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

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A Hybrid Algorithm with Fuzzy Logic for Population Diversity Maintenance of Genetic Algorithm 4th

A Hybrid Algorithm with Fuzzy Logic for Population Diversity...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Srivaishnavi, J. V. Kumar, J. Krishna Thangavelu, S. Amrita Vishwa Vidyapeetham Dept Comp Sci & Engn Amrita Sch Comp Coimbatore Tamil Nadu India Aarini Consulting Amsterdam North Holland Netherlands

ISBN: (纸本)9783031837821;9783031837838

Computational intelligence (CI) algorithms are designed to derive the biological behavior of various natural species. The evolving, behavioral, and adapting nature of the species are taken as the basis for the components of CI viz., Evolutionary Algorithms (EAs), Fuzzy Logic (FL), and Neural Networks (NN). The EAs, FL, and NN have their algorithmic structure, merits, and demerits. Hybridization is the most open research problem, as the hybridized versions of the algorithms could balance the merits and demerits of the constituent algorithms and show improved performance. The study presented in this paper is an attempt towards the hybridization of an EA called Genetic Algorithm (GA) and FL. Initially, various existing approaches to hybridizing GA and FL are explored. This led to the identification of balancing the population diversity of GA by a Fuzzy Logic Inference System (FLIS) as a research problem. This paper proposes a HyAGaFlis (Hybrid Algorithm of GA and FLIS) algorithm which adapts its crossover and mutation probabilities using a FLIS, to maintain the population diversity to reach the global optimum value without trapping in local optimum value(s). The performance of the classical GA and the HyAGaFlis algorithms are compared on a multimodal benchmarking function. The comparative study revealed that the proposed HyAGaFlis outperforms the classical GA in reaching the global optimum without loss of population diversity. The proposed algorithm is useful to practitioners and scientists who intend to use GA to solve their optimization problems, with less effort for GA's parameter tuning. The novelty of the HyAGaFlis algorithm lies in the way how it controls the population diversity during the evolution process of GA.

关键词： Genetic Algorithm (GA) Fuzzy Logic (FL) Population diversity Mamdani fuzzy system Local optimum Global optimum

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Attentive A* for Visual Cue Based Path Planning in Complex Environments 4th

Attentive A* for Visual Cue Based Path Planning in Complex E...

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4th Workshops on Agents and Robots for Reliable Engineered Autonomy

作者： Kumar, Abhay Verma, Kunal Garg, Armaan Jha, Shashi Shekhar Indian Inst Technol Ropar Rupnagar Punjab India

ISBN: (纸本)9783031731792;9783031731808

Navigating through complex environments has always been a big challenge for smart robots and AI agents. This paper focuses on a specific problem within the domain of path planning, aiming to aid agents in navigating through intricate environments using raw images as input. Data-driven path-planning algorithms have emerged as a cogent solution to such problems. These algorithms are a fusion of deep learning models for cost approximation, and differentiable versions of classical planning algorithms. While these methods excel at handling combinatorial data, their computational demands are high. These often leads to poor function approximation due to ineffective feature extraction in complex environments. Drawing from the significance of the attention mechanism in various deep learning applications, we present Attentive A*, introducing attention to data-driven path-planning algorithms. The proposed approach provides precise cost approximations by leveraging visual cues, resulting in enhanced performance across various metrics. A comprehensive assessment is conducted on diverse datasets, including MP, Tiled MP, CSM and Warcraft, to assess the effectiveness of the proposed approach compared to other baselines.

关键词： Path Planning Attention mechanism Raw Images

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Evaluation of Computational and Power Performance in Matrix Multiplication Libraries - MKL Vs cuBLAS 11th

Evaluation of Computational and Power Performance in Matrix ...

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11th Latin American Conference on High Performance Computing

作者： Torres, L. A. Barrios, Carlos J. H. Denneulin, Yves Grupo Invest Computo Avanzado Gran Escala CAGE Supercomputac Calculo Cientifico SC3UIS Bucaramanga 680002 Colombia Lab Informat Grenoble 700 Ave Centrale F-38400 Grenoble France Inst Natl Rech Informat & Automat INRIA Le Chesnay Rocquencourt France Collaborat France Colombie Technol Avancees IInfo Le Chesnay Rocquencourt France

ISBN: (纸本)9783031800832;9783031800849

Matrix multiplication is one of the most essential linear algebra operations in many scientific applications requiring HPC. The matrix multiplication operation has been highly optimized and parallelized thanks to research for more than two decades. Libraries like Intel's MKL or NVIDIA's cuBLAS implemented new and optimized matrix multiplication techniques that increase performance and reduce computational cost. The study compares the execution times, performance, accuracy, and power consumption of these libraries. Power consumption was measured using PAPI and PERF. The hardware used in the experiments has different architectures, such as third- and fourth-generation Intel processors and NVIDIA V100 and NVIDIA A100 GPUs. Intel's MKL library results showed better execution times and higher performance under certain conditions than NVIDIA's cuBLAS library. On the other hand, cuBLAS with Tensor Cores yielded better execution times than without their use, but at the cost of precision. The data obtained from different architectures indicated that the MKL library, with specific processor architectures, can achieve performance close to or surpass that the GPU provided, albeit with higher power consumption. These results are contingent upon specific hardware specifications, such as the number of cores, clock frequency, processor generation, PCI bus speed and bandwidth, and GPU architecture (compute capability).

关键词： Matrix Multiplication MKL cuBLAS Power consumption Performance

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PayLater Adoption in Payment Transactions 4th

PayLater Adoption in Payment Transactions

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Surwanti, Arni Nurazizah, Alizura Nabila Univ Muhammadiyah Yogyakarta Kasihan Indonesia

ISBN: (纸本)9783031840777;9783031840784

This study analyzes the influence of Social Influence, Hedonic Motivation, Habit, and Financial Literacy on PayLater Adoption. This study used primary data, with non-probability sampling: purposive sampling methods techniques. The number of samples used in this study are 200 respondents. Data collection is carried out through the distribution of questionnaires using google form. The data analysis used is Structural Equation Modeling (SEM) using Smart PLS 4 software. The results of this study show that Social Influence, Hedonic Motivation, and Habit have a positive and significant effect on PayLater Adoption. Financial literacy had a positive insignificant effect on PayLater adoption. Based on the study's findings, researchers make recommendations to the government, academic institutions, and other interested parties on how to raise public knowledge of the advantages and drawbacks of PayLater use in order to prevent detrimental effects on community welfare.

关键词： Social Influence Hedonic Motivation Habit Financial Literacy PayLater Adoption

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Pre-training a Transformer-Based Generative Model Using a Small Sepedi Dataset 5th

Pre-training a Transformer-Based Generative Model Using a Sm...

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5th Southern African Conference for Artificial Intelligence Research

作者： Ramalepe, Simon Phetole Modipa, Thipe I. Davel, Marelie H. Univ Limpopo Dept Comp Sci Polokwane South Africa Northwest Univ Fac Engn Potchefstroom South Africa Ctr Artificial Intelligence Res Pretoria South Africa Natl Inst Theoret & Computat Sci Stellenbosch South Africa

ISBN: (纸本)9783031782541;9783031782558

Due to the scarcity of data in low-resourced languages, the development of language models for these languages has been very slow. Currently, pre-trained language models have gained popularity in natural language processing, especially, in developing domain-specific models for low-resourced languages. In this study, we experiment with the impact of using occlusion-based techniques when training a language model for a text generation task. We curate 2 new datasets, the Sepedi monolingual (SepMono) dataset from several South African resources and the Sepedi radio news (SepNews) dataset from the radio news domain. We use the SepMono dataset to pre-train transformer-based models using the occlusion and non-occlusion pre-training techniques and compare performance. The SepNews dataset is specifically used for fine-tuning. Our results show that the non-occlusion models perform better compared to the occlusion-based models when measuring validation loss and perplexity. However, analysis of the generated text using the BLEU score metric, which measures the quality of the generated text, shows a slightly higher BLEU score for the occlusion-based models compared to the non-occlusion models.

关键词： Transformers Text generation Pre-training Occlusion-based training Datasets

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Layout Optimization Strategy Based on Three-Stage Cutting Pattern 5th

Layout Optimization Strategy Based on Three-Stage Cutting Pa...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Li Jiaojiao Hou Zeqiang Tan Diaoyin Liu Xin Peng Jiawu Natl Univ Def Technol Natl Key Lab Informat Syst Engn Changsha Peoples R China Beijing Res Ctr Space Informat Relay Transmiss Te Beijing Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China

ISBN: (纸本)9783031807749;9783031807756

The popularity of "personalized customization" has put forward higher requirements for industrial production, and how to achieve mass customization production has become one of the main problems plaguing manufacturing enterprises. This paper focuses on the layout optimization problem of square parts, and adopts a three-stage 2D rectangular cutting method, i.e., following the cutting idea of "sheet-strip-stack-item". To solve this problem, we develop a mixed-integer linear programming model with the optimization objective of using as few square sheets as possible while satisfying the order demand and related constraints. Afterwards, we adopt the CPLEX solver to invoke the branch-and-bound algorithm package to solve the model. The experimental results show that the method can accomplish the scheduling of all the orders in the given four datasets and obtain a satisfactory solution within 30 min. The utilization rate of sheets in all datasets reaches more than 85%, proving that the method can utilize raw materials more efficiently, and achieve good economic benefits.

关键词： Scheduling optimization Rectangular cuts Mixed-integer programming model Branch-and-cut

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Enhancing and Analyzing Log Generation for Collaborative Problem-Solving Activities: Video Analysis and OCR Techniques 17th

Enhancing and Analyzing Log Generation for Collaborative Pro...

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17th India Compute Conference-COMPUTE-Annual

作者： Jadhav, Suprabha Jain, Parth Raith, Harjot Singh Arya, Kavi Iyer, Sridhar Indian Inst Technol Educ Technol Mumbai Maharashtra India Atharva Coll Engn Informat Technol Malad India NMIMS Artificial Intelligence & Data Sci Navi Mumbai India ITT Bombay Comp Sci & Engn Mumbai Maharashtra India

ISBN: (纸本)9783031843907;9783031843914

This study explores the use of video analysis and Optical Character Recognition (OCR) to generate accurate logs for tracking user search behaviors for external resources during collaborative problem-solving activities on the RoboReady educational platform. The research included 15 teams of engineering students who interacted with a website designed around the PISA 2015 collaborative problem-solving framework. Video recordings of the users screens were analyzed using image processing and OCR to extract URLs and timestamps of visited web pages. The study compares these logs with those collected through server-side tracking, noting the limitations of the latter in capturing external resource usage. The methodology includes frame extraction, URL isolation, and text recognition using Tesseract OCR and Google Cloud Vision API. Challenges like noisy OCR output and the necessity for manual verification are addressed. The generated logs provide insights into users navigation and resource usage. However, limitations such as video quality dependency, scalability issues, and the need for manual intervention are also discussed. This research enhances the understanding of users behavior in online learning environments and offers methodological insights for studying digital learning interactions.

关键词： Collaborative Problem-Solving Educational Data Mining Optical Character Recognition Video Analysis User Behavior Tracking

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Trilinear Distillation Learning and Question Feature Capturing for Medical Visual Question Answering 5th

Trilinear Distillation Learning and Question Feature Capturi...

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

作者： Long, Shaopei Li, Yong Weng, Heng Tang, Buzhou Wang, Fu Lee Hao, Tianyong South China Normal Univ Sch Comp Sci Guangzhou Peoples R China Guangzhou Univ Chinese Med Affiliated Hosp 2 Guangzhou Peoples R China Harbin Inst Technol Dept Comp Sci Shenzhen Grad Sch Shenzhen Guangdong Peoples R China Hong Kong Metropolitan Univ Sch Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Medical Visual Question Answering (Med-VQA) targets at answering a clinical question associated with a corresponding medical image. Med-VQA has revealed huge potential in the field of medicine, but it still faces many challenges in practice. Existing Med-VQA models have not fully utilized medical answer features, though Compact Trilinear Interaction (CTI) model has proven that the answer has close correlations with question and image. However, directly applying existing CTI model in general domain on the small volume of Med-VQA datasets would lead to over-fitting and obtain poor performance. Therefore, this paper proposes a novel trilinear distillation learning framework called TDL for MedVQA to learn correlations between medical answer, image and question from the trilinear model by distillation learning. In addition, considering the clinical questions are harder to understand due to the professionalism of medical scenarios, we design a question feature capturing (QFC) module to capture the fine-grained intramodality relationships and characteristics of clinical questions. Furthermore, we take account of the unbalanced-labels of Med-VQA datasets, and propose a novel Label Smoothing Regularization Focal Loss to enhance the generalization capability of the model while dynamically adjust the sample weights during training. Based on the standard benchmark dataset VQA-RAD, our proposed TDL model achieves the state-of-the-art performance, including the best overall Accuracy 76.71%, Precision 83.78%, Recall 76.71% and F1-score 78.94%.

关键词： Medical Visual Question Answering Trilinear Distillation Learning Question Feature Capturing

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Dental Caries Segmentation from Orthopantomograms Using Optimized DeepLabV3+ 5th

Dental Caries Segmentation from Orthopantomograms Using Opti...

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5th International Symposium on Intelligent Computing Systems

作者： Neeraja, R. Anbarasi, L. Jani Geetha, S. Revanth, Gadhamsetty Prasad, Prudhvi Raj, Benson Edwin Mohan, Geetha Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India Higher Coll Technol Dept Comp Informat Sci Fujairah U Arab Emirates Inst Appl Technol Comp Sci Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031829307;9783031829314

Orthopantomograms are essential tools for dentists in analyzing dental conditions like dental caries. Typically, dentists diagnose cavities by manually examining these images. However, manual detection can sometimes miss cavities due to factors such as negligence from excessive workloads and lack of expertise. Hence, the demand arises for computer-based smart vision technologies that employ machine learning, image processing approaches and artificial intelligence techniques to tackle these challenges. This study leverages an advanced deep CNN based DeepLabV3+ techniques such as atrous convolutions and encoder-decoder architectures to capture rich contextual information and fine-grained spatial details from orthopantomograms (OPGs). The encoder-decoder architecture enhances the ability to capture minute details in the segmentation of tooth caries, by capturing multi-scale context with multiple parallel filters employing different atrous rates, enabling the model to consider features at various scales simultaneously. The OPGs were obtained from the Nanded Rural Dental College & Research Centre in Maharashtra. A total of 153 OPGs selected and processed by an experienced dentist to identify the caries regions in the original images. The mask dataset is generated by extracting the caries region for the segmentation process. Various augmentation approaches such as zooming, rotation, horizontal and vertical flipping were applied to expand the dataset and achieved improved segmentation results. The results indicate that the model is highly effective in diagnosing dental caries, achieving an accuracy score of 99.2%, IoU score as 99.3% and Dice coefficient as 99.68%, respectively.

关键词： Orthopantomograms Tooth Caries DeepLabV3+ Segmentation

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CyMed: A Framework for Testing Connected Medical Devices 4th

CyMed: A Framework for Testing Connected Medical Devices

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4th International Conference on Society 5.0

作者： Scherb, Christopher Hadayah, Adrian Heitz, Luc Bryan Grieder, Hermann Asprion, Petra Maria Univ Appl Sci & Arts Northwestern Switzerland Windisch Switzerland

ISBN: (纸本)9783031714115;9783031714122

Connected Medical Devices (CMDs) significantly benefit patients but are also vulnerable to malfunctions that can harm. Despite strict safety regulations for market entry, there's a notable shortage of specific cybersecurity frameworks for CMDs. Existing regulations on cybersecurity practices are often broad and lack detailed implementation steps. This paper introduces the CyMed framework, designed for vendors and end-users, offering explicit strategies to enhance the cybersecurity of CMDs. The effectiveness of CyMed is assessed through practical testing and expert interviews.

关键词： Software Weaknesses Fuzzing Symbolic Execution Medical Devices Connected Medical Devices

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