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arXiv 2024年

作者： Behutiye, Woubshet Nema Rodriguez, Pilar Oivo, Markku Tosun, Ayşe University of Oulu Oulu Finland Faculty of Computer Engineering and Informatics Istanbul Technical University Turkey

Context: Technical debt (TD) is a metaphor that is used to communicate the consequences of poor software development practices to non-technical stakeholders. In recent years, it has gained significant attention in agile software development (ASD). Objective: The purpose of this study is to analyze and synthesize the state of the art of TD, and its causes, consequences, and management strategies in the context of ASD. Research Method: Using a systematic literature review (SLR), 38 primary studies, out of 346 studies, were identified and analyzed. Results: We found five research areas of interest related to the literature of TD in ASD. Among those areas, "managing TD in ASD" received the highest attention, followed by "architecture in ASD and its relationship with TD". In addition, eight categories regarding the causes and five categories regarding the consequences of incurring TD in ASD were identified. "Focus on quick delivery" and "architectural and design issues" were the most popular causes of incurring TD in ASD. "Reduced productivity", "system degradation" and "increased maintenance cost" were identified as significant consequences of incurring TD in ASD. Additionally, we found 12 strategies for managing TD in the context of ASD, out of which "refactoring" and "enhancing the visibility of TD" were the most significant. Conclusion: The results of this study provide a structured synthesis of TD and its management in the context of ASD as well as potential research areas for further investigation. © 2024, CC BY-NC-ND.

关键词： Software design

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Predictive Analysis of Sarajevo’s AQI using Machine Learning Models for Varied Data Granularity and Prediction Windows

Predictive Analysis of Sarajevo’s AQI using Machine Learnin...

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Proceedings of the International Convention MIPRO

作者： Emina Zolota Vahidin Hasić Amina Mević Amra Delić Senka Krivić Department for Computer Science and Informatics Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina

ISBN: (数字)9798350382501

ISBN: (纸本)9798350382518

This study scrutinizes five years of Sarajevo’s Air Quality Index (AQI) data using diverse machine learning models — Fourier autoregressive integrated moving average (Fourier ARIMA), Prophet, and Long short-term memory (LSTM)—to forecast AQI levels. Focusing on various prediction frames, we evaluate model performances and identify optimal strategies for different temporal granularities. Our research unveils subtle insights into each model’s efficacy, shedding light on their strengths and limitations in predicting AQI across varied timeframes. This research presents a robust framework for automatic optimization of AQI predictions, emphasizing the influence of temporal granularity on prediction accuracy, automatically selecting the most efficient models and parameters. These insights hold significant implications for data-driven decision-making in urban air quality control, paving the way for proactive and targeted interventions to improve air quality in Sarajevo and similar urban environments.

关键词： Accuracy Atmospheric modeling Urban areas Machine learning Predictive models Air quality Data models

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Deep Reinforcement Learning Based Bus Stop-Skipping Strategy

Deep Reinforcement Learning Based Bus Stop-Skipping Strategy

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Tham, Mau-Luen Tay, Bee-Sim Khor, Kok-Chin Phon-Amnuaisuk, Somnuk Universiti Tunku Abdul Rahman Lee Kong Chian Faculty of Engineering and Science Department of Electrical and Electronic Engineering Malaysia Department of Internet Engineering and Computer Science Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Malaysia School of Computing and Informatics Universiti Teknologi Brunei Brunei

ISBN: (纸本)9798350326413

Stop-skipping strategy can benefit both bus operators and passengers if the control is intelligent enough to adapt to the changes in passenger demands and traffic conditions. This is possible via deep reinforcement learning (DRL), where an agent can learn the optimal policy by continuously interacting with the dynamic bus operating environment. In this paper, one express bus lane followed by one no-skip flow is treated as one episode for bus route optimization. The objective is to maximize the passenger satisfaction level while minimizing the bus operator expenditures. To this end, a reward function is formulated as a function of passenger waiting time, passenger in-vehicle time, and total bus travel time. By training an agent of a double deep Q-network (DDQN), simulation results show that the agent can intelligently skip the stations and outperform the noskip method, under different passenger distribution patterns. © 2023 IEEE.

关键词： Reinforcement learning

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Effect of Interest Variation and Prefix Length on Pending Interest Table (PIT) Performance in Named Data Networking (NDN) 9

Effect of Interest Variation and Prefix Length on Pending In...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Khaderani, Helmi Ahmad Muhammadi, Ishlah Muhammad, Lutfi Razan Sucipto, Adi Kusumastuti, Luthfi Restu Nurhayati, Ade Ahdan, Syaiful Syambas, Nana Rachmana Bandung Institute of Technology Dept. of Telecommunication Engineering Bandung40132 Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia Telkom University Faculty of Engineering Jakarta Barat11710 Indonesia

ISBN: (纸本)9798350305029

Pending interest table (PIT) is one of the data structures on each named data network router. The speed of delivery of interest as well as the interest served at PIT are several important parameters for measuring PIT performance. This research was conducted to analyze the performance of PIT by measuring the round trip time (RTT) of sending data and sending interests from consumers to producers on the Palapa Ring Sumatra topology. Testing is carried out using miniNDN by varying the length of the prefix and the number of interests sent. From the simulation and results of the analysis that has been carried out, it is known that the correlation between the length of the prefix and RTT is directly proportional. The longer the prefix of an interest packet, the higher the RTT value. Apart from the simulation results, the number of interest packets sent is the same as those received at each node. © 2023 IEEE.

关键词： Topology

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Classification of Arrhythmias 12-Lead ECG Signals Based on 1 Dimensional Convolutional Neural Networks

Classification of Arrhythmias 12-Lead ECG Signals Based on 1...

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Smart Computing, IoT and Machine Learning (SIML), International Conference on

作者： Arief Kurniawan Bayu Aditya Triwibowo Dion Hayu Fandiantoro Department of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember

ISBN: (数字)9798350364101

ISBN: (纸本)9798350364118

Heart disease is the highest cause of death in the world. Arrhythmia is an abnormality in the rhythm of the heartbeat. The heart beats too fast, too slow, or irregularly. Arrhythmias are not always dangerous, e.g., someone who does excessive activity has a faster heart rate. Then, a diagnosis is needed to classify arrhythmias. One method used is ECG (Electrocardiogram) signal analysis. The ECG signal consists of P, QRS Complex, and T waves. The morphology of the QRS is used for arrhythmia classification. Currently, cardiologists analyze ECG signals by observing directly. This method is depending on the level of expertise of the cardiologist. Previous research classified arrhythmias based on the QRS morphology from a single ECG lead. As 12-lead ECG devices have now become standard in ECG examinations because abnormalities can be observed from multiple angles. This study proposes the classification of arrhythmias in 12-lead ECG signals based on the morphology of QRS complex waves using a deep learning 1-dimensional Convolutional Neural Network. The output of deep learning is the classification of arrhythmias into four classes, namely: Normal, Right Bundle Branch Block, Premature Ventricular Contraction, and Atrial Premature Beat. The outcome of the proposed system is that each QRS segment is used as input for deep learning, which can improve classification performance compared to the classification carried out by each lead. The experimental results show the method can be done well, with an average Accuracy, Precision, Sensitivity, and F1-Score were 98.8%, 99.2%, 99.2%, and 99.2%, respectively.

关键词： Deep learning Accuracy Sensitivity Heart beat Arrhythmia Morphology Electrocardiography

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Fine-Tuning GPT-4o-Mini for Programming Questions Generation

Fine-Tuning GPT-4o-Mini for Programming Questions Generation

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International Conference on computer and Applications (ICCA)

作者： Alaa Hesham Abeer Hamdy Department of Software Engineering Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Creating programming questions that are both meaningful and educationally relevant is a critical task in computer science education. This paper introduces a fine-tuned GPT4o-mini model (C2Q). It is designed to generate meaningful questions by leveraging semantic feature extraction and well- crafted prompts. The approach addresses the limitations of traditional generative models, offering a deeper understanding of programming code and producing questions that are precise, diverse, and relevant to a given code snippets. The proposed framework incorporates essential code elements, such as control structures and method attributes, to generate questions that align with programming concepts. Evaluation metrics used were BLEU, ROUGE-1, and ROUGE-L to evaluate the model's performance. The findings reveal that the model achieves better structural coherence and conceptual relevance while focusing on contextual understanding over exact term matching. This work highlights the potential of the proposed approach to advance teaching and assessment methods in computer science.

关键词： Measurement Codes Computational modeling Semantics Natural language generation Focusing Feature extraction Question generation Prompt engineering Programming profession

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Embedded Real-Time Analogue NeoPixel Clock

Embedded Real-Time Analogue NeoPixel Clock

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Mehmed Mujić Ehlimana Cogo Ingmar Bešić Department of Computer Science and Informatics Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina

Embedded real-time clock systems have a large number of applications in practice. The main issue is the accuracy of time they show, which is why performing time synchronization is very important for their usability and reliability. This paper proposes an embedded real-time analogue clock that uses an AdaFruit NeoPixel LED ring for visualizing current time. Three different colors are used for showing hour, minute and second values, whereas different levels of brightness are used for describing accurate values of time to the level of milliseconds. An Ethernet LAN module is used for performing time synchronization via a remote NTP server. Dynamic synchronization interval change is used for removing the effect of the microcontroller clock error on the accuracy of the shown time. After being put to use, the system was able to perform multiple functions successfully, including the conveying of information to the user when the clock is out of sync.

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Happy and Sad Classification using HOG Feature Descriptor in SVM Model Selection 3

Happy and Sad Classification using HOG Feature Descriptor in...

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3rd International Conference on informatics, Multimedia, Cyber, and Information System, ICIMCIS 2021

作者： Alamsyah, Derry Fachrurrozi, Muhammad Mdp University Informatics Faculty of Computer Science and Engineering Palembang Indonesia Sriwijaya University Informatics Faculty of Computer Science Indralaya Indonesia

ISBN: (纸本)9781665427333

Facial Expression Recognition (FER) of the image is one of the potential research fields. It remains some open problems to be solved such as various head positions, backgrounds, occlusion, face attribute etc., where the FER 2013 dataset give such conditions. In this research, the small balanced dataset used to recognize two common fundamental expression, happy and sad face image as our set conditions. Using SVM as classifier and HOG as feature expression method, this research shows best performance, that is 72% accuracy, in quadratic polynomial kernel with intercept constant $\mathrm{b}=1$ and tolerance constant $\mathrm{C}=0.1$. By using such conditions, minimized pose variant, a conventional approach in FER such SVM and HOG has shown fair performance in the FER 2013 dataset. © 2021 IEEE.

关键词： Support vector machines

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An Energy-Efficient Protocol for Internet of Things Based Wireless Sensor Networks

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computers, Materials & Continua 2023年第5期75卷 2397-2412页

作者： Mohammed Mubarak Mustafa Ahmed Abelmonem Khalifa Korhan Cengiz Nikola Ivkovic Department of Information Technology Sri Krishna College of Engineering&TechnologyCoimbatoreIndia Department of Networking and Security College of Information TechnologyUniversity of FujairahFujairahUAE Switching and Traffic Department National Telecommunication InstituteCairoEgypt Department of Computer Engineering Istinye UniversityZeytinburnuIstanbulTurkey Faculty of Organization and Informatics University of ZagrebVarazdinCroatia

The performance of Wireless Sensor Networks(WSNs)is an important fragment of the Internet of Things(IoT),where the current WSNbuilt IoT network’s sensor hubs are enticing due to their critical *** grouping hubs,a clustering convention offers a useful solution for ensuring energy-saving of hubs andHybridMedia Access Control(HMAC)during the course of the ***,current grouping standards suffer from issues with the grouping structure that impacts the exhibition of these conventions *** this investigation,we recommend an Improved Energy-Proficient Algorithm(IEPA)for HMAC throughout the lifetime of the WSN-based *** consecutive segments are *** the covering of adjusted clusters,an ideal number of clusters is determined ***,fair static clusters are shaped,based on an updated calculation for fluffy cluster heads,to reduce and adapt the energy use of the sensor *** heads(CHs)are,ultimately,selected in optimal locations,with the pivot of the cluster heads working among cluster ***,the proposed convention diminishes and balances the energy utilization of hubs by improving the grouping structure,where the IEPAis reasonable for systems that need a long *** assessment results demonstrate that the IEPA performs better than existing conventions.

关键词： Energy consumption improved energy-proficient algorithm internet of things wireless sensor network

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Trans-Compiler-Based Database Code Conversion Model for Native Platforms and Languages 11th

Trans-Compiler-Based Database Code Conversion Model for Nati...

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11th International Conference on Model and Data engineering, MEDI 2022

作者： Barakat, Rameez Radwan, Moataz-Bellah A. Medhat, Walaa M. Yousef, Ahmed H. School of Information Technology and Computer Science Nile University Giza Egypt Center for Informatics Science Nile University Giza Egypt Department of Computer and Systems Faculty of Engineering Ain Shams University Cairo Egypt Faculty of computers and artificial intelligence Benha University Banha Egypt

ISBN: (纸本)9783031215940

Cross-platform mobile application development frameworks are now widely used among software companies and developers. Despite their time and cost-effectiveness, they still lack the performance and experience of natively developed applications. Many research tools have been proposed to solve this problem by converting a natively developed application from one platform to another. The Trans-Compiler Based Android to iOS Converter (TCAIOSC) was proposed to convert the front-end and back-end code of Android Java applications to iOS applications. Since databases are essential for mobile applications, this paper proposes a new database code conversion model based on trans-compilation and pattern matching. It proposes a model that can be used to support database code conversion between native languages and platforms and applies the proposed model to support the conversion of Firebase Firestore database code from Android (Java) to iOS (Swift) using TCAIOSC. The enhanced tool’s results show high accuracy for the converted database code and a noticeable improvement in the overall conversion results for TCAIOSC. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pattern matching

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