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Scheduled Sampling for Recursive Multi-Step GPU Temperature Forecasting 23

Scheduled Sampling for Recursive Multi-Step GPU Temperature ...

引用

23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Wang, Harold Jiang, Xunfei Ebrahimi, Mahdi University of California San Diego Department of Cognitive Science San DiegoCA92092 United States California State University Department of Computer Science NorthridgeCA91330 United States

ISBN: (纸本)9798350374889

Energy-efficient workload management is increasingly important in data centers as demand for cloud computing continues to grow. Recent findings in workload management point to thermal-aware load balancers as an effective approach to reduce energy consumption via cooling costs. Due to the high energy cost of Data Centers (DCs) and possible risk of disrupting critical services, simulations can be used to evaluate temperature-aware load-balancing algorithms. In this paper, we leverage an existing DC simulator's environmental parameters for integrating a temperature prediction model to construct a temperature-aware workload distribution simulation. Using scheduled sampling, we implemented a custom recursive multi-step forecasting model that reduced the discrepancy between training error and simulation predictions. Compared with a baseline GPU temperature prediction model, our hybrid CNN-LSTM model achieved a reduction in RMSE from 2.54 to 0.73. © 2024 IEEE.

关键词： Prediction models

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Comparative Analysis of CNN Models for Multiclass Detection on Colon Disease 9

Comparative Analysis of CNN Models for Multiclass Detection ...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Sulistyawan, Jason Sebastian Nugroho, Kuncahyo Setyo Isnan, Mahmud Pardamean, Bens Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

Gastrointestinal diseases are significant health concerns that mostly affect the digestive and biliary tracts. These can only be observed internally through endoscopy, or a modern approach named Wireless Capsule Endoscopy. Though the approach has proven to work, the process relies heavily on medical experts, which is prone to error when given a considerably large load. The use of deep learning in detecting diseases with images has been proven to work in various cases and can be implemented for gastrointestinal diseases. This study compared four deep learning models based on Convolutional Neural Network architectures consisting of the VGG16, ResNet152V2, InceptionV3, and Xception for classifying colon disease images. The experiment showed that the ResNet152V2 performed the best, compared to the other three with a testing accuracy score of 0.9837. On the other hand, the VGG16 had the lowest performance with around accuracy score of 0.89 while the Xception and InceptionV3models yielded similar accuracy scores of 0.9587 and 0.9549, respectively. The results highlight the effectiveness of ResNet152V2 in handling the complexity of colon disease detection © 2024 IEEE.

关键词： Convolutional neural networks

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Microprocessor Instruction Design Tool for RISC-V Architecture 22

Microprocessor Instruction Design Tool for RISC-V Architectu...

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22nd International Symposium on Communications and Information Technologies, ISCIT 2023

作者： Jiahui, Luo Morimoto, Tomoyuki Ogita, Tadahiro Kawamata, Ryota Wang, Ziming Tsutsumi, Toshiyuki Meiji University Computer Science Program Graduate School of Science and Technology Kanagawa Japan Meiji University School of Science and Technology Department of Computer Science Kanagawa Japan

ISBN: (纸本)9781665457316

We have been developing MEIMAT (MEiji Microprocessor Architecture Design Tools). The MEIMAT is essentially designed to be able to represent any instruction of various processors by the MEIMAT meta-instruction in two ways of semantic and functional expressions. In this paper, we have verified the RISC-V support to the MEIMAT, and have improved so that MEIMAT further supports RISC-V. First, we have verified whether the RISC-V instructions can be expressed by the MEIMAT semantic expression. It is also verified that the functional expression is automatically generated from the semantic instruction, and the corresponding correct circuit diagrams are generated in the MEIMAT instruction visualization tool. Then, we have newly developed a stage configuration mechanism to implement the RISC-V instructions. Because the RISC-V architecture is implemented by 4 or 5 processing stages in most actual processors. To develop further an easy-to-understand design tool, we have also introduced illustration windows for RISC-V specified hardware modules such as a register file, especially a status register in the instruction visualization tool. Finally, through these verifications and improvements, we have successfully confirmed that the 43 instructions in the RISCV (RV32I) instructions set of the RISC-V architecture can be converted into MEIMAT meta-instructions, and then they can be represented as circuit diagrams with the RISC-V stage configuration in the MEIMAT instruction visualization tool. © 2023 IEEE.

关键词： Integrated circuit design

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Automatic question generation for bahasa indonesia examination using copynet 9th

Automatic question generation for bahasa indonesia examinati...

引用

9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Henry, Matthew Martianus Elwirehardja, Gregorius Natanael Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

In educational institutions, an educator is responsible for assessing the student's knowledge grasp through examination. Creating exam questions, even the low-level factoid questions, is time-consuming, especially for inexperienced educators. Therefore, this study aims to create a sequence-to-sequence model using CopyNet by exploiting its copying mechanism advantage to automatically generate Bahasa Indonesia factoid questions to ease the educator's burden. Indonesian records in the TyDi QA dataset are used as the model input. GRU and Bi-GRU are employed as the CopyNet encoder, while LSTM is used as the CopyNet decoder. The model that utilizes GRU as the encoder achieves BLEU1, BLEU2, BLEU3, BLEU4, and ROUGE-L scores of 0.28, 0.19, 0.14, 0.1, and 0.32, respectively. Bi-GRU utilization as the model encoder achieves BLEU1, BLEU2, BLEU3, BLEU4, and ROUGE-L scores of 0.26, 0.17, 0.12, 0.09, and 0.30, respectively. Models using either encoder still achieve low scores. However, compared with the previous work, the result is still on par regarding the BLEU score. Further examination found that the generated questions do not adhere to semantic and syntactical correctness. Adding more records to the dataset and utilizing a more advanced architecture like CopyBERT are encouraged to improve the model performance in future work. Despite the result, this study has shown that CopyNet, primarily designed for text summarization or single-turn dialogue, can be tailored for factoid question generation. © 2024 The Authors.

关键词： Semantics

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Automatic Multiple Choice Question Generation: A Systematic Literature Review 9

Automatic Multiple Choice Question Generation: A Systematic ...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Henry, Matthew Martianus Heryanto, Nur Adhianti Isnan, Mahmud Nugroho, Kuncahyo Setyo Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

Despite their drawbacks, multiple-choice questions (MCQ) have been widely used to assess the students' understanding of lectures through examinations. The development of automatic MCQ generation is beneficial, especially for educators. As a starting point for further development, a Systematic Literature Review (SLR) is conducted to uncover current trends, future challenges, and opportunities in automatic MCQ generation. Previously, an SLR was conducted, but it lacks coverage of the utilization of transformer-based models. This SLR covers the development of automatic MCQ generation using either traditional or advanced approaches such as Transformers. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was used to gather the data from Scopus, IEEE Xplore, SpringerLink, arXiv, and Semantic Scholar. The included articles must be open-access computer science conference papers or journal articles and written in English less than five years ago. Four independent reviewers analyzed the research workflow, evaluation metric, and dataset used in each study. There are 18 included studies, where 17% (n = 3) studies are from 2024, 33% (n = 6) studies are from 2023, 22% (n = 4) studies are from 2022, 11% (n = 2) studies are from 2021, and 17% (n = 3) studies are from 2020. There are 33% (n = 6) of the studies used the traditional feature-based engineering approach, 39% (n = 7) of the studies used the Transformer-based model fine-tuning approach, and the remaining used novel approaches. The study found that BERT variants are the most utilized Transformer-based model in automatic MCQ. The research notes some challenges, but also open various opportunities for further research, including Large Language Model (LLM) utilization for automatic MCQ generation, the utilization BERT-based models for standardized machine-learned evaluation metrics, and the initiative for the creation of an MCQ dataset benchmark. © 2024 IEEE.

关键词： Semantics

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The Effect of Social Media Influencers on Business Sustainability: Understanding Customer Behavior Change Towards Sustainable Usage Patterns 6

The Effect of Social Media Influencers on Business Sustainab...

引用

6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Herlina, Maria Grace Budiharto, Alexandra Li Lukito, Elizabeth Margaretha Iskandar, Karto Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

The study aims to fill existing research gaps by creating a new framework that explores the impact of social media influencers on sustainable usage patterns, focusing on business sustainability as a mediating factor. In this research, non-probability judgmental and purposive sampling methods were used. Due to the size and distribution of the population, sampling was also chosen based on the researcher's own judgment and experience. Structural equation modeling (SEM) is used as the data analysis method. Path coefficient analysis yielded significant findings. Social media influencers were found to influence business sustainability significantly. Apart from that, social media influencers have a significant negative affect on sustainable usage patterns. Business sustainability also significantly influences sustainability usage patterns, with a powerful influence. This enriches the dynamics of business sustainability, proposing new knowledge about how it influences this relationship. © 2024 IEEE.

关键词： Sustainable development goals

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Darwinian Lion Swarm Optimization-Based Extreme Learning Machine with Adaptive Weighted Smote for Heart Disease Prediction 1

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1st International Conference on Intelligent Computing for Sustainable Development, ICICSD 2023

作者： Sasirega, D. Krishnapriya, V. Sri Ramakrishna College of Arts and Science Coimbatore India KG College of Arts and Science Tamil Nadu Coimbatore India Department of Computer Science and Cognitive Systems Sri Ramakrishna College of Arts and Science Tamil Nadu Coimbatore India

ISBN: (数字)9783031612985

ISBN: (纸本)9783031612978

Predicting Cardiovascular Diseases (CVD) is essential in reducing the deaths associated with different heart diseases. However, there are complexity and class imbalance problems in existing methods that degrade the overall effectiveness. Therefore, advanced frameworks are essential for heart disease detection models. This paper proposes a competent heart disease recognition framework using proficient machine learning (ML)-based methods for each clinical data analysis stage. Initially, the pre-processing stage introduces three methods for enhancing the input clinical data quality. Firstly, the Hierarchical Density-based Spatial Clustering of Applications with Noise based on the Neighbor Similarity (HDBSCAN-NS) is proposed to eliminate the data outliers without increasing the complexity. Secondly, Class Median based Missing Value Imputation (CMMVI) approach is proposed to impute the missing values based on the class distribution. Thirdly, Adaptive Weighted Synthetic Minority Oversampling Technique with Natural Neighbors (AWSMOTE-NN) is proposed to resolve the drawbacks of collinearity in the imbalanced dataset and improve the class balancing. After pre-processing, the features are extracted, and optimal feature subsets are selected using the Owl Optimization algorithm (OOA). These selected features are used by the proposed hyper-parameter optimized classifier of Darwinian Lion Swarm Optimization-based Extreme Learning Machine (DLSO-ELM) is utilized for detecting heart disease. UCI heart disease datasets are used to validate the proposed framework, and the results showed that the OOA-DLSO-ELM-based approach provides better heart disease prediction with high accuracy and low complexity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Balancing

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Communicative Need Modulates Lexical Precision Across Semantic Domains: A Domain-Level Account of Efficient Communication 44

Communicative Need Modulates Lexical Precision Across Semant...

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44th Annual Meeting of the cognitive science Society: cognitive Diversity, CogSci 2022

作者： Bradford, Laurestine Thomas, Guillaume Xu, Yang Department of Linguistics McGill University Canada Department of Linguistics University of Toronto Canada Department of Computer Science Cognitive Science Program University of Toronto Canada

Different domains exhibit different degrees of lexical precision. Existing work has suggested that communicative need may modulate the precision of word meaning in individual domains. We extend this proposal across domains by asking why languages have more precise vocabulary in some domains than others. We hypothesize that lexical precision for a domain reflects how frequently speakers need to refer to it. We test this proposal using a cross-linguistic dataset of word-concept mappings for nine diverse domains from seven languages, and word frequencies from independent corpora. We find that the more frequent domains (except for kinship) tend to be more precise in every language, supporting a domain-level account of efficient communication on the precision of the lexicon. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Statistical tests

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ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models 13

ChatGPT is fun, but it is not funny! Humor is still challeng...

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13th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2023

作者： Jentzsch, Sophie Kersting, Kristian Cologne Germany Computer Science Department Technical University Darmstadt Darmstadt Germany Centre for Cognitive Science TU Darmstadt Germany Germany

ISBN: (纸本)9781959429876

Humor is a central aspect of human communication that has not been solved for artificial agents so far. Large language models (LLMs) are increasingly able to capture implicit and contextual information. Especially, OpenAI’s ChatGPT recently gained immense public attention. The GPT3-based model almost seems to communicate on a human level and can even tell jokes. Humor is an essential component of human communication. But is ChatGPT really funny? We put ChatGPT’s sense of humor to the test. In a series of exploratory experiments around jokes, i.e., generation, explanation, and detection, we seek to understand ChatGPT’s capability to grasp and reproduce human humor. Since the model itself is not accessible, we applied prompt-based experiments. Our empirical evidence indicates that jokes are not hard-coded but mostly also not newly generated by the model. Over 90% of 1008 generated jokes were the same 25 Jokes. The system accurately explains valid jokes but also comes up with fictional explanations for invalid jokes. Joke-typical characteristics can mislead ChatGPT in the classification of jokes. ChatGPT has not solved computational humor yet but it can be a big leap toward "funny" machines. © 2023 Association for Computational Linguistics.

关键词： Computational linguistics

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Object-Driven Fuzzy Decision Model for Assessing the Employees' Work from Home Eligibility 4

Object-Driven Fuzzy Decision Model for Assessing the Employe...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Utama, Ditdit Nugeraha Immanuel, Christiawan Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331506490

Work from Home (WFH), especially in the post-pandemic era, has emerged as an alternative work system adopted by many companies. Efficiency and effectiveness are the two main reasons why WFH is favored. In addition, the implementation of the WFH system in conducting business processes within the company allows for greater flexibility in work methods. For certain types of jobs or specific industry domains, this WFH system can be quite reliable. Workers can perform their tasks from anywhere and at any time. Also, monitoring systems enable employees to be remotely tracked by the company. However, the question remains: is an employee's work environment suitable enough to ensure the successful implementation of WFH? To answer this, a standardized model is needed to assess the level of eligibility for WFH implementation. This study aims to develop a fuzzy-based decision model to evaluate the eligibility of an employee's work environment for implementing WFH. An object-oriented approach is used to describe the design of the decision model, while fuzzy logic serves as the primary method for building the model. The model successfully simulated 24 raw employee data points, yielding a highest eligibility score of 88.61 with a similarity index of 0.78 when compared to the model proposed in previous research. © 2024 IEEE.

关键词： Personnel

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