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International Conference on Information Technology (ICIT)

作者： Wael Alzyadat Mohammad Al-Madi Mohammad Muhairat Nagham Al-Madi Feras Altarawneh Department of Software Engineering Faculty of Science and IT Alzaytoonah University of Jordan Amman Jordan Computer Science Al-Zaytoonah University of Jordan Amman Jordan Artificial Intelligence Faculty of Science and IT Alzaytoonah University of Jordan Amman Jordan Department of Software Engineering Faculty of Science and IT Al-Zaytoonah University of Jordan Amman Jordan

Artificial Intelligence Augmented software Development Life Cycle is a process for developing software that incorporates AI techniques and technologies. One aspect of AIASDLC is knowledge vaporization, which refers to the process of extracting and encapsulating knowledge from the AI model during development and making it available for use in other parts of the software development life cycle. The advantages of AIASDLC include; improved performance, increased efficiency, better decision-making, better user experience, better scalability, better security and compliance, and knowledge vaporization. Requirement engineering is the process of gathering, analyzing, and specifying the requirements for a software system. The context boundary, domain knowledge, application domain, and system boundary are all important concepts in requirement engineering that help define the scope and constraints of the system being developed. Understanding the context boundary is important because it helps identify the external factors that will affect the requirements of the system. Domain knowledge is the understanding and expertise in a specific field or area that is necessary for effective requirement engineering. The application domain is the specific area or field where the system will be used, and it is important to understand the application domain to gather and document the correct requirements for the system

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SYNFER: TOWARDS BOOSTING FACIAL EXPRESSION RECOGNITION WITH SYNTHETIC DATA

arXiv

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arXiv 2024年

作者： He, Xilin Luo, Cheng Xian, Xiaole Li, Bing Song, Siyang Khan, Muhammad Haris Xie, Weicheng Shen, Linlin Ge, Zongyuan Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China AIM Lab Faculty of IT Monash University Australia KAUST Saudi Arabia University of Exter United Kingdom MBZUAI United Arab Emirates

Facial expression datasets remain limited in scale due to privacy concerns, the subjectivity of annotations, and the labor-intensive nature of data collection. This limitation poses a significant challenge for developing modern deep learning-based facial expression analysis models, particularly foundation models, that rely on large-scale data for optimal performance. To tackle the overarching and complex challenge, we introduce SynFER (Synthesis of Facial Expressions with Refined Control), a novel framework for synthesizing facial expression image data based on high-level textual descriptions as well as more fine-grained and precise control through facial action units. To ensure the quality and reliability of the synthetic data, we propose a semantic guidance technique to steer the generation process and a pseudo-label generator to help rectify the facial expression labels for the synthetic images. To demonstrate the generation fidelity and the effectiveness of the synthetic data from SynFER, we conduct extensive experiments on representation learning using both synthetic data and real-world data. Experiment results validate the efficacy of the proposed approach and the synthetic data. Notably, our approach achieves a 67.23% classification accuracy on AffectNet when training solely with synthetic data equivalent to the AffectNet training set size, which increases to 69.84% when scaling up to five times the original size. Our code will be made publicly available. © 2024, CC BY.

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Hybrid Deep Learning Model of LSTM and BiLSTM for Transjakarta Passenger Prediction

Hybrid Deep Learning Model of LSTM and BiLSTM for Transjakar...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Joko Siswanto Hendry Untung Rahardja Irwan Sembiring Erick Alfons Lisangan Muhammad Iman Nur Hakim Feri Wibowo Road Transportation System Engineering Polytechnic of Road Transportation Safety Tegal Indonesia Faculty of Information Technology Satya Wacana Christian University Indonesia Faculty of Science and Technology University of Raharja Tangerang Indonesia Faculty of Computer Science Satya Wacana Christian University Indonesia Computer Science and Software Engineering The University of Western Australia Perth Australia Automotive Engineering Technology Polytechnic of Road Transportation Safety Tegal Indonesia Faculty of Engineering and Science Muhammadiyah University Purwokerto Banyumas Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

A crucial role in the BRT transportation system’s planning, development, and operation is the prediction of passenger numbers. Using time-series data, it is necessary to develop careful prediction models, appropriate techniques, and an indepth understanding of the number of BRT Transjakarta passengers. A prediction model is sought based on comparing combined LSTM and BiLSTM experiments using three evaluation matrices and time. Historical data used from daily passenger data for 13 BRT Transjakarta corridors (1/01/2021-31/12/2023). The best prediction model was obtained from the BiLSTM-CNN combination with the lowest MSLE (0.0425), MAPE (0.1598), and SMAPE (15.9387) evaluation matrix values. However, it required a longer time (00:02:14). Predictions of passenger numbers on the 13 Transjakarta BRT corridors for the next 12 months can be made simultaneously by predicting fluctuations occurring simultaneously. The strongest positive correlation is in corridor 9-6, while the strongest negative correlation is in corridor 12-5. The prediction results must be understood by stakeholders, managers, and technopreneurs to develop and support appropriate strategies to increase the number of BRT passengers.

关键词： Deep learning Training Correlation Accuracy System performance Predictive models Data models Planning Stakeholders Long short term memory

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Measurement of Performance Parameters in the GPON Optical Network 23

Measurement of Performance Parameters in the GPON Optical Ne...

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23rd Slovak-Czech-Polish Optical Conference on Wave and Quantum Aspects of Contemporary Optics

作者： Trubák, Kamil Štípal, Jiří Látal, Jan Šiška, Petr Vojtěch, Josef Hristov, Hristo Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. listopadu 2172/15 Ostrava-Poruba708 00 Czech Republic CESNET a. l. e. Zikova 1903/4 Prague160 00 6 Czech Republic Department of Software Technologies Plovdiv University "Paisii Hilendarski" Plovdiv4000 Bulgaria

ISBN: (数字)9781510688094

ISBN: (纸本)9781510688087

With the increasing number of end users that are using multimedia services, demand for access network high bitrate systems with sufficient quality of services also increases. However, this might not always be ensured by the telecom operators, as they must perform network optimization concerning Quality of Service (QoS) and multimedia data transmission. In this work, we focused on Gigabit Passive Optical Network (GPON) performance testing with the help of various tools (iPerf, RFC 6349 or ITU-T Y.1564). The Grafana software tool is used to monitor data streams. Measurements were made to limit the downstream bitrate of up to 20 end users at 100 Mbit/s. Based on the repeated measurements, an aggregation curve is modelled indicating the available bitrate with respect to the network load. © 2025 SPIE.

关键词： Internet protocols

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Benchmarking Cryptographic Protocols for (IoT) Malware Defence

Benchmarking Cryptographic Protocols for (IoT) Malware Defen...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Khalil Ben Khaled Dönìz Borsos Jozsef Katona Kandó Kalmán Faculty of Electrical Engineering Institute of Microelectronics and Technology Obuda University Budapest Hungary Kándó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

This study presents a comprehensive benchmarking analysis of cryptographic protocols for Internet of Things (IoT) malware defense. The framework was specifically tailored to evaluate cryptographic protocols such as AES-128, AES-256, ChaCha20, RSA (1024-bit, 2048-bit, and 4096-bit), SHA256, SHA512, and HMAC-SHA256 were tested in both standalone and emulated environments. The primary objective was to evaluate the performance and resource consumption of these protocols, focusing on their encryption and hashing efficiency. Key innovations include the comparative analysis of resource consumption and performance efficiency across diverse cryptographic operations, under both real-world and emulated conditions. By identifying protocols like ChaCha20 for high efficiency and minimal resource usage, and RSA 4096-bit for enhanced security at higher computational costs, this study provides actionable insights into the trade-offs between security and performance. These findings offer a foundational reference for selecting optimized cryptographic protocols, advancing IoT malware defense strategies through informed decision-making.

关键词： Performance evaluation Technological innovation Benchmark testing Malware Encryption Internet of Things Reliability Informatics Machine intelligence Cryptographic protocols

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Multimedia Services Problems in Hybrid Network 23

Multimedia Services Problems in Hybrid Network

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23rd Slovak-Czech-Polish Optical Conference on Wave and Quantum Aspects of Contemporary Optics

作者： Štípal, Jiří Trubák, Kamil Látal, Jan Šiška, Petr Vojtěch, Josef Hristov, Hristo Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17. listopadu 2172/15 Ostrava-Poruba708 00 Czech Republic CESNET a. l. e. Zikova 1903/4 Prague160 00 6 Czech Republic Department of Software Technologies Plovdiv University "Paisii Hilendarski" Plovdiv4000 Bulgaria

ISBN: (数字)9781510688094

ISBN: (纸本)9781510688087

This work is dealing with actual topic in the field of internet service providers that are solving the question of metallic and optical networks convergence. The aim was to construct an experimental network topology in the form of WAN or LAN topology consisting of GPON network with VDSL2 technology, which led to the creation of a hybrid access network, on which the parameters of multimedia services were then measured. In particular, focusing on the distribution of video streams through SDTV & HDTV (MPEG-2) + HDTV (MPEG-4). As part of the network integrity measurements, tests are performed according to the recommendations of RFC 6349 and ITU-T Y.1564 standard. © 2025 SPIE.

关键词： Multimedia services

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Investigating Efficacy of Perplexity in Detecting LLM-Generated Code

arXiv

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arXiv 2024年

作者： Xu, Jinwei Zhang, He Yang, Yanjin Cheng, Zeru Lyu, Jun Liu, Bohan Zhou, Xin Yang, Lanxin Bacchelli, Alberto Chiam, Yin Kia Chiew, Thiam Kian State Key Laboratory of Novel Software Technology Software Institute Nanjing University Nanjing210008 China Department of Informatics University of Zürich Zürich8006 Switzerland Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

Large language model-generated code (LLMgCode) has become increasingly prevalent in software development. Many studies report that LLMgCode has more quality and security issues than human-authored code (HaCode). It is common for LLMgCode to mix with HaCode in a code change, while the change is signed by only human developers, without being carefully checked. Many automated methods have been proposed to detect LLMgCode from HaCode, in which the perplexity-based method (PERPLEXITY for short) is the state-of-the-art method. However, the efficacy evaluation of PERPLEXITY has focused on the detection accuracy. In this article, we are interested in whether PERPLEXITY is good enough in a wider range of realistic evaluation settings. To this end, we devise a large-scale dataset that includes 11,664 HaCode snippets and 13,164 LLMgCode snippets, and based on that, we carry out a family of experiments to compare PERPLEXITY against feature-based and pre-training-based methods from three perspectives: (1) detection accuracy in terms of programming language, degree of difficulty, and scale of solution, (2) generalization capability, and (3) inference efficiency. The experimental results show that PERPLEXITY has the best generalization capability while it has low accuracy and efficiency in most cases. Based on the experimental results and detection mechanism of PERPLEXITY, we discuss implications into both the strengths and limitations of PERPLEXITY, e.g., PERPLEXITY is unsuitable for high-level programming languages while it has good interpretability. As the first large-scale investigation on detecting LLMgCode from HaCode, this article provides a wide range of evidence for future improvement. Copyright © 2024, The Authors. All rights reserved.

关键词： High level languages

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Dynamic knowledge graph completion through time-aware relational message passing

Dynamic knowledge graph completion through time-aware relati...

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International CSI computer Conference, CSICC

作者： Amirhossein Baqinejadqazvini Saedeh Tahery Saeed Farzi Department of Software Engineering Faculty of Computer Engineering K. N. Toosi University of Technology Tehran Iran Department of Artificial Intelligence Faculty of Computer Engineering K. N. Toosi University of Technology Tehran Iran School of Computer Science Institute for Research in Fundamental Sciences (IPM) Tehran Iran

As the structure of knowledge graphs may vary over time, static knowledge graph completion methods do not deal with time-varying knowledge graphs. However, examining the paths between entities and entities' context information can lead to more accurate completion methods. This paper attempts to complete dynamic (time-varying) knowledge graphs by combining time-aware relational paths and relational context. The proposed model can improve dynamic knowledge graph completion methods by leveraging neural networks. Experimental results conducted on two standard datasets, ICEWS14 and ICEWS05-15, indicate our model's superiority in terms of Mean Reciprocal Rank (MRR) and Hit@k over its well-known counterparts, such as DE-TransE and DE-DistMult.

关键词： Computational modeling Message passing Neural networks Knowledge graphs Time factors Standards Context modeling

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Discovery of association rules between factors affecting user satisfaction in software project by FP-GROWTH Algorithm 18

Discovery of association rules between factors affecting use...

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18th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2021

作者： Kaewbanjong, Katawut Intakosum, Sarun King Mongkut's Institute of Technology Ladkrabang Software System Engineering Laboratory Department of Computer Science Faculty of Science Bangkok Thailand

ISBN: (纸本)9780738111278

The main objective of this work was to find the association rules between factors affecting user satisfaction in software project by using an association rule discovery technique. Data from 191 software projects were collected and association rules between 15 Selected factors were discovered by FP-growth algorithm. Primarily, 281 association rules were discovered. Rules that were not directly related to user satisfaction were filtered out by simple criteria. In the end, 11 final association rules that passed those criteria were obtained with an average confidence value of 65.30%. These rules can incorporated in the planning and administration of a software project to gain utmost user satisfaction © 2021 IEEE.

关键词： Association rules

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The Good and Bad Seeds of CNN Parallelization in Forensic Facial Recognition

The Good and Bad Seeds of CNN Parallelization in Forensic Fa...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Jaouhar Fattahi Baha Eddine Lakdher Mohamed Mejri Ridha Ghayoula Feriel Sghaier Laila Boumlik Department of Computer Science and Software Engineering Laval University Quebec Canada DArS DGSSI MCN Québec Canada Faculty of Science University of Monastir Monastir Tunisia Faculty of Engineering University of Moncton New Brunswick Canada Carthage National Engineering School Carthage University Tunis Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

In forensic investigations, facial recognition techniques serve as critical tools for identifying and apprehending suspects. In this study, we investigate the impact of Convolutional Neural Networks (CNNs) parallelization on the performance of facial recognition models within forensic contexts. Through experiments, we demonstrate the potential benefits of parallelization in enhancing model accuracy and robustness. Leveraging a reduced dataset, we employ augmentation techniques to expand the diversity of training samples. Our findings highlight the advantages of CNN parallelization in achieving superior recognition outcomes. Nevertheless, we identify constraints linked to excessive parallelization, which may induce model overfitting.

关键词： Training Navigation Face recognition Forensics Robustness System identification Convolutional neural networks Information technology Faces Research and development

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