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Congress on Image and Signal Processing, CISP

作者： Keju Zu Fuqiang Liu Zhipeng Li Key Laboratory of Embedded System and Service Computing University of Tongji Shanghai China

When the high occlusion occurs in crowded scene, face detection is a better substitute for detecting pedestrian. In this paper, we present a novel crowd analysis method based on discriminative descriptor of faces and support vector machine (SVM) ensemble. Through manipulating the input features in the same sample set, the different input features of faces are extracted to train two SVM classifiers. The classification scores of two generated classifiers are combined adaptively to make a collective decision. The first SVM, as the principal classifier gives out most of face hypotheses, while the second SVM serves as secondary one to rejecting the false positive. We present experiment to test the proposed method in crowded subway video, and the result shows that the SVM ensemble outperforms the single SVM in counting the pedestrian.

关键词： Layout Support vector machines Support vector machine classification Face detection Histograms Surveillance Humans Lighting Cameras Feature extraction

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Real-Time Communication Method for mHealth Base on Extended XMPP Protocol

Real-Time Communication Method for mHealth Base on Extended ...

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The 9th International Conference on Wireless Communications,Networking and Mobile computing(WICOM 2013)

作者： Chenjie Shi Yu Fang The Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University

Considering characteristic of mHealth communication and problems of existing methods, this paper presents a real-time communication method for mHealth based on extended XMPP protocol. The method can maintain the role status efficiently and reduce data latency during the communication process. Meanwhile, it can be extended flexibly to meet increasing communication demands of mHealth services. Furthermore, a system framework is presented to support telemonitoring scene. Finally, system implementation and feasibility tests verify the effectiveness of the method and framework.

关键词： mHealth XMPP Protocol Real-Time Communication

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Vehicle classification based on the fusion of deep network features and traditional features

Vehicle classification based on the fusion of deep network f...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Hua Qian Yaying Zhang Chunmei Liu Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Consider to the defect of traditional features which have high empirical components. A vehicle classification algorithm based on the fusion of higher-layer features of a deep network and traditional features was proposed. Firstly, the traditional features of PHOG and LBP-EOH were extracted. Secondly, the higher-layer features excavated from the vehicle pictures by deep belief networks were added, making these three kinds of features together by feature fusion. Finally, support vector machine is used to train and classify the vehicle. When the number of training samples is large enough, the algorithm has a significant effect compared to those with traditional features. It can achieve the accuracy of 95% in the six categories of vehicles.

关键词： Vehicles Support vector machines Feature extraction Algorithm design and analysis

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Vehicle to Vehicle and Roadside Sensor Communication for Intelligent Navigation

Vehicle to Vehicle and Roadside Sensor Communication for Int...

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International Conference on Wireless Communications, Networking and Mobile computing (WiCom)

作者： Rongyan Xia Chen Ye Dongdong Zhang Key Laboratory of Embedded System and Service Computing University of Tongji Shanghai China

Hybrid sensor network technology is a key component of future ITS applications. In this paper, we propose a hybrid architecture that combined VANETs and roadside WSNs for intelligent navigation. Different from traditional ITS application, Sensors in WSNs and VANETs are used to perceive and exchange roadside and vehicular information to support the intelligent navigating decision process. We firstly give the system and protocol architecture, and then we discuss the scenarios and use cases of our system in intelligent navigation. After that, we describe the software and hardware implementation of our prototype, conduct a simulation on the discussed scenarios, and present a detailed data communication experimental result to prove the feasibility of our prototype.

关键词： Vehicles Wireless sensor networks Navigation Data communication Roads Prototypes Path planning

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Accelerating Support Vector Machine Learning with GPU-Based MapReduce

Accelerating Support Vector Machine Learning with GPU-Based ...

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IEEE International Conference on systems, Man and Cybernetics

作者： Tianyao Sun Hanli Wang Yun Shen Jun Wu Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

ISBN: (纸本)9781479986989

With the exploding growth of data, the computational complexity required by learning Support Vector Machine (SVM) lays a heavy burden on real-world applications. To address this issue, parallel computational techniques can be employed such as the Graphics Processing Units (GPUs) and MapReduce model. As it is well known, GPUs are microprocessors on a multi-core architecture which reveal high performance in mass data parallel computing, and MapReduce allows computational tasks to be divided into a plurality of parts, distributed to various computing nodes and combined on a single node. In this paper, we propose a GPU-based MapReduce framework to accelerate SVM learning by jointly utilizing the parallel computing power of GPU and MapReduce. Extensive experimental results have verified the effectiveness and efficiency of the proposed approach.

关键词： Graphics processing units Support vector machines Kernel Training Optimization Acceleration Parallel processing

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An Efficient Webpage Classification Algorithm Based on LSH

An Efficient Webpage Classification Algorithm Based on LSH

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International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015

作者： Junjun Liu Haichun Sun Zhijun Ding The Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University

With the explosive growth of Internet information,it is more and more important to fetch real-time and related *** it puts forward higher requirement on the speed of webpage classification which is one of common methods to retrieve and manage *** get a more efficient classifier,this paper proposes a webpage classification method based on locality sensitive hash *** which,three innovative modules including building feature dictionary,mapping feature vectors to fingerprints using Localitysensitive hashing,and extending webpage features are *** compare results show that the proposed algorithm has better performance in lower time than the na?ve bayes one.

关键词： Explosive growth Webpage classification Locality-sensitive hashing Fingerprint Extending webpage features

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A recent survey on controllable text generation: A causal perspective

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Fundamental Research 2024年第3期5卷 1194-1203页

作者： Wang, Junli Zhang, Chenyang Zhang, Dongyu Tong, Haibo Yan, Chungang Jiang, Changjun Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai 201804 China National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University Shanghai 201804 China

As an important subject of natural language generation, Controllable Text Generation (CTG) focuses on integrating additional constraints and controls while generating texts and has attracted a lot of attention. Existing controllable text generation approaches mainly capture the statistical association implied within training texts, but generated texts lack causality consideration. This paper intends to review recent CTG approaches from a causal perspective. Firstly, according to previous research on basic types of CTG models, it is discovered that their essence is to obtain the association, and then four kinds of challenges caused by absence of causality are introduced. Next, this paper reviews the improvements to address these challenges from four aspects, namely representation disentanglement, causal inference, knowledge enhancement and multi-aspect CTG respectively. Additionally, this paper inspects existing evaluations of CTG, especially evaluations for causality of CTG. Finally, this review discusses some future research directions for the causality improvement of CTG and makes a conclusion. © 2024

关键词： Causal inference Causality Controllable text generation Knowledge enhancement Representation disentanglement

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Adopting WirelessHART for In-Vehicle-Networking

Adopting WirelessHART for In-Vehicle-Networking

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IEEE International Symposium on Cyberspace Safety and Security

作者： Zhuo Bi Deji Chen Cheng Wang Changjun Jiang Ming Chen Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University

ISBN: (纸本)9781479989386

It is estimated that the breakthrough in the broad deployment of Internet of Things (IoT) could come from smart cars. Indeed, we have seen multi-facet advances around cars: new material, in vehicle infotainment, driverless cars, smart transportation, electrical vehicles, etc. However, in-vehicle-networking has been mainly by wire;the wiring for a car is largely prebuilt during the design phase. With more and more things networked within a car, wiring has taken up 1-2 percent of the total weight. This translates into burning up to 0.1 kilogram fuel over 100 kilometers. On the other hand, the advances in wireless technology, especially the broad acceptance of WirelssHART in the industrial settings, has proved its capability in harsh environments. This paper studies what could happen if we use WirelessHART mesh network for in-vehicle communication. While new wireless network protocols are needed to perform the task of CAN, the dominant in-vehicle fieldbus, WirelessHART could take on the work performed by LIN, the fieldbus for peripheral devices. A detailed study is provided to compare these buses. Road tests were performed, in which a WirelessHART network keeps running for the whole 20 minute period.

关键词： Reliable Wireless Sensor Network Smart Car CAN LIN WirelessHART

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A Traffic Flow Prediction Approach: LSTM with Detrending

A Traffic Flow Prediction Approach: LSTM with Detrending

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IEEE International Conference on Progress in Informatics and computing (PIC)

作者： Zheng Zhao Yaying Zhang The Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

ISBN: (纸本)9781538676738;9781538676721

Traffic flow prediction plays a key role in many Intelligent Transportation system research and applications. It aims to forecast the forthcoming traffic conditions with the help of historical data. Urban traffic always has its morning and afternoon peak hours. We also observed that the urban traffic flow can always be divided into main trend data and its residual part. The main trend data presents a similar trend on different days. The residual data is time-variant part which reflects the short-term fluctuation of traffic condition over each day. Enlighted by detrending, Principal Component Analysis (PCA) method is applied to extract the main trend data in this paper. The residual data is obtained by subtracting the main trend data from the overall traffic flow data. Then Long Short-Term Memory (LSTM) model is proposed to predict the residual data. With main trend data and predicted residual data, the urban traffic flow can be predicted by the joint PCA and LSTM approach. Finally, the empirical study demonstrates the propose method outperforms similar traffic prediction models.

关键词： Market research Predictive models Principal component analysis Logic gates Roads Feature extraction Artificial neural networks

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HSim: A novel method on similarity computation by hybrid measure

HSim: A novel method on similarity computation by hybrid mea...

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International Conference on Information and Communication systems (ICICS)

作者： Qin Zhao Cheng Wang Changjun Jiang The Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

ISBN: (纸本)9781479973507

Link similarity is widely applied in measuring the similarity between objects, e.g., web pages, scientific papers and social networks. However, there are a lot of drawbacks in existing methods of measuring link similarity. In brief, these methods can not handle some semantic-similar content. Moreover, the computation of them are not accurate in some scenes. In this paper, we present a novel method of measuring link similarity called HSim. It introduces the semantic similarity to calculate the similarity between objects, and overcomes the drawback that existing methods ignore the semantic information of objects. We also develop a novel computation function to make the result of similarity more accurate.

关键词： Semantics Web pages Complexity theory Data mining Internet Ontologies

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