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2024 Computational Humanities Research Conference, CHR 2024

作者： Hicke, Rebecca M.M. Kristensen-McLachlan, Ross Deans Department of Computer Science Cornell University United States Center for Humanities Computing Aarhus University Denmark Department of Linguistics Cognitive Science and Semiotics Aarhus University Denmark

Metaphors are everywhere. They appear extensively across all domains of natural language, from the most sophisticated poetry to seemingly dry academic prose. A significant body of research in the cognitive science of language argues for the existence of conceptual metaphors, the systematic structuring of one domain of experience in the language of another. Conceptual metaphors are not simply rhetorical flourishes but are crucial evidence of the role of analogical reasoning in human cognition. In this paper, we ask whether Large Language Models (LLMs) can accurately identify and explain the presence of such conceptual metaphors in natural language data. Using a novel prompting technique based on metaphor annotation guidelines, we demonstrate that LLMs are a promising tool for large-scale computational research on conceptual metaphors. Further, we show that LLMs are able to apply procedural guidelines designed for human annotators, displaying a surprising depth of linguistic knowledge. © 2024 Copyright for this paper by its authors.

关键词： Linguistics

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A Machine Learning algorithm for Predicting Translation Initiation Start Positions 21

A Machine Learning algorithm for Predicting Translation Init...

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21st IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2024

作者： Digenis, Stefanos Grigoriadis, Dimitris Miliotis, Marios Hatzigeorgiou, Artemis G. Hellenic Pasteur Institute Greece University of Thessaly Department of Computer Science and Biomedical Informatics Greece

ISBN: (纸本)9798350356632

Translation Initiation Start (TIS) sites are specific regions in genes, where the protein synthesis starts. In this study, the specific problem of RNA sequence classification is addressed through the deployment of convolutional neural networks (CNNs) employing features extracted from genomic sequences. Translation initiation is a crucial step in protein synthesis, indicating the sites where the translation mechanism takes place and such, accurate prediction of its start sites is a significant step towards deeper understanding and harnessing of its complex characteristics. To this aim, a state-of-the-art deep learning model has been developed which predicts the presence/absence of translation initiation start sites within RNA sequences. The proposed deep learning model leverages the power of CNNs to effectively capture relevant features in RNA sequences. Trained on a comprehensive dataset of manually annotated translation initiation sites, the model learns to discern key patterns and motifs associated with initiation starts. Through rigorous evaluation, a model comprising sequence, conservation and hexamer features exhibits a remarkably high accuracy rate of 93.75%. This research holds promise for various biological applications, including gene annotation, drug target identification, and understanding the mechanisms of protein synthesis. The ability to reliably predict translation initiation start sites using deep learning provides a valuable tool for computational biologists and experimental researchers alike. © 2024 IEEE.

关键词： Convolutional neural networks

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Comparative Analysis of Memory-Based Collaborative Filtering and Deep Learning Models for Resolving Cold Start and Data Sparsity Issues in E-commerce Recommender Systems 3

Comparative Analysis of Memory-Based Collaborative Filtering...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Shetty, Amrithkala M Manjaiah, D.H. Aljunid, Mohammed Fadhel Yogesh, K.M. Mangalore University Department of Computer Science Karnataka India Thamar University Computer and Informatics Center Thamar Yemen M S Ramaiah University of Applied Sciences Department of Computer Science and Engineering India

ISBN: (纸本)9798350384598

In today's digital landscape, where online activities and e-commerce play pivotal roles, Recommender Systems (RS) have become indispensable tools for enhancing user experiences by providing personalized recommendations. However, collaborative filtering (CF) algorithms often face challenges such as the cold start problem and data sparsity. This work addresses these challenges using memory-based CF approaches such as Singular Value Decomposition (SVD), SVD++, K-Nearest Neighbors (KNN) Baseline, and K-Nearest Neighbors (KNN) Basic. Hyperparameters for these algorithms are optimized using GridSearchCV with cross-validation values set to 5, 10, and 15. Additionally, Deep Learning (DL) models including LSTM, BiLSTM, Att+BiLSTM, User-Product Fusion (UPF), and Deep Collaborative Filtering (DCF) are explored. The BiLSTM model achieves a lower Root Mean Squared Error (RMSE) of 1.1224, while the DCF model achieves a Mean Absolute Error (MAE) of 0.6897. Among the evaluated models, SVD++ achieves an RMSE of 0.9881 and an MAE of 0.7003 with cross-validation set to 15, indicating superior predictive accuracy. The best performance is observed with cross-validation set to 15. These results highlight the effectiveness of optimized memory-based CF algorithms in overcoming the cold start and data sparsity issues, leading to more accurate and reliable recommendations. The findings suggest that memory-based CF algorithms, specifically SVD++, can significantly enhance the development of robust recommender systems, ultimately improving user experience in e-commerce and other online platforms. © 2024 IEEE.

关键词： Collaborative filtering

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Disease Classification in the Cotton Using Convolution Neural Networks

Disease Classification in the Cotton Using Convolution Neura...

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International Conference on Advances in Data science and Computing Technologies, ASDC 2022

作者： Sharma, Aviral Chhabra, Gunjan School of Computer Science Department of Informatics UPES Uttarakhand Dehradun India Department of Computer Science and Engineering GEHU Uttarakhand Dehradun India

ISBN: (纸本)9789819936557

The present research focuses on an integrated architecture for extraction and identification of disease in the cotton plants. The deep learning architecture provided in the paper is capable of extracting the features from the cotton images and then create feature map of the same. The methods provide an automated approach for disease detection using image parts of various plant parts. The proposed methodology provides a unified approach for detection of major cotton diseases, namely bacterial blight, cotton leaf curl virus and wilt. The model can implemented in mobile applications for mass deployments and timely intervention be provided for protection of crops. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cotton

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Optimizing Symbol Visibility Through Displacement 19

Optimizing Symbol Visibility Through Displacement

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19th Scandinavian Symposium on Algorithm Theory, SWAT 2024

作者： Gärtner, Bernd Kalani, Vishwas Reddy, Meghana M. Meulemans, Wouter Speckmann, Bettina Stojaković, Miloš Department of Computer Science ETH Zürich Switzerland Department of Computer Science and Engineering I.I.T. Delhi India Department of Mathematics and Computer Science TU Eindhoven Netherlands Department of Mathematics and Informatics Faculty of Sciences University of Novi Sad Serbia

ISBN: (纸本)9783959773188

In information visualization, the position of symbols often encodes associated data values. When visualizing data elements with both a numerical and a categorical dimension, positioning in the categorical axis admits some flexibility. This flexibility can be exploited to reduce symbol overlap, and thereby increase legibility. In this paper we initialize the algorithmic study of optimizing symbol legibility via a limited displacement of the symbols. Specifically, we consider unit square symbols that need to be placed at specified y-coordinates. We optimize the drawing order of the symbols as well as their x-displacement, constrained within a rectangular container, to maximize the minimum visible perimeter over all squares. If the container has width and height at most 2, there is a point that stabs all squares. In this case, we prove that a staircase layout is arbitrarily close to optimality and can be computed in O(n log n) time. If the width is at most 2, there is a vertical line that stabs all squares, and in this case, we give a 2-approximation algorithm (assuming fixed container height) that runs in O(n log n) time. As a minimum visible perimeter of 2 is always trivially achievable, we measure this approximation with respect to the visible perimeter exceeding 2. We show that, despite its simplicity, the algorithm gives asymptotically optimal results for certain instances. © Bernd Gärtner, Vishwas Kalani, Meghana M. Reddy, Wouter Meulemans, Bettina Speckmann, and Miloš Stojaković;licensed under Creative Commons License CC-BY 4.0.

关键词： Containers

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Resource Based Automatic Calibration System (RBACS) Using Kubernetes Framework

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Intelligent Automation & Soft Computing 2023年第1期35卷 1165-1179页

作者： Tahir Alyas Nadia Tabassum Muhammad Waseem Iqbal Abdullah S.Alshahrani Ahmed Alghamdi Syed Khuram Shahzad Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science&IT Virtual University of PakistanLahore54000Pakistan Department of Software Engineering The Superior UniversityLahore54000Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan

Kubernetes,a container orchestrator for cloud-deployed applications,allows the application provider to scale automatically to match thefluctuating intensity of processing *** cluster technology is used to encapsulate,isolate,and deploy applications,addressing the issue of low system reliability due to interlocking ***-based platforms usually entail users define application resource supplies for eco container *** is a constant problem of over-service in data centers for cloud service *** operating costs and incompetent resource utilization can occur in a waste of *** revolutionized the orchestration of the container in the cloud-native *** can adaptively manage resources and schedule containers,which provide real-time status of the cluster at runtime without the user’s *** clusters face unpredictable traffic,and the cluster performs manual expansion configuration by the *** to operational delays,the system will become unstable,and the service will be *** work proposed an RBACS that vigorously amended the distribution of containers operating in the entire Kubernetes *** allocation pattern is analyzed with the Kubernetes *** estimate the overall cost of RBACS,we use several scientific benchmarks comparing the accomplishment of container to remote node migration and on-site *** experiments ran on the simulations to show the method’s effectiveness yielded high precision in the real-time deployment of resources in eco *** to the default baseline,Kubernetes results in much fewer dropped requests with only slightly more supplied resources.

关键词： Docker container virtualization cloud resource kubernetes

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Data-Driven Design for Educational Game: Leveraging Insights from Educational Game Reviews by the k-means Clustering 8

Data-Driven Design for Educational Game: Leveraging Insights...

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8th International Conference on Information Technology, InCIT 2024

作者： Wang, Qian Polpinij, Jantima Liamthaisong, Khachakrit Chan, Chunpong Singhakam, Dolsun Mahasarakham University Faculty of Informatics Department of New Media Mahasarakham Thailand Mahasarakham University Faculty of Informatics Department of Computer Science Mahasarakham Thailand

ISBN: (纸本)9798350366303

This study proposes a data-driven approach to educational game design, utilizing user feedback and reviews to identify key factors contributing to the success or failure of educational games. By analyzing reviews of popular educational games from Common Sense Media, we employed k-means clustering to group similar reviews and extract meaningful insights. The clustering process, validated through Silhouette Scores and Davies-Bouldin Index metrics, revealed distinct themes in user feedback, highlighting areas for potential enhancement in educational game design. Our results demonstrate that clustering reviews can effectively differentiate between positive and mixed feedback, providing actionable guidance for game developers. This study underscores the value of incorporating systematic user feedback analysis into the educational game design process, offering a pathway to creating more engaging and educationally effective games. The findings contribute to a more structured and evidence-based approach to educational game development, ultimately enhancing learning outcomes and user satisfaction © 2024 IEEE.

关键词： Game design

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BiLSTM for Resume Classification 22

BiLSTM for Resume Classification

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2024 IEEE 22nd World Symposium on Applied Machine Intelligence and informatics, SAMI 2024

作者： Jalili, Amirreza Tabrizchi, Hamed Razmara, Jafar Mosavi, Amir College of Science University of Tehran School of Mathematics Statistics and Computer Science Department of Computer Science Tehran Iran University of Tabriz Faculty of Mathematics Statistics and Computer Science Department of Computer Science Tabriz Iran Obuda University John von Neumann Faculty of Informatics Budapest Hungary Ludovika University of Public Service Budapest Hungary

ISBN: (纸本)9798350317206

In the era of digital recruitment and increasing volumes of job applications, the effective categorization and classification of resumes have become essential for streamlining the hiring process. The purpose of this paper is to present a Bidirectional LSTM architecture method designed to enhance the accuracy and efficiency of resume screening and classification. Leveraging the power of the presented bidirectional LSTM architecture allows the network to effectively capture complex information and context. To enhance the model's performance, we also incorporate word embedding, further enriching textual data representation. We evaluate the proposed method using a comprehensive dataset of resumes across various industries and job roles, demonstrating its superior performance in terms of classification accuracy and speed compared to traditional methods. Furthermore, we discuss the potential applications of this method in recruitment automation and offer insights into its scalability and adaptability in real-world scenarios, providing a valuable tool for human resource (HR) professionals and recruitment agencies seeking to optimize their hiring processes. © 2024 IEEE.

关键词： Data mining

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ClipArtGAN: An Application of Pix2Pix Generative Adversarial Network for Clip Art Generation

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Multimedia Tools and Applications 2025年 1-25页

作者： Elnabawy, Reham H. Abdennadher, Slim Hellwich, Olaf Eldawlatly, Seif Technische Universität Berlin Berlin Germany Computer Science and Engineering Department Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Computer Science Department Faculty of Informatics and Computer Science German International University New Administrative Capital Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University 1 El-sarayat st. Abbassia Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt School of Information Technology New Giza University Giza Egypt

An image can convey a thousand words. This statement emphasizes the importance of illustrating ideas visually rather than writing them down. Although detailed image representation is typically instructive, there are several contexts where simplification of image representations may be more informative. One way to provide an abstract representation of an object is via clip art, which is a simple graphic illustration of an object that can be included in documents or presentations. Clip art representation can be utilized in graphic design for concepts expression and daily life objects illustration. Furthermore, clip art had shown remarkable advantages when compared to real images in terms of editing and manipulation since clip art geometry can be easily edited in addition to the feasibility of storing it in a resolution-independent manner. However, it can be difficult to automatically create clip arts that resemble any object. Here, we propose a novel deep learning model, named ClipArtGAN, that utilizes Generative Adversarial Networks (GANs) to convert a photo of an object to its corresponding clip art. To our knowledge, this is the first GAN model based on Pix2Pix architecture that is capable of automatically generating clip art for any arbitrary object. Three training datasets were collected to train and compare two GAN models based on Pix2Pix and CycleGAN architectures. Our results demonstrate that the mean accuracy obtained using ClipArtGAN utilizing Pix2Pix outperforms that of CycleGAN by 58% and that of Google Images-based search by 54%. These results indicate the efficacy of ClipArtGAN in generating representative clip art images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Efficient Computation Offloading of IoT-Based Workflows Using Discrete Teaching Learning-Based Optimization

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computers, Materials & Continua 2022年第11期73卷 3685-3703页

作者： Mohamed K.Hussein Mohamed H.Mousa Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailiaEgypt Department of Information Technology College of Computer Science at AlKamilUniversity of JeddahJeddahSaudi Arabia

As the Internet of Things(IoT)and mobile devices have rapidly proliferated,their computationally intensive applications have developed into complex,concurrent IoT-based workflows involving multiple interdependent *** exploiting its low latency and high bandwidth,mobile edge computing(MEC)has emerged to achieve the high-performance computation offloading of these applications to satisfy the quality-of-service requirements of workflows and *** this study,we propose an offloading strategy for IoT-based workflows in a high-performance MEC *** proposed task-based offloading strategy consists of an optimization problem that includes task dependency,communication costs,workflow constraints,device energy consumption,and the heterogeneous characteristics of the edge *** addition,the optimal placement of workflow tasks is optimized using a discrete teaching learning-based optimization(DTLBO)*** experimental evaluations demonstrate that the proposed offloading strategy is effective at minimizing the energy consumption of mobile devices and reducing the execution times of workflows compared to offloading strategies using different metaheuristics,including particle swarm optimization and ant colony optimization.

关键词： High-performance computing internet of things(IoT) mobile edge computing(MEC) workflows computation offloading teaching learning-based optimization

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