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Blockchain Applications in Emergency Logistics: A Bibliometric Study and Future Research Horizons 4th

Blockchain Applications in Emergency Logistics: A Bibliometr...

引用

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

作者： Sassaoui, Imane El Alami, Jamila Hlyal, Mustapha Univ Med V Rabat Grad Sch Technol LASTIMI Lab Sale Morocco ESITH Sch Casablanca CELOG Rd El Jadida Casablanca Morocco

ISBN: (纸本)9783031686740;9783031686757

Improving emergency logistics in order to effectively respond to disasters is becoming vital, with the constant rise of disasters frequency. Conventional supply chains often face challenges in different phases of a disaster, such as the lack of transparency or coordination issues. To examine this potential, the proposed study sheds light on the crucial role of emerging technologies, mainly Blockchain Technology in optimising emergency supply chain management. Blockchain technology has the ability to transform logistics operations deployment during a disaster, through its decentralised and secure features. This paper uses a systematic approach based on science mapping and performance analysis, it offers insights into relevant literature, key scientific works, leading authors, journals, and countries in this topic. The study considers 405 articles from Scopus database and deploys vOSviewer software for visual analysis. The findings highlight the significant contributions of authors like Ivanov. AND Cherrafi, A. Sustainability Switzerland Journal, setting the standard followed by International Journal of Production Research. Regarding countries analysis, the publications are originally from 74 different country with India, China and the United States taking the lead in scientific production. In conclusion, the proposed paper suggests a deeper exploration of potential research opportunities, mainly in the integration of Blockchain technology with other emerging technologies, and the deployment of dynamic modelling techniques in disaster management.

关键词： Blockchain Applications Emergency Logistics Bibliometric analysis

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Did Somebody Say 'Gest-IT'? A Pilot exploration of Multimodal data Management 10

Did Somebody Say 'Gest-IT'? A Pilot Exploration of Multimoda...

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10th Italian conference on Computational Linguistics, CLiC-it 2024

作者： Pannitto, Ludovica Albanesi, Lorenzo Marion, Laura Martines, Federica Maria Caruso, Carmelo Bianchini, Claudia S. Masini, Francesca Mauri, Caterina Alma Mater Studiorum University of Bologna Italy University of Poitiers FoReLLIS Laboratory France

The paper presents a pilot exploration of the construction, management and analysis of a multimodal corpus. Through a three-layer annotation that provides orthographic, prosodic, and gestural transcriptions, the Gest-IT resource allows to investigate the variation of gesture-making patterns in conversations between sighted people and people with visual impairment. After discussing the transcription methods and technical procedures employed in our study, we propose a unified CoNLL-U corpus and indicate our future steps. © 2024 CEUR-WS. All rights reserved.

关键词： Information management

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Natural Language Processing: A Literature Survey of Approaches, Applications, Current Trends, and Future Directions

Natural Language Processing: A Literature Survey of Approach...

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2024 Asian conference on Intelligent Technologies, ACOIT 2024

作者： Jha, Shruti Mahamuni, Chaitanya v. Garewal, Ishmeen Kaur Department of Computer Engineering Agnel Charities’ Fr. C Rodrigues Institute of Technology Navi Mumbai India

ISBN: (纸本)9798350374933

This comprehensive survey explores the diverse and rapidly evolving landscape of Natural Language Processing (NLP), covering foundational approaches and wide-ranging applications across various industries. NLP, which lies at the intersection of computer science, artificial intelligence, and linguistics, aims to enable machines to understand, interpret, and generate human language. The paper delves into methodologies such as rule-based systems, statistical models, neural networks, and hybrid techniques. The survey examines NLP’s pivotal roles in key sectors. In healthcare, it enhances patient care and medical research through data analysis. In finance, it aids in sentiment analysis, risk assessment, and fraud detection. Education benefits from automated grading and personalized learning. Detailed analyses highlight applications in customer service with chatbots, legal compliance through document automation, disaster risk mitigation via data processing, and social media analysis for sentiment tracking. The paper also identifies current challenges, such as handling ambiguous language and ensuring data privacy, and discusses emerging research trends like deep learning advancements and sophisticated language models. By illuminating these aspects, the survey underscores NLP’s transformative potential and provides valuable insights for further exploration in this dynamic field. © 2024 IEEE.

关键词： Deep learning

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Exploring data Workers’ Behaviours in data Quality Discovery 44

Exploring Data Workers’ Behaviours in Data Quality Discover...

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44th International conference on Information Systems: Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies, ICIS 2023

作者： Chen, Tianwa Demartini, Gianluca Indulska, Marta Sadiq, Shazia School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia Business School The University of Queensland Brisbane Australia

ISBN: (纸本)9781713893622

The rise of data re-purposing has resulted in an unprecedented opportunity to create new value from existing data assets. Unlike previous approaches that relied on well-understood data quality requirements, this open-ended setting demands a discovery-oriented approach. Despite a growing market of tools that assist with data exploration and profiling, data quality discovery continues to drain the time of data workers due to its cognitively demanding and contextual nature. Accordingly, we study data quality discovery through purpose-built experimental platforms that mimic typical data exploration platforms, and explore implicit sensemaking behaviours of data workers when required to write code or use available functions, by exploiting eye-tracking and log data. Our analysis of visual scanning and attention patterns reveals various sensemaking loops amongst data workers with different performance levels in different settings. Our findings provide insights for researchers and tool designers to consider how to support different data workers in data quality discovery. Copyright © 2023 Tianwa Chen, Gianluca Demartini, Marta Indulska, Shazia Sadiq.

关键词： Eye tracking

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Jobviewer: Graph-based visualization for Monitoring High-Performance Computing System 9

JobViewer: Graph-based Visualization for Monitoring High-Per...

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IEEE/ACM 15th International conference on Utility and Cloud Computing (UCC) / 9th International conference on Big data Computing, Applications and Technologies (BDCAT)

作者： Dang, Tommy Nguyen, Ngan v. T. Li, Jie Sill, Alan Hass, Jon Chen, Yong Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA Texas Tech Univ Ctr High Performance Comp Lubbock TX 79409 USA Dell Inc Austin TX USA

ISBN: (纸本)9781665460903

visualization aims to strengthen data exploration and analysis, especially for complex and high-dimensional data. High-performance computing (HPC) systems are typically large and complicated instruments that generate massive performance and operation time series. Monitoring HPC systems' performance is a daunting task for HPC admins and researchers due to their dynamic natures. This work proposes a visual design using the bi-partite graph's idea to visualize HPC clusters' structure, metrics, and job scheduling data. We built a web-based prototype, called Jobviewer, that integrates advanced methods in visualization and human-computer interaction (HCI) to demonstrate the benefits of visualization in real-time monitoring HPC centers. We also showed real use cases and a user study to validate the efficiency and highlight the current approach's drawbacks.

关键词： High-Performance Computing Center Job Scheduling Radar Charts Time-Series data analysis Multidimensional data visualization

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Enhancing the Perception of Color Casting and Contrasting in Underwater Image using Fusion Methods

Enhancing the Perception of Color Casting and Contrasting in...

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2024 International conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Rajesh, v. Radhika, S. Sathyabama Institute of Science & Technology Manakula Vinayagar Institute of Technology Dept of Ece Puducherry India School of Electrical & Electronics Sathyabama Institute of Science & Technology Tamilnadu India

ISBN: (纸本)9798331510022

A thorough method for improving color casting and contrast perception in underwater images is described, coupled with a compression and reconstruction technique to maintain the enhanced visual quality of the images while reducing storage and transmission costs. The suggested approach combines color correction and image enhancement techniques, followed by a robust compression algorithm leverages the underlying statistical properties of the enhanced image. The reconstruction process subsequently recovers the high-quality image using a decompression technique. Both subjective and objective image quality measures are used to assess the effectiveness of the suggested strategy, showing notable gains in visual perception and compression efficiency. The outcomes show that the suggested strategy has the potential to enable more efficient underwater data analysis and interpretation. © 2024 IEEE.

关键词： Underwater imaging

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Fine-Tuning Large Language Models for Automatic Font Skeleton Generation: exploration and analysis 17th

Fine-Tuning Large Language Models for Automatic Font Skelet...

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17th Asian conference on Computer vision, ACCv 2024

作者： Liu, Yuxuan Fujii, Yasuhisa Zhu, Xinru Nohara, Kayoko School of Environment and Society Institute of Science Tokyo Tokyo Japan Google DeepMind Tokyo Japan

ISBN: (纸本)9789819609079

Despite the pivotal role that font skeletons could play in typeface research and font design, the availability of font skeleton data is sparse and limited. This research explores the possibility of using Large Language Models (LLMs) to generate font skeleton data based on font outline data. Our method represents font skeletons and font outlines as sequences of text tokens derived from SvG commands, and formulates the font skeleton task as a language modeling task predicting the token sequence for a font skeleton given the token sequence of a font outline. As a first attempt, we fine-tuned GPT-3.5 on a dataset of 8,213 Japanese font outlines and corresponding skeletons. Both quantitative and qualitative evaluations show the effectiveness of the approach in terms of rasterized pixel distance, Chamfer distance, and visual analysis. Our proposed method achieved average results of 14.678 for rasterized pixel distance and 1.713 for Chamfer distance, both better than the baseline method (Polyvectorization). In visual analysis, we found better generation results for complex shapes which logograms such as Chinese characters tend to have, than for the simple shapes of syllabograms such as Japanese kana, phonograms such as Latin alphabets, and symbols. Although our fine-tuned model has limitations in generating the skeletons of other font styles, this research establishes a foundation for the automatic generation of font skeletons using LLMs, setting the stage for future work on automatic skeleton generation and the wider application of font skeletons in typography. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Musculoskeletal system

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Exploring data Workers’ Behaviours in data Quality Discovery 34

Exploring Data Workers’ Behaviours in Data Quality Discover...

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34th Australasian conference on Information Systems, ACIS 2023

关键词： Eye tracking

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Feeling Textiles through AI: An exploration into Multimodal Language Models and Human Perception Alignment 24

Feeling Textiles through AI: An Exploration into Multimodal ...

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Companion International conference on Multimodal Interaction

作者： Zhong, Shu Gatti, Elia Cho, Youngjun Obrist, Marianna UCL Dept Comp Sci London England

ISBN: (纸本)9798400704628

Human-artificial intelligence (AI) alignment ensures that AI systems align with human goals and behaviors. This paper introduces perceptual alignment as a critical aspect of this alignment, focusing on the concurrence between human judgments and AI evaluations across sensory modalities. We particularly explore how Multimodal Large Language Models (MLLMs), which process both visual and textual data, interpret the tactile qualities of textiles-a significant challenge in online shopping environments. Our research analyzes six vision-based MLLMs to see how they describe the tactile experience of textiles and compares these AI-generated descriptions with human assessments. Through semantic similarity measures and in-person evaluations, we investigate the extent of alignment between human perceptions and AI descriptions. Our findings indicate significant variability in the AI's ability to interpret different textiles, highlighting both the potential and limitations of current AI models in achieving perceptual alignment. This work contributes to understanding the complexities of aligning AI capabilities with human touch sensory experiences.

关键词： Human-AI Alignment Human-AI interaction Touch Experience Textile Hand Multimodal Large Language Models

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Real Time Phishing Detection Using Lexical analysis and visual Similarity 14th

Real Time Phishing Detection Using Lexical Analysis and Vis...

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14th EAI International conference on Broadband Communications, Networks, and Systems, BROADNETS 2024

作者： Gnanesh, A. Deepesh, Dasa A. Hegde, Bhargav vyasamudri, Shreehari Sarasvathi, v. PES University Bengaluru560100 India

ISBN: (纸本)9783031811678

Phishing, a form of social engineering, involves deceptive practices to extract sensitive information from individuals. Typically, attackers manipulate their messages to mimic genuine communication from reputable entities like financial institutions, social networks, or online marketplaces. Phishing attempts manifest across various communication channels, encompassing email, text messages, and social media platforms. The acquired sensitive data fuels identity theft and financial fraud, underscoring the importance of vigilance when encountering unsolicited communications or hyperlinks soliciting personal information or immediate action. This paper offers a comprehensive approach to tackle phishing hazards, employing diverse features including URL structure for lexical analysis and screen-shot for visual similarity using transfer learning. Notably, the XGBoostClassifier attains an outstanding accuracy rate of 96.89%. To bolster this, a hybrid approach is adopted, integrating the MobileNet model for visual similarity-based detection. The incorporation of the MobileNet model introduces a visual similarity-based detection layer, augmenting the system’s capabilities. Implemented as a Chrome plug-in, this system dynamically scrutinizes website URLs, promptly alerting users about potential phishing threats. Rigorous testing on real-world phishing websites showcases the method’s robust performance, offering a reliable and user-friendly solution to detect and prevent phishing attacks. The hybrid integration of feature based detection through XGBoostClassifier and visual similarity analysis using MobileNet fortifies the system, elevating its effectiveness in safeguarding against evolving phishing assaults. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Phishing

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