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Watermelons Talk: Predicting Ripeness through Tapping

IEEE Internet of Things Magazine 2024年第4期7卷 154-161页

作者： Lin, Yun-Wei Lin, Yi-Bing Chen, Wen-Liang Chang, Chia-Hui Li, Han-Kuan College of Artificial Intelligence Yang Ming Chiao Tung University (NYCU) NYCU China Medical University Miin Wu School of Computing National Cheng Kung University Research Center for Information Technology Innovation Academia Sinica College of Biological Science and Technology NYCU Industrial Development Graduate Program of College of Biological Science and Technology

During the commercial production of watermelons, farmers must swiftly assess fruit ripeness post-harvest to minimize losses through sorting based on edibility time. This process enhances marketability and productivity but is often very tedious in traditional approaches. This article delves into the multifaceted realm of Internet of Things (IoT) based real-time watermelon ripeness evaluation. Watermelons, subject to diverse degrees of ripeness, significantly impact the fruit's taste and texture. Notably, watermelons cease to mature after detachment from the vine, underscoring the importance of selecting the ripest specimens at purchase. Prompt post-harvest fruit ripeness assessment is pivotal to mitigate losses, ensuring accurate sorting based on edibility timeline. Consequently, diligent watermelon ripeness assessment by farmers gains importance for enhanced marketability and productivity. While manual techniques like tapping, color examination, and day counting serve practical purposes, their accuracy relies on subjective judgment. Currently, the prevailing method for assessing watermelon ripeness is the sound test. This tapping technique surprisingly rests on logical grounds, as the resulting sounds offer an adequate ripeness indicator. However, personal interpretations of these sounds are influenced by subjective experiences and traditional wisdom. This article investigates non-destructive methodologies for evaluating watermelon ripeness. Then we propose WatermelonTalk, an IoT based real-time deep learning platform designed for acoustic watermelon testing. We also introduce the concept of the 'tapping ensemble,' not previously found in the literature, which significantly enhances prediction accuracy. The article's contributions encompass the most comprehensive categorization of watermelons in the literature, specifically categorizing 1698 watermelons across 343 varieties by ripeness. Previous studies have considered either the 2-level test (unripe and ripe) or th

关键词： Productivity Deep learning Accuracy Real-time systems Acoustics Crops Farming Agricultural products Commercialization Color Testing

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An Analysis of Interventions with Students with Autistic Spectrum Disorder (ASD) Using Gamified Geometric Thinking

An Analysis of Interventions with Students with Autistic Spe...

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Frontiers in Education (FIE) Conference

作者： Lidiane Maciel Pereira Regina Barwaldt Vinícius Menezes de Oliveira Mateus Oliveira Jung Graduate Program in Science Education Federal University of Rio Grande Rio Grande Brazil Graduate Program in Computing Federal University of Rio Grande Rio Grande Brazil Graduate Program in Computional Modeling Federal University of Rio Grande Rio Grande Brazil

Studies have been indicating the use of digital resources for interventions with children diagnosed with Autism Spectrum Disorder (ASD), especially when it comes to supporting communication and social interaction. However, there is research that suggests the use of digital environments not only in these aspects but also to assist them in their academic knowledge, such as in their mathematical skills, specifically related to geometric thinking. From this context, Active Methodologies point to various ways of rethinking traditional teaching, one of them being gamification, which can be understood as the use of game elements in non-game situations. Gamification has shown significant benefits for this audience, proving to be an effective tool to aid in the development and learning of these individuals. Therefore, this article in its complete form presents an excerpt from a doctoral thesis that seeks to understand the geometric thinking of nine autistic participants through gamified activities in their real mode. The research is divided into two stages: the first stage involves a preliminary survey with five teachers through structured interviews, aiming to investigate the demand for gamified activities for students with ASD, as well as indicating possibilities for such activities. From the Discursive Textual Analysis (DTA) of these interviews, three categories emerged: opportunities, skills, and limitations. As for the second stage, conducted with nine participants, who are students with ASD, interventions were carried out with these activities in their real mode, which will be implemented digitally later. Through the researcher's observation analysis, based on recorded videos of the interventions, three subcategories related to the initial categories can be identified: potential interactions (opportunities); strategies for geometric thinking (skills); and gamification in the real mode (limitations). The first subcategory emerged due to the need to describe how students

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Text and Speech Adjustable Bimodal Emotion Recognition in Conversational Interface

Text and Speech Adjustable Bimodal Emotion Recognition in Co...

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IEEE International Symposium on Consumer Electronics (ISCE)

作者： Komang Rinartha Lun-Wei Ku Social Networks and Human Centered Computing Program Taiwan International Graduate Program Institute of Information Science Nangang Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

Speech emotion recognition (SER) is a Machine Learning (ML) topic that is now receiving a lot of research attention. This can be attributed to its growing capacity, improvements in algorithms, and utilization in practical situations. Furthermore, speech contains not just auditory information but also lexical information. Both speech-based emotion recognition and text-based emotion recognition, called bimodal emotion recognition, can be highly beneficial for human interaction. Take an example, maintaining the communication between elder parents who are left behind after their children leave for education, employment, and family dynamics who can be greatly affected, both physically and emotionally, by feelings of loneliness and social isolation. Bimodal emotion recognition employs both text-based and audio-based methods to identify emotions. Each algorithm was trained separately and then combined using a late fusion technique. RoBERTa text-based emotion recognition can be combined with CNN audio-based emotion recognition by using late fusion to create a bimodal emotion recognition that can obtain more than 50% of accuracy by combining text and audio in a similar proportion or relying on text more than audio. By adjusting the proportion of text or audio emotion recognition, we can get better result of recognition and it can be customized by the user's preference. We elaborate on creating bimodal emotion recognition, conversational interface, a framework for emotion monitoring system, and give the example of a combined algorithm to perform emotion recognition.

关键词： Emotion recognition Accuracy Machine learning algorithms Text recognition Computational modeling Heuristic algorithms Speech recognition Machine learning Real-time systems Monitoring

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Importance of complementary data to histopathological image analysis of oral leukoplakia and carcinoma using deep neural networks

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Intelligent Medicine 2023年第4期3卷 258-266页

作者： Leandro Muniz de Lima Maria Clara Falcão Ribeiro de Assis Júlia Pessini Soares Tânia Regina Grão-Velloso Liliana Aparecida Pimenta de Barros Danielle Resende Camisasca Renato Antonio Krohling Nature-inspired Computing Lab Federal University of Espirito SantoVitoriaBrazil Graduate Program in Computer Science Federal University of Espirito SantoVitoriaBrazil School of Dentistry Departamento de Clinica OdontologicaFederal University of Espirito SantoVitoriaBrazil Graduate Program in Clinical Dentistry Federal University of Espirito SantoVitoriaBrazil

Background Oral cancer is one of the most common types of cancer in men causing mortality if not *** recent years,computer-aided diagnosis(CAD)using artificial intelligence techniques,in particular,deepneural networks have been investigated and several approaches have been proposed to deal with the automateddetection of various pathologies using digital *** studies indicate that the fusion of images with thepatient’s clinical information is important for the final clinical *** such dataset does not yet exist fororal cancer,as far as the authors are aware,a new dataset was collected consisting of histopathological images,demographic and clinical *** study evaluated the importance of complementary data to histopathologicalimage analysis of oral leukoplakia and carcinoma for *** A new dataset(NDB-UFES)was collected from 2011 to 2021 consisting of histopathological imagesand *** 237 samples were curated and analyzed by oral pathologists generating the gold standardfor ***-of-the-art image fusion architectures and complementary data(Concatenation,MutualAttention,MetaBlock and MetaNet)using the latest deep learning backbones were investigated for 4 distincttasks to identify oral squamous cell carcinoma,leukoplakia with dysplasia and leukoplakia without *** evaluate them using balanced accuracy,precision,recall and area under the ROC curve *** Experimental results indicate that the best models present balanced accuracy of 83.24%using images,demographic and clinical information with MetaBlock fusion and ResNetV2 *** represents an improvement in performance of 30.68%(19.54 pp)in the task to differentiate samples diagnosed with oral squamous cellcarcinoma and leukoplakia with or without *** This study indicates that cured demographic and clinical data may positively influence the performance of artificial intelligence models in automated classification of

关键词： Leukoplakia Squamous carcinoma Pathology Deep learning Transfer learning

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A massively parallel speciation-based differential evolution algorithm applied to the 3D-AB protein structure prediction

A massively parallel speciation-based differential evolution...

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作者： Stubs Parpinelli, Rafael Boiani, Mateus Dias, André E. P. Graduate Program in Applied Computing State University of Santa Catarina Santa Catarina Joinville Brazil Rio Grande do Sul Porto Alegre Brazil Computer Science Department State University of Santa Catarina Santa Catarina Joinville Brazil

One of the most challenging problems in Bioinformatics is the finding of a protein conformation and it is known as the Protein Structure Prediction (PSP) problem. The main feature present in the AB off-lattice model is the use of polarity as the main driving force to guide the optimization process. The present work proposes an adaptive evolutionary algorithm based on GPU that is applied to the 3D-AB off-lattice PSP problem. The proposed approach is named cuDSMjDE, and is composed of a Dynamic Speciation-based Mutation Strategies Differential Evolution that employs the jDE mechanism to control F and CR parameters. A crowding strategy is also adopted, increasing competition between individuals and maintaining the diversity of solutions. All routines of the proposed algorithm are developed to run in GPU. During the experiments, eight Protein Data Bank sequences are employed. Results obtained concerning potential energy are competitive when compared with state-of-the-art algorithms. The use of a massively parallel architecture promoted the necessary scalability with speedups up to 708.78 (Figure presented.). © 2021 John Wiley & Sons, Ltd.

关键词： Evolutionary algorithms

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Ontology-Driven IoT System for Monitoring Hypertension 25

Ontology-Driven IoT System for Monitoring Hypertension

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25th International Conference on Enterprise Information Systems, ICEIS 2023

作者： de Souza, Pedro Lopes de Souza, Wanderley Lopes Pires, Luís Ferreira Moreira, João Luiz Rebelo da Silva Rodrigues, Ronitti Juner Ciferri, Ricardo Rodrigues Semantics Cybersecurity & Services Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente P.O. Box 217 Enschede Netherlands Ubiquitous Computing Group Graduate Program in Computer Science Computing Department Federal University of São Carlos P.O. Box 676 São Carlos Brazil

ISBN: (纸本)9789897586484

Hypertension is a noncommunicable disease (NCD) that causes global concern, high costs and a high number of deaths. Internet of Things, Ubiquitous computing, and Cloud computing enable the development of systems for remote and real-time monitoring of patients affected with NCDs like hypertension. This paper reports on a system for monitoring hypertension patients that was built by employing these techniques. This system allows the vital signs of a patient (blood pressure, heart rate, body temperature) to be captured via sensors built in a wearable device similar to a wristwatch. These signals are transmitted to the patient's mobile device for processing, and the generated clinical data are sent to the cloud to be properly presented and analysed by the health professionals responsible for the patient. To deal with semantic interoperability issues that arise when multiple different devices and system components must interoperate, a semantic model was conceived for this system in terms of ontologies for diseases and devices. This paper also presents the semantic module that we developed and implemented in the cloud to perform reasoning based on this model, demonstrating the potential benefits of incorporating semantic technologies in our system. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Ontology

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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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An Efficient Hardware Design for the VVC Prediction Refinement with Optical Flow

IEEE Design and Test

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IEEE Design and Test 2025年

作者： Perleberg, Murilo Munoz, Marcello Maass, Denis Afonso, Vladimir Agostini, Luciano Porto, Marcelo Federal University of Pelotas Video Technology Research Group - Graduate Program in Computing Pelotas Brazil Video Technology Research Group - Sul-rio-grandense Federal Institute Pelotas Brazil

The Prediction Refinement with Optical Flow (PROF) is incorporated in the Affine Prediction of the Versatile Video Coding (VVC) standard. While the Affine Prediction allows the representation of complex motions in the scene, the PROF enhances video encoding efficiency by generating a virtual rotation on each subblock of the current block. Due to the intensive computational processing, this work presents a dedicated hardware design for the PROF and its integration with the Affine Motion Estimation Reconstructor. The synthesis results show that the proposed PROF Core design can process 120 frames per second of 4K Ultra-High-Definition resolution videos while dissipating 2.2mW. © 2013 IEEE.

关键词： Image coding

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ICD-10 - ORPHA: An Interactive Complex Network Model for Brazilian Rare Diseases

ICD-10 - ORPHA: An Interactive Complex Network Model for Bra...

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2023 International Conference on ENTERprise Information Systems, CENTERIS 2023 - International Conference on Project MANagement, ProjMAN 2023 - International Conference on Health and Social Care Information Systems and Technologies, HCist 2023

作者： Neiva, Mariane Barros De Oliveira, Bibiana Mello Schmidt, Amanda Maria Scheibe, Victoria Machado Milke, Julia Cordeiro Dos Santos, Mariana Lopes Yamada, Diego Bettiol Colombo Filho, Marcio Eloi Soares, Giovane Thomazini De Araujo Ribeiro, Yasmin Bruno, Odemir Martinez Felix, Temis Maria Alves, Domingos Raras Network Group Scientific Computing Group University of São Paulo SP Sao Carlos Brazil Medical Genetics Service Porto Alegre Clinical Hospital RS Porto Alegre Brazil Ribeirao Preto Faculty of Medicine University of Sao Paulo São Paulo Brazil Graduate Program in Genetics and Molecular Biology Federal University of Rio Grande Do Sul RS Porto Alegre Brazil Graduate Program in Medicine: Medical Sciences Federal University of Rio Grande Do Sul RS Porto Alegre Brazil

A disease is considered rare if it has a low prevalence. It is estimated that around 400 million people worldwide have a rare disease, including 15 million in Brazil. Consequently, it became a public health priority for the World Health Organization and the Brazilian Health Ministry. In 2014, the Brazilian government launched a national policy regarding the care for rare patients', the Ordinance nº199. The national politic defines guidelines, procedures, and descriptions of rare disease codes to provide access and diagnosis in the public health system to reduce mortality and improve patient's quality of life. Diseases are identified according to the International Classification of Diseases 10th Revision, a widely used terminology in this context. However, there are also different terminologies to codify a rare disease, such as the ORPHAcode provided by Orphanet. This paper proposes a complex network model using the terminologies' relationship to show that the International Classification of Diseases 10th Revision may be generic for diagnosing rare Brazilian patients. Moreover, there is no perfect nomenclature to define rare diseases, but each context has a better application. So, mapping the relationship between each terminology is fundamental for creating consistent semantic relationships in biomedical ontologies, providing a functional environment for carrying out tasks involving more than one terminology. © 2024 Elsevier B.V.. All rights reserved.

关键词： Biomedical Ontology Complex Network Graph Modeling Rare Disease

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On Efficient Processing of Queries for Live Multi-Streaming Soiree Organization

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IEEE Transactions on Services computing 2023年第4期16卷 2812-2826页

作者： Kankeu Fotsing, C.P. Shen, Chih-Ya Huang, Liang-Hao Chen, Yi-Shin Lee, Wang-Chien Yang, De-Nian International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei115 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu300 Taiwan National Tsing Hua University Department of Computer Science Hsinchu300 Taiwan Academia Sinica Institute of Information Science Taipei115 Taiwan Pennsylvania State University Department of Computer Science and Engineering State CollegePA16801 United States

Real-time social interactions and multi-streaming are two critical features of live streaming services. In this paper, we formulate a new fundamental service query, Social-aware Diverse and Preferred Organization Query (SDSQ), that jointly selects a set of diverse and preferred live streaming channels and a group of socially tight viewers for organization of a live multi-streaming soiree. We prove that SDSQ is NP-hard and inapproximable within any factor, and design SDSSel, a 2-approximation algorithm with a guaranteed error bound. Moreover, we study SDSQ-T, a special case of SDSQ, where the social graph is a threshold graph, and propose TDSSel, a 2-approximation algorithm without any error to SDSQ-T. We propose two pruning strategies, PCP and CDP to boost SDSSel and TDSSel. We further propose a more challenging but practical service query, Generalized Social-aware Maximum Preferred and Diverse Query (GSPQ), a generalization of SDSQ. We design GPDSel, a 4-approximation algorithm for GSPQ with a guaranteed error bound. We propose a strategy to improve the approximation ratios of the proposed algorithms. A user study on Twitch validates SDSQ, and the large-scale experiments on real datasets demonstrate the superiority of the proposed algorithms over several baselines for live-streaming services. © 2008-2012 IEEE.

关键词： Approximation algorithms

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