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检索条件"丛书名=Communications in Computer and Information Science"

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Algorithm for Circuit Covering the Euclidean Complete Graph 2nd

Algorithm for Circuit Covering the Euclidean Complete Graph

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2nd International Conference on information Technologies and Their Applications

作者： Nuriyeva, Fidan Dokuz Eylul Univ Izmir Turkiye Minist Sci & Educ Republ Azerbaijan Inst Control Syst Baku Azerbaijan

ISBN: (纸本)9783031734168;9783031734175

Computational Geometry is a branch of computer science that focuses on algorithms solving geometric problems. Computational Geometry has applications in various fields such as Modern Engineering and Mathematics, computer Graphics, Robotics, VLSI Design, computer-Aided Design, Molecular Modeling, Metallurgy, Manufacturing, Textile Design, Forestry, and Statistics. Graph theory is useful in defining problems and determining structural relationships. Hundreds of interesting computational problems are stated in graphs. In this study, the term of a circuit that more tightly covers the vertex set of the Euclidean complete graph has been defined, and an algorithm has been proposed to find this circuit. The complexity of the proposed algorithm is O(n). The proposed Boundary Point term in our presented work given in [5] allows for the expansion of the defined set of Boundary Vertices, enabling a more tightly construction of the Circuit Covering the Graph. The proposed algorithm can be applied to geometric design problems.

关键词： Graph Circuit Covering the Graph Convex Hull Geometric Design Problem

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Pervasive Aided Screening System of Multiple Sclerosis from Retinal OCT Images 8th

Pervasive Aided Screening System of Multiple Sclerosis from ...

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8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Ouni, Sabrin Elloumi, Yaroub Ben Djemaa, Raoudha Univ Sousse Higher Inst Comp Sci & Commun Technol Sousse Sousse Tunisia Univ Monastir Med Technol & Image Proc Lab Fac Med Monastir Tunisia Univ Sousse Miracl Lab Higher Inst Comp Sci & Commun Technol Sousse Tunisia

ISBN: (纸本)9783031838446;9783031838453

Multiple Sclerosis (MS) is a neurodegenerative disease. As an irreversible disease, the MS screening in early stage is highly recommended. Several works suggested to diagnose MS from the retinal Optical Coherence Tomography (OCT) images, which implies layer thickness. However, a delay on MS screening is registered, caused by the unavailability of medical equipment and the low-rate of medical staff. We propose in this paper a novel method for MS screening from OCT images captured by a pervasive devices. The main challenges is to insure a higher accurate MS screening through a processing workflow executed into smartphone device. For this purpose, we fine tune the convolutive deep neural network "Inception-V4" to extract features from OCT images, which are provided to an Extreme Learning Machine (ELM) classifier to deduce MS disease. A cross-validation process is conducted where 92.00% accuracy, 94.00% sensitivity, 90.00% specificity and 90.38% precision in average are achieved. In addition, the whole method is implemented into a mobile device, where execution time is under one second whatever the OCT image is, which is adequate to employ screening on clinical context.

关键词： Multiple Sclerosis Pervasive-aided screening system Deep CNN

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To Ensemble or Not: Assessing Majority Voting Strategies for Phishing Detection with Large Language Models 4th

To Ensemble or Not: Assessing Majority Voting Strategies for...

引用

4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Trad, Fouad Chehab, Ali Amer Univ Beirut Elect & Comp Engn Beirut Lebanon

ISBN: (纸本)9783031821493;9783031821509

The effectiveness of Large Language Models (LLMs) significantly relies on the quality of the prompts they receive. However, even when processing identical prompts, LLMs can yield varying outcomes due to differences in their training processes. To leverage the collective intelligence of multiple LLMs and enhance their performance, this study investigates three majority voting strategies for text classification, focusing on phishing URL detection. The strategies are: 1) a prompt-based ensemble, which utilizes majority voting across the responses generated by a single LLM to various prompts;2) a model-based ensemble, which entails aggregating responses from multiple LLMs to a single prompt;and 3) a hybrid ensemble, which combines the two methods by sending different prompts to multiple LLMs and then aggregating their responses. Our analysis shows that ensemble strategies are most suited in cases where individual components-whether prompts or LLMs-exhibit equivalent performance levels. However, when there is a significant discrepancy in individual performances, the effectiveness of the ensemble method may not exceed that of the highest-performing single component. In such instances, opting for ensemble techniques is not recommended.

关键词： Ensemble LLMs Majority Voting Phishing Detection

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Role of Artificial Intelligence in Facilitating Open Innovation in VAT Management 5th

Role of Artificial Intelligence in Facilitating Open Innovat...

引用

5th 5th Olympus - International Conference on Supply Chain

作者： Yordanova, Zornitsa Univ Natl & World Econ Sofia Bulgaria

ISBN: (纸本)9783031693434;9783031693441

In the dynamic landscape of fiscal policy and tax administration, the integration of Artificial Intelligence (AI) within Value-Added Tax (VAT) management systems offers transformative opportunities to enhance efficiency, compliance, and transparency. Amidst a complex global economic environment characterized by intricate transactions and international trade, there is a critical need for innovative approaches to VAT collection and compliance. This study embarks on an exploratory analysis of AI's role in revolutionizing VAT management practices through the prism of open innovation-a paradigm that advocates leveraging external knowledge and technological advancements to drive internal innovation. Utilizing a qualitative research methodology, inclusive of a literature review and case studies, this paper examines the current and potential impacts of AI on VAT management practices. It delves into the application of AI in automating the processing of tax regulations, facilitating data modeling, and enhancing decision-making processes within VAT systems. Furthermore, this research evaluates the contributions of open innovation strategies, such as collaboration with external partners and engagement with user communities, to the development and continuous improvement of AI-enhanced VAT management tools. Drawing insights from interviews with development teams and tax professionals, alongside an examination of documentary evidence, this study presents a novel framework for understanding and advancing AI applications in VAT administration. It highlights the synergistic effects of AI and open innovation in creating more resilient, efficient, and transparent VAT systems. The results show that AI technologies and open innovation practices have a lot of potential to bring VAT management up to date, as long as problems like data privacy, engaging stakeholders, and the fact that tax laws are always changing are properly dealt with. This research contributes to the burgeoning discourse

关键词： Artificial Intelligence Open Innovation Value-added tax Digital Transformation process automation ERP Data Analytics Business Intelligence

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Evaluation of Intermodal Transport Routes: Environment and Biodiversity Perspectives 5th

Evaluation of Intermodal Transport Routes: Environment and B...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Krstic, Mladen Tadic, Snezana Agnusdei, Leonardo Miglietta, Pier Paolo Porrini, Donatella Univ Salento Dept Econ Sci Via Monteroni snc I-73100 Lecce Italy Univ Belgrade Fac Transport & Traff Engn Logist Dept Vojvode Stepe 305 Belgrade 11000 Serbia Natl Biodivers Future Ctr NBFC I-90133 Palermo Italy Univ Salento Dept Innovat Engn Via Monteroni snc I-73100 Lecce Italy Univ Salento Dept Biol & Environm Sci & Technol Via Monteroni snc I-73100 Lecce Italy

ISBN: (纸本)9783031807749;9783031807756

Intermodal transportation integrates multiple modes of transport, enhancing logistics efficiency and reducing environmental footprints. It alleviates road congestion, reduces greenhouse gas emissions, and supports sustainable economic growth. One of the main issues in intermodal transport planning is the selection of adequate routes, especially in terms of their impact on the environment. Strategic route planning can mitigate transportation impacts on biodiversity and ecosystems, preventing habitat fragmentation and wildlife disruption. Thus, incorporating ecological considerations in transport planning is vital for sustainability. This paper aims to define a framework for evaluating and ranking intermodal transport routes based on their environmental and biodiversity impacts, promoting resilient and environmentally responsible transportation systems. For solving the defined problem this paper proposes a geographic information system (GIS)-based multi-criteria decision-making (MCDM) framework. It employs a Comprehensive distance-based Ranking (COBRA) MCDM method that uses GIS-based input data to evaluate alternative intermodal transport routes and to rank them. The proposed framework is tested on a real-life case study, i.e., on evaluating and comparing intermodal transport routes between North European ports and North Italy intermodal terminals, performing on two Trans-European Transport Network (TEN-T) corridors. The results indicate that the Scandinavian-Mediterranean corridor between the port of Hamburg and the intermodal terminal Verona Quadrante Europa is more favorable regarding environmental impact and biodiversity protection. The main contributions of the study include the development of a framework for route evaluation and the establishment of criteria that can be used for evaluating and ranking routes.

关键词： Intermodal transport Route selection Geographic information system (GIS) Multi-criteria decision-making (MCDM) Comprehensive distance-based ranking (COBRA)

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Research on Ethical Issues of Classroom Dialogue in the Era of Large Language Model 7th

Research on Ethical Issues of Classroom Dialogue in the Era ...

引用

8th Asia Pacific Web and Web-Age information Management Joint International Conference on Web and Big Data

作者： Ling, Jianxia Zhu, Jia Shi, Jianyang De Meo, Pasquale Zhejiang Normal Univ Coll Educ Jinhua Zhejiang Peoples R China Univ Messina Dept Ancient & Modern Civilizat Messina Italy

ISBN: (纸本)9789819600540;9789819600557

As society moves towards the intelligence era, the large language model ChatGPT has garnered widespread attention, particularly regarding its applications in education and the ethical issues it raises. This study examines the ethical dimensions of classroom dialogue for novice and expert teachers using a method that combines human verification with ChatGPT's judgment. The content of classroom dialogue from expert teachers was used as prompts for ChatGPT to predict and generate subsequent classroom dialogue, which was then compared with those of the expert teachers. Results show that expert teachers exhibit significantly higher ethical levels in classroom dialogue compared to novice teachers. While there are differences in the characteristics of dialogue content predicted by ChatGPT and those of expert teachers, there is no significant difference in ethical levels between the two types of dialogue.

关键词： ChatGPT Classroom dialogue Ethics Expert teachers Novice teachers

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Optimization of Resource Allocation and Distribution in Industrial Supply Chains and Logistics Networks Using Genetic Algorithm and Game Theory 5th

Optimization of Resource Allocation and Distribution in Indu...

引用

5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Giri, Chandadevi Harale, Nitin Univ Boras Allegatan 1 S-50332 Boras Sweden

ISBN: (纸本)9783031807749;9783031807756

Efficient resource allocation and distribution are essential for optimizing industrial supply chains and logistics networks. This paper presents a novel hybrid approach that combines genetic algorithm (GA) with game-theoretic analysis to tackle the complex, multi-objective challenges inherent in these systems. The proposed framework focuses on minimizing transportation costs and carbon emissions while optimizing routing from source locations through intermediate facilities to final demand points. The genetic algorithm effectively navigates the search space to identify near-optimal solutions, while the game-theoretic analysis models strategic interactions among stakeholders, promoting stable and equitable resource allocation. The proposed approach exhibits advantages as it enhances cost efficiency and reduces emissions. The results provide the practical implications for improving the efficiency and sustainability of logistics operations across various industries. This study contributes to the advancement of resilient and adaptive supply chains by offering a comprehensive optimization framework that leverages the strengths of both genetic algorithms and game theory.

关键词： Resource allocation Distribution Logistics network optimization Genetic algorithm Game theory

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"Bibliometric Analysis of Possibilities, Legal and Technical Prerequisites for Establishing and Operating an International Freight-Logistic Hub" 5th

"Bibliometric Analysis of Possibilities, Legal and Technical...

引用

5th 5th Olympus - International Conference on Supply Chain

作者： Sofia, Mavridou Aristotelis, Mavidis Dimitrios, Folinas Int Hellen Univ Panagioti Kanelopoulou 2 Katerini 60100 Greece

ISBN: (纸本)9783031693502;9783031693519

This study presents a comprehensive bibliometric analysis exploring the multifaceted aspects of establishing and operating an international freight logistics hub. Our research methodology encompasses four core components: network analysis, co-occurrence analysis, thematic mapping, and conceptual structure examination. This paper presents a comprehensive bibliometric analysis to explore the possibilities, legal, and technical prerequisites for establishing and operating an International Freight-Logistic Hub. Drawing on data from 2003 to 2023, the study analyses 241 documents from various sources, indicating a substantial and growing body of research in this field. The findings highlight a robust annual growth rate of 13.46% in scholarly output, significant international collaboration (21.16% of documents) and a diverse authorship base of 646 contributors. Key methodologies employed include network analysis, thematic mapping, co-occurrence analysis, and conceptual structure mapping using the Biblioshiny tool. The network analysis reveals influential authors and research clusters essential for policy engagement and strategic collaborations. Thematic mapping identifies crucial areas such as "containers," "port terminals," and "railroad yards and terminals" as Motor Themes, indicating their centrality and extensive research in hub operations. Co-occurrence analysis underscores the importance of supportive legal frameworks and technological advancements. At the same time, conceptual structure mapping highlights the role of specific technical aspects and geographical considerations in logistic hub operations. Our findings elucidate the critical legal and technical prerequisites for establishing a thriving hub and potential challenges and opportunities. The study provides valuable insights for policymakers and practitioners, emphasising the need to foster advanced technologies, comprehensive legal regulations, and international collaborations to enhance the efficiency and e

关键词： International Freight Logistics Bibliometric Analysis Legal and Technical Prerequisites Network Analysis Thematic Mapping

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Automatic Inspection of Static Application Security Testing (SAST) Reports via Large Language Model Reasoning 4th

Automatic Inspection of Static Application Security Testing ...

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4th International Conference on Artificial Intelligence Logic and Applications

作者： Li, Kaixuan Liu, Han Zhang, Lyuye Chen, Yixiang East China Normal Univ Shanghai Key Lab Trustworthy Comp Shanghai Peoples R China Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9789819603534;9789819603541

Static Application Security Testing (SAST) tools are widely employed to detect bugs and vulnerabilities in software due to their extensive coverage and independence from their execution environment. However, these tools often produce numerous false positives, necessitating manual inspection and confirmation by developers, which is both arduous and time-consuming. Large Language Models (LLMs) have exhibited superior code semantic understanding, presenting an opportunity to effectively act as human experts in analyzing source code and SAST reports. This paper introduces a versatile and easily extensible approach that leverages LLM reasoning to automatically inspect extensive SAST reports. We developed a GPT-based prototype, named FPShield, to automatically identify and eliminate potential false positives in SAST reports. Our experiments, conducted on the smartbugs-curated dataset containing 207 vulnerabilities across 31 Solidity smart contracts, demonstrate the practical effectiveness of our approach. By integrating FPShield with the prominent SAST tool Slither, we improved its precision from 9.43% to 52.11% and its F1 score from 13.41% to 26.62%. Moreover, FPShield reduced the number of issues reported by Slither by approximately 86%, decreasing from 509 to 71. Our research presents a novel perspective on harnessing the capabilities of LLMs to markedly improve the precision of SAST tools and significantly reduce the manual effort required by developers.

关键词： SAST LLM Vulnerability detection

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Pedestrian Fall Detection Algorithm Based on Improved YOLOv7 5th

Pedestrian Fall Detection Algorithm Based on Improved YOLOv7

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Wang, Fei Zhang, Yunchu Zhang, Xinyi Liu, Yiming Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Peoples R China Shandong Key Lab Intelligent Bldg Technol Jinan 250101 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

To address the problems of light interference, target occlusion, wide range of target scale variation and uneven quality of training images during fall, a fall recognition algorithm based on an improved YOLOv7 model is presented in this paper. By incorporating Coordinate Attention mechanism in Backbone, the system pays attention to the location information of the fall target. And the InceptionNeXt module is employed, which aims to maximise the feature information extraction, to enhance the accuracy of target detection at different scales and improve the performance when there are light interference and occlusion situations in the environment. In addition, the loss is replaced with Wise-IoU, which is applied to address the negative impact of low-quality images on the model performance and obtain more accurate findings. The findings of the experiments indicate that the improved models mAP0.5 and mAP0.5:0.95 in this paper improve by 1.7% and 6.4%, respectively, compared to the original YOLOv7 model. Therefore, the model presented in this paper has an advanced performance in the fall detection task.

关键词： YOLOv7 pedestrian fall detection attention mechanism InceptionNeXt Wise-IoU Transfer Learning

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