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Decisions in Continuous Integration and Delivery: An Exploratory Study 34

Decisions in Continuous Integration and Delivery: An Explora...

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34th international conference on Software Engineering and Knowledge Engineering, SEKE 2022

作者： Luo, Yajing Liang, Peng Shahin, Mojtaba Li, Zengyang Yang, Chen School of Computer Science Wuhan University Wuhan China School of Computing Technologies RMIT University Melbourne Australia School of Computer Science Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan China School of Artificial Intelligence Shenzhen Polytechnic Shenzhen China

ISBN: (纸本)1891706543

In recent years, Continuous Integration (CI) and Continuous Delivery (CD) has been heatedly discussed and widely used in part or all of the software development life cycle as the practices and pipeline to deliver software products in an efficient way. There are many tools, such as Travis CI, that offer various features to support the CI/CD pipeline, but there is a lack of understanding about what decisions are frequently made in CI/CD. In this work, we explored one popular open-source project on GitHub, Budibase, to provide insights on the types of decisions made in CI/CD from a practitioners' perspective. We first explored the GitHub Trending page, conducted a pilot repository extraction, and identified the Budibase repository as the case for our study. We then crawled all the closed issues from the repository and got 1,168 closed issues. Irrelevant issues were filtered out based on certain criteria, and 370 candidate issues that contain decisions were obtained for data extraction. We analyzed the issues using a hybrid approach combining predefined types and the Constant Comparison method to get the categories of decisions. The results show that the major type of decisions in the Budibase closed issues is Functional Requirement Decision (67.6%), followed by Architecture Decision (11.1%). Our findings encourage developers to put more effort on the issues and making decisions related to CI/CD, and provide researchers with a reference of decision classification made in CI/CD. © 2022 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Pipelines

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Automatic Student Attendance and Activeness Monitoring System

Automatic Student Attendance and Activeness Monitoring Syste...

引用

international conference on Intelligent Systems and Sustainable computing, ICISSC 2021

作者： Poojari, Naveena Narayana Sangeetha, J. Shreenivasa, G. Prajwal Department of Computer Science and Technology Ramaiah Institute of Technology Karnataka Bangalore India

ISBN: (纸本)9789811900105

Managing the student attendance is a very important functionality of an educational institution. Institutions manage this task in their own ways. Tracking of each and every student will be a job which demands high precisions. Imagine tracking the attendance as well as activeness of students throughout the class along with teaching subject is a tedious task. It is nearly impossible where the student count is very high. In our work, we have used this computational power of the computers and implemented an automated system which monitors both attendance and activeness of each individual in its covered area and stores the data in a database for later use. Our model is multitasking and has reasonable computational speed, which can easily replace the work of manually taking attendance in a very effective and fast way. This work also focusses on the activeness detection. Hence, this is a multitasking system. Nowadays, many students wear spectacles and our work can easily recognize the faces wearing spectacles also. The activeness system also works fine on students wearing spectacles. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Aspect ratio

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Fraud Detection Using Decision Tree Algorithm to Curb Identity Theft 1

引用

international conference on advances in Communication Technology and Computer Engineering, ICACTCE 2023

作者： Hai, Tao Zhou, Jincheng Ajoboh, Oluwakemi A. Olatunji, Timothy Zhou, Xiaoshan Iwendi, Celestine Oyesola, Boluwatife School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China School of Creative Technologies University of Bolton Bolton United Kingdom School of Information and Artificial Intelligence Nanchang Institute of Science and Technology Nanchang China

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

Identity theft is a growing concern that can cause significant financial and emotional harm to individuals. One way to detect and prevent identity theft is by using machine learning algorithms, such as decision tree. In this study, we investigate the effectiveness of using a decision tree algorithm in detecting and preventing identity theft. A dataset consisting of personal information, as well as information on suspicious activity, was collected from a financial institution. The dataset included a total of 284,807 rows of data and 30 columns. The decision tree algorithm was implemented using the Python programming language and the scikit-learn library. The algorithm was trained on the training set and used to classify new cases as either fraudulent or non-fraudulent. The performance of the decision tree algorithm was evaluated using several performance metrics such as accuracy, precision, recall and F1-score. Results showed that the decision tree algorithm was effective in detecting and preventing identity theft, with an overall accuracy of 99%. These findings demonstrate the potential of using decision tree algorithms in detecting and preventing identity theft, which can help to curb the increasing problem of identity theft and protect individuals from financial and emotional harm. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision trees

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Whole-File Chunk-Based Deduplication Using Reinforcement Learning for Cloud Storage

Whole-File Chunk-Based Deduplication Using Reinforcement Lea...

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international conference on advances in Social Network Analysis and Mining, ASONAM

作者： Xincheng Yuan Melody Moh Teng-Sheng Moh Department of Computer Science San José State University San José CA USA

Deduplication is the process of removing replicated data content from storage facilities like online databases, cloud datastore, local file systems, etc. It is commonly performed as part of data preprocessing to eliminate redundant data that requires extra storage spaces and computing power and is crucial for data storage management in cloud computing. Deduplication is essential for file backup systems since duplicated files will presumably consume more storage space, especially with a short backup period such as daily. A common technique in this field involves splitting files into chunks whose hashes can be compared using data structures or techniques like clustering. This paper explores the possibility of performing such file chunk deduplication leveraging an innovative reinforcement learning approach to achieve a high deduplication ratio. The proposed system is named SegDup, which achieves 13% higher deduplication ratio than Extreme Binning, a state-of-the art deduplication algorithm.

关键词： Cloud computing Social networking (online) File systems databases data preprocessing Memory Clustering algorithms

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Fog based Healthcare Monitoring System in SDN-IoT Networks

Fog based Healthcare Monitoring System in SDN-IoT Networks

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： Neeraj Kumari Vivek Kumar Jain Department of Computing Science & IT Teerthanker Mahaveer University Moradabad India Department of Engineering & Technology Jaipur National University Jaipur India

ISBN: (纸本)9781665493970

IoT based health-monitoring systems and applications have been a game-changer in the realm of cutting-edge healthcare facilities, medical data processing. In this research, we improved a similar IoT-enabled real-time heart monitoring system by making use of the cloud computing concept for obtaining sensor data, visualizing it with less cost and power, storing it at local storage, tracking it, and interacting with it remotely. We created a novel fog computing interface by combining Software Defined Networking with three sensing devices that retrieve health data. Analysis of medical data, bio signals, and sensor-generated data, signals, demonstrated the system's viability. Cost, power usage, and latency were also factored in to the analysis of the system's performance and compatibility. The wearable health system has been developed and tested, and the results show that it is ideal for relieving medical staff while providing round-the-clock, remote patient care.

关键词： Heart Cloud computing Costs System performance data visualization Medical services Real-time systems

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2nd international conference on data science, Machine Learning and Applications, ICDSMLA 2020

2nd International Conference on Data Science, Machine Learni...

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2nd international conference on data science, Machine Learning and Applications, ICDSMLA 2020

ISBN: (纸本)9789811636899

The proceedings contain 157 papers. The special focus in this conference is on data science, Machine Learning and Applications. The topics include: Twitter Spam Review Detection Using Hybrid Machine Learning Techniques;throughput Improvement in Energy Efficient Heterogeneous Wireless Sensor Network;predicting Expected Time of Arrival of Shipments Through Multiple Linear Regression;Learning via Long Short-Term Memory (LSTM) Network for Predicting Strains in Railway Bridge Members Under Train Induced Vibration;smart Transportation Using Fog computing;detection of Learning Disability: A Survey;EEG Based Emotion Investigation from Various Brain Region Using Deep Learning Algorithm;electric Motor Drive Using Single Input Fuzzy Logic Controller for Husk Extraction in Rice Mill Industry;states Categorization in India Based on Health Security Capacity with Machine Learning Technique;CovidSORT: Detection of Novel COVID-19 in Chest X-ray Images by Leveraging Deep Transfer Learning Models;a Real Time Radio Spectrum Measurement Campaign for Machine Learning Based Spectrum Inference in Cognitive Radio Network;application of Disruptive technologies in Intelligent Transportation System;deep Learning Models for Rubik’s Cube with Entropy Modelling;performance Analysis of Software Enabled Accelerator Library for Intel Architecture;blind Descent: A Prequel to Gradient Descent;a Survey: Modelling Strategies for Predictive Maintenance;abstract and Image Analysis of High-Temperature Materials from Scientific Journals Using Deep Learning and Rule-Based Machine Learning Approaches;a Time Dependent Epidemiology Model for Hospital Resource Management in Usual Scenarios and Pandemic;Load Forecasting Using LSTM and Its Comparison with Other Machine Learning Techniques and Their Implementation;application of Machine Learning to Detect Neuroticism in Individuals Using Handwriting Analysis;preface.

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An Energy Efficient Routing for Emergency Rescue in IoT-Based WSN 4th

An Energy Efficient Routing for Emergency Rescue in IoT-Base...

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4th international conference on Futuristic Trends in Networks and computing technologies, FTNCT 2021

作者： Shreyas, J. Shilpa, S. Udayaprasad, P.K. Srinidhi, N.N. Dilip Kumar, S.M. Department of Computer Science and Engineering UVCE Bengaluru India

ISBN: (纸本)9789811950360

The rapid expansion demand of sensor devices have attained significant attention for wireless sensor network (WSN) and demands many Internet of Things (IoT) applications for real-time usage. The routing technique must capable of efficiently delivering packets to sink to achieve large-scale IoT application’s reliability. How to guarantee real-time emergency reaction capabilities during data transmission is now a difficult topic for research under the demand of large IoT network scale expansion. Existing routing scheme fails to deliver data and has higher routing latency due to transmissions failures and lower energy nodes. In this article, an energy efficient emergency response routing scheme (EEER) is proposed. The proposed EEER delivers the critical data efficiently under the emergency with lower latency. Extended transmission range covers wide range of intermediate nodes for data communication. This scheme selects nodes with higher residual energy for forwarding emergency data towards sink. To ensure this, the output of the proposed device is validated with an comprehensive simulation test. Results tested using NS2 simulation method indicate an improvement of 5% decrease in energy usage, 6% increase in packet delivery ratio and 7% decrease in end to end delay. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Decentralized M-Learning Platform with Trusted Execution Environment 20th

Decentralized M-Learning Platform with Trusted Execution Env...

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20th international conference on Web-based Learning (ICWL)

作者： Wang, Wennan Zhu, Linkai Wang, Baoping Guang, Li Peng, Sheng Cai, Zhiming City Univ Macau Inst Data Sci Macau Peoples R China Chinese Acad Sci Inst Software Beijing Peoples R China Zhuhai Yingying Technol Co Ltd Zhuhai Peoples R China

ISBN: (纸本)9783030907853;9783030907846

With the rapid growth in E-learning, Mobile learning (M-learning) is one of the most widely used ways in distance education compare to the traditional classroom environment. However, Mobile learning has accentuated the problem of data privacy disclosure and low performance. Blockchain is an important step in smart contracts with peer execution, immutability, and provenance may bring a new level of protection and confidence to M-learning. In combination with Trusted Execution Environment, we proposed a decentralized M-learning platform prototype within TrustZone technology for ARM devices where the learner identity key is encrypted and stored in Hyperledger Fabric with a peer-to-peer network. Our platform resolves the difficulties with privacy data in E-learning and reduced workload. An evaluation demonstrated that It is available to execute encryption algorithms in a trusted execution environment.

关键词： M-learning E-learning Blockchain TrustZone Privacy

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Real-Time Face Mask Detection Using Convolution Neural Network and Computer Vision 4th

Real-Time Face Mask Detection Using Convolution Neural Netwo...

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4th international conference on computing, Communications, and Cyber-Security, IC4S 2022

作者： Anusuya, V. Vignesh Saravanan, K. Vishnu Praba, V. Department of Computer Science and Engineering Ramco Institute of Technology Rajapalayam India

ISBN: (纸本)9789819914784

One strategy to prevent COVID-19 as a result of the pandemic is to use a face mask. Although many countries have made it a requirement for citizens to do so, the majority of people continue to disobey this order. In the current situation, police frequently check for face masks in public locations and fine anyone found not to be wearing one. The existing system identifies the person wearing the mask or not, but our proposed method identifies the person those who are not wearing the mask properly and also identifies the multiple person wearing the mask or not. On the other side, some governments have implemented technology to identify individuals wearing face masks and communicate their information to a patrol team so that they may catch them. The proposed model identifies individuals who are in the public without face masks. The proposed approach is able to identify these persons using facial detection technologies, and the data is then combined with a database of public identity information to gather information about the individual and deliver the fine amount to his home address and mobile numbers. Using the convolution neural network (CNN) model, we have identified people wearing and not wearing masks. When compared to many other algorithms, CNN can more precisely recognize data down to the pixel level. In the implementation of our model, the activation functions for the hidden and fully connected layers, respectively, were rectified linear unit (ReLU) and softmax. Two convolution layers with 100 filters each were used. The 91.21% accurate Cascade classifier is used to recognize faces. Adam is the optimizer, and cross-entropy serves as the loss function. More than 1500 images were used to train the model, which comprises classes with and without masks. The public will start wearing masks in public areas because of the terror that this AI-based mask recognition system instils in them, helping to stop the spread of diseases that are otherwise beneficial to society.

关键词： Convolution

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Automatic Life Event Tree Generation for Older Adults 1

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24th international conference on Human-Computer Interaction, HCII 2022

作者： Gui, Fang Wu, Xi Hu, Min Yang, Jiaoyun School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

Studies have shown that learning personal stories could help provide individualized eldercare services. However, personal stories are often disordered because of the scattered collection, including informal interviews or daily interactions, which brings difficulties in acquiring valuable information quickly. One solution to this problem is to extract events from personal stories and automatically organize them in chronological order. Events extracted by current methods from social media or news corpus are mainly organized in a linear structure. These works usually focus on the event time and ignore the consistency of event contents when organizing events. This paper aims to organize events into a tree structure based on an event network, with stem nodes representing key event topics and branch nodes representing detailed events. Social workers or caregivers can clarify the life experience of the older adults quickly through the event tree and have a preliminary understanding of them. The experiments show that the event tree generated by our method has a better performance in consistency than current event organization methods. A survey study shows that our method achieves the highest logical coherence for the event tree branches compared with other algorithms. © 2022, Springer Nature Switzerland AG.

关键词： Trees (mathematics)

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