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检索条件"机构=Beijing Key Laboratory on Integration and Analysis of Large-cale Stream Data"

共 139 条记录，以下是51-60 订阅

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Web Service Clustering Method Based on Word Vector and Biterm Topic Model 6

Web Service Clustering Method Based on Word Vector and Biter...

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6th IEEE International Conference on Cloud Computing and Big data Analytics, ICCCBDA 2021

作者： Yang, Dongju He, Dan Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Data Engineering Institute North China University of Technology Beijing China

ISBN: (纸本)9780738105338

The increasing number of Web services brings challenges for accurate and efficient service discovery. Service clustering technology can narrow the retrieval range of service discovery and improve the efficiency of service discovery. However, due to the short text features with few words and difficult feature extraction in service description text, the traditional text clustering model can not get effective clusters. In this paper, a web service clustering method based on word vector and BTM (Biterm Topic Model) is proposed. Firstly, the sparse feature problem of short text is alleviated by expanding the word vector. Secondly, the extended service description text is modeled by the BTM based on Gibbs sampling. Finally, the k-means algorithm is used to cluster web services. Compared with LDA (Latent Dirichlet Allocation) and BTM, this method improves the F-Measure of clustering index. Experimental results show that the proposed method can effectively alleviate the problem of sparse service description text features and improve the clustering effect of services. © 2021 IEEE.

关键词： Web services

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Research on Ship Classification Method Based on AIS data 15th

Research on Ship Classification Method Based on AIS Data

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15th CCF Conference on Computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020

作者： Luo, Pu Gao, Jing Wang, Guiling Han, Yanbo Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China North China University of Technology Beijing China

ISBN: (纸本)9789811625398

It is important for maritime authorities to effectively identify and classify unknown types of ships in historical trajectory data. A method of using trajectory image and training based on deep residual network (ResNet) to obtain ship type classifier is proposed. First, a method of integrating speed information into the trajectory image is proposed, then the trajectory image is input into the ResNet model for training to obtain the classifier. Finally, the real AIS data of 5 ship types are used for experiments. The experimental results show that the method can meet the requirements of type identification and classification of ships based on AIS data, and provides technical support for further research on the identification of camouflaged ships, mining of ship behavior patterns and anomaly detection. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Ships

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The establishment and adaptive iteration of service community based on context association mining

The establishment and adaptive iteration of service communit...

引用

Electronic Technology, Communication and Information (ICETCI), IEEE International Conference on

作者： Lijun Wang Beijing Key Laboratory on Integration and Analsis of Large-Scale Stream Data North China University of Technology Beijing China

The scale and types of service resources on the internet are becoming increasingly complex, and providing services for users' personalized needs is a research hotspot. This article proposes a user-centered service community concept to create a service community that can meet their needs. Existing methods for constructing service communities typically cannot meet the requirements of a user-centered service community as proposed in this article. The main method for creating a user-centered service community is to analyze the QoS data of users' historical service calls and incorporate temporal and spatial contexts into a collaborative filtering algorithm to obtain resources that are relevant to the user. The obtained resources are then added to the service community. Finally, the community mean square error is used to verify the effectiveness and feasibility of the algorithm. In order to ensure the dynamic nature of the service community and consider changes in user needs, this article defines two rules to trigger adaptive iteration and updates the resources of the service community based on the triggering rules.

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Efficient Time Series Predicting with Feature Selection and Temporal Convolutional Network 4

Efficient Time Series Predicting with Feature Selection and ...

引用

4th IEEE International Conference on Computer and Communication Engineering Technology, CCET 2021

作者： Li, Xiaoqi Liu, Chen He, Yiru North China University of Technology Beijing Key Laboratory Integration and Analysis of Large-Scale Stream Data School of Information Science and Technology Beijing China

ISBN: (纸本)9781665438902

TCN (Temporal Convolutional Network) has been proved that it outperforms canonical recurrent networks across different tasks and demonstrates longer effective memory. Besides, it speeds up the prediction with parallel calculation. The Granger causality test is used to detect temporal causality between variables to select the most influential features. In this paper, we adopt the Granger causality test and introduce TCN with two-dimensional convolution kernel to predict the temperature of superheater in thermal power plants. To reduce the noise of dataset, we use the Granger causality test to select features which have the greatest impact on the temperature prediction. The challenge of efficiency is addressed by applying the TCN with two-dimensional convolution kernel. To evaluate our method, we conducted experiments on time series predicting with the real-sensor data from thermal power plant. Compared with canonical recurrent network, our method has improved the predicting ability in terms of accuracy and efficiency. © 2021 IEEE.

关键词： Time series

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A Neural Network Based High-Rise Building Block Detection 4

A Neural Network Based High-Rise Building Block Detection

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4th IEEE International Conference on Computer and Communication Engineering Technology, CCET 2021

作者： Shu, Jie Guan, Yanjie Wang, Renxiang North China University of Technology School of Information Science and Technology Beijing China Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing China

ISBN: (纸本)9781665438902

High-rise building block detection is very useful for urban morphology analysis, such as urban wind field analysis. However, in previous studies, high-rise buildings are determined by data from several different sources. This paper presents a new method of high-rise building block detection based only on high-resolution satellite images. It first detects the potential regions of high-rise building blocks through a neural network. Then a filtering algorithm is used to distinguish these potential regions to obtain final high-rise building blocks. Experimental results show that this method is much better than other methods in detecting high-rise building blocks, with F1-score 0.87. This method also gives the possibility of using only a single source of data to detect high-rise buildings. © 2021 IEEE.

关键词： Morphology

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In-Air Handwriting Recognition Using Acoustic Impulse Signals 19th

In-Air Handwriting Recognition Using Acoustic Impulse Signal...

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19th International Conference on Smart Living and Public Health, ICOST 2022

作者： Niu, Kai Zhang, Fusang Fu, Xiaolai Jin, Beihong Peking University Beijing China Beijing Xiaomi Mobile Software Co. Ltd. Beijing China Institute of Software Chinese Academy of Science Beijing China Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China

ISBN: (纸本)9783031095924

This paper presents AcousticPAD, a contactless and robust handwriting recognition system that extends the input and interactions beyond the touchscreen using acoustic signals, thus very useful under the impact of the COVID-19 epidemic. To achieve this, we carefully exploit acoustic pulse signals with high accuracy of time of fight (ToF) measurements. Then we employ trilateration localization method to capture the trajectory of handwriting in air. After that, we incorporate a data augmentation module to enhance the handwriting recognition performance. Finally, we customize a back propagation neural network that leverages augmented image dataset to train a model and recognize the acoustic system generated handwriting characters. We implement AcousticPAD prototype using cheap commodity acoustic sensors, and conduct extensive real environment experiments to evaluate its performance. The results validate the robustness of AcousticPAD, and show that it supports 10 digits and 26 English letters recognition at high accuracies. © 2022, The Author(s).

关键词： Backpropagation

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"Failure" Service Pattern Mining for Exploratory Service Composition 17th

"Failure" Service Pattern Mining for Exploratory Service Com...

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17th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Yuan, Yunjing Wang, Jing Han, Yanbo Li, Qianwen Chen, Gaojian Jiao, Boyang Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data School of Information Science and Technology No.5 Jinyuanzhuang Road Beijing Shijingshan District China

ISBN: (纸本)9783030926342

To adapt to uncertain and dynamic requirements, exploratory service composition enables business users to construct service composition processes in a trial-and-error manner. A large number of service composition processes are generated, which can be learned to improve the reusability of the service composition processes. By mining these service composition processes and abstracting the mining results to service patterns, the efficiency of service composition can be effectively improved. At present, there has been some research on the methods of service pattern mining. Most of the work focuses on successful service composition processes, but the ones that fail are also valuable. For example, by using the mining results of failure service composition processes, the accuracy of service recommendations can be improved. To solve this problem, this paper proposes a "failure" service pattern mining algorithm (FSPMA) for exploratory service composition, which extends the gSpan algorithm, and can mine "failure" service patterns from service composition processes for further reuse. Meanwhile, the exploratory service composition model and the service pattern model are explained for the FSPMA. The prototype implementation of the exploratory service composition environment is introduced, which integrates the FSPMA. The experimental evaluation is explained to verify the algorithm, and the result shows that the efficiency of the FSPMA has a significant improvement in mining "failure" service patterns compared with the gSpan algorithm and the TKG algorithm. Finally, the application of "failure" service patterns in service recommendations is given. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Reusability

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Service-Based Event Penetration from IoT Sensors to Businesses: a Case Study

Service-Based Event Penetration from IoT Sensors to Business...

引用

2022 CCF International Conference on Service Science, CCF ICSS 2022

作者： Wang, Guiling Fang, Jun Wang, Jing Yu, Jian Zhang, Liang Han, Yanbo Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China North China University of Technology School of Information Science and Technology Beijing China Auckland University of Technology Department of Computer Science Auckland New Zealand Shanghai Key Laboratory of Data Science Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China

ISBN: (数字)9781665498616

ISBN: (纸本)9781665498616

By leveraging IoT Big data, BPM can gain real-time physical world information to make faster and more accurate decisions, but there is a technical gap between IoT sensors and businesses. To bridge the gap, an event penetration mechanism from IoT sensors to business processes is proposed along a practical case study. This paper presents a concrete IoT-BPM application case dealing with seaborne safety ensurance in transporting liquefied natural gas (LNG), analyzes its technical challenges, and examines the feasibility and supposed effects of the BRIBOT approach. © 2022 IEEE.

关键词： Internet of things

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A Novel Heuristic Method for Emergency Path Planning Based on Dynamic Spatial-Temporal Characteristics Map

A Novel Heuristic Method for Emergency Path Planning Based o...

引用

2020 International Conference on Industrial Applications of Big data and Artificial Intelligence, BDAI 2020

作者： Yang, Bowen Yuan, Lei Yan, Jin Ding, Zhiming Cai, Zhi Guo, Limin Beijing University of Technology Beijing100124 China Institute of Software Chinese Academy of Sciences Beijing100190 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing100144 China

Emergency path planning technology is one of the hot research points in intelligent transportation systems. There are many methodologies and applications in emergency path planning. However, due to the complexity of the urban network and crowded road conditions, the difficulty of emergency path planning. The objective of emergency path planning is to get the vehicle out of the emergency areas and to its destination in the shortest time. Road congestion caused by emergency situations in cities directly affects the original road network structure. Then the weight of the original road network is no longer suitable as a basis for path recommendation and the value of edges of weight will change over time. To handle the dynamic road network, a novel situational time-stamp heuristic search algorithm (STH) is introduced for the situation space. This algorithm can effectively solve the problem of diversity of situational networks. STH can build a heuristic that adapts to time changes based on the map refresh time, and ensures that the path given in the time window T is optimal. Moreover, STH can give a pruning strategy according to the search time window T, which significantly improves the efficiency of the algorithm. Finally, the path planned by STH is better than the baseline algorithm. © Published under licence by IOP Publishing Ltd.

关键词： Motion planning

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AISCW: Artificial Intelligence Supported Cooperative Work The New Frontier of CSCW 19th

AISCW: Artificial Intelligence Supported Cooperative Work Th...

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19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Shi, Meilin Wang, Guiling Zhang, Shaohua Jiang, Jinlei Xiang, Yong Computer Science Department Tsinghua University Beijing100084 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing China School of Information Science and Technology North China University of Technology Beijing100144 China Camford Royal School Beijing102211 China

ISBN: (纸本)9789819623723

Under today’s natural environmental, social and technological conditions, the global and large-scale challenges faced by humanity are becoming increasingly significant, making collaborative work increasingly important. Computer-Supported Cooperative Work (CSCW) is anticipated to assume a more pivotal role in modern society and within the fabric of production and daily life. In recent years, Artificial Intelligence has witnessed remarkable advancements, and the "cooperative intelligence" of AI systems has been enhanced. The role of computers in supporting human collaboration is no longer limited to an auxiliary role but also serves as a cooperative role, bringing new challenges to the study of CSCW, ranging from basic collaborative theories to key technologies. This paper proposes a new frontier in CSCW, AI Supported Cooperative Work (AISCW) and its system research. The objective is to take use of AI technologies to enhance and optimize human collaborative activities. By delving into the interplay between CSCW and AI, this paper outlines the foundational concepts, key issues and potential research focal points of AISCW. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： AISCW Cooperative AI CSCW

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