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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： You Xin Liu Chen Yang Yang School of Information North China University of Technology Beijing China Beijing Key Laboratory of Large-scale Stream Data Integration and Analysis Technology Beijing China Beijing Huaxia Qixin Technology Co. Ltd China

There are many types of documents in fossil-fuel power station to describe equipment failures, including maintenance records, treatment diagnosis suggestions, historical cases, and equipment knowledge. The knowledge of equipment anomaly diagnosis and handling is scattered in different documents. Extract and fuse scattered knowledge from these scattered documents to generate a knowledge graph for equipment fault di-agnosis, providing necessary decision support for maintenance personnel to discover and handle equipment faults. This article proposes an implementation method for extracting and integrating equipment fault knowledge from diverse and multi type text records to form a knowledge graph. A thermal power plant equipment fault Q&A system based on the fusion of open source large language models and knowledge graphs has been developed. Main contributions: (1) A knowledge extraction algorithm integrating BERT-WWM model and pointer annotation method is proposed to extract entity relations of fault text jointly. Experiments show that the method performs well in extracting overlapped triples, and F1 is improved by 8.51 % compared with existing algorithms; (2) A knowledge fusion model based on RoBERTa-BiLSTM is proposed, which fully utilizes the feature information of the entity text to be disambiguated and the entity mention text, and cap-tures the interdependent features within the sentence through attention mechanism. The experiment shows that this method improves F1 by 9.56% compared to existing fusion algorithms. (3) Based on the open-source large model ChatGLM, a fusion method of knowledge graph and large model ChatGLM was explored, and a device fault question answering system for thermal power plants was implemented, achieving high accuracy in practical applications.

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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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Unpaired Learning of Roadway-Level Traffic Paths from Trajectories 17th

Unpaired Learning of Roadway-Level Traffic Paths from Traj...

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

作者： Jia, Weixing Wang, Guiling Yang, Xuankai Zhang, Fengquan School of Information Science and Technology North China University of Technology Beijing100144 China Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of Technology Beijing100144 China

ISBN: (纸本)9783030926342

Traffic path data can be used as the basis for traffic monitoring and other technologies, which is essential for developing traffic-related technologies. Traditional methods of traffic path data extraction can no longer meet the needs because they cannot solve the problem of lacking standard benchmark data that may exist in the traffic field. Deep learning-based path extraction methods using large-scale data are a class of promising approaches. However, most of the deep learning-based path extraction methods are supervised and rely on paired training data. This paper proposes an unpaired learning method for fine-grained roadway-level paths from trajectory data based on CycleGAN. The method constructs spatio-temporal features based on HSV color space from trajectories which can enhance the model’s ability to recognize the roadway details. It transforms the features using convolutional layers, which can preserve the spatio-temporal information of the features, thus making the extraction results more accurate. We conduct experiments using urban and maritime traffic trajectory data and compare the proposed method with the state-of-the-art methods. The results of our model have more roadway level details, higher precision and F1 score than the other existing unsupervised traffic path learning methods. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Trajectories

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H-MGSR: A Hierarchical Motif-based Graph Attention Neural Network for Service Recommendation

H-MGSR: A Hierarchical Motif-based Graph Attention Neural Ne...

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IEEE International Conference on Web Services (ICWS)

作者： Xin Zheng Guiling Wang Jiayu Zhang Yuqi Zhang Nancy Wang Jian Yu Yanbo Han 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 Beijing China Department of Computer Science Auckland University of Technology Auckland New Zealand

The rapid development of web services has made it increasingly challenging for developers to find desired web services. To address this issue, researchers have developed various powerful models for service recommender systems. Recently, graph neural networks have shown promising performance in various deep learning tasks including service recommendation. This paper proposes a novel graph neural network for web service recommendation using a hierarchical attention mechanism that combines a node-level and a motif-level attention mechanisms. The node-level attention mechanism is responsible for aggregating information by the importance of different neighbors, while the motif-level attention mechanism performs a weighted combination of the node embeddings generated from different motif adjacency matrices. Finally, the generated node embeddings are optimized by the multi-layer perceptron (MLP), which in turn provide recommendations. Experimental results on real-world datasets demonstrate that our proposed model outperforms state-of-the-art approaches. Additionally, we conduct a model analysis to investigate the importance of different motifs. Overall, our proposed method shows promising performance for web service recommendation and highlights the potential of using graph neural networks in this domain.

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Spatial-Temporal Aware Business Event Forecasting for Proactive Services from IoT Sensory data

Spatial-Temporal Aware Business Event Forecasting for Proact...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jiayu Zhang Guiling Wang Yongpeng Shi Xin Zheng Jian Yu 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 Beijing China Department of Computer Science Auckland University of Technology Auckland New Zealand

With the development of IoT and AI, better knowledge and information can be learned and extracted from IoT sensory data which enables business systems to proactively provide services to customers. This paper is the first study that attempts to forecast high-level business events from raw IoT sensory event data to improve the proactivity of services and applications. We propose a deep learning based business event forecasting framework, i.e., IoT2BE, which extracts prior knowledge to identify business events from IoT sensory data, extracts features in multi-views including the spatial and temporal view, generates spatio-temporal business event embeddings, and uses a seq2seq model with attention to predict the future business events. Extensive experiments are based on two datasets including one real-world maritime ship trajectory dataset and one publicly available raw sensor dataset from a smart home environment. The results demonstrate that our framework can be effectively applied in various business scenarios.

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A Dynamic Binding Method for Situation-aware IoT Services Targeting Proactive BPM

A Dynamic Binding Method for Situation-aware IoT Services Ta...

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International Conference on Computer Supported Cooperative Work in Design

作者： Xiuxian Li Guiling Wang Yongpeng Shi Jian Yu 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 Beijing China Department of Computer Science Auckland University of Technology Auckland New Zealand

By leveraging IoT data, Business Process Management (BPM) systems can sense the physical world situation and make more accurate decisions proactively, but the current BPM systems lack the effective method to take good use of IoT data. In this paper, a situation-aware dynamic binding method for IoT services targeting proactive BPM is proposed to meet this requirement. The method first establishes a prediction model to predict the bindable IoT services under given situations by constructing an encoder-decoder structure network model using bi-directional gated recurrent units (Bi-GRU) and incorporating an attention mechanism. Then our method creates a Dynamic IoT Service Task (DIT) model for BPM. This enables the BPM system to integrate the prediction model with dynamic switching of IoT services and a parallel running architecture for multiple IoT services. A prototype system is developed and a case study is conducted to evaluate the performance of the method. The study focuses on the safety supervision scenario for the transportation of hazardous Liquefied Natural Gas (LNG) by sea. Results show that the prediction model proposed in this paper outperforms other models on multiple indicators. Also, the case study and experimental results verify the effectiveness of the method.

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A GAN-Based Power Quality Anomaly Detection Method for Imbalanced Multivariate Time Series

A GAN-Based Power Quality Anomaly Detection Method for Imbal...

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IEEE International Conference on Computer and Communication Engineering Technology (CCET)

作者： Jiachen Huang Chen Liu Yang Yang Yunjia Liu Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of Technology Beijing China School of Computer Science. North China University of Technology Beijing China Huaxia Qixin Limited Company Beijing China

Time series anomaly detection is an important and well-researched topic with extensive applications in finance, healthcare, biological data, and industrial production. However, in the field of power quality, detecting anomalies in complex and imbalanced multivariate long time series data remains a challenge. This paper proposes a GAN-based method for multivariate time series anomaly detection, which directly trains on raw data captured by power monitoring stations. It addresses the issues of the lack of original examples, difficulty in acquisition, and simulation of abnormal data in power quality. Additionally, the proposed method introduces an attention mechanism and a multi-scale feature fusion mechanism, reducing the accumulation of errors during model training and effectively addressing the problem of overfitting due to the scarcity of abnormal samples and high noise in power quality data. The comprehensive experiments conducted on four original power quality datasets demonstrate that the proposed method significantly improves the performance of anomaly detection in the field of power quality compared to baseline methods.

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Service dependency mining method based on service call chain analysis

Service dependency mining method based on service call chain...

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2021 CCF International Conference on Service Science, ICSS 2021

作者： Lan, Yuanyuan Fang, Lei Zhang, Mingzhu Su, Jianhua Yang, Zhongguo Li, Han North China University of Technology Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data School of Information Science and Technology Beijing China Oilfield Company Institute of PetroChina Changqing Exploration and Development Reserch Shanxi Xi'an China

ISBN: (纸本)9781665440455

In the cloud computing environment, the release and dynamic deployment of microservices face many challenges, and the services' dependency is an important consideration for service deployment. The service call chain logs record rich information, such as the time and delay of each service call in the business tracking process, and present the logs in the form of a call chain. Existing research does not fully consider the local composite service dependencies and their discontinuous dependencies in the service chain. In order to obtain these complex dependencies and provide basic supporting data for the dynamic deployment and adjustment of microservices, a dependency model of microservices is proposed, and a dependency mining method based on call chain logs is designed to extract local dependencies and the discontinuous dependency relationship. The effectiveness of the algorithm is verified by testing on Alibaba Cloud Computing's public data set. © 2021 IEEE.

关键词： Mining

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Multi-graph Spatio-temporal Graph Convolutional Network for Traffic Flow Prediction

arXiv

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arXiv 2023年

作者： Ding, Weilong Zhang, Tianpu Wang, Jianwu Zhao, Zhuofeng The School of Information Science and Technology North China University of Technology Beijing100144 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing100144 China Department of Information Systems University of Maryland Baltimore County United States

Inter-city highway transportation is significant for urban life. As one of the key functions in intelligent transportation system (ITS), traffic evaluation always plays significant role nowadays, and daily traffic flow prediction still faces challenges at network-wide toll stations. On the one hand, the data imbalance in practice among various locations deteriorates the performance of prediction. On the other hand, complex correlative spatiotemporal factors cannot be comprehensively employed in long-term duration. In this paper, a prediction method is proposed for daily traffic flow in highway domain through spatio-temporal deep learning. In our method, data normalization strategy is used to deal with data imbalance, due to long-tail distribution of traffic flow at network-wide toll stations. And then, based on graph convolutional network, we construct networks in distinct semantics to capture spatio-temporal features. Beside that, meteorology and calendar features are used by our model in the full connection stage to extra external characteristics of traffic flow. By extensive experiments and case studies in one Chinese provincial highway, our method shows clear improvement in predictive accuracy than baselines and practical benefits in business. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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A Dual-Level Graph Attention Network and Transformer for Enhanced Trajectory Prediction Under Road Network Constraints

SSRN

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SSRN 2024年

作者： Zhang, Jiayu Guo, Lucas Wang, Guiling Yu, Jian Zheng, Xin Mei, Yusheng Han, Boyang 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 Beijing China Department of Computer Science Auckland University of Technology Auckland New Zealand Southwest Jiaotong University Chengdu China JD Intelligent Cities Research

Predicting vehicle future trajectories enables various Location-Based Services (LBS), including traffic flow monitoring, vehicle route planning, etc. Vehicles are constrained to predefined road networks. Their traveling behaviors are influenced by both their own attributes and road network characteristics hidden in other vehicles' travel histories. Most of the existing methods fail to sufficiently leverage such collaborative trajectory data under road network *** paper proposes a novel model, called HGT-RN (Hierarchical Graph Attention Network and Transformer Networks for Enhanced Trajectory Prediction under Road Network Constraints), which exploits all vehicles' trajectories and integrates road network constraints. Specifically, HGT-RN creates a dual-level Graph Attention Network (GAT) based on global traffic flow and local individual trajectory graph to learn trajectory embeddings: (i) Weighted-directional global traffic flow graph and Weighted-Directional GAT to learn the global-level trajectory embedding by aggregating the neighbors' embeddings based on the spatial transitional relationships among road intersections over all vehicles' trajectories, considering the preference of current trajectory;and (ii) Local individual trajectory graph attention network to learn the local-level trajectory embedding by modeling the transitions within the current trajectory at each intersection. When incorporating the trajectory embeddings into a transformer model for future trajectory prediction, a road network topology-aware loss function is designed to improve accuracy. Extensive experiments conducted on three city-scale real-world taxi trajectory datasets demonstrate that HGT-RN significantly outperforms existing baseline models. The code is available at https://***/zjy9826/HGT-RN. © 2024, The Authors. All rights reserved.

关键词： Trajectories

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