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检索条件"丛书名=Lecture Notes in Networks and Systems"

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3D Traversability Analysis in Forest Environments Based on Mechanical Effort 17th

3D Traversability Analysis in Forest Environments Based on M...

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17th International Conference on Intelligent Autonomous systems (IAS)

作者： Carvalho, Afonso E. Ferreira, Joao Filipe Portugal, David Univ Coimbra Inst Syst & Robot Coimbra Portugal Nottingham Trent Univ Sch Sci & Technol Nottingham England

ISBN: (纸本)9783031222153;9783031222160

Autonomous navigation in harsh and dynamic 3D environments poses a great challenge for modern Robotics. This work presents a novel traversability analysis and path-planning technique that processes 3D pointcloud maps to generate terrain gradient information. An analysis of terrain roughness and presence of obstacles is applied on the perceived environment in order to generate efficient paths. These avoid major hills when more conservative paths are available, potentially promoting fuel economy and reducing the wear of the equipment and the associated risks. The proposed approach outperforms existing techniques based on results in realistic 3D simulation scenarios, which are discussed in detail.

关键词： Forestry robotics Traversability analysis 3d navigation Path planning UGV Mechanical effort

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The Effect of Using the Chatbot to Improve Digital Literacy Skill of Thai Elderly 1

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： Sriwisathiyakun, Kanyarat King Mongkuts Inst Technol Sch Ind Educ & Technol Bangkok 10520 Thailand

ISBN: (数字)9783031456459

ISBN: (纸本)9783031456442;9783031456459

This research aims to demonstrate the learning success and contentment of elderly people in Thailand using a chatbot innovation for improving digital literacy. Three parts of the research were carried out: Phase 1 of the research focused on developing the chatbot and any relevant educational digital media;Phase 2 engaged in pre-experimental by experts' validation;and Phase 3 involved an experiment where the chatbot was deployed with elderly people. Samples were collected from 33 elderly people. The information was gathered through expert interviews, pretests, and posttests on chatbot usage, and satisfaction surveys. The data was then analyzed using the dependent t-test, percentage, mean, standard deviation, and content analysis. Results showed that our Senior See Net chatbot was simple to use and navigate, with an appropriate artistic appearance. Findings demonstrate that this chatbot was simple to use and access, had an appropriate creative composition, and was made up of sufficiently thorough media and content on digital literacy. Furthermore, the statistical analysis revealed that after using the chatbot, the elderly's understanding of digital literacy improved statistically significant at the 0.05 level. Furthermore, their overall satisfaction was rated high.

关键词： Digital literacy Chatbot Elderly Learning outcome User satisfaction

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Digital Wellness of Preschool Children: The Story of Cyber-cat and the Consequences of Hacking 9th

Digital Wellness of Preschool Children: The Story of Cyber-c...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Mnisi, Given Drevin, Gunther Drevin, Lynette Esterhuizen, Joshua Croucamp, Christo North West Univ Potchefstroom South Africa

ISBN: (纸本)9789819735587;9789819735594

In the modern world, there has been a significant increase in digital usage leading to heightened concerns about digital safety, particularly for children since they lack the necessary knowledge to protect themselves. More than 95% of South African children have access to cyberspace, indicating that they are driving digital uptake, which implies that they aremore exposed to online risks. Introducing children to the notion of being digitally safe in cyberspace can have tremendous benefits, as childhood experiences profoundly impact their overall growth. Children learn by playing, discovering, and posing questions to overcome any obstacles that arise. Therefore incorporating elements of both enjoyment and seriousness into a game to spread cybersecurity awareness engagingly and enjoyably would be highly effective. The aim of this paper is to report on a study where a game was developed to raise awareness of dangers and consequences of hacking among preschool children. The intent is that the game be played under the supervision of a parent or caregiver. The game was evaluated by reviewers to determine its suitability and shortcomings. From this evaluation, future improvements to the game were identified.

关键词： Digital wellness Preschool children Cyberspace Cybersecurity Hacking Serious games

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Defining the Scope of IoT systems at the Pre-contract Time 16th

Defining the Scope of IoT Systems at the Pre-contract Time

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16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Vera, Tomas Ochoa, Sergio F. Santos, Rodrigo Perovich, Daniel Univ Chile Dept Comp Sci Santiago Chile Univ Nacl Sur Dept Elect & Comp CONICET Bahia Blanca Buenos Aires Argentina

ISBN: (纸本)9783031775703;9783031775710

Performing the scoping of IoT systems is recognized as a challenging task, and it becomes harder when must be addressed at pre-contract time. The literature reports both the use of regular elicitation techniques to deal with this activity, and their low cost-effectiveness in determining the product scope when these techniques are used. Trying to deal with that limitation, this paper presents a digital collaborative canvas that allows stakeholders and providers to build, validate, and agree on the scope of IoT systems, at the pre-contract time of development and evolution projects. It was preliminary evaluated by three experts in the design of IoT systems;the obtained results, although still preliminary, are highly encouraging.

关键词： Scoping of IoT systems requirements engineering at pre-contract time elicitation technique digital canvas

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Knowledge Integration in Vector Quantization Models and Corresponding Structured Covariance Estimation 15th

Knowledge Integration in Vector Quantization Models and Corr...

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15th International Workshop on Self-Organizing Maps, Learning Vector Quantization and Beyond (WSOM)

作者： Kaden, M. Voigt, J. Bohnsack, K. S. Lange-Geisler, M. Villmann, Thomas Mittweida Univ Appl Sci Saxon Inst Computat Intelligence & Machine Learni Mittweida Germany

ISBN: (纸本)9783031671586;9783031671593

We propose a knowledge-informed variant for learning covariance-dependent data representations using unsupervised vector quantization. In particular, we consider linear data mappings included in vector quantization models, such as c-means++, neural gas, or self-organizing maps, to achieve representations in a lower-dimensional data space. To this end, we show how additional data knowledge can be integrated into the models. The additional data structure information is used to generate an appropriate data mapping depending on the corresponding structured data covariances.

关键词： Knowledge-informed covariance estimation unsupervised vector quantization neural gas self-organizing maps principal component analysis

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Census2Vec: Enhancing Socioeconomic Predictive Models with Geo-Embedded Data

Census2Vec: Enhancing Socioeconomic Predictive Models with G...

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Intelligent systems Conference

作者： Bevara, Ravi Varma Kumar Wagenvoord, Isabelle Hosseini, Farahnaz Sharma, Himanshu Nunna, Vandana Xiao, Ting Univ North Texas Dept Informat Sci Denton TX 76203 USA Colorado Coll Dept Comp Sci Colorado Springs CO 80903 USA Univ North Texas Dept Comp Sci Denton TX 76203 USA

ISBN: (纸本)9783031664304;9783031664311

Artificial intelligence's role in distilling insights from data has emerged as a pivotal solution to contemporary challenges within our data-centric society. Census data, while offering rich demographic and socioeconomic insights, is limited by its complex dimensionality posing obstacles to developing universally applicable models. This study introduces an approach to leverage census data through the creation of location embeddings across various domains. Utilizing the Optuna framework, this research tuned autoencoders to optimize the bottleneck layer size, producing compact, low-dimensional embeddings that encapsulate critical relationships. These embeddings are further enriched with Federal Information Processing Standard (FIPS) codes, maintaining geographic identifiers. The methodology's effectiveness is demonstrated through regression models trained on the American Community Survey, accurately predicting key indicators like median gross rent and per capita income with an 8-10% higher average accuracy compared to traditional PCA-based methods. These findings suggest a novel paradigm for overcoming the limitations of geographically specific huge dimensional data for synthesizing insights. In practical terms, such as in public policy, this approach could enable more precise targeting of socio-economic interventions based on nuanced community profiles. This innovative technique in representation learning shows considerable promise for enhancing machine learning applications across diverse sectors, including marketing, and real estate.

关键词： Census data Location embeddings Autoencoders Principal Component Analysis (PCA) FIPS codes Predictive analytics

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Inception Time Model for Structural Damage Detection Using Vibration Measurements 1

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4th Congress on Intelligent systems (CIS)

作者： Singh, Vikramaditya Bharali, Kunal Kalita, Indrajit Roy, Moumita Debnath, Nirmalendu Saharia, Manashi Das, Ayan IIIT Guwahati Dept Comp Sci & Engn Gauhati Assam India NIT Silchar Dept Civil Engn Silchar Assam India Ctr Excellence CYENS Nicosia Cyprus

ISBN: (数字)9789819990405

ISBN: (纸本)9789819990399;9789819990405

Structural health monitoring (SHM), facilitating the detection and identification of damages at an early stage in any important large Engineering structures, can aid significantly in proper functionality and maintenance, thereby enhancing structural safety to a great extent. In recent times, autonomous approaches using deep learning (DL)-based models have played a critical part in the solution of various SHM problems such as structural damage detection (SDD). However, the majority of these DL-based approaches are not capable of capturing time series information with limited computational complexity. To handle this, a deep learning model based on the inception module has been investigated to perform global-level structural damage detection by using acceleration response (time series) data under the ambient vibration scenario. An ensemble of deep convolutional neural networks is a major component of the inception module and it is inspired by the inception module to capture the time series information. This module focuses on increasing network width rather than depth, thereby reducing the problem of over-fitting. The proposed methodology is validated numerically using data of ten storied building structures and the ASCE benchmark structure considering multiple damage-case scenarios. The results are found to be encouraging and considerably better in terms of performance accuracy and computation as compared to other well-known approaches. The proposed methodology thus establishes itself as an interesting SHM tool for SDD.

关键词： Convolutional neural network Damage detection Deep learning Inception time model Structural health monitoring

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AI Art Authenticator: Deep Learning Image Classification 1

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Angeline, R. Nambiar, Abhiram Suji Jacinth, K. Samuel Christo, P. Alan Joseph, Princeton Antony SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (数字)9789819713264

ISBN: (纸本)9789819713257;9789819713264

The AI Art Authenticator: Deep Learning Image Classification project leverages deep learning techniques to tackle the challenging task of distinguishing between images that are produced using artificial intelligence and those that are not. With a comprehensive dataset consisting of 10,000 images that are produced using artificial intelligence and those made by humans for training, and an additional 3000 images in each category for testing, this project achieves an accuracy rate of 87.95%. A CNN architecture was developed for the project's core component utilizing Tensor-Flow and Keras. This CNN has layers that include convolutional, pooling, and fully connected layers, and its final layer uses sigmoid activation for binary classification. When training the model, enhanced picture data is utilized to increase the model's robustness and generalizability. The project also includes a testing phase where a custom function processes images and predicts their authenticity. Notably, the confusion matrix reveals the model's effectiveness, cleanly separating photos produced by AI from those that are not. This AI Art Authenticator holds significant potential for applications such as content verification, copyright protection, and art authentication. It showcases the power of deep learning in distinguishing between human-generated and AI-produced content, contributing to the ongoing discourse around AI ethics and the authenticity of digital media.

关键词： Deep learning CNN Image classification

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Hardware-Based Blockchain Architecture with Physical Unclonable Function for Securing IoT systems 4th

Hardware-Based Blockchain Architecture with Physical Unclona...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Lhore, Houda El-Hadbi, Assia Bousselam, Kaouthar Elissati, Oussama Chami, Mouhcine Inst Natl Postes & Telecommun STRS Lab Rabat Morocco

ISBN: (纸本)9783031686597;9783031686603

The Internet of Things (IoT) represents a new era of interconnected devices that seamlessly communicate and share information through internet technology, eliminating the need for external intervention. As the exchanged information can often be of a confidential nature, ensuring its security is paramount. Traditional security solutions prove inadequate for safeguarding IoT systems, prompting the exploration of alternative measures. Blockchain technology has emerged as a viable solution, gaining traction for its adaptability to address the security challenges inherent in IoT deployments. However, the integration of Blockchain with IoT systems presents challenges, particularly in incorporating hardware accelerators effectively. This paper introduces a novel approach to enhancing security in IoT systems by proposing a hardware-based Blockchain architecture leveraging a hardware security primitive known as the Physical Unclonable Function (PUF). The inclusion of PUFs is advocated for their unique advantages in bolstering security.

关键词： Internet of Things (IoT) Blockchain technology Security Physical Unclonable Function (PUF)

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Assessment of Environmental Performance of Ports Utilizing an Integrated LBWA-MARCOS Decision-Making Approach Based on Picture Fuzzy Sets 1

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Tatar, Veysel Ayvaz, Berk Artvin Coruh Univ Dept Maritime & Port Management Artvin Turkiye Istanbul Ticaret Univ Fac Engn Ind Engn Istanbul Turkiye

ISBN: (数字)9783031700187

ISBN: (纸本)9783031700170;9783031700187

The management of a port is of critical economic significance because of its part in global trade. However, the expansion of port operations and the continued use of antiquated methods of labor have resulted in detrimental effects on the environment, thereby posing challenges to sustainability of ports. In order for ports to compete in the global market and to ensure customer satisfaction, the importance that port managers attach to environmental indicators comes to the fore. In this context, this study suggests a fuzzy integrated MCDM strategy that combines the picture fuzzy LBWA (PF-LBWA) and picture fuzzy MARCOS (PF-MARCOS) approaches in order to achieve this goal. The proposed model is also demonstrated through environmental performance analysis of ten major ports in Turkiye.

关键词： Environmental performance Fuzzy logic LBWA MARCOS MCDM Picture fuzzy sets Port sustainability

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