检索结果-内蒙古大学图书馆

Joint learning of discriminative low-dimensional image representations based on dictionary learning and two-layer orthogonal projections

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Wei, Xian Shen, Hao Li, Yuanxiang Tang, Xuan Jin, Bo Zhao, Lijun Murphey, Yi Lu Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences China Technical University of Munich Germany and fortiss GmbH Munich Germany Shanghai Key Lab for Trustworthy Computing School of Computer Science and Software Engineering East China Normal University China School of Aeronautics & Astronautics Shanghai Jiao Tong University Shanghai200240 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150006 China Department of Electrical and Computer Engineering University of Michigan-Dearborn DearbornMI48128 United States

This work investigates the problem of efficiently learning discriminative low-dimensional representations of multi-class large-scale image objects. We propose a generic deep learning approach by taking advantages of Convolutional Neural Networks (CNN), sparse dictionary learning, and orthogonal projections. CNN is not only powerful on feature extraction, but also robust to spatial variance and changes. Sparse dictionary learning is well known for disentangling nonlinear underlying discriminative factors in data. The orthogonal projection is a notable efficient tool to project multi-class data onto low dimensional discriminative subspace. The proposed procedure can be summarized as follows. At first, a CNN is employed to extract high-dimensional (HD) preliminary convolutional features. Secondly, to avoid the high computational cost of direct sparse coding on HD CNN features, we present to learn sparse representation (SR) in an orthogonal projected space over a task-driven sparsifying dictionary. We then exploit the discriminative projection on SR. The whole learning process is treated as a joint optimization problem of trace quotient maximization, which involves the CNN parameters, the orthogonal projection on CNN features, the dictionary and the discriminative projection on sparse codes. The related cost function is well defined on product manifold of the Stiefel manifold, the Oblique manifold, and the Grassmann manifold. It is optimized via a geometrical stochastic gradient descent algorithm. Finally, the quality of dictionary and projections learned by the proposed approach is further investigated in terms of image classification. The experimental results show that the approach can achieve a strongly competitive performance with state-of-the-art image classification methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

METTLE: a METamorphic Testing approach to assessing and validating unsupervised machine LEarning systems

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Xie, Xiaoyuan Zhang, Zhiyi Yueh Chen, Tsong Liu, Yang Poon, Pak-Lok Xu, Baowen School of Computer Science Wuhan University Wuhan430072 China Department of Computer Science and Software Engineering Swinburne University of Technology HawthornVIC3122 Australia School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Engineering and Technology Central Queensland University Australia MelbourneVIC3000 Australia State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

Unsupervised machine learning is the training of an artificial intelligence system using information that is neither classified nor labeled, with a view to modeling the underlying structure or distribution in a dataset. Since unsupervised machine learning systems are widely used in many real-world applications, assessing the appropriateness of these systems and validating their implementations with respect to individual users’ requirements and specific application scenarios/contexts are indisputably two important tasks. Such assessment and validation tasks, however, are fairly challenging due to the absence of a priori knowledge of the data. In view of this challenge, we develop a METamorphic Testing approach to assessing and validating unsupervised machine LEarning systems, abbreviated as METTLE. Our approach provides a new way to unveil the (possibly latent) characteristics of various machine learning systems, by explicitly considering the specific expectations and requirements of these systems from individual users’ perspectives. To support METTLE, we have further formulated 11 generic metamorphic relations (MRs), covering users’ generally expected characteristics that should be possessed by machine learning systems. To demonstrate the viability and effectiveness of METTLE, we have performed an experiment involving six commonly used clustering systems. Our experiment has shown that, guided by user-defined MR-based adequacy criteria, end users are able to assess, validate, and select appropriate clustering systems in accordance with their own specific needs. Our investigation has also yielded insightful understanding and interpretation of the behavior of the machine learning systems from an end-user software engineering’s perspective, rather than a designer’s or implementor’s perspective, who normally adopts a theoretical approach. Copyright © 2018, The Authors. All rights reserved.

关键词： software testing

Auto-contouring FDG-PET/MR images for cervical cancer radiation therapy: An intelligent sequential approach using focally trained, shallow U-Nets

学校读者我要写书评

暂无评论

Intelligence-Based Medicine 2021年 5卷 100026-100026页

作者： Atallah Baydoun Ke Xu Latoya A. Bethell Feifei Zhou Jin Uk Heo Kaifa Zhao Elisha T. Fredman Rodney J. Ellis Pengjiang Qian Raymond F. Muzic Bryan J. Traughber Department of Radiation Oncology University Hospitals Cleveland Medical Center Cleveland OH USA School of Artificial Intelligence and Computer Science Jiangnan University Wuxi Jiangsu 214122 China Jiangsu Key Laboratory of Media Design and Software Technology Jiangnan University Wuxi Jiangsu 214122 China Department of Radiology School of Medicine Case Western Reserve University Cleveland OH 44106 USA Department of Biomedical Engineering Case Western Reserve University Cleveland OH 44106 USA Department of Computing Hong Kong Polytechnic University Hung Hom 999077 Hong Kong Department of Radiation Oncology Penn State Cancer Institute Hershey PA 17033 USA

Background Manual contouring for radiation therapy planning remains the most laborious and time consuming part in the radiation therapy workflow. Particularly for cervical cancer, this task is complicated by the complex female pelvic anatomy and the concomitant dependence on 18 F-labeled Fluorodeoxyglucose (FDG) positron emission tomography (PET) and magnetic resonance (MR) images. Using deep learning, we propose a new auto-contouring method for FDG-PET/MR based cervical cancer radiation therapy by combining the high level anatomical topography and radiological properties, to the low-level pixel wise deep-learning based semantic segmentation. Materials/methods The proposed method: 1) takes advantage of PET data and left/right anatomical symmetry, creating sub-volumes that are centered on the structures to be contoured. 2) Uses a 3D shallow U-Net (sU-Net) model with an encoder depth of 2.3) Applies the successive training of 3 consecutive sU-Nets in a feed forward strategy. 4) Employs, instead of the usual generalized dice loss function (GDL), a patch dice loss function (PDL) that takes into account the Dice similarity index (DSI) at the level of each training patch. Experimental analysis was conducted on a set of 13 PET/MR images was using a leave-one-out strategy. Results Despite the limited data availability, 5 anatomical structures - the gross tumor volume, bladder, anorectum, and bilateral femurs - were accurately (DSI = 0.78), rapidly (1.9 s/structure), and automatically delineated by our algorithm. Overall, PDL achieved a better performance than GDL and DSI was higher for organs at risk (OARs) with solid tissue (e.g. femurs) than for OARs with air-filled soft tissues (e.g. anorectum). Conclusion The presented workflow successfully addresses the challenge of auto-contouring in FDG-PET/MR based cervical cancer. It is expected to expedite the cervical cancer radiation therapy workflow in both, conventional and adaptive radiation therapy settings.

关键词： Cervical cancer Deep learning Image segmentation PET/MR Based radiation therapy U-net

Graph-based ontology summarization: A survey

学校读者我要写书评

暂无评论

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

Automated localization for unreproducible builds

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Ren, Zhilei Xuan, Jifeng Jiang, He Yang, Zijiang Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China School of Computer Science Wuhan University Wuhan China Department of Computer Science Western Michigan University KalamazooMI United States

Reproducibility is the ability of recreating identical binaries under pre-defined build environments. Due to the need of quality assurance and the benefit of better detecting attacks against build environments, the practice of reproducible builds has gained popularity in many open-source software repositories such as Debian and Bitcoin. However, identifying the unreproducible issues remains a labour intensive and time consuming challenge, because of the lacking of information to guide the search and the diversity of the causes that may lead to the unreproducible binaries. In this paper we propose an automated framework called RepLoc to localize the problematic files for unreproducible builds. RepLoc features a query augmentation component that utilizes the information extracted from the build logs, and a heuristic rule-based filtering component that narrows the search scope. By integrating the two components with a weighted file ranking module, RepLoc is able to automatically produce a ranked list of files that are helpful in locating the problematic files for the unreproducible builds. We have implemented a prototype and conducted extensive experiments over 671 real-world unreproducible Debian packages in four different categories. By considering the topmost ranked file only, RepLoc achieves an accuracy rate of 47.09%. If we expand our examination to the top ten ranked files in the list produced by RepLoc, the accuracy rate becomes 79.28%. Considering that there are hundreds of source code, scripts, Makefiles, etc., in a package, RepLoc significantly reduces the scope of localizing problematic files. Moreover, with the help of RepLoc, we successfully identified and fixed six new unreproducible packages from Debian and Guix. Copyright © 2018, The Authors. All rights reserved.

关键词： Quality assurance

Graph convolutional neural networks via motif-based attention

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Peng, Hao Li, Jianxin Gong, Qiran Wang, Senzhang Ning, Yuanxing Yu, Philip S. Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Department of Computer Science University of Illinois at Chicago

Many real-world problems can be represented as graph-based learning problems. In this paper, we propose a novel framework for learning spatial and attentional convolution neural networks on arbitrary graphs. Different from previous convolutional neural networks on graphs, we first design a motif-matching guided subgraph normalization method to capture neighborhood information. Then we implement subgraph-level self-attentional layers to learn different importances from different subgraphs to solve graph classification problems. Analogous to image-based attentional convolution networks that operate on locally connected and weighted regions of the input, we also extend graph normalization from one-dimensional node sequence to two-dimensional node grid by leveraging motif-matching, and design self-attentional layers without requiring any kinds of cost depending on prior knowledge of the graph structure. Our results on both bioinformatics and social network datasets show that we can significantly improve graph classification benchmarks over traditional graph kernel and existing deep models. Copyright © 2018, The Authors. All rights reserved.

关键词： Graphic methods

Effective Query Grouping Strategy in Clouds

学校读者我要写书评

暂无评论

Journal of computer science & technology 2017年第6期32卷 1231-1249页

作者： Qin Liu Yuhong Guo Jie Wu Guojun Wang College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing 100876 China School of Computer Science Carleton University Ottawa ON K155B6 Ganada Department of Computer and Information Sciences Temple University Philadelphia PA 19122 U.S.A. School of Computer Science and Educational Software Guangzhou University Guangzhou 510006 China

As the demand for the development of cloud computing grows, more and more organizations have outsourced their data and query services to the cloud for cost-saving and flexibility. Suppose an organization that has a great number of users querying the cloud-deployed multiple proxy servers to achieve cost efficiency and load balancing. Given n queries, each of which is expressed as several keywords, and k proxy servers, the problem to be solved is how to classify n queries into k groups, in order to minimize the difference between each group and the number of distinct keywords in all groups. Since this problem is NP-hard, it is solved in mathematic and heuristic ways. Mathematic grouping uses a local optimization method, and heuristic grouping is based on k-means. Specifically, two extensions are provided： the first one focuses on robustness, i.e., each user obtains search results even if some proxy servers fail; the second one focuses on benefit, i.e., each user can retrieve as many files as possible that may be of interest without increasing the sum. Extensive evaluations have been conducted on both a synthetic dataset and real query traces to verify the effectiveness of our strategies.

关键词： cloud computing cost efficiency load balancing robustness benefit

A Multi-Objective Data Placement Method for IoT Applications Over Big Data Using NSGA-II

学校读者我要写书评

暂无评论

A Multi-Objective Data Placement Method for IoT Applications...

IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Xiaolong Xu Renhao Gu Yuan Yuan Kai Peng Yadang Chen Wanchun Dou Alex X. Liu Department of Computer Science and Engineering Michigan State University MI USA Jiangsu Engineering Centre of Network Monitoring Nanjing University of Information Science and Technology Nanjing China College of Engineering Huaqiao University Quanzhou Fujian China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

The Internet of Things has been developing fast since it connects various devices for human beings to use, monitor or configure these devices, which brings great convenience. Generally, the big data generated by the sensors of IoT applications needs to be collected, stored and processed to realize the services of such applications. For the execution of task from the IoT applications, it may need a variety of datasets for tasks to access and process. However, it is still a challenge to realize the data placement method when taking into account the resource utilization and the energy consumption in data centers along with the data access time for the task sets to access the datasets. In view of this challenge, a multi-objectives data placement method for IoT applications over big data is proposed in this paper. Technically, multi-objectives are analyzed first. Then a heuristic data placement method is designed based on Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Consequentially, experimental evaluation and comparison analysis are conducted to validate the efficiency and effectiveness of our proposed method.

关键词： Manganese Handheld computers Internet of Things Switches Green computing Social computing Blockchain

An Energy-Aware Virtual Machine Scheduling Method for Cloudlets in Wireless Metropolitan Area Networks

学校读者我要写书评

暂无评论

An Energy-Aware Virtual Machine Scheduling Method for Cloudl...

IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Xiaolong Xu Yuancheng Li Yuan Yuan Kai Peng Wenbin Yu Wanchun Dou Alex X. Liu Department of Computer Science and Engineering Michigan State University MI USA Jiangsu Engineering Centre of Network Monitoring Nanjing University of Information Science and Technology Nanjing China College of Engineering Huaqiao University Quanzhou Fujian China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Currently, mobile applications, such as interactive gaming, virtual reality, and natural language processing, are becoming more computational intensive, but the computing capacity of mobile devices is limited. An effective way to solve this problem is offloading the tasks to nearby cloudlets, which are some small mobile data centers deployed in wireless metropolitan area network (WMAN). and consume a large amount of energy. Excessive energy consumption brings about some negative effects, including increased carbon dioxide emissions, aggravating the greenhouse effect, etc. In view of this challenge, we propose an energy-aware virtual machine (VM) scheduling method for cloudlets in WMAN. Technically, an optimization model is designed to formalize our problem. Then, based on VM Live Migration technology, a corresponding energy saving VM scheduling method abbreviated as MFEA is proposed to determine which VMs should be migrated and where they should be migrated. Finally, experimental data demonstrate that the proposed method is effective.

关键词： Cloud computing Performance evaluation Energy consumption Internet of Things Green computing Social computing Blockchain