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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

共 10372 条记录，以下是4461-4470 订阅

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Virtual Machine Introspection for Anomaly-Based Keylogger Detection

Virtual Machine Introspection for Anomaly-Based Keylogger De...

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IEEE Workshop on high performance Switching and Routing

作者： Huseyn Huseynov Kenichi Kourai Tarek Saadawi Obinna Igbe Department of Electrical Engineering City University of New York City College New York United States Department of Computer Science and Networks Kyushu Institute of Technology Fukuoka Japan

ISBN: (数字)9781728148465

ISBN: (纸本)9781728148472

Software Keyloggers are dominant class of malicious applications that surreptitiously logs all the user activity to gather confidential information. Among many other types of keyloggers, API-based keyloggers can pretend as unprivileged program running in a user-space to eavesdrop and record all the keystrokes typed by the user. In a Linux environment, defending against these types of malware means defending the kernel against being compromised and it is still an open and difficult problem. Considering how recent trend of edge computing extends cloud computing and the Internet of Things (IoT) to the edge of the network, a new types of intrusion-detection system (IDS) has been used to mitigate cybersecurity threats in edge computing. Proposed work aims to provide secure environment by constantly checking virtual machines for the presence of keyloggers using cutting edge artificial immune system (AIS) based technology. The algorithms that exist in the field of AIS exploit the immune system's characteristics of learning and memory to solve diverse problems. We further present our approach by employing an architecture where host OS and a virtual machine (VM) layer actively collaborate to guarantee kernel integrity. This collaborative approach allows us to introspect VM by tracking events (interrupts, system calls, memory writes, network activities, etc.) and to detect anomalies by employing negative selection algorithm (NSA).

关键词： Immune system Linux Keyboards Artificial intelligence Virtual machining Detectors Malware

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CCGraph: a PDG-based code clone detector with approximate graph matching

CCGraph: a PDG-based code clone detector with approximate gr...

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IEEE International conference on Automated Software Engineering (ASE)

作者： Yue Zou Bihuan Ban Yinxing Xue Yun Xu Key Laboratory on High Performance Computing Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9781450367684

ISBN: (纸本)9781728172811

Software clone detection is an active research area, which is very important for software maintenance, bug detection, etc. The two pieces of cloned code reflect some similarities or equivalents in the syntax or structure of the code representations. There are many representations of code like AST, token, PDG, etc. The PDG (program Dependency Graph) of source code can contain both syntactic and structural information. However, most existing PDG-based tools are quite time-consuming and miss many clones because they detect code clones with exact graph matching by using subgraph isomorphism. In this paper, we propose a novel PDG-based code clone detector, CCGraph, that uses graph kernels. Firstly, we normalize the structure of PDGs and design a two-stage filtering strategy by measuring the characteristic vectors of codes. Then we detect the code clones by using an approximate graph matching algorithm based on the reforming WL (Weisfeiler-Lehman) graph kernel. Experiment results show that CCGraph retains a high accuracy, has both better recall and F1-score values, and detects more semantic clones than other two related state-of-the-art tools. Besides, CCGraph is much more efficient than the existing PDG-based tools.

关键词： Software maintenance Cloning Detectors Tools Syntactics Kernel Software engineering

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Exploring the Relationship Between Collaborative Discourse, programming Actions, and Cybersecurity and Computational Thinking Knowledge

Exploring the Relationship Between Collaborative Discourse, ...

引用

IEEE International conference on Teaching, Assessment and Learning for Engineering (TALE)

作者： Bernard Yett Caitlin Snyder Nicole Hutchins Gautam Biswas Institute for Software Integrated Systems Vanderbilt University Nashville USA

ISBN: (数字)9781728169422

ISBN: (纸本)9781728169439

Computational thinking (CT) skills are necessary for solving the real-world problems of today and are therefore being incorporated into K-12 curricula. Cybersecurity is of similar importance; however, it can be difficult for young learners to grasp the required concepts and use them to construct meaningful algorithms. We discuss our approach that combines a hands-on robotics platform with a block-based programming environment to facilitate the learning and application of cybersecurity and CT concepts. Throughout a week-long intervention, high school students were introduced to cybersecurity and CT and given the opportunity to apply this knowledge in a collaborative setting to solve security problems on the robotics platform with instructor and peer support. A series of competitions between groups of students further motivated students to translate their learned concepts to practice, often leading to breakthroughs as students incorporated new algorithms into their existing projects to counteract previous security flaws. We present evidence of the learning behaviors of several such groups through mixed-method case studies integrating data collected from learning performance, collaborative discourse, and analysis of program development. We discuss the impact of this approach on cybersecurity and CT learning and then present future directions for this work.

关键词： Knowledge engineering Collaboration computer security Robots programming profession STEM programming environments

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Automatic computer Aided System for Lung Cancer in Chest CTs Using MD-RFCN Combined with Tri-Level Region Proposal Network 17

Automatic Computer Aided System for Lung Cancer in Chest CTs...

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17th IEEE International conference on Industrial Informatics (INDIN)

作者： Masood, Anum Sheng, Bin Li, Ping Yang, Po Kim, Jinman Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Hong Kong Polytech Univ Dept Comp Kowloon Hong Kong Peoples R China Liverpool John Moores Univ Dept Comp Sci Liverpool Merseyside England Univ Sydney Sch Informat Technol Sydney NSW Australia

ISBN: (纸本)9781728129273

Pulmonary cancer is one of the major causes of deaths caused by cancer around the globe. Early stage lung cancer detection can prove to be essential for the patients, for which the computed tomography (CT) images are analyzed by the radiologists to determine the presence of nodules and diagnose the disease. Conventional techniques used by the radiologists for nodule detection in CT images is time-consuming and inefficient;to assist in the diagnosis process and further enhance its efficiency and accuracy, decision support systems have been developed in the past few years. In our paper, we proposed a Multi-Dimension Region-based Fully Convolutional Network based decision support system for detection and classification of lung nodule. The Multi-Dimension RFCN serves as an image classifier backbone for our feature extraction step in addition to the proposed Tri-Level Region Proposal Network (3L-RPN) along with the position-sensitive score maps (PSSM) being explored. A novel median intensity projection method is used to leverage the multi-dimensional information from CT images and introduced an additional deconvolutional layer to adopt the proposed TriLevel Region Proposal Network in our architecture to automatically identify the potential Region of Interest. We trained and evaluated our proposed decision support system using LIDC-IDRI dataset. The evaluation results demonstrated the high level performance of our proposed model in comparison to the state-of-the-art nodule detection and classification methods by attaining classification accuracy of 97.61% and sensitivity of 97.4%.

关键词： Lung cancer nodule classification computer aided systems convolutional neural network

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Dynamic remote attestation service for virtual machine on the IaaS cloud platform 3

Dynamic remote attestation service for virtual machine on th...

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3rd Annual International conference on Network and Information Systems for computers, ICNISC 2017

作者： Zhou, Huaizhe Ba, Haihe Ren, Jiangchun Chen, Yong Wang, Yongjun Wang, Zhiying College of Computer National University of Defense Technology Changsha Hunan410073 China

ISBN: (纸本)9781538616185

While the Infrastructure-as-a-Service (IaaS) cloud computing model has become a compelling computing solution, the security concerns on the data and application integrity in the virtual machines (VMs) have drastically restricted its widespread adoption. Although numerous researches have been dedicated to deal with the aforementioned issues, it still remains a challenge for now. In this paper, we present DRAS, a novel framework for remote attestation on VMs in IaaS cloud. It combines trusted computing with virtual machine introspection to provide flexible measurement for targeted VMs in a stealthy manner, which is more robust to malicious attackers. Moreover, we minimize the impact on platform performance and reduce trusted computing base by separating integrity measurement and attestation service from privileged domain to a dedicated secure VM. We show a concrete implementation of DRAS and a prototype based on Xen hypervisor. © 2019 IEEE.

关键词： Virtual machine

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Scale-aware camera localization in 3D LiDAR maps with a monocular visual odometry

Scale-aware camera localization in 3D LiDAR maps with a mono...

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conference on computer Animation and Social Agents (CASA)

作者： Sun, Manhui Yang, Shaowu Liu, Henzhu Natl Univ Def Technol Coll Comp State Key Lab High Performance Comp Changsha 410073 Hunan Peoples R China

Localization information is essential for mobile robot systems in navigation tasks. Many visual-based approaches focus on localizing a robot within prior maps acquired with cameras. It is critical where the Global Positioning System signal is unreliable. In contrast to conventional methods that localize a camera in an image-based map, we propose a novel approach that localizes a monocular camera within a given three-dimensional (3D) light detection and ranging (LiDAR) map. We employ visual odometry to reconstruct a semidense set of 3D points from the monocular camera images. These points are continuously matched against the 3D prior LiDAR map by a modified feature-based point cloud registration method to track a full six-degree-of-freedom camera pose. Since the monocular camera suffers from the scale-drift problem due to the lack of depth information, the proposed method solves it by adopting updatable scale estimation. Experiments carried out on a publicly large-scale data set demonstrate that the camera and LiDAR multimodal data matching problem is solved, and the localization accuracy of our method is comparable to state-of-the-art approaches.

关键词： multimodal data matching point cloud registration visual localization

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Adaptive hybrid projective models for aerial video stitching 6

Adaptive hybrid projective models for aerial video stitching

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6th International conference on Systems and Informatics, ICSAI 2019

作者： Xi, Runping Han, Tianyi Zhang, Yaqing Han, Qianqian Natl. Engineering Laboratory of Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech Image Information Processing School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781728152561

Aerial video stitching has been widely used in many fields such as large-scale videos surveillance, disaster monitoring and unmanned aerial vehicle (UAV) navigation. Ghosting and parallax are challenging problems when the video streams are captured by UAV. This paper presents a novel method based on hybrid projective model which combines local warp alignment and global warp projection for better registration accuracy. The geometric alignment model is used for global registration of images and the content-preserving alignment model is used for local refinement. Simultaneously, reference frame is updated adaptively according to the shift of scenery, which is used to eliminate the cumulative error appearing in long video streams. The proposed approach has been tested under multiple datasets in different scenarios, which shows good performance. © 2019 IEEE.

关键词： Security systems

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high-speed Railway Fastener Detection Using Minima Significant Region and Local Binary Patterns 4

High-speed Railway Fastener Detection Using Minima Significa...

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2019 4th Annual International conference on Information System and Artificial Intelligence, ISAI 2019

作者： Fan, Hong Xiong, Ying Fei, Yaodong Southwest Jiaotong University School of Mechanical Engineering Chengdu610031 China

Railway fastener is an important part of the railway system. Keeping the fasteners effective is essential to ensure the safe operation of the railway, so abnormal railway fastener detection is a main task of railway maintenance. With the development of railway system, the traditional manual fastener detection method has been unable to meet the application requirements, because it is very slow, costly, and dangerous. In this paper, we propose a novel method for abnormal fastener detection based on computer vision, which can detect missing fasteners automatically. In this method, Minima Significant Region is extracted in order to improve the fastener localization accuracy. Then, fastener recognition is operated using local binary features and Support Vector Machine classifier based on the fastener sub-images which are obtained by fastener localization. The proposed method is evaluated in our own database which is obtained by railway inspection system in different environments. The experimental results have shown improved performance against the state-of-the-art algorithm. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Railroads

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Retrieval by classification: Discriminative binary embedding for sketch-based image retrieval 2nd

Retrieval by classification: Discriminative binary embedding...

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2nd Chinese conference on Pattern Recognition and computer Vision, PRCV 2019

作者： Shi, Yufeng You, Xinge Wang, Wenjie Zheng, Feng Peng, Qinmu Wang, Shuo School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Research Institute of Huazhong University of Science and Technology in Shenzhen Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9783030317256

Sketch-based image retrieval (SBIR) intends to use free-hand sketch drawings as query to retrieve correlated real-world images from database. Hashing based methods gradually become the mainstream approaches in SBIR with its low memory usage and high query speed. Existing hashing based methods are incapable of guiding hash codes to preserve inter-class relationship and improving object recognition ability of hash functions simultaneously, which limits the higher performance. Hence, we propose Discriminative Binary Embedding (DBE), a novel algorithm of considering inter-class relationship and object recognition ability in a joint manner by treating retrieval as classification. Specifically, we apply NLP methods to encode category labels as binary embedding and then build classifiers for images and sketches, so as to obtain hash codes of instances based on binary embedding of predicted labels. Experimental results on two benchmarks show that DBE outperforms several state-of-the-arts. © Springer Nature Switzerland AG 2019.

关键词： Object recognition

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Predicting Survived and Killed Mutants

Predicting Survived and Killed Mutants

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IEEE International conference on Software Testing Verification and Validation Workshop, ICSTW

作者： Alejandra Duque-Torres Natia Doliashvili Dietmar Pfahl Rudolf Ramler Institute of Computer Science University of Tartu Tartu Estonia Software Competence Center Hagenberg GmbH Hagenberg Austria

ISBN: (数字)9781728110752

ISBN: (纸本)9781728110769

Mutation Testing (MT) is a state-of-the-art technique for assessing test suite effectiveness. The MT principle is to inject variants, known as mutants, into the System Under Test (SUT). Then, the behaviour of the original SUT is compared to that of the mutated SUT when running the same test suite. If no difference in behaviour is observed, the mutant is said to have survived; otherwise, it is said to have been killed. Despite its strengths, the applicability of MT in practice has been limited by its high computational cost. To mitigate this problem, Predictive Mutation Testing (PMT) has been proposed. PMT uses a classification model based on features related to the mutated code and the test suite to predict the execution results of a mutant without actually executing it. In other words, PMT predicts whether a mutant will be killed or will survive. In previous studies, PMT has been evaluated on several projects in two application scenarios, involving cross-project and crossversion learning. The goal of our research is to investigate how well the proposed PMT method, which has been evaluated on Java, can be extended to other programming languages. For that purpose, we first replicated the previous study and then extended the PMT approach to a single C program. We used random forrest classifiers as our supervised learning approach of choice. Our results indicate that PMT is able to predict the execution results of mutants with high accuracy. On the Java projects, we achieved Area Under Curve (AUC) values above 0.90 with a Prediction Error (PE) below 10%. On the C project, we achieved an AUC value above 0.90 with a PE below 1%. In our analyses we also investigated how sensitive the performance of PMT is to the set of selected features. In particular, we wanted to understand whether adding programming language specific features to a language independent core set of features significantly improve the performance of PMT. Our results are an indicator that, overall, PMT has

关键词： Java Tools Predictive models Software testing Software

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