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An Ordinance-Tweet Mining App to Disseminate Urban Policy Knowledge for Smart Governance 19th

An Ordinance-Tweet Mining App to Disseminate Urban Policy Kn...

引用

19th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2020

作者： Varghese, Christina Varde, Aparna S. Du, Xu Department of Computer Science Montclair State University MontclairNJ United States Department of Earth and Environmental Science Montclair State University MontclairNJ United States Environmental Science and Management PhD Program Montclair State University MontclairNJ United States

ISBN: (纸本)9783030450014

This paper focuses on how populations by the use of technology, more specifically an app, can comprehend the enactment of ordinances (local laws) in an urban area along with their public reactions expressed as tweets. Furthermore, they can understand how well the area is developing and enhancing as a Smart City. The main goal of this research is to develop an Ordinance-Tweet Mining App that disseminates the results of analyzing ordinances and tweets about them, especially related to Smart City Characteristics such as Smart Environment, Smart Mobility etc. This app would be beneficial to various users such as environmental scientists, policy makers, city committees as well as the common public in becoming more aware of legislative bodies, and possibly contributing in different aspects to make the urban area improve as a Smart City. This work fits the realm of Smart Governance due to transparency via public involvement. © 2020, IFIP International Federation for Information Processing.

关键词： Sentiment analysis

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Public participation in museums and cultural heritage sites: iCommunity mobile application

Public participation in museums and cultural heritage sites:...

引用

2020 AVI2CH Workshop on Advanced Visual Interfaces and Interactions in Cultural Heritage, AVI2CH 2020

作者： Nasrolahi, Ahmad Messina, Vito Gena, Cristina Tech4Culture Phd program University of Torino Turin Italy Historical Studies Dept. University of Torino Turin Italy Computer Science Dept. University of Torino Turin Italy

This paper presents iCommunity, an application that will be aimed at finding a universal method for inclusive societies in decision-making processes for cultural heritage management. iCommunity is being designed as a mobile application facilitating public participation in cultural heritage management. The main idea is to give all people, who are affected by a decision, the opportunity to be involved in the decision-making process. Any cultural heritage institution will be able to publish its activities on the application so as to evaluate users' feedback before the implementation of the activity itself. Based on the user's location, the application displays ongoing activities to the user himself. As a part of a participatory approach, users share each other's experience or data related to the posted activity. They also can show their agreement or disagreement to the activities by voting. The comments, votes and user's activities will be analysed real time for helping authorities to consider the consequence of their activities for more transparent and effective decision-making. © Copyright 2020 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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Economic comparison of deloaded-wind and wind-battery systems

Economic comparison of deloaded-wind and wind-battery system...

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2014 6th Annual IEEE Green Technologies Conference, GREENTECH 2014

作者： Singarao, Venkatesh Yadav Nimmagadda, Sandeep Rao, Vittal Multi-Disciplinary Wind Science and Engineering PhD Program Texas Tech University Lubbock United States Electrical and Computer Engineering Texas Tech University Lubbock United States Department of Electrical and Computer Engineering Texas Tech University Lubbock United States

ISBN: (纸本)9781479939336

With the increasing grid penetration levels of wind power, there is a large need for the energy reserves for grid support during under-frequency conditions. Such energy reserves are created by operating the wind farm at non-optimum/deloaded operating points, as well as through storage of excess energy in Batteries. This paper provides technical and economic analysis of the above strategies and thus provides a case comparison of deloaded wind farm to that of wind-Battery system. A simplified Excel-based Proforma financial model has been developed to perform sensitivity analysis, as well as to obtain the impact of these strategies on the cost of energy. © 2014 IEEE.

关键词： Electric batteries

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Towards cross-modal causal structure and representation learning 2

Towards cross-modal causal structure and representation lear...

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2nd Machine Learning for Health Symposium, ML4H 2022

作者： Mao, Haiyi Liu, Hongfu Xiaotian Dou, Jason Benos, Panayiotis V. Joint CMU-Pitt PhD Program in Computational Biology University of Pittsburgh United States Michtom School of Computer Science Brandeis University United States Department of Electrical and Computer Engineering University of Pittsburgh United States Department of Epidemiology University of Florida United States

Does the SARS-CoV-2 virus cause patients' chest X-Rays ground-glass opacities? Does an IDH-mutation cause differences in patients' MRI images? Conventional causal discovery algorithms, although well developed to uncover the cause-effect relationships on structured data, cannot elucidate causal relations between unstructured images and structured scalar variables due to the complexity of the former. In this paper, we consider causal discovery between images and structured (scalar) variables. Specifically, we derive low dimensional image representations to analyze with structured variables. We propose a two-module amortized variational algorithm named Cross-Modal Variational Causal representation and structure Learning (CMCL). CMCL jointly learns identifiable representations given a set of independent structured variables and causal relations via formulating latent representations and structured variables into a direct acyclic graph. Moreover, we further enforce counterfactual invariance/variance onto representations. We demonstrate that CMCL outperforms other related methods on synthetic datasets and validate causal relations on semi-synthetic datasets by visualization. © 2022 P.N. Argaw, E. Healey & I.S. Kohane.

关键词： Coronavirus

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Belief update using graphs

Belief update using graphs

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21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21

作者： Georgatos, Konstantinos Department of Mathematics and Computer Science John Jay College of Criminal Justice CUNY 445 West 59th Street New York NY 10019 United States PhD Program in Computer Science Graduate Center CUNY 365 Fifth Avenue New York NY 10016 United States

ISBN: (纸本)9781577353652

The purpose of this paper is to introduce a form of update based on the minimization of the geodesic distance on a graph. We provide a characterization of this class using set-theoretic operators and show that such operators bijectively correspond to geodesic metrics. As distance is generated by distinguishability, our framework is appropriate in contexts where distance is generated by threshold, and therefore, when measurement is erroneous. Copyright © 2008, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Geodesy

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Certified computation of the sign of a matrix determinant 99

Certified computation of the sign of a matrix determinant

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Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms

作者： Victor Y. Pan Yanqiang Yu Department of Mathematics and Computer Science Lehman College CUNY Bronx NY PhD Program in Computer Science CUNY New York NY

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

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

引用

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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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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