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Toward Enhancing Diabetes Self-Management with Personalization Through Human Digital Twins for Behavior Change 9th

Toward Enhancing Diabetes Self-Management with Personalizati...

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9th International Congress on Information and Communication Technology (ICICT)

作者： de Oliveira, Catarina Dias James, Lorenzo Khanshan, Alireza Van Gorp, Pieter Univ Minho LASI ALGORITMI Res Ctr Braga Portugal Eindhoven Univ Technol Ind Design Dept Eindhoven Netherlands Eindhoven Univ Technol Ind Engn Dept Eindhoven Netherlands

ISBN: (纸本)9789819750344;9789819750351

Patients with type 2 diabetes need to adhere to a healthy lifestyle to prevent suffering from short-term and long-term escalations. A key aspect of treatment is to supplement educational content with techniques for behavior change. Unfortunately, introducing change and maintaining it is notoriously hard. We explore a novel approach to personalization in the context of Human Digital Twins (HDTs), that facilitates a bidirectional data flow between patients and their digital counterparts. While current mHealth tools show potential, there is room for improvement in incorporating the concept of such twins and considering the complexity of behavior change. We developed a novel architecture that enables mHealth tools, through the lens of the Transtheoretical Model of behavior change, to focus on multiple stages of change and personalize functionalities based on an HDT model that is trained more holistically. Our modular HDT is made of three subcomponents: a health literacy model, a daily activities model, and a model of the user's player type. Our primary contribution is the demonstration of its relevance and feasibility toward the application of personalization for behavior change in patients with diabetes.

关键词： Personalization Diabetes Self-management Human digital twins Behavior change Education Health literacy

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Adaptive Traffic Light Control Through Reinforcement Learning Based on Sensor Integration 21st

Adaptive Traffic Light Control Through Reinforcement Learnin...

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21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Uribe-Chavert, Pedro Lopez-Cuerva, Luis Posadas-Yague, Juan-Luis Poza-Lujan, Jose-Luis Univ Politecn Valencia Doctoral Sch Camino VeraSn Valencia 46022 Spain Univ Politecn Valencia Informat Sch Camino VeraSn Valencia 46022 Spain Univ Politecn Valencia Res Inst Ind Comp & Automat Camino VeraSn Valencia 46022 Spain

ISBN: (纸本)9783031820724;9783031820731

This paper explores intelligent traffic light management advancements, focusing on controlling intersection traffic opening times. The decision-making process is influenced by factors such as traffic density. The information for these decisions is gathered from sensors placed on the streets, whose accuracy can vary. Data collected are processed to aid control agents in decision-making. The paper proposes an intersection control algorithm that operates under the assumption of lacking sensorisation. To balance raw sensor data, control nodes implement a reinforced learning algorithm to select the most suitable combination of sensors to improve traffic parameters. The paper also introduces a method for calculating traffic density by combining sensors with imprecise data. This research contributes to intelligent traffic management by providing a novel approach to intersection control and traffic density calculation.

关键词： Intelligent Traffic Light Management Reinforced Learning Algorithm Traffic Density Calculation

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The Factors Influencing Continuance Intention and Herd Behaviour in Social Commerce: Ease of Use and Online Customer Experience 9th

The Factors Influencing Continuance Intention and Herd Behav...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Zhu, Kaiyan Tan, Caroline Swee Lin Panwar, Tarun RMIT Univ Melbourne Vic 3000 Australia

ISBN: (纸本)9789819754403;9789819754410

Social commerce is proliferating worldwide, but it has only attracted scant academic attention on what psychological factors influence consumer decision-making. Guided by the stimulus-organism-response (S-O-R) framework, we examined the influences from ease of use and online customer experience (OCE) on consumers' continuance intention and herd behaviour in China's two prominent social commerce formats: live streaming commerce and group buying. An online structured questionnaire was distributed among the social commerce consumers, and 694 valid respondents were collected. To evaluate the proposed conceptual model, we employed the statistical analysis approach of exploratory factor analysis (EFA) and partial least square structural equation modelling (PLS-SEM), specifically, SPSS 28.0 and SmartPLS 4.0. This empirical study yields some significant results. First, OCE contributes significantly to continuance intention, followed by herd behaviour. Second, ease of use directly influences continuance intention and OCE but not herd behaviour. OCE plays a mediation role in transferring the effect of ease of use to herd behaviour. Third, we found OCE, one of the uncontrollable factors in social commerce, is critical within the consumer decision-making process. In contrast, ease of use plays a less significant contribution role as a controllable factor. The results of this study provide managerial implications to the social commerce adopters, who are targeting consumers with Chinese backgrounds around the world. The limitations and suggestions for future research are also discussed.

关键词： Ease of use Online customer experience Social commerce Consumer decision-making Continuance intention Herd behaviour

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An Explainable AI-Based Demand Response Optimization Framework for Smart Buildings 21st

An Explainable AI-Based Demand Response Optimization Framewo...

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21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Ibrar, Muhammad Nahom, Hayla Mohammed, Abegaz Marquez-Sanchez, Sergio Fernandez, Javier Hernandez Corchado, Juan Manuel Erbad, Aiman Hamad Bin Khalifa Univ Ar Rayyan Qatar Univ Salamanca BISITE Res Grp Calle Espejo Multiusos S-N Salamanca 37007 Spain Iberdrola Innovat Middle East Doha 210177 Qatar Qatar Univ Coll Engn Doha 2713 Qatar

ISBN: (纸本)9783031820724;9783031820731

Smart building energy demand response (DR) plays a paramount role in the decarbonization of energy use by leveraging different techniques that adjust energy consumption in response to fluctuating prices and demands. In this paper, we present an explainable AI-integrated DR optimization framework for smart buildings. This adaptive XAI-based DR approach intelligently forecasts energy demands and schedules the energy consumption of the buildings based on the forecasted demand patterns, grid conditions, and energy prices. This approach allows the stakeholders to make informed and optimal DR decisions based on the forecasted demand to maximize energy efficiency and user comfort while minimizing electricity costs. The simulation results demonstrate that the proposed approach promotes energy efficiency and user comfort.

关键词： Demand response optimization Energy management Explainable artificial intelligence K-means Random forest regressor

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Bayesian Optimization-Based Hyper-parameter-Tuned Neural Network Regression for Smart Home Energy Consumption Modelling Using Weather Information 9th

Bayesian Optimization-Based Hyper-parameter-Tuned Neural Net...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Alzimami, Ahmed Das, Saptarshi Mueller, Markus Univ Exeter Fac Environm Sci & Econ Ctr Environm Math Penryn Campus Exeter TR10 9FE Cornwall England Univ Exeter Inst Data Sci & Artificial Intelligence North Pk Rd Exeter EX4 4QE Devon England Univ Exeter Environm & Sustainabil Inst Penryn Campus Exeter TR10 9FE Cornwall England

ISBN: (纸本)9789819754403;9789819754410

This paper presents an illustrative study on the predictive analysis of household energy consumption patterns in the context of hybrid energy-powered smart homes having wide variety of appliances. Leveraging the proliferation of smart sensors and the integration of the Internet of Things (IoTs), our research focuses on the integration of weather information in predicting the user behaviour for different household appliances and the total energy consumption. By employing nonlinear regression modelling using artificial neural networks (ANNs), we develop black-box predictive models aimed at predicting total energy usage given the user behaviour patterns and weather information in order seamlessly design an optimized smart home energy system. We use the adjusted R-2 or the coefficient of determination as the performance metric in deciding the best fitted model. In such data-driven modelling, the regression methods need to be further fine-tuned over a wide choice of hyper-parameters which significantly affect the accuracy, speed, and consistency of the machine learning (ML) models. We here benchmark the hyper-parameter optimized model with a simpler hyper-parameter optimized linear model to show the efficacy of the nonlinear model over the linear counterpart. The reported research endeavours to advance the paradigm of sustainable urban living by leveraging datadrivenMLmodels for hybrid energy-powered smart homes, with the goal of fostering energy-efficient and ecologically responsible urban environments.

关键词： Hyper-parameter optimization Bayesian optimization Artificial neural network (ANN) regression Hybrid energy systems Smart homes

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Deep Information Extractor (DIE): A Multipurpose Information Extractor with Shifted Vectors Pre-processing Methods 9th

Deep Information Extractor (DIE): A Multipurpose Information...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Sharma, Raghav Sanger, Amit Tomer, Yatin Pandey, Rohit Hughes Syst Corp Gurugram India

ISBN: (纸本)9789819754403;9789819754410

Information Extraction (IE) from text is a challenging data mining task. Recently, numerous research studies have been proposed in this domain, but most of them used Pre-Processed Embeddings (PPE) inefficiently. PPE converts Pre-Trained Embedding (PTE) into a different format to achieve a more accurate outcome. Most of the existing PPE methods concatenate the embeddings, leading to high computational costs and low accuracy of the extracted information. This paper introduces a novel deep learning model and PPE methods for IE from any text. The proposed PPE uses information from different pre-trained models via establishing sensible relationships between this information without enhancing the computational cost. Proposed PPE methods exploit Bert and Deberta embeddings. To select the most relevant characters and words from a sentence, the bi-directional encoder-decoder Recurrent Neural Network (RNN) model was used, followed by Multi-head Attention (MHAT) to learn the relationship between words. Therefore, results have significantly improved target information estimation accuracy on the benchmark dataset across domains.

关键词： DIE Information extraction Pre-trained embeddings Pre-processed embeddings Bi-directional RNN encoder-decoder

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ChatGPT and Semiotic Representation Theory: Innovating the Measurement of Cognitive Demand in Mathematical Tasks 21st

ChatGPT and Semiotic Representation Theory: Innovating the M...

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21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Puraivan, Eduardo Cofre-Morales, Connie Rodriguez, Miguel Lasnibat-Godoy, Tamara Tapia, Juan Hervas-Gomez, Carlos Diaz-Noguera, Maria Dolores Univ Vina Mar Escuela Ciencias Valparaiso Chile Univ Valparaiso Escuela Ingn Informat Valparaiso Chile Univ Playa Ancha Fac Ciencias Educ Valparaiso Chile Pontificia Univ Catolica Valparaiso Inst Matemat Valparaiso Chile Univ Bielefeld Fak Math Bielefeld Germany Univ Seville Fac Ciencias Educ Andalucia Spain Univ Vina Mar VRA Valparaiso Chile

ISBN: (纸本)9783031820724;9783031820731

Determining the level of cognitive demand in school tasks is a significant challenge, marked by the lack of consensus among evaluators when assigning cognitive complexity in different areas of knowledge. In view of this problem, we propose a methodology based on the Theory of Registers of Semiotic Representation to guide the assignment of cognitive demand in mathematical tasks, specifically focused on the quadratic function. We have used ChatGPT to implement this methodology, applying it to mathematical tasks extracted from school textbooks. The results show that the analysis methodology proposed for the classification of these tasks is simple to implement with ChatGPT and with the experts. Moreover, on the classification of the 3 tasks analyzed, the experts evaluated ChatGPT's performance well with respect to 2 of them, and on a third one there were discrepancies.

关键词： ChatGPT Level of cognitive demand Mathematics tasks

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Smart Mobility Operation at Signalized Intersection Level in Microsimulation Environment 20th

Smart Mobility Operation at Signalized Intersection Level in...

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20th Transport systems Theory and Practice Conference

作者： Tumminello, Maria Luisa Zare, Nazanin Macioszek, Elzbieta Grana, Anna Univ Palermo Dept Engn Palermo Italy Silesian Tech Univ Dept Transport Syst Traff Engn & Logist Katowice Poland

ISBN: (纸本)9783031857508;9783031857515

In the transition toward exclusive operation of connected and automated vehicles (CAVs), many outstanding issues remain regarding methodologies for assessing the performance of CAVs mixed with conventional vehicles (CVs), especially at intersections where their interaction significantly increases. This research proposes a microsimulation-based approach to assess the operational performance of a mixed fleet of CAVs and CVs at signalized intersections. Aimsun Next was used to model CAV driving behavior across increasing market penetration rates (MPR) of CAVs. The model parameters were redefined by comparing simulated capacity data with benchmark capacity functions, which were built based on CAV adjustment factors provided by the Highway Capacity Manual. Simulation results indicate that the throughput of CAV operations depends on their MPR. The microsimulation-based approach provides a methodology for analyzing the assumptions behind the assessment of CAVs' ability to efficiently operate at signalized intersections.

关键词： Signalized intersection connected and automated vehicles traffic microsimulation Aimsun Next

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Text Categorization Can Enhance Domain-Agnostic Stopword Extraction 3rd

Text Categorization Can Enhance Domain-Agnostic Stopword Ext...

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3rd International Conference on Language Processing and Knowledge Management-LPKM

作者： Turki, Houcemeddine Etori, Naome A. Taieb, Mohamed Ali Hadj Omotayo, Abdul-Hakeem Emezue, Chris Chinenye Ben Aouicha, Mohamed Awokoya, Ayodele Lawan, Falalu Ibrahim Nixdorf, Doreen Univ Sfax Data Engn & Semant Res Unit Sfax Tunisia Univ Minnesota Minneapolis MN USA Univ Calif Davis Davis CA USA Tech Univ Munich Munich Germany McPherson Univ Lagos Nigeria Kaduna State Univ Kaduna Nigeria Masakhane Res Community Johannesburg South Africa

ISBN: (纸本)9783031850660;9783031850677

This paper investigates the role of text categorization in streamlining stopword extraction in natural language processing (NLP), specifically focusing on nine African languages alongside French. By leveraging the MasakhaNEWS, African Stopwords Project, and MasakhaPOS datasets, our findings emphasize that text categorization effectively filters out domain-specific keywords for most examined languages, allowing a simpler identification of stopwords. Nevertheless, linguistic variances result in lower detection rates for specific languages. Interestingly, we find that while over 40% of stopwords are common across news categories, less than 15% are unique to a single category. Uncommon stopwords add depth to the text, but their classification as stopwords depends on context. Therefore, combining statistical and linguistic approaches creates comprehensive stopword lists, highlighting the value of our hybrid method. This research enhances NLP for African languages and underscores the importance of text categorization in stopword extraction.

关键词： Stopword Extraction Text Categoization African Languages Domain-agnostic Stopwords

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Novel Methods and Application of Visualizing Web Survey Responses: Technology-Enhanced Quality Improvement in Surveys (TEQUIS) 9th

Novel Methods and Application of Visualizing Web Survey Resp...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Matsumoto, Sayaka Takamatsu, Kunihiko Imai, Shotaro Inakura, Tsunenori Mori, Masao Tokyo Inst Technol Tokyo 1528550 Japan

ISBN: (纸本)9789819750344;9789819750351

Information and Communication Technology (ICT) has made surveys easier to conduct. However, the data collected may show that the response rate in surveys may decline toward the latter half, as participants stop answering, or sometimes forget to answer the questions;the resultant missing data affects the quality of the survey results. We faced this impediment in our institutional research activities at a higher education institution, where we were involved in the tabulation and analysis of surveys targeting students. Therefore, recognizing the necessity to determine the reasons why questions are not answered and to adopt countermeasures, this paper visualizes the presence/absence of responses for each question by introducing two ways, i.e., heatmap style and bar chart style: Technology-Enhanced Quality Improvement in Surveys (TEQUIS). The application of TEQUIS to the dummy data of the questionnaire allowed us to discover problems with the questionnaire and to make suggestions for improvement. Visualization of the distribution of missing data makes it possible to share the shortcomings of the questionnaire among the parties involved. The two visualization methods presented in this paper, i.e., the heatmap and bar chart format diagrams could facilitate monitoring of the survey to ensure that it is not too long or difficult for participants to answer. This paper proposes to incorporate these methods of visualizing the presence/absence of answers to each question while checking the questionnaire to maintain and improve not only the quality of surveys, but also their outcomes.

关键词： Web survey Questionnaire Missing data Visualization of response behavior Technology-enhanced quality improvement in surveys (TEQUIS)

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