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SSRN 2023年

作者： Fuada, Syifaul Duttagupta, Subhasri Adiono, Trio Siregar, Lindawani Program Studi Sistem Telekomunikasi Universitas Pendidikan Indonesia West Java Province Bandung Indonesia Department of Computer Science and Engineering Amrita Viswa Vidyapeetham Keralam Amritapuri-Campus 690525 India Electrical Engineering Department School of Electrical Engineering and Informatics Institut Teknologi Bandung West Java Province Bandung Indonesia Teaching Factory of Manufacturing Electronics Politeknik Negeri Batam Kepulauan Riau Province Batam Indonesia

Street lighting is a public facility that gives highway users’ convenience during their nighttime trips, making it essential to be controlled and monitored using IoT technology. This study aimed to develop a smart lighting system connected through a Mesh network. The system monitors the street light and automatically controls it using the ESP8266 module, a microcontroller with Wi-Fi built-in. The options in selecting the lamp type include Halogen, Fluorescent, LED, or Incandescent. In general, LED is used because it has an advantage in energy-saving over other lamp types. Moreover, the system is equipped with automatic light intensity control through a dimming technique by Pulse Width Modulation (PWM), while BH1750 is used as a light sensor. Furthermore, it monitors the environmental temperature and humidity using DHT22. The communication between the lighting module is based on ESP-Mesh because each node is connected and forwarding data to the destination and the coverage area. ESP-Mesh allows the wireless network to have a scalable architecture in a broader area. It has features to self-organize and self-configure, implying the network could be built and maintained autonomously. Additionally, the system has a web server service with access to control each node and find out the mesh routing table. The results showed that each node could be configured as a mesh network using ESP8266, which sends dimming commands and reads the sensors. The dimming and intensity control was tested using real public street light called NL200D LED module and an off-the-self dimming control driver module, N-Channel MOSFET FQP30N06L. Furthermore, a Mesh connection was tested using five super bright 3mm LEDs representing real street light due to the limitation of the LED module in the laboratory. The street light has three control modes, including on/off-based, intensity control-based or dimming control manually through the website, and intensity control-based automatically using the two se

关键词： Mesh generation

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Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records

arXiv

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arXiv 2023年

作者： Sivarajkumar, Sonish Huang, Yufei Wang, Yanshan Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Department of Health Information Management University of Pittsburgh PittsburghPA United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States

Objective: To pre-train fair and unbiased patient representations from Electronic Health Records (EHRs) using a novel weighted loss function that reduces bias and improves fairness in deep representation learning models. Methods: We defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features. We applied the proposed model, called Fair Patient Model (FPM), to a sample of 34,739 patients from the MIMIC-III dataset and learned patient representations for four clinical outcome prediction tasks. Results: FPM outperformed the baseline models in terms of three fairness metrics: demographic parity, equality of opportunity difference, and equalized odds ratio. FPM also achieved comparable predictive performance with the baselines, with an average accuracy of 0.7912. Feature analysis revealed that FPM captured more information from clinical features than the baselines. Conclusion: FPM is a novel method to pre-train fair and unbiased patient representations from EHR data using a weighted loss function. The learned representations can be used for various downstream tasks in healthcare and can be extended to other domains where bias and fairness are important. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data

arXiv

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arXiv 2024年

作者： Chen, Shan Gallifant, Jack Guevara, Marco Gao, Yanjun Afshar, Majid Miller, Timothy Dligach, Dmitriy Bitterman, Danielle S. Program Mass General Brigham Harvard Medical School BostonMA United States Department of Radiation Oncology Brigham and Women's Hospital Dana-Farber Cancer Institute BostonMA United States Computational Health Informatics Program Boston Children's Hospital Harvard Medical School BostonMA United States Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Department of Critical Care Guy's & St Thomas' NHS Trust London United Kingdom Department of Medicine University of Wisconsin School of Medicine and Public Health MadisonWI United States Department of Computer Science Loyola University Chicago ChicagoIL United States

Brief communication 70 words: Generative models have been showing potential for producing data in mass. This study explores the enhancement of clinical natural language processing performance by utilizing synthetic data generated from advanced language models. Promising results show feasible applications in such a high-stakes domain. © 2024, CC BY-NC-SA.

关键词： Natural language processing systems

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A Clinical Decision Support System of Pressure Ulcers Tissue Classification 21

A Clinical Decision Support System of Pressure Ulcers Tissue...

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5th International Conference on Medical and Health informatics, ICMHI 2021

作者： Chang, Dun-Hao Chu, Po-Jui Li, Yi-Jhen Ning, Chun-Kai Chien, Ting-Ying Department of Information Management Yuan Ze University Taoyuan City Taiwan Department of Plastic and Aesthetic Surgery Far Eastern Memorial Hospital Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9781450389846

Pressure ulcers (PUs) are a common problem associated with great morbidity and cost. Because of the lack of professional personnel in the institutions or home care system, the diagnosis and treatment of PUs were sometimes delayed. We aim to develop an automatic tissue classification and severity evaluation system of PUs using AI technology. All PU images were collected from patients in the Far Eastern Memorial Hospital (FEMH) in recent 3 years and labeled according to different tissue types. The labeled images were used to train the tissue segmentation model with U-net convolutional neural network (CNN). The percentage of the different tissue can be calculated automatically and would be compared with the manually labeled one. The output of tissue classification was transformed to a clinical decision support system (CDSS) with a rule-base formula. Our study showed that proposed system can classify each tissue automatically and reach an accuracy about 93.6%. However, the CDSS's accuracy rate was only 64% for the referral suggestion and 62% for the care recommendation. Hence, the system should be modified with more data and information. © 2021 ACM.

关键词： Tissue

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Analysis A parallel combination between the Number Theorists aRe Us, Riverst Shamir Adleman and Triple Data Encryption Standard methods for measuring the speed of document security 2

Analysis A parallel combination between the Number Theorists...

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2nd International Conference on Mechanical, Electronics, computer, and Industrial Technology, MECnIT 2018

作者： Hardiaman Hulu, Luken Candawan William Sipayung, David Hafis, Muhammad Christnatalis Informatics Engineering Study Program Deparment of Technology and Computer Science Universitas Prima Indonesia Medan Indonesia

computer security is a very important thing in an information system. The strength of the NTRU algorithm is the difficulty of finding a short vector of a lattice (a discrete subgroup of a collection of vectors that includes the entire vector environment) of a random polynomial that has a large degree. The strength of the RSA algorithm lies in the level of difficulty in factoring non-prime numbers into the primary factor. As long as no efficient algorithm has been found to find the prime factors of large integers, the RSA algorithm is highly recommended for message encryption. And finally this is the TRIPEL DES algorithm designed using a 56-bit key, and that size is enough to run a secure encryption technique. This algorithm provides a simple solution that is running the DES Algorithm 3 times for data blocks. The results of our study of measuring speed on encryption and decryption using the same file are superior to the RSA algorithm compared to the NTRU and TRIPLE DES algorithms. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Security of data

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Traffic sign detection using histogram of oriented gradients and max margin object detection 2

Traffic sign detection using histogram of oriented gradients...

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2nd International Conference on Mechanical, Electronics, computer, and Industrial Technology, MECnIT 2018

作者： Reinaldo Manurung, Natanael Simbolon, Juara Immanuel Christnatalis Informatics Engineering Study Program Deparment of Technology and Computer Science Universitas Prima Indonesia Medan Indonesia

Traffic signs are important markers in two-wheeled and four-wheeled vehicles. However, there is a change in direction or arrangement on the road that cannot be opened on a map which can cause incorrect information, which can cause traffic jams. In this journal the author uses a camera mounted on a car that provides a solution for drivers who issue problems that occur on the road that show directions or arrangements that are not directly updated using the HOG and MMOD methods. HOGs and MMODs are methods that can refute objects well, and move images and will be recognized immediately. The information received can be sent directly to an electronic map so that it can be accessed automatically by the driver's information and assistance and other information finds the right path, so that it can help the driver and can avoid traffic jams. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Traffic signs

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Speech AI for All: Promoting Accessibility, Fairness, Inclusivity, and Equity

Speech AI for All: Promoting Accessibility, Fairness, Inclus...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Wu, Shaomei Wenzel, Kimi Li, Jingjin Li, Qisheng Pradhan, Alisha Kushalnagar, Raja Lea, Colin Koenecke, Allison Vogler, Christian Hasegawa-Johnson, Mark Su, Norman Makoto Ratner, Nan Bernstein *** Mountain ViewCA United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States *** SeattleWA United States Department of Informatics Ying Wu College of Computing New Jersey Institute of Technology NewarkNJ United States Gallaudet University WashingtonDC United States Apple CupertinoCA United States Cornell University IthacaNY United States Technology Access Program Gallaudet University WashingtonDC United States The Grainger College of Engineering University of Illinois Urbana-Champaign Urbana-ChampaignIL United States Department of Computational Media University of California Santa Cruz Santa CruzCA United States Department of Hearing and Speech Sciences University of Maryland College Park College ParkMD United States Neuroscience and Cognitive Science University of Maryland College Park College ParkMD United States

ISBN: (纸本)9798400713958

Trained and optimized for typical and fluent speech, speech AI works poorly for people with speech diversities, often interrupting them and misinterpreting their speech. The increasing deployment of speech AI in automated phone menus, AI-conducted job interviews, and everyday devices poses tangible risks to people with speech diversities. To mitigate these risks, this workshop aims to build a multidisciplinary coalition and set the research agenda for fair and accessible speech AI. Bringing together a broad group of academics and practitioners with diverse perspectives, including HCI, AI, and other relevant fields such as disability studies, speech language pathology, and law, this workshop will establish a shared understanding of the technical challenges for fair and accessible speech AI, as well as its ramifications in design, user experience, policy, and society. In addition, the workshop will invite and highlight first-person accounts from people with speech diversities, facilitating direct dialogues and collaboration between speech AI developers and the impacted communities. The key outcomes of this workshop include a summary paper that synthesizes our learnings and outlines the roadmap for improving speech AI for people with speech diversities, as well as a community of scholars, practitioners, activists, and policy makers interested in driving progress in this domain. © 2025 Copyright held by the owner/author(s).

关键词： Speech enhancement

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Position: Bayesian deep learning is needed in the age of large-scale AI 24

Position: Bayesian deep learning is needed in the age of lar...

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Proceedings of the 41st International Conference on Machine Learning

作者： Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig José Miguel Hernández-Lobato Aliaksandr Hubin Alexander Immer Theofanis Karaletsos Mohammad Emtiyaz Khan Agustinus Kristiadi Yingzhen Li Stephan Mandt Christopher Nemeth Michael A. Osborne Tim G. J. Rudner David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang Department of Mathematics The University of Manchester Manchester UK Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge Spotify London UK Computational Neuroscience Unit University of Bristol Bristol UK Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany and Department of Computer Science Technical University of Munich Munich Germany and Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge UK Department of Mathematics University of Oslo Oslo Norway and Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative California Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London UK Department of Computer Science UC Irvine Irvine Department of Mathematics and Statistics Lancaster University Lancaster UK Department of Engineering Science University of Oxford Oxford UK Center for Data Science New York University New York Munich Center for Machine Learning Munich Germany and Department of Statistics LMU Munich Munich Germany DeepMind London UK and Department of Statistics University of Oxford Oxford UK Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences and Center for Data Science Computer Science Department New York University New York Department of Computer Science Purdue University West Lafayette

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.

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Automating the Practice of science – Opportunities, Challenges, and Implications

arXiv

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arXiv 2024年

作者： Musslick, Sebastian Bartlett, Laura K. Chandramouli, Suyog H. Dubova, Marina Gobet, Fernand Griffiths, Thomas L. Hullman, Jessica King, Ross D. Nathan Kutz, J. Lucas, Christopher G. Mahesh, Suhas Pestilli, Franco Sloman, Sabina J. Holmes, William R. Institute of Cognitive Science Osnabrück University Osnabrück49090 Germany Department of Cognitive Linguistic & Psychological Sciences Brown University ProvidenceRI02912 United States Centre for Philosophy of Natural and Social Science London School of Economics Lakatos Building Houghton Street LondonWC2A 2AE United Kingdom AaltoFI-00076 Finland Department of Computing Science University of Alberta 8900 114 St NW EdmontonABT6G 2S4 Canada Cognitive Science Program Indiana University 1101 E 10th St BloomingtonIN47405 United States School of Psychology University of Roehampton LondonSW15 4JD United Kingdom Departments of Psychology and Computer Science Princeton University PrincetonNJ United States Department of Computer Science Northwestern University IL United States Department of Chemical Engineering and Biotechnology University of Cambridge CambridgeCB3 0AS United Kingdom Department of Computer Science and Engineering Chalmers University of Technology Gothenburg412 96 Sweden Department of Applied Mathematics and Electrical and Computer Engineering University of Washington Seattle98195 United States School of Informatics University of Edinburgh 10 Crichton St. EH8 9AB United Kingdom Department of Materials Science and Engineering University of Toronto Canada Department of Psychology Department of Neuroscience The University of Texas AustinTX United States Department of Computer Science University of Manchester M13 9PL United Kingdom

Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: Where do the greatest opportunities lie for automation in scientific practice?;What are the current bottlenecks of automating scientific practice?;and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice. © 2024, CC BY.

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Feature Selection in Cross-Project Software Defect Prediction 3

Feature Selection in Cross-Project Software Defect Predictio...

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3rd International Conference on science and Technology 2019, ICST 2019

作者： Saifudin, A. Trisetyarso, A. Suparta, W. Kang, C.H. Abbas, B.S. Heryadi, Y. Informatics Engineering Pamulang University Jalan Raya Puspitek 46 Banten15310 Indonesia Computer Science Program Bina Nusantara University Jalan Kebon Jeruk Raya 27 Jakarta11530 Indonesia Informatics Department Pembangunan Jaya University Banten Indonesia Department of Electronics and Communication Engineering Kwangwoon University Korea Republic of

Advances in technology have increased the use and complexity of software. The complexity of the software can increase the possibility of defects. Defective software can cause high losses. Fixing defective software requires a high cost because it can spend up 50% of the project schedule. Most software developers don't document their work properly so that making it difficult to analyse software development history data. Software metrics which use in cross-project software defects prediction have many features. Software metrics usually consist of various measurement techniques, so there are possibilities for their features to be similar. It is possible that these features are similar or irrelevant so that they can cause a decrease in the performance of classifiers. In this study, several feature selection techniques were proposed to select the relevant features. The classification algorithm used is Naive Bayes. Based on the analysis using ANOVA, the SBS and SBFS models can significantly improve the performance of the Naïve Bayes model. © Published under licence by IOP Publishing Ltd.

关键词： Defects

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