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Using Others' Tests to Identify Breaking Updates

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Using Others' Tests to Identify Breaking Updates

International Mining Software Repositories, MSR

作者： Suhaib Mujahid Rabe Abdalkareem Emad Shihab Shane McIntosh Dept. of Computer Science and Software Engineering Data-driven Analysis of Software (DAS) Lab Concordia University Montreal Quebec Canada Software Analysis and Intelligence Lab (SAIL) School of Computing Queen's University Kingston Ontario Canada Dept. of Electrical and Computer Engineering Software Repository Excavation and Build Eng. Labs McGill University Montreal Quebec Canada

The reuse of third-party packages has become a common practice in contemporary software development. Software dependencies are constantly evolving with newly added features and patches that fix bugs in older versions. However, updating dependencies could introduce new bugs or break backward compatibility. In this work, we propose a technique to detect breakage-inducing versions of third-party dependencies. The key insight behind our approach is to leverage the automated test suites of other projects that depend upon the same dependency to test newly released versions. We conjecture that this crowd-based approach will help to detect breakage-inducing versions because it broadens the set of realistic usage scenarios to which a package version has been exposed. To evaluate our conjecture, we perform an empirical study of 391,553 npm packages. We use the dependency network from these packages to identify candidate tests of third-party packages. Moreover, to evaluate our proposed technique, we mine the history of this dependency network to identify ten breakage-inducing versions. We find that our proposed technique can detect six of the ten studied breakage-inducing versions. Our findings can help developers to make more informed decisions when they update their dependencies.

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Generalized random surfer-pair models

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arXiv 2019年

作者： Narayanaswami, Sai Kiran Ravindran, Balaraman Ramaiyan, Venkatesh Robert Bosch Centre for Data Science and AI Indian Institute of Technology Madras Chennai India Dept. of Computer Science and Engineering Robert Bosch Centre for Data Science and AI Indian Institute of Technology Madras Chennai India Dept. of Electrical Engineering Robert Bosch Centre for Data Science and AI Indian Institute of Technology Madras Chennai India

SimRank is a widely studied link-based similarity measure that is known for its simple, yet powerful philosophy that two nodes are similar if they are referenced by similar nodes. While this philosophy has been the basis of several improvements, there is another useful, albeit less frequently discussed interpretation for SimRank known as the Random Surfer-Pair Model. In this work, we show that other well known measures related to SimRank can also be reinterpreted using Random Surfer-Pair Models, and establish a mathematically sound, general and unifying framework for several link-based similarity measures. This also serves to provide new insights into their functioning and allows for using these measures in a Monte Carlo framework, which provides several computational benefits. Next, we describe how the framework can be used as a versatile tool for developing measures according to given design requirements. As an illustration of this utility, we develop a new measure by combining the benefits of two existing measures under this framework, and empirically demonstrate that it results in a better performing measure. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

Multiclass Single Label Model for Web Page Classification

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Multiclass Single Label Model for Web Page Classification

2019 International Conference on Recent Advances in Energy-efficient Computing and Communication, ICRAECC 2019

作者： Kag, Aakash Jenila Livingston, L.M. Livingston Merlin, L.M. Agnel Livingston, L.G.X. Mirabel Technologies Big Data Analyst Hyderabad HyderabadTG500032 India VIT Chennai School of Computing Science and Engineering 600127 India Jeppiaar Institute of Technology ECE Dept SriperumbudurTamil Nadu631604 India St. Xaviers Catholic college of Engineering CSE Dept. Nagercoil629003 India

ISBN: (纸本)9781538694824

Web is a huge repository of information and there is a need of categorization of web pages to facilitate better search and retrieval of pages. Web page classification has become a challenging task due to the exponential growth of the World Wide Web and this study augments classification model with a general facility for automatically assigning class label (e.g., sport, news) to web pages based on the output of a Naive Bayes classifier. For the purpose of build classification model, yahoo Open Directory Project (ODP) data set has been used for create training and testing set. In this research work web page classification was done using Uniform Resource Locator (URL) features, Meta data, Meta keywords, Internal Links and text, which gives better result than URLs based method. © 2019 IEEE.

关键词： Websites

Task Offloading for Automatic Speech Recognition in Edge-Cloud Computing Based Mobile Networks

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Task Offloading for Automatic Speech Recognition in Edge-Clo...

IEEE Symposium on Computers and Communications (ISCC)

作者： Shitong Cheng Zhenghui Xu Xiuhua Li Xiongwei Wu Qilin Fan Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong SAR China College of Intelligence and Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Dept. Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9781728180861

ISBN: (纸本)9781728180878

Explosively increasing multimedia services and applications, e.g., automatic speech recognition (ASR), have aggravated the burden on the cloud server in mobile networks. To address the challenge, mobile edge computing has emerged for partially alleviating the workload of the cloud server and enhancing the quality of service of mobile users. In this paper, we aim to employ the technique of edge-cloud computing to accelerate the processing of ASR tasks generated by users in mobile networks. Particularly, we deploy a convolutional neural network based encoder in each edge server to extract features of the audio data. Based on certain network constraints (i.e., user association and edge servers' storage/computing capacity), we propose a low-complexity and distributed iterative greedy method to address the formulated nonlinear mixed-integer nonconvex optimization problem. Simulation results demonstrate the effectiveness of the proposed scheme on reducing the total delay in the network.

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Advancing eosinophilic esophagitis diagnosis and phenotype assessment with deep learning computer vision

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arXiv 2021年

作者： Adorno, William Catalano, Alexis Ehsan, Lubaina von Eckstaedt, Hans Vitzhum Barnes, Barrett McGowan, Emily Syed, Sana Brown, Donald E. Dept. of Engineering Systems and Environment University of Virginia CharlottesvilleVA United States College of Dental Medicine Columbia University New York CityNY United States School of Medicine University of Virginia CharlottesvilleVA United States Department of Pediatrics School of Medicine University of Virginia CharlottesvilleVA United States Department of Medicine University of Virginia CharlottesvilleVA United States School of Data Science University of Virginia CharlottesvilleVA United States

Eosinophilic Esophagitis (EoE) is an inflammatory esophageal disease which is increasing in prevalence. The diagnostic gold-standard involves manual review of a patient’s biopsy tissue sample by a clinical pathologist for the presence of 15 or greater eosinophils within a single high-power field (400× magnification). Diagnosing EoE can be a cumbersome process with added difficulty for assessing the severity and progression of disease. We propose an automated approach for quantifying eosinophils using deep image segmentation. A U-Net model and post-processing system are applied to generate eosinophil-based statistics that can diagnose EoE as well as describe disease severity and progression. These statistics are captured in biopsies at the initial EoE diagnosis and are then compared with patient metadata: clinical and treatment phenotypes. The goal is to find linkages that could potentially guide treatment plans for new patients at their initial disease diagnosis. A deep image classification model is further applied to discover features other than eosinophils that can be used to diagnose EoE. This is the first study to utilize a deep learning computer vision approach for EoE diagnosis and to provide an automated process for tracking disease severity and progression. © 2021, CC BY.

关键词： Image segmentation

Learning Two-Stream CNN for Multi-Modal Age-related Macular Degeneration Categorization

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arXiv 2020年

作者： Wang, Weisen Li, Xirong Xu, Zhiyan Yu, Weihong Zhao, Jianchun Ding, Dayong Chen, Youxin The MoE Key Lab of Data Engineering and Knowledge Engineering Renmin University of China Beijing100872 China Aimc Lab School of Information Renmin University of China Beijing100872 China Vistel Ai Lab Visionary Intelligence Ltd. Beijing100872 China The Key Lab of Ocular Fundus Disease Chinese Academy of Medical Sciences Dept. of Ophthalmology Peking Union Medical College Hospital Beijing100730 China

This paper tackles automated categorization of Agerelated Macular Degeneration (AMD), a common macular disease among people over 50. Previous research efforts mainly focus on AMD categorization with a single-modal input, let it be a color fundus photograph (CFP) or an OCT B-scan image. By contrast, we consider AMD categorization given a multi-modal input, a direction that is clinically meaningful yet mostly unexplored. Contrary to the prior art that takes a traditional approach of feature extraction plus classifier training that cannot be jointly optimized, we opt for end-to-end multi-modal Convolutional Neural Networks (MM-CNN). Our MM-CNN is instantiated by a two-stream CNN, with spatially-invariant fusion to combine information from the CFP and OCT streams. In order to visually interpret the contribution of the individual modalities to the final prediction, we extend the class activation mapping (CAM) technique to the multi-modal scenario. For effective training of MM-CNN, we develop two data augmentation methods. One is GAN-based CFP/OCT image synthesis, with our novel use of CAMs as conditional input of a high-resolution image-to-image translation GAN. The other method is Loose Pairing, which pairs a CFP image and an OCT image on the basis of their classes instead of eye identities. Experiments on a clinical dataset consisting of 1,094 CFP images and 1,289 OCT images acquired from 1,093 distinct eyes show that the proposed solution obtains better F1 and Accuracy than multiple baselines for multimodal AMD categorization. Code and data are available at https://***/li-xirong/mmc-amd. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

Time threshold for classification between 2D-touch and 3D-touch based on user's 3D-touch input 8

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Time threshold for classification between 2D-touch and 3D-to...

2018 8th International Workshop on Computer science and engineering, WCSE 2018

作者： Park, JinHyuck Nam, ChoonSung Lee, JangYeol Seo, Young Hoon Shin, DongRyeol Dept. Electric and Electrical Computer Engineering SungKyunKwan University Suwon Korea Republic of Dept. Applied Data Science SungKyunKwan University Suwon Korea Republic of

ISBN: (纸本)9789811178610

As 3D-Touch technology has recently developed, 3D-Touch technology, which was applied only to the touch display of mobile devices, has been introduced to various devices. Although many researches have proposed applying the 3D-Touch technology to various devices, these researches have been confined only to apply 3D-Touch technology without considering 2D-Touch technology. Therefore, there is no clear criterion for distinguishing between 2D-Touch and 3D-Touch. In this paper, we suggest a time threshold for distinguishing between 2D-Touch and 3D-Touch on a display that supports 3D-Touch. © Proceedings of 2018 the 8th International Workshop on Computer science and engineering, WCSE 2018. All rights reserved.

关键词： Three dimensional displays

Machine learning approach for segmenting glands in colon histology images using local intensity and texture features

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arXiv 2019年

作者： Khatun, Rupali Chatterjee, Soumick Dept of Computer Applications Amity University Kolkata India Biomedical Magnetic Resonance Faculty of Natural Science Data & Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Germany

— Colon Cancer is one of the most common types of cancer. The treatment is planned to depend on the grade or stage of cancer. One of the preconditions for grading of colon cancer is to segment the glandular structures of tissues. Manual segmentation method is very time-consuming, and it leads to life risk for the patients. The principal objective of this project is to assist the pathologist to accurate detection of colon cancer. In this paper, the authors have proposed an algorithm for an automatic segmentation of glands in colon histology using local intensity and texture features. Here the dataset images are cropped into patches with different window sizes and taken the intensity of those patches, and also calculated texture-based features. Random forest classifier has been used to classify this patch into different labels. A multilevel random forest technique in a hierarchical way is proposed. This solution is fast, accurate and it is very much applicable in a clinical setup. Copyright © 2019, The Authors. All rights reserved.

关键词： Histology

Traffic Optimization Algorithms in Optical Networks For Real Time Traffic Analysis

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Optik 2022年

作者： Suresh Kumar. K Swati Vijay Shinde S. Venkata Lakshmi R.S. Sabeenian Koppisetti Durga Bhavani K.V.S.R. Murthy Department of Information Technology Saveetha Engineering College Tamil Nadu India Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune India Dept. of Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore India Department of ECE Sona College of Technology Salem Department of Management Studies Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India EEE Department & Dean (R&D) Aditya Group of Engineering Colleges

The rapid growth of metropolitan areas poses significant traffic control issues. Advanced telecommunication structures, such as the IoT (Internet of Things) technology, including wireless connections, produce enormous amounts of diverse traffic information. Conventional networking control approaches for tracking and information analysis confront certain obstacles and problems in specific systems, like reliability and efficient actual-time analysis of massive datasets. Furthermore, due to variables like gadget portability and connection diversity, the overall structure of internet activity, particularly in wireless networks, exhibits extremely complicated activity. The article aims to create a method for collecting high-quality, comprehensive information like transportation flowing strength, travel routes, and mean vehicle velocity. Simultaneously, information is gathered throughout the whole operational zone of junctions and neighboring highway segments under the city video security sensor perspective. More profound learning methods will be used within the communication domain for Networking Transport Management and Analytics (NTMA) activities such as traffic categorization and forecasting. The YOLOv3 artificial routing topology, as well as a SORT-accessible monitor, are used in our approach. Utilizing extended masking branches and improving its geometry of anchoring, overall essential efficiency in YOLO has been enhanced. We employed a technique of viewpoint conversion using parameters from actual pictures to global dimensions for computing overall vehicular velocities.

关键词： Networking Traffic Management and Analytics (NTMA) real-time surveillance traffic management and database processing