2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)最新文献_第6页

Real-time PTV system implementation on multi-SoC architecture accelerated by OpenCL 通过 OpenCL 加速在多 SoC 架构上实现实时 PTV 系统

2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) Pub Date : 2024-02-01 DOI: 10.1109/ACDSA59508.2024.10467455

Ran Guo, E. Dekneuvel, Gilles Jacquemod, P. Biwole

{"title":"Real-time PTV system implementation on multi-SoC architecture accelerated by OpenCL","authors":"Ran Guo, E. Dekneuvel, Gilles Jacquemod, P. Biwole","doi":"10.1109/ACDSA59508.2024.10467455","DOIUrl":"https://doi.org/10.1109/ACDSA59508.2024.10467455","url":null,"abstract":"Measuring discrete particle trajectories in the air and monitoring airflow movement through 3D particle tracking technology (3D PTV) have numerous applications in smart homes, environments, energy, and other fields. In this study, an intelligent instrument for a real-time 3D PTV system is designed and developed based on the data flow streaming model. A high-level set of various functions is implemented following the client-server architectural model to provide services like 3D tracking and camera calibration. The model is deployed on several master-slave-based SoC FPGA acceleration boards to meet strict constraints like a high frame rate required for high trajectory precision. A functional decomposition of the 3D tracking service is elaborated to map particle detection and temporal tracking processes on three slave boards, one per camera. The remaining processing (spatial matching and 3D reconstruction) and the client requests management are mapped on the master board. On the FPGA processors of slave boards, treatment has been accelerated with a pipeline structure of the internal processes interleaved by FIFOs (First-In, First-Out) with the help of OpenCL. Experiments have been conducted using low-cost Intel DE10 standard boards.","PeriodicalId":518964,"journal":{"name":"2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)","volume":"286 2","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140528994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unlocking Transparency in Credit Scoring: Leveraging XGBoost with XAI for Informed Business Decision-Making 提高信用评分的透明度：利用 XGBoost 和 XAI 实现明智的商业决策

2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) Pub Date : 2024-02-01 DOI: 10.1109/ACDSA59508.2024.10467573

Maryam Alblooshi, Hessa Alhajeri, Meera Almatrooshi, Maher Alaraj

{"title":"Unlocking Transparency in Credit Scoring: Leveraging XGBoost with XAI for Informed Business Decision-Making","authors":"Maryam Alblooshi, Hessa Alhajeri, Meera Almatrooshi, Maher Alaraj","doi":"10.1109/ACDSA59508.2024.10467573","DOIUrl":"https://doi.org/10.1109/ACDSA59508.2024.10467573","url":null,"abstract":"Credit score analysis is vital to modern banking systems, allowing banks and other financial institutions to determine a borrower's creditworthiness. In such a situation, accurate and robust prediction models are vital because they allow lenders to make rational decisions regarding loan approvals and risk management. This paper provides an overview of using XGBoost, a sophisticated machine learning algorithm, to improve credit score evaluation, and the XAI model, LIME, to describe the black box machine learning algorithm. XGBoost, a gradient boosting-based ensemble learning algorithm, has gained prominence for its capacity to give improved predicted accuracy while handling vast and complicated datasets. Its algorithmic characteristics, including regularization, parallel processing, and decision tree optimisation, make it especially well-suited for credit scoring problems. Because of its complexity, implementing XAI is critical since it will help lenders grasp the reasons for the result of the XGBoost. The results show how the XAI model, LIME, helps simplify the complexity of these models. It is critical to integrate XAI models since they will improve lender decision-making. The fundamental goal of this research is to evaluate the XAI model, LIME, and determine how well the XAI model explains the findings of our experimental tests. Furthermore, it illustrates the possibility of incorporating LIME into credit score analysis, resulting in more efficient lending procedures, enhanced risk management, and better decision-making. Finally, this paper emphasizes the importance of using advanced machine learning techniques such as XGBoost in credit scoring analysis, which has the potential to transform the way banks and other financial institutions assess credit risk, as well as include LIME for a better understanding of the results.","PeriodicalId":518964,"journal":{"name":"2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)","volume":"56 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Experiment as Code - An Automated Testbed for Cloud Scaling and Operations 实验即代码--云扩展和运营的自动化测试平台

2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) Pub Date : 2024-02-01 DOI: 10.1109/ACDSA59508.2024.10468018

Vincent Latzko, Zhenzhen Jia, Christian L. Vielhaus, Mahshid Mehrabi, F. Fitzek

{"title":"Experiment as Code - An Automated Testbed for Cloud Scaling and Operations","authors":"Vincent Latzko, Zhenzhen Jia, Christian L. Vielhaus, Mahshid Mehrabi, F. Fitzek","doi":"10.1109/ACDSA59508.2024.10468018","DOIUrl":"https://doi.org/10.1109/ACDSA59508.2024.10468018","url":null,"abstract":"Cloud Computing has been widely adopted in industry, administrations, as well as research. At the same time, development and testing of new technologies is troubled by inconsistent environments. Exchanging modules of a system during research becomes a major obstacle. As a result, baselines for comparisons are lacking, and evaluating new developments may lead to heavy time cost. Big industrial players typically circumvent this problem by relying on the scale of their datacenters, experiments, and problems, which makes them inherently interesting.We propose a softwarised testbed that abstracts away inevitable hardware differences. Its design closely follows the established philosophy to separate workloads and declare expressive interfaces between them. The result is a coherent testbed, consisting of interchangeable, but state of the art, components. It establishes Experiment-as-Code, where an experiment is declared to load generators programmatically. In sum, the testbed yields reproducible results. We verify networking, computational and interacting components by reproducing known results. We illustrate one use case of scaling under uncertainty the applicability for research. The whole system is hosted on-premise, with minimal hardware requirements, which enables especially academia to contribute. The low-effort in setup also allows handover to students and staff with the goal to ensure knowledge transfer.","PeriodicalId":518964,"journal":{"name":"2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)","volume":"877 22","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140528743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reinforcement learning based control for torque allocation in electric vehicles: a preliminary analysis 基于强化学习的电动汽车扭矩分配控制：初步分析

2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) Pub Date : 2024-02-01 DOI: 10.1109/ACDSA59508.2024.10467491

Henrique de Carvalho Pinheiro, M. Carello

引用次数: 0

Towards Semantic Layer for Enhancing Blocking Entity Resolution Accuracy in Big Data 建立语义层，提高大数据中块状实体的解析精度

2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) Pub Date : 2024-02-01 DOI: 10.1109/ACDSA59508.2024.10467666

Reham Afifi Abd El Aziz, Doaa Elzanfaly, Marwa Salah Farhan

引用次数: 0

ACDSA 2024 Foreword ACDSA 2024 前言

2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) Pub Date : 2024-02-01 DOI: 10.1109/acdsa59508.2024.10467756

引用次数: 0

Optimizing Efficiency and Performance in DC-DC Boosters: A Newton-Raphson Approach for Enhanced Model Predictive Control 优化直流-直流升压器的效率和性能：用于增强模型预测控制的牛顿-拉斐森方法

2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) Pub Date : 2024-02-01 DOI: 10.1109/ACDSA59508.2024.10467663

Osamah N. Neamah, Aadil S. Abumelh, R. Bayir

引用次数: 0

Data analysis and triangulation for problem source elimination in smart cities: the societal city 为消除智慧城市中的问题源进行数据分析和三角测量：社会城市

2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) Pub Date : 2024-02-01 DOI: 10.1109/ACDSA59508.2024.10467629

Leonidas Anthopoulos, Maria Mitsiou

{"title":"Data analysis and triangulation for problem source elimination in smart cities: the societal city","authors":"Leonidas Anthopoulos, Maria Mitsiou","doi":"10.1109/ACDSA59508.2024.10467629","DOIUrl":"https://doi.org/10.1109/ACDSA59508.2024.10467629","url":null,"abstract":"A recent ambition for smart city (SC) is to become people centric, which means to realize sustainability, inclusiveness, prosperity, and human rights for the benefit of all. In this respect, the local authorities aim to utilize technology to meet the community needs, and among others to identify and address the sources of local problems. This work in progress describes the concept of the \"social/societal city\" and attempts to justify the reasons that the SC should evolve to a societal city. A societal city focuses on the people and living SC dimensions, and promote social well-being, justice, equality, and participation. To improve the performance of the services provided to their citizens, the municipal authorities must collect data from different sources, analyze them, identify local problems and their sources, and do the appropriate policy making to deal with the sources instead of simply responding with the consequences of the problems. This ongoing study uses a multi-method research methodology: it defines the societal city with findings from literature review. Then, it investigates accidents in the SC of Trikala, Greece and uses data triangulation to define their sources. Finally, it interviews the municipal authority to propose measures that require emergent strategic changes.","PeriodicalId":518964,"journal":{"name":"2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)","volume":"392 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140528763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A troyan Diffie-Hellman-like protocol based on proof of gullibility 基于轻信证明的特洛伊-迪菲-赫尔曼类协议

2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) Pub Date : 2024-02-01 DOI: 10.1109/ACDSA59508.2024.10468029

M. Ceria, Alessandro De Piccoli, Theo Moriarty, Andrea Visconti

引用次数: 0

The relation between use of car-sharing services and use of digital technology in Sweden 瑞典汽车共享服务的使用与数字技术的使用之间的关系

2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) Pub Date : 2024-02-01 DOI: 10.1109/ACDSA59508.2024.10467810

John Magnus Roos

引用次数: 0