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

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Human-Robot Interaction Based on Hand Gesture Detection Using YOLO Algorithm 4th

Human-Robot Interaction Based on Hand Gesture Detection Usin...

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

作者： Garcia, Ivan Moya, Viviana Pilco, Andrea Vilcapoma, Piero Guevara, Leonardo Guaman-Rivera, Robert Menendez, Oswaldo Pablo Vasconez, Juan Univ Andres Bello Energy Transformat Ctr Fac Engn Santiago 7500971 Chile Univ Int Ecuador UIDE Fac Ciencias Tecn Quito 170411 Ecuador Univ Lincoln Lincoln Inst Agri Food Technol Lincoln LN2 2LG England Univ OHiggins Inst Engn Sci Rancagua 2820000 Chile Univ Catolica Norte Dept Ingn Sistemas & Computac Antofagasta 1249004 Chile

ISBN: (纸本)9783031832093;9783031832109

Recognizing human intentions to make robots able to collaborate with humans has been a high-interest research topic in recent years. In particular, non-verbal communication methods can provide useful approaches to reach a fluid and natural interaction between humans and robots. This article investigates the role of hand gesture recognition in human-robot interaction (HRI) scenarios, in which a human communicates with a robot using hand gestures. In particular, we emphasize the training and calibration of YOLOv2 (You Only Look Once) for real-time hand gesture recognition. We use YOLOv2 to detect five distinct hand gestures (index finger, full open hand, fist, thumb up, and peace sign). Then, we program 6 degrees C of freedom (DoF) manipulator educational robot to perform a specific task based on the recognized gesture. We explore the creation of user-specific models, which achieve a mean average accuracy of up to 99.02%, and user-general models, with a mean average accuracy of up to 90.52% respectively. The outcomes of this research could significantly influence the HRI field and herald new developments enhancing the efficiency and fluidity of human-robot non-verbal communication.

关键词： Human-robot interaction Non-verbal communication Hand Gesture Recognition Robot manipulator YOLO

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DeepMatch: A BERT-Powered Talent Matchmaking Approach 4th

DeepMatch: A BERT-Powered Talent Matchmaking Approach

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

作者： Gozuacik, Necip Topaloglu, Atakan Evren, Ayse Mine Karakus, Serkan Siemens Advanta R&D Ctr Istanbul Turkiye Istanbul Hlth & Technol Univ Fac Engn & Nat Sci Software Engn Istanbul Turkiye

ISBN: (纸本)9783031821493;9783031821509

Consultancy companies aim to match their employees to customer projects based on their employee's talents. Traditional matchmaking methodologies are founded on manual processes that rely on rules of thumb or algorithms that are based on handcrafted heuristics, which cause the matchings to be not only sub-optimal, but also time-consuming, subjective, and prone to human errors. In this paper, we propose a novel consultancy matching algorithm that utilizes BERT to semantically find the most optimal consultant-project matchings for a given set of consultants and projects, pairing relevant project specifications with consultant specifications using the JVSAP algorithm. In doing so, our proposed talent matchmaking system may be utilized to improve the accuracy and efficiency of consultancy matching, thereby facilitating more effective consultancy engagements. Our findings suggest that the pairings demonstrate a discernible alignment with human intuition, as evidenced by the consistent correlation between consultants possessing domain-specific expertise and projects characterized by corresponding thematic descriptions.

关键词： Deep Learning Jonker-Volgenant Algorithm Natural Language Processing Talent Management Text Similarity

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A Multi-objective Clustering Algorithm Integrating Intra-Clustering and Inter-Clustering Measures 7th

A Multi-objective Clustering Algorithm Integrating Intra-Clu...

引用

7th International Conference on Optimization and Learning

作者： Azevedo, Beatriz Flamia Rocha, Ana Maria A. C. Pereira, Ana I. Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Braganca Portugal Univ Minho ALGORITMI Res Ctr LASI Campus Gualtar P-4710057 Braga Portugal Inst Politecn Braganca Lab Assoc Sustentabilidade & Tecnol SusTEC P-5300253 Braganca Portugal

ISBN: (纸本)9783031779404;9783031779411

This study delves into bio-inspired approaches and clustering methodologies to introduce an automated clustering algorithm named Multi-objective Clustering Algorithm (MCA). Using multi-objective strategies and several combination measures, this method calculates the optimal number of clusters and element partitioning by minimizing intra-clustering measures and maximizing inter-clustering ones. Through experimentation on three benchmark datasets, the results highlight the success of the MCA in obtaining a set of optimal solutions (Hybrid Pareto front) through the integration of multi-objective strategies and clustering measures. Moreover, the Dunn clustering validity index is used to support the decision maker in selecting the optimal solution among the ones presented in the Hybrid Pareto front. This approach allows decision-makers to choose the most suitable solution by incorporating additional insights beyond the model.

关键词： Multi-objective bio-inspired methods partitioning-clustering

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A Non-blind Audio Watermarking in 3D Mesh Models Using Daubechies Wavelet Technique 5th

A Non-blind Audio Watermarking in 3D Mesh Models Using Daube...

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5th International Symposium on Intelligent Computing Systems

作者： Sharine, G. Julie Sai, S. D. Aditya Anbarasi, L. Jani Raj, Benson Edwin Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India Higher Coll Technol Dept Comp Informat Sci Fujairah U Arab Emirates

ISBN: (纸本)9783031829307;9783031829314

Digital watermarking is a copyright protection technique that helps to identify the ownership of the data through a process of hiding digital data. Watermarking in 3D mesh models includes challenges like robustness against attacks and imperceptibility. Embedding Audio in 3D mesh model is used for protecting intellectual property rights and adhering the authenticity of the 3D models. A watermarking process using audio in 3D mesh models is proposed which embeds imperceptible audio watermarks into the mesh while protecting the visual quality of the model. 3D mesh models are pre-processed using Principal Component Analysis to attain normalized model vertices which helps in standardizing the data and improving the efficiency of subsequent watermark embedding. This pre-processed 3D mesh model is transformed into the discrete wavelet transform using Daubechies wavelets and an audio watermark is embedded into the transformed vertices of the 3D mesh model. The watermark is extracted from the watermarked model by performing the Inverse DWT and reconstruction of the audio file is performed to enhance the authentication process thus reducing the risk of false positives or negatives. The extracted watermark and original watermark are compared to assess the effectiveness of the proposed work by evaluating the robustness and imperceptibility. The proposed audio watermarking scheme was evaluated using metrics like PSNR and Hausdorff distance yielding approximately 43 dB and 3.75 x 10(-5) respectively.

关键词： 3D Watermarking 3D mesh models Audio signal Imperceptibility

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LLM for Uniform information Extraction Using Multi-task Learning Optimization 7th

LLM for Uniform Information Extraction Using Multi-task Lear...

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

作者： Li, Ying Tan, Zhen Xiao, Weidong Natl Univ Def Technol Natl Key Lab Informat Syst Engn Kaifu Peoples R China

ISBN: (纸本)9789819600540;9789819600557

With the increasing maturity of deep learning technology, large language models have shown excellent performance in the field of natural language processing, but the performance of information extraction is yet to be further improved. In this paper, we use 7B Llama-2 as a base model training to obtain a large language model capable of using natural language to guide information extraction tasks, which can solve the challenges faced by traditional information extraction methods, and also innovatively use multi-task learning optimization to improve the performance of the large model. We performed large-scale pretraining and instruction tuning on the big model LLama-2. Based on the highquality and richly typed training data automatically constructed by ChatGPT, remote supervision, and other algorithms, which contains a total of 1 million entities, relations, and events, we designed corresponding English templates for instruction tuning. Second, we performed supervised fine-tuning of the model using manually labeled high-quality training sets. To further improve the model performance, we innovatively adopt a multi-task learning optimization strategyGradNorm, which can dynamically adjust the weights of different tasks, thus balancing the losses among tasks during the training process and reducing the overall training loss. After information extraction experiments, our model is compared with other models to test the performance of uniform information extraction for large models, and the experimental results show that our model performs well.

关键词： Large Language Model Uniform information Extraction Instruction Fine-tuning Multi-task Learning Optimization

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Average Merging and Prompting Training for Policy Generalization 5th

Average Merging and Prompting Training for Policy Generaliza...

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5th China Intelligent Robotics Academic Conference

作者： Zhao, Dongdong Yao, Song Yan, Shi Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China

ISBN: (纸本)9789819616138;9789819616145

Recent research has demonstrated that the flexible creation of generic policies can be achieved by merging multiple task-specific, individually trained policies, although this approach has been limited to tasks within the distribution. The present study investigates whether a prompt-based transformer architecture can enable merged multi-task models to generalize to unseen tasks. In this study, multi-task models are obtained without the requirement for centralized training by averaging and merging the attention layer parameters of single models trained on three different tasks. These models are then fine-tuned based on the prompting trajectories of the test tasks. Additionally, a language pretraining model is introduced as an initialization during the individual model training phase. Experiments conducted in four environments of Mujoco and Meta-World demonstrate that the merge-fine-tuning model performs at least as well as the single-task model in unseen test tasks while significantly reducing the number of convergence steps. This work facilitates the extension of generic models to out-of-distribution environments and provides a novel approach for building generic strategies capable of generalizing to unseen tasks.

关键词： policy generalization average merging prompt-based training

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Image Segmentation Network Based on Convolutional Layers, a Clustering Layer and Cluster Classification 37th

Image Segmentation Network Based on Convolutional Layers, a ...

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37th International Conference on computer Applications in Industry and Engineering

作者： Rosas-Romero, Roberto Univ Amer Puebla San Andres Cholula Puebl Mexico

ISBN: (纸本)9783031762727;9783031762734

This work contributes to the detection of regions of interest on images and their corresponding classification in medical imaging applications by introducing an image segmentation network that consists of four stages. In the first stage, multi-resolution processing is applied to outline regions where further segmentation and classification are to be conducted. Subsequently, a quad-tree division stage followed by a clustering stage deliver an image that is divided into unlabeled clusters. The output stage assigns each cluster to one class. This architecture offers flexibility in the input and output stages since this network can be fed with images of any size and the output stage can be implemented with any traditional classification model such as k-nearest neighbors, multi-layer perceptron, and support vector machine. Another contribution of this work is that this network does not rely on a very large number of annotated images for its training.

关键词： Image segmentation Convolutional Neural Networks Multi-Resolution Analysis Clustering

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Evaluating Cloud-Native Deployment Options with a Focus on Reliability Aspects 18th

Evaluating Cloud-Native Deployment Options with a Focus on R...

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18th Symposium and Summer School on Service Oriented Computing

作者： Knoch, Franka Lichtenthaeler, Robin Wirtz, Guido Univ Bamberg Distributed Syst Grp Bamberg Germany

ISBN: (纸本)9783031725777;9783031725784

For deploying applications in the cloud various options exist that can be compared based on their impacts on different quality attributes. In this work, we evaluate such deployment options with a focus on the quality aspect of reliability. More specifically, we consider characteristics relevant to reliability in the context of so-called cloud-native applications. The chosen characteristics stem from a quality model for cloud-native applications. By implementing different options to realize these characteristics in an exemplary application on AWS, we make them comparable. This evaluation is then used to extend the modeling approach for the quality model so that the different implementation options can be represented and evaluated using the quality model. Our contribution therefore is an investigation of deployment options for cloud-native applications considering reliability, as well as its transfer into an existing quality evaluation approach for further experimentation.

关键词： cloud-native cloud deployment reliability evaluation

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Competence of Teachers and Ethical Aspects of Implementing AI Technologies in Education

Competence of Teachers and Ethical Aspects of Implementing A...

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

作者： Iryna, Vorotnykova Borys Grinchenko Kyiv Metropolitan Univ Kyiv Ukraine

ISBN: (纸本)9783031834318;9783031834325

The integration of artificial intelligence (AI) technologies into education presents both significant opportunities and challenges for educators. For AI to be effectively and ethically implemented in the educational environment, teachers must possess the necessary competencies and understanding of the ethical implications associated with these technologies. The article examines critical aspects of teacher competence and ethical aspects that must be paid attention to for the successful implementation of AI in education. Using data from a survey conducted among 102 participants, we analyse the distribution of teachers' qualification levels and their views on ethical challenges, advantages and disadvantages arising from the use of AI in education and teachers' readiness to use the new technology (opinions on how AI will improve their work, how easy the use of AI, what is the general attitude and intention to use technology in the future and what is the organisational support and individual characteristics of the teachers themselves). The results show that the majority of teachers have the highest qualifications an average level of digital competence and a low level of AI literacy, which indicates the need to improve the skills of teachers in the implementation of AI, the self-assessment by teachers of the ethics of using AI is higher than the assessment of understanding, knowledge and consideration in the practice of ethical norms It is important to plan and develop critical competencies for the effective use of AI in education, ensuring its safe and ethical implementation based on technological, pedagogical training and the formation of ethical literacy.

关键词： Artificial Intelligence Teacher Competencies AI Competence Digital Competence Ethics Of Using AI

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The Application of Imaging Methods and Machine Learning in the Agroindustry Sector at Production Activity 4th

The Application of Imaging Methods and Machine Learning in t...

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

作者： Vardasca, Ricardo Pratas, Antonio Tereso, Marco Bento, Fernando ISLA Santarem Santarem Portugal Univ Porto LAETA INEGI Porto Portugal Univ Evora VISTA Lab ALGORITMI Evora Portugal ISCTE IUL ISTAR Lisbon Portugal

ISBN: (纸本)9783031832093;9783031832109

The agricultural production sector is of great interest to national economies, their GDP and the population's food chain. During digitalization and modernization, imaging techniques and machine learning are important driving factors. In this research, a narrative critical literature review was conducted on the most used imaging modalities and their use with machine learning methods, which are examined along with their applications in the agricultural industry in production activities, as well as their limitations and existing challenges. This research is intended to support a further development of a technological framework for intelligent precision agroindustry production. It was found that the most used imaging methods are hyperspectral and multispectral imaging, infrared thermal imaging, magnetic resonance imaging, X-ray imaging, scanning electron microscopy and ultraviolet imaging. The majority of employments of machine learning along with imaging was using supervised learning algorithms, there were a few applications using unsupervised and reinforcement learning algorithms. From the results and analysis, it can be concluded that the use of imaging techniques enables an increase in quality and profit maximization of agro-industrial products and their characterization data, which can be better analyzed with the help of machine learning methods and lead to a more sustainable and innovative agro-industrial productive sector.

关键词： Agroindustry Imaging Machine Learning Production activity

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