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6th IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2024

作者： Liu, Shu Wu, Qiang Yang, Xin Computational and Data Science PhD Program Middle Tennessee State University MurfreesboroTN United States University of Tennessee Department of Mathematics KnoxvilleTN United States Middle Tennessee State University Department of Computer Science MurfreesboroTN United States

ISBN: (纸本)9798350396133

This paper performs a classification task on data obtained from the Autism Brain Imaging Data Exchange (ABIDE) repository. In real-world case analysis, the number of autism spectrum disorder (ASD) patients is much smaller than typically developed people. To address this issue, this paper proposes the utilization of pairwise robust support vector machine (PRSVM) algorithms to classify autism spectrum disorder (ASD) patients. In this project's experiments, the correlation matrix derived from functional magnetic resonance imaging (fMRI) data was employed as a classification feature. A comprehensive evaluation was conducted to compare the classification performance of PRSVM with various machine learning methods. The comparative analysis encompassed various aspects, including different data dimensions, imbalanced ratios, and sample sizes, providing valuable insights into the relative performance of the algorithms under different experimental conditions. The experimental results demonstrate that PRSVM can detect autistic patients more accurately when the data is imbalanced. Moreover, the results indicate that PRSVM outperforms or achieves comparable performance to other conventional classification methods in a variety of situations. Furthermore, our approach can be further improved by augmenting the training set with either exclusively normal person samples or by incorporating patient samples and normal people samples in a proportionate manner. This augmentation strategy holds promising application value, as it contributes to improving the performance and robustness of our method. © 2024 IEEE.

关键词： Diseases

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LLaVA-NDiNO: Empowering LLMs with Multimodality for the Italian Language 8

LLaVA-NDiNO: Empowering LLMs with Multimodality for the Ital...

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8th Workshop on Natural Language for Artificial Intelligence, NL4AI 2024

作者： Musacchio, Elio Siciliani, Lucia Basile, Pierpaolo Semeraro, Giovanni Italian National PhD Program in Artificial Intelligence University of Bari Aldo Moro Bari Italy Dept. of Computer Science University of Bari Aldo Moro Via E. Orabona 4 Bari70125 Italy

Since their initial inception, large language models have undergone many innovations. One of these innovations concerns multimodality. Several adaptation strategies have been developed to expand LLMs to process multimodal signals. However, the training procedure for these multimodal models is performed on English-only vision-language datasets in the current literature, limiting their capabilities for other languages. This work proposes the first family of LMMs for the Italian language. We trained them using state-of-the-art backbone models and datasets, translated into Italian using the most up-to-date machine translation model available. In support of open science, we publicly release the data, models, and code used to develop these models. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Visual languages

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Adapting Large Language Models to Narrative Content 3

Adapting Large Language Models to Narrative Content

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3rd Workshop on Artificial Intelligence and Creativity, CREAI 2024

Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains. However, adapting them to narrative content remains challenging. This paper explores the opportunities in adapting open-source LLMs to narrative contexts, where coherence, plot development, and character consistency are paramount. We investigate existing techniques for adapting and then fine-tuning LLMs on narrative data and propose a solution tailored to the specific demands of narrative generation. Furthermore, we analyze the performance of the proposed approach on the standard dataset WritingPrompts by exploring several corpora for the adaptation step. Moreover, we propose a qualitative evaluation involving human feedback. Results show that the adaption helps the model improve the generation and accuracy of prompts ranking. © 2024 Copyright for this paper by its authors.

关键词： Artificial Intelligence Generative AI Large Language Models Narrative Content Generation Narrative Content Understanding

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Real-Time 3D Object Detection, Recognition and Presentation Using a Mobile Device for Assistive Navigation

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SN computer science 2023年第5期4卷 1-18页

作者： Chen, Jin Zhu, Zhigang Visual Computing Laboratory Computer Science Department The City College-CUNY New York 10031 NY United States Nearabl Inc. New York 10023 NY United States PhD Program in Computer Science The Graduate Center-CUNY New York 10016 NY United States

This paper presents an integrated solution for 3D object detection, recognition, and presentation to increase accessibility for various user groups in indoor areas through a mobile application. The system has three major components: a 3D object detection module, an object tracking and update module, and a voice and AR-enhanced interface. The 3D object detection module consists of pre-trained 2D object detectors and 3D bounding box estimation methods to detect the 3D poses and sizes of the objects in each camera frame. This module can easily adapt to various 2D object detectors (e.g., YOLO, SSD, mask RCNN) based on the requested task and requirements of the run time and details for the 3D detection result. It can run on a cloud server or mobile application. The object tracking and update module minimizes the computational power for long-term environment scanning by converting 2D tracking results into 3D results. The voice and AR-enhanced interface integrates ARKit and SiriKit to provide voice interaction and AR visualization to improve information delivery for different user groups. The system can be integrated with existing applications, especially assistive navigation, to increase travel safety for people who are blind or have low vision and improve social interaction for individuals with autism spectrum disorder. In addition, it can potentially be used for 3D reconstruction of the environment for other applications. Our preliminary test results for the object detection evaluation and real-time system performance are provided to validate the proposed system. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： 3D object detection ARKit Assistive technology Blind or low vision Voice assistance

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Prediction Tool on Fine Particle Pollutants and Air Quality for Environmental Engineering

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SN computer science 2022年第3期3卷 184页

作者： Varde, Aparna S. Pandey, Abidha Du, Xu Dept. of Computer Science Environmental Science and Management PhD Program Montclair State University Montclair NJ United States Visiting Researcher at Max Planck Institute for Informatics Saarbrücken Germany Department of Computer Science Montclair State University Montclair NJ United States Department of Earth and Environmental Science Environmental Science and Management PhD Montclair State University Montclair NJ United States

This article focuses on the research, design and implementation of a prediction tool for air quality to estimate pollutant concentrations, contributing to environmental engineering. It addresses prediction of fine particle air pollutants of diameter less than 2.5 µm (particulate matter 2.5), their concentration being substantially influenced by urban traffic. We collect worldwide multicity data from health-related public sources on which mining is performed using classical data mining/machine learning paradigms: association rules, clustering and classification. Challenges include adapting appropriate techniques based on data, and capturing subtle domain-specific aspects. The prediction tool is built using knowledge discovered by mining, leveraging health standards, catering to novice, intermediate and expert users. The prediction output is accurate, efficient, interpretable and useful as evident from our experiments. The tool is helpful for urban decision support. This work is beneficial in developing software systems such as intelligent tutors, mobile device apps and smart city tools. It contributes to smart environment, mobility and living, making a positive impact on smart cities and sustainability. In this work, we claim that classical computational paradigms in their fundamental form can be adapted to solve environmental engineering problems, with easy comprehension, as per the Occam’s razor principle that advocates simplicity. This article constitutes applied research: using computational techniques to solve domain-specific problems. Future work includes exploring models in deep learning such as CNN and Bi-LSTM, and considering different types of pollutants as well as other sources besides multicity traffic data, to conduct further studies. This would address additional challenges with enhancements. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Applied research Decision support Environmental engineering Health informatics Machine learning Urban studies

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Vegetation Coverage and Urban Amenity Mapping Using computer Vision and Machine Learning

Vegetation Coverage and Urban Amenity Mapping Using Computer...

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3rd International Conference on Image Processing and Vision Engineering, IMPROVE 2023

作者： Karkut, Nicholas Kiriluk, Alexey Zhang, Zihao Zhu, Zhigang Computer Science Department The City College of New York - CUNY New YorkNY10031 United States Graduate Landscape Architecture Program The City College of New York - CUNY New YorkNY10031 United States PhD Program in Computer Science The Graduate Center - CUNY New YorkNY10016 United States

ISBN: (纸本)9789897586422

This paper proposes a computer vision-based workflow that analyses Google 360-degree street views to understand the quality of urban spaces regarding vegetation coverage and accessibility of urban amenities such as benches. Image segmentation methods were utilized to produce an annotated image with the amount of vegetation, sky and street coloration. Two deep learning models were used -- Monodepth2 for depth detection and YoloV5 for object detection -- to create a 360-degree diagram of vegetation and benches at a given location. The automated workflow allows non-expert users like planners, designers, and communities to analyze and evaluate urban environments with Google Street Views. The workflow consists of three components: (1) user interface for location selection;(2) vegetation analysis, bench detection and depth estimation;and (3) visualization of vegetation coverage and amenities. The analysis and visualization could inform better urban design outcomes. © 2023 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Vegetation mapping

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An Integrated Mobile Vision System for Enhancing the Interaction of Blind and Low Vision Users with Their Surroundings

An Integrated Mobile Vision System for Enhancing the Interac...

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3rd International Conference on Image Processing and Vision Engineering, IMPROVE 2023

作者： Chen, Jin Ramnath, Satesh Samaroo, Tyron Maksakuli, Fani Ruci, Arber Sturdivant, E’edresha Zhu, Zhigang Nearabl Inc New YorkNY10023 United States Computer Science Department The City College of New York - CUNY New YorkNY10031 United States PhD Program in Computer Science The Graduate Center - CUNY New YorkNY10016 United States

ISBN: (纸本)9789897586422

This paper presents a mobile-based solution that integrates 3D vision and voice interaction to assist people who are blind or have low vision to explore and interact with their surroundings. The key components of the system are the two 3D vision modules: the 3D object detection module integrates a deep-learning based 2D object detector with ARKit-based point cloud generation, and an interest direction recognition module integrates hand/finger recognition and ARKit-based 3D direction estimation. The integrated system consists of a voice interface, a task scheduler, and an instruction generator. The voice interface contains a customized user request mapping module that maps the user’s input voice into one of the four primary system operation modes (exploration, search, navigation, and settings adjustment). The task scheduler coordinates with two web services that host the two vision modules to allocate resources for computation based on the user request and network connectivity strength. Finally, the instruction generator computes the corresponding instructions based on the user request and results from the two vision modules. The system is capable of running in real time on mobile devices. We have shown preliminary experimental results on the performance of the voice to user request mapping module and the two vision modules. © 2023 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Palmprint recognition

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Linking Alternative Fuel Vehicles Adoption with Socioeconomic Status and Air Quality Index

arXiv

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arXiv 2023年

作者： Singh, Anuradha Yadav, Jyoti Shrestha, Sarahana Varde, Aparna S. Environmental Science and Management PhD Program Department of Computer Science Montclair State University MontclairNJ07043 United States

Understanding adoption of new technology by its consumers helps us deal with the challenges it faces in any new market. Moreover, the impact it creates on society in terms of the environment, health, and justice is significantly based on the extent of adoption. Alternative fuel vehicles (AFVs) is such an area that faces challenges in terms of consumers’ diverse social status and resistance to change. In order to achieve a cleaner transportation sector, we need to address these challenges. In this paper, we conduct a study via machine learning techniques to correlate the adoption of AFVs across various regions, their socioeconomic ranking, and their impact on the air quality index (AQI);and furthermore to predict the AQI as per the region’s AFV adoption. This is an empirical study with predictive modeling based on a regional panel data analysis where we use real US census data, air quality data, and data on the number of AFVs purchased per region. Research in this area can help to promote appropriate policies for AFV adoption in the future with due justice to different population groups. This work exemplifies a modest utilization of AI techniques to enhance social good. More specifically, it makes a considerable impact on energy, climate, transportation, and environmental sustainability. © 2023, CC BY.

关键词： Population statistics

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A Balanced Pedagogical Approach toward AI Readiness Education for STEM Learners: Instilling a balanced view of AI capabilities through active learning in both traditional classroom and self-directed online environments 22

A Balanced Pedagogical Approach toward AI Readiness Educatio...

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Proceedings of the 2022 6th International Conference on Education and E-Learning

作者： Alvis C. Fong Ajay K. Gupta Steve M. Carr Shameek Bhattacharjee Michael Harnar Department of Computer Science Western Michigan University USA Interdisciplinary PhD in Evaluation Program Western Michigan University USA

ISBN: (纸本)9781450398428

Artificial intelligence (AI) is increasingly being applied to disciplines beyond computer science (CS). Engineers, statisticians, business analysts, biologists, physicists, physicians, and pharmacists, are among the many non-CS professionals who leverage the power of AI algorithms and systems for solving their domain-specific problems. Although AI has been found useful for solving a wide range of previously unsolvable problems, there are important limitations associated with contemporary AI. It is therefore important to inform current and future AI users regarding both strengths and weaknesses of AI in its current form, as well as what AI will be like in the foreseeable future. In this paper, the authors describe a pedagogical approach toward educating AI users from a range of STEM disciplines so that they can best exploit what AI has to offer. Specifically, a balanced approach is taken to ensure that learners gain knowledge and skills in what AI can or cannot do for them. A growing suite of experiential learning modules, which complement existing educational resources, serve as a vehicle for getting STEM learners ready for a future workplace characterized by significant use of AI technologies. These learning modules promote active learning and can be applied in a traditional classroom setting, self-directed online study, or a mix of the two modes. The paper ends with a presentation of encouraging results of actual use of the experiential learning modules in a mixed mode setting across multiple quantitative disciplines. All project artifacts, including the developed experiential learning modules, recommended uses, and best practices, are freely available on the project website. Interested educators and researchers are welcome to use the available resources and/or contribute to the on-going research.

关键词： artificial intelligence readiness active learning STEM education experiential learning computing education

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A Model and Tool for Community Engagement: Case Study: Community Engagement in the Bisotun World Heritage Site 14

A Model and Tool for Community Engagement: Case Study: Commu...

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14th AVI-CH Workshop on Advanced Visual Interfaces and Interactions in Cultural Heritage, AVI-CH 2022

作者： Nasrolahi, Ahmad Messina, Vito Gena, Cristina Tech4Culture PhD Program University of Torino Turin Italy Historical Studies Department University of Torino Turin Italy Computer Science Department University of Torino Turin Italy

Participating local community in cultural heritage management has always been a concern since the Venice Charter so far (1964). In addition, the Faro Convention (2005) shifted focus from cultural heritage values to the values of cultural heritage for society. In this case, there is required to design a simple method and tool in order to facilitate the interaction between local people and authority. This paper introduces a method and tool (iCommunity) that are now using in community-centered approach in the Bisotun World Heritage Site. The main goal of this application is to find a simple and applicable method and tool for inclusion the local community in decision-making processes in cultural heritage management. The proposed model is designed based on the general public participation principles, on the one hand, and the users' need and requirement in the world heritage site, on the other hand. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Decision making

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