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9th International Conference on Mobile Wireless Middleware, Operating Systems and Applications, MOBILWARE 2020

作者： Zhang, Chijun jin, Jing Huang, Qiuyang Du, Zhanwei Yuan, Zhilu Tang, Shengjun Liu, Yang School of Management Science and Information Engineering Jilin University of Finance and Economics Changchun130117 China Jilin Province Key Laboratory of Fintech Jilin University of Finance and Economics Changchun130117 China College of Computer Science and Technology Jilin University Changchun130012 China Research Institute for Smart Cities and Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services School of Architecture and Urban Planning Shenzhen University Shenzhen China Jilin Big Data Research Center for Business Jilin University of Finance and Economics Changchun130117 China

ISBN: (纸本)9783030622046

Traffic accidents seriously threaten people’s lives and property all over the world. Therefore, it is of great significance to human society to have a long-term traffic accidents data with detail temporal and geographic information in a specific space, which can be used for traffic accident hotspots identification to reduce the incidence of traffic accidents. Here, we obtain a one-year dataset of traffic accidents of the city center in Changchun, Northeast China, in 2017. In this paper, we analyze the risk of traffic accident in urban area, and then discover the characteristics of traffic accidents at the temporal and spatial aspect. We construct a traffic network, which takes crossings as nodes and road sections as edges and weighted by the total number of traffic accidents. In addition, we integrate road structure data and meteorological data to explore the characteristics of the traffic network. © 2020, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Risk analysis

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Rational Delegation Learning Outsourcing Scheme Based on Game Theory

Rational Delegation Learning Outsourcing Scheme Based on Gam...

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International Conference on Networking and Network Applications (NaNA)

作者： Kang Xiang Youliang Tian Sheng Gao Changgen Peng Weijie Tan Institute of Cryptography and Data Security Guizhou University Guiyang P. R. China School of Information Central University of Finance and Economics Beijing P. R. China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang P. R. China

ISBN: (数字)9781728189543

ISBN: (纸本)9781728189550

At present, many enterprises or individuals with a lot of data are limited by their own computing power, so can't mine useful information from the data. To address this problem, this paper proposes a rational delegation machine learning pattern. In the proposed pattern, we firstly construct a reliable game model, and then derive a formal model of rational delegation machine learning according to delegation computation thought. Finally, we design an outsourcing scheme for decision tree model. We analyze the security and performance of the proposed scheme, the analysis results show that the proposed scheme can not disclose any useful information. Last but not least, the experimental result demonstrates that the proposed scheme can obtain a decision tree model with high accuracy.

关键词： Training Computational modeling Machine learning data models Outsourcing Decision trees Security

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Unsupervised vehicle re-identification with progressive adaptation

arXiv

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arXiv 2020年

作者： Peng, Jinjia Wang, Yang Wang, Huibing Zhang, Zhao Fu, Xianping Wang, Meng College of Information and Science Technology Dalian Maritime University LiaoningDalian China Pengcheng Laboratory ShenzhenGuangdong China Key Laboratory of Knowledge Engineering with Big Data Ministry of education Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology China

Vehicle re-identification (reID) aims at identifying vehicles across different non-overlapping cameras views. The existing methods heavily relied on well-labeled datasets for ideal performance, which inevitably causes fateful drop due to the severe domain bias between the training domain and the real-world scenes;worse still, these approaches required full annotations, which is labor-consuming. To tackle these challenges, we propose a novel progressive adaptation learning method for vehicle reID, named PAL, which infers from the abundant data without annotations. For PAL, a data adaptation module is employed for source domain, which generates the images with similar data distribution to unlabeled target domain as "pseudo target samples". These pseudo samples are combined with the unlabeled samples that are selected by a dynamic sampling strategy to make training faster. We further proposed a weighted label smoothing (WLS) loss, which considers the similarity between samples with different clusters to balance the confidence of pseudo labels. Comprehensive experimental results validate the advantages of PAL on both VehicleID and VeRi-776 dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Vehicles

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Adaptive layer-wise personalized federated learning via dual delay update in future communication networks

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computer Communications 2025年 239卷

作者： Xu, Haowen Mao, Yingchi Chen, Si Rong, Yi Muazu, Tasiu He, Xiaoming College of Computer Science And Software Engineering Hohai University No. 8 Focheng West Road Jiangning District Nanjing211100 China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing210098 China China Huaneng Group Co. Ltd Beijing100000 China College of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210003 China

The future communication networks refers to large-scale mass-connected networks consisting of billions of cloud, edge, and end devices, which are expected to support the ever-growing communication demands. In the future communication networks, billions of end devices generate massive amount of data that needs to be processed and analyzed (e.g., model training). Artificial Intelligence of Things (AIoT) is a groundbreaking technology that leverages artificial intelligence models to process and analyze data generated by a large number of internet of things devices. As an emerging AIoT method, personalized Federated Learning (pFL) has emerged prominently in distributed model training using massive data from the future communication networks. However, it is challenging to accomplish high-performance and communication-efficient model training by existing pFL methods in the future communication networks, due to the following limitations. a) Dynamic role differences in each layer of a multi-layer model are neglected, leading to poor accuracy in customized models deployed on end devices. b) Owing to numerous end devices in the future communication networks, the communication frequency between a cloud server and end devices is extremely high in each communication round, resulting in expensive communication cost. To solve these two limitations, this paper presents a novel pFL framework for distributed model training in the future communication networks, called Adaptive Layer-wise personalized Federated Learning via Dual Delay Update (ALpFLDDU). First, in end devices, a layer-wise aggregation scheme based on an adaptive weight calculation mechanism is designed to capture the dynamic role differences of model layers. Second, in each communication round, we develop a dual delay update strategy to reduce communication frequency between a cloud server and end devices while ensuring model performance. Simulation experiments on text and image classification datasets are conducted. Th

关键词： Internet of things

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An Enterprise Competitiveness Assessment Method Based on Ensemble Learning 16th

An Enterprise Competitiveness Assessment Method Based on Ens...

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16th Web Information Systems and Applications Conference, WISA 2019

作者： Chang, Yaomin Li, Yuzheng Chen, Chuan Cao, Bin Li, Zhenxing School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion Sun Yat-sen University Guangzhou China Information Technology Co. Ltd. Beijing China

ISBN: (纸本)9783030309510

It is of great significance to assess the competitiveness of enterprises based on big data. The current methods cannot help corporate strategists to judge the status quo and prospects of enterprises’ development at a relatively low cost. In order to make full use of big data to evaluate enterprise competitiveness, this paper proposes an enterprise competitiveness assessment method based on ensemble learning. The experimental results show that our method has a significant improvement in the task of the enterprise competitiveness assessment. © 2019, Springer Nature Switzerland AG.

关键词： data mining

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Rely-Guarantee Reasoning about Messaging System for Autonomous Vehicles

Rely-Guarantee Reasoning about Messaging System for Autonomo...

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Theoretical Aspects of Software Engineering (TASE)

作者： Wenjing Xu Yongwang Zhao Dianfu Ma YuXin Zhang Qian Xiao State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang Univerisity Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China State Key Laboratory of Automotive Simulation and Control College of Automotive Engineering Jilin University Jilin China School of Computer Science and Technology Guizhou University Guiyang China

Messaging system as a communicating infrastructure is a safety-critical component of autonomous vehicles. For the purpose of safety certification of Level 4 autonomous driving systems, its necessary to provide a formally verified specification of messaging systems. This paper presents a realistic case study using the PiCore rely-guarantee framework to formally verify the DGPS (Differential Global Positioning System) of UISEE autonomous driving systems. We first create an axiom model of messaging systems by extending PiCore, which is reusable for concrete applications. The model supports dynamic configuration of message buffers as well as the automatic generation of the rely and guarantee conditions for compositional reasoning. It is instantiated when developing the formal specification of DGPS. Then, we use the rely-guarantee proof system of PiCore to verify the functional correctness and invariant of DGPS. The specification and its proof provide a strong evidence for the ongoing safety certification of DGPS.

关键词： Cognition Safety Formal specifications Vehicle dynamics Autonomous vehicles Certification Global Positioning System

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Comparing Recommendation Algorithms in Session-based E-commerce Sites

Comparing Recommendation Algorithms in Session-based E-comme...

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International Conference on Intelligent Computing, Automation and Systems (ICICAS)

作者： Mingtian Peng Jiahe Zhang Shilin Wen Chi Harold Liu TravelSky Technology Limited Beijing Engineering Research Center of Civil Aviation Big Data and Key Laboratory of Intelligent Passenger Service of Civil Aviation-CAAC Beijing China College of Computer Science Beijing Institute of Technology Beijing China

Recommender systems have become widely used in various website applications. With the integration of deep learning and recommender systems, the classic Session-based Recommender System (SRS) appears, which can obtain implicit feedback from explicit interactions. Some scholars have pro-posed many effective recommendation algorithms to provide better recommendation service in SRS. In order to compare the performance of these session-based recommendation algorithms, we consider a simple E-commerce SRS scenario and choose four representative session-based recommendation algorithms in this paper. Then we do some evaluation experiments. The experimental results show that the combination of local preferences and global preferences will improve the recommendation performance significantly, and for GNNs and RNN s in session recommendation based on deep learning, we also conclude that the prediction effect of GNN s is slightly superior to RNN s on long sessions.

关键词： Deep learning Automation Prediction algorithms Recommender systems

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Fine-grained attribute-aware analysis for person re-identification

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Procedia computer science 2022年 199卷 276-283页

作者： Kunlong Bai Saiji Fu Linrui Yang Dalian Liu School of Computer Science & Technology University of Chinese Academy of Sciences Beijing 100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 of Zhongguancun East Street Haidian District Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences No. 80 of Zhongguancun East Street Haidian District Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences No. 80 of Zhongguancun East Street Haidian District Beijing 100190 China Sino-Danish College University of Chinese Academy of Sciences Beijing 100049 China Institute of Mathematics and Physics Beijing Union University Beijing 100101 China Institute of Fundamental and Interdisciplinary Sciences Beijing Union University Beijing 100101 China

How and where to learn the discriminative feature has always been a critical issue of person re-identification (re-ID). Most of the previous methods focus on extracting global representation from overall identity image and lack of high-level semantic attribute information. Based on the considerations above, we focus on looking for a way to make people’s attribute play a role in person re-identification task. In this paper, a novel multitask-like network, namely, Attribute-Identity Complementary Network (AICNet), is designed. It contains two branches to learn the identity and attribute feature separately, and a reciprocal interaction process is added to enrich the discrimination of the resulting feature. In addition, in order to better extract high-level semantic attribute information, multi-scale features are combined in the process of attribute extraction. With the help of this attribute-identity complementary strategy, the generated feature can be guided to learn most distinctive attribute feature that is most relevant to identity feature. The present paper carries out extensive experiments on two large-scale datasets, including Market-1501, and DukeMTMC-reID, after which it is found that our attribute-identity complementary framework significantly outperforms the baseline model and achieves competitive performance compared with the state-of-the-art person re-ID methods.

关键词： Person re-identification Attribute recognition Multi-task learning Deep neural network

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A symmetric uniform formula and sole index method for sieving (twin) primes

TechRxiv

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TechRxiv 2020年

作者： Ren, Wei School of Computer Science China University of Geosciences Wuhan430074 China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Guizhou Provincial Key Laboratory of Public Big Data GuiZhou University Guizhou China

All primes can be indexed by k, as primes must be in the form of 6k+1 or 6k-1. In this paper, we explore for the set of k such that either 6k+1 or 6k-1 is not a prime. Our method provides a uniform formula for k that can sieve primes and twin primes as well. The uniform presents symmetry in terms of k that works as single index in sieving. We also propose a new conjecture that is equivalent to Twin Prime Conjecture but possibly be easier to approach by merely exploring of sole index in term of k. The uniform formula for prime sieving are as follows: k ∈ Sl ⇒ 6k−1 6∈ , where Sl = [−I]6I+1 = [I]6I−1\min([I]6I−1), I ∈ . k ∈ Sr ⇒ 6k + 1 6∈ , where Sr = [−I]6I−1 ∪ ([I]6I+1\min([I]6I+1)), I ∈ . © 2020, CC BY.

关键词： Number theory

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Supervised hashing with recurrent scaling 3rd

Supervised hashing with recurrent scaling

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3rd APWeb and WAIM Joint Conference on Web and big data, APWeb-WAIM 2019

作者： Fu, Xiyao Bin, Yi Wang, Zheng Wei, Qin Chen, Si Guizhou Provincial Key Laboratory of Public Big Data Guizhou University GuiyangGuizhou550025 China School of Computer Science and Engineering University of Electronic Science and Technology of China ChengduSichuan611731 China Information Science Academy China Electronics Technology Group Corporation Beijing China

ISBN: (纸本)9783030260743

Learning to hash is a method that can deal with content-based information retrieval efficiently. Traditional learning to hash methods, however, lack the ability to map the generated hash codes to the high-level semantic space. Attributes, as a kind of higher level of visual data representation compared to features, have the potential ability in deep learning to boost the performance. Utilizing attributes from visual data in deep learning to hash can link every bit of the hash codes and a certain type of attributes, therefore giving the hash code an explicit explanation. This paper presents a novel framework, named Deep Recurrent Scaling Hashing (DRSH), to solve the traditional image retrieval problem. The hash codes generated from DRSH are a combination of the outputs of each step of an enhanced LSTM and features generated from convolutional neural nets and are learned through images' attributes. This RNN is reformed to adjust the decorrelation of data flowing between each cell step, which not only makes the learning phase benefit from the ability of recurrent neural nets to learn with recurrent memory but also enable the availability of each hash bit to preserve distinct information. Experiments show that this framework can achieve appreciable performance on major datasets, and also have the ability to explain the meaning of hash codes based on attributes. © 2019, Springer Nature Switzerland AG.

关键词： Long short-term memory

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