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31st Annual IEEE International Conference on High Performance computing, Data, and Analytics, HiPC 2024

作者： Xuan, Zhibo You, Xin Yang, Hailong Li, Mingzhen Luan, Zhongzhi Liu, Yi Qian, Depei School of Computer Science and Engineering Beihang University China State Key Lab of Processors Institute of Computing Technology Chinese Academy China

ISBN: (纸本)9798331509095

As the performance gap between hardware and software widens, performance analysis tools are essential for understanding the behavior of large-scale High-Performance computing (HPC) programs. These tools provide insights into the performance bottlenecks and help in optimizing the performance of the programs. In this paper, we present a comprehensive study of performance analysis tools for large-scale HPC systems including both sampling-based and instrumentation-based tools that are commonly adopted in the HPC community. We investigate the abundance and overheads of data collection as well as the analysis capabilities of HPCToolkit, TAU, and Scalasca with representative programs at scale. Our study shows that different performance analysis tools have distinct strengths and weaknesses, and the choice of a performance analysis tool depends on the specific requirements of the user. We also discuss the challenges and future directions in the field of performance analysis tools for large-scale HPC systems. © 2024 IEEE.

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Advancing Color Perception: An IoT-driven Pedagogical System for Visually Impaired Children 1

Advancing Color Perception: An IoT-driven Pedagogical System...

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1st IEEE International Conference on Advances in Signal Processing, Power, Communication, and computing, ASPCC 2024

作者： Balage, Prasandika Jayawardene, Nuwan University of Westminster School of Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798350355093

Visual impairment is characterized by the loss of vision, encompassing both complete blindness and partial vision loss. Studies reveal a notable prevalence of visual impairment among school-aged children. The significance of colors in acquiring and discerning visual information is crucial, aiding children in effectively perceiving and comprehending their surroundings. This study presents an Internet of Things (IoT) system designed for teaching colors to visually impaired children. The system achieved an accuracy rate surpassing 77.8%, and it is operated effectively regardless of surrounding lighting conditions. Given that the target users of the system are visually impaired children, this research was conducted in close alignment with Human-computer Interaction (HCI) principles. © 2024 IEEE.

关键词： Color vision

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OGGPS: An Automatic Intelligence Driven Approach for Ontology Generation on Gandhian Philosophy and Peace Studies 23th

OGGPS: An Automatic Intelligence Driven Approach for Ontolog...

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

作者： Sharma, Ojasvi Sawarn, Shivam Deepak, Gerard School of Computing DIT University Dehradun India Department of Computer Science and Engineering BMS Institute of Technology and Management Yelahanka Bangalore India

ISBN: (纸本)9783031647789

This research aims to exploit the integration and competition of Generative Reciprocal Networks (GANs). An Algorithm to Facilitate Automatic Ontology Generation and Knowledge Graph (KG) Synthesis of Gandhi and Peace Studies. Combining the power of GANs to generate synthetic data and a competitive analysis algorithm and ordering concepts, comprehensive ontology and data graph can be created. This approach allows Systematic organization and presentation of concepts and relationships central to Gandhi's philosophy and peace studies. The iterative process allows for refinement and improvement, ensuring the accuracy of the ontology and KG to capture the complexity of the domain. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Ontology

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The Progression of Competencies and Dispositions: How do Maturing Software Engineers Compare? 54

The Progression of Competencies and Dispositions: How do Mat...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Clear, Tony Clear, Alison Auckland University of Technology Department of Computer Science and Software Engineering Auckand New Zealand School of Computing Eastern Institute of technology Auckland New Zealand

ISBN: (纸本)9798350351507

This research paper describes and extends the outcomes from an in- depth study investigating the difference in the expected skills requirements from junior software engineers to senior software engineers, and reflections on the findings from that study. It is a given that senior software engineers have more experience and skills than junior software engineers. However, a focus on their differing competencies and dispositions provides an enhanced mechanism for comparison. Gaps were identified in assessing 'professional knowledge' as categorized by the IEEE/ACM computing Curriculum Overview Report (CC2020), and in assessing 'dispositions'. It appeared that the specific scenario of comparing the expected competencies between junior and senior software engineers, tested the framework for assessing competencies developed in the CC2020 project and applied in its mapping to the IEEE/ACM computer science (CS2013) approved curriculum. In this study into the difference between Junior and Senior Software Engineers, an initial review of relevant literature was conducted. The review found that research analyzing job requirements for software engineers of different levels was limited;'experience' as a keyword was seldom mentioned;and a common distinction was made between 'soft' and 'hard' skills - the latter being skills that were 'technical', such as programming languages, frameworks, libraries, and tools, whereas soft skills referred to skills such as personality traits, attitudes, and teamwork skills. In our extension of that work the notion of soft skills was unpacked into professional skills and dispositions. The process of mapping from the CC2020 competency framework to the CS2013 curriculum had deliberately modelled how to represent a competency-based rather than a knowledge-based curriculum. The critical deficiency identified here was the limitation imposed by adopting a skills framework based on the cognitive taxonomy, and thereby unwittingly omitting the crucial compani

关键词： Mapping

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A survey for light field super-resolution

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High-Confidence computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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Snowfall Forecasting with Enhanced Accuracy: Leveraging Multi-Class SVM for Meteorological Predictions 7th

Snowfall Forecasting with Enhanced Accuracy: Leveraging Mult...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Ranjith Kumar, M. Sudeesh Kumar, V. Tharun Kaarthik, G.K. Chatiyode, Veda Srinivasan Anusha, Janani Revathi, P. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering C Abdul Hakeem College of Engineering and Technology Tamil Nadu Melvisharam India

ISBN: (纸本)9783031699856

The use of multi-class Support Vector Machines (SVM) for snowfall prediction is an advanced meteorological application. The study emphasizes how valuable snowfall forecasts are in various contexts, including agriculture, aviation, transportation management, and climate reporting. This approach presents a novel methodology for classifying snowfall types by analyzing various meteorological factors such as temperature range, wind dynamics, and precipitation. A 400-entries of datasets were pulled from GitHub to demonstrate the methodology. The sample is selected to reduce the complexity and to increase the performance. The dataset is split into a testing set (30%) and a training set (70%), which aids in assessing the model’s performance in a wide range of parameters. When the algorithm was first tested, it could distinguish different types of snowfall with a remarkable 83% accuracy rate based on indicators such as Temperature (min & max), Precipitation, Wind Speed, and Wind Gust. By optimizing the SVM configuration through hyper-parameter tuning approaches such as grid search, we were able to further refine our model’s accuracy to an astounding 91%. This improvement in forecast accuracy signifies a significant move away from older, more expensive instrument-based methods and towards data-driven snowfall analysis. Our results show how machine learning can revolutionize weather prediction systems, providing a more effective and economical method of comprehending and predicting snowfall patterns. © IFIP International Federation for Information Processing 2024

关键词： Snow

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Deep Learning and Quantum-Enabled Cloud Platform Approach on Optimized Crop Health Predictions 5th

Deep Learning and Quantum-Enabled Cloud Platform Approach on...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Jagendra Thakur, Hardeo Kumar Sharma, Neetu Alsahlanee, Abbas Thajeel Rhaif Sahunthala, S. Shaik, Nazeer Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chembarambakkam India Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India

ISBN: (纸本)9789819763177

This study is an innovative investigation into the field of plant disease detection and plant health prediction using state-of-the-art machine learning models and Quantum Support Vector Machines (QSVM) for feature extraction. A diverse dataset of 700 images, including a variety of plant species and disease states, served as the foundation for this study. The study evaluated the performance of four machine learning models: CNN (ConvNet), RNN (Recurrent), KNN (k-Nearest Neighbors), and SVM (Support Vector Machine) for accurately identifying and classifying plant diseases. Convolutional neural network (CNN) model unexpectedly prevailed, achieving a remarkable accuracy rate of 98.77%. Accuracy, recall, and F1 score of the model further demonstrated its propensity for disease diagnosis. QSVMs were used to extract intricate features from pictures of sick leaves in order to increase the models’ precision and prognostication ability. The findings have significant impact for agriculture. And the sophisticated toolkit they offer has provided an important step forward in proactive disease management for better crop health. Carrying out an early intervention for disease can result in higher crop output, lower agricultural losses, and improved global food security. The QSVM’s application of quantum computing principles also opens up fresh frontiers for creating machine learning applications in different industries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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A Systematic Literature Review of XAI-based Approaches on Brain Disease Detection using Brain MRI Images 4

A Systematic Literature Review of XAI-based Approaches on Br...

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4th International Conference on advanced Research in computing, ICARC 2024

作者： Madapatha, Savin Fernando, Pumudu University of Westminster Department of Computer Science and Engineering London United Kingdom School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (纸本)9798350384864

Brain Magnetic Resonance Imaging (MRI) analysis is a widely used medical procedure for the early diagnosis of various brain diseases. Accurate pathology identification during the brain MRI analysis procedure is crucial as misdiagnoses or missed findings can greatly affect a patient's treatment and long-term prediction. With the recent advancement of Artificial Intelligence (AI) in the medical field, researchers have approached various techniques to detect brain diseases using AI. Although AI models exhibit high accuracy, they suffer from a lack of transparency and interpretability, paving the way for the development of eXplainable Artificial Intelligence (XAI) methods in brain disease diagnosis. Image segmentation, machine learning, deep learning and XAI are important for assisting the diagnostic procedure. In this paper, a comprehensive overview of various existing techniques in brain disease detection using MRI is presented, starting with image segmentation techniques, followed by classification techniques, and finally, XAI techniques. In conclusion, the paper identifies a critical need for further research on XAI integration to advance brain disease detection. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Enhancing TCP Congestion Control: A Comparative Analysis, and Algorithmic Modifications using NS2

Enhancing TCP Congestion Control: A Comparative Analysis, an...

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Thangam, S. Trisha, K. Hima Sree, N. Reddy P, Srinithya Balakrishnan, Sivaneasan Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India Singapore Institute of Technology Singapore

ISBN: (纸本)9798350309140

The transmission control protocol is essentially a computer network protocol and ensures the reliability of the transmitted data. TCP's congestion control algorithms support network performance and stability. Network congestion has become a major issue in network analysis. TCP (Transmission Control Protocol) Congestion control algorithms ensure smooth transmission of data across networks. This work examines TCP congestion algorithms. The main objective is to increase its efficiency and modify the algorithm. A detailed performance analysis of the enhanced TCP congestion control algorithms namely Tahoe, New Reno, and Vegas is performed and simulations are used to evaluate its performance under different network conditions such as high latency, constant bit rate (CBR) bandwidth load, and varying traffic loads. Metrics such as latency, throughput, and round-trip time are analyzed under the scenarios using ns-2 simulator. © 2024 IEEE.

关键词： Transmission control protocol

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Cyber-Attacks and Anomaly detection on CICIDS-2017 dataset using ER-VEC 2

Cyber-Attacks and Anomaly detection on CICIDS-2017 dataset u...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Jha, Ravi Shankar Ojha, Kushagra Mishra, Aryan Mishra, Riya Kaushik, Abha School of Computing Dit University Dehradun India Computer Science and Engineering Gl Bajaj Institute of Technology and Management Greater Noida India

ISBN: (纸本)9798350371055

Securing a network the preliminary defense mechanisms of applied Security Compliance along with IDS (Intrusion Detection System) and IPS (Intrusion Prevention System) that are applied all over the networks to block the passage of malicious content into it. With the advent of modern technologies and innovations in every field, there has been a significant load on the security architecture to provide for faultless, secure, and robust infrastructure. In today's modern day, firewalls are capable of blocking a major portion of malicious activity from creeping into the network. Still, attackers have been successful in penetrating in the form of internal attacks and can perform their targeted exploitation, like denial of service, link failures, ransomware, and so on, which has disastrous consequences. Thus, such anomalies do occur that pose a threat to data security and the underlying structure. This study aims to propose a novel technique, including the use of ER-VEC (Extra-Tree Random Voting Ensemble Classifier), to classify various attack network packets into benign and suspicious categories. We also did comparisons with SVM(support vector machine), DT (decision tree), RF(random Foreset), and EXTC(extra tree extension) proposed by other researchers, to testify our models we have also tested and compared on the same datasets, and compared the results proved to be more effective and efficient than the previous methodologies. This article started with a section introduction then section two related work which included with problem statement, section three with the proposed predicted model, then we covered result analysis and comparative study with proposed strategies by other researchers and conclusion and future scope. © 2024 IEEE.

关键词： anomaly cyber-attack IDS cybersecurity detection DT ER-VEC EXTC firewall IPS RF

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