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International Conference on computer Supported Cooperative Work in Design

作者： Marcos Antonio de Almeida António Correia Daniel Schneider Jano Moreira de Souza Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil

ISBN: (纸本)9781728165974;9781728165981

We report the first findings of an empirical study aimed at investigating how COVID-19 pandemic has impacted the work practices and lifestyles of digital nomads (DN). To do this, we analyzed messages, questions and comments posted by digital nomads in a specific online discussion community of the Reddit social network. Preliminary findings indicate COVID-19 as an opportunity to test DN lifestyle by aspiring digital nomads who want to plan their careers and also present evidence of an overload of online channels for actual DNs. On the other hand, we found that much of the literature on digital nomadism is fragmented and scattered through different disciplines and perspectives, with a strong focus on digital nomads' lifestyles. In order to obtain a holistic and unified understanding of digital nomads, we conducted a comprehensive literature review to further conceptualize the phenomenon under study.

关键词： COVID-19 Social networking (online) Pandemics engineering profession Conferences Bibliographies Collaborative work

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Crowdsourcing Urban Narratives for a Post-Pandemic World

Crowdsourcing Urban Narratives for a Post-Pandemic World

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International Conference on computer Supported Cooperative Work in Design

作者： Ramon Chaves Daniel Schneider Claudia Motta António Correia Hugo Paredes Bárbara Caetano Jano Moreira de Souza Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research (NCE) Federal University of Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro UTAD Vila Real Portugal Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil

ISBN: (纸本)9781728165974;9781728165981

Over the past decades, the use of digital technologies to support participatory urban planning and design has been repeatedly described as a crucial instrument and critical building block for tackling historical problems of participation in such processes. Social media, e-participation platforms, and crowdsourcing applications are examples of technologies that can involve citizens in decision-making processes and thus leverage the benefits of collective intelligence. However, despite the extensive use of social media platforms, old problems related to engagement and participation still occur in digital initiatives. Successful collaboration examples between citizens, policymakers, and strategic stakeholders are still scarce based on online social practices. This study aims to introduce a collective intelligence model, which combines crowdsourcing and social storytelling to support participatory urban planning and design from a bottom-up perspective. The paper concludes by discussing a scenario where citizens can engage in mapping, taking photos, sending ideas, or even creating collective stories about their university issues in a post-pandemic future.

关键词： Crowdsourcing Social networking (online) Smart cities Instruments Urban planning Collective intelligence Tools

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Improving debris flow evacuation alerts in Taiwan using machine learning

arXiv

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arXiv 2022年

作者： Tsai, Yi-Lin Irvin, Jeremy Chundi, Suhas Ng, Andrew Y. Field, Christopher B. Kitanidis, Peter K. Department of Civil and Environmental Engineering Stanford University StanfordCA United States Department of Computer Science Stanford University StanfordCA United States Woods Institute for the Environment Stanford University StanfordCA United States Interdisciplinary Environmental Studies Program Stanford University StanfordCA United States Department of Earth System Science Stanford University StanfordCA United States Institute for Computational and Mathematical Engineering Stanford University StanfordCA United States

Taiwan has the highest susceptibility to and fatalities from debris flows worldwide. The existing debris flow warning system in Taiwan, which uses a time-weighted measure of rainfall, leads to alerts when the measure exceeds a predefined threshold. However, this system generates many false alarms and misses a substantial fraction of the actual debris flows. Towards improving this system, we implemented five machine learning models that input historical rainfall data and predict whether a debris flow will occur within a selected time. We found that a random forest model performed the best among the five models and outperformed the existing system in Taiwan. Furthermore, we identified the rainfall trajectories strongly related to debris flow occurrences and explored trade-offs between the risks of missing debris flows versus frequent false alerts. These results suggest the potential for machine learning models trained on hourly rainfall data alone to save lives while reducing false alerts. Copyright © 2022, The Authors. All rights reserved.

关键词： Rain

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Blockchain Technology Based Credibility Verification in E-Journal Entities

Blockchain Technology Based Credibility Verification in E-Jo...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Sri Watini Zulkarnain Kedah Chandra Lukita Petrus Sokibi Gadis Mustika Reymund Rahardja Arsi Yulianjani Husni Teja Sukmana Early Childhood Teacher Education University of Panca Sakti Bekasi Bekasi Indonesia Vice Chancellor University of Malaysian Computer Science Selangor Malaysia Information System University of Catur Insan Cendekia Cirebon Indonesia Information System University of Raharja Tangerang Indonesia Digital Business University of Raharja Tangerang Indonesia Information Management Study Program University of Raharja Tangerang Indonesia Lecture of Information Engineering Syarif Hidayatullah Islamic University Jakarta Indonesia

In the industrial era 4.0, it has surpassed increasingly complex technological advances in the information system that required a very high infrastructure and facilities and prevented fraud. Counterfeiting is a procedure following an entity, statistics, or documents (observe forged documents) with plans to deceive. Falsification of extensive information certificates is exacerbated by the current economic situation in Indonesia, which is currently occurring due to the widespread epidemic, namely COVID-19. The spread of the COVID-19 outbreak has had a significant impact and changes in all sectors, especially certificate forgery in Indonesia. The imitation of busy certificates is the main focus of this journal, focusing on journal publications combined with Blockchain technology. The sophistication of blockchain technology as authentication is comparing two or more items or additional tests to ensure the accuracy, correctness, or correctness of the information. This method uses qualitative methods with data sources based on case studies in controlled supervision with the basic concept of cryptography as the basis of analysis. From the decisions made, it is hoped that this will minimize the level of forgery of certificates widely measured during the COVID-19 era.

关键词： COVID-19 Economics Epidemics Soft sensors Focusing Forgery Blockchains

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Implantable Biosensor for Continuous Serotonin Detection in Freely Moving Crayfish

Implantable Biosensor for Continuous Serotonin Detection in ...

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International Conference on Solid State Sensors and Actuators (TRANSDUCERS)

作者： Jinjing Han Tawen Ho Justin M. Stine Michael A. Straker Jens Herberholz Reza Ghodssi Department of Electrical and Computer Engineering University of Maryland College Park MD USA Institute for System Research University of Maryland College Park MD USA Robert E. Fischell Institute for Biomedical Devices University of Maryland College Park MD USA Department of Psychology University of Maryland College Park MD USA Program in Neuroscience and Cognitive Science University of Maryland College Park MD USA Fischell Department of Bioengineering University of Maryland College Park MD USA

This work presents, for the first time, an implantable biosensor for continuous in vivo serotonin (5-HT) monitoring in freely moving crayfish. 5-HT is an important neurotransmitter that regulates animal behaviors. Conventional methods for detecting 5-HT do not provide real-time information. Our approach facilitates continuous electrochemical monitoring of 5-HT levels without impeding animal natural behavior. The sensing system shows a linear range of 0.4 - 1.2 μΜ with a sensitivity of 0.07 μΑ/μΜ for 5-HT detection. With a 3D-printed package, the sensor can be easily attached to crayfish dorsal carapace allowing electrodes implantation into crayfish heart to monitor neurohormonal dynamics of 5-HT. The implantable biosensor system displayed a significant increase in oxidation currents after 5-HT injections. The device successfully demonstrates the application for in vivo detection of 5-HT in the hemolymph (i.e., blood) of freely behaving crayfish underwater.

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Application of Deep Artificial Neural Network to Model Characteristic Fluctuation of Multi-Channel Gate-All-Around Silicon Nanosheet and Nanofin MOSFETs Induced by Random Nanosized Metal Grains

Application of Deep Artificial Neural Network to Model Chara...

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Sagarika Dash Yiming Li Wen-Li Sung Parallel and Scientific Computing Laboratory National Yang-Ming Chiao Tung University Hsinchu Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang-Ming Chiao Tung University Hsinchu Taiwan Institute of Communications Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Institute of Biomedical Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Center for mm Wave Smart Radar System and Technologies National Yang-Ming Chiao Tung University Hsinchu Taiwan

In this work, we propose a deep artificial neural network (D-ANN) to estimate the work function fluctuation (WKF) on 4-channel stacked gate-all-around (GAA) silicon (Si) nanosheet (NS) and nanofin (NF) MOSFET devices for the first time. The 2-layered simple deep model can well predict the transfer characteristics for both NS/NF FET with a large number of (128) input features, utilizing considerably lesser (1100 samples) data uniformly. The resultant model is evaluated by the $\mathrm{R}^{2}$ score and RMSE to witness its competency and the average error is $< 4\%$ . We do also discuss the circuit simulation possibility by applying the ANN approach.

关键词： Semiconductor device modeling MOSFET Gallium arsenide Artificial neural networks Predictive models Logic gates Silicon

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Biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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A catalog of bad agile smells for agility assessment 22

A catalog of bad agile smells for agility assessment

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22nd Ibero-American Conference on Software engineering, CIbSE 2019

作者： Telemaco, Ulisses Oliveira, Toacy Alencar, Paulo Cowan, Don System Engineering and Computing Science Program Federal University of Rio de Janeiro Brazil David R. Cheriton School of Computer Science University of Waterloo Ontario Canada

Background: The adoption of Agile methods has grown significantly and now almost all software companies state that they are using some aspects of Agile in their software processes. However, the introduction of Agile methods into a software development environment is not a straightforward process. Thus, Agility Assessment (AA) is an important tool that supports the evaluation of how well a company has adopted the agile approach. Problem: Agility Assessment is usually a manual process and there are few automatic steps during the various phases. Part of this limitation arises because the requirements to check the agility of a project/company are still described in natural language and spread across many sources of documents such as manifestos and company policies. Aim: The aim of this paper is to organize the body of knowledge related to Agile Methods and identify a set of requirements that can be used in an Agility Assessment process. We extend the metaphor of a bad smell to an Agility Assessment context and propose the term Bad Agile Smell to describe an early warning signal that something in an agile project may need to be adjusted. Method: A literature review, as well a survey with practitioners, were conducted to identify a set of Agility Bad Smells. The resulting set of identified Agility Bad Smells is presented as a structured catalog. Result: We identified a set of 20 Bad Agile Smells for agile methods that were considered relevant according to research criteria. The Bad Agile Smells are presented in a structured catalog. © XXII Ibero-American Conference on Software engineering, CIbSE 2019. All rights reserved.

关键词： Software design

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A metamodel to represent agile software development projects 22

A metamodel to represent agile software development projects

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22nd Ibero-American Conference on Software engineering, CIbSE 2019

Agile Software Development is an important paradigm in the software engineering community and a significant movement in the software industry. According to Version One Survey 2018[28], 97% of the surveyed organizations are using some aspect of agile practices. However, the adoption of agile practices is not a straightforward task and there are many open questions related to agile methods that the academic community is trying to answer. One of the challenges of academic research related to Agile Software Development is the lack of metamodels to support the representation of agile projects. A preliminary investigation conducted in this study revealed that existing software development metamodels focus on representing elements related to software process definition and neglect aspects related to software project execution such as Iterations, Deliveries and Task Instances. This study aims at investigating how existing software development metamodels support the representation of agile software development projects and how they can be extended to fill possible gaps. To achieve this goal, we first identified a set of concepts, entities, and information related to agile projects. Then, we investigated how existing software development metamodels represent the identified elements. Finally, we proposed a metamodel, that we named Agile Software Development Project Metamodel (ASDPM), which contains the elements necessary to represent agile software development projects. © XXII Ibero-American Conference on Software engineering, CIbSE 2019. All rights reserved.

关键词： Software design

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Dynamical phases of short-term memory mechanisms in RNNs

arXiv

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arXiv 2025年

作者： Kurtkaya, Bariscan Dinc, Fatih Yuksekgonul, Mert Blanco-Pozo, Marta Cirakman, Ege Schnitzer, Mark Yemez, Yucel Tanaka, Hidenori Yuan, Peng Miolane, Nina CNC Program Stanford University Stanford United States KUIS AI Center Department of Computer Engineering Koc University Turkey Geometric Intelligence Lab UC Santa Barbara Santa Barbara United States Kavli Institute for Theoretical Physics UC Santa Barbara Santa Barbara United States Stanford University Stanford United States James H. Clark Center for Biomedical Engineering & Sciences Stanford University Stanford United States Howard Hughes Medical Institute Stanford University Stanford United States Phi Lab NTT Research Sunnyvale United States Center for Brain Science Harvard University Cambridge United States State Key Laboratory of Medical Neurobiology Institute for Translational Brain Research MOE Frontiers Center for Brain Science Fudan University China Research Institute of Intelligent Complex System Fudan University China

Short-term memory is essential for cognitive processing, yet our understanding of its neural mechanisms remains unclear. A key focus in neuroscience has been the study of sequential activity patterns, where neurons fire one after another within large networks, to explain how information is maintained. While recurrent connections were shown to drive sequential dynamics, a mechanistic understanding of this process still remains unknown. In this work, we first introduce two unique mechanisms that can subserve short-term memory: slow-point manifolds generating direct sequences or limit cycles providing temporally localized approximations. Then, through analytical models, we identify fundamental properties that govern the selection of these mechanisms, i.e., we derive theoretical scaling laws for critical learning rates as a function of the delay period length, beyond which no learning is possible. We empirically verify these observations by training and evaluating more than 35,000 recurrent neural networks (RNNs) that we will publicly release upon publication. Overall, our work provides new insights into short-term memory mechanisms and proposes experimentally testable predictions for systems neuroscience. © 2025, CC BY.

关键词： Long short-term memory

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