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2023 Eelco Visser Commemorative Symposium, EVCS 2023

作者： Thompson, Simon Horpácsi, Dániel School of Computing University of Kent Canterbury United Kingdom Faculty of Informatics Eötvös Loránd University Budapest Hungary

ISBN: (纸本)9783959772679

Eelco Visser’s work has always encouraged stepping back from the particular to look at the underlying, conceptual problems. In that spirit we present an approach to describing refactorings that abstracts away from particular refactorings to classes of similar transformations, and presents an implementation of these that works by substitution and subsequent rewriting. Substitution is language-independent under this approach, while the rewrites embody language-specific aspects. Intriguingly, it also goes back to work on API migration by Huiqing Li and the first author, and sets refactoring in that general context. © Simon Thompson and Dániel Horpácsi;licensed under Creative Commons License CC-BY 4.0.

关键词： API upgrade generic language independent refactoring rewriting substitution

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The synergy of Linear Regression, Fuzzy Functional Dependencies and Linguistic Summaries: A Case of Heat Islands in Bratislava 29

The synergy of Linear Regression, Fuzzy Functional Dependenc...

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29th International Conference on Information, Communication and Automation Technologies, ICAT 2023

作者： Minarikova, Erika Vuceticc, Miljan Hudec, Miroslav University of Economics in Bratislava Faculty of Economic Informatics Bratislava Slovakia Vlatacom Institute of High Technology Artificial Intelligence Department Belgrade Serbia Singidunum University Faculty of Informatics and Computing Belgrade Serbia VSB-Technical University of Ostrava Faculty of Economics Ostrava Czech Republic

ISBN: (纸本)9798350399837

The last decade has witnessed a rapid development of computational intelligence models in many scientific and industry fields. One of challenges is the lack of interpretability and explainability of the mechanism underlying the models. In this paper, we propose a methodology for evaluating and explaining influences by linear regression and by fuzzy logic techniques, which have been shown as promising approaches for the explainable computational intelligence. Precisely, fuzzy functional dependencies are able to verify regression result and explain intensities linguistically, whereas linguistic summaries can reveal intensities of relations among the subdomains of considered features. The proposed synergy is experimentally evaluated on the problem of explaining the influence of geographical features on the urban heat islands in the Bratislava downtown. Urban planning and development should consider environmental aspects. Thus, a support which is validated by three diverse approaches is more reliable. © 2023 IEEE.

关键词： Smart city

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Cybersecurity and Smart Cities: Current Status and Future

Cybersecurity and Smart Cities: Current Status and Future

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2023 IEEE International Conference on Emerging Trends in Engineering, Sciences and Technology, ICES and T 2023

作者： Habib, Muhammad Yasir Qureshi, Haseeb Ahmad Khan, Shujahat Ali Mansoor, Zara Chishti, Abdul Rehman The Islamia University of Bahawalpur Faculty of Engineering Dept. of Electronic Engineering Pakistan Bournemouth University Faculty of Computing Dept. of Computing and Informatics Bournemouth United Kingdom Charles Darwin University College of Engineering IT and Environment Darwin Australia

ISBN: (纸本)9781665455602

A Smart city implements the latest IoT and information and communication technologies (ICT) to improve the quality of urban city administrations, decrease expenditures, asset management and interconnect citizens of a Smart city. Smart cities offer numerous advantages like improved energy productivity, management, healthcare facilities, efficient transport systems, proper waste and water management, and individual security. Nonetheless, this reliance on ICT and IoT technologies makes a Smart city prone to digital cyber assaults. These technologies are vulnerable to many security issues like Information theft, Eaves-dropping attack, Denial of service, Communication delays, Data manipulation, IoT security attacks, Communication interception, Jamming, Sensor failure, insecure API, and Remote exploitation. This research study intends to address opinions on cybersecurity technologies, vulnerabilities, and cybercrime awareness based on the systematic literature review 'PRISMA Model' as our research method and help researchers and practitioners to look for innovative Smart City solutions. Our research endeavors to momentarily depict the central ideas of digital security and protection issues related to Smart city areas and uncover digital cyber-Attacks that focus on Smart city communities in the literature. In brief, the focus of this research is to explore and review the aspects of Smart city cybersecurity issues, Smart city vulnerabilities related to information security, and provide a comprehensive research framework that will help the researchers and practitioners explore this area of research. © 2023 IEEE.

关键词： Cybersecurity

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PSO Based Multi-Objective Approach for Controlling PID Controller

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Computers, Materials & Continua 2022年第6期71卷 4409-4423页

作者： Harsh Goud Prakash Chandra Sharma Kashif Nisar Ag.Asri Ag.Ibrahim Muhammad Reazul Haque Narendra Singh Yadav Pankaj Swarnkar Manoj Gupta Laxmi Chand Indian Institute of Information Technology NagpurIndia School of Computing&Information Technology Manipal UniversityJaipur(Rajasthan)India Faculty of Computing and Informatics University Malaysia SabahJalan UMS88400Kota Kinabalu SabahMalaysia Faculty of Computing&Informatics Multimedia UniversityPersiaran Multimedia63100CyberjayaSelangorMalaysia Maulana Azad National Institute of Technology BhopalIndia Department of Electronics and Communication Engineering JECRC UniversityJaipur(Rajasthan)India

CSTR(Continuous stirred tank reactor)is employed in process control and chemical industries to improve response characteristics and system *** has a highly nonlinear characteristic that includes complexities in its control and *** performance is compassionate to change in system parameterswhich need more effort for planning a significant controller for *** reactor temperature changes in either direction from the defined reference *** is important to note that the intensity of chemical actions inside the CSTR is dependent on the various levels of temperature,and deviation from reference values may cause degradation of biomass *** and implementation of an appropriate adaptive controller for such a nonlinear system are *** this paper,a conventional Proportional Integral Derivative(PID)controller is *** conventional techniques to deal with constraints suffer severe limitations like it has fixed controller ***,A novel method is applied for computing the PID controller parameters using a swarm algorithm that overcomes the conventional controller’s *** the proposed technique,PID parameters are tuned by Particle Swarm Optimization(PSO).It is not easy to choose the suitable objective function to design a PID controller using PSO to get an optimal *** this article,a multi-objective function is proposed for PSO based controller design of CSTR.

关键词： Particle swarm optimization multi-objective PSO continuous stirred tank reactor proportional integral derivative controller

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Supervised Machine Learning Techniques for Power Consumption Usage Level Prediction 6

Supervised Machine Learning Techniques for Power Consumption...

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6th International Conference on Software Engineering and Computer Science, CSECS 2023

作者： Tan, Yi-Fei Zhao, Ge-Zhi Cheeng, Tze-Hang Ooi, Chee-Pun Tan, Wooi-Haw Cheong, Soon-Nyean Multimedia University Faculty of Engineering Cyberjaya Malaysia University of Technology Faculty of of Engineering Chongqing Chongqing China Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia Multimedia University Faculty of Creative Multimedia Cyberjaya Malaysia

ISBN: (纸本)9798350306378

Electricity is an essential part of modern life and is crucial for many aspects of society and the economy. It is essential for industries to operate and create products, for transportation systems to function, and for communication and information technology to function. As the world continues to become more connected and technologically advanced, the demand for electricity is likely to increase, making it an even more critical resource in the future. Overusing electricity will lead to higher energy bills, which can put a strain on household or business budgets. In this paper, the authors developed a model to predict the level of electricity consumption usage. The model is developed in 2 stages. First stage is to categorize the electricity usage into several levels by using equally divided method. Second stage is to use supervised machine learning techniques to develop a prediction model for the level of electricity consumption. The experimental results indicated that using the Random Forest classifier leads to better performance in terms of accuracy, precision, recall, F1 score, and kappa score. Besides that, the results show that 2-level prediction performed better than 4-level prediction. © 2023 IEEE.

关键词： Forecasting

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Superficial Wound Classification Using CNN Image Classifier for Initial Wound Care

Superficial Wound Classification Using CNN Image Classifier ...

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IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

作者： Sitti Wan Fazirah Ambo'Miri Lai Po Hung Florence Sia Fui Sze Tan Soo Fun Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

Proper diagnosis of the types of wounds is the first step in effective wound management. Healthcare quality can be improved with the use of artificial intelligence technology. In this project, convolutional neural network (CNN) models are employed for superficial wound image classification. The goal of this project is to implement carefully selected CNN models with fine-tuned parameters (batch sizes, augmentation parameters and epoch size) to categorize superficial wounds of abrasion, bruise and superficial skin burns. The library and functions from TensorFlow, Keras, MatplotLib and OpenCv will be used alongside the Python programming language to create this project in a Google Colaboratory notebook, also widely known as Colab. This work evaluates the CNN models of VGG-16, InceptionV3 and ResNet-50 to find out which best classifies the dataset of non-fatal superficial wounds. These models have significant differences especially in their number of layers and concepts of capturing the features of images input. By utilizing these models, the accuracy rate of more than 40% is observed after models being trained and validated on 90% of the dataset (80% for training and 20 for validation), then tested and prediction with the remaining 10% of the dataset for classification report and confusion matrix generation. Out of 3 CNN models used in this study, it is observed that InceptionV3 model's highest accuracy is 44% while both ResNet50 and VGG-16 model achieved 45% accuracy against the superficial wound database (non-fatal wound).

关键词： Training Accuracy Medical services Wounds Predictive models Internet Convolutional neural networks Artificial intelligence Image classification Biomedical imaging

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RT-PPS: Real-time privacy-preserving scheme for cloud-hosted IoT data

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Journal of High Speed Networks 2025年第1期31卷 71-89页

作者： Elhoseny, Mohamed Riad, Khaled College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates Faculty of Computers and Information Mansoura University Mansoura35516 Egypt Computer Science Department College of Computer Sciences & Information Technology King Faisal University Al-Ahsa 31982 Saudi Arabia Mathematics Department Faculty of Science Zagazig University Zagazig44519 Egypt

The Internet of Things (IoT) is a rapidly growing network of devices that can communicate with each other and with cloud-based services. These devices generate vast amounts of data that can be used to provide valuable insights into user behavior, environmental conditions, and other important factors. However, as this data is collected and processed by cloud-hosted services, there is a growing concern about privacy and security. Without adequate protection, sensitive information could be exposed to hackers or other malicious actors, putting both individuals and organizations at risk. To address this challenge, real-time privacy-preserving techniques can be used to protect IoT data without compromising its value. This paper introduces an efficient Real-time privacy-preserving scheme (RT-PPS) for cloud-hosted IoT data. RT-PPS employs multi-authority attribute-based encryption on a hybrid cloud environment to keep data secure and private, while still allowing it to be processed and analyzed by cloud-hosted services. RT-PPS has efficient response time and resource consumption, which gives it the ability to handle a huge number of concurrent users at the same time without notable delay. The proposed RT-PPS has been validated through extensive experimental evaluation on a variety of configurations. Moreover, the proposed scheme has been computationally compared with the state-of-the-artwork. RT-PPS has shown excellent performance, effectiveness, and efficiency. The RT-PPS encryption time for a 1 GB dataset while considering 1024 slices is approximately 1000 ms. Also, the RT-PPS decryption time for a 1 GB ciphertext while considering 1024 slices are approximately 235 ms. Finally, RT-PPS is proven secure against any polynomial-time attacks and their variations that have at most a negligible advantage in the introduced security model. Moreover compared to most of the state-of-the-artwork, RT-PPS reduced the ciphertext size and lowered the computations in the encryption, key g

关键词： Ciphertext

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Developing Deep Learning Models to Predict Benign and Malicious Attacks in IoT Networks

Developing Deep Learning Models to Predict Benign and Malici...

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computing (ICOCO), IEEE International Conference on

作者： Nik Muhammad Danial Bin Nik Ram Zaedi Amy Lim Hui Lan Goh Hui-Ngo Faculty of Computing & Informatics Multimedia University Malaysia Cyberjaya Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

The Internet of Things (IoT) has afforded increased connectivity across various domains which increased cybersecurity vulnerabilities. Threats like Distributed Denial of Service (DDoS) attacks, Spoofing, and web-based attacks pose a significant danger to IoT networks, which require more advanced Network Intrusion Detection Systems (NIDS) to effectively counter these threats. The aim of this paper is to investigate the viability of DL-based classifiers for NIDS. The CICIoT2023 dataset is used in this study. Feature reduction is performed using the Pearson’s Correlation Coefficient (PCC) Matrix and features that contain all-zero values are removed. To resolve the class imbalanced issue, Synthetic Minority Oversampling (SMOTE) is applied exclusively on the training set only. For comprehensive comparison studies, a total of nine classifiers have been considered which can be categorized into machine learning (ML)-based classifiers and Deep Learning (DL)-based classifiers. MLbased classifiers consist of Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbors (KNN) and Logistic Regression (LR), and while DL-based classifiers consist of Long Short-Term Memory (LSTM), Bidirectional Long-Short Term Memory (BiLSTM), 1-dimensional convolutional neural network (1D-CNN) and recurrent neural network (RNN). The findings revealed that the DL-based classifiers have consistently recorded high accuracy, precision, recall, F1 score and AUC of more than 0.9. Although ML-based classifiers have recorded high precision and AUC, the accuracy, recall and F1 score have a wider variation in scores. The consistency and high scores across various evaluation metrics indicate the viability of adopting DL-based classifiers for building NIDS.

关键词： Training Deep learning Measurement Accuracy Computational modeling Buildings Predictive models Nearest neighbor methods Internet of Things Testing

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Integrating Serious Game in Personalized Learning Environment

Integrating Serious Game in Personalized Learning Environmen...

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computing (ICOCO), IEEE International Conference on

作者： Vivian Gek-Ting Wee Fang-Fang Chua Faculty of Computing and Informatics Multimedia University Cyberjaya Selangor Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

Computer science skills have become very important in the careers of STEM fields in today’s digital economy. However, many students often struggle in grasping programming concepts, leading to high dropout rates in computer science courses. The students have also found traditional learning environment to be ineffective in engaging and supporting them to tackle the issue. This paper recognizes the need for a pedagogical shift towards personalized learning experience to address the shortcomings of traditional teaching approaches. It aims to develop a personalized learning system named Codecaster Odyssey that caters to the diverse needs of learners. It also aims to leverage the intrinsic appeal of gaming to enhance student engagement and foster sustained interest in learning programming by integrating serious game into the learning system.

关键词： Learning systems Education Turning Encoding Real-time systems Serious games Reliability Mentoring Programming profession STEM

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Embodied Narrative: Data Storytelling of Online Artwork Experiences 8th

Embodied Narrative: Data Storytelling of Online Artwork Expe...

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8th International Visual informatics Conference, IVIC 2023

作者： Baharin, Hanif Harun, Afdallyna Fathiyah Mohd Norowi, Noris Institute of Visual Informatics Universiti Kebangsaan Malaysia UKM Selangor Bangi43600 Malaysia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Selangor Shah Alam40450 Malaysia Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9789819973385

This paper describes a pilot ethnomethodology study of visiting a 3D virtual gallery that exhibits 3D models of physical artworks and of viewing photographs of artworks in a 2D online artist portfolio. Artwork, with its intrinsic information and layers of meaning, is a form of data which narrates a compelling and evocative story to engage audience. The digital landscape is a compelling medium to deliver this storytelling. However, such approach demands a delicate balance between technology incorporation and artistic vision. In an era of converging data and art, this paper explores the aesthetics of data storytelling and immersive online art experiences. Amid CoVID-19 lockdowns, physical art exhibitions migrated online, prompting a study of 3D virtual gallery visits and 2D online artist portfolios. Participants engaged in think-aloud protocols while navigating both formats, revealing insights into rationalizing actions and interpreting artworks online. Challenges emerged, including disrupted responses due to the inability to discern texture in 3D models or artwork photos. Navigational control, viewing distance, detailed artwork descriptions, and stable internet connections further influenced the online viewing experience. This prompts a further exploration on means to curate online exhibitions thoughtfully, where data aesthetics and artistic storytelling is harmonized for realistic interpretation within the digital expanse. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Textures

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