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| 1 | A Truncated SVD-Based ARIMA Model for Multiple QoS Prediction in Mobile Edge Computing显示文摘In the mobile edge computing environments,Quality of Service(QoS)prediction plays a crucial role in web service recommendation.Because of distinct features of mobile edge computing,i.e.,the mobility of users and incomplete historical QoS data,traditional QoS prediction approaches may obtain less accurate results in the mobile edge computing environments.In this paper,we treat the historical QoS values at different time slots as a temporal sequence of QoS matrices.By incorporating the compressed matrices extracted from QoS matrices through truncated Singular Value Decomposition(SVD)with the classical ARIMA model,we extend the ARIMA model to predict multiple QoS values simultaneously and efficiently.Experimental results show that our proposed approach outperforms the other state-of-the-art approaches in accuracy and efficiency. | Chao Yan Yankun Zhang Weiyi Zhong Can Zhang Baogui Xin | 2022 | Tsinghua Science and Technology2022，27，2： | 9 |
| 2 | 基于机器学习的人体步态检测智能识别算法研究显示文摘为实现快速步态状态判断,以更好地对下肢外骨骼进行高精度的步态识别和控制,进行了基于可穿戴惯性测量装置检测人体姿态变化的算法研究。通过对人体下肢的跌倒、转弯、蹲坐与起立等非周期性步态变化活动进行测算试验,获得了受试者实验过程中身体角度、下肢关节角速度和加速度变化等数据,随后应用随机森林等4种机器学习经典分类算法对受试者进行了活动识别对比分析,结果表明,决策树监督学习算法相对于其他算法,能够快速、准确地检测并判断出人体非周期性变化中的多种活动状态,历次识别精度均可达到99%以上,为可穿戴智能装备的开发与应用提供理论基础。 | 高经纬 马超 姚杰 王少红 | 2021 | 电子测量与仪器学报2021，35，3： | 8 |
| 3 | Security Issues and Defensive Approaches in Deep Learning Frameworks显示文摘Deep learning frameworks promote the development of artificial intelligence and demonstrate considerable potential in numerous applications.However,the security issues of deep learning frameworks are among the main risks preventing the wide application of it.Attacks on deep learning frameworks by malicious internal or external attackers would exert substantial effects on society and life.We start with a description of the framework of deep learning algorithms and a detailed analysis of attacks and vulnerabilities in them.We propose a highly comprehensive classification approach for security issues and defensive approaches in deep learning frameworks and connect different attacks to corresponding defensive approaches.Moreover,we analyze a case of the physical-world use of deep learning security issues.In addition,we discuss future directions and open issues in deep learning frameworks.We hope that our research will inspire future developments and draw attention from academic and industrial domains to the security of deep learning frameworks. | Hongsong Chen Yongpeng Zhang Yongrui Cao Jing Xie | 2021 | Tsinghua Science and Technology2021，26，6： | 2 |
| 4 | 基于多核SVM的AdaBoost心力衰竭死亡率评估模型显示文摘【目的】心力衰竭简称心衰,是一种复杂的临床综合征,具有高发病率、高死亡率和预后效果不佳等显著特点,是各类心脏疾病发展的终末期,严重危害人类健康。因此,对心衰患者进行早期的预后评估研究至关重要,可以最大程度地帮助患者生存。【方法】提出一种基于多核支持向量机(multi kernel support vector machine,MK-SVM)和自适应提升算法(adaptive boosting,AdaBoost)的心力衰竭死亡率评估模型(MK-SVM-AdaBoost).该算法利用MK-SVM将特征映射到高维空间,并依据AdaBoost算法将基本分类器进行集成,实现死亡率的精确预测。同时,将合成少数过采样技术(synthetic minority oversampling technique,SMOTE)和Tomek links欠采样技术相结合的混合抽样方法引入到预测模型中,减轻不平衡数据集对模型性能的影响。【结果】在收集于白求恩医院的小型心衰数据集上进行心衰患者30 d内死亡率预测实验。实验结果表明,MK-SVM-AdaBoost模型的准确率和召回率分别达到了85.63%和86.33%,优于现有方法,ROC曲线下与坐标轴围成的面积(area under curve,AUC)和其微观平均值(micro-mean AUC,MiA-AUC)分别达到了91.00%和92.00%,表明提出的模型具有良好的稳定性。【结论】提出的模型具有较高的准确率和稳定性,可以为医生的临床决策提供一定的参考。今后课题将继续对数据集进行扩充,并对分级预警进行研究,以便对患者进行更有效的评估。 | 刘晓玉 李灯熬 赵菊敏 | 2023 | 太原理工大学学报2023，54，5： | 0 |
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