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TechRxiv 2021年

作者： Li, Leixiao Lin, Hao Wan, Jianxiong Wang, Yongsheng Gao, Jing College of Data Science and Application Inner Mongolia University of Technology China School of Computer Science and Engineering Tianjin University of Technology China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service China College of Computer and Information Engineering Inner Mongolia Agricultural University China Inner Mongolia Autonomous Region Key Laboratory of big data research and application for agriculture and animal husbandry China

Efficient and accurate driving path planning can help drivers drive. To solve the problem of low efficiency of traditional heuristic algorithms such as PSO and GA in solving driving path planning, we introduce Excellence Coefficient into heuristic algorithms and make a parallel design based on Spark, which called EC-SPPSOGA. Excellence Coefficient can increase the probability of good edges being left, simultaneously, preserves the possibility of longer side being selected. The parallel design is based on time-consuming analysis of heuristic algorithms. We validate the performance of EC-SPPSOGA based on the data in TSPLIB. It is verified that the EC-SPPSOGA can improve efficiency of driving path planning and has good scalability. © 2021, CC BY.

关键词： Heuristic algorithms

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Improving read performance of lsm-tree based KV stores via dual grained caches 21

Improving read performance of lsm-tree based KV stores via d...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data science and Systems, HPCC/SmartCity/DSS 2019

作者： Li, Xiang Xu, Guangping Fan, Hao Lu, Hongli Tang, Bo Xue, Yanbing Zong, Ziliang School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Department of Computer Science Texas State University San MarcosTX United States

ISBN: (纸本)9781728120584

Key-value (KV) stores based on the Log-Structure Merge tree (LSM-tree) have been widely used in modern storage systems (e.g. LevelDB and RocksDB) to achieve high write throughput. However, conventional LSM-tree design has high latency for random read operations, especially in the workloads with concurrent read and write operations. This paper proposes a dual-grained cache scheme to improve the performance of LSM-tree based KV stores, in which a coarse-grained cache improves read performance by leveraging spatial locality and a fine-grained cache tracks the frequently accessed KV pairs. We implement the dual-grained caching on a LSM-tree simulator and evaluate its performance using various Yahoo! Cloud Service Benchmark (YCSB) workloads. Our experimental results show that the proposed way of dual-grained caches can significantly improve the read performance of KV stores without sacrificing the advantages of the LSM-tree for write-intensive workloads. © 2019 IEEE.

关键词： Compaction Throughput Conferences Indexes computer science Benchmark testing Distributed databases

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HSNP-Miner: High Utility Self-Adaptive Nonoverlapping Pattern Mining

HSNP-Miner: High Utility Self-Adaptive Nonoverlapping Patter...

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IEEE International Conference on Big Knowledge (ICBK)

作者： Md Motaher Hossain Youxi Wu Philippe Fournier-Viger Zhao Li Lei Guo Yan Li School of Artificial Intelligence Hebei University of Technology Tianjin China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Alibaba Group Hangzhou Zhejiang China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjing China School of Economics and Management Hebei University of Technology Tianjing China

ISBN: (纸本)9781665438599

Sequential pattern mining (SPM) under the nonoverlapping condition (or nonoverlapping SPM) is a type of data mining used to extract frequent gapped subsequences (known as patterns) from sequences, which is more valuable and versatile than other related methods. In nonoverlapping SPM, two occurrences cannot reuse the same sequence letter in the exact location as the occurrences. This method evaluates the frequency of the patterns in the sequence, and ignores the impact of external utility (item price or profit). Therefore, some low-frequency and essential patterns are overlooked. To address this issue, this paper introduces High Utility Self-adaptive Nonoverlapping Pattern (HSNP) mining and proposes HSNP-Miner, which includes two steps: support calculation and candi-date pattern generation. To calculate the support, we propose the NoSup algorithm, which can effectively calculate support while avoiding the creation of redundant nodes. An advanced upper bound method is employed to generate the candidate patterns more efficiently. Compared to other competitive methods, the experimental results demonstrate the efficiency of the proposed algorithm and the uniqueness of nonoverlapping sequence pat-tarns.

关键词： Upper bound Conferences DNA Data mining Viruses (medical)

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Predicting Electricity Usage Based on Deep Neural Network*

Predicting Electricity Usage Based on Deep Neural Network*

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IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Ran Wei Jinhai Wang Qirui Gan Xin Dang Huiquan Wang College of Life Sciences Tianjin Polytechnic University Tianjin China School of Electronics and Information Engineering Tianjin Polytechnic University Tianjin China School of Computer Science and Software Engineering Tianjin Polytechnic University Tianjin China

ISBN: (数字)9781538683446

ISBN: (纸本)9781538683453

This paper describes a deep neural network (DNN) based method for forecasting short-term hospital electricity usage. In Experiment One, a 4-layer DNN stack auto-encoder (SAE) based model is constructed to verify the accuracy of the method. Kilowatt-hours (kwh), capacitance (pf), power factor (phi), voltage (v), electricity reactive power (var), and electricity active power (w) are the main input variables. After training the model, the prediction accuracy can reach 77.60%. In the improvement phase, the model is altered to use more common variables; specifically, kilowatt-hours (kwh), electric charge (charg), average active power (avg-w), and maximum active power (max-w) are used as input variables. In order to optimize the training of the model, Experiment Two improves on the basis of the original DNN model. As a result, the prediction accuracy can be increased to 85.17%. Finally, the four power data with the best measurement are used, namely current(I), voltage(V), reactive power(Var) and active power(W), and the predicted result is 98.14%. This method indicates that the planning and scheduling of the hospital' s electricity usage will also be improved.

关键词： Training Hospitals Predictive models Neural networks Forecasting Reactive power Mathematical model

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SOC estimation based on data driven exteaded Kalman filter algorithm for power battery of electric vehicle and plug-in electric vehicle

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Journal of Central South university 2019年第6期26卷 1402-1415页

作者： LIU Fang MA Jie SU Wei-xing CHEN Han-ning TIAN Hui-xin LI Chun-qing School of Computer Science & Technology Tianjin Polytechnic UniversityTianjin 300387China Tianjin Qingyuan Electric Vehicle Limited Liability Company Tianjin 300457China Control Theory and Control Engineering Tianjin UniversityTianjin 300350China

State of charge(SOC)estimation has always been a hot topic in the field of both power battery and new energy vehicle(electric vehicle(EV),plug-in electric vehicle(PHEV)and so on).In this work,aiming at the contradiction problem between the exact requirements of EKF(extended Kalman filter)algorithm for the battery model and the dynamic requirements of battery mode in life cycle or a charge and discharge period,a completely data-driven SOC estimation algorithm based on EKF algorithm is *** innovation of this algorithm lies in that the EKF algorithm is used to get the SOC accurate estimate of the power battery online with using the observable voltage and current data information of the power battery and without knowing the internal parameter variation of the power *** the combination of data-based and model-based SOC estimation method,the new method can avoid high accumulated error of traditional data-driven SOC algorithms and high dependence on battery model of most of the existing model-based SOC estimation methods,and is more suitable for the life cycle SOC estimation of the power battery operating in a complex and ever-changing environment(such as in an EV or PHEV).A series of simulation experiments illustrate better robustness and practicability of the proposed algorithm.

关键词： state of charge extended Kalman filter autoregressive model power battery

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Hybrid-attention guided network with multiple resolution features for person re-identification

arXiv

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arXiv 2020年

作者： Zhang, Guoqing Yang, Junchuan Zheng, Yuhui Wang, Ye Wu, Yi Chen, Shengyong School of Computer and Software Nanjing University of Information Science Technology Nanjing210044 China School of Marketing Logistics Management Nanjing University of Finance and Economics Nanjing China Wormpex AI Research School of Computer Science Engineering Tianjin University of Technology

Extracting effective and discriminative features is very important for addressing the challenges of person re-identification (re-ID). Prevailing deep convolutional neural networks usually use high-level features for identifying pedestrian. However, some essential spatial information resided in low-level features will be lost when learning the high-level features. Most existing person re-ID methods mainly rely on hand-craft bounding boxes, where person images are precisely aligned. It is unrealistic in practical applications due to the inaccuracy of automatic detection algorithms. To address these problems, we propose a hybrid-attention guided network with multiple resolution features for person re-ID. We first construct a multi-resolution fusion strategy to ensure that multi-resolution features can be spatially aligned during feature fusion and at the same time ensure the discriminability of features after fusion. Then, we introduce the attention mechanism and multi-granularity operation to reduce the impact of irregular bounding boxes by gently expanding the size of feature maps. In addition, a new multi-pool feature extractor is designed to obtain different types of information by using two different pools and the feature representation capability can be further improved. Extensive experiments display the superiority of our approach. Our code is available at https://***/libraflower/MutipleFeature-for-PRID. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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2019 ACM Turing Celebration Conference - China, ACM TURC 2019

作者： Li, Xiuhua Wang, Xiaofei Sheng, Zhengguo Hu, Chunqiang Leung, Victor C.M. School of Big Data and Software Engineering Chongqing University Chongqing China School of Computer Science and Technology Tianjin University Tianjin China Dept. of Engineering and Design University of Sussex Falmer Brighton United Kingdom Dept. Electrical and Computer Engineering University of British Columbia Vancouver Canada

ISBN: (纸本)9781450371582

Content requests from mobile users are explosively increasing due to the growing popularity of mobile social activities. However, current mobile network infrastructure cannot effectively support the generated massive mobile data traffic while maintaining consistent and acceptable network performances. To deal with such a serious challenge, caching popular content at the edges of mobile networks has been recognized as a promising solution. In this paper, we introduce the content caching technique in Fog-Cloud based radio access networks for offering joint Fog-enabled and Cloud-enabled data services. Then we propose a collaborative hierarchical Fog-Cloud caching framework based on predicting content popularity with the Auto Regressive model. Our objective is to explore the maximum capacity of the considered network infrastructure by effectively offloading network traffic. Trace-driven evaluations demonstrate the effectiveness of our proposed caching mechanism in terms of traffic offload. © 2019 Association for Computing Machinery.

关键词： Fog

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An Empirical Evaluation of GDPR Compliance Violations in Android mHealth Apps

An Empirical Evaluation of GDPR Compliance Violations in And...

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International Symposium on software Reliability engineering (ISSRE)

作者： Ming Fan Le Yu Sen Chen Hao Zhou Xiapu Luo Shuyue Li Yang Liu Jun Liu Ting Liu School of Cyber Science and Engineering MoE KLINNS Xi’an Jiaotong University China The Hong Kong Polytechnic University China College of Intelligence and Computing Tianjin University China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9781728198705

ISBN: (纸本)9781728198712

The purpose of the General Data Protection Regulation (GDPR) is to provide improved privacy protection. If an app controls personal data from users, it needs to be compliant with GDPR. However, GDPR lists general rules rather than exact step-by-step guidelines about how to develop an app that fulfills the requirements. Therefore, there may exist GDPR compliance violations in existing apps, which would pose severe privacy threats to app users. In this paper, we take mobile health applications (mHealth apps) as a peephole to examine the status quo of GDPR compliance in Android apps. We first propose an automated system, named HPDROID, to bridge the semantic gap between the general rules of GDPR and the app implementations by identifying the data practices declared in the app privacy policy and the data relevant behaviors in the app code. Then, based on HPDROID, we detect three kinds of GDPR compliance violations, including the incompleteness of privacy policy, the inconsistency of data collections, and the insecurity of data transmission. We perform an empirical evaluation of 796 mHealth apps. The results reveal that 189 (23.7%) of them do not provide complete privacy policies. Moreover, 59 apps collect sensitive data through different measures, but 46 (77.9%) of them contain at least one inconsistent collection behavior. Even worse, among the 59 apps, only 8 apps try to ensure the transmission security of collected data. However, all of them contain at least one encryption or SSL misuse. Our work exposes severe privacy issues to raise awareness of privacy protection for app users and developers.

关键词： Privacy Data privacy Systematics Semantics software reliability Security General Data Protection Regulation

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The 2D Materials Roadmap

arXiv

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arXiv 2025年

作者： Ren, Wencai Bøggild, Peter Redwing, Joan Marie Novoselov, Kostya Sun, Luzhao Qi, Yue Jia, Kaicheng Liu, Zhongfan Burton, Oliver Alexander-Webber, Jack Hofmann, Stephan Cao, Yang Long, Yu Yang, Quan-Hong Li, Dan Choi, Soo Ho Kim, Ki Kang Lee, Young Hee Li, Mian Huang, Qing Gogotsi, Yury Clark, Nicholas Carl, Amy Gorbachev, Roman Olsen, Thomas Rosen, Johanna Thygesen, Kristian Sommer Efetov, Dmitri K. Jessen, Bjarke S. Yankowitz, Matthew Barrier, Julien Kumar, Roshan Krishna Koppens, Frank H.L. Deng, Hui Li, Xiaoqin Dai, Siyuan Basov, D.N. Wang, Xinran Das, Saptarshi Duan, Xiangfeng Yu, Zhihao Borsch, Markus Ferrari, Andrea C. Huber, Rupert Kira, Mackillo Xia, Fengnian Wang, Xiao Wu, Zhong-Shuai Feng, Xinliang Simon, Patrice Cheng, Hui-Ming Liu, Bilu Xie, Yi Jin, Wanqin Nair, Rahul Raveendran Xu, Yan Zhang, Qing Katiyar, Ajit K. Ahn, Jong-Hyun Aharonovich, Igor Hersam, Mark C. Roche, Stephan Hua, Qilin Shen, Guozhen Ren, Tianling Zhang, Hao-Bin Koo, Chong Min Koratkar, Nikhil Pellegrini, Vittorio Young, Robert J. Qu, Bill Lemme, Max Pollard, Andrew J. Shenyang National Laboratory for Materials Science Institute of Metal Research Chinese Academy of Sciences 72 Wenhua Road Shenyang110016 China Technical University of Denmark Denmark The Pennsylvania State University United States University of Manchester United Kingdom Institute for Functional Intelligent Materials National University of Singapore Singapore Beijing Graphene Institute China University of Cambridge United Kingdom Department of Chemical Engineering The University of Melbourne Victoria Australia Nanoyang Group Tianjin Key Laboratory of Advanced Carbon and Electrochemical Energy Storage School of Chemical Engineering and Technology Tianjin University Tianjin300072 China The Hong Kong University of Science and Technology Hong Kong Center for Integrated Nanostructure Physics Institute for Basic Science Suwon16419 Korea Republic of Sungkyunkwan University Suwon16419 Korea Republic of Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences China Drexel University United States Linköping University Sweden Ludwig-Maximilians-Universität München Germany University of Washington United States ICFO The Institute of Photonic Sciences Castelldefels Barcelona08860 Spain University of Michigan United States University of Texas Austin United States Auburn University United States Columbia University United States State Key Laboratory of Catalysis Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian China University of California Los Angeles United States Suzhou Laboratory Suzhou China School of Integrated Circuit Science and Engineering Nanjing University of Posts and Telecommunications Nanjing210023 China Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States University of Regensburg Germany Department of Electrical and Computer Engineering Yale University New Have

Over the past two decades, 2D materials have rapidly evolved into a diverse and expanding family of material platforms. Many members of this materials class have demonstrated their potential to deliver transformative impact on fundamental research and technological applications across different fields. In this roadmap, we provide an overview of the key aspects of 2D material research and development, spanning synthesis, properties and commercial applications. We specifically present roadmaps for high impact 2D materials, including graphene and its derivatives, transition metal dichalcogenides, MXenes as well as their heterostructures and moiré systems. The discussions are organized into thematic sections covering emerging research areas (e.g., twisted electronics, moiré nano-optoelectronics, polaritronics, quantum photonics, and neuromorphic computing), breakthrough applications in key technologies (e.g., 2D transistors, energy storage, electrocatalysis, filtration and separation, thermal management, flexible electronics, sensing, electromagnetic interference shielding, and composites) and other important topics (computational discovery of novel materials, commercialization and standardization). This roadmap focuses on the current research landscape, future challenges and scientific and technological advances required to address, with the intent to provide useful references for promoting the development of 2D materials. © 2025, CC BY.

关键词： Integrated optoelectronics

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IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive Computing (Plenary Panel Report-II) 20

IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive...

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20th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2021

作者： Kinsner, Witold Zhu, Haibin Baciu, George Luo, Guiming Rubio, Fernando Sui, Jie Huang, Runhe Hiraishi, Hironori Peng, Jun Chen, Liang Univ. of Manitoba FI2CICC Dept of ECE Winnipeg Canada Nipissing University FI2CICC ON Canada Hong Kong Polytechnic University FI2CICC Dept. of Computing Hong Kong Tsinghua University FI2CICC Software Institute Beijing China Complutense Univ. of Madrid FI2CICC Dept. of Computer Systems and Computation Spain Aberdeen University FI2CICC Aberdeen United Kingdom Hosei University FI2CICC Faculty of Computer and Information Sciences Japan Ashikaga University Faculty of Engineering Tochigi326-8558 Japan Chongqing Univ. of Science and Technology FI2CICC School of AI Chongqing China Univ. of Northern British Columbia Dept. of Computer Science BC Canada

ISBN: (纸本)9781665421195

Cognitive Computing (CC) is a contemporary field of fundamental intelligence theories and general AI technologies triggered by the transdisciplinary development in intelligence, computer, brain, knowledge, cognitive, robotic, and cybernetic sciences for engineering implementations. This paper presents a summary of the plenary panel (Part II) on the theoretical foundations of CI/CC and recent breakthroughs in AI engineering reported in the 20th IEEE International ICCI*CC Conference (ICCI*CC'21). The latest advances in CI and CC towards general AI are presented by twenty-Two distinguished panelists. Strategic AI engineering applications in CI, CC, and cognitive systems are elaborated for abstract intelligence, cognitive robots, autonomous systems, intelligent vehicles, and safety-And-mission-critical systems. © 2021 IEEE.

关键词： Cognitive systems

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