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The Correlation Between Architecture Smells, Package Stability and Reusability: A Case Study 4

The Correlation Between Architecture Smells, Package Stabili...

4th IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer engineering, MI-STA 2024

作者： Benidris, Mrwan Elfallah, Ehab A. Omar Cheema, Marwa Aslam Alsma, Mohammed Salah Addin Kadiki, Tareq Ziad Elbarsha, Abdelfattah University of Benghazi Faculty of Information Technology Dep. of Computer Science Libya Libyan International Medical University Faculty of Information Technology Dept. of Software Engineering Libya University of Benghazi Faculty of Public Health Health Informatics Department Libya

ISBN: (纸本)9798350372632

the design decisions made in the architecture of a software system are essential to its maintainability, and thus its quality is of great importance. Architecture smells (ASs) can be used to identify any quality issues within the system. To further explore the relationships between ASs, stability, and reusability, Spearman correlation coefficients will be calculated. This paper seeks to examine the connection between architecture smells, software stability, and reusability. To do this, we utilized a dataset created by other researchers and conducted an empirical study of the associations between five types of architecture smells and software stability and reusability. This research is necessary as there is a lack of in-dept. research on architecture smells and their relationship to software stability and reusability. We found a relationship between the presence of Feature Concentration in package and the stability and reusability of the package. Also, package's stability is affected by both Cyclic Dependency and Unstable Dependency. A slight positive correlation has been found between God Class and stability, as well as a very weak negative correlation between Unstable Dependency and reusability. Finally, we found a slight negative correlation between package stability and reusability. © 2024 IEEE.

关键词： Reusability

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Cocopal - A Deep Learning Based Intelligent System to Certify and Standardize the Quality of Coconut Based Products 4

Cocopal - A Deep Learning Based Intelligent System to Certif...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Gunawardana, K.H.R. Hemachandra, M.G.S.P. Hettiarachchi, N.M. Deshan, M.P.N. Ganegoda, Devanshi Weerasinghe, Lokesha Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

The procedure of certifying and standardizing the quality of the coconut-based products is done manually in Sri Lanka at precent. It is a time consuming and labor- intensive task and is conducted by experts. In most cases, the quality is decided solely by visual inspections by buyers and suppliers, with no scientific basis. The paper reports the capacity of bringing modern technology solutions such as Artificial Intelligence (AI), Machine Learning (ML), Image Processing (IP), and decentralized storage to aid in the certification and standardization of the quality of raw *** showed that the accuracy of the proposed system is in the 86% to 90% range and showed that this technique could beimproved and used as an alternative to manual techniques. © 2022 IEEE.

关键词： Deep learning

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Train a Robot to Climb Staircase Using Vision-Base System 10

Train a Robot to Climb Staircase Using Vision-Base System

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10th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2022

作者： Jayawardana, J.T.H. Dilshan, H.V.V. Wijethilaka, R.G.K.H Balasooriya, T.D. Rajapaksha, U.U.S. Harshanath, S.M.B. Jayawardena, Chandimal Sri Lanka Institute of Information Technology Dept. Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. Information Technology Malabe Sri Lanka

ISBN: (数字)9781665401562

ISBN: (纸本)9781665401562

Currently, robots are used for different types of work, such as the manufacturing industry, healthcare, and the hotel industry. According to the current epidemic situation, the usage of robots was increased because of the need to reduce human interaction. As a result, they have to walk around the workplace, because of that, they may have to climb staircases. The world has many types of robots. Here the selected robot is a humanoid. This proposal is concerned with how to detect the staircase, count steps, get dimensions of it, and move the robot on it by keeping body balance. First, want to know what the objects are, then walk. The identified images from the image sensors will get as input. The technology stack that is used for image analysis is a method related to computer vision in deep learning. Other than that, while climbing the stairs robot needs to identify whether the staircase is over or not. Here we introduce a new concept: get the number of steps required to climb before climbing the stairs. It is related to how humans identify things by seeing and making decisions. The need to take the dimensions of the stairs is that when considering the stairs, they have their height, width, and range. Therefore, it is imperative to calculate the dimensions of each staircase separately. Creating a balance system similar to human balance is a great advantage in robotics. To implement such a system, there is an upright pose controller to allow the robot to walk stably by preventing tilting of the robot during walking on uneven floor. In general, for us to do that we need to calculate the global inclination of the floor is a key factor. It can be measured with a 2-axis accelerator meter, and it is installed in the inertial sensor. © 2022 IEEE.

关键词： Stairs

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Towards Eco-Sustainability and Green Analytics Model to Measure the Performance of Big Data Systems 9

Towards Eco-Sustainability and Green Analytics Model to Meas...

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9th International Conference on information Management, ICIM 2023

作者： Jusoh, Yusmadi Yah Abdullah, Rusli Nor, Rozi Nor Haizan Yahaya, Jamaiah Hj. Muhammad, Shireen Arunachalam, Aishwharya Raani Universiti Putra Malaysia Faculty of Computer Science and Information Technology Dept. of Software Engineering and Information Systems Serdang Malaysia Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Bangi Malaysia Faculty of Communication Selangor Malaysia

ISBN: (纸本)9798350336382

Many BDS is doomed to failure because of the missing knowledge on measuring the performance of BDS. The failure to identify the performance measurement and correct will make the problems worsen. This will complicate the efforts of fixing such problems and more importantly, will affect the quality of knowledge, insights, and results expected by the users in the future. In achieving durable performance, the organization needsto understand the factors that contribute to the performance of the eco-sustainability and green analytics metrics for BDS. Although the existence of its importance and awareness was largely neglected. Therefore, the eco-sustainability and green analytic measurement of the BDS is examined in this research. Measuring the BDS performance has the benefit of identifying problems and launching corrective actions before these problems happen and become worsen. The research will investigate the eco-sustainability and green analytic measures which will be proposed and used to capture performance for each process of BDS. Then, based on such measures and metrics, as well as existing performance concepts, frameworks, and models, a measurement model for BDS will be created. The model will be validated through expert evaluations and confirmatory studies with users and practitioners of BDS users. The research has a greater significance in leveraging existing performance concepts in BDS settings and the research proposes users' participation in the sustainability and continuous performance evaluation of theirBDS. © 2023 IEEE.

关键词： Big data

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A Comparative Analysis of Data Resampling Techniques for Addressing Class Imbalance in Mosquito Classification

A Comparative Analysis of Data Resampling Techniques for Add...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Reshma Pise Abhishek Singh Chauhan Bharati Ainapure Aditya Shenolkar Leena Deshpande Shivraj Jadhav Computer Engineering Dept. Vishwakarma University Pune India CE (Software Engineering) Dept. Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Mosquitoes, as vectors of life-threatening diseases such as dengue, Zika, and West Nile virus, present a significant public health challenge. Precise and rapid identification of mosquito species is crucial for early detection and prevention of these diseases. Deep learning techniques have proven to be highly effective for automatic visual insect classification tasks. However, the imbalanced distribution of mosquito species within training datasets severely impacts the algorithm’s classification performance. This study investigates data sampling solutions for learning from imbalanced mosquito images dataset. We transfer-learn InceptionV3, a pre-trained convolutional neural network to classify images of two vector mosquito species Aedes Aegypti and Anopheles stephensi. The effectiveness of these data sampling solutions is evaluated and compared in terms of their impact on the performance of CNN classifier.

关键词： Training Visualization Technological innovation Accuracy Sensitivity Prevention and mitigation Vectors Convolutional neural networks Viruses (medical) Diseases

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Ossintegrity: Collaborative Open Source Code Integrity Verification

SSRN

引用

SSRN 2024年

作者： Nahum, Mor Grolman, Edita Maimon, Inbar Mimran, Dudu Elyashar, Aviad Brodt, Oleg Elovici, Yuval Shabtai, Asaf Dept. of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel

Open-source software (OSS) libraries have become popular among developers due to their ability to reduce development time and costs. However, OSS can also be exploited and used as a means of conducting OSS supply chain attacks. In OSS attacks, malicious code is injected into libraries that the target is using. Previous studies have proposed various methods for preventing and detecting such attacks, however most of them focused on untargeted attacks. In contrast, this paper focuses on targeted OSS supply chain attacks which are performed by skilled and persistent attackers with strong technical aptitude. Targeted OSS attacks are crafted towards a specific target (i.e., developer). Since these attacks do not appear in the general OSS repositories, they tend to go under the radar for a long period of time allowing an attacker to gain access to sensitive data or systems. In this paper, we propose (SC)2V – secure crowdsource-based code verification, a novel distributed and scalable framework for verifying OSS libraries. (SC)2V is aimed at preventing targeted supply chain attacks and is integrated with the build phase of software production, serving as an additional code verification step before packaging the application and deploying it. (SC)2V involves both users (developers seeking to verify an OSS library) and verifiers contributing to the collaborative verification effort. (SC)2V considers a library as verified and safe when a consensus is reached among verifiers. We evaluated the proposed method using eight different attack scenarios (including cold start and edge cases), on around 900 popular OSS libraries and their dependencies, each of which included an average of 10 files and was verified by at least five participants;a total of 127,000 files were evaluated, and the results indicate that it took our framework an average of just 26 seconds to issue an alert against the attacks. © 2024, The Authors. All rights reserved.

关键词： Open source software

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A Novel Method for Estimate the Structure Design Complication of Multi-Agents Based Systems 2

A Novel Method for Estimate the Structure Design Complicatio...

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2nd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer engineering, MI-STA 2022

作者： Elmarzaki, Howayda A. Abdelaziz, Tawfig M. Elakeili, Salwa Maatuk, Abdelsalam University of Benghazi Dept. Software Engineering Department Benghazi Libya Libyan International Medical University Dept. Information System Department Benghazi Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

Making a design decision in architecture design (AD) is challenging, especially when the system has many components and interactions. The complication is evident in the design phase of agents-based systems that are assigned various responsibilities, according to most works of literature. The study's purpose is to assist system designers in developing effective architectures for agents systems that are not complicated and easy to understand. Complex Task Measurement is a novel estimation of entanglement between agents' tasks presented in this paper (CTM). This method is built on the use-case point perspective, however with several adjustments to meet the demands of agents. Before making a design decision, this innovative measurement can be utilized to estimate the architecture design. The findings will be put to use in a variety of situations. As a case study, the research will be applied to the book recommendations system, and the results will be presented after the method has been implemented. © 2022 IEEE.

关键词： Design

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Exploring Mobility and Scalability of Cloud Computing Servers Using Logical Regression Framework 2

Exploring Mobility and Scalability of Cloud Computing Server...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sriramulugari, Shiva Kumar Gorantla, Venkata Ashok K Gude, Venkataramaiah Gupta, Ketan Yuvaraj, N. Solution Architect United States Software Engineer United States University of the Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Shakthi Institute of Engineering and Technology Department of Cse Coimbatore India

ISBN: (纸本)9798350371055

This research paper explores the possibilities of using the Logical Regression Framework as a means of controlling the scalability and mobility of cloud computing servers. The aim of this research paper is to identify the key factors that serve as predictors of good scalability and mobility performance in cloud computing servers. In doing so, the research undertakes a comparative analysis of different cloud computing architectures in order to identify which architectures exhibit better scalability and mobility performance. This comparative evaluation is done through the use of a Logical Regression Framework, which is based on two main data sources: (1) the server performance logs and (2) the server utilization rate. Each of these datasets is explored in dept. to find correlations between certain features and scalability/mobility performance. Using this information, an optimal cloud architecture is determined that shows better scalability and mobility characteristics. The hope is that this research can help cloud computing architects and engineers to design and implement better architectures for their cloud-based applications. © 2024 IEEE.

关键词： Automation Cloud Computing Logical Regression Mobility Performance Scalability

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Investigating Online Searching Behavior Based on Google Trends in MENA Region Before and After COVID-19 53

Investigating Online Searching Behavior Based on Google Tren...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Belkacem, Abdelkader Nasreddine Jamil, Nuraini Alrabaee, Saed College of It Uae University Dept. of Computer and Network Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Information Systems and Security Al Ain United Arab Emirates

ISBN: (纸本)9798350336429

The outbreak of the coronavirus disease (COVID-19) has had a profound impact on education worldwide. The rise of remote learning is one of the most significant changes in this regard, as many schools and universities were forced to close down by regional health authorities. This has also caused people to become more conservative in trade-offs between healthcare and education. Google Trends is the most common tool for analyzing online search behaviors. It is a free resource that provides information on the trends and changes in users' online interests over time based on certain terms and subjects. The online search queries on Google can be used to assess users' behaviors concerning online learning to forecast their choices regarding online education. This paper examines the frequency of users' web searches for online communication tools, courses, and learning terms. We statistically compared users in the Middle East and North Africa regions by using the volumes of searches recorded on Google Trends from January 2016 to August 2022. Moreover, we used machine learning techniques to identify differences among the keywords used. The findings statistically show that COVID-19 has led to an increase in the extent of students' attention to and interest in online learning. © 2023 IEEE.

关键词： COVID-19 Google Trends MENA Online learning Remote learning Web Behavior

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DeliverAI: Reinforcement Learning Based Distributed Path-Sharing Network for Food Deliveries

arXiv

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arXiv 2023年

作者： Mehra, Ashman Saha, Snehanshu Raychoudhury, Vaskar Mathur, Archana Dept. of CSIS BITS Pilani K K Birla Goa Campus Goa India Dept. of CSIS and APPCAIR BITS Pilani Goa India HappyMonk AI Bangalore India Department of Computer Science and Software Engineering Miami University Miami United States Dept. of Information Science and Engineering Nitte Meenakshi Institute of Technology India

Delivery of items from the producer to the consumer has experienced significant growth over the past decade and has been greatly fueled by the recent pandemic. Amazon Fresh, Shopify, UberEats, InstaCart, and DoorDash are rapidly growing and are sharing the same business model of consumer items or food delivery. Existing food delivery methods are sub-optimal because each delivery is individually optimized to go directly from the producer to the consumer via the shortest time path. We observe a significant scope for reducing the costs associated with completing deliveries under the current model. We model our food delivery problem as a multi-objective optimization, where consumer satisfaction and delivery costs, both, need to be optimized. Taking inspiration from the success of ride-sharing in the taxi industry, we propose DeliverAI - a reinforcement learning-based path-sharing algorithm. Unlike previous attempts for path-sharing, DeliverAI can provide real-time, time-efficient decision-making using a Reinforcement learning-enabled agent system. Our novel agent interaction scheme leverages path-sharing among deliveries to reduce the total distance traveled while keeping the delivery completion time under check. We generate and test our methodology vigorously on a simulation setup using real data from the city of Chicago. Our results show that DeliverAI can reduce the delivery fleet size by 12%, the distance traveled by 13%, and achieve 50% higher fleet utilization compared to the baselines. © 2023, CC BY.

关键词： Reinforcement learning

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