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Top-n recommendation algorithms for cloud data

Tien Tzu Hsueh Pao/Acta Electronica Sinica 2015年第1期43卷 54-61页

作者： Huang, Zhen-Hua Department of Computer and Technology Tongji University Shanghai200092 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China

Top-n recommendation technology has recently received a lot of attention in information service community. In this paper, we study the problem of top-n recommendation under the cloud data. Firstly, we propose a multilayer distributed storage architecture(MDSA)which can fit the need of top-n recommendation. Secondly, based on the consideration of reducing the cost of network transmission, we present the data encoding pattern which is based on the MDSA architecture. Lastly, we fast realize top-n recommendation using the map/reduce distributed programming model. For satisfying the realistic requirement, we further present three application variants of top-n recommendation. Massive experiments demonstrate that our approach is efficient and has high overall performance. ©, 2015, Chinese Institute of Electronics. All right reserved.

关键词： MapReduce

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Combining feature selection and resample to improve classification accuracy

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Journal of Computational Information systems 2012年第22期8卷 9539-9544页

作者： Liu, Jiaming Wang, Xiao Zhao, Qingce You, Mingyu Li, Guozheng The Key Laboratory of Embedded System and Service Computing Department of Control Science and Engineering Tongji University Shanghai Shanghai 201804 China

The paper provides the solution of the campion for CDMC2011, a data mining contest. The task for the data mining contest organized in conjunction with the ICONIP20II conference was to learn three predictive models (i.e. a classifier) capable of distinguishing between different classes for three separate tasks. Different combinations of data preprocess, feature selection and classifier learning methods were tried to obtain the best results. The performance of classifiers are improved by ensemble learning method. The final accuracy on test dataset shows our methods outperformed others. Copyright © 2012 Binary Information Press.

关键词： Intrusion detection

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FedWNS: Data Distribution-Wise Node Selection in Federated Learning via Reinforcement Learning 26

FedWNS: Data Distribution-Wise Node Selection in Federated L...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Tu, Chengwu Zhao, Shengjie Deng, Hao Tongji University School of Software Engineering Shanghai201800 China Key Laboratory of Embedded System and Service Computing Shanghai201800 China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai201800 China

ISBN: (纸本)9798350331684

To deal with the discrepancy between global and local objectives in the federated learning invoked by the non-independent, identically distributed (non-IID) data and mitigate the impact of catastrophic forgetting in the training phase, we propose a federated learning framework with data distribution-wise reinforcement learning to perform node selection to accelerate the convergence process and alleviate the accuracy degradation. In this framework, the agent on the central server observes the number of samples every node owns, the derived distribution information of every dataset, and the current local and global accuracy. Then infer the selected node-set to participate in the current federated learning round through policy network in reinforcement learning. Finally, we conduct simulations with publicly data sets. Simulation results indicate that our FedWNS outperforms the existing FedAvg and CSFedAvg on the testing accuracy and the communication rounds to reach target accuracy under different settings. © 2023 IEEE.

关键词： Reinforcement learning

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A Prediction Based on Clustering and Personality Questionnaire Data for IGD Risk

A Prediction Based on Clustering and Personality Questionnai...

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International Conference on Natural Computation, Fuzzy systems and Knowledge Discovery

作者： Xiaoliang Gong Kun Fang Yichu Hou Bozhong Long Zongling Di Lei Cao National Key Laboratory of Embedded System and Service Computing (Ministry of Education) Tongji University Shanghai China

ISBN: (纸本)9781509040940

With the development of the Internet, a lot of people trapped in the network, especially the adolescent depending on the network game and disturbing their normal life. 579 freshmen participated in this work who were collected the personality questionnaires in the first week they came in university and their grades points average (GPA) after half year. The questionnaires were including Self-Control (SCS), Barratt impulse Inventory (BIS) and Chinese Big Five Personality (CBF). This work used multi-clustering algorithms to construct the models of predicting for Internet game disorder (IGD) risk, including FCM, K-means, and Hierarchical for training model. This is the first try to predict the risk of IGD by personality traits. The results shown the questionnaire data were well separated by different clustering algorithms into three groups who were shared the analogous personality traits which has a relationship with the behavior of IGD. But compared to the GPA of each group, the efficiency of the prediction model seems not so satisfactory. There need more efforts to optimized the model in the future.

关键词： Personality questionnaire Data IGD Risk FCM K-means Hierarchical

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Study and implementation of agent-based grid resource monitoring model

Study and implementation of agent-based grid resource monito...

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2nd International Conference on Information Science and Engineering, ICISE2010

作者： Ming, Liu Zhang, Junqi Wang, Yu Li, Jie Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China

ISBN: (纸本)9781424480968

Grid is a promising infrastructure which enables scientists and engineers to access geographically distributed resources. Grid computing is a new technology which focuses on aggregating various kinds of resource (e.g., processor cycles, disk storage, and contents) into one computing platform. The realization of grid computing requires a resource agent to manage and monitor available resources. Based on study of past models, this paper presents a new agent-based resource monitoring model whose main feature is multi-layered monitoring architecture, which enhances the ability to monitor related resource efficiently. In the meantime, the new model has been implemented in the development of a resource monitoring module integrated within a academic grid project. © 2010 IEEE.

关键词： Grid computing

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A three-stage method to find refining schedules of crude oil operations in refinery

A three-stage method to find refining schedules of crude oil...

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2011 IEEE International Conference on service Operations, Logistics and Informatics, SOLI 2011

作者： Wu, Naiqi Bai, Liping Zhou, Mengchu School of Mechatronics Engineering Guangdong University of Technology Guangzhou 510090 China MoE Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 200092 China ECE NJIT Newark NJ 07 102 United States

ISBN: (纸本)9781457705731

A short-term scheduling problem for crude oil operations is highly challenging. There lack efficient techniques and software tools for its solution. Our prior research shows that it may be solved in a hierarchical way. At the upper level, we find a realizable refining schedule to optimize concerned objectives. At the lower level, a detailed schedule is obtained to realize it. This work attempts to present a three-stage method to solve the the upper level problem from a control perspective. The first stage solves a linear programming problem to determine the maximal production rate. The second one solves a transportation problem to optimally assign crude oil types and volume to the distillers. The last one adjusts and sequences oil parcels. Consequently, the proposed method is computationally efficient. An industrial case study is presented to show the result. © 2011 IEEE.

关键词： Crude oil

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Study on stability of traffic flow for cooperative driving control

Study on stability of traffic flow for cooperative driving c...

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IEEE Symposium on Intelligent Vehicle

作者： Zhipeng Li Ping Wang Fuqiang Liu Key Laboratory of Embedded System and ServIce Computing supported by Ministry of Education University of Tongji Shanghai China

ISBN: (纸本)9781424435036

In the literature of traffic flow theory, the research on the effect on stability of traffic flow for cooperative driving control possesses an important significance. However, presently the the study on the problem is unsatisfactory because it is difficult to determine the impact qualitatively or quantitatively in real traffic experiment. In this paper, some efforts have been made for better understanding the effect on stability of traffic flow for cooperative driving control by investigating the stability for lattice traffic models, which are presented here by incorporating motion information of cars preceding. From linear stability analysis and direct simulations validation, we learn some properties of the effect on the stability and congestion waves by using the information of many other cars. First, cooperative driving behavior of many cars preceding can efficiently stabilize the traffic flow. Second, cooperative driving behavior of the cars nearby plays a prominent role in stability. Third, when the car number participating in cooperative driving policy exceeds a certain value, the congestion waves will disappear.

关键词： Traffic control Control systems Communication system traffic control Roads Laboratories embedded system embedded computing Stability analysis Transportation Mathematical model

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ST-ReP: Learning Predictive Representations Efficiently for Spatial-Temporal Forecasting

arXiv

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arXiv 2024年

作者： Zheng, Qi Yao, Zihao Zhang, Yaying The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China

Spatial-temporal forecasting is crucial and widely applicable in various domains such as traffic, energy, and climate. Benefiting from the abundance of unlabeled spatial-temporal data, self-supervised methods are increasingly adapted to learn spatial-temporal representations. However, it encounters three key challenges: 1) the difficulty in selecting reliable negative pairs due to the homogeneity of variables, hindering contrastive learning methods;2) overlooking spatial correlations across variables over time;3) limitations of efficiency and scalability in existing self-supervised learning methods. To tackle these, we propose a lightweight representation-learning model ST-ReP, integrating current value reconstruction and future value prediction into the pre-training framework for spatial-temporal forecasting. And we design a new spatial-temporal encoder to model fine-grained relationships. Moreover, multi-time scale analysis is incorporated into the self-supervised loss to enhance predictive capability. Experimental results across diverse domains demonstrate that the proposed model surpasses pre-training-based baselines, showcasing its ability to learn compact and semantically enriched representations while exhibiting superior scalability. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Visual Tracking Based on Mixture Motion Model and Incorporate Observation Distribution

Visual Tracking Based on Mixture Motion Model and Incorporat...

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Congress on Image and Signal Processing, CISP

作者： Keju Zu Yu Yang Zhipeng Li Fuqiang Liu Min Tian The Key Laboratory of Embedded System and Service Computing supported by Ministry ofEducation University of Tongji Shanghai China

Accurate visual object tracking through long sequences is a challenging task since object's appearance changes and complex motion happens. We present mixture motion model and incorporate observation model within the Monte Carlo framework to achieve robust visual tracking. The mixture motion model which employs important history motion information of the target is built according to a motion measurement matrix to model the target's transition state. Meanwhile, the incorporate observation model is established by introducing SVM classification scores into normal tracking observation model. A particles filter's implementation with these mixture models is demonstrated, which leads to robust tracking results, especially in occlusion and complex scene.

关键词： Target tracking Particle tracking Monte Carlo methods Robustness Support vector machines Support vector machine classification Particle filters History Signal processing Laboratories

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A Remote Debugging system for OpenMP Parallel Program

A Remote Debugging System for OpenMP Parallel Program

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International Conference on Transportation, Mechanical, and Electrical Engineering

作者： Hua Chai Qinqin Shi The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China

ISBN: (纸本)9781457717000

A remote debugging system for OpenMP parallel program is presented in this paper. The system consists of two parts, namely, an integrated debugging environment running on the clent-side and a background daemon running on the server-side. Information exchange between the two sides is accomplished through the socket-based network communication technology. The remote debugging function is realized by the automatic instrumentation technology at code level based on syntax tree. Instance tests show that the system can provide remote users with correctness checking, performance analysis and other debugging functions for OpenMP parallel programs. The user interface is simple and very easy to use.

关键词： Parallel Program OpenMP Remote Debugging Syntax Tree Instrumentation Technology

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