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2023 International Seminar on computer Science and engineering Technology, SCSET 2023

作者： Liu, Haiou Ma, Qingyun Software Engineering Institute of Guangzhou Department of Electronics Guangzhou China Software Engineering Institute of Guangzhou Department of Computer Guangzhou China

ISBN: (纸本)9798350301472

The relay node is the transit center of the sensor Internet of Things communication, and the rationality of its selection is of great significance to ensure the stability of the Internet of Things communication and information throughput. This paper proposes a method of selecting relay nodes based on optical fiber to maximize the throughput of the sensing Internet of Things. By building a sensor Internet of Things model, find the main lobe in the target fiber sensor base station, and identify the signal transmitted by the light source node. Select the relay nodes that can transmit the known optical channel status to other nodes in the candidate nodes, remove the nodes that cannot maximize the throughput, obtain the average received signal to noise ratio of the sensor Internet of Things based on statistical independent distribution, and select the appropriate fiber sensor antenna to complete the relay node selection. Simulation results show that the outage probability of the proposed method approaches zero when the number of nodes is 22;When the power of the light source node is 30 dBm, the network throughput reaches 98%. © 2023 IEEE.

关键词： Signal to noise ratio

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Adaptive Resource Planning for AI Workloads with Variable Real-Time Tasks

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computers, Materials & Continua 2023年第3期74卷 6823-6833页

作者： Sunhwa Annie Nam Kyungwoon Cho Hyokyung Bahn Department of Computer Science and Engineering Ewha UniversitySeoul03760Korea Embedded Software Research Center Ewha UniversitySeoul03760Korea

AI(Artificial Intelligence)workloads are proliferating in modernreal-time *** the tasks of AI workloads fluctuate over time,resourceplanning policies used for traditional fixed real-time tasks should be *** particular,it is difficult to immediately handle changes inreal-time tasks without violating the deadline *** cope with thissituation,this paper analyzes the task situations of AI workloads and findsthe following two ***,resource planning for AI workloadsis a complicated search problem that requires much time for ***,although the task set of an AI workload may change over time,thepossible combinations of the task sets are known in *** on theseobservations,this paper proposes a new resource planning scheme for AIworkloads that supports the re-planning of *** of generatingresource plans on the fly,the proposed scheme pre-determines resourceplans for various combinations of ***,in any case,the workload isimmediately executed according to the resource plan ***,the proposed scheme maintains an optimized CPU(Central Processing Unit)and memory resource plan using genetic algorithms and applies it as soonas the workload *** proposed scheme is implemented in the opensourcesimulator SimRTS for the validation of its *** show that the proposed scheme reduces the energy consumptionof CPU and memory by 45.5%on average without deadline misses.

关键词： Resource planning artificial intelligence real-time system task scheduling optimization problem genetic algorithm

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An Empirical Study on Small-Sized Datasets Based on Eubank’s Optimal Spacing Theorem

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SN computer Science 2025年第1期6卷 1-19页

作者： Abedu, Samuel Mensah, Solomon Boafo, Frederick Department of Computer Science and Software Engineering Concordia University Montreal Canada Department of Computer Science University of Ghana Accra Ghana Department of Computer Science Lancaster University Ghana Accra Ghana

Conventional machine learning methods for software effort estimation (SEE) have seen an increase in research interest. Conversely, there are few research that try to evaluate how well deep learning techniques work in SEE. This can be attributed to the relatively small sizes of SEE datasets. The goal of the study is to establish a threshold for small-sized datasets in SEE. Additionally, it looks into how well certain deep learning and traditional machine learning models perform on small-sized datasets. From the body of existing literature, plausible SEE datasets are extracted and ranked along with their attributes and number of project cases. The ranking of the project instances is discretized into three classes (small, medium, and large) using Eubank’s optimal spacing theory. Using the leave-one-out cross-validation, each small-sized dataset is used to train two deep learning models and five conventional machine learning models. Each model’s ability to make predictions is evaluated using its mean absolute error. Results show that, on small-scale datasets, deep learning models outperform traditional machine learning models in terms of prediction accuracy, which contradicts what is previously known. Regularisation techniques can be used in conjunction with deep learning to address SEE. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Eubank’s optimal spacing theory Small-sized software effort estimation Traditional Machine learning

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Road Defect Detection Model Based on YOLOv8

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第4期52卷 986-994页

作者： Cong, Shengqiang Zhang, Chunna Yu, Yang Yue, Xiaoping Zhao, Jinchi Shen, Yuming School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China University of Science and Technology Liaoning Liaoning Anshan114051 China

The detection of road defects is crucial for ensuring vehicular safety and facilitating the prompt repair of roadway imperfections. Existing YOLOv8-based models face the following issues: extraction capabilities and insufficient feature representation in complex scenarios, slow bounding box regression speed. To address these challenges, we propose the YOLOv8-DSW model. Firstly, we incorporate the Dilation-wise Residual (DWR) module into the C2f module in the neck, improving the detection performance of multi-scale feature maps. Secondly, We incorporate the SENetv2 attention mechanism into the neck of the YOLOv8 model to augment feature expression capabilities. Finally, we introduce Wise-IoU (WIoU) to enhance the speed of bounding box regression. Experimental results indicate that YOLOv8-DSW enhances the mean Average Precision at 50% (mAP50) by 2.5% in comparison to the YOLOv8n, significantly improving detection accuracy and provide valuable method for the development of road defect detection. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Deep learning

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Characterization of Memory Access in Deep Learning and Its Implications in Memory Management

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computers, Materials & Continua 2023年第7期76卷 607-629页

作者： Jeongha Lee Hyokyung Bahn Institute of Artificial Intelligence and Software Ewha UniversitySeoul03760Korea Department of Computer Science and Engineering Ewha UniversitySeoul03760Korea

Due to the recent trend of software intelligence in the Fourth Industrial Revolution,deep learning has become a mainstream workload for modern computer *** the data size of deep learning increasingly grows,managing the limited memory capacity efficiently for deep learning workloads becomes *** this paper,we analyze memory accesses in deep learning workloads and find out some unique characteristics differentiated from traditional ***,when comparing instruction and data accesses,data access accounts for 96%–99%of total memory accesses in deep learning workloads,which is quite different from traditional ***,when comparing read and write accesses,write access dominates,accounting for 64%–80%of total memory ***,although write access makes up the majority of memory accesses,it shows a low access bias of 0.3 in the Zipf ***,in predicting re-access,recency is important in read access,but frequency provides more accurate information in write *** on these observations,we introduce a Non-Volatile Random Access Memory(NVRAM)-accelerated memory architecture for deep learning workloads,and present a new memory management policy for this *** considering the memory access characteristics of deep learning workloads,the proposed policy improves memory performance by 64.3%on average compared to the CLOCK policy.

关键词： Memory access deep learning machine learning memory access memory management CLOCK

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Real-time Volume Preserving Constraints for Volumetric Model on GPU

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computers, Materials & Continua 2022年第10期73卷 831-848页

作者： Hongly Va Min-Hyung Choi Min Hong Department of Software Convergence Soonchunhyang UniversityAsan31538Korea Department of Computer Science and Engineering University of Colorado DenverDenver80217COUSA Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Korea

This paper presents a parallel method for simulating real-time 3D deformable objects using the volume preservation mass-spring system method on tetrahedron *** general,the conventional mass-spring system is manipulated as a force-driven method because it is fast,simple to implement,and the parameters can be ***,the springs in traditional mass-spring system can be excessively elongated which cause severe stability and robustness issues that lead to shape restoring,simulation blow-up,and huge volume loss of the deformable *** addition,traditional method that uses a serial process of the central processing unit(CPU)to solve the system in every frame cannot handle the complex structure of deformable object in ***,the first order implicit constraint enforcement for a mass-spring model is utilized to achieve accurate visual realism of deformable objects with tough constraint *** this paper,we applied the distance constraint and volume conservation constraints for each tetrahedron element to improve the stability of deformable object simulation using the mass-spring system and behave the same as its real-world *** reduce the computational complexity while ensuring stable simulation,we applied a method that utilizes OpenGL compute shader,a part of OpenGL Shading Language(GLSL)that executes on the graphic processing unit(GPU)to solve the numerical problems *** applied the proposed methods to experimental volumetric models,and volume percentages of all objects are *** average volume percentages of all models during the simulation using the mass-spring system,distance constraint,and the volume constraint method were 68.21%,89.64%,and 98.70%,*** proposed approaches are successfully applied to improve the stability of mass-spring system and the performance comparison from our experimental tests also shows that the GPU-based method is faster than CPU-based implementation for all cases.

关键词： Deformable object simulation mass-spring system implicit constraint enforcement volume conservation constraint GPU parallel computing

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An Overview for Trustworthy and Explainable Artificial Intelligence in Healthcare 28

An Overview for Trustworthy and Explainable Artificial Intel...

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2024 28th International Conference on Information Technology, IT 2024

作者： Arslanoglu, Kubra Karakose, Mehmet The University of Firat The Faculty of Engineering Department of Software Engineering Elazig Turkey The University of Firat The Faculty of Engineering Department of Computer Engineering Elazig Turkey

ISBN: (纸本)9798350369618

Recently, the increased use of artificial intelligence in healthcare has significantly changed the developments in the field of medicine. Medical centres have adopted AI applications and used it in many applications to predict disease diagnosis and reduce health risks in a predetermined way. In addition to Artificial Intelligence (AI) techniques for processing data and understanding the results of this data, Explainable Artificial Intelligence (XAI) techniques have also gained an important place in the healthcare sector. In this study, reliable and explainable artificial intelligence studies in the field of healthcare were investigated and the blockchain framework, one of the latest technologies in the field of reliability, was examined. Many researchers have used blockchain technology in the healthcare industry to exchange information between laboratories, hospitals, pharmacies, and doctors and to protect patient data. In our study, firstly, the studies whose keywords were XAI and Trustworthy Artificial Intelligence were examined, and then, among these studies, priority was given to current articles using Blockchain technology. Combining the existing methods and results of previous studies and organizing these studies, our study presented a general framework obtained from the reviewed articles. Obtaining this framework from current studies will be beneficial for future studies of both academics and scientists. © 2024 IEEE.

关键词： Blockchain

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Predictive Caching of File System for Database Workload Management 1

Predictive Caching of File System for Database Workload Mana...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Jadhav, Rohini Jadhav, Veena Pawar, Prajakta Kannaujia, Abhishek Kumar, Abhishek Gohil, Jayesh College of Engineering Department of Information Technology Pune India College of Engineering Department of Computer Engineering Pune India Bharati Vidyapeeth College of Engineering Department of Computer Engineering Lavale Pune India Veritas Principal Software Engineering India

ISBN: (纸本)9798350355611

The proactive caching technique known as 'predictive caching' attempts to improve file system performance by anticipating and pre-fetching data that is likely to be requested in the future. Conventional caching strategies, such LRU (Least Recently Used) and LFU (Least Frequently Used), base their decision on which material to keep in the cache on historical access patterns. Although these methods work well in many situations, they could not completely take advantage of the temporal and geographical proximity of file system access patterns, which could result in less-than-ideal cache use. Utilizing machine learning algorithms, statistical analysis, or heuristics, predictive caching makes predictions about future access patterns based on past data, system status, file properties, or user activity. So we propose a predictive caching technique which reduce access latency and boost system performance by pre-fetching and storing pertinent data ahead of time in anticipation of incoming data accesses. © 2025 IEEE.

关键词： Dynamic Data File Access Pattern Predictive Caching Random Forest Classifier

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TSN vs I3D vs Pose-C3D: Action Recognition in Basketball using SpaceJam Dataset 24

TSN vs I3D vs Pose-C3D: Action Recognition in Basketball usi...

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7th International Conference on Computational Intelligence and Intelligent Systems, CIIS 2024

作者： Chiura, Tafadzwa Blessing Van Der Haar, Dustin Academy of Computer Science and Software Engineering Faculty of Science University of Johannesburg Gauteng Johannesburg South Africa

ISBN: (纸本)9798400717437

Most of the research conducted in action recognition is mainly focused on general human action recognition, and most of the available datasets support studies in general human action recognition. In more specific contexts, such as basketball, datasets that are as comprehensive and publicly available are limited. This study proposes taking three popular and mature methods in the field of action recognition, namely Temporal Segment Networks (TSN), Two-Stream CNN using Inflated 3D-convolutional Neural Networks (I3D) and Pose-C3D, and applying them to the SpaceJam dataset, which is a basketball-specific action dataset. All three experiments used pre-trained ImageNet models and were fine-tuned on the SpaceJam dataset. TSN was the oldest of the methods but obtained the best results of the three experiments, scoring a top-1 and top-5 accuracy of 59% and 96%, respectively. I3D was second best, with a top-1 and top-5 accuracy of 41% and 85%, respectively. Pose-C3D came in third, scoring a top-1 and top-5 accuracy of 15% and 50%, respectively. The results show that the models cannot distinguish significantly between some actions, such as ball in hand, pass and dribble. The study shows that it is feasible for context-specific fine-grain action recognition, but more needs to be done to discriminate against similar actions. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Multi-Task Visual Semantic Embedding Network for Image-Text Retrieval

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Journal of computer Science & Technology 2024年第4期39卷 811-826页

作者： Xue-Yang Qin Li-Shuang Li Jing-Yao Tang Fei Hao Mei-Ling Ge Guang-Yao Pang School of Computer Science and Technology Dalian University of TechnologyDalian 116024China School of Computer Science Shaanxi Normal UniversityXi’an 710119China School of Computer Engineering Weifang UniversityWeifang 261061China Guangxi Colleges and Universities Key Laboratory of Intelligent Industry Software Wuzhou UniversityWuzhou 543002 China

Image-text retrieval aims to capture the semantic correspondence between images and texts,which serves as a foundation and crucial component in multi-modal recommendations,search systems,and online *** mainstream methods primarily focus on modeling the association of image-text pairs while neglecting the advantageous impact of multi-task learning on image-text *** this end,a multi-task visual semantic embedding network(MVSEN)is proposed for image-text ***,we design two auxiliary tasks,including text-text matching and multi-label classification,for semantic constraints to improve the generalization and robustness of visual semantic embedding from a training ***,we present an intra-and inter-modality interaction scheme to learn discriminative visual and textual feature representations by facilitating information flow within and between ***,we utilize multi-layer graph convolutional networks in a cascading manner to infer the correlation of image-text *** results show that MVSEN outperforms state-of-the-art methods on two publicly available datasets,Flickr30K and MSCOCO,with rSum improvements of 8.2%and 3.0%,respectively.

关键词： image-text retrieval cross-modal retrieval multi-task learning graph convolutional network

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