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27th International Conference on software Engineering and Knowledge Engineering, SEKE 2015

作者： Wang, Yue Sun, Hao Zeng, Qingkai State Key Lab for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210023 China

ISBN: (纸本)1891706373

Fork-based symbolic execution would waste large amounts of computing time and resource on invulnerable paths when applied to vulnerability detection. In this paper, we propose a statically-guided fork-based symbolic execution technique for vulnerability detection to mitigate this problem. In static analysis, we collect all valid jumps along vulnerable paths, and define the priority for each program branch based on the ratio of vulnerable paths over total paths in its subsequent program. In fork-based symbolic execution, path exploration can be restricted to vulnerable paths, and code segments with higher proportion of vulnerable paths can be analyzed in advance by utilizing the result of static analysis. We implement a prototype named SAF-SE and evaluate it with ten benchmarks from GNU Coreutils version 6.11. Experimental results show that SAF-SE outperforms KLEE in the efficiency and accuracy of vulnerability detection.

关键词： Static analysis

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Compile-Time Automatic Synchronization Insertion and Redundant Synchronization Elimination for GPU Kernels

Compile-Time Automatic Synchronization Insertion and Redunda...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Lifeng Liu Meilin Liu Chong-Jun Wang Jun Wang Department of Computer Science and Engineering Wright State University Dayton OH USA National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Electrical Engineering and Computer Science University of Central Florida Orlando FL USA

ISBN: (纸本)9781509053827

In most of the GPU kernel programs, the synchronization statements are inserted manually by the programmers, which is very labor intensive, and error-prone. In this paper, we propose a synchronization optimization framework to automatically insert synchronization statements into the GPU kernels at compile time, while eliminating the redundant synchronization statements. We have shown that our framework can not only insert the synchronizations correctly, but also eliminate the redundant synchronizations, which outperforms the existing compiler frameworks that introduce redundant synchronizations using the most conservative strategy. Taking the GPU kernels as the input, our framework leverages data dependence analysis to insert synchronizations. We extend CETUS, a source-to-source compiler framework, to implement our synchronization optimization framework. Experimental results show that our proposed framework achieved 100% correctness by combining extensive evaluation and manual comparison. In addition, the number of synchronization statements in GPU kernels is reduced by 32.5%, and the number of synchronization statements executed is reduced by 28.2% on average by our synchronization optimization framework compared to the original GPU kernels.

关键词： Synchronization Graphics processing units Kernel Optimization computer architecture Indexes Algorithm design and analysis

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TextLogger: Inferring longer inputs on touch screen using motion sensors 15

TextLogger: Inferring longer inputs on touch screen using mo...

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8th ACM Conference on Security and Privacy in Wireless and Mobile Networks, WiSec 2015

作者： Ping, Dan Sun, Xin Mao, Bing State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9781450336239

Today's smartphones are equipped with precise motion sensors like accelerometer and gyroscope, which can measure tiny motion and rotation of devices. While they make mobile applications more functional, they also bring risks of leaking users' privacy. Researchers have found that tap locations on screen can be roughly inferred from motion data of the device. They mostly utilized this side-channel for inferring short input like PIN numbers and passwords, with repeated attempts to boost accuracy. In this work, we study further for longer input inference, such as chat record and email content, anything a user ever typed on a soft keyboard. Since people increasingly rely on smartphones for daily activities, their inputs directly or indirectly expose privacy about them. Thus, it is a serious threat if their input text is leaked. To make our attack practical, we utilize the shared memory side-channel for detecting window events and tap events of a soft keyboard. The up or down state of the keyboard helps triggering our Trojan service for collecting accelerometer and gyroscope data. Machine learning algorithms are used to roughly predict the input text from the raw data and language models are used to further correct the wrong predictions. We performed experiments on two real-life scenarios, which were writing emails and posting Twitter messages, both through mobile clients. Based on the experiments, we show the feasibility of inferring long user inputs to readable sentences from motion sensor data. By applying text mining technology on the inferred text, more sensitive information about the device owners can be exposed. Copyright 2015 ACM.

关键词： side-channel attacks edit distance model shared memory side-channel machine learning language model smartphone security keystroke inference using motion sensors

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Model-driven indoor scenes modeling from a single image 41

Model-driven indoor scenes modeling from a single image

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41st Graphics Interface 2015, GI 2015

作者： Liu, Zicheng Zhang, Yan Wu, Wentao Liu, Kai Sun, Zhengxing State Key Lab for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9780994786807

In this paper, we present a new approach of 3D indoor scenes modeling on single image. With a single input indoor image (including sofa, tea table, etc.), a 3D scene can be reconstructed using existing model library in two stages: image analysis and model retrieval. In the image analysis stage, we obtain the object information from input image using geometric reasoning technology combined with image segmentation method. In the model retrieval stage, line drawings are extracted from 2D objects and 3D models by using different line rendering methods. We exploit various tokens to represent local features and then organize them together as a star-graph to show a global description. Finally, by comparing similarity among the encoded line drawings, models are retrieved from the model library and then the scene is reconstructed. Experimental results show that, driven by the given model library, indoor scenes modeling from a single image could be achieved automatically and efficiently. Copyright held by authors.

关键词： Image analysis

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Facilitating reusable and scalable automated testing and analysis for android apps 15

Facilitating reusable and scalable automated testing and ana...

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7th Asia-Pacific Symposium on Internetware, Internetware 2015

作者： Meng, Zhanshuai Jiang, Yanyan Xu, Chang State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9781450336413

Mobile apps are prevalent in everyone's daily life. However, apps are oftentimes defective, undermining their convenience, and therefore automated testing and analysis of apps are developed to enhance apps' quality. As these advanced technologies become increasingly effective and complicated, prototyping such a tool also becomes a challenge. To facilitate easy development of high-quality testing and analysis tools that work with real-world apps, we in this paper present the design and implementation of ATT (Android Testing Toolkit), for crafting reusable and scalable testing and analysis on Android apps. ATT consists of integrated tools and APIs for event generation, profiling and program instrumentation, and can be distributed over cloud platforms for scalable testing and analysis. We qualitatively evaluated the applicability of ATT by demonstrating implementation of a series of existing and enhanced work (RERAN, Monkey+ and UGA) upon ATT, and quantitatively studied the scalability of parallelized UGA on a cloud platform with five real-world apps. We believe that our ATT tool would facilitate development of more advanced testing and analysis approaches for Android apps. © 2015 ACM.

关键词： Quality control

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Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

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Scientific data 2021年第1期8卷 242页

作者： Julien Cohen-Adad Eva Alonso-Ortiz Mihael Abramovic Carina Arneitz Nicole Atcheson Laura Barlow Robert L Barry Markus Barth Marco Battiston Christian Büchel Matthew Budde Virginie Callot Anna J E Combes Benjamin De Leener Maxime Descoteaux Paulo Loureiro de Sousa Marek Dostál Julien Doyon Adam Dvorak Falk Eippert Karla R Epperson Kevin S Epperson Patrick Freund Jürgen Finsterbusch Alexandru Foias Michela Fratini Issei Fukunaga Claudia A M Gandini Wheeler-Kingshott Giancarlo Germani Guillaume Gilbert Federico Giove Charley Gros Francesco Grussu Akifumi Hagiwara Pierre-Gilles Henry Tomáš Horák Masaaki Hori James Joers Kouhei Kamiya Haleh Karbasforoushan Miloš Keřkovský Ali Khatibi Joo-Won Kim Nawal Kinany Hagen H Kitzler Shannon Kolind Yazhuo Kong Petr Kudlička Paul Kuntke Nyoman D Kurniawan Slawomir Kusmia René labounek Maria Marcella Laganà Cornelia Laule Christine S Law Christophe Lenglet Tobias Leutritz Yaou Liu Sara Llufriu Sean Mackey Eloy Martinez-Heras Loan Mattera Igor Nestrasil Kristin P O'Grady Nico Papinutto Daniel Papp Deborah Pareto Todd B Parrish Anna Pichiecchio Ferran Prados Àlex Rovira Marc J Ruitenberg Rebecca S Samson Giovanni Savini Maryam Seif Alan C Seifert Alex K Smith Seth A Smith Zachary A Smith Elisabeth Solana Y Suzuki George Tackley Alexandra Tinnermann Jan Valošek Dimitri Van De Ville Marios C Yiannakas Kenneth A Weber Ii Nikolaus Weiskopf Richard G Wise Patrik O Wyss Junqian Xu NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. jcohen@polymtl.ca. Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal QC Canada. jcohen@polymtl.ca. Mila - Quebec AI Institute Montreal QC Canada. jcohen@polymtl.ca. NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. Department of Radiology Swiss Paraplegic Centre Nottwil Switzerland. Centre for Advanced Imaging The University of Queensland Brisbane Australia. Department of Radiology University of British Columbia Vancouver BC Canada. Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA. Department of Radiology Harvard Medical School Boston MA USA. Harvard-Massachusetts Institute of Technology Health Sciences & Technology Cambridge MA USA. School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia. NMR Research Unit Queen Square MS Centre UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London London UK. Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany. Department of Neurosurgery Medical College of Wisconsin Milwaukee WI USA. Aix-Marseille Univ CNRS CRMBM Marseille France. APHM Hopital Universitaire Timone CEMEREM Marseille France. Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville TN USA. Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada. CHU Sainte-Justine Research Centre Montreal QC Canada. Centre de Recherche CHUS CIMS Sherbrooke Canada. Sherbrooke Connectivity Imaging Lab (SCIL) Computer Science department Université de Sherbrooke Sherbrooke Canada. Université de Strasbourg CNRS ICube Strasbourg France. UHB - University Hospital Brno and Masaryk University Department of Radiology and Nuclear

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New Sharpness Features for Image Type Classification Based on Textual Information

New Sharpness Features for Image Type Classification Based o...

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IAPR International Workshop on Document Analysis Systems, DAS

作者： K. S. Raghunandan Palaiahnakote Shivakumara G. Hemantha Kumar Umapada Pal Tong Lu Department of Studies in Computer Science University of Mysore Karnataka India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Narional Key Lab for Novel Software Technology Nanjing University Nanjing China

Achieving good recognition results from a single method for text lines in video/natural scene images captured by high resolution cameras or low resolution mobile cameras, and images in web pages, is often hard. In this paper, we propose new sharpness based features of textual portion of each input text line image using HSI color space for the classification of an input image into one of the four classes (video, scene, mobile or born digital). This helps in choosing an appropriate method based on the class type of the input text for its improved recognition rate. For a given input text line image, the proposed method obtains H, S and I images. Then Canny edge images are obtained for H, S and I spaces, which results in text candidates. We perform sliding window operation over the text candidate image of each text line of each color space to estimate new sharpness by calculating stroke width and gradient information. The sharpness values of the text lines of the three color spaces are then fed to k-means clustering with maximum, minimum and average guesses, which results in three respective clusters. The mean of each cluster for respective color spaces outputs a feature vector having nine feature values for image classification with the help of an SVM classifier. Experimental results on standard datasets, namely, ICDAR 2013, ICDAR 2015 video, ICDAR 2015 natural scene data, ICDAR 2013 born digital data and the images captured by a mobile camera (our own data) show that the proposed classification method helps in improving recognition results.

关键词： Mobile communication Text recognition Image edge detection Image resolution Image color analysis Digital images Optical character recognition software

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Detection, classification and characterization of android malware using API data dependency 11th

Detection, classification and characterization of android ma...

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11th International Conference Security and Privacy in Communication Networks, SecureComm 2015

作者： Li, Yongfeng Shen, Tong Sun, Xin Pan, Xuerui Mao, Bing State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9783319288642

With the popularity of Android devices, more and more Android malware are manufactured every year. How to filter out malicious app is a serious problem for app markets. In this paper, we propose DroidADDMiner, an efficient and precise system to detect, classify and characterize Android malware. DroidADDMiner is a machine learning based system that extracts features based on data dependency between sensitive APIs. It extracts API data dependence paths embedded in app to construct feature vectors for machine learning. While DroidSIFT [13] also attempts automated detection of Android applications according to data flow analysis, DroidADDMiner can not only reduce the run time but also characterize malware’s behaviors automatically. We implement DroidADDMiner based on FlowDroid [14] and evaluate it using 5648 malware samples and 14280 benign apps. Experiments show that, for malware detection, DroidADDMiner achieves a 98% detection rate, with a 0.3% false positive rate. For malware classification, the accuracy of classifying malicious apps under their proper family labels is 96%. Although performing data flow analysis, most of the experimental samples can be examined in 60 seconds. © Institute for computer sciences, Social Informatics and Telecommunications Engineering 2015.

关键词： Android malware

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Ontology-based metadata matching for emergency decision-making using MapReduce

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International Journal of Metadata, Semantics and Ontologies 2015年第2期10卷 75-83页

作者： Zhu, Li Hu, Wei School of Economics and Management Nanjing University of Information Science and Technology Nanjing Jiangsu210044 China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing Jiangsu210023 China

Ontology matching techniques can help improve the accuracy of emergency decisionmaking on heterogeneous data. In this paper, we propose a practical approach to leverage ontology and MapReduce for matching metadata of emergency supplies. We use this approach in the domain of emergency food. Specifically, by extending the AGROVOC ontology developed by the Food and Agriculture Organisation (FAO) of United Nations, ontological descriptions of food metadata are established. Then, based on the classification of emergency functionality, the instances of food metadata within the same category are matched in a two-stage TF-IDF fashion, which is further improved with the MapReduce framework for efficient parallel computation. Our experiments on grain, meat and milk metadata retrieved from several e-commerce websites demonstrate the good performance of the proposed approach. Additionally, case study is provided to illustrate the potential use of our approach. © 2015 Inderscience Enterprises Ltd.

关键词： MapReduce

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Parallel surface reconstruction on GPU 15

Parallel surface reconstruction on GPU

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7th International Conference on Internet Multimedia Computing and Service, ICIMCS 2015

作者： Gao, Heng Tang, Jie Wu, Gangshan State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9781450335287

Marching Cubes is the most frequently used method to reconstruct isosurface from a point cloud. However, the point clouds are getting denser and denser, thus the efficiency of Marching cubes method has become an obstacle. This paper presents a novel GPU-based parallel surface reconstruction algorithm. The algorithm firstly creates a GPU-based uniform grid structure to manage point cloud. Then directed distances from vertices of cubes to the point cloud are computed in a newly put forwarded parallel way. Finally, after the generation of triangles, a space indexing scheme is adopted to reconstruct the connectivity of the resulted surface. The results show that our algorithm can run more than 10 times faster compared to the CPU-based implementations. © 2015 ACM.

关键词： Graphics processing unit

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