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arXiv 2024年

作者： Viswanath, Siddharth Bhaskar, Dhananjay Johnson, David R. Rocha, João Felipe Castro, Egbert Grady, Jackson D. Grigas, Alex T. Perlmutter, Michael A. O’Hern, Corey S. Krishnaswamy, Smita Department of Computer Science Yale University United States Department of Genetics Yale University United States Program in Computing Boise State University United States Computational Biology and Bioinformatics Program Yale University United States Department of Mathematics Boise State University United States

Understanding the dynamic nature of protein structures is essential for comprehending their biological functions. While significant progress has been made in predicting static folded structures, modeling protein motions on microsecond to millisecond scales remains challenging. To address these challenges, we introduce a novel deep learning architecture, Protein Transformer with Scattering, Attention, and Positional Embedding (ProtSCAPE), which leverages the geometric scattering transform alongside transformer-based attention mechanisms to capture protein dynamics from molecular dynamics (MD) simulations. ProtSCAPE utilizes the multi-scale nature of the geometric scattering transform to extract features from protein structures conceptualized as graphs and integrates these features with dual attention structures that focus on residues and amino acid signals, generating latent representations of protein trajectories. Furthermore, ProtSCAPE incorporates a regression head to enforce temporally coherent latent representations. Importantly, we demonstrate that ProtSCAPE generalizes effectively from short to long trajectories and from wild-type to mutant proteins, surpassing traditional approaches by delivering more precise and interpretable upsampling of dynamics. © 2024, CC BY.

关键词： Molecular docking

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Multi-Objective optimized Complexity Control for the AV1 Video Encoder

Multi-Objective optimized Complexity Control for the AV1 Vid...

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Picture Coding Symposium, PCS

作者： Gustavo Rehbein Isis Bender Guilherme Corrêa Luciano Agostini Marcelo Porto Federal University of Pelotas (UFPel) – Graduate Program in Computing (PPGC) Video Technology Research Group (ViTech)

ISBN: (纸本)9781665492584

AOMedia Video 1 (AV1) is an open-source video encoding format launched in 2018 by Alliance for Open Media. To achieve high coding efficiency, AV1 brings significant innovations in comparison to its predecessor, the VP9 format. However, this came at the cost of a higher encoding complexity due to the newly introduced tools and block partitioning structures. To allow for wide deployment of AV1 in different multimedia devices, adjusting its encoding process according to the available computational resources is strongly desirable. Thus, this work presents an adaptive complexity controller for the AV1 encoder based on multi-objective optimization. Experimental results show that the proposed controller reduces encoding complexity in a target range from 10% to 40%, with satisfactory precision varying between 0.04 and 4.10 percentage points, and a BD-Rate between 0.22% and 5.24%.

关键词： Technological innovation Costs Sensitivity analysis Operating systems Video sequences Focusing Media

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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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Vision of the Seas: Open Visual Perception Framework for Autonomous Sailing Vessels 30

Vision of the Seas: Open Visual Perception Framework for Aut...

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30th International Conference on Systems, Signals and Image Processing, IWSSIP 2023

作者： Andre, Araujo P. D. Chandrasekharan, Ganadev Prajapathy Clua, Esteban Walter Gonzalez Preux, Phillipe Vasconcellos, Eduardo Charles Goncalves, Luiz Marcos Garcia Institute of Computing Universidade Federal Fluminense Niteroi24210-346 Brazil Université de Lille Inria Villeneuve d'Ascq59650 France Univ. Fed. Do Rio Grande Do Norte Elec. and Comp. Eng. Graduate Program Natal59078-970 Brazil

ISBN: (纸本)9798350337297

We propose an Open Visual Perception framework for autonomous navigation and obstacle detection in marine sailing vessels. The proposed framework lays the groundwork for perceiving marine objects needed for vessel navigation and obstacle avoidance using only image processing as the primary method. Various methods are explored to extract features such as waterline, terrain, and other vessels. The proposed framework also incorporates image stabilization techniques without the need for an Inertial Measurement Unit to compensate for ocean wave oscillations. The work also introduces the system architecture of an autonomous marine vessel, besides the components of the open visual perception framework. Results demonstrate the capabilities of obstacle detection, horizon line identification, and water texture detection. © 2023 IEEE.

关键词： Stabilization

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Online Visual Merchandising of Indonesian Traditional Woven Fabric Store through Social Media Marketing

Online Visual Merchandising of Indonesian Traditional Woven ...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Artha Sejati Ananda Janita Sembiring Meliala Miranda Tanjung Sekar Wulan Prasetyaningtyas Ratna Dewi Paramita Management Department BINUS Business School Master Program Bina Nusantara University Jakarta Indonesia Business Creation Program Management Department Binus Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Fashion Design Program Product Design Department School of Computing and Creative Art Bina Nusantara University Jakarta Indonesia

The purpose of this study is to explore the social media marketing strategy of Indonesian traditional woven fabric retailing stores and describe the visual merchandising elements of the social media marketing strategy. This study uses a single case study of an Indonesian small-and-medium enterprise (SME) that produces and sells "uis karo" ("uis nipes") traditional woven fabric. This study identifies how the SME traditional woven store has utilized social media marketing to promote and sell its products and how it incorporates online visual merchandising in its social media marketing activities. The study provides a novel contribution into the study of online visual merchandising and its intersection with social media marketing. This paper suggests traditional woven fabric industry on how three elements of online visual merchandising (path-finding, environmental, and product presentation factors) can be incorporated into social media marketing actions.

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Universal Spam Detection using Transfer Learning of BERT Model

arXiv

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arXiv 2022年

作者： Tida, Vijay Srinivas Hsu, Sonya Center of Advanced Computer Studies School of Computing and Informatics University of Louisiana Lafayette United States Informatics Program School of Computing and Informatics University of Louisiana Lafayette United States

Deep learning transformer models become important by training on text data based on self-attention mechanisms. This manuscript demonstrated a novel universal spam detection model using pre-trained Google's Bidirectional Encoder Representations from Transformers (BERT) base uncased models with four datasets by efficiently classifying ham or spam emails in real-time scenarios. Different methods for Enron, Spamassain, Lingspam, and Spamtext message classification datasets, were used to train models individually in which a single model was obtained with acceptable performance on four datasets. The Universal Spam Detection Model (USDM) was trained with four datasets and leveraged hyperparameters from each model. The combined model was finetuned with the same hyperparameters from these four models separately. When each model using its corresponding dataset, an F1-score is at and above 0.9 in individual models. An overall accuracy reached 97%, with an F1 score of 0.96. Research results and implications were discussed. © 2022, CC BY-NC-ND.

关键词： Classification (of information)

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TERMS: textual emotion recognition in multidimensional space

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Applied Intelligence 2023年第3期53卷 2673-2693页

作者： Ghafoor, Yusra Jinping, Shi Calderon, Fernando H. Huang, Yen-Hao Chen, Kuan-Ta Chen, Yi-Shin Social Networks and Human-Centered Computing Taiwan International Graduate Program Institute of Information Science Academia Sinica. Institute of Information Systems and Applications National Tsing Hua University Hsinchu City Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu City Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Tsing Hua University Hsinchu City Taiwan

Microblogs generate a vast amount of data in which users express their emotions regarding almost all aspects of everyday life. Capturing affective content from these context-dependent and subjective texts is a challenging task. We propose an intelligent probabilistic model for textual emotion recognition in multidimensional space (TERMS) that captures the subjective emotional boundaries and contextual information embedded in a text for robust emotion recognition. It is implausible with discrete label assignment;therefore, the model employs a soft assignment by mapping varying emotional perceptions in a multidimensional space and generates them as distributions via the Gaussian mixture model (GMM). To strengthen emotion distributions, TERMS integrates a probabilistic emotion classifier that captures the contextual and linguistic information from texts. The integration of these aspects, the context-aware emotion classifier and the learned GMM parameters provide a complete coverage for accurate emotion recognition. The large-scale experimentation shows that compared to baseline and state-of-the-art models, TERMS achieved better performance in terms of distinguishability, prediction, and classification performance. In addition, TERMS provide insights on emotion classes, the annotation patterns, and the models application in different scenarios. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Emotion Recognition

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Model for Anomaly Detection in Reducing Water Losses in the Distribution Network of Peru using Machine Learning Algorithms

Model for Anomaly Detection in Reducing Water Losses in the ...

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Software Engineering and Computer Science (CSECS), International Conference on

作者： Mayra Rivera Pricila Lucero José Luis Castillo-Sequera Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Peru Department of Computer Science Group: Health Computing and Intelligent System Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Water leakage in distribution networks is a significant challenge, especially in regions with limited infrastructure like Huancayo, Peru, where losses account for 32.82% of the distributed volume. This study introduces a machine learning-based approach to detect leaks using four algorithms: Autoencoder LSTM, Isolation Forest, One-Class SVM, and K-Nearest Neighbors (KNN). The methodology involved preprocessing historical consumption data (2018–2024) into 12-month temporal sequences per client and evaluating the models based on F1 Score, Precision, and Mean Absolute Error (MAE). Among the algorithms, the Autoencoder LSTM demonstrated superior performance with the highest precision (0.89) and the lowest MAE (0.00402). Its robustness in high-variability contexts enables early and reliable leak detection, providing a cost-effective solution for optimizing water management in resource-constrained environments.

关键词： Support vector machines Machine learning algorithms Autoencoders Distribution networks Forestry Nearest neighbor methods Robustness Leak detection Long short term memory Water resources

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Concept, Technology, Barriers To Smart Village: A Systematic Literature Review

Concept, Technology, Barriers To Smart Village: A Systematic...

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International Conference on Cyber and IT Service Management (CITSM)

作者： Joy Nashar Utamajaya Meyliana Harjanto Prabowo Achmad Nizar Hidayanto Arief Ramadhan Computer Science Department BINUS Graduate Program - Computer Science Department Bina Nusantara University Jakarta Indonesia School of Computing Telkom University Bandung Indonesia

Smart villages that utilize digital technology are a solution to assist rural community services in meeting their needs, especially for developing countries. However, financial problems, namely the high cost of infrastructure, the size of the informal economy and governance are challenges in limiting the government’s aspirations regarding the smart village concept. The presentation of this article examines the state of smart village development in developing countries, which includes conceptual understanding, motivations, drivers and barriers to smart villages. The purpose of this study is to find answers to research questions by reviewing several selected study articles. The method used was to search for selected studies and there were 60 selected studies identified from a search of five literature databases which initially contained 3394 articles. The results of the data were analyzed using synthetic analysis and thematic analysis. The results of this study found data related to Concept, Technology, Barriers To Smart Village which came from several articles by answering questions from the research, namely RQ1: How many articles from the 10 most populous countries discussed Smart Village, RQ2: What are the basic dimensions of Smart Village, RQ3: What new technologies are discussed in Smart Village digitization, RQ4: What are the Challenges in Smart Village. The results of this SLR can also later be used as a reference for further researchers to find out the background of research related to smart villages. The conclusion obtained in this research is the end of the explanation of this article concludes that there are still many shortcomings in this research and it is hoped that in the future many studies will be able to explore other aspects such as regulations and others that are not discussed in the article so that it is expected to be a reference for further researchers to examine information related to smart villages. in order to find an ideal and up-to-date con

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Experimental Evaluation of a Checklist-Based Inspection Technique to Verify the Compliance of Software Systems with the Brazilian General Data Protection Law

arXiv

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arXiv 2023年

作者： Cerqueira, Diego André de Mello, Rafael Maiani Travassos, Guilherme Horta Systems Engineering and Computer Science Graduate Program Federal University of Rio de Janeiro Brazil Computing Institute Federal University of Rio de Janeiro Brazil

Background/Context: Recent laws to ensure the security and protection of personal data establish new software requirements. Consequently, new technologies are needed to guarantee software quality under the perception of privacy and protection of personal data. Therefore, we created a checklist-based inspection technique (LGPDCheck) to support the identification of defects in software artifacts based on the principles established by the Brazilian General Data Protection Law (LGPD). Objective/Aim: To evaluate the effectiveness and efficiency of LGPDCheck for verifying privacy and data protection (PDP) in software artifacts compared to ad-hoc techniques. Method: To assess LGPDCheck and ad-hoc techniques experimentally through a quasi-experiment (two factors, five treatments). The data will be collected from IoT-based health software systems built by software engineering students from the Federal University of Rio de Janeiro. The data analyses will compare results from ad-hoc and LGPDCheck inspections, the participant's effectiveness and efficiency in each trial, defects' variance and standard deviation, and time spent with the reviews. The data will be screened for outliers, and normality and homoscedasticity will be verified using the Shapiro-Wilk and Levene tests. Nonparametric or parametric tests, such as the Wilcoxon or Student's t-tests, will be applied as appropriate. © 2023, CC BY-NC-ND.

关键词： Data privacy

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