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2024 Global Digital Health Knowledge Exchange and Empowerment Conference, gDigiHealth.KEE 2024

作者： Naji, Areen Subha, Muath Arab American University PhD in Information Technology Engineering Ramallah Palestine Arab American University Department of Computer Science PHD Coordinator Ramallah Palestine

ISBN: (纸本)9798331531331

This paper evaluates the effect of initial center selection on cluster quality and the rate of convergence in K-means clustering. In this paper, a new approach is proposed that uses random initialization and spatial distribution scores. The main objective is to enhance K-means clustering by creating a simple, novel, and efficient technique shredded for data classification and other high-level image processing tasks. Our approach relies on image color segmentation through the application of a transformation to the image's RGB color space, representing every pixel by three values of different components of color, divided into blocks, and then a calculation of the density of dots in every block. This value of density mark provided information about the distribution of color values around various parts of an image. It uses a tactical approach in selecting initial centers, namely Scored Centers Initialization, or SCI. It chooses the block with the highest score as the initial center block chosen randomly. Then, it selects subsequent centers based on the probability assessed by the Euclidean distance from the nearest one picked center and the density score of the block. It continuously does this until the desired number of centers is reached. The experimental results indicate that the clustering effect of the approach proposed was better than the traditional Random Centers Initialization method, thus proving the proposed strategy was effective. Based on the consideration of point-to-center distances and the spatial distribution score, it captured better data structures to get quicker convergence and higher quality in the cluster assignment. © 2024 IEEE.

关键词： K-means clustering

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A study on the soundness of closed-ended evaluation of Large Language Models adapted to the Italian language 10

A study on the soundness of closed-ended evaluation of Large...

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

作者： Musacchio, Elio Siciliani, Lucia Basile, Pierpaolo Michielon, Edoardo Pasqualini, Marco Uboldi, Asia Beatrice Semeraro, Giovanni Department of Computer Science University of Bari Aldo Moro Italy National PhD in Artificial Intelligence University of Pisa Italy Fastweb SpA Milan Italy

With the rising interest in Large Language Models, deep architectures capable of solving a wide range of Natural Language Generation tasks, an increasing number of open weights architectures have been developed and released online. In contrast with older architectures, which were aimed at solving specific linguistic assignments, Large Language Models have shown outstanding capabilities in solving several tasks at once, raising the question of whether they can truly comprehend natural language. Nevertheless, evaluating this kind of capability is far from easy. One of the proposed solutions so far is using benchmarks that combine various types of tasks. This approach is based on the premise that achieving good performance in each of these individual tasks can imply having developed a model capable of understanding language. However, while this assumption is not incorrect, it is evident that it is not sufficient, and the evaluation of Large Language Models still remains an open challenge. In this paper, we conduct a study aimed at highlighting the potential and limitations of current datasets and how a new evaluation setting applied to language-adapted Large Language Models may provide more insight than traditional approaches. © 2024 CEUR-WS. All rights reserved.

关键词： Natural language processing systems

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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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Implementation of Secure Storage for Maintaining Characteristic Contracts in Civil Law 6

Implementation of Secure Storage for Maintaining Characteris...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Shukurillayevich, Gulomov Akmaljan Mahmudovich, Kuldashev Muhammad Ziyodullayevna, Muqimova Muslima Ananth, Christo Ananth Kumar, T. PhD in Law Samarkand State University Uzbekistan Samarkand State University Faculty of International Education Programs Uzbekistan Computer Science and Engineering IFET College of Engineering India

ISBN: (纸本)9798350304480

In public life, the contract, which is one of the oldest legal instruments, is widely used to regulate relations related to the satisfaction of certain needs of participants in civil law relations. Property relations that develop at the stage of sale of manufactured goods, performance of work, provision of services is based on the construction of the contract. Property interest is secured by an agreement. A contract is an agreement between two or more persons on the emergence, change or termination of their civil rights and obligations. It can be seen from the definition that the interested parties in the contract are counterparties directly participating in it. Civil law relations arising under this agreement apply only to both of them and create relative legal relations. According to the principle of relativity, the contract, as a rule, obliges the parties that have concluded it does not affect third parties. Contracts concluded for the benefit of third parties are one of the very important exceptions to this principle. When it comes to cyber security, the characteristic contract made for the favour of a third party may involve the exchange, processing, or storage of sensitive information. This article discusses the features of the secure storage for contract concluded in favour of a third party as a subject of research. The secure MD5 fused AES algorithm is proposed here to improve data privacy and security in the storage. © 2023 IEEE.

关键词： contract law contracts obligation security

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Pairwise Learning for Autism Spectrum Disorder Imbalanced Classification 6

Pairwise Learning for Autism Spectrum Disorder Imbalanced Cl...

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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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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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Classification of Images using Multi-Layer Neural Network using Machine Learning 14

Classification of Images using Multi-Layer Neural Network us...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Chanchlani, Akshita S. Thakare, Vilas M. Wadhai, Vijay M. Department Computer Science and Engineering PhD Research Scholar SGBA University Amravati India Departments of CSE SGBA University Amravati India Astute Career Counseling Academy United States

This paper is focused on development of system for analysis with advanced Imaging Technologies with Machine Learning and Deep Learning. It also focuses on research and development of technological solutions for detection and analysis of brain cancer disease from health care domain. This paper focuses on implementation and analysis of convolution neural network algorithm in combination with multi-layer neural network algorithm on brain cancer MRI image dataset. This system will be useful in detection, classification, segmentation, analysis and prediction of brain cancer disease by using machine learning and deep learning technology. Proposed system is to detect whether brain tumor is cancerous (malignant) or not (benign) as well prediction of tumor grade on segmented region of interest. This implemented research work will enable all the stake holders like doctors, radiologists, neurosurgeons to detect and predict brain cancer precisely, efficiently and accurately with speedy performance. © Grenze Scientific Society, 2023.

关键词： Image analysis

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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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Improvement of K-Means Clustering Through Center's Hybrid Initialization

Improvement of K-Means Clustering Through Center's Hybrid In...

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Global Digital Health Knowledge Exchange & Empowerment Conference (gDigiHealth.KEE)

作者： Areen Naji Muath Subha Technology Engineering Arab American University PhD in Information Palestine Ramallah Department of Computer Science PHD Coordinator Arab American University Palestine Ramallah

ISBN: (数字)9798331531331

ISBN: (纸本)9798331531348

关键词： Image segmentation Graphical models Accuracy Image color analysis Clustering algorithms Euclidean distance Indexes Standards Distribution functions Convergence

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