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IEEE International Conference on Big Knowledge (ICBK)

作者： Md Motaher Hossain Youxi Wu Philippe Fournier-Viger Zhao Li Lei Guo Yan Li School of Artificial Intelligence Hebei University of Technology Tianjin China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Alibaba Group Hangzhou Zhejiang China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjing China School of Economics and Management Hebei University of Technology Tianjing China

ISBN: (纸本)9781665438599

Sequential pattern mining (SPM) under the nonoverlapping condition (or nonoverlapping SPM) is a type of data mining used to extract frequent gapped subsequences (known as patterns) from sequences, which is more valuable and versatile than other related methods. In nonoverlapping SPM, two occurrences cannot reuse the same sequence letter in the exact location as the occurrences. This method evaluates the frequency of the patterns in the sequence, and ignores the impact of external utility (item price or profit). Therefore, some low-frequency and essential patterns are overlooked. To address this issue, this paper introduces High Utility Self-adaptive Nonoverlapping Pattern (HSNP) mining and proposes HSNP-Miner, which includes two steps: support calculation and candi-date pattern generation. To calculate the support, we propose the NoSup algorithm, which can effectively calculate support while avoiding the creation of redundant nodes. An advanced upper bound method is employed to generate the candidate patterns more efficiently. Compared to other competitive methods, the experimental results demonstrate the efficiency of the proposed algorithm and the uniqueness of nonoverlapping sequence pat-tarns.

关键词： Upper bound Conferences DNA Data mining Viruses (medical)

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The study of searchable encryption mechanism

The study of searchable encryption mechanism

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International Conference on Intelligent Computing, Communication and Devices, ICCD 2017

作者： Cui, Ranran Zhang, Yongsheng Li, Nengneng School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China

ISBN: (纸本)9789811089435

With the development of information technology, more and more data information uploaded to the cloud server, cloud computing encryption storage technology for data security has become particularly important. This paper mainly studies and analyzes the searchable encryption technology in cloud computing. Firstly, the research status of domestic and foreign research is analyzed. Secondly, the classification of searchable encryption technology is introduced from different aspects, and the classification model of searchable encryption technology A detailed elaboration. Finally, some problems encountered in searchable confidentiality technology and the prospect of this technology are introduced. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Cryptography

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Relaxing Relationship Queries on Graph Data

arXiv

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arXiv 2020年

作者： Li, Shuxin Cheng, Gong Li, Chengkai National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Computer Science and Engineering University of Texas at Arlington ArlingtonTX United States

In many domains we have witnessed the need to search a large entity-relation graph for direct and indirect relationships between a set of entities specified in a query. A search result, called a semantic association (SA), is typically a compact (e.g., diameter-constrained) connected subgraph containing all the query entities. For this problem of SA search, efficient algorithms exist but will return empty results if some query entities are distant in the graph. To reduce the occurrence of failing query and provide alternative results, we study the problem of query relaxation in the context of SA search. Simply relaxing the compactness constraint will sacrifice the compactness of an SA, and more importantly, may lead to performance issues and be impracticable. Instead, we focus on removing the smallest number of entities from the original failing query, to form a maximum successful sub-query which minimizes the loss of result quality caused by relaxation. We prove that verifying the success of a sub-query turns into finding an entity (called a certificate) that satisfies a distance-based condition about the query entities. To efficiently find a certificate of the success of a maximum sub-query, we propose a best-first search algorithm that leverages distance-based estimation to effectively prune the search space. We further improve its performance by adding two fine-grained heuristics: one based on degree and the other based on distance. Extensive experiments over popular RDF datasets demonstrate the efficiency of our algorithm, which is more scalable than baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Resource Description Framework (RDF)

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A patient outcome prediction based on random forest 19

A patient outcome prediction based on random forest

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4th International Conference on Crowd science and Engineering, ICCSE 2019

作者： Yang, Shan Zheng, Xiangwei Yuan, Feng School of Information Science and Engineering Shandong Normal University Ji'nan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Ji’nan China

ISBN: (纸本)9781450376402

Since the research and development value of electronic health records (EHRs) which contains a large number of patient treatment data is very high and meaningful, EHRs has gained attention by researchers in recent years. EHRs has some characteristics such as temporality, sparsity, complexity, irregularity, noisiness and so on, which bring many challenges to direct study the medical data. Thus, an effective feature extraction, or phenotyping from patient EHRs is a key step before any further applications. In this paper, MIMIC-III intensive care database is selected for the experiments. To predict the patient’s death outcome (namely death due to illness or still alive), we make full use of the visit records of patients and propose a prediction method that combines the medical concept representation model Med2Vec with random forest algorithm. Experimental results indicate that the proposed method is robust to parameter variations and noise. Besides, compared with other prediction methods, the performance metrics of the proposed method are very well. Finally, the effect of the Med2Vec model is superior to that obtained by raw data (i.e., no feature learning applied to EHRs data). © 2019 Association for Computing Machinery. All rights reserved.

关键词： Forecasting

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Efficient Medical Image Segmentation Based on Knowledge Distillation

arXiv

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arXiv 2021年

作者： Qin, Dian Bu, Jia-Jun Liu, Zhe Shen, Xin Zhou, Sheng Gu, Jing-Jun Wang, Zhi-Hua Wu, Lei Dai, Hui-Fen Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Ningbo Research Institute School of Software Technology Zhejiang University The Fourth Affiliated Hospital Zhejiang University School of Medicine

Recent advances have been made in applying convolutional neural networks to achieve more precise prediction results for medical image segmentation problems. However, the success of existing methods has highly relied on huge computational complexity and massive storage, which is impractical in the real-world scenario. To deal with this problem, we propose an efficient architecture by distilling knowledge from well-trained medical image segmentation networks to train another lightweight network. This architecture empowers the lightweight network to get a significant improvement on segmentation capability while retaining its runtime efficiency. We further devise a novel distillation module tailored for medical image segmentation to transfer semantic region information from teacher to student network. It forces the student network to mimic the extent of difference of representations calculated from different tissue regions. This module avoids the ambiguous boundary problem encountered when dealing with medical imaging but instead encodes the internal information of each semantic region for transferring. Benefited from our module, the lightweight network could receive an improvement of up to 32.6% in our experiment while maintaining its portability in the inference phase. The entire structure has been verified on two widely accepted public CT datasets LiTS17 and KiTS19. We demonstrate that a lightweight network distilled by our method has non-negligible value in the scenario which requires relatively high operating speed and low storage usage. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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Automated Cardiac Vortex Ring Identification and Characterization Based on Recurrent All-Pairs Field Transforms and Lagrangian Averaged Vorticity Deviation

SSRN

引用

SSRN 2023年

作者： Yang, Ke Zeng, Shan Shi, Ruizheng Ghista, Dhanjoo N. Hu, Xin Lv, Site Zhang, Chris W.J. Zhang, Yi Wong, Kelvin K.L. School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China Department of Cardiovascular Medicine Xiangya Hospital Central South University Changsha410008 China University 2020 Foundation San JoseCA95126 United States The State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Mechanical Engineering College of Engineering University of Saskatchewan SaskatoonSKS7N 5A9 Canada Machine Intelligence Laboratory College of Computer Science Sichuan University Chengdu610041 China School of Electrical and Electronic Engineering University of Adelaide AdelaideSA5005 Australia

Automated identification of cardiac vortices is a formidable task due to the complex nature of blood flow within the heart chambers. This study proposes a novel approach that algorithmically characterizes the identification criteria of these cardiac vortices based on Lagrangian Averaged Vorticity Deviation (LAVD). For this purpose, the Recurrent All-Pairs Field Transforms (RAFT) on the optical flow of Phase Contrast Magnetic Resonance Imaging (PC-MRI) is introduced to construct a continuous velocity field and reduce errors that arise from the integral process of LAVD. Additionally, Generalized Hough Transform (GHT) is applied for automated depiction of the structure of cardiac vortices. The effectiveness of this method is demonstrated and validated by the computation of the acquired cardiac flow data. The results of this comprehensive visual and analytical study show that the evolution of cardiac vortices can be effectively described and displayed, and the RAFT framework for optical flow can synthesize the in-between PC-MRIs with high accuracy. This allows cardiologists to acquire a deeper understanding of intracardiac hemodynamics and its impact on cardiac functional performance. © 2023, The Authors. All rights reserved.

关键词： Vortex flow

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Cloud Storage Forensics: BaiduNetDisk, WeiYun, and 115yun on a Wireless Network

Cloud Storage Forensics: BaiduNetDisk, WeiYun, and 115yun on...

引用

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

作者： Yuzhe Chen Xiao Fu Bin Luo Xiaojiang Du Mohsen Guizani State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Temple University Philadelphia USA Qatar University Qatar

ISBN: (数字)9781728182988

ISBN: (纸本)9781728182995

With the development of cloud computing and wireless networks, cloud storage services are widely used in daily life. People can get access to cloud storage anytime and anywhere. Cloud storage forensics is a computer forensic scenario that currently appears frequently. In this paper, we first briefly introduce the general methods of traditional cloud storage forensics and then introduce forensic investigations that are conducted on three cloud storage services: BaiduNetDisk, 115yun, and WeiYun. The findings of the forensic analysis are presented in detail to help the development of forensics for cloud storage services.

关键词： Cloud computing Forensics Wireless networks Conferences Tools Global communication

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ARCH-COMP20 category report: Hybrid systems with piecewise constant dynamics and bounded model checking 7th

ARCH-COMP20 category report: Hybrid systems with piecewise c...

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7th International Workshop on Applied Verification of Continuous and Hybrid Systems, ARCH 2020

作者： Bu, Lei Abate, Alessandro Adzkiya, Dieky Mufid, Muhammad Syifa’Ul Ray, Rajarshi Wu, Yuming Zaffanella, Enea State Key Laboratory of Novel Software Techniques Nanjing University Jiangsu Nanjing China University of Oxford Oxford United Kingdom Department of Mathematics Institut Teknologi Sepuluh Nopember Indonesia Indian Association for the Cultivation of Science Kolkata India Department of Mathematical Physical and Computer Sciences University of Parma Italy

This report presents the results of a friendly competition for formal verification of continuous and hybrid systems with piecewise constant dynamics. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in 2020. In this fourth edition, five tools have been applied to solve six different benchmark problems in the category for piecewise constant dynamics: BACH, PHAVerLite, PHAVer/SX, TROPICAL, and XSpeed. Compared to last year, we combine the HBMC and HPWC categories of ARCH-COMP 2019 to a new category PCDB (hybrid systems with Piecewise Constant bounds on the Dynamics (HPCD) and Bounded model checking (BMC) of HPCD systems). The result is a snapshot of the current landscape of tools and the types of benchmarks they are particularly suited for. Due to the diversity of problems, we are not ranking tools, yet the presented results probably provide the most complete assessment of tools for the safety verification of continuous and hybrid systems with piecewise constant dynamics up to this date. © 2020 EasyChair. All rights reserved.

关键词： Hybrid systems

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Research on education resource recommendation algorithm based on implicit feedback and pairwise 10

Research on education resource recommendation algorithm base...

引用

10th International Conference on Information technology in Medicine and Education, ITME 2019

作者： Huang, Xiaoxiang Zhang, Yongsheng Zheng, Zhihua Zhang, Jie Duan, Jiaxi School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China

ISBN: (纸本)9781728139173

In view of the problem of how to improve the accuracy of education resources recommendation in Internet era, a recommendation algorithm based on implicit feedback and pairwise education resources are proposed. Firstly, the feature extraction of learner-education resource is carried out, and the implicit features of learner-education resource are extracted by implicit semantic model, and the prediction results of factor decomposition are used as input to sorting learning algorithm. Converting sorting problems to classification problems uses the theory of ordering learning. Based on the experiment comparison with the traditional recommendation algorithm, it is compared with the comparison algorithm, which has better effect on the recommendation of educational resources. The algorithm increases by nearly 30% on metrics MAP, NDGG. © 2019 IEEE.

关键词： Learning algorithms

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On Improving Write Throughput in Commodity RFID Systems

On Improving Write Throughput in Commodity RFID Systems

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2019 IEEE Conference on computer Communications, INFOCOM 2019

作者： Liu, Jia Chen, Xingyu Liu, Xiulong Zhang, Xiaocong Wang, Xia Chen, Lijun State Key Laboratory for Novel Software Technology Nanjing University China Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781728105154

High write throughput plays a vital role in improving the time efficiency of RFID-enabled applications, such as password update and over-the-air programming. In this paper, we make a fresh attempt to study the under-investigated problem of group write and discuss how to improve the write throughput in the commodity RFID system. By conducting extensive experiments, we reveal that the select operation specified by the C1G2 standard takes up the large overhead of a write cycle, which is the key factor in determining the write efficiency. Based on this finding, we propose an efficient write bundling (WB) scheme that bundles multiple writes up and executes them together in a burst mode, which greatly reduces the number of selects and thereby amplifies the write throughput. In WB, a carefully designed bit vector is assigned to each tag for connecting multiple tags into a run, such that a single select command is able to pick these tags concurrently. Besides, WB integrates multiple select commands into one by running a series of logic operations on the inventoried flags, which are used to indicate whether or not a tag is active. We implement WB in a commodity RFID system, with no need of any software modifications or hardware argument. Extensive experiment results show that WB is able to reduce the number of selects by 87.5% and produce a 2× write amplification, compared with the C1G2-compatible exclusive write. © 2019 IEEE.

关键词： Efficiency

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