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

作者： Alam Ahmad Hidayat Rudi Nirwantono Mahmud Isnan Joko Pebrianto Trinugroho Bens Pardamean Mathematics Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department Bioinformatics and Data Science Research Center BINUS Graduate Program Jakarta Indonesia

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features.

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Multi-Task Learning Frameworks to Classify Food and Estimate Weight From a Single Image

Multi-Task Learning Frameworks to Classify Food and Estimate...

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International Joint Conference on computer science and Software Engineering (JCSSE)

作者： Pakin Siwathammarat Panas Jesadaporn Jakarin Chawachat Data Science Research Center Faculty of Science Chiang Mai University Thailand Department of Internal Medicine Faculty of Medicine Chiang Mai University Thailand Department of Computer Science Faculty of Science Chiang Mai Thailand

Usually, elderly patients in hospitals suffer from malnutrition because they are unable to consume food as prescribed by doctors or nutritionists. Analyzing food intake is labor-intensive and time-consuming. Therefore, machine learning is used to analyze the food intake. Major food analysis tasks include food classification and food weight estimation. The basic machine learning approach to this problem is to combine a food classification model with a food weight estimate model sequentially. When we deployed it, we found that a large amount of memory and models were required. One solution is to use multi-task learning. In this study, we proposed multi-task learning frameworks that could recognize food and predict weight based on a single image. The performance of our frameworks was compared to the baseline models, which only utilized either regression or classification. Although baseline accuracy is higher, our framework has MAPE values that are lower than the baseline. To improve the performance, we explored different approaches for weighting loss, including manual weighting and auto weighting, using uncertainty and auxiliary tasks. From the experiment, our results showed that our multi-task learning framework that adjusted the weight of loss using auxiliary tasks outperformed the baseline models in terms of MAPE and Accuracy. Moreover, we demonstrate our framework when scaling up the backbone from ResNet50 to ResNet101 and ResNet152.

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Connecting Cross-Modal Representations for Compact and Robust Multimodal Sentiment Analysis with Sentiment Word Substitution Error

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IEEE Transactions on Affective Computing 2024年

作者： Sun, Qiyuan Zuo, Haolin Liu, Rui Li, Haizhou The Department of Computer Science Inner Mongolia University Hohhot010021 China Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong CUHKShenzhen Shenzhen518172 China University of Bremen Faculty3 Computer Science / Mathematics Enrique-Schmidt-Str. 5 Cartesium Bremen28359 Germany

Multimodal Sentiment Analysis (MSA) seeks to fuse textual, acoustic, and visual information to predict a speaker’s sentiment states effectively. However, in real-world scenarios, the text modality received by MSA systems is often obtained through automatic speech recognition (ASR) models. Unfortunately, ASR may erroneously recognize sentiment words as phonetically similar neutral alternatives, leading to sentiment degradation in text and impacting MSA accuracy. Recent attempts aim to first identify the sentiment word substitution (SWS) error in ASR results and then refine the corrupted word embeddings using multimodal information for final multimodal fusion. However, such a method includes a burdensome and ambiguous detection operation and ignores the inherent correlations and heterogeneity among different modalities. To address these issues, we propose a more compact system, termed ARF-MSA consisting of three key components to achieving robust MSA with SWS errors: 1) Alignment: we establish connections between the "text-acoustic’ and "text-visual" representations to effectively map the "text-acoustic-visual" data into a unified sentiment space by leveraging their multimodal correlation knowledge;2) Refinement: we perform fine-grained comparisons between the text modality and the other two modalities in the unified sentiment space, enabling refinement of the sentiment expression within the text modality more concisely;3) Fusion: Finally, we hierarchically fuse the dominant and non-dominant representation from three heterogeneity modalities to obtain the multimodal feature for MSA. We conduct extensive experiments on the real-world datasets and the results demonstrate the effectiveness of our model. © 2010-2012 IEEE.

关键词： Alignment

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Sentiment Analysis: Indonesia Netflix User's Comment Using Multiple Lexicon-Based Dictionaries

Sentiment Analysis: Indonesia Netflix User's Comment Using M...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Sebastianus Adi Santoso Mola Tiwuk Widiastuti Rosa V. K. Isabela Ohe Roma Andrea Stevens Karnyoto Bens Pardamean Faculty of Science and Engineering University of Nusa Cendana Kupang Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

A subscription movie streaming service from the United States, Netflix, has been present in Indonesia since 2016 and provides a wide variety of films without showing any single advertisements that can be viewed from anywhere, as long as there is access to the internet. Despite these advantages, many users have complained through the Google Play Store's comments column. Some of the common complaints include frequent buffering and connectivity issues, dissatisfaction with the limited selection of Indonesian movies, lack of subtitles for specific languages, or pricing concerns have also been raised. In this study developed two combined scenario methods using the InSet and SentiStrength_id dictionary to obtain better performance and compare them against independent InSet and SentiStrength_id. This study collected users' comments for the Netflix app in the Google Play Store as a dataset using web scraping techniques through the Google Collaboration tool. The dataset contains 3250 rows spanning the period from January 22, 2024, through June 6, 2024. To ensure proper processing of the text, data cleaning, lowercasing, normalization, tokenization, stemming, and stopwords removal are conducted. The results show that most user opinions are negative. The InSet dictionary has an accuracy of 92%, SentiStrength_id 78%, Combined scenario 1 is 87%, and scenario 2 reaches the highest among others which is 92.52%.

关键词： Sentiment analysis Dictionaries Accuracy Films Collaboration Pricing Motion pictures Tokenization Cleaning Internet

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TCNAS: Transformer Architecture Evolving in Code Clone Detection

TCNAS: Transformer Architecture Evolving in Code Clone Detec...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hongyan Xu Xiaohuan Pei Xiu Su Shan You Chang Xu School of Computer Science and Engineering University of New South Wales Australia Data61 The Commonwealth Scientific and Industrial Research Organisation (CSIRO) School of Computer Science Faculty of Engineering The University of Sydney Australia Big Data Institute Central South University SenseTime Research

Code clone detection aims at finding code fragments with syntactic or semantic similarity. Most of current approaches mainly focus on detecting syntactic similarity while ignoring semantic long-term context alignment, and these detection methods encode the source code using human-designed models, a process which requires both expert input and a significant cost of time for experimentation and refinement. To address these challenges, we introduce the Transformer Code Neural Architecture Search (TCNAS), an approach designed to optimize transformer-based architectures for detection. In TCNAS, all channels are trained and evaluated equitably to enhance search efficiency. Besides, we introduce the dataflow of the code by extracting the semantic information from the code fragments. TCNAS facilitates the discovery of an optimal model structure geared towards the detection, eliminating the need for manual design. The searched optimal architecture is utilized to detect the code pairs. We conduct various empirical experiments on the benchmark, which covering all four types of code clone detection. The results demonstrate our approach consistently yields competitive detection scores across a range of evaluations.

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A Temporal Graphlet Kernel for Classifying Dissemination in Evolving networks

arXiv

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arXiv 2022年

作者： Oettershagen, Lutz Kriege, Nils M. Jordan, Claude Mutzel, Petra Institute of Computer Science University of Bonn Bonn Germany Faculty of Computer Science University of Vienna Vienna Austria Research Network Data Science University of Vienna Vienna Austria

We introduce the temporal graphlet kernel for classifying dissemination processes in labeled temporal graphs. Such dissemination processes can be spreading (fake) news, infectious diseases, or computer viruses in dynamic networks. The networks are modeled as labeled temporal graphs, in which the edges exist at specific points in time, and node labels change over time. The classification problem asks to discriminate dissemination processes of different origins or parameters, e.g., infectious diseases with different infection probabilities. Our new kernel represents labeled temporal graphs in the feature space of temporal graphlets, i.e., small subgraphs distinguished by their structure, time-dependent node labels, and chronological order of edges. We introduce variants of our kernel based on classes of graphlets that are efficiently countable. For the case of temporal wedges, we propose a highly efficient approximative kernel with low error in expectation. We show that our kernels are faster to compute and provide better accuracy than state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： computer viruses

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An Exploratory Study of Automated Anti-phishing System 9th

An Exploratory Study of Automated Anti-phishing System

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9th International Conference on Computing and Informatics, ICOCI 2023

作者： Aziz, Mochamad Azkal Azkiya Riskhan, Basheer Zakaria, Nur Haryani Jambli, Mohamad Nazim Albukhary International University Kedah Alor Setar05200 Malaysia Data Management & Software Solution Research Lab School of Computing Universiti Utara Malaysia Kedah Sintok06010 Malaysia Faculty of Computer Science and Information Technology University Malaysia Sarawak Sarawak Kota Samarahan Malaysia

ISBN: (纸本)9789819995882

Phishing attacks have emerged as a major problem in the digital world due to a rising trend in their frequency. While various approaches have been developed to detect and prevent phishing attacks, a definitive solution to the problem has yet to be discovered. This study discusses automated anti-phishing systems while analyzing and comparing various anti-phishing strategies using exploratory research. Traditional, machine learning, and deep learning-based anti-phishing systems are discussed in the article. The study highlights the use of Artificial Intelligence (AI) based systems, particularly utilizing methods such as Convolutional Neural networks, Support Vector Machines, and Recurrent Neural networks. These AI-based approaches dominate the current trend in the field. This study could potentially be helpful for researchers who wish to delve deeper into the topic of automated phishing detection and prevention systems with a comprehensive review. It is advised to carry out further research to investigate the strengths and limitations of different methods and algorithms used in automated anti-phishing systems to understand their performance and effectiveness better. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Relative magnetic field and slipping effect on Casson dusty fluid of two phase fluctuating flow over inclined parallel plate

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South African Journal of Chemical Engineering 2023年 44卷 135-146页

作者： khan, Dolat Kumam, Poom Kumam, Wiyada Suttiarporn, Panawan Rehman, Ali Fixed Point Research Laboratory Fixed Point Theory and Applications Research Group Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) 126 Pracha Uthit Rd. Bang Mod Bangkok Thung Khru 10140 Thailand Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) 126 Pracha Uthit Rd. Bang Mod Bangkok Thung Khru 10140 Thailand Applied Mathematics for Science and Engineering Research Unit (AMSERU) Program in Applied Statistics Department of Mathematics and Computer Science Faculty of Science and Technology Rajamangala University of Technology Thanyaburi (RMUTT) Pathum Thani 12110 Thailand Faculty of Science Energy and Environment King Mongkut's University of Technology North Bangkok Rayong Campus Rayong 21120 Thailand Special Interest Group for Modelling and Data Analytics Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Kuala Nerus 21030 Malaysia

Due to its significant applications in magnetic devices for cell separation, magnetic drugs for cancer tumor treatment, blood flow adjustment during surgery, magnetic endoscopy, and fluid pumping in industrial and engineering processes, magnetohydrodynamics (MHD) has a wide range of applications. Therefore, the aim of this study to investigate the Relative Magnetic Field and slipping effect on Casson dusty fluid of two-phase fluctuating flow over inclined parallel plate. More exactly, the relative magnetic phenomena are fixed relatively to the plate (MFFRP) or fixed relatively to the fluid (MFFRF). The mathematical modeling was established as the set of partial differential equations for dusty Casson fluid. Buckingham's pi theorem is used to find out the dimensionless variables, to make our system dimensionless. The solution in perturb form is to find out by incorporating Poincare–Lighthill perturbation technique (PLPT). To know in-depth different parameters the graphical results for both velocities are plotted with the help of software Mathcad- 15. It is clearly detected that the relative magnetic field performs a vital part in the fluid as well as particle motion. Moreover, the relative magnetic field play a vital role in the decreasing of skin friction of both plates. © 2023

关键词： Brinkman fluid Dust particles Fluctuating vertical plate Inclined relative magnetic field PLPT

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arXiv

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arXiv 2024年

作者： Klimczak, Jakub Hamed, Ahmed Abdeen Faculty of Computer Science AGH Kawiory 31 Cracow30-055 Poland Clinical Data Science - Network Medicine and AI Sano Centre for Computational Medicine Nawojki 11 Cracow30-072 Poland

Background - The emergence of generative AI tools, empowered by Large Language Models (LLMs), has shown powerful capabilities in generating content. To date, the assessment of the usefulness of such content, generated by what is known as prompt engineering, has become an interesting research question. Objectives - Using the mean of prompt engineering, we assess the similarity and closeness of such contents to real literature produced by scientists. Methods - In this exploratory analysis, (1) we prompt-engineer ChatGPT and Google Bard to generate clinical content to be compared with literature counterparts, (2) we assess the similarities of the contents generated by comparing them with counterparts from biomedical literature. Our approach is to use text-mining approaches to compare documents and associated bigrams and to use network analysis to assess the terms' centrality. Results - The experiments demonstrated that ChatGPT outperformed Google Bard in cosine document similarity (38% to 34%), Jaccard document similarity (23% to 19%), TF-IDF bigram similarity (47% to 41%), and term network centrality (degree and closeness). We also found new links that emerged in ChatGPT bigram networks that did not exist in literature bigram networks. Conclusions - The obtained similarity results show that ChatGPT outperformed Google Bard in document similarity, bigrams, and degree and closeness centrality. We also observed that ChatGPT offers linkage to terms that are connected in the literature. Such connections could inspire asking interesting questions and generate new hypotheses. © 2024, CC BY.

关键词： data mining

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Joining Cast Mg AZ91 and Wrought Al 6082 Through Friction Stir Welding 1

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12th Symposium on Friction Stir Welding and Processing, held at the TMS Annual Meeting and Exhibition, TMS 2023

作者： Mroczka, Krzysztof Dymek, Stanislaw Pietras, Adam Węglowska, Aleksandra Hamilton, Carter Kopyściański, Mateusz Department of Materials Engineering Faculty of Materials Science and Physics Cracow University of Technology Krakow Poland Faculty of Metal Engineering and Industrial Computer Science AGH University of Science and Technology Krakow Poland The Lukasiewicz Research Network Institute of Welding Gliwice Poland Department of Mechanical and Manufacturing Engineering Miami University College of Engineering and Computing 56N Garland Hall OxfordOH45056 United States

ISBN: (数字)9783031226618

ISBN: (纸本)9783031226601

Dissimilar welds of cast magnesium AZ91 with wrought aluminium 6082-T6 were fabricated by friction stir welding (6 mm thick) with the alloys alternately placed on the advancing and retreating sides. The unique weld microstructures were characterized through light and electron microscopies (SEM, EDS), tensile tests were performed on samples containing the entire welds and the microhardness was measured across the weld cross-sections. Preliminary results demonstrate that some regions contain a significant amount of the brittle Al12Mg17 phase. To complement the microstructural and mechanical characterization, numerical simulations of the welding process provided deeper insight into the material flow during mixing and to the temperature distributions across the weld zones. Future work will include micro tensile specimens (cross-sectional area of 0.7 × 0.7 mm) to supplement the conventional tensile tests. These micro tensile specimens will be excised from distinct weld areas to highlight differences in local properties that influence weld behavior. © 2023, The Minerals, Metals & Materials Society.

关键词： Welds

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