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International Workshops on Polystores and other Systems for Heterogeneous Data, Poly 2018, and Data Management and Analytics for Medicine and Healthcare, DMAH 2018 held in conjunction with the 44th International conference on Very Large Data Bases, VLDB 2018

作者： Amarbayasgalan, Tsatsral Lee, Jong Yun Kim, Kwang Rok Ryu, Keun Ho Database Laboratory School of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of School of Law Chungbuk National University Cheongju28644 Korea Republic of Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam Department of Computer Science College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of

ISBN: (纸本)9783030337513

The World Health Organization (WHO) reported that coronary heart disease (CHD) is one of the top causes of global mortality, and it is also highly ranked in Korea. The wrong lifestyle such as alcohol, tobacco, and high fatty food is directly involved in the main risk factors for CHD. In the early stage, it is possible to prevent suffering from CHD by an appropriate drug and healthy lifestyle which lead to effective treatment. In this paper, we propose a deep autoencoder based neural networks (DAE-NNs) to predict CHD risk. First, a dataset is divided into two groups by their divergence using a deep autoencoder model. Then, deep neural network (NN) classifiers are trained on each group of dataset separately. As a result, the performance measurements including accuracy, F-measure and AUC score reached 83.53%, 84.36%, and 84.02%, respectively in the Korean population. These results show that our proposed DAE-NNs approach outperformed typical data mining based classifiers for CHD risk prediction. © 2019, Springer Nature Switzerland AG.

关键词： Data mining

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performance Prediction for Power-Capped Applications based on Machine Learning Algorithms

Performance Prediction for Power-Capped Applications based o...

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International conference on high performance Computing & Simulation (HPCS)

作者： Bo Wang Jannis Klinkenberg Daniel Ellsworth Christian Terboven Matthias Müller IT Center RWTH Aachen University Germany Mathematics and Computer Science Colorado College Colorado Springs CO USA

ISBN: (数字)9781728144849

ISBN: (纸本)9781728144856

Growing high performance computing (HPC) clusters are encountering a power wall due to limitations in the surrounding infrastructure. Maximizing a cluster's performance in the presence of a limited power budget is an open problem with high relevance and requires a deep understanding of application performance and power *** components with the same technical specification have distinct power efficiencies and applications running on those components have diverse power profiles. Enforcing a power limit on individual components changes the performance characteristics. In this work, we investigate and quantity power- and performance-characteristics of various applications. Further, we present a systematic methodology to collect corresponding monitoring data and apply machine learning (ML) techniques to predict the performance under particular power caps. The observed prediction error is under 3% in most cases, which is in the same range of performance variation as application runs without a power cap.

关键词： program processors Benchmark testing Sockets Hardware Random access memory Runtime Power measurement

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Minimizing energy expenditures using genetic algorithm for scalability and longlivety of multi hop sensor networks 9

Minimizing energy expenditures using genetic algorithm for s...

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9th IEEE International conference on Electronics Information and Emergency Communication, ICEIEC 2019

作者： Abro, Adeel Zhongliang, Deng Memon, Kamran Ali Mohammadani, Khalid H. Ul Ain, Noor Memon, Saleemullah Memon, Imran Panhwar, Muhammad A. School of Electronic Engineering Beijing University of Posts and Telecommunications China School of Information Communication Engineering Beijing University of Posts and Telecommunications Beijing China Department of Computer Science Bahria University Karachi Campus Sindh Pakistan

ISBN: (纸本)9781728111896

Along with implementations in tracking and monitoring systems, Sensor Networks (SNs) have evolved for many years and proved as an ultimate solution for dealing with sensing, controlling and mobility issues of physical phenomenon. Depending on the efficiency of the routing paradigm that are being used, the computing and processing power is minimal given the limited SNs batteries. In this paper, we use a Genetic Algorithm (GA) for multi hop scenario in extensive experiments with 20-90 nodes and analyze the performance of the proposed algorithm in terms of energy expenditures, scalability and longlivety of the SN. GA sinks nearly all packets in 18000 rounds as compared less efficient threshold sensitive energy efficient sensor network (TEEN) protocol under various deployments. In analysis for distance of multiple hops from/to the respective sink, the proposed algorithm performed fairly better than the TEEN approach in maximizing the sensor activity by saving energy resulting the increased lifetime of the network. Further, the algorithm is scalable and any number of nodes can produce the optimized results. The work can be extended to format some new scenarios and optimize routes with the help of GA and other algorithms typically used in optimization. © 2019 IEEE.

关键词： Energy utilization

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Quality Meets Diversity: A Model-Agnostic Framework for computerized Adaptive Testing

Quality Meets Diversity: A Model-Agnostic Framework for Comp...

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IEEE International conference on Data Mining (ICDM)

作者： Haoyang Bi Haiping Ma Zhenya Huang Yu Yin Qi Liu Enhong Chen Yu Su Shijin Wang Anhui Province Key Laboratory of Big Data Analysis and ApplicationSchool of Computer Science and Technology University of Science and Technology of China CN Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Anhui University CN IFLYTEK Research CN

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

computerized Adaptive Testing (CAT) is emerging as a promising testing application in many scenarios, such as education, game and recruitment, which targets at diagnosing the knowledge mastery levels of examinees on required concepts. It shows the advantage of tailoring a personalized testing procedure for each examinee, which selects questions step by step, depending on her performance. While there are many efforts on developing CAT systems, existing solutions generally follow an inflexible model-specific fashion. That is, they need to observe a specific cognitive model which can estimate examinee's knowledge levels and design the selection strategy according to the model estimation. In this paper, we study a novel model-agnostic CAT problem, where we aim to propose a flexible framework that can adapt to different cognitive models. Meanwhile, this work also figures out CAT solution with addressing the problem of how to generate both high-quality and diverse questions simultaneously, which can give a comprehensive knowledge diagnosis for each examinee. Inspired by Active Learning, we propose a novel framework, namely Model-Agnostic Adaptive Testing (MAAT) for CAT solution, where we design three sophisticated modules including Quality Module, Diversity Module and Importance Module. Specifically, at one CAT selection step, Quality Module first quantifies the informativeness of questions and generates candidate subset with the highest quality. Then, Diversity Module selects one question at each step that maximizes the concept coverage. Additionally, we propose Importance Module to estimate the importance of concepts that optimizes the CAT selection. Under MAAT, we prove that the goal of maximizing both quality and diversity is NP-hard, but we provide efficient algorithms by exploiting the inherent submodular property. Extensive experimental results on two real-world datasets clearly demonstrate that our MAAT can support CAT with guaranteeing both quality and diversity

关键词： Adaptation models Cats Computational modeling Estimation Games Testing Recruitment

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Simulation of Energy-Efficient Demand Response for high performance Computing Systems

Simulation of Energy-Efficient Demand Response for High Perf...

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Simulation Winter conference

作者： Kishwar Ahmed Jason Liu Department of Computer Science University of South Carolina Beaufort Bluffton SC USA School of Computing and Information Sciences Florida International University Miami FL USA

ISBN: (数字)9781728132839

ISBN: (纸本)9781728120522

Energy consumption is a critical issue for high-performance computing (HPC) systems. Demand response is a program offered by power service providers to reduce energy consumption. In this paper, we present the simulation of an energy-efficient economic demand-response model based on contract theory. Simulation is developed to examine the effectiveness of the demand-response model in a variety of real-world scenarios. Results from simulation show that the model can achieve energy efficiency through the rewarding mechanism of the contract-based design. In particular, the HPC users can be properly compensated to ensure their willing participation in energy reduction.

关键词： Load management Contracts Economics Energy consumption Computational modeling Power demand program processors

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Automated Patch Correctness Assessment: How Far are We?

Automated Patch Correctness Assessment: How Far are We?

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IEEE International conference on Automated Software Engineering (ASE)

作者： Shangwen Wang Ming Wen Bo Lin Hongjun Wu Yihao Qin Deqing Zou Xiaoguang Mao Hai Jin College of Computer Science National University of Defense Technology Changsha China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781450367684

ISBN: (纸本)9781728172811

Test-based automated program repair (APR) has attracted huge attention from both industry and academia. Despite the significant progress made in recent studies, the overfitting problem (i.e., the generated patch is plausible but overfitting) is still a major and long-standing challenge. Therefore, plenty of techniques have been proposed to assess the correctness of patches either in the patch generation phase or in the evaluation of APR techniques. However, the effectiveness of existing techniques has not been systematically compared and little is known to their advantages and disadvantages. To fill this gap, we performed a large-scale empirical study in this paper. Specifically, we systematically investigated the effectiveness of existing automated patch correctness assessment techniques, including both static and dynamic ones, based on 902 patches automatically generated by 21 APR tools from 4 different categories. Our empirical study revealed the following major findings: (1) static code features with respect to patch syntax and semantics are generally effective in differentiating overfitting patches over correct ones; (2) dynamic techniques can generally achieve high precision while heuristics based on static code features are more effective towards recall; (3) existing techniques are more effective towards certain projects and types of APR techniques while less effective to the others; (4) existing techniques are highly complementary to each other. For instance, a single technique can only detect at most 53.5% of the overfitting patches while 93.3% of them can be detected by at least one technique when the oracle information is available. Based on our findings, we designed an integration strategy to first integrate static code features via learning, and then combine with others by the majority voting strategy. Our experiments show that the strategy can enhance the performance of existing patch correctness assessment techniques significantly.

关键词： Industries Semantics Tools Syntactics Maintenance engineering Benchmark testing Software engineering

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A structure-based de-anonymization attack on graph data using weighted neighbor match 4

A structure-based de-anonymization attack on graph data usin...

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4th IEEE International conference on Data Science in Cyberspace, DSC 2019

作者： Fang, Junbin Li, Aiping Jiang, Qianyue Li, Shudong Han, Weihong College of Computer National University of Defense Technology Changsha China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Cyberspace Institute of Advanced Technology Guangzhou University Institute of Electronic and Information Engineering of UESTC in Guangdong Guangzhou China

ISBN: (纸本)9781728145280

Nowadays, many large-scale graph datasets, such as social networks data, are often published for scientific research, information sharing and data mining. Privacy protection issues in publishing datasets have also received widespread attention. Usually, in order to preserve the individual privacy, the datasets will be anonymized before released. Many researchers have proposed various of De-anonymization algorithms to re-identify nodes in the anonymized dataset which is an effective way to evaluate the anonymity of the published datasets. There are many problems when the de-anonymization algorithms apply to the anonymized datasets. The method might require high-quality seeds, or had a low noise tolerance, so that the de-anonymization process can only achieve poor results. In this paper, we propose a structure-based weighted neighborhood matching algorithm, named SWNM, which have a comprehensive consideration of local and global features when calculating the nodes similarity between anonymized nodes and auxiliary nodes. SWNM also using a dynamic similarity matrix and weighted neighborhood matching algorithm to ensure a nice noise tolerance and de-anonymization accuracy. Finally, experiments on real graph datasets show that the SWNM has better performance than baseline method. © 2019 IEEE.

关键词： Data mining

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performance of high-strength concrete walls exposed to fire

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ADVANCES IN STRUCTURAL ENGINEERING 2018年第8期21卷 1173-1182页

作者： Nguyen, Kate T. Q. Tuan Ngo Mendis, Priyan Heath, David Univ Melbourne Dept Infrastruct Engn Melbourne Vic Australia

high-strength concrete is becoming very popular around the world due to its many advantages over normal-strength concrete. There are significant behavioural differences between high-strength concrete and normal-strength concrete, most notably the brittleness and sudden spalling under elevated temperatures, whereby pieces of hardened concrete explosively dislodge. Although all high-rise and even many medium-rise buildings have high-strength concrete walls, the spalling of high-strength concrete walls in fire has generally been ignored by the designers and the fire resistance of walls has been calculated using the rules specified for normal-strength concrete. Catastrophic failures could occur due to this ignorance of an important issue. Major design codes including the American and Australian Codes do not cover spalling adequately. Even the Eurocode rules are based on limited research. After a brief discussion on the present design practice, this article presents a summary of spalling research. The relevant results from a comprehensive study conducted at the University of Melbourne are briefly discussed. The authors are not aware of any other comprehensive research projects covering the fire behaviour of normal-strength concrete and high-strength concrete walls exposed not only to standard fires but also hydrocarbon fires. The results showed that spalling in high-strength concrete is more significant when subjected to hydrocarbon fire compared to normal-strength concrete. The level of compressive load on the panels was also found to have a significant effect on the fire performance of the high-strength concrete panels. The finite analysis element program, ANSYS, was used to model the concrete walls subjected to load and fire (both ISO834 Standard fire and hydrocarbon fire). The test results were used to validate the computer model.

关键词： construction finite element analysis fire performance high-strength concrete spalling

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MFBN: An efficient base model for person re-identification 2019

MFBN: An efficient base model for person re-identification

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4th International conference on Mathematics and Artificial Intelligence, ICMAI 2019

作者： He, Shengyi Wu, Junmin Li, Yuqian Department of computer Science and Technology University of China Hefei China Suzhou Institute for Advanced Study of USTC Suzhou China

ISBN: (纸本)9781450362580

Person Re-IDentification (Re-ID) has developed rapidly with deep learning methods, as for these methods, the base mod- els used in most of them are not customized for Re-ID task. Although some studies have carefully designed the models special for Re-ID task, these models are always not easy to be the base model and expand with new methods due to their great complexity. In this paper, we propose a novel efficient base model named as Multi-granularity Feature Boosting Network (MFBN). MFBN consists of branches with information in different granularities. MFBN combines these branches into one whole, so MFBN is easy to be ex- tended as a base model. Moreover, MFBN applies feature boosting technique to boost fine granularitiy branch features with coarse granularity branch features, and applies channel- wise attention to increase diversities between features in multiple granularities. With these improves, MFBN has surpassed popular base models and got state-of-the-art results on mainstream Re-ID datasets including Market-1501, DukeMTMC-reID and CUHK-03. MFBN achieves results of rank-1/mAP=95.2%/93.2% on Market-1501 dataset and rank-1/mAP=90.3%/88.3% on DukeMTMC-reID dataset. Code and pretrained models are available at https://***/hsyi/Multi-granularity-Feature-Boosting- Network vision researchers due to its significant importance in wide area video surveillance. Despite the re- cent great progress, person Re- ID remains challenging due to the following reasons: 1) notable changes in human posture across space and time;2) variations in visual appearance caused by different viewpoints;3) occlusions and camera resolutions;moreover, 4) person Re-ID is considered to be the high-level task based on a pedestrian detection system, so inaccurate person detection boxes also impact the performance of Re-ID methods. See Fig. 1 for some detail difficult query examples. In the figure, Images in same blue block are true positive image, query image, false positive image, from left

关键词： computer vision

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Poster: Obfuscating program control flow with Intel SGX 40

Poster: Obfuscating program control flow with Intel SGX

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40th ACM/IEEE International conference on Software Engineering, ICSE 2018

作者： Wang, Yongzhi Shen, Yulong Cheng, Ke Yang, Yibo Su, Cuicui Faree, Anter School of Computer Science and Technology Xidian University Xian Shaanxi China

ISBN: (纸本)9781450356633

Control flow obfuscation is a direct approach in protecting the confidentiality of program logic. However, existing works in this direction either failed to offer high confidentiality guarantees or incurred high performance overheads. In this paper, we propose CFHider, a high security and high performance control flow obfuscation technique. By leveraging program transformation and Intel Software Guard Extension (SGX) technology, CFHider hides control flow information to an opaque yet trusted execution environment, i.e., the SGX enclave. Our evaluation showed that, CFHider extensively raises the bar for reverse-engineering attacks targeting on the control flow confidentiality, and incurs a moderate performance overhead. © 2018 Authors.

关键词： Reverse engineering

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