNN 10

Research on college teachers teaching ability evaluation mod...

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10th International Conference on Information Technology in Medicine and Education, ITME 2019

作者： Zhang, Jie Zhang, Yongsheng Wei, Yu Zheng, Zhihua Fan, Shaojun School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China

ISBN: (纸本)9781728139173

The teaching ability of teachers is one of the core abilities of colleges. Its level has gradually become an important indicator for evaluating a school. However, the existing evaluation models of college teachers are timeconsuming, empirical, complex, subjective and speculative. Therefore, based on the teaching features of colleges in China, this paper constructs a fast and effective college teacher evaluation model by discrete Hopfield neural network technology, and introduces the basic ideas and steps of the model. © 2019 IEEE.

关键词： Hopfield neural networks

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A patient outcome prediction based on random forest 19

A patient outcome prediction based on random forest

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4th International Conference on Crowd Science and Engineering, ICCSE 2019

作者： Yang, Shan Zheng, Xiangwei Yuan, Feng School of Information Science and Engineering Shandong Normal University Ji'nan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Ji’nan China

ISBN: (纸本)9781450376402

Since the research and development value of electronic health records (EHRs) which contains a large number of patient treatment data is very high and meaningful, EHRs has gained attention by researchers in recent years. EHRs has some characteristics such as temporality, sparsity, complexity, irregularity, noisiness and so on, which bring many challenges to direct study the medical data. Thus, an effective feature extraction, or phenotyping from patient EHRs is a key step before any further applications. In this paper, MIMIC-III intensive care database is selected for the experiments. To predict the patient’s death outcome (namely death due to illness or still alive), we make full use of the visit records of patients and propose a prediction method that combines the medical concept representation model Med2Vec with random forest algorithm. Experimental results indicate that the proposed method is robust to parameter variations and noise. Besides, compared with other prediction methods, the performance metrics of the proposed method are very well. Finally, the effect of the Med2Vec model is superior to that obtained by raw data (i.e., no feature learning applied to EHRs data). © 2019 Association for Computing Machinery. All rights reserved.

关键词： Forecasting

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BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images

arXiv

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arXiv 2022年

作者： Cong, Runmin Yang, Haowei Jiang, Qiuping Gao, Wei Li, Haisheng Wang, Cong Zhao, Yao Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing100048 China The Department of Computer Science City University of Hong Kong Hong Kong The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The School of Information Science and Engineering Ningbo University Ningbo315211 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China The Distributed and Parallel Software Lab Huawei Technologies Shenzhen518129 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

The spread of COVID-19 has brought a huge disaster to the world, and the automatic segmentation of infection regions can help doctors to make diagnosis quickly and reduce workload. However, there are several challenges for the accurate and complete segmentation, such as the scattered infection area distribution, complex background noises, and blurred segmentation boundaries. To this end, in this paper, we propose a novel network for automatic COVID-19 lung infection segmentation from CT images, named BCS-Net, which considers the boundary, context, and semantic attributes. The BCS-Net follows an encoder-decoder architecture, and more designs focus on the decoder stage that includes three progressively Boundary-Context-Semantic Reconstruction (BCSR) blocks. In each BCSR block, the attention-guided global context (AGGC) module is designed to learn the most valuable encoder features for decoder by highlighting the important spatial and boundary locations and modeling the global context dependence. Besides, a semantic guidance (SG) unit generates the semantic guidance map to refine the decoder features by aggregating multi-scale high-level features at the intermediate resolution. Extensive experiments demonstrate that our proposed framework outperforms the existing competitors both qualitatively and quantitatively. © 2022, CC BY-NC-SA.

关键词： COVID-19

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Research and implementation of teaching system for higher vocational specialty courses based on hybrid teaching mode 10

Research and implementation of teaching system for higher vo...

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10th International Conference on Information Technology in Medicine and Education, ITME 2019

作者： Zhang, Penglong Zhang, Yongsheng Fan, Shaojun Huang, Xiaoxiang Zhang, Jie School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China

ISBN: (纸本)9781728139173

In recent years, with the diversified development of education and training in China and the continuous 'Internet +', enterprises and society have put forward higher requirements for the ability of skilled talents. However, in the vocational specialty colleges that transport a large number of skilled talents, it is increasingly difficult to meet the needs of students' learning and development. At the same time, it is difficult for students to select appropriate content for independent learning in the vast information on the Internet. Hybrid teaching is a teaching mode that combines the advantages of both traditional teaching and networked teaching. The specialty courses teaching system is based on hybrid teaching mode. It uses Java Web technology as the basis for adopting B/S development mode, MySQL database and eclipse, Navicat as Development tools. The research and implementation of the hybrid teaching system is conducive to the school to help bridging the communication between the students and the enterprise, so that students can learn more directly and provide high-skilled talents with basic professional knowledge and skilled skills. On the other hand, through practice, it also helps to promote the development of hybrid teaching reform, thus solving the complexity and particularity of the informationization process in higher vocational colleges. © 2019 IEEE.

关键词： Teaching

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Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency

arXiv

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arXiv 2023年

作者： Liu, Xiaogeng Li, Minghui Wang, Haoyu Hu, Shengshan Ye, Dengpan Jin, Hai Wu, Libing Xiao, Chaowei School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab China School of Cyber Science and Engineering Wuhan University China Arizona State University United States

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo)1, a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability. Copyright © 2023, The Authors. All rights reserved.

关键词： Crime

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A grouping approach based on non-uniform binary grid partitioning for crowd evacuation simulation

A grouping approach based on non-uniform binary grid partiti...

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作者： Liu, Hong Li, Yan Li, Wenhao Lu, Dianjie Zhang, Guijuan School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China University of South Australia Adelaide Australia

Small social groups based on kinship or friendships are ubiquitous in human crowds. Therefore, the effect of social groups on crowd evacuations and that of crowd evacuations on social groups must be investigated. To simulate the group phenomenon when an emergency occurs, we propose an improved social force model that takes into account the social group relationship among the population, and based on our proposed model, a novel grouping algorithm predicated on non-uniform binary grid partitioning is put forward. The approach initially maps the individuals into the plane space, and then it adopts top-down binary grid partitioning iteratively until the divided grid contains only the individuals with relations;then, the values of the relation and density of the non-empty grid cells are calculated, and the grids are sorted according to these values. After sorting, selecting, merging, and forming the core grids, the other grids are merged to the core grids. We have compared the algorithm with the hierarchical classification algorithm and the grid-based algorithm. The results show that the accuracy, speed, and scalability are all advantages. We also establish a simulation platform to illustrate the proposed grouping algorithm and the improved social force model for crowd evacuation simulation. © 2018 John Wiley & Sons, Ltd.

关键词： Simulation platform

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