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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Patrícia Da Costa Morgana Da Rosa Rafael Soares Eduardo Da Costa Sergio Bampi Graduate Program on Microelectronics Federal University of Rio Grande do Sul Porto Alegre Brazil Graduate Program on Computing Federal University of Pelotas (UFPel) Pelotas Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil Graduate Program on Microelectronics (PGMICRO) Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

This work presents an optimized exponential function VLSI hardware design by Taylor series expansion. The proposed architecture implements the exponential by approximating the logic design of a 4 th -order Taylor series and explores efficient CMOS arithmetic operation strategies. It implements a shift-based divider and explores an efficient 4-2 adder compressor in the adder tree. The proposal with a −7 to 11 input values range shows an output error of around 2% of MRED with a reduced energy consumption of 3.63 pJ/operation for 32-bit output. For a 64-bit output, the energy per operation of the VLSI exponential unit is 14.97pJ/op, being able to process a more comprehensive input range (i.e., −14 to 22) for a negligible mean output error of around 1.7% of MRED.

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

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

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

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

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

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.

关键词： Lungs Genomics Medical services Colorectal cancer Breast Multitasking Oncology Graph neural networks Prognostics and health management Cancer

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Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge computing

arXiv

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arXiv 2024年

作者： Moore, Ervin Imteaj, Ahmed Hossain, Md Zarif Rezapour, Shabnam Amini, M. Hadi MiamiFL33199 United States Center FIU School of Computing Southern Illinois University United States Knight Foundation School of Computing and Information Sciences Florida International University MiamiFL33199 United States Enterprise and Logistics Engineering program at FIU

Federated Learning (FL) is a privacy-preserving distributed machine learning scheme, where each participant’s data remains on the participants’ devices and only the local model generated utilizing the local computational power is transmitted throughout the database. However, the distributed computational nature of FL creates the necessity to develop a mechanism that can remotely trigger any network agents, track their activities, and prevent threats to the overall process posed by malicious participants. Particularly, the FL paradigm may become vulnerable due to an active attack from the network participants, called a poisonous attack. In such an attack, the malicious participant acts as a benign agent capable of affecting the global model quality by uploading an obfuscated poisoned local model update to the server. This paper presents a cross-device FL model that ensures trustworthiness, fairness, and authenticity in the underlying FL training process. We leverage trustworthiness by constructing a reputation-based trust model based on agents’ contributions toward model convergence. We ensure fairness by identifying and removing malicious agents from the training process through an outlier detection technique. Additionally, we establish authenticity by generating a token for each participating device through a distributed sensing mechanism and storing that unique token in a blockchain smart contract. Further, we insert the trust scores of all agents into a blockchain and validate their reputations using various consensus mechanisms that consider the computational task. © 2024, CC BY.

关键词： Federated learning

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Graphene-functionalized textile composites for wearable Joule heating applications

Advanced Nanocomposites

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Advanced Nanocomposites 2025年 2卷 108-123页

作者： Omar Faruk Abbas Ahmed Ashfaqul Hoque Khadem Lu Jia Luyi Sun Department of Materials Science and Engineering Binghamton University State University of New York at Binghamton NY 13902 USA Polymer Program Institute of Materials Science and Department of Chemical & Biomolecular Engineering University of Connecticut Storrs CT 06269 USA Department Mechanical Engineering College of Engineering Computing and Applied Science Clemson University Clemson SC 29634 USA College of Textile Engineering Taiyuan University of Technology Jinzhong Shanxi 030600 China

Thermal comfort is essential for maintaining physiological well-being, with textile materials traditionally serving as the primary medium for regulating heat exchange between the body and its environment. However, conventional textiles often fall short of maintaining optimal thermal balance. So, there is an increasing demand for advanced thermoregulation systems that can effectively reduce heat loss and enhance warmth, ensuring consistent comfort. Recent research has highlighted the promise of advanced functional materials, especially graphene and its composites, for modifying textiles to improve thermal properties. This article comprehensively reviews recent advancements in graphene-functionalized textile composites, focusing on their applications in wearable Joule heaters designed for personalized thermal comfort or thermal therapy. Various graphene-functionalized textile composites are reviewed from the perspectives of material properties, processing strategies and device fabrication methods. Key challenges and future opportunities are summarized for graphene-functionalized textile-based Joule heaters as innovative solutions in wearable thermal regulation.

关键词： Graphene Textile Composite Wearable electronics Joule heater

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Minimizing the Number of Stunting Prevalence Using the Euclid Algorithm Clustering Approach 7

Minimizing the Number of Stunting Prevalence Using the Eucli...

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7th IEEE International Conference of Computer Science and Information Technology, ICOSNIKOM 2023

作者： Zarlis, Muhammad Oktavia, Tanty Buaton, Relita Ernawan, Ferda Andrian, Kevin Bina Nusantara University Information System Management Department BINUS Graduate Program - Master of Information System Management Jakarta11480 Indonesia STMIK Kaputama Informatics Engineering Binjai20714 Indonesia Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Pekan26600 Malaysia BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Department of Information Systems Management Jakarta Indonesia

ISBN: (纸本)9798350360752

Superior and quality human resources are based on healthy human resources with indicators of adequate nutritional intake according to age development. However, the world still faces the problem of hunger and malnutrition today. According to a UNICEF report, the number of people suffering from malnutrition in the world will reach 767.9 million people in 2021. The World Health Organization (WHO) said that malnutrition is a dangerous threat to the health of the world's population. Stunting also has an impact in Indonesia, the prevalence of toddlers experiencing stunting in Indonesia is 24.4% in 2021. The solution created is to classify and cluster the prevalence of stunting to produce a pattern that can be used as best practice to be transmitted to other affected areas. The algorithm used is Euclid. The Euclid algorithm can cluster stunting prevalence data into 4 clusters with the very little category at 79%, the little category at 67%, the many categories at 51%, and the very much category at 21%. The results of the classification and clustering of the best stunting prevalence in cluster one with a very small number, can be used as a source of accurate and updated information that can be used by the government in its efforts to optimize stunting handling in each district/city based on artificial intelligence which can provide handling and optimization patterns. stunting in every district/city. © 2023 IEEE.

关键词： Classification (of information)

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A Novel Approach for Commercial Opportunities Qualification Using the BANT Methodology under the Fuzzy Set Theory Framework.

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Procedia Computer Science 2024年 246卷 1271-1280页

作者： Marcus Vinicius Leite Jair Minoro Abe Marcos Leandro Hoffmann Souza Graduate Program in Production Engineering-Paulista University-UNIP Rua Dr. Bacelar 1212-São Paulo-SP-04026002 Brazil Graduate Program in Applied Computing-Vale do Rio dos Sinos University-UNISINOS Av. Unisinos 950 - São Leopoldo-RS-93022-000- Brazil

Demand generation is crucial for organizations, supplying sales teams with well-qualified commercial opportunities. Despite the wide variety of existing opportunity qualification methodologies, the subjective nature of experts’ final evaluation remains an obstacle to efficiency and productivity in the business process. This research investigated how Fuzzy Set Theory and Fuzzy Logic could be applied to the BANT methodology for qualifying commercial opportunities, aiming to replace these deliberative evaluations by experts to increase the sales cycle performance. A fuzzy inference system was developed to emulate the assessments of the experts. The analysis of the ratings obtained after processing a sample of commercial opportunities from 2022 and 2023 confirmed the system’s effectiveness in aligning with expert perceptions. While the study indicated room for refinements in the model, the findings underscore the potential to streamline the qualification of opportunities and improve sales cycle performance.

关键词： Fuzzy Set Theory Fuzzy Logic Decision Making Commercial Opportunity Qualification BANT Methodology Sales Force Support Commercial Process Optimization

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Comprehensive Averaged Model of DCM-Operated Boost Converter Fed by PV Source

Comprehensive Averaged Model of DCM-Operated Boost Converter...

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Workshop on Smart Grid and Renewable Energy (SGRE)

作者： Fatima A Fairouz Mohammed A Elgendy Musbahu Muhammed School of Engineering Newcastle University Newcastle Upon Tyne United Kingdom Renewable Energy program Kuwait Institute for Scientific Research Industrial Shuwaikh Kuwait School of Computing Engineering & Digital Technologies Teesside University Middlesbrough United Kingdom

The average model of DC-DC converters is commonly used to analyze the control stability and dynamics of switched-mode converters. Although the average model of the boost converter (BC) in the continuous conduction mode (CCM) and the discontinuous conduction modes (DCM) is well-covered in the literature, the modelling and control of the DCM-operated BC with the maximum power point tracking (MPPT) has not been paid much attention. In this paper, a comprehensive state-space averaged model of DCM-operated BC fed by photovoltaic (PV) source is derived. Higher-order, lower-order, reduced-order, and full-order models are considered. MATLAB simulation is used to verify the derived models. The converter dynamics were analyzed based on the bode-plot and the step-response for each model. Results show that the lower-full-order model is considered the most suitable choice for modelling the DCM-operated boost converter.

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Measuring Design in Businesses and Government—A Framework to Measure Design Impact 8th

Measuring Design in Businesses and Government—A Framework t...

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8th International Conference on Research into Design, ICoRD 2021

作者： Venkatesh, Ashreya M. Wood, Kristin L. Silva, Arlindo Program Singapore University of Technology and Design Tampines Singapore College of Engineering Design and Computing University of Colorado Denver Denver United States Program Director for the MEng in Innovation By Design Tampines Singapore

ISBN: (纸本)9789811601187

Private and public sector organizations are realizing significant and lasting economic and social value through the adoption of design (Sheppard et al. in The business value of design. McKinsey & Company (2018), [1]);(2015 dmi: Design value index results and commentary—design management institute (2015), [2]);(Brown in Design thinking. Harvard Business Review, [3, 4]). As such, projects and initiatives that are design focused are increasingly being undertaken by businesses and governments. However, there is only a nascent understanding of how the impact of design should be measured. A comprehensive framework has been developed and tested with businesses and the government of Singapore to measure the outcomes of their designs. The framework consists of four impact areas—enabling economic transformation, raising quality of life, advancing brand and culture, and making ground-breaking achievements in design. The design impact framework considers different types of impact and shows that while certain types of "impact" can be quantified, we also now have other ways for measuring impact and widening its definition. The design impact framework provides a basis and common language for discussing designs across disciplines and scale. The framework and tool jointly create a comprehensive and robust structure that may assist designers and organizations communicate their designs more effectively. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Design in Singapore Impact assessment framework Measuring design impact

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OGrES Welcome! Toward a systematic theory for serious game design 21

OGrES Welcome! Toward a systematic theory for serious game d...

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8th ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play, CHI PLAY 2021

作者： Blackburn, Nancy N. Cardona-Rivera, Rogelio E. Laboratory for Quantitative Experience Design School of Computing and the Entertainment Arts and Engineering Program University of Utah Salt Lake CityUT United States

ISBN: (纸本)9781450383561

Despite the vast scholarship around serious games, there is still no consensus methodology for how to best go about designing them. This paper presents work-in-progress toward synthesizing an actionable, systematic serious game design theory. We introduce mIBO as a computational model to aid in thinking about game design processes and rationales, and OGrES as a method for defining the function of a serious game. © 2021 Owner/Author.

关键词： Serious games

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Generating reward structures on a parameterized distribution of dynamics tasks

Generating reward structures on a parameterized distribution...

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2021 Conference on Artificial Life, ALIFE 2021

作者： Leite, Abe Izquierdo, Eduardo J. Cognitive Science Program Indiana University Bloomington United States Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

In order to make lifelike, versatile learning adaptive in the artificial domain, one needs a very diverse set of behaviors to learn. We propose a parameterized distribution of classic control-style tasks with minimal information shared between tasks. We discuss what makes a task trivial and offer a basic metric, time in convergence, that measures triviality. We then investigate analytic and empirical approaches to generating reward structures for tasks based on their dynamics in order to minimize triviality. Contrary to our expectations, populations evolved on reward structures that incentivized the most stable locations in state space spend the least time in convergence as we have defined it, because of the outsized importance our metric assigns to behavior fine-tuning in these contexts. This work paves the way towards an understanding of which task distributions enable the development of learning. © 2021 ALIFE. All Rights Reserved.

关键词： Multi-task learning

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