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GPU Acceleration of MIP Intra Prediction in VVC 31

GPU Acceleration of MIP Intra Prediction in VVC

引用

31st European Signal Processing Conference, EUSIPCO 2023

作者： Storch, Iago Roma, Nuno Palomino, Daniel Bampi, Sergio Graduate Program in Computing Institute of Informatics Federal University of Rio Grande do Sul Porto Alegre Brazil Lisboa Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Federal University of Pelotas Graduate Program in Computing Video Technology Research Group Pelotas Brazil

ISBN: (纸本)9789464593600

The Versatile Video Coding (VVC) standard introduced several novel encoding tools for intra frame prediction, increasing the encoder complexity when compared to previous standards. Among these novelties is the MIP tool, whose acceleration has not been tackled in the literature. Therefore, an efficient parallelization of MIP prediction targeting GPU platforms is now proposed. The presented technique makes use of alternative reference samples and computes the distortion in an approximate manner to expose and potentiate massive parallelism. Moreover, the adopted prediction scheduling and memory communication were tailored by considering the GPUs' architecture and memory hierarchy. When compared with a CPU execution, this work is capable to accelerate the MIP prediction up to 105 times at the cost of a negligible coding efficiency loss of 0.284% BD-BR. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Graphics processing unit

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Multi-task Learning Graph Neural Networks for Cancer Prognosis Prediction with Genomic Data 46

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

引用

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

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

ISBN: (纸本)9798350371499

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. © 2024 IEEE.

关键词： Lung cancer

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Fine Tuning Swin Transformer Based Pretrained Model for Microscopic Fungi Images Classification 11

Fine Tuning Swin Transformer Based Pretrained Model for Micr...

引用

11th International Conference on Computer, Control, informatics and its Applications, IC3INA 2024

作者： Anggara, Muchamad Galih Hindasyah, Ahmad Amda, Julfa Muhammad Waskita, A.A. Graduate Program of Informatics Engineering Universitas Pamulang Pamulang Indonesia Research Center for Computing National Research and Innovation Agency Cibinong Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798331542313

Accurate image classification of microscopic pathogenic fungi is a challenge in the field of mycology. This study proposes a Swin Transformer-based approach to automate the identification process. The Swin Transformer model, known for its ability to capture complex spatial interactions, is adapted from ImageNet’s pre-trained model. The adaptation process is focused on fine-tuning the final classification layer to match the 5 classes of fungi present in the dataset. This classification layer is replaced by a new fully-connected layer that has the number of output according to the number of fungal classes. The weights on these layers are randomly initialized and trained in conjunction with several final convolutional layers. In addition, regularization techniques such as dropouts are applied to prevent overfitting. Adaptation using 5-fold cross-validation was carried out to find the best fold with hyperparameters including AdamW optimizer with a learning rate of 10−3, StepLR scheduler, batch size of 128 images, and a total of 9 epochs / iterations. After that, fine-tuning is carried out on the best fold with hyperparameters including AdamW optimizer with a learning rate of 10−5, StepLR scheduler, batch size of 32 images, and a total of 100 epochs / iterations. The final result showed excellent performance, with accuracy, precision, recall and an average F1-score of 95%. These results show the potential of the Swin Transformer in becoming an effective tool for mycologists. © 2024 IEEE.

关键词： Deep learning

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Perpetual Crossers without Sensory Delay: Revisiting the Perceptual Crossing Simulation Studies

Perpetual Crossers without Sensory Delay: Revisiting the Per...

引用

2022 Conference on Artificial Life, ALIFE 2022

作者： Izquierdo, Eduardo J. Severino, Gabriel J. Merritt, Haily Cognitive Science Program Indiana University Bloomington Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

We revisit the perceptual crossing simulation studies, which are aimed at challenging methodological individualism in the analysis of social cognition by studying multi-agent real-time interactions. To date, all of these simulation studies have reported that it is practically impossible to evolve artificially a robust behavioral strategy without introducing temporal delays into the simulation. Also, all of the studies report on a single strategy: a perpetually crossing agent pair. Here, we systematically report on the evolutionary success of neural circuits on the perceptual crossing task, with and without sensory delay. We also report on two different strategies in the ensemble of successful solutions, only one of which had been discussed in the literature previously. © Artificial Life Conference Proceedings 2022.

关键词： Delay circuits

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Acceptance Analysis of IWB Systems in Education using UTAUT2 Model: The Moderating Role of Perceived Technological Innovations

Acceptance Analysis of IWB Systems in Education using UTAUT2...

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2023 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2023

作者： De Jesus, Noelyn Castro, Eltimar T. Buyan, Cory Antonio Batangas State University College of Informatics & Computing Sciences Batangas Philippines Technological Institute of the Philippines Graduate Program Department Manila Philippines Ballard Power Systems Business Technology Burnaby Canada

ISBN: (纸本)9798350321302

Interactive Whiteboard (IWB) Systems are increasingly being used to replace traditional whiteboards in educational activities, making them more efficient and interesting for both teachers and students. However, no studies have been conducted to investigate how age, educational level, and employment status can impact the adoption and usage of IWB systems in the educational setting. The study aims to analyze the acceptance and the influencing factors on faculty members' intention and usage behavior towards IWB systems in teaching, utilizing the Modified-Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. 135 faculty members from various colleges and departments across Batangas State University ARASOF-Nasugbu Campus were surveyed and data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS software for hypotheses testing. The results of the study indicate that faculty members' behavioral intention to use interactive whiteboard systems in teaching is positively influenced by performance expectancy and social influence. Moreover, facilitating conditions and habit positively impact usage behavior. This study, therefore, concludes that there is significant roles of performance expectancy and social influence as well as the moderating effect of age on the behavioral intention to use interactive whiteboard (IWB) systems in education. Furthermore, the study confirms that effort expectancy, hedonic motivation, and status as moderating variables significantly influence faculty members' usage behavior when using IWB systems in teaching. © 2023 IEEE.

关键词： Economic and social effects

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Strengthening the Security of Deniable Authentication Scheme Using Zero-Knowledge Proof 11

Strengthening the Security of Deniable Authentication Scheme...

引用

11th International Conference on Communication and Network Security, ICCNS 2021

作者： Moesriami Barmawi, Ari Rizal Nurjaman, Asep Informatics Graduate Program School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9781450386425

In an electronic voting system, authentication is used to ensure that the voter is legitimate without knowing his/her identity, while the vote collectors verify the data is received from a legitimate user without knowing the identity of the voter. One of the authentication schemes that fulfilled this requirement is called a deniable authentication scheme, where the receiver can prove the source of the message while another party cannot identify the source of the message. In 2013, Li-Takagi et al. proposed a deniable authentication scheme. However, Li-Takagi's scheme has weaknesses if the receiver fully cooperates with the third party. In this case, the third party can identify the source of a given message. In the proposed method, zero-knowledge proof is introduced to preserve the anonymity of the deniable authentication scheme when the receiver fully cooperates with the third party. Based on the analysis, the proposed scheme fulfills the requirement of the deniable authentication scheme when the receiver fully cooperates with the third party. However, the proposed scheme has additional computation costs for securing the shared secret key. Two attack schemes that are carried out on both Li-Takagi and the proposed scheme are the MITM attack and the impersonation attack. The probability of breaking the proposed scheme using an MITM attack is lower than when using Li-Takagi's scheme, but the probability of breaking the proposed scheme using an impersonation attack is the same as Li-Takagi's scheme. © 2021 ACM.

关键词： Authentication

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Classification Based on Texture Analysis for Synthetic Hazy Image from Mount Kelud Haze Image Density 8

Classification Based on Texture Analysis for Synthetic Hazy ...

引用

8th International Conference on Electrical, Electronics and Information Engineering, ICEEIE 2023

作者： Nugroho, Fresy Basid, Puspa Miladin Safitri A. Arif, Yunifa Miftachul Diah, Norizan Mat Bahtiar, Firma Sahrul Nurhayati, Hani Kurniawan, Fachrul Priandani, Nurizal Dwi Dermawan, Dodik Arwin Universitas Islam Negeri Maulana Malik Ibrahim Informatics Engineering Faculty of Science and Technology Malang Indonesia School of Computing Sciences College of Computing Informatics and Media Universiti Teknologi Mara Shah Alam Malaysia Library and Information Science Universitas Islam Negeri Maulana Malik Ibrahim Faculty of Science and Technology Malang Indonesia Manajemen Informatika Program Vokasi Universitas Negeri Surabaya Surabaya Indonesia

ISBN: (纸本)9798350340501

This paper classifies the simulations of homogeneous synthetic images, heterogeneous synthetic hazy images, and original hazy images taken from CCTV (Close Circuit Television) of Mt. Kelud crater using the GLCM (Gray Level Co-Occurrence Matrix) method. The average feature values obtained using the GLCM (Gray Level Co-Occurrence Matrix) method are used to compare the similarity of gray feature values of the three and then classify thin, medium, and thick images. The results for classifying thin haze, medium haze, and thick haze on the homogeneous synthetic hazy image test data obtained an accuracy value of 50%, a precision value of 46%, and a sensitivity value of 65%. As for the classification of thin, medium, and thick fog on heterogeneous synthetic hazy images, test data obtained an accuracy value of 42%, a precision value of 32%, and a sensitivity value of 48%. © 2023 IEEE.

关键词： Textures

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Personality Analysis Based on The Handwriting Shape using Integration of Image Processing and Machine Learning Algorithm

Personality Analysis Based on The Handwriting Shape using In...

引用

Conference on New Media Studies (CoNMedia)

作者： Rosalina Genta Sahuri Hasanul Fahmi Joe Wesley Moerland Rila Mandala Informatics Study Program President University Indonesia Information System Study Program President University Indonesia Magister of Informatics Study Program President University Indonesia Faculty of Computing President University Indonesia

In Indonesia, mental health awareness is not as prominent as physical health, and many adolescents have experienced mental health problems. Some people hide their mental and emotional problems by appearing cheerful and peaceful, but research shows that body language and voice tone can reveal a person's inner psychological state. Similarly, handwriting can be analyzed through graphology to reveal personality traits such as honesty, emotional stability, and fears. Graphology is a time-consuming and expensive process, using image processing and machine learning, graphology can be done quickly without human bias. This research aims to predict the personality traits by implement image processing combined with machine learning to analyze handwriting features. Using lazy learner algorithm of K-Nearest Neighbor algorithm, SVM and Naïve Bayes, the application can predict the MBTI personality from handwriting image with an accuracy of 94%, 78%, and 93% respectively. From the experiment, it is shown that the application able to do the analysis of one's handwriting and then predict the personality traits, fully automated using a computer

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Evaluation of an agent-based reinforcement learning framework for model-based testing

Evaluation of an agent-based reinforcement learning framewor...

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Inteligencia Ambiental, Ingeniería de Software y Salud Electrónica y Móvil (AmITIC), V Congreso Internacional en

作者： Jose Ramírez-Méndez Christian Quesada-López Graduate Program in Computing and Informatics University of Costa Rica San Pedro Costa Rica Department of Computer Science and Informatics University of Costa Rica San Pedro Costa Rica

ISBN: (数字)9798350364538

ISBN: (纸本)9798350364545

This study evaluates an agent-based reinforcement learning framework for model-based testing (MBT). The framework’s performance was assessed on three key metrics: effectiveness and efficiency in achieving model coverage objectives, the quantity and uniqueness of generated test cases, and code coverage. The results show improved metrics for the framework compared to traditional testers in model coverage evaluation and test case metrics. Specifically, the framework achieved higher effectiveness and efficiency, generating a higher average number of test cases with a substantial proportion of unique cases, indicating more diverse and thorough testing. Additionally, the framework, along with random and greedy testers, achieved over 70% coverage across branch, method, and line code metrics. The framework showed slightly higher values in method and line coverage compared to other testers. The evaluation highlights the use of agent-based reinforcement learning to support model-based testing in planning test case generation, exploring models using multiple strategies, and learning guided by testing metrics. Future work will focus on further refining the framework learning model, including error coverage evaluation, and testing its applicability across different software systems.

关键词： Measurement Software testing Codes Calculators Refining Reinforcement learning Software systems Planning Testing

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Maternal Health Risk Classification - A Comparison between Algorithm Decision Tree and k-Nearest Neighbor (kNN)

Maternal Health Risk Classification - A Comparison between A...

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2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology, ICSEIET 2023

作者： Fatmawati, Maylia Novita, Mega Saputro, Nugroho Dwi Chauhan, Alok Singh Nada, Noora Qotrun Sinha, Amit Herlambang, Bambang Agus Gupta, Shruti Universitas Pgri Semarang Faculty of Engineering and Informatics Department of Informatics Semarang Indonesia Universitas Pgri Semarang Postgraduate Program of Science Education Semarang Indonesia School of Computing Science & Engineering Galgotias University Greater Noida India ABES Engineering College Department of Computer Science & Engineering Uttar Pradesh Ghaziabad201009 India

ISBN: (纸本)9798350329186

Maternal health is a critical concern, particularly for individuals who are pregnant and will shape the future generations. However, not all expectant mothers receive tailored attention and care for their unique health needs. Consequently, many mothers face high levels of health risks during pregnancy, highlighting the importance of classifying maternal health risks. Classification can help identify influential factors affecting maternal health and predict risk levels for individual mothers. In the field of data mining, various classification methods exist for analyzing complex datasets. This study focuses on two widely used algorithms: decision tree and k-Nearest Neighbor (kNN). By employing these algorithms, we aim to classify a dataset comprising 1,014 records of maternal health risk levels. Through cross-validation and T-tests, we rigorously evaluate the performance of the decision tree and kNN algorithms in this context. Our findings indicate that the kNN algorithm is more suitable for classifying maternal health risks, achieving a significantly higher accuracy of 71.50% compared to the decision tree algorithm's accuracy of 70.71%. This result establishes the superiority of the kNN algorithm in accurately classifying maternal health risks. These findings have implications for developing targeted interventions and tailored healthcare strategies to mitigate identified risks. © 2023 IEEE.

关键词： Health risks

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