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6th International Conference on Advancements in Computing, ICAC 2024

作者： Ajmal, Farhath Wijekoon, Poorna Dhanamina, Haritha Ravishan, Yasiru Nawinna, Dasuni Attanayaka, Buddhima Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

The automation of business process modelling has become crucial for organizations seeking to improve their operational efficiency. This research presents a novel methodology that leverages fine-tuned GPT models to automate the conversion of textual business process descriptions into Business Process Model and Notation (BPMN) diagrams. The study builds on previous work, addressing gaps related to the handling of unstructured text and the absence of anaphora resolution in our solution we use fine-tuned GPT models to accurately extract and structure business process activities from text input from end users, which are then converted into BPMN diagrams using a custom generation framework. The evaluation results demonstrate the system's accuracy with an average precision of 80%, recall of 76%, and F1 score of 78%. These findings outline the potential of using advanced language models to streamline business process modelling, offering significant improvements in accuracy and efficiency. The research concludes by suggesting enhancements for future iterations, including fine-tuning with advanced models and expanding support for complex BPMN elements, aiming to further optimize workflow for organizations. © 2024 IEEE.

关键词： Efficiency

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xPython, Novel Secure Real- Time Operating System for IoT Subsystems Development using Micro-Python Language 11

xPython, Novel Secure Real- Time Operating System for IoT Su...

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11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Elalem, Mervat Sobh, Mohamed A. Bahaa-Eldin, Ayman M. Ain Shams University Faculty of Engineering Computer & Systems Engineering Department Cairo Egypt ElSewedy University of Technology On leave from Ain Shams University Cairo Egypt

ISBN: (纸本)9798350369328

The deep integration of IoT into our daily lives has made the security of IoT everyone's priority, from IoT device manufacturers to IoT developers and final customers. Even though every year millions of IoT devices are introduced to the market, security, and privacy have not been competently addressed. Based on the survey of IoT challenges, IoT applications face many threats in Network communication, storage access, device identification and Authentication, program execution, firmware update, Secure Boot, etc. IoT devices are vulnerable to attacks, which breach the security and privacy of users and systems. Commonly most IoT applications are deployed using common operating systems that do not address IoT specialized security requirements. This paper introduces xPython, a novel secure-by-design operating system for IoT subsystems (devices and gateways). xPython provides transparent security solutions for common threats in addition to a security library for custom security implementation. xPython minimizes the time required to develop security features. xPython is secure by design to ensure security features are included at all stages of product creation and deployment. This paper proposes a new architecture for IoT operating systems, and it ends with a complete prototype implementation. The proposed architecture integrates security services inside operating system layers. It affects device initiation, communication protocols, application deployment, and application execution, ensuring software is executed securely, including non-secure applications (secure boot) and storage access. The proposed security solutions are inspired by previous work in Smart Cards. Smart Cards are considered a trusted environment for identification, authentication, and confidentiality. Finally, xPython is built over a real-time operating system to fulfill embedded systems requirements. The xPython development is also performed using Micro-Python to simplify the development process and reduc

关键词： Python

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Development of a Web Application for Evaluating Corporate Social Responsibility Practices

Development of a Web Application for Evaluating Corporate So...

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8th International Conference on Science, Technology and Innovation for Society, CITIS 2023

作者： Cuvi, Jonnathan Sigcha, Erik Siguenza-Guzman, Lorena Faculty of Engineering University of Cuenca Cuenca010107 Ecuador Department of Applied Chemistry and Systems of Production Faculty of Chemical Sciences University of Cuenca Cuenca010107 Ecuador Department of Computer Sciences Faculty of Engineering University of Cuenca Cuenca010107 Ecuador Faculty of Economic and Business Research Centre Accountancy KU Leuven Leuven Belgium

ISBN: (纸本)9783031519819

Corporate Social Responsibility (CSR) is an increasingly relevant concept for companies as it improves competitiveness and market perception. Incorporating CSR elements in commercial operations requires considering various aspects, such as employee and customer satisfaction, product quality, and analysis of the environmental impacts generated. In line with this, companies have shown a growing interest in CSR concepts and the application of tools to determine its level of integration in business activities. This paper aims to present a web application to assess the level of integration of CSR elements in Ecuadorian MSMEs (Micro, Small and Medium Enterprises). The development of the proposed software required the analysis of CSR concepts, norms, and standards to establish a basic framework adapted to the context of Ecuadorian companies. The web application and the evaluation method based on an adapted CSR framework are presented as results of this work. The developed application, tested with personnel from local textile MSMEs, automates the evaluation process and can be considered as a first component for a comprehensive evaluation platform. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Customer satisfaction

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Modeling of COVID-19 outbreak in Gaza Strip using SEIR model

Modeling of COVID-19 outbreak in Gaza Strip using SEIR model

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2023 International Conference on computer Science, Information Technology and engineering, ICCoSITE 2023

作者： Radwan, Akram M. Mortaja, Waleed Alzaq, Husam University College of Applied Sciences Information Technology Faculty Gaza Palestine University College of Applied Sciences Engineering & Smart Systems Faculty Gaza Palestine Istinye University Computer Engineering Department Istanbul Turkey

ISBN: (纸本)9798350320954

The novel coronavirus (COVID-19) pandemic is a major global health threat that was spreading very quickly around the world. Many mathematical models are being formulated to study the spread of COVID-19 and to predict its evolution, in order to help mitigating future outbreaks. The Susceptible-Exposed-Infectious-Recovered (SEIR) model is considered a prevalent model that describes various diseases outbreaks including COVID-19. The model divides the individuals into four groups;susceptible, exposed, infectious, recovered. To predict the evolution of the pandemic using the SEIR model, we first estimate the parameters as well as initial conditions. In this work, we model the spreading of COVID-19 pandemic using the SEIR in Gaza Strip to identify the better fitting model for forecasting future spread. The data required for estimation was collected between March 1st, 2021 and December 31st, 2021. The simulation results using SEIR model showed a significant model fitting, as there is no considerable difference from the real data compared to the respective model values. We also study the severity of the pandemic by identifying two important parameters;the basic reproduction rate (Ro), which determines the speed of the spread of COVID-19;and the infection fatality ratio (IFR) which estimates the proportion of deaths among all infected individuals. We obtained a value of Ro = 0. 89. IFR value is ranged between 0.079% and 0.085%, which is less than the global average of about 0.15%. © 2023 IEEE.

关键词： COVID-19

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A Process Oriented Integration Model for Smart Health Services

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Intelligent Automation & Soft Computing 2023年第2期35卷 1369-1386页

作者： Farzana Kausar Gondal Syed Khuram Shahzad Muhammad Arfan Jaffar Muhammad Waseem Iqbal Department of Computer Science Superior UniversityLahore54600Pakistan Department of Informatics and Systems University of Management and TechnologyLahore54000Pakistan Faculty of Computer Science&IT Superior UniversityLahore54600Pakistan Department of Software Engineering Superior UniversityLahore54600Pakistan

Cities are facing challenges of high rise in population number and con-sequently need to be equipped with latest smart services to provide luxuries of life to its *** integrated solutions are also a need to deal with the social and environmental challenges,caused by increasing ***,the development of smart services’integrated network,within a city,is facing the bar-riers including;less efﬁcient collection and sharing of data,along with inadequate collaboration of software and *** to resolve these issues,this paper recommended a solution for a synchronous functionality in the smart services’integration process through modeling *** this integration modeling solution,atﬁrst,the service participants,processes and tasks of smart services are identiﬁed and then standard illustrations are developed for the better understand-ing of the integrated service group *** process modeling and notation(BPMN)language based models are developed and discussed for a devised case study,to test and experiment i.e.,for remote healthcare from a smart *** research is concluded with the integration process model application for the required data sharing among different service *** outcomes of the modeling are better understanding and attaining maximum automation that can be referenced and replicated.

关键词： Process modeling business process business process integration business process modeling and notation

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Feed-Forward Probabilistic Error Cancellation with Noisy Recovery Gates 17

Feed-Forward Probabilistic Error Cancellation with Noisy Rec...

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17th IEEE International Symposium on Embedded Multicore/Many-core systems-on-Chip, MCSoC 2024

作者： Kurosawa, Leo Saito, Yoshiyuki Lee, Xinwei Yan, Xinjian Xie, Ningyi Cai, Dongsheng Shin, Jungpil Asai, Nobuyoshi University of Aizu School of Computer Science and Engineering Aizu-Wakamatsu Fukushima Japan Singapore Management University School of Computing and Information Systems Singapore University of Tsukuba School of Science and Technology Tsukuba Japan University of Tsukuba Faculty of Engineering Information and Systems Tsukuba Japan

ISBN: (纸本)9798331530471

Probabilistic Error Cancellation (PEC) aims to improve the accuracy of expectation values for observables. This is accomplished using the probabilistic insertion of recovery gates, which correspond to the inverse of errors. However, the inserted recovery gates also induce errors. Thus, it is difficult to obtain accurate expectation values with PEC since the estimator of PEC has a bias due to noise induced by recovery gates. To address this challenge, we propose an improved version of PEC that considers the noise resulting from gate insertion, called Feed-Forward PEC (FFPEC). FFPEC provides an unbiased estimator of expectation values by cancelling out the noise induced by recovery gates. We demonstrate that FFPEC yields more accurate expectation values compared to the conventional PEC method through analytical evaluations. Numerical experiments are used to evaluate analytical results. © 2024 IEEE.

关键词： Inverse problems

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Predicting software vulnerability based on software metrics: a deep learning approach

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Iran Journal of computer Science 2024年第4期7卷 801-812页

作者： Agbenyegah, Francis Kwadzo Asante, Micheal Chen, Jinfu Akpaku, Ernest Department of Computer Science Kwame Nkrumah University of Science and Technology Kumasi Ghana School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Department of Information Technology Faculty of Computing and Information Systems Ghana Communication Technology University Accra Ghana

The security of IT systems is the topmost priority of software developers. Software vulnerabilities undermine the security of computer systems. Lately, there have been a lot of reported issues of software vulnerabilities recorded in individuals and corporate systems. Software metrics-based techniques have been used and found to be effective due to low false positives and false negatives. However, there are software metrics that have not been investigated and could have an impact on the performance of the predictive model. In this study, we developed an ensemble deep learning algorithm made of LSTM, CNN, and MLP models with the non-investigated metrics as a feature to learn the latent representation of code metrics to predict anomalies in source code effectively. We trained the proposed model on the SySeVR datasets and compared the model's performance against five deep learning algorithms and four state-of-the-art SVPs. The proposed model performs better than the other deep learning with a classification accuracy of 96.17%, and a precision of 97.72%. The model's classification accuracy is 4.98% higher than that of the five deep learning models and 1.71% lower in precision than the state-of-the-art SVP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Deep learning Machine learning Software metrics Software security Software vulnerabilities

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Enhancing resilience in complex energy systems through real-time anomaly detection: a systematic literature review

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Energy Informatics 2024年第1期7卷 96页

作者： Aghazadeh Ardebili, Ali Hasidi, Oussama Bendaouia, Ahmed Khalil, Adem Khalil, Sabri Luceri, Dalila Longo, Antonella Abdelwahed, El Hassan Qassimi, Sara Ficarella, Antonio Department of Engineering for Innovation University of Salento Lecce Italy Bologna Italy Computer Science Department Faculty of Sciences Semlalia Cadi Ayyad University Marrakesh Morocco Computer Science Department Faculty of Science and Technology Cadi Ayyad University Marrakesh Morocco Department of Research and Development HSPI SpA-Roma Rome Italy Department of Embedded Systems and Artificial Intelligence MAScIR UM6P Rabat Morocco

As real-time data sources expand, the need for detecting anomalies in streaming data becomes increasingly critical for cutting edge data-driven applications. Real-time anomaly detection faces various challenges, requiring automated systems that adapt continuously to evolving data patterns due to the impracticality of human intervention. This study focuses on energy systems (ES), critical infrastructures vulnerable to disruptions from natural disasters, cyber attacks, equipment failures, or human errors, leading to power outages, financial losses, and risks to other sectors. Early anomaly detection ensures energy supply continuity, minimizing disruption impacts, an enhancing system resilience against cyber threats. A systematic literature review (SLR) is conducted to answer 5 essential research questions in anomaly detection due to the lack of standardized knowledge and the rapid evolution of emerging technologies replacing conventional methods. A detailed review of selected literature, extracting insights and synthesizing results has been conducted in order to explore anomaly types that can be detected using Machine Learning algorithms in the scope of Energy systems, the factors influencing this detection success, the deployment algorithms and security measurement to take in to consideration. This paper provides a comprehensive review and listing of advanced machine learning models, methods to enhance detection performance, methodologies, tools, and enabling technologies for real-time implementation. Furthermore, the study outlines future research directions to improve anomaly detection in smart energy systems. © The Author(s) 2024.

关键词： Cyber attacks

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Light field depth estimation:A comprehensive survey from principles to future

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High-Confidence Computing 2024年第1期4卷 92-107页

作者： Tun Wang Hao Sheng Rongshan Chen Da Yang Zhenglong Cui Sizhe Wang Ruixuan Cong Mingyuan Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Zhongfa Aviation Institute Beihang UniversityHangzhou 311115China Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF *** this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this *** summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep *** analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed *** addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.

关键词： Light field Depth estimation Deep learning Sub-aperture image Epipolar-plane image

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An Optimized Algorithm for Resource Allocation for D2D in Heterogeneous Networks

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computers, Materials & Continua 2022年第2期70卷 2923-2936页

作者： Abdul Kadir Hamid Fahd N.Al-Wesabi Nadhem Nemri Ammar Zahary Imran Khan Department of Electrical&Computer Engineering University of SharjahSharjahUnited Arab Emirates Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversitySanaaYemen Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Electrical Engineering University of Engineering and TechnologyPeshawarPaki

With the emergence of 5G mobile multimedia services,end users’demand for high-speed,low-latency mobile communication network access is *** them,the device-to-device(D2D)communication is one of the considerable *** D2D communication,the data does not need to be relayed and forwarded by the base station,but under the control of the base station,a direct local link is allowed between two adjacent mobile *** flexible communicationmode reduces the processing bottlenecks and coverage blind spots of the base station,and can be widely used in dense user communication scenarios such as heterogeneous ultra-dense wireless *** of the important factors which affects the quality-of-service(QoS)of D2D communications is co-channel *** order to solve this problem of co-channel interference,this paper proposes a graph coloring based *** main idea is to utilize the weighted priority of spectrum resources and enables multiple D2D users to reuse the single cellular user *** proposed algorithm also provides simpler power *** heterogeneous pattern of interference is determined using different types of interferences and UE and the priority of color is *** results show that the proposed algorithm effectively reduced the co-channel interference,power consumption and improved the system throughput as compared with existing algorithms.

关键词： 5G device-to-device communication wireless networks spectrum

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