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9th Asia-Pacific Symposium on Internetware, Internetware 2017

作者： Liu, Yi Wang, Jue Xu, Chang Ma, Xiaoxing State Key Lab for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)1595930361

Many smartphone applications suffer from energy inefficiency problems, but locating these problems is quite difficult and laborintensive. Automated tools for detecting energy inefficiency bugs have been shown to be effective. Existing approaches generally consist of two parts, namely the simulation part and the monitor part. The simulation part explores an application's state space guided by an application execution model, and the monitor part checks for occurrences of energy inefficiency patterns. However, existing approaches might miss energy inefficiency bugs due to their imprecise application execution models and oversimplified energy inefficiency diagnosis policies. In this paper, we proposed NavyDroid, an approach to diagnosing energy inefficiency problems more *** summarized a comprehensive application execution model from Android specifications and expressed it as a state machine. By considering multiple patterns of wake lock misuses, our approach is able to detect more complex energy bugs caused by wake lock *** implemented NavyDroid on top of Java Pathfinder (JPF) and applied it to real-world applications. We evaluated NavyDroid with 17 real-world Android applications, and NavyDroid located more energy inefficiency bugs in these applications than the existing work E-GreenDroid did. The results of our experiments demonstrate that our approach can effectively locate real energy inefficiency bugs in Android applications, suggesting its effectiveness. © 2017 Association for Computing Machinery.

关键词： Locks (fasteners)

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Robust frequent directions with application in online learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2019年第1期20卷

作者： Luo Luo Cheng Chen Zhihua Zhang Wu-Jun Li Tong Zhang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China National Engineering Lab for Big Data Analysis and Applications School of Mathematical Sciences Peking University Beijing China National Key Laboratory for Novel Software Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Department of Computer Science and Technology Nanjing University Nanjing China Computer Science & Mathematics Hong Kong University of Science and Technology Hong Kong

The frequent directions (FD) technique is a deterministic approach for online sketching that has many applications in machine learning. The conventional FD is a heuristic procedure that often outputs rank deficient matrices. To overcome the rank deficiency problem, we propose a new sketching strategy called robust frequent directions (RFD) by introducing a regularization term. RFD can be derived from an optimization problem. It updates the sketch matrix and the regularization term adaptively and jointly. RFD reduces the approximation error of FD without increasing the computational cost. We also apply RFD to online learning and propose an effective hyperparameter-free online Newton algorithm. We derive a regret bound for our online Newton algorithm based on RFD, which guarantees the robustness of the algorithm. The experimental studies demonstrate that the proposed method outperforms state-of-the-art second order online learning algorithms.

关键词： frequent directions matrix approximation online Newton algorithm online convex optimization sketching

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A comparative study of consistent snapshot algorithms for main-memory database systems

arXiv

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arXiv 2018年

作者： Li, Liang Wang, Guoren Wu, Gang Yuan, Ye Chen, Lei Lian, Xiang Department of Computer Science Northeastern University of China 100819 United States Department of Computer Science Beging Institute of Technology 100081 China Department of State Key Lab. for Novel Software Technology Nanjing University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Computer Science Kent State University United States

In-memory databases (IMDBs) are gaining increasing popularity in big data applications, where clients commit updates intensively. Specifically, it is necessary for IMDBs to have efficient snapshot performance to support certain special applications (e.g., consistent checkpoint, HTAP). Formally, the in-memory consistent snapshot problem refers to taking an in-memory consistent time-in-point snapshot with the constraints that 1) clients can read the latest data items and 2) any data item in the snapshot should not be overwritten. Various snapshot algorithms have been proposed in academia to trade off throughput and latency, but industrial IMDBs such as Redis adhere to the simple fork algorithm. To understand this phenomenon, we conduct comprehensive performance evaluations on mainstream snapshot algorithms. Surprisingly, we observe that the simple fork algorithm indeed outperforms the state-of-the-arts in update-intensive workload scenarios. On this basis, we identify the drawbacks of existing research and propose two lightweight improvements. Extensive evaluations on synthetic data and Redis show that our lightweight improvements yield better performance than fork, the current industrial standard, and the representative snapshot algorithms from academia. Finally, we have opensourced the implementation of all the above snapshot algorithms so that practitioners are able to benchmark the performance of each algorithm and select proper methods for different application scenarios. Copyright © 2018, The Authors. All rights reserved.

关键词： Database systems

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Value based Curriculum Design for Engineering Studies 5

Value based Curriculum Design for Engineering Studies

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5th IEEE International Conference on MOOCs, Innovation and technology in Education (MITE)

作者： Subramanian, Shriram Dorairaju, Bharathi Pallavarapu, Mayur Department of Computer Science Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin China

ISBN: (纸本)9781538631898

Education apart from the basic needs of food, clothing and shelter, is another key factor to empower an individual. It acts as an enabler to elevate a person socially and from within. Education system has been evolving since human existence and various approaches are being used to teach. Focusing on professional courses, the quality of education is questioned and stands as a concern along with the unemployment issues. In India, there is a huge potential for technology growth. However, as per the national employability report, only 3.84% of engineers qualify for a startup technology role. Further, 2016 employability rate has been 17.91%, 3.67% and 40.57% for IT Services, IT Products and Business Process Outsourcing respectively. Apart from this, there are many forums in which the quality of education is being questioned, since education is linked with the job or business opportunity. One of the essentials in the professional courses is the curriculum design. It is very important to start with the curriculum that can enhance the knowledge of students and also make a prospective career. With these in mind, the improvement options have to be deeply thought and charted out. In this regard, a survey has been taken up with engineering professionals to get their feedback on how they view the education system. This paper focuses on the feedback from working engineering professionals, compares the existing and other curriculum options and provides a proposal for a value based curriculum design.

关键词： Education system quality career engineering studies employability value based education curriculum

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The medical segmentation decathlon

arXiv

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arXiv 2021年

作者： Antonelli, Michela Reinke, Annika Bakas, Spyridon Farahani, keyvan Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. van Ginneken, Bram Bilello, Michel Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc J. Heckers, Stephan H. Huisman, Henkjan Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Golia Pernicka, Jennifer S. Rhode, Kawal Tobon-Gomez, Catalina Vorontsov, Eugene Hulsmon, Hankan Meakin, James A. Ourselin, Sebastien Wiesenfarth, Manuel Arbeláez, Pablo Bae, Byeonguk Chen, Sihong Daza, Laura Feng, Jianjiang He, Baochun Isensee, Fabian Ji, Yuanfeng Jia, Fucang Kim, Namkug Kim, Ildoo Merhof, Dorit Pai, Akshay Park, Beomhee Perslev, Mathias Rezaiifar, Ramin Rippel, Oliver Sarasua, Ignacio Shen, Wei Son, Jaemin Wachinger, Christian Wang, Liansheng Wang, Yan Xia, Yingda Xu, Daguang Xu, Zhanwei Zheng, Yefeng Simpson, Amber L. Maier-Hein, Lena Cardoso, M. Jorge School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany University of Pennsylvania PhiladelphiaPA United States BethesdaMD United States Heidelberg Germany Electrical Engineering and Computer Science Vanderbilt University NashvilleTN United States Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen Netherlands Quantitative Biomedicine University of Zurich Zurich Switzerland DeepMind London United Kingdom School of Computing Department of Biomedical and Molecular Sciences Queen’s University KingstonON Canada Department of Surgery Memorial Sloan Kettering Cancer Center New YorkNY United States BethesdaMD United States Department of Informatics Technische Universität München Germany Department of Radiology Memorial Sloan Kettering Cancer Center New YorkNY United States Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center NashvilleTN United States Department of Radiology Stanford University StanfordCA United States Department of Computer Science and Software Engineering École Polytechnique de Montréal Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Medical Faculty University of Heidelberg Heidelberg Germany VUNO Inc. Seoul Korea Republic of Universidad de los Andes Colombia Department of Automation Tsinghua University Beijing China Heidelberg Germany Department of Computer Science Xiamen University Xiamen361005 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Kakao Brain Korea Republic of Institute of Imaging & Computer Vision RWTH Aachen University Aachen Germany Cere

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks;(2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability;(3) the training of accurate AI segmentation models is now commoditized to non AI *** Codes 68T07. © 2021, CC BY-NC-SA.

关键词： Image segmentation

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

引用

Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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NjuLink: Results for instance matching at OAEI 2017 12

NjuLink: Results for instance matching at OAEI 2017

引用

12th International Workshop on Ontology Matching, OM 2017

作者： Lyu, Xinze Zhang, Qingheng Hu, Wei Sun, Zequn Qu, Yuzhong State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China

njuLink is a tool designed for instance matching. It mainly matches instances by finding discriminative property pairs. Also, to meet 1:1 equivalence relationship for the OAEI 2017 DORUMES task, we make several improv...

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Graph-based ontology summarization: A survey

arXiv

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arXiv 2018年

作者： Pouriyeh, Seyedamin Allahyari, Mehdi Liu, Qingxia Cheng, Gong Arabnia, Hamid Reza Qu, Yuzhong Kochut, Krys Computer Science Department University of Georgia AthensGA United States Computer Science Department Georgia Southern University StatesboroGA United States National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Ontologies have been widely used in numerous and varied applications, e.g., to support data modeling, information integration, and knowledge management. With the increasing size of ontologies, ontology understanding, which is playing an important role in different tasks, is becoming more difficult. Consequently, ontology summarization, as a way to distill key information from an ontology and generate an abridged version to facilitate a better understanding, is getting growing attention. In this survey paper, we review existing ontology summarization techniques and focus mainly on graph-based methods, which represent an ontology as a graph and apply centrality-based and other measures to identify the most important elements of an ontology as its summary. After analyzing their strengths and weaknesses, we highlight a few potential directions for future research. Copyright © 2018, The Authors. All rights reserved.

关键词： Ontology

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B-spline surface fitting to mesh vertices

引用

science China(Information sciences) 2017年第7期60卷 259-261页

作者： Yang LU Junhai YONG Kanle SHI Hejin GU Ping ZHENG Jean-Claude PAUL School of Software Tsinghua University Department of Computer Science and Technology Tsinghua University Key Laboratory for Information System Security Ministry of Education of China Tsinghua National Laboratory for Information Science and Technology Tsinghua University Department of Research & Development Jiangxi Academy of Science Department of Mathematics and Computer Science Nanchang Normal University

Here we propose a novel method of fitting B-spline surfaces to input mesh *** a mesh model that is difficult to fit with one B-spline surface,we divide it into several patches that form a tree structure(which is called hierarchical splines[1]),and for each patch,we use one B-spline surface for ***,only a few patches are required,and G2continuity can be achieved between the adjacent surfaces.

关键词： B-spline surface fitting to mesh vertices

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Data flow analysis on android platform with fragment lifecycle modeling 12th

Data flow analysis on android platform with fragment lifecyc...

引用

12th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2016

作者： Li, Yongfeng Ouyang, Jinbin Guo, Shanqing Mao, Bing State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China School of Computer Science and Technology Shandong University Jinan China

ISBN: (纸本)9783319596075

Smartphones carry a large quantity of sensitive information to satisfy people’s various requirements, but the way of using information is important to keep the security of users’ privacy. There are two kinds of misuses of sensitive information for apps. On the one hand, careless programmers may leak the data by accident. On the other hand, the attackers develop malware to collect sensitive data intentionally. Many researchers apply data flow analysis to detect data leakages of an app. However, data flow analysis on Android platform is quite different from the programs on desktop. Many researchers have solved some problems of data flow analysis on Android platform, like Activity lifecycle, callback methods, inter-component communication. We find that Fragment’s lifecycle also has an effect on the data flow analysis of Android apps. Some data will be leaked if we don’t take Fragment’s lifecycle into consideration when performing data flow analysis in Android apps. So in this paper, we propose an approach to model Fragment’s lifecycle and its relationship with Activity’s lifecycle, then introduce a tool called FragDroid based on FlowDroid [7]. We conduct some experiments to evaluate the effectiveness of our tool and the results show that there are 8% of apps in our data set using Fragment. In particular, for popular apps, the result is 50.8%. We also evaluate the performance of using FragDroid to analyze Android apps, the result shows the average overhead is 17%. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2017.

关键词： Data flow analysis

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