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11th International Conference on Soft Computing and Machine Intelligence, ISCMI 2024

作者： Ala'anzy, Mohammed Alaa Mazhit, Zhanar Ala'anzy, Alaa Fadhil Algarni, Abdulmohsen Akhmedov, Ramis Bauyrzhan, Abdiyev SDU University Department of Computer Science Almaty Kazakhstan Al-Hikma University College Department of Business Administration Baghdad Iraq King Khalid University Department of Computer Science Abha61421 Saudi Arabia Eurasian National University Department of Computer and Software Engineering Astana Kazakhstan

ISBN: (纸本)9798331518127

In the realm of computer science, sorting algorithms play a pivotal role in optimising data organisation and retrieval processes across various applications. This paper presents a comprehensive examination and comparative analysis of the most commonly used sorting algorithms, focusing on their time, space complexity, and efficiency. By scrutinising both traditional methods like bubble sort, selection sort, and insertion sort, and advanced techniques such as merge sort, quick sort, heap sort, and radix sort, this study sheds light on the performance metrics of each algorithm. Through meticulous implementation and evaluation across datasets of varying magnitudes, ranging from 100 to 100,000 integers, the research provides valuable insights into the strengths and weaknesses of these sorting techniques. The outcomes of this study offer practical guidance for professionals in data engineering and software development, aiding in the informed selection of sorting algorithms tailored to specific application needs. Utilising a rigorous experimental setup with the pseudocode, the study meticulously measures the time complexity of each algorithm, recording CPU time in seconds. The analysis reveals significant performance differentials among the algorithms, showcasing the efficiency of quick sort, shell sort, and heap sort on small datasets, while highlighting the scalability of merge sort, radix sort, and quick sort as dataset sizes increase. This research contributes to the advancement of algorithmic knowledge and provides a valuable resource for practitioners seeking to optimise sorting processes in their computational tasks. © 2024 IEEE.

关键词： software design

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Finite-difference time-domain methods

NATURE REVIEWS METHODS PRIMERS

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NATURE REVIEWS METHODS PRIMERS 2023年第1期3卷 1-19页

作者： [Anonymous] Department of Electrical and Computer Engineering The Ohio State University Columbus OH USA Department of Electrical and Computer Engineering University of Toronto Toronto Ontario Canada Department of Electrical and Computer Engineering University of Utah Salt Lake City UT USA Department of Electrical Engineering Pohang University of Science and Technology Pohang Korea Department of Electrical and Electronic Engineering The University of Manchester Manchester UK Biomedical Engineering Department Northwestern University Evanston IL USA Department of Electrical and Software Engineering University of Calgary Calgary Canada Department of Electrical and Computer Engineering Purdue University West Lafayette IN USA

This PrimeView highlights how algorithm and hardware develops could expand the application range of finite-difference time-domain methods.

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The Human Behind the Data: Reflections from an Ongoing Co-Design and Deployment of a Data-Navigation Interface for Front-Line Emergency Housing Shelter Staff

The Human Behind the Data: Reflections from an Ongoing Co-De...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Masrani, Teale W. He, Helen Ai Messier, Geoffrey Department of Computer Science University of Calgary Calgary Canada Electrical and Software Engineering University of Calgary Calgary Canada

ISBN: (纸本)9781450394222

On any night in Canada, at least 35,000 individuals experience homelessness. These individuals use emergency shelters to transition out of homelessness and into permanent housing. We designed and deployed a technology to support front-line staff at the largest emergency housing shelter in Calgary, Canada. Over a period of five months in 2022, we worked closely with front-line staff to co-design an interface for supporting a holistic understanding of client context and facilitating decision-making. The tool is currently in-use and our collaboration is ongoing. In this paper, we reflect on preliminary findings regarding the second iteration of the tool. We find that supporting shelter staff in understanding the human behind the data was a critical component of design. This work contributes to literature on how data tools may be integrated into homeless shelters in a way that aligns with shelters' values. © 2023 Owner/Author.

关键词： Housing

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Security Challenges and Solutions in Smart Cities 2nd

Security Challenges and Solutions in Smart Cities

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2nd International Conference on Advances in Computing Research, ACR 2024

作者： Alzyoud, Faisal Al-Falah, Ruba Tarawneh, Monther Tarawneh, Omar Computer Science Department Isra University Amman Jordan Ministry of Local Administration Amman Jordan Software Engineering Department Amman Arab University Amman Jordan

ISBN: (纸本)9783031569494

The increase of smart devices and vast amount of telecommunication growth raise the tendency to convert convolution cities in to emerging smart cities. This paper synthesizes key findings and insights from a comprehensive presentation, addressing critical aspects of smart city security. It underscores the pivotal role of robust security strategies in the development and sustainability of smart urban environments. This paper, encompass three main areas: the integration of Artificial Intelligence (AI) and machine learning for real-time threat detection, the imperative of adopting quantum-safe cryptography, and the evolving nature of security threats in smart cities. These trends and challenges necessitate proactive measures, reinforcing the need for security to be a fundamental component of smart city blueprints. In conclusion, this paper advocates for collaborative efforts and ongoing research in smart city security. It calls upon stakeholders from diverse sectors to join forces, share knowledge, and develop innovative solutions. By doing so, we can navigate the complex security landscape of smart cities and shape a future where technology and security coexist harmoniously, ensuring the well-being and prosperity of urban communities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Smart city

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RAT: A Refactoring-Aware Tool for Tracking Code History 24

RAT: A Refactoring-Aware Tool for Tracking Code History

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46th International Conference on software engineering: Companion, ICSE-Companion 2024

作者： Niu, Feifei Shao, Junqian Xu, Chaofan Mayr-Dorn, Christoph Assunção, Wesley K. G. Huang, Liguo Li, Chuanyi Ge, Jidong Luo, Bin Egyed, Alexander State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute for Software Systems Engineering Johannes Kepler University Linz Austria Department of Computer Science North Carolina State University North Carolina United States Department of Computer Science and Engineering Southern Methodist University DallasTX United States

ISBN: (纸本)9798400705021

History of code elements is essential for software maintenance tasks. However, code refactoring is one of the main causes that makes obtaining a consistent view on code evolution difficult as renaming or moving source code elements break such history. To this end, this paper presents RAT, a refactoring-aware tool for keeping track of code elements evolution across time, not just in terms of revisions but also in terms of refactoring. This is the first tool that enables fine-grained code element traceability of the whole repository. Empirical evaluation of leveraging our tool in three bug localization techniques relying on code history shows significant improvement in localization accuracy. Based on our findings, we believe that many of the state-of-the-art approaches using past source code data would benefit from our tool. Demo Tool: https://***/feifeiniu-se/RAT-Demo. Demo Video: https://***/VI-xwUaIPp4. © 2024 IEEE computer Society. All rights reserved.

关键词： Rats

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MQTT based monitoring and control system for remote stations in automotive industry

MQTT based monitoring and control system for remote stations...

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2023 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2023

作者： Ivanovic, Denis Job, Josip Puntaric, Marko Herceg, Marijan TTTech-Auto CX Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Department of Software Engineering Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Department of Communications Osijek Croatia

ISBN: (纸本)9798350347722

In this paper is presented implementation of a lightweight supervisory control and data acquisition system for remote test stations as a part of a larger test house in the automotive industry. It is necessary to allow remote set up and monitoring of the test stations, which means defining the structure of a system with numerous test stations as endpoints. The test stations are meant to be virtual machines but can be both virtual and physical machines. A communication protocol must be used that enables this within the organization's network, regardless of actual distance and location. The parameters of the test stations to be monitored can be defined according to the needs and purpose. Even when creating your own tool, this function is not unique, i.e. on one test station parameters of operating system resources such as memory usage can be monitored, while on another test station logged-in users or running programs, reported system errors and others must be monitored. Data storage is one of the mandatory functions of this system. This process allows viewing data throughout history and more detailed analysis of events. There are numerous benefits that can be drawn and used from the data stored in the database. It is necessary to pay attention to the data stored as well as the scope and retention throughout history. The correct and secure setup of a database is one of the prerequisites for any serious system that processes data. Appropriate views should be created for users involved in the data monitoring process using all the elements mentioned so far. Creating a visually acceptable and easy-To-use interface that serves the main purpose is the final presentation of the entire system to users. A complete system leads to higher productivity, profitability and better organization of the company. It also allows project managers to better plan the use and occupancy of test stations. At any time, they have insight into the status of individual test stations, their activitie

关键词： Automotive industry

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Extracting Driving Styles from Automotive Sensor Data to Develop Personas 9

Extracting Driving Styles from Automotive Sensor Data to Dev...

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9th International Conference on computer science and engineering, UBMK 2024

作者： Kaya, M. Cagri Bouraffa, Tayssir Wnuk, Krzysztof Chalmers University of Technology University of Gothenburg Department of Computer Science and Engineering GothenburgSE-41296 Sweden Blekinge Institute of Technology Department of Software Engineering KarlskronaSE-37179 Sweden

ISBN: (纸本)9798350365887

Creating accurate and insightful personas for automotive applications requires understanding the diverse driving styles that emerge from sensor data. This paper introduces initial results of our methodology for developing personas based on the analysis of automotive sensor data, which captures various driving behaviors. We present a comprehensive process that begins with the extraction of driving events from sensor data, followed by the identification of distinct driving styles through K-Means clustering. Our approach is innovative in its use of dynamic, real-world driving data as opposed to static or direct user interaction data commonly employed in persona development. This allows for a deeper understanding of driver behaviors, which are indirectly inferred from sensor data, thus providing a foundation for creating detailed personas. © 2024 IEEE.

关键词： Automotive sensor data clustering driving styles event detection persona development

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A novel deep learning approach for investigating liquid fuel injection in combustion system

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Discover Artificial Intelligence 2025年第1期5卷 1-14页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Ahmed, Noor Mazhar, Tehseen Shahzad, Tariq Khan, Sunawar Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Science Sindh Madressatul Islam University Sindh Karachi Pakistan Department of Computer Science and Information Technology Shaheed Benazir Bhutto University Lyari Karachi Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar62300 Pakistan Sana’a00967 Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg South Africa Av. Grandes Ecoles BP 1989 Libreville Gabon Bridges for Academic Excellence Spectrum Center-ville Tunis Tunisia

The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system. © The Author(s) 2025.

关键词： Deep learning

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Implementation of an Effective Framework in Merging Cybersecurity and software engineering 6

Implementation of an Effective Framework in Merging Cybersec...

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6th International Conference of Women in Data science at Prince Sultan University, WiDS-PSU 2023

作者： Alhuqail, Sara Khalid Jamail, Nor Shahida Mohd Prince Sultan University College of Computer Science and Information Sciences Department of Software Engineering Riyadh Saudi Arabia

ISBN: (纸本)9781665477239

Cyberattacks have increased. Security throughout software development reduces the program's attack surface. Despite several fixes. new research indicates software security weaknesses and hackers are rising. SSD security methods must be studied. 28 academic and government SSD approaches are examined in this study. Security is implemented throughout SDLC. We studied organizational. behavioral. legal. policy. and governance issues as well as SSD approaches. Method effectiveness strategies were investigated. We conclude with scientific problems. Our survey should help academics an institutions understand software security development (SSD). Nontechnical software security experts may benefit from our bridge. Our literature gaps may improve software security. Today's fast-paced. modern world requires software security for product success. CIA-classified data. software. and services (Confidentiality. Integrity. and Availability). © 2023 IEEE.

关键词： Life cycle

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Enhancing engineering Education Through LLM-Driven Adaptive Quiz Generation: A RAG-Based Approach 54

Enhancing Engineering Education Through LLM-Driven Adaptive ...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Gopi, Sreekanth Sreekanth, Devananda Dehbozorgi, Nasrin Kennesaw State University Department of Computer Science KennesawGA United States Kennesaw State University Department of Software Engineering KennesawGA United States

ISBN: (纸本)9798350351507

This research-to-practice study aims to develop an Artificial Intelligence (AI) MCQ generation system for engineering students, with a focus on adaptive learning, educational technology, and innovative assessment tools, to enhance personalized learning. engineering education faces significant academic performance challenges, with first-year retention rates in STEM fields ranging between 27% to 46%, largely due to poor academic achievements. Multiple Choice Questions (MCQs) identify misconceptions, reinforce knowledge retention, and offer efficient assessment methods for engineering education. This interactive method improves attention and memory retention, reinforces knowledge, and improves comprehension. In this context, the emergence of Large Language Models (LLMs) such as GPT-4 has marked a significant advancement. Our literature review method employed a systematic approach, analyzing peer-reviewed articles, conference papers, and authoritative reports to uncover the trends and challenges in AI-driven quiz generation. The notable gap identified in our literature review is the lack of LLM-based adaptive quiz generation methods specifically for engineering education. Our methodology involved sourcing relevant structured datasets, data pre-processing, embedding generation, vector database storage, hybrid-search retrieval, LLM query results feed, prompt engineering, and context-based response. In this research, we adopted Vectara as a vector database tool for its automatic data ingestion capabilities and seamless integration with generative AI applications. Prompt engineering involves a dual-prompt approach, where the Contextual Question Prompt formulates questions based on user topics and chat history, while the Answer Question Prompt manages MCQ responses with explanations, ensuring relevant and contextually accurate interactions. Evaluation includes topic relevancy, answer relevancy, and a contextual relevancy score. Preliminary results indicate promising results

关键词： engineering education

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