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arXiv 2022年

作者： Sim, Sunghyun Liu, Ling Bae, Hyerim Industrial Management & Big Data Engineering Major Division of Industrial Convergence System Engineering Dongeui University 176 Eomgwang No Gaya Dong 24 Busanjin Gu Busan47340 Korea Republic of The College of Computing Georgia Institute of Technology 266 Ferst Drive AtlantaGA30332-0765 United States Industrial Data Science & Engineering Department of Industrial Engineering Pusan National University Pusan National University 30-Jan-jeon Dong Geum-Jeong Gu Busan609-753 Korea Republic of

Process mining is a methodology for derivation and analysis of processes models based on the event log. When process mining is employed to analyze business processes, the process discovery step, the conformance checking step, and the enhancements step are repeated. If a user wants to analyze a process from multiple perspectives (such as activity perspectives, originator perspectives, and time perspectives), the above procedure, inconveniently, has to be repeated over and over again. Although past studies involving process mining have applied detailed stepwise methodologies, no attempt has been made to incorporate and optimize multi-perspective process mining procedures. This paper contributes to developing solution approach to this problem. First, we propose an automatic discovery framework of a multi-perspective process model based on deep Q-Learning. Our Dual Experience Replay with Experience Distribution (DERED) approach can automatically perform process model discovery steps, conformance check steps, and enhancements steps. Second, we propose a new method that further optimizes the experience replay (ER) method, one of the key algorithms of deep Q-learning, to improve the learning performance of reinforcement learning agents. Finally, we validate our approach using six real world event datasets collected in port logistics, steel manufacturing, finance, IT, and government administration. We show that our DERED approach can provide users with multi-perspective, high-quality process models that can be employed more conveniently for multi-perspective process mining. © 2022, CC BY.

关键词： Reinforcement learning

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ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT data

arXiv

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arXiv 2022年

作者： Yang, Xinquan Li, Xuguang Li, Xuechen Wu, Peixi Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China The School of Dentistry Shenzhen University Shenzhen China

Implant prosthesis is the most appropriate treatment for dentition defect or dentition loss, which usually involves a surgical guide design process to decide the implant position. However, such design heavily relies on the subjective experiences of dentists. In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to automatically predict the implant position based on the oral CBCT data. We creatively propose to predict the implant position using the 2D axial view of the tooth crown area and fit a centerline of the implant to obtain the actual implant position at the tooth root. Convolutional stem and decoder are designed to coarsely extract image features before the operation of patch embedding and integrate multi-level feature maps for robust prediction, respectively. As both long-range relationship and local features are involved, our approach can better represent global information and achieves better location performance. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed ImplantFormer achieves superior performance than existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Optimal Margin Distribution Machine with Sparsity Inducing Penalty

Optimal Margin Distribution Machine with Sparsity Inducing P...

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International Conference on big data and Smart computing (bigCOMP)

作者： Teng Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Recently a promising research direction of statistical learning has been advocated, i.e., the optimal margin distribution learning, with the central idea of optimizing the margin distribution. As the most representative approach of this new learning paradigm, the optimal margin distribution machine (ODM) considers maximizing the margin mean and minimizing the margin variance simultaneously. The standard ODM exploits the ℓ_2-norm penalty, which gives rise to a dense decision boundary. However, in some situations, the model with parsimonious representation is more preferred, due to the redundant noisy features or limited computing resources. In this paper, we propose the sparse optimal margin distribution machine (Sparse ODM), which aims to achieve better generalization performance with moderate model size. For optimization, we extends an efficient coordinate descent method to solve the final problem since the variables are decoupled. In each iteration, we propose a modified Newton method to solve the one-variable sub-problem. Experimental results on both synthetic and real data sets show the superiority of the proposed method.

关键词： Standards Newton method Noise measurement Optimization Support vector machines Computational modeling Training

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A Survey on Graph Processing Accelerators:Challenges and Opportunities

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Journal of Computer Science & technology 2019年第2期34卷 339-371页

作者： Chuang-Yi Gui Long Zheng Bingsheng He Cheng Liu Xin-Yu Chen Xiao-Fei Liao Hai Jin National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China School of Computing National University of Singapore Singapore 117418 Singapore Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

Graph is a well known data structure to represent the associated relationships in a variety of applications,e.g.,data science and machine *** a wealth of existing efforts on developing graph processing systems for improving the performance and/or energy efficiency on traditional architectures,dedicated hardware solutions,also referred to as graph processing accelerators,are essential and emerging to provide the benefits significantly beyond what those pure software solutions can *** this paper,we conduct a systematical survey regarding the design and implementation of graph processing ***,we review the relevant techniques in three core components toward a graph processing accelerator:preprocessing,parallel graph computation,and runtime *** also examine the benchmarks and results in existing studies for evaluating a graph processing ***,we find that there is not an absolute winner for all three aspects in graph acceleration due to the diverse characteristics of graph processing and the complexity of hardware *** finally present and discuss several challenges in details,and further explore the opportunities for the future research.

关键词： graph processing accelerator domain-specific architecture performance energy efficiency

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High Performance DDoS Attack Detection system Based on Distribution Statistics 16th

High Performance DDoS Attack Detection System Based on Distr...

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16th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2019

作者： Xie, Xia Li, Jinpeng Hu, Xiaoyang Jin, Hai Chen, Hanhua Ma, Xiaojing Huang, Hong National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783030307080

Nowadays, web servers often face the threat of distributed denial of service attacks and their intrusion prevention systems cannot detect those attacks effectively. Many existing intrusion prevention systems detect attacks by the state of per-flow and current processing speed cannot fulfill the requirements of real-time detection due to the high speed traffic. In this paper, we propose a powerful system TreeSketchShield which can improve sketch data structure and detect attacks quickly. First, we discuss a novel structure TreeSketch to obtain statistics of network flow, which utilizes the stepped structure of binary tree to map the distribution and reduces the complexity of the statistic calculation. Second, we present a two-level detection scheme that could make a compromise between the detection speed and detection accuracy. Experimental results show that our method can process more than 100,000 records per second. The false alarm rate can achieve 2% to 25% performance improvement. © 2019, IFIP International Federation for Information Processing.

关键词： Binary trees

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Multi-stage network embedding for exploring heterogeneous edges

arXiv

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arXiv 2021年

作者： Huang, Hong Song, Yu Ye, Fanghua Xie, Xing Shi, Xuanhua Jin, Hai The National Engineering Research Center for Big Data Technology Service Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China The Department of Computer Science University College London London United Kingdom Microsoft Research Asia Beijing China

The relationships between objects in a network are typically diverse and complex, leading to the heterogeneous edges with different semantic information. In this paper, we focus on exploring the heterogeneous edges for network representation learning. By considering each relationship as a view that depicts a specific type of proximity between nodes, we propose a multi-stage non-negative matrix factorization (MNMF) model, committed to utilizing abundant information in multiple views to learn robust network representations. In fact, most existing network embedding methods are closely related to implicitly factorizing the complex proximity matrix. However, the approximation error is usually quite large, since a single low-rank matrix is insufficient to capture the original information. Through a multi-stage matrix factorization process motivated by gradient boosting, our MNMF model achieves lower approximation error. Meanwhile, the multi-stage structure of MNMF gives the feasibility of designing two kinds of non-negative matrix factorization (NMF) manners to preserve network information better. The united NMF aims to preserve the consensus information between different views, and the independent NMF aims to preserve unique information of each view. Concrete experimental results on realistic datasets indicate that our model outperforms three types of baselines in practical applications. © 2021, CC BY.

关键词： Non-negative matrix factorization

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Failure order: A missing piece in disk failure processing of data centers 21

Failure order: A missing piece in disk failure processing of...

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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and systems, HPCC/SmartCity/DSS 2019

作者： Yi, Yusheng Xiao, Jiang Wu, Song Li, Huichuwu Jin, Hai National Engineering Research Center for Big Data Technology System Services Computing Technology System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781728120584

To avoid data loss, data centers adopt disk failure prediction (DFP) technology to raise warnings ahead of actual disk failures, and process the warnings in the order they are raised, i.e., a first-in-first-out (FIFO) warning order. The FIFO-guided warning order can process warnings timely when disk failures are rare in data centers. With the growing scale of data centers, the increasing number of disk failures leads to a complex situation that multiple warnings are raised simultaneously, where the FIFO-guided warning order neither processes warnings timely, nor manages warnings properly due to lack of the priority of warnings. Thus, a real-time and finer-grained priority guidance for warning order management is an urgent need. To this end, we turn our attention to the failures since each warning corresponds to a fail event. The key insight is that the interdependence of failures, i.e., the order failure occurred, indicates the order of warning processing. With an accurate failure order, data centers can decrease the probability of data loss and the downtime of latency-sensitive applications by processing urgent warnings in advance. In this paper, we predict the failure order with a LambdaMART model, which is a state-of-the-art ranking algorithm in information retrieval. To avoid overly concerning on the correctness of high-rank warnings in information retrieval, we design a symmetric metric to evaluate the prediction evaluation of failure order. Experiment on a public dataset, provided by the Backblaze company, shows that our model outperforms the FIFO order and the order from previous DFP models. © 2019 IEEE.

关键词： Information retrieval

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data Anonymization for big Crowdsourcing data

Data Anonymization for Big Crowdsourcing Data

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2019 INFOCOM IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019

作者： Deng, Xiaofeng Zhang, Fan Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781728118789

In traditional database systems, data anonymization has been extensively studied, it provides an effective solution for data privacy preservation, and multidimensional anonymization scheme among them is widely used. However, without delicate parameter settings, these technologies may cause uncontrollable information loss and decrease the accuracy of data analytic tasks. Furthermore, crowdsourcing data is usually huge in amount and must be distributed stored in clouds, which makes the conventional data anonymization technologies not applicable. In this paper, we propose a framework that uses MapReduce to anonymize large-scale data before disseminating them to human workers. In order to guarantee the number and distribution of data records to be similar in all nodes, our framework first redistributes the original data to all participating nodes. Then a heuristic two-phase anonymization schema, which can be seamlessly integrated into the framework, is proposed. Experimental results show that with the same objective of privacy, our approach is scalable for large-scale data and can improve the average accuracy of human worker's analytic tasks. © 2019 IEEE.

关键词： Crowdsourcing

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Predicting Friendship Using a Unified Probability Model 7th

Predicting Friendship Using a Unified Probability Model

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7th CCF Academic Conference on bigdata, CCF bigdata 2019

作者： Kou, Zhijuan Wang, Hua Yuan, Pingpeng Jin, Hai Xie, Xia National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811518980

Now, it is popular for people to share their feelings, activities tagged with geography and temporal information in Online Social Networks (OSNs). The spatial and temporal interactions occurred in OSNs contain a wealth of information to indicate friendship between persons. Existing researches generally focused on single dimension: spatial or temporal dimension. The simplified model only works in limited scenarios. Here, we aim to understand the probability of friendship and the place and time of interactions. First, spatial similarity of interactions is defined as a vector of places where persons checked in. Second, we employ exponential functions to characterize the change of strength of interactions as time goes on. Finally, a unified probability model to predict friendship between two persons is given. The model contains two sub-models based on spatial similarity and temporal similarity respectively. The experimental results on four data sets including spatial data sets (Gowalla and Weeplaces) and temporal data sets (Higgs Twitter data set, High school Call data set) show that our model works as expected. © Springer Nature Singapore Pte Ltd 2019.

关键词： Social networking (online)

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Advancing low-light image enhancement through deep learning: A comprehensive experimental study

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Knowledge-Based systems 2025年

作者： Muhammad Tahir Rasheed Hufsa Khan Junsong Wang Yan Kang College of Big Data and Internet Shenzhen Technology University Shenzhen 518118 China Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound National Engineering Laboratory for Big Data System Computing Technology School of Biomedical Engineering Shenzhen University Shenzhen 518060 China College of Electronics and Information Engineering Shenzhen University Shenzhen 518060 China College of Health Science and Environmental Engineering Shenzhen Technology University Shenzhen 518118 China

Low-light photography severely degrades the perceptual quality of images, which adversely affects the performance of computer vision algorithms. Deep learning-based low-light image enhancement (LLIE) methods are dominating in improving the quality of degraded and corrupted images taken in non-optimal lighting conditions. Either the designed methods are evaluated on a limited set of test datasets or they are not evaluated for machine vision applications. A detailed examination of the recent developments, their generalization, and their application to computer vision tasks is required. This experimental review highlights the future trend of recent learning-based LLIE methods through statistical analysis, experimentally analyzing their generalization capability on a wide spectrum of test datasets, examining the effectiveness of LLIE in computer vision applications, and discussing a correlation between them. The test data used for the generality of these methods covers diversified scenes/contents as well as complex degradation in real scenarios. Rich variety of full-reference and no-reference metrics are applied to compare the relative performances. Furthermore, the application of enhancement methods in low-light face detection is also validated to examine the effectiveness of these LLIE methods as a preprocessing step in machine vision tasks. The discussion on correlation of experimental results from the perspective of both human and machine vision in the subsequent part provides broader view of the field. This systematic review concludes with the limitations of enhancement methodologies and unresolved issues.

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