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2023 IEEE International Conference on Big Data, BigData 2023

作者： Bonomi, Luca Lionts, Marilyn Fan, Liyue Vanderbilt University Medical Center Dept. Biomedical Informatics NashvilleTN United States Vanderbilt University Dept. Computer Science NashvilleTN United States University of North Carolina College of Computing and Informatics CharlotteNC United States

ISBN: (纸本)9798350324457

Effective disease surveillance systems require large-scale epidemiological data to improve health outcomes and quality of care for the general population. As data may be limited within a single site, multi-site data (e.g., from a number of local/regional health systems) need to be considered. Leveraging distributed data across multiple sites for epidemiological analysis poses significant challenges. Due to the sensitive nature of epidemiological data, it is imperative to design distributed solutions that provide strong privacy protections. Current privacy solutions often assume a central site, which is responsible for aggregating the distributed data and applying privacy protection before sharing the results (e.g., aggregation via secure primitives and differential privacy for sharing aggregate results). However, identifying such a central site may be difficult in practice and relying on a central site may introduce potential vulnerabilities (e.g., single point of failure). Furthermore, to support clinical interventions and inform policy decisions in a timely manner, epidemiological analysis need to reflect dynamic changes in the data. Yet, existing distributed privacy-protecting approaches were largely designed for static data (e.g., one-time data sharing) and cannot fulfill dynamic data requirements. In this work, we propose a privacy-protecting approach that supports the sharing of dynamic epidemiological analysis and provides strong privacy protection in a decentralized manner. We apply our solution in continuous survival analysis using the Kaplan-Meier estimation model while providing differential privacy protection. Our evaluations on a real dataset containing COVID-19 cases show that our method provides highly usable results. © 2023 IEEE.

关键词： Data Privacy Distributed Data Survival Analysis

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GranDIHC-BR 2025-2035 - GC7: Interaction with Emerging Technologies: An Ecosystem Integrating Humans Technologies and Contexts 24

GranDIHC-BR 2025-2035 - GC7: Interaction with Emerging Techn...

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23rd Brazilian Symposium on Human Factors in computing Systems, IHC 2024

作者： Zaina, Luciana Choma, Joelma Prates, Raquel Oliveira Valentim, Natasha Malveira Costa Bicho, Alessandro de Lima Delabrida Silva, Saul Emanuel Frigo, Luciana Bolan Department of Computing Federal University of São Carlos SP Sorocaba Brazil Computing Science Department Federal University of Minas Gerais MG Belo Horizonte Brazil Department of Informatics Federal University of Paraná PR Curitiba Brazil Center for Computational Science Federal University of Rio Grande RS Rio Grande Brazil Department of Computing Federal University of Ouro Preto MG Ouro Preto Brazil Department of Computing Federal University of Santa Catarina SC Araranguá Brazil

ISBN: (纸本)9798400712241

This article discusses the grand challenge of Emerging Technologies, which is part of the seven Grand Challenges of the Human-Computer Interaction (HCI) community for the years 2025-2035 in Brazil. Emerging technologies are characterized by significant scientific advances and rapid adoption, such as the Internet of Things (IoT), 5G, Big Data, Analytics, Artificial Intelligence (AI), Robotics, and Virtual/Augmented/Mixed Reality (VR/AR/MR). Alongside the development of these technologies, various applications are emerging from a symbiosis among individuals, technologies, and contexts that make up a complex ecosystem. The emerging use of these technologies by human beings reveals specific demands for interaction in this ecosystem, highlighting the relevance of this topic. From an HCI perspective, this article discusses how the Brazilian scientific community should contribute over the next decade to effectively adopt emerging technologies and/or the emerging use of technologies in different contexts. Throughout the text, the identified issues pertinent to this challenge are analyzed from the perspective of how the elements of the ecosystem interact, contextualizing them at a national and global level. An agenda of six research topics is presented, actions to overcome the challenge are discussed, and the expected results for each are listed. Finally, ethical issues regarding research with emerging technologies are highlighted, and a final discussion regarding the other six challenges that are part of the HCI Grand Challenges is presented. © 2024 Copyright held by the owner/author(s).

关键词： Economic and social effects

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CHILLAX - at Arabic Hate Speech 2022: A Hybrid Machine Learning and Transformers based Model to Detect Arabic Offensive and Hate Speech 5

CHILLAX - at Arabic Hate Speech 2022: A Hybrid Machine Learn...

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5th Workshop Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection, OSACT 2022

作者： Makram, Kirollos Hany Nessim, Kirollos George Abd-Almalak, Malak Emad Roshdy, Shady Zekry Salem, Seif Hesham Thabet, Fady Fayek Mohamed, Ensaf Hussein Research Support Center in Computing and Informatics Department of Computer Science Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt

ISBN: (纸本)9791095546757

Hate speech and offensive language have become a crucial problem nowadays due to the extensive usage of social media by people of different gender, nationality, religion and other types of characteristics, allowing anyone to share their thoughts and opinions. In this research paper, we proposed a hybrid model for the first and second tasks of OSACT2022. This model used the Arabic pre-trained Bert language model MARBERT for feature extraction of the Arabic tweets in the dataset provided by the OSACT2022 shared task, then fed the features to two classic machine learning classifiers (Logistic Regression, Random Forest). The best results achieved for the offensive tweet detection task were achieved by the Logistic Regression model with accuracy, precision, recall, and f1-score of 80%, 78%, 78%, and 78%, respectively. The results for the hate speech tweet detection task were 89%, 72%, 80%, and 76%. © European Language Resources Association (ELRA).

关键词： Classification (of information)

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SIDE: Sequence-Interaction-Aware Dual Encoder for Predicting circRNA Back-Splicing Events

SIDE: Sequence-Interaction-Aware Dual Encoder for Predicting...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： He, Chengxin Duan, Lei Zheng, Huiru Qu, Yuening Yu, Zhenyang Sichuan University School of Computer Science Chengdu China Sichuan University Med-X Center for Informatics Chengdu China Ulster University School of Computing Northern Ireland United Kingdom

ISBN: (纸本)9798350337488

Circular RNAs (circRNAs) play a critical role in gene regulation and association with diseases due to their specialized structure, which is formed as a closed loop structure during a non-canonical splicing process where the donor site back-spliced to an upstream acceptor site. As fundamental work to clarify their functions and mechanisms, a large number of computational methods for predicting circRNA formation have been proposed, among which, in particular, deep learning is utilized to capture relevant patterns from raw RNA sequences and model their interactions to facilitate prediction. However, these methods fail to fully utilize the important characteristics of back-splicing events, i.e., the positional information of the splice sites and the interaction features of its flanking sequences, for prediction. To this end, we hereby propose a novel approach called SIDE for predicting circRNA back-splicing events using only nucleotide sequences. Our model employs a dual encoder to capture global and interactive features of the sequence, and then a decoder designed by the contrastive learning to fuse out discriminative features improving the prediction of circRNAs formation. Empirical results on three real-world datasets have shown the effectiveness of SIDE. Our code is publicly available at https://***/scu-kdde/Bioinfo-SIDE-2023. © 2023 IEEE.

关键词： back-splicing events prediction circRNA formation contrastive learning deep learning

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Building Community Consensus for Scientific Metadata with YAMZ

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Data Intelligence 2023年第1期5卷 242-260页

作者： Jane Greenberg Scott McClellan Christopher Rauch Xintong Zhao Mat Kelly Yuan An John Kunze Rachel Orenstein Claire Porter Vanessa Meschke Eric Toberer Metadata Research Center College of Computing and InformaticsDrexel University3675 Market St 10th floorPhiladelphiaPA19104USA 2Dept.of PhysicsColorado School of Mines1500 Illinois StGoldenCO 80401USA

This paper reports on a demonstration of YAMZ(Yet Another Metadata Zoo)as a mechanism for building community consensus around metadata *** demonstration is motivated by the complexity of the metadata standards environment and the need for more user-friendly approaches for researchers to achieve vocabulary *** paper reviews a series of metadata standardization challenges,explores crowdsourcing factors that offer possible solutions,and introduces the YAMZ system.A YAMZ demonstration is presented with members of the Toberer materials science laboratory at the Colorado School of Mines,where there is a need to confirm and maintain a shared understanding for the vocabulary supporting research documentation,data management,and their larger metadata *** demonstration involves three key steps:1)Sampling terms for the demonstration,2)Engaging graduate student researchers in the demonstration,and 3)Reflecting on the *** results of these steps,including examples of the dialog provenance among lab members and voting,show the ease with YAMZ can facilitate building metadata vocabulary *** conclusion discusses implications and highlights next steps.

关键词： Metadata standards FAIR metadata Consensus building Community metadata Domain metadata

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Blowing-up solutions for the Moorea–Gibson–Thompson equation with a viscoelastic memory and an external force

Blowing-up solutions for the Moorea–Gibson–Thompson equati...

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作者： Kirane, Mokhtar Draifia, Ala Eddine Jlali, Lotfi Department of Mathematics College of Computing and Mathematical Sciences Khalifa University of Science and Technology Abu Dhabi United Arab Emirates Department of Mathematics and Informatics Institute of Sciences University Center Aflou Laghouat Algeria Riyadh Saudi Arabia

The Moore–Gibson–Thompson equation with a viscoelastic memory and a forcing term is considered. The existence and uniqueness of a local solution are obtained via Faedo–Galerkin's method. Furthermore, it is shown that solutions with or without a positive initial energy experience blowing-up due to the nonlinear forcing term. © 2024 The Authors. Mathematical Methods in the Applied Sciences published by John Wiley & Sons Ltd.

关键词： Galerkin methods

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IoMT-Based Smart Healthcare of Elderly People Using Deep Extreme Learning Machine

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Computers, Materials & Continua 2023年第7期76卷 19-33页

作者： Muath Jarrah Hussam Al Hamadi Ahmed Abu-Khadrah Taher M.Ghazal School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia Center for Cyber Security Faculty of Information Science and TechnologyUniversity Kebangsaan Malaysia(UKM)43600BangiSelangorMalaysia Center for Cyber-Physical Systems Khalifa University127788Abu DhabiUAE

The Internet of Medical Things(IoMT)enables digital devices to gather,infer,and broadcast health data via the cloud *** phenomenal growth of the IoMT is fueled by many factors,including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based *** is a growing interest in providing solutions for elderly people living assistance in a world where the population is rising *** IoMT is a novel reality transforming our daily *** can renovate modern healthcare by delivering a more personalized,protective,and collaborative approach to ***,the current healthcare system for outdoor senior citizens faces new *** healthcare systems are inefficient and lack user-friendly technologies and interfaces appropriate for elderly people in an outdoor ***,in this research work,a IoMT based Smart Healthcare of Elderly people using Deep Extreme Learning Machine(SH-EDELM)is proposed to monitor the senior citizens’*** performance of the proposed SH-EDELM technique gives better results in terms of 0.9301 accuracy and 0.0699 miss rate,respectively.

关键词： ICT ML FN DELM SH-EDELM

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Integrating Deep Learning and Ant Colony Optimization for Improved Shortest Route Problem Solving 5

Integrating Deep Learning and Ant Colony Optimization for Im...

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5th International Conference on Big-data Service and Intelligent Computation, BDSIC 2023

作者： Turky, Ayad Hussain, Abir Bettayeb, Anissa M. Serhani, Mohamed Adel Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering College of Engineering University of Sharjah Sharjah United Arab Emirates Information Technology Center University of Sharjah Sharjah United Arab Emirates Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798400708923

Determining the best shortest path between locations in intelligent transportation systems is crucial but challenging. Traditional approaches, which assume fixed travel times, fall short of accurately reflecting dynamic factors such as weather, time, and day. This paper introduces a novel two-stage approach that integrates deep learning and ant colony optimization to address this limitation. Firstly, deep learning is employed to predict travel times, incorporating weather conditions as input for improved accuracy. Secondly, an ant colony optimization algorithm is utilized to identify the shortest route based on the predicted travel times. We evaluated the proposed approach using the New York City taxi benchmark dataset and observed superior performance compared to current state-of-the-art algorithms. © 2023 ACM.

关键词： Travel time

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VINECS: Video-based Neural Character Skinning

VINECS: Video-based Neural Character Skinning

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhouyingcheng Liao Vladislav Golyanik Marc Habermann Christian Theobalt Max Planck Institute for Informatics Saarland Informatics Campus The University of Hong Kong Saarbrücken Research Center for Visual Computing Interaction and AI

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Rigging and skinning clothed human avatars is a challenging task and traditionally requires a lot of manual work and expertise. Recent methods addressing it either generalize across different characters or focus on capturing the dynamics of a single character observed under different pose configurations. However, the former methods typically predict solely static skinning weights, which perform poorly for highly articulated poses, and the latter ones either require dense 3D character scans in different poses or cannot generate an explicit mesh with vertex correspondence over time. To address these challenges, we propose a fully automated approach for creating a fully rigged character with pose-dependent skinning weights, which can be solely learned from multi-view video. Therefore, we first acquire a rigged template, which is then statically skinned. Next, a coordinate-based MLP learns a skinning weights field parameterized over the position in a canonical pose space and the respective pose. Moreover, we introduce our pose- and view-dependent appearance field allowing us to differentiably render and supervise the posed mesh using multi-view imagery. We show that our approach outperforms state-of-the-art while not relying on dense 4D scans. More details can be found on our project page 1 1 https://***/~mhaberma/projects/2023-Vinecs.

关键词： Geometry Surface reconstruction Computer vision Three-dimensional displays Computational modeling Manuals Rendering (computer graphics)

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Multi-task Learning Graph Neural Networks for Cancer Prognosis Prediction with Genomic Data 46

Multi-task Learning Graph Neural Networks for Cancer Prognos...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Lin, Tsung-Wei Arriagada, Sofia Ormazabal Lin, Che Taiwan Taiwan International Graduate Program AIoT Ntu Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan Department of Electrical Engineering Ntu Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taiwan Smart Medicine and Health Informatics Program Ntu Taiwan

ISBN: (纸本)9798350371499

Providing robust prognosis predictions for cancers with limited data samples remains a challenge for precision oncology. In this study, we propose a novel approach that combines multi-task learning (MTL) and graph neural networks (GNNs) to address this issue. By representing gene-gene interactions as a graph network, our approach leverages multi-task learning to effectively capture the relationships of genes relevant to the oncogenesis and progression of breast, lung, and colon cancer. We demonstrate that our approach improves the cancer prognosis prediction for cancers with fewer samples, such as colon adenocarcinoma, by leveraging the shared gene-gene interactions across different cancer types, obtaining increases in the area under the precision-recall curve (AUPRC) of 24%. Our work contributes to the field of smart healthcare by demonstrating the potential of MTL and GNNs for enhancing cancer prognosis prediction, even with limited data samples. © 2024 IEEE.

关键词： Lung cancer

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