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10th International Conference on Software Defined Systems, SDS 2023

作者： Daraghmeh, Mustafa Agarwal, Anjali Jararweh, Yaser Concordia University Electrical and Computer Engineering Montreal Canada Jordan University of Science and Technology Computer Science Irbid Jordan

ISBN: (纸本)9798350382235

Businesses increasingly lean on cloud-based solutions in the contemporary digital landscape, drawn by their scalability and adaptability. Navigating the financial intricacies of cloud subscription models, particularly when intertwined with software-defined systems, remains a formidable challenge. This challenge is accentuated by dynamic pricing structures, making accurate cost forecasting a critical but complex endeavor. This research unveils an innovative methodology designed to meticulously forecast the financial costs of cloud subscription tasks based on resource allocation characteristics. Our approach centers around a robust model carefully created through particular preparation and modeling stages that incorporate a pricing model for various virtual machines and utilize different advanced regression algorithms to navigate the complex world of cloud subscription services. The results of our study, which analyzed real cloud workloads, indicate that equipping businesses with a powerful predictive tool can lead to improved financial planning and strategic decision-making in their cloud operations. This study aims to guide businesses in the ever-changing field of cloud technology, focusing on promoting financial efficiency and improving decision-making strategies in their cloud initiatives. © 2023 IEEE.

关键词： Forecasting

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Medical Reports Simplification Using Large Language Models 23th

Medical Reports Simplification Using Large Language Models

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Almezhghwi, Khaled Hassan, Morad Ali Ghadedo, Adel Belhaj, Fairouz Shwehdi, Rabei Electrical and Electronics Engineering College of Electronics Technology Tripoli Tripoli Libya Computer Science Bani Waleed University Bani Waleed Libya Computer Science College of Electronics Technology Tripoli Tripoli Libya Computer Information Systems Higher Institute of Science and Technology Tripoli Libya Electrical and Electronics Engineering College of Electronics Technology Tripoli Tripoli Libya

ISBN: (纸本)9783031648120

Making medical reports easily understandable for a wider audience is a significant endeavor, and the recent advancements in deep learning and large language models offer a promising solution. In our research, we have introduced a fine-tuned Text-to-Text Transformer (T5) model to efficiently summarize these medical reports. Our model is trained and assessed on the publicly accessible Indiana Dataset. To gauge its effectiveness, we employ the ROUGE metrics. The outcomes we've achieved hold promise for the potential of this approach in enhancing medical report summarization. By harnessing the power of cutting-edge technology, we aim to make medical information more accessible and comprehensible to everyone. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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A Multilevel Learning Model for Predicting CPU Utilization in Cloud Data Centers

A Multilevel Learning Model for Predicting CPU Utilization i...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber science and technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

ISBN: (纸本)9798350304602

In the contemporary era of cloud computing, efficient and precise prediction of CPU utilization ensures optimal performance and energy efficiency in data centers. Traditional predictive models often need to be improved as these data centers grow in complexity and scale, necessitating more nuanced and integrative solutions. This paper introduces an advanced multi-layered learning framework meticulously designed to meet the demands of modern cloud data centers. Our innovative approach synergistically combines anomaly detection, data clustering, and ensemble-based regression prediction. The Isolation Forest algorithm is used during the preliminary stage to identify and address anomalies within the data. Subsequent phases harness the K-Means clustering algorithm, refine data categorization based on recurrent CPU usage patterns, and employ multilevel ensemble-based prediction models for accurate forecasting rooted in historical and real-time data trends. Through comprehensive evaluations, our model demonstrates significant improvements in prediction accuracy and robustness against the dynamism inherent in cloud environments. Our research paves the way for a more resilient, proactive, and efficient approach to CPU utilization prediction, laying the foundational stone for future innovations in cloud computing resource management. © 2023 IEEE.

关键词： Forecasting

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Coexistence behavior of asymmetric attractors in hyperbolic-type memristive Hopfield neural network and its application in image encryption

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Chinese Physics B 2024年第3期33卷 302-315页

作者：李晓霞何倩倩余天意才壮徐桂芝 Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province Hebei University of TechnologyTianjin 300130China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of TechnologyTianjin 300130China School of Life Science & Health Engineering Hebei University of TechnologyTianjin 300130China

The neuron model has been widely employed in neural-morphic computing systems and chaotic *** study aims to develop a novel circuit simulation of a three-neuron Hopfield neural network(HNN)with coupled hyperbolic memristors through the modification of a single coupling connection *** bistable mode of the hyperbolic memristive HNN(mHNN),characterized by the coexistence of asymmetric chaos and periodic attractors,is effectively demonstrated through the utilization of conventional nonlinear analysis *** techniques include bifurcation diagrams,two-parameter maximum Lyapunov exponent plots,local attractor basins,and phase trajectory ***,an encryption technique for color images is devised by leveraging the mHNN model and asymmetric structural *** method demonstrates significant benefits in correlation,information entropy,and resistance to differential attacks,providing strong evidence for its effectiveness in ***,an improved modular circuit design method is employed to create the analog equivalent circuit of the memristive *** correctness of the circuit design is confirmed through Multisim simulations,which align with numerical simulations conducted in Matlab.

关键词： hyperbolic-type memristor Hopfield neural network(HNN) asymmetric attractors image encryption

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Exploring the Usage of Mobile Phones in the Assessment Process: A Case Study 47

Exploring the Usage of Mobile Phones in the Assessment Proce...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Kovačević, Željko Bubnic, Bostjan Kosar, Tomaž Zagreb University Of Applied Sciences Department Of Computer Science And Information Technology Zagreb Croatia University Of Maribor Faculty Of Electrical Engineering And Computer Science Maribor Slovenia

ISBN: (纸本)9798350382495

technology is changing how students learn in the 21st century significantly. Integrating mobile devices in teaching, learning, and assessment processes has emerged as an important strategy for improving teaching methodologies and students' learning. However, adopting mobile devices presents several challenges, including understanding the students' performance, impact on time, and efficiency in the assessment process. This study explores this area with an empirical study of using mobile devices and compares the results to those using traditional desktop computers. In the educational environment, we investigated students' effective use while interacting with mobile and desktop versions of the Second Introductory programming summative assessment. Multinational/multi-institutional research involved a comparative analysis between two student groups undertaking identical tasks on mobile phones and desktop computers. Our results suggest that the students' results are not affected significantly by using the mobile version of the assessment application. These results highlight the usability of mobile devices as a flexible and effective tool for various educational applications, strengthening their role in the modern learning environment. © 2024 IEEE.

关键词： Object oriented programming

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De-biased knowledge distillation framework based on knowledge infusion and label de-biasing techniques

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Journal of Electronic science and technology 2024年第3期22卷 57-68页

作者： Yan Li Tai-Kang Tian Meng-Yu Zhuang Yu-Ting Sun School of Economics and Management University of Electronic Science and Technology of ChinaChengdu611731China School of Economics and Management Beijing University of Posts and TelecommunicationBeijing100876China School of Electrical Engineering and Computer Science The University of QueenslandBrisbane4072Australia

Knowledge distillation,as a pivotal technique in the field of model compression,has been widely applied across various ***,the problem of student model performance being limited due to inherent biases in the teacher model during the distillation process still *** address the inherent biases in knowledge distillation,we propose a de-biased knowledge distillation framework tailored for binary classification *** the pre-trained teacher model,biases in the soft labels are mitigated through knowledge infusion and label de-biasing *** on this,a de-biased distillation loss is introduced,allowing the de-biased labels to replace the soft labels as the fitting target for the student *** approach enables the student model to learn from the corrected model information,achieving high-performance deployment on lightweight student *** conducted on multiple real-world datasets demonstrate that deep learning models compressed under the de-biased knowledge distillation framework significantly outperform traditional response-based and feature-based knowledge distillation models across various evaluation metrics,highlighting the effectiveness and superiority of the de-biased knowledge distillation framework in model compression.

关键词： De-biasing Deep learning Knowledge distillation Model compression

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Is the VGG-19 Road Segmentation Method better than the Customized UNET Method? 3

Is the VGG-19 Road Segmentation Method better than the Custo...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Chanda, Moumita Mazumder, Olive Patwary, Md. Fazlul Karim Paul, Souvik Iubat - International University of Business Agriculture and Technology Computer Science and Engineering Dhaka Bangladesh Computer Science and Engineering Dhaka Bangladesh Jahangirnagar University Institute of Information Technology Dhaka Bangladesh University of Ottawa Electrical and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350384598

The main reason for coming up with self-driving cars is to enhance the safety of cars on the road by improving and reducing the occurrence of traffic accidents. Self-driving cars rely on advanced systems known as Advanced Driver Assistance Systems (ADASs) that help in perceiving the driving environment despite being on the road. Automobile lane detection is repeatedly considered effective in building up the recognition of the nearest lanes. Is it capable of discerning lane markings and objects on the side of the road or, topologically, it does not have to make a rolling contact? To answer this query, hence we apply road segmentation techniques that involve delineation of objects of interest along the road. In this paper, we compared two models named VGG-19 and UNET by observing their accuracy in detecting road lanes, objects, and cars. We find that the VGG-19 model achieves superior accuracy, about 40% accuracy with 93% precision in detecting complex road structures compared to the UNET model in our 95 images. This research contributes to finding a novel technique for semantic segmentation in the context of autonomous driving. © 2024 IEEE.

关键词： Advanced driver assistance systems

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Text Augmentation-Based Model for Emotion Recognition Using Transformers

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computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer

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Incorporating Data Preparation and Clustering Techniques for Workload Segmentation in Large-Scale Cloud Data Centers 3

Incorporating Data Preparation and Clustering Techniques for...

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3rd International Conference on Intelligent Data science Technologies and Applications, IDSTA 2023

ISBN: (纸本)9798350339253

The interconnected dependencies of various cloud-hosted services and applications make managing cloud resources more difficult. Observing fully operational virtual computing instances based on task profiles can aid in identifying workload characteristics. Scheduling and managing workloads may be optimized by tailoring selection and decision-making processes in response to workload segmentation. Cloud data center managers use models that group operations and tasks with similar structures, allowing for a more straightforward comparison of outcomes and improved cloud service performance and availability. However, conventional clustering algorithms can cluster the cloud workload into a single data grouping pattern. Since cloud workload data is open to diverse interpretations, several legitimate categories are hiding in the various data outlooks. Considering high-dimensional data, where several attribute profiles define each job, we provide a strategy for grouping cloud workloads at the task scheduling level. The proposed model is applied to a real-world data center workload, which includes a trace of virtual instances derived from the Microsoft Azure public dataset. Several data clustering methods and pipelines are examined and contrasted. The results show that different feature engineering methods used in data pipelines lead to various valid clustering schemes that can be used to improve the performance of workload segmentation in big cloud data centers. © 2023 IEEE.

关键词： Cluster analysis

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Challenges in platforming and digitizing decentralized energy services

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Energy Informatics 2022年第1期5卷 1-29页

作者： Idries, Ahmed Krogstie, John Rajasekharan, Jayaprakash Department of Computer Science Norwegian University of Science and Technology Trondheim Norway Department of Electrical Power Engineering Norwegian University of Science and Technology Trondheim Norway

This paper aims to identify and classify the challenges and issues faced by the energy sector in digitizing distributed energy operation and services using digital platforms. It contributes to two fields: information systems (IS) in the domain of platforms ecosystems and digital services innovation and the field of energy informatics in the domain of digital business models and service innovation. Through a systematic literature review, we investigate current research on digitalization and digital platforms in the context of electrical energy services and identify challenges and areas for future research. The key challenges are then classified into two categories, each with three subcategories: Architectural challenges, including aspects of design, ecosystem management, and agility and openness;and business and regulatory challenges, including contracts and relationship management, business models, and standards. The main limitation of this study is that it does not focus on a specific geographic domain, which means that the results are somewhat general and may not be applicable to certain countries or regions. The paper concludes with recommendations for future research directions in the digitalization and platformization of the energy sector in all six-sub-areas. © 2022, The Author(s).

关键词： Ecosystems

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