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48th International Conference on Computers and Industrial engineering, CIE 2018

作者： Zhuang, Li Wang, Long Zhang, Zijun School of Data Science City University of Hong Kong Hong Kong Beijing100083 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China

A double-layer data-driven framework for the automated vision inspection of the rail surface cracks is proposed in this paper. Based on images of rails, the proposed framework is capable to detect the location of cracks firstly and next automatically obtain the boundary of cracks via a feature-based linear iterative crack aggregation (FLICA). Extended Haar-like features are applied to develop significant features for identifying cracks in images. Built on extended Haar-like features, a cascading classifier ensemble integrating three single cascading classifiers with a major voting scheme is proposed to decide the presence of cracks in the image. Each single cascading classifier is composed of a sequence of stage classifiers trained by the LogitBoost algorithm. A scalable sliding window carrying the cascading classifier ensemble is applied to scan images of rail tracks, which is identified by the Otsu’s method, and detect cracks. After completing the crack registration in the first layer, the FLICA is developed to discover boundaries of cracks. The effectiveness of the proposed data-driven framework for identifying rail surface cracks is validated with the rail images provided by the China Railway Corporation and Hong Kong Mass Transit Railway (MRT). Six benchmarking methods, the Otsu’s method, mean shift, the visual detection system, the geometrical approach, fully convolutional networks and the U-net, are utilized to prove advantages of the proposed framework. Results of the validation and comparative analyses demonstrate that the proposed framework is most effective in the rail surface crack detection. © 2018, Curran Associates Inc. All rights reserved.

关键词： Crack detection

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A Multiple-Blockchains based Service Monitoring Framework in Edge-Cloud Computing 23

A Multiple-Blockchains based Service Monitoring Framework in...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Fu, Hang Wang, Puwei Li, Haoran Zhan, Ying Chen, Jinchuan Du, Xiaoyong Guizhou University of Finance and Economics Guiyang City550025 China School of Information Renmin University of China China Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Beijing100872 China

ISBN: (纸本)9781665494571

In the edge-cloud computing, the applications usually are delivered as services, each of which runs independently and can cooperate to construct the complicated applications. However, it is difficult to monitor the services in the decentralized edge-cloud environment since there usually does not exist the central authorization to guarantee trustiness of the service quality data collected. To address the trust issue, the blockchain techniques have been introduced to the service monitoring. Considering the services usually are distributed on the edge nodes, if we deploy only a blockchain on the edge nodes near to the services, it may have to support both the service registration and monitoring because of the loose-coupling principle. That would influence the performance, even would crash the edge nodes. In this paper, we propose a multiple-blockchains based framework, including a registration blockchain that focuses on the service registration and discovery, and a set of monitoring blockchains that focus on the service invocation and monitoring. The registration blockchain is deployed on the cloud nodes, and the monitoring blockchains are deployed on the edge nodes near to the services for collecting efficiently and precisely the service quality data. Based on the cross-chain technology, we design a cross-chain service discovery between the monitoring blockchain and the registration blockchain for finding the services the requesters need. Finally, we implement the registration and monitoring blockchains based on the blockchain platform Cosmos and conduct a set of experiments to show the effectiveness of the proposed framework. © 2021 IEEE.

关键词： Authorization Cloud computing Smart cities Computer crashes Blockchains Monitoring

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Marginal Value-Based Edge Resource Pricing and Allocation for Deadline-Sensitive Tasks 42

Marginal Value-Based Edge Resource Pricing and Allocation fo...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Wang, Puwei Sun, Zhouxing Zhan, Ying Li, Haoran Du, Xiaoyong Renmin University of China School of Information China Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Beijing100872 China Guizhou University of Finance and Economics Guiyang City550025 China

ISBN: (纸本)9798350334142

In edge computing (EC), resource allocation is to allocate computing, storage and networking resources on the edge nodes (ENs) efficiently and reasonably to tasks generated by users. Due to the resource-limitation of ENs, the tasks often need to compete for the resources. Pricing mechanisms are widely used to deal with the resource allocation problem, and the valuations of tasks play a critical role in the price mechanisms. However, users naturally are not willing to expose the valuations of their tasks due to conflicts of interests. Current research works usually adopt truthful auctions to motivate the users to report honestly the valuations of their tasks. In this paper, we introduce the marginal value to estimate the valuations of tasks, and propose a marginal value-based pricing mechanism using the incentive theory, which motivates the tasks with higher marginal values to actively request more resources. The EC platform sets the resource prices using the price mechanism, and then the users determine their resource requests relying on the resource prices and the valuations of their tasks. After receiving the deadline-sensitive tasks from the users, the resource allocation can be modeled as a knapsack problem with the deadline constraints. Extensive experimental results demonstrate that our approach is computationally efficient and is promising in enhancing the utility of the EC platform and the tasks. © 2023 IEEE.

关键词： Resource allocation

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Topology-based data compression in wireless sensor networks

Topology-based data compression in wireless sensor networks

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13th International Conference on Web-Age Information Management, WAIM 2012

作者： Mo, Shangfeng Chen, Hong Li, Yinglong Key Laboratory of Data Engineering and Knowledge Engineering of MOE Renmin University of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China Hunan University of Science and Technology Xiangtan 411201 China

ISBN: (纸本)9783642322808

In this paper, we address the problem of data Compression which is critical in wireless sensor networks. We proposed a novel Topology-based data Compression (TDC) algorithm for wireless sensor networks. We utilize the topological structure of routing tree to reduce the transmission of message packets. We analyzed the differences and relations between our algorithm and other compression algorithms. Extensive experiments are conducted to evaluate the performance of the proposed TDC approach by using two kinds of data sets: real data set and synthetic data set. The results show that the TDC algorithm substantially outperforms Non-compression algorithm in terms of packets transmitted. © 2012 Springer-Verlag.

关键词： Topology

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Towards comprehensive integration management of business continuity, records and knowledge

Towards comprehensive integration management of business con...

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作者： An, Xiaomi Wang, Wang Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Beijing 100872 China School of Information Resource Management Renmin University of China Beijing 100872 China

ISBN: (纸本)9788988678251

Many of management systems for business continuity, records and knowledge (BRK) are standalone. Connections and networks of business continuity management systems (BCMS), records management systems (RMS) and knowledge management systems (KMS) are expected to study for facing challenges from evidence-based collaborations in a networked working environment, in terms of control of documented information for business efficiency;guarantee of evidentiality and quality of documented information for organizational effectiveness;and sharing, protecting and increasing values of records as business and knowledge assets for knowledge economy. Based on review of ideas of integration management thinking, best practice frameworks of business continuity management, records management and knowledge management, management models of BCMS, RMS and KMS, an architecture framework towards comprehensive integration management of BRK is proposed by vertical integration design and crosswise integration operations. Case studies demonstrate that such architecture framework has implications to enhance controls of efficiency, effectiveness, and economy of organizational resources and assets adaptable to complex global competition;to improve quality assurance of documented information for supporting evidence-based governance adaptable to dynamic changing of information and communication technologies;to consistent accumulation, sharing and exchanging evidence, memory and knowledge along e-business processes to promote collaboration, optimization and innovation of BCMS, RMS and KMS.

关键词： knowledge management

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knowledge Enhanced Coreference Resolution via Gated Attention

Knowledge Enhanced Coreference Resolution via Gated Attentio...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： He, Kai Mao, Bing Zhou, Xiangyu Li, Yufei Gong, Tieliang Li, Chen Wu, Jialun Xi'an Jiaotong University School of Computer Science and Technology Xi'an China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an China

ISBN: (纸本)9781665468190

Coreference resolution aims at linking all mentions that refer to the same entity, which are widely adopted in many biomedical and bioinformatics tasks, such as biomedical knowledge graph construction and metabolic pathway integration. Many recent studies focus on improving neural model structures. However, we argue that a practical method that integrates commonsense knowledge can further improve coreference resolution performance, because commonsense delivers extra prior knowledge for reasoning and can enhance related representations, rather than naive mention-context occurrence modeling. In this work, we propose an effective method to integrate external commonsense knowledge into a neural coreference resolution model. Specially, a gated attention mechanism is employed in our method to leverage commonsense according to different contexts. By using ConceptNet as the knowledge base in three span-ranking backbone models, the models can yield significant performance gains on used datasets. We also achieve improvements in tasks of long-term mention detection and cross-sentence coreferences after incorporating knowledge. © 2022 IEEE.

关键词： Model structures

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Interference and power constrained broadcast and multicast routing in wireless ad hoc networks using directional antennas

Interference and power constrained broadcast and multicast r...

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作者： Li, Zheng Li, Deying Liu, Ming School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 610054 China

In this paper, we address the interference and power constrained broadcast/multicast routing problem (D-IPCB/M) in wireless ad hoc networks using directional antenna as a starting point, which jointly considers low-interference and energy-efficiency issues. Then we study the delay-bounded interference and power constrained broadcast/multicast routing problem (DB-D-IPCB/M). An approximation algorithm and a heuristic algorithm with low time complexity are proposed for the D-IPCB/M and DB-D-IPCB/M problem, respectively. Finally, we explore and investigate the multi-constrained subgraph optimization (MCSO) problem. We prove its NP-hard and propose an approximation scheme with theoretical performance guarantee for this class of optimization problems. This is a general result originated from the study of D-IPCB/M and DB-D-IPCB/M problems, which can be applicable to solve different optimization problems such as the multi-constrained broadcast/multicast routing problems. Broadcast/multicast message by using the routing trees found by our algorithms tends to not only have less channel collisions but also save energy to extend network lifetime. The theoretical results are evaluated by simulation studies. © 2010 Elsevier B.V. All rights reserved.

关键词： Approximation algorithms

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Multi-fuzzy-constrained graph pattern matching with big graph data

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Intelligent data Analysis 2020年第4期24卷 941-958页

作者： Liu, Guliu Li, Lei Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei Anhui 230009 China School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Mininglamp Academy of Sciences Mininglamp Technologies Beijing China

Graph pattern matching has been widespread used for protein structure analysis, expert finding and social group selection, ect. Recently, the study of graph pattern matching using the abundant attribute information of vertices and edges as constraint conditions has attracted the attention of scholars, and multi-constrained simulation has been proposed to address the problem in contextual social networks. Actually, multi-constrained graph pattern matching is an NP-complete problem and the fuzziness of constraint variables may exist in many applications. In this paper, we introduce a multi-fuzzy-constrained graph pattern matching problem in big graph data, and propose an efficient first-k algorithm Fuzzy-ETOF-K for solving it. Specifically, exploration-based method based on edge topology is adopted to improve the efficiency of edge connection, and breadth-first bounded search is used for edge matching instead of shortest path query between two nodes to improve the efficiency of edge matching. The results of our experiments conducted on three datasets of real social networks illustrate that our proposed algorithm Fuzzy-ETOF-K significantly outperforms existing approaches in efficiency and the introduction of fuzzy constraints makes our proposed algorithm more efficient and effective. © 2020 - IOS Press and the authors. All rights reserved.

关键词： big graph data Graph pattern matching multi-fuzzy-constrained

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Recurring Drift Detection and Model Selection-Based Ensemble Classification for data Streams with Unlabeled data

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New Generation Computing 2021年第2期39卷 341-376页

作者： Li, Peipei Wu, Man He, Junhong Hu, Xuegang Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei 230009 China School of Computer Science and Information Engineering Hefei University of Technology Hefei 230009 China CloudWalk Technology Co. Ltd. Guangzhou China

data stream classification is widely popular in the field of network monitoring, sensor network and electronic commerce, etc. However, in the real-world applications, recurring concept drifting and label missing in data streams seriously aggravate the difficulty on the classification solutions. And this challenge has received little attention from the research community. Motivated by this, we propose a new ensemble classification approach based on the recurring concept drifting detection and model selection for data streams with unlabeled data. First, we build an ensemble model based on the classifiers and clusters. To improve the classification accuracy, we use the ensemble model to predict each data chunk and partition clusters according to the distribution of predicted class labels. Second, we adopt a new concept drifting detection method based on the divergence of concept distributions between adjoining data chunks to distinguish recurring concept drifts. All historical new concepts will be maintained. Meanwhile, we introduce the time-stamp-based weights for base models in the ensemble model. In the selection of the base model, we consider the time-stamp-based weight and the divergence between concept distributions simultaneously. Finally, extensive experiments conducted on four benchmark data sets show that our approach can quickly adapt to data streams with recurring concept drifts, and improve the classification accuracy compared to several state-of-the-art classification algorithms for data streams with concept drifts and unlabeled data. © 2021, Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature.

关键词： data stream classification Ensemble learning Recurring concept drift Unlabeled data

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An approximate sequential pattern mining approach over data stream based on Levenshtein-Automata

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Journal of Computational Information Systems 2007年第5期3卷 2025-2030页

作者： Li, Haifeng Chen, Hong School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering China

Sequential pattern mining is an important method in data mining. Traditional mining algorithms are not adapted to the fast, unlimited, continuous and dynamic data stream because they are multiple pass in scanning database. Some approximate sequential pattern mining algorithms are proposed recently which cost too many system resources in sequence compare process. A sequential compare method based on Levenshtein-Automata is proposed in this paper. This method build state conversion model with pretreatment which can finish computing the sequences' similarity in linear time. A combination of Levenshtein-Automata computation and common computation of edit distance is presented in allusion to the Levenshtein-Automata's problem of using too much memory, so a tradeoff between time cost and space cost is implemented. The experiment result shows this method is effective and efficient.

关键词： data mining

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