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检索条件"机构=Postgraduate Program in Computer science"

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‘Do I Belong Here, There, or Elsewhere?’: The Materiality and Intangible Benefits of Technology in Digital Nomads' Place-Belongingness

‘Do I Belong Here, There, or Elsewhere?’: The Materiality ...

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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Hesam Mohseni Marcos Antonio de Almeida Daniel Schneider António Correia Faculty of Information Technology University of Jyväskylä Jyväskylä Finland Systems Engineering and Computer Science Program (PESC) Federal University of Rio de Janeiro Rio de Janeiro Brazil Postgraduate Program in Informatics (PPGI) Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9798350354423

ISBN: (纸本)9798350354430

The advent of digital technology has contributed to experiencing liquid lifestyles and flexible work arrangements with different mobility routes and targets. Escaping the exhausting routine of commuting to and from work has been a key driver of digital nomadism, a phenomenon that has become increasingly widespread across the globe and is often centered around cross-border travel and expatriate settlement. Due to their frequent mobility, digital nomads face unique challenges in fostering a sense of place-belongingness compared to most other types of workers. Despite the popularity of digital nomadic work/life in the current public discourse, there is no standardized guidebook for understanding how digital technology already supports or could potentially support the sense of place-belongingness among these voluntary migrants. Motivated by this line of research, in our paper, we propose an initial guide with practical methodological considerations on how to investigate this phenomenon using qualitative and quantitative materials. Such studies can assist strategists and organizations in attracting new talent and fostering more inclusive environments.

关键词： Liquids Standards organizations Organizations Mediation Faces

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A Method Based on Deep Neural Network for Instance Segmentation of Retinal Lesions Caused by Diabetic Retinopathy

A Method Based on Deep Neural Network for Instance Segmentat...

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Carlos Santos Marilton Aguiar Daniel Welfer Marcelo Silva Alejandro Pereira Marcelo Ribeiro Bruno Belloni Computer Center Federal Institute of Education Science and Technology Farroupilha Alegrete Brazil Postgraduate Program in Computing Federal University of Pelotas Pelotas Brazil Postgraduate Program in Computer Science Federal University of Santa Maria Santa Maria Brazil Federal University of Pelotas Pelotas Brazil Federal Institute of Education Science and Technology Sul-Rio-Grandense Passo Fundo Brazil

Diabetic Retinopathy is one of the main causes of vision loss and can be identified through ophthalmological exams that aim to locate the presence of retinal lesions such as microaneurysms, hemorrhages, soft exudates, and hard exudates. The development of computerized methods to perform the instance segmentation of lesions may support in the early diagnosis of the disease. However, the instance segmentation of retinal artifacts is a complex task due to factors such as the size of objects and their morphological characteristics. This article proposes a method based on a Mask R-CNN neural network architecture to perform instance segmentation of lesions associated with diabetic retinopathy. The proposed method was trained, adjusted, and tested using the public DDR and IDRiD Diabetic Retinopathy datasets, and implemented with the Detectron2 and OpenCV libraries. The proposed method reached in the DDR dataset, using the SGD optimizer, the mAP of 0.2660 for the limit of I oU of 0.5 in the validation step. The results obtained in the experiments demonstrate that the proposed method showed promising results in the instance segmentation of fundus lesions.

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Crowd and Urban Storytelling: Evaluating a Collective Intelligence Model to Support Discussions about the City 25

Crowd and Urban Storytelling: Evaluating a Collective Intell...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Chaves, Ramon Motta, Claudia Correia, Antonio Paredes, Hugo Caetano, Barbara De Souza, Jano Moreira Schneider, Daniel Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Federal University of Rio de Janeiro Brazil Inesc Tec University of Tr s-os-Montes e Alto Douro Utad Quinta de Prados Apartado 1013 Vila Real Portugal Systems Engineering and Computer Science Program PESC/COPPE/UFRJ Rio de Janeiro Brazil

ISBN: (纸本)9781665405270

In recent years, digital technologies have been used to support discussions about the city and also to involve citizens in participatory public processes. However, despite the widespread use of social media platforms, old issues related to engagement and participation still persist in digital initiatives. The main goal of this study is to carry out an empirical evaluation of a collective intelligence model that combines crowdsourcing and social storytelling to support discussions about the city from a bottom-up perspective. Within a design science research approach we designed a participatory action study that was carried out through a workshop with students and professionals from different areas, such as architecture, urban design and information technology. As a result, we were able to assess whether the collective intelligence model was acceptable to the participants by investigating whether the behavioral assumptions were valid and thus outlining some contributions to the field of urban informatics. © 2022 IEEE.

关键词： Crowdsourcing

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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International Conference on computer Applications and Industrial Electronics (ICCAIE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM 2.5 and PM 10 ). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM 2.5 and PM 10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results.

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Investigating Author Research Relatedness through Crowdsourcing: A Replication Study on MTurk 26

Investigating Author Research Relatedness through Crowdsourc...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Correia, António Paulino, Dennis Paredes, Hugo Guimarães, Diogo Schneider, Daniel Fonseca, Benjamim Inesc Tec University of Trás-os-Montes e Alto Douro Utad Quinta de Prados Vila Real Portugal University of Nebraska at Omaha College of Information Science & Technology OmahaNE68182 United States Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil

ISBN: (纸本)9798350331684

Determining the relatedness of publications by detecting similarities and connections between researchers and their outputs can help science stakeholders worldwide to find areas of common interest and potential collaboration. To this end, many studies have tried to explore authorship attribution and research similarity detection through the use of automatic approaches. Nonetheless, inferring author research relatedness from imperfect data containing errors and multiple references to the same entities is a long-standing challenge. In a previous study, we conducted an experiment where a homogeneous crowd of volunteers contributed to a set of author name disambiguation tasks. The results demonstrated an overall accuracy higher than 75% and we also found important effects tied to the confidence level indicated by participants in correct answers. However, this study left many open questions regarding the comparative accuracy of a large heterogeneous crowd with monetary rewards involved. This paper seeks to address some of these unanswered questions by repeating the experiment with a crowd of 140 online paid workers recruited via MTurk's microtask crowdsourcing platform. Our replication study shows high accuracy for name disambiguation tasks based on authorship-level information and content features. These findings can be of greater informative value since they also explore hints of crowd behavior activity in terms of time duration and mean proportion of clicks per worker with implications for interface and interaction design. © 2023 IEEE.

关键词： Crowdsourcing

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Mapping Tokenomics Arrangements to Expand the Digital Nomad Ecosystem 26

Mapping Tokenomics Arrangements to Expand the Digital Nomad ...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： De Almeida, Marcos Antonio Correia, António De Souza, Jano Moreira Schneider, Daniel Rio de Janeiro Brazil Inesc Tec University of Trás-os-Montes e Alto Douro Vila Real Portugal University of Nebraska at Omaha College of Information Science & Technology OmahaNE68182 United States Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil

ISBN: (纸本)9798350331684

The Massification of remote work, in response to the COVID-19 pandemic, has been causing significant changes in productive and working arrangements, both for individuals, organizations, and society. At the level of personal life philosophy, for example, this transformation can be evidenced in the dissemination of digital nomadism values such as work/leisure balance among corporate workers. On the other hand, at the level of gig/crowd work platforms, the emergence of tensions may indicate the exhaustion of sociotechnical design models adopted by big techs. We show how evidence collected from digital nomads in empirical ethnography studies can inform HCI/CSCWD researchers of design-oriented strands to explore emerging opportunities in new creative digital crypto-economic ecosystems. Finally, we present a proposed research agenda to explore DNs' activities in the crypto-economic ecosystem. © 2023 IEEE.

关键词： COVID-19

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Predicting Relegation Clubs in Italian Serie A with Method based C4.5 Decision Tree Algorithm 1

Predicting Relegation Clubs in Italian Serie A with Method b...

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1st Bukittinggi International Conference on Education, BICED 2019

作者： Syukmana, F. Wahyudi, E. Gata, W. Wahono, H. Febianto, N.I. Kuntoro, A.Y. Nitra, R.O. Effendi, L. Saputra, D.D. Sulaeman, O.R. Computer Science Postgraduate Program STMIK Nusa Mandiri Indonesia Graduate School Master's Degree Computer Science STMIK Nusa Mandiri Indonesia

The purpose of this study is to help small clubs from Italian Serie A in finding the minimum targets to avoid relegation into Serie B competition (below Serie A league). Relegation will reduce the club's income from TV revenues and the decline of enthusiastic supporters. Based on the data from the final standings (seasons 2006 until 2018), this can be explained by the Decision Tree method using the C4.5 algorithm. The methods used in this study are data collection, data pre-processing, model proposal, model testing, and model validation. In this study, it is expected that the value of accuracy exceeds 85% to achieve a proper classification. © Published under licence by IOP Publishing Ltd.

关键词： Decision trees

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Privacy Preserving Electronic Health Record with Consortium Blockchain

Privacy Preserving Electronic Health Record with Consortium ...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Adri Gautama Adian Fatchur Rochim Luhur Bayuaji Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Computer Engineering Diponegoro University Semarang Indonesia Computer Science Budi Luhur University Jakarta Indonesia

Every patient has health record, it was written as statement of patient's conditions, treatments, and medications, and nowadays it is become digitalized, it can be copied and shared easily, but the nature of EHR is private, not for public, private between patients and healthcare provider. Privacy preserving can be done by implementing Blockchain network for storing EHR, only authorized users or nodes can access and write data to the network. New block in blockchain created as per consensus among nodes, every node can join a blockchain network if it is public, to meet a consensus required resources intensive cryptographic challenge, in private blockchain, only selected can join and involved in the network or specific transaction. In healthcare, data privacy is the main concerned, need a secure, scalable, and efficient blockchain hence implementing consortium blockchain is perfect match for EHR where multiple healthcare provider can operate in a single platform to conduct transaction or sharing EHR among members. This study analyzes 15 articles derived from IEEE Explore, science Direct and Scopus by implementing screening with keyword Consortium Blockchain and EHR and proposing new blockchain model accommodating solution for challenges on access, storage, sharing, privacy, and performance.

关键词： Electrical engineering Data privacy Privacy Analytical models Monte Carlo methods Medical services Blockchains

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The Effect of Data Augmentation on the Accuracy of Fish Species Classification Using Deep Learning

The Effect of Data Augmentation on the Accuracy of Fish Spec...

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International Conference on Informatics and Computing (ICIC)

作者： Ellya Helmud Catur Edi Widodo Oky Dwi Nurhayati Doctor Information System Postgraduate Diponegoro University Semarang Indonesia Faculty of Information Technology Information Systems Study Program Atma Luhur Institute of Science and Business Pangkalpinang Indonesia Physics Department Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a convolutional neural network. The dataset used consists of $\mathbf{3 0 1 0}$ fish images, divided into training, validation, and testing sets. The convolutional neural network model was trained both with and without data augmentation. Evaluation results show that the model trained with data augmentation achieved an accuracy of $95 \%$ with a loss value of 0.0983, slightly better than the model without augmentation which achieved an accuracy of $94.56 \%$ with a loss value of $\mathbf{0. 1 7 9 4}$. This indicates that data augmentation techniques are effective in improving model performance, likely because augmentation helps the model generalize better to variations in fish image data. The results of this research demonstrate the significant potential of convolutional neural network for fish image classification tasks. The developed model can serve as a foundation for the development of computer vision-based applications such as automatic fish species identification in fisheries or educational applications. Further research can be conducted by exploring different convolutional neural network architectures, more advanced data augmentation techniques, and larger datasets to improve model performance.

关键词： Deep learning Training Accuracy Computational modeling computer architecture Fish Data augmentation Data models Convolutional neural networks Image classification

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Information Retrieval System to Find Articles and Clauses in UUD 1945 Using Vector Space Model Method 1

Information Retrieval System to Find Articles and Clauses in...

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1st Bukittinggi International Conference on Education, BICED 2019

作者： Sulaeman, O.R. Gata, W. Wahyudi, E. Hakim, M.J. Subandi, R. Setiyawan, R. Pratama, B. Master of Computer Science Postgraduate Program STMIK Nusa Mandiri Indonesia Graduate School Master's Degree Computer Science STMIK Nusa Mandiri Indonesia

This study aims to find articles and clauses from the 1945 Constitution (UUD 1945) using the Vector Space Model method that calculates the similarity of many documents. One document is represented by one clause from each article of the 1945 Constitution. The next step is pre-processing by deleting unnecessary words (stopwords) and changing it into basic words (stemmer) in the Indonesian language. Each document will be indexed to speed up query and simplify the weighting. Words weighting in documents is performed using the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm by calculating the frequency of words in documents and all documents. The document search results will be presented in the ranking with the largest number of scoring appears at the top (descend sorting). The word search in this system more or less takes 90-100 milliseconds in 73 documents. © Published under licence by IOP Publishing Ltd.

关键词： Vector spaces

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