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检索条件"机构=Program Computer Science"

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Team Sharingans at SimpleText: Fine-Tuned LLM based approach to Scientific Text Simplification 25

Team Sharingans at SimpleText: Fine-Tuned LLM based approach...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Ali, Syed Muhammad Sajid, Hammad Aijaz, Owais Waheed, Owais Alvi, Faisal Samad, Abdul Computer Science Program Dhanani School of Science and Engineering Habib University Karachi75290 Pakistan

This paper reports Habib University's Team Sharingans' participation in the CLEF 2024 SimpleText track, which aims to simplify scientific texts for improved readability and comprehension for non-experts. Our goal is to use state-of-the-art language models for simple yet accurate explanations of scientific texts for the general public. Our solution is based on a multi-step approach utilizing the GPT-3.5 model to solve Tasks 1, 2, and 3 i.e. passage extraction, identification and explanation of difficult concepts, and summarization. Our approach for Task 1 involved sentence embedding-based vector database for narrowing the corpus, MS-Marco for document ranking, and GPT-3.5 for selecting informative passages. For Task 2, we fine-tuned the GPT-3.5 model to identify and explain difficult terms and generate explanations. For Task 3 also, we fine-tuned the GPT-3.5 model with a specific prompt to simplify given scientific abstracts and sentences. The effectiveness of our approach was assessed based on the quality of results, demonstrating the potential of advanced language models in making scientific education more accessible to the general public. Our solution proposes using fine-tuned large language models as a reliable source for scientific education. © 2024 Copyright for this paper by its authors.

关键词： BERT Elastic Search GPT-3.5 Turbo Large Language Models SimpleText Text simplification

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Trend analysis in competitive programming using topic modeling 9th

Trend analysis in competitive programming using topic modeli...

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

作者： Ramahalim, Albert Yulius Karuniawan, Christoffer Edbert Purwanto, Eko Setyo Ario, Muhamad Keenan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Competitive programming (CP) is a mind sports activity where people solve problems using command-line computer programs to provide correct output for the given test cases. Competitors need to practice problem-solving and mathematics as well as study algorithms and data structures to perform well in CP. This study aims to provide an original way to perform a trend analysis in CP, distinguishing topics frequently used in CP contests. To fulfill our goal, we create topic models based on previous topic modeling works to do natural language processing tasks using Latent Dirichlet Allocation (LDA) and Biterm Topic Model (BTM). For our dataset, we constructed a corpus from Codeforces blog posts, a popular website for competitive programmers, by extracting its content and user comments. Our results indicate that BTM is powerful enough to do trend analysis in CP. The trend analysis recognized that dynamic programming and complexity analysis have been the most prominent topics for the last ten years. Data structures and string algorithms are runners-up that may have potential trends in the future. This study opens up further research on other methods to perform trend analysis using better topic models and corpora. © 2024 The Authors.

关键词： Modeling languages

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Quantitative analysis of sign language translation using artificial neural network model 9th

Quantitative analysis of sign language translation using art...

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

作者： Wijaya, Fendy Dahendra, Leonardo Purwanto, Eko Setyo Ario, Muhamad Keenan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Sign language is one of the technique to support communication with deaf and speech impaired people. Nowadays, human needs become more complex, so are the needs of people with those disabilities. Therefore, with the sophistication of modern technology in the field of computer science, it is necessary to have a sign language translation system which is capable to convert human gestures into words or spelling letters in a natural human language, especially Indonesian language and SIBI sign language. This research is conducted to analyze the capabilities of an Artificial Neural Network (ANN) system to translate SIBI sign language into Indonesian language. The analysis is performed quantitatively from the experiment results to gain a descriptive insights for the generated models. By creating two datasets containing alphabets and words in SIBI sign language, two models were generated to predict alphabets and words, respectively. The result shows that ANN model could effectively and efficiently perform a sign language translation with accuracy of alphabet prediction reached 96.15% and words prediction reached 99.45% in duration of prediction without exceeding 0.15 seconds. Regarding to the results, it can be concluded that ANN model quantitatively suitable to be implemented as an Indonesian sign language translator system. © 2024 The Authors.

关键词： Neural machine translation

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Harnessing Deep Learning for Ocular Disease Diagnosis 9th

Harnessing Deep Learning for Ocular Disease Diagnosis

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

作者： Ryan, Jessica Nathaniel, Dave Andrew Purwanto, Eko Setyo Ario, Muhamad Keenan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Vision impairment, often caused by preventable ocular diseases can be challenging to diagnose accurately and prone to human error. Automation using technology, particularly deep learning, offers a promising solution to aid in accurate and efficient disease detection. This study explores the use of different CNN models specifically VGG-16, VGG-19, ResNet-50, and ResNet-152v2, for detecting ocular diseases. Simple fine-tuning is applied to these models, and their performance is compared to identify the most effective model. The purpose is to show how different models contribute to establishing reliable illness detection systems. The results reveal that most of these models perform well with even minimal fine-tuning. Among the models, ResNet-152v2 achieved the highest training accuracy of 90.36% demonstrating its capacity to learn from the training data. In contrast, ResNet-50 offered a more balanced performance with marginally lower accuracy, making it a robust choice for general application. © 2024 The Authors.

关键词： classification deep learning ocular diseases

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Enhancing Waste Classification with YOLOv8 Models for Efficient and Accurate Sorting 9th

Enhancing Waste Classification with YOLOv8 Models for Effici...

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

作者： Vieri, Deverel Susanto, Rendy Purwanto, Eko Setyo Ario, Muhamad Keenan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

As of 2023, Indonesia ranks as the second largest waste-producing country globally. The waste produced is not segregated properly, leading to the vast amount of waste piling up in local landfills. Traditional methods, such as manual sorting, have been widely used to segregate waste but suffer from inefficiencies and inaccuracies. In contrast, deep learning models offer an alternative solution for waste classification, overcoming the limitations of traditional methods. A deep learning approach using YOLOv8 was proposed to classify waste into six distinct categories. Three different YOLOv8 variants: nano, small, and medium, are trained after the dataset has been augmented into 3,500 labeled images. The results indicate that these models were able to achieve high accuracy in classifying images, with the nano variant having the least training time and an accuracy of approximately 89%. © 2024 The Authors.

关键词： Contrastive Learning

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A fine-tuned vision transformer-based on limited dataset for facial expression recognition 9th

A fine-tuned vision transformer-based on limited dataset for...

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

作者： Febrian, Rio Huang, Ronald Richie Setiono, Nicholas Ramdhan, Dimas Chowanda, Andry Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

With the increase of interest in Facial Expression Recognition (FER) in the past few decades. Several challenges surfaced with the invention of many different FER models which are often based on Convolution Neural Network (CNN) architectures. Recently, an attention-based transformer model has been presented to address FER. One of the major issues with Transformers is the need for a large data quantity for training. Therefore, in this paper, we propose to learn how to fine-tune a vision transformer-based (ViT) model using a limited dataset. We will be using the JAFFE Dataset, which consists of only 213 images containing seven different emotions. The proposed method is evaluated using several fine-tuning methods, such as adding dropout, data augmentation, and layer freezing. We compared the models implemented with 5% dropout regularization, augmented dataset (up to 5000 images), and freezing the initial model's layers, fine-tuning around a fourth of the last layers. The best model was achieved by fine-tuning ViT L-16 with 96.06% accuracy, trained with 5% dropout in the augmented dataset, and freezing the initial 21st layers. We also compared our model to the other previous work model and the results showed that our model reached the state-of-the art for the JAFFE dataset. © 2024 The Authors.

关键词： Convolutional neural networks

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Visualization and Simulation of Any MPU Architecture Design with Meta-Instruction 23

Visualization and Simulation of Any MPU Architecture Design ...

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23rd International Symposium on Communications and Information Technologies, ISCIT 2024

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

ISBN: (纸本)9798350353952

Our conventional architecture design tool converted instructions of microprocessor (MPU) into meta-instructions with both semantic and functional expressions, and it displayed a circuit diagram of the meta-instruction. The tool was able to perform dynamic simulation only on an educational processor called COMET. However, the meta-instruction must be able to express the arbitrary instruction of at least different significant existing MPUs in order to design arbitrary MPUs. Furthermore, it should be possible to visualize all instructions and then execute them. Therefore, we have improved the meta-instruction so that the architecture design tool applies to major MPUs: MIPS, ARM, and RISC-V in addition to COMET. The improved architecture design tool visualizes and simulates the circuit diagram of the improved meta-instruction. The result shows the possibility of the meta-instruction-based architecture design tool being able to design any MPU. © 2024 IEEE.

关键词： Microprocessor chips

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Ransomware Resilience: Investigating Organizational Security Culture and Its Impact on Cybersecurity Practices against Ransomware Threats

Ransomware Resilience: Investigating Organizational Security...

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2023 International Conference on Informatics Engineering, science and Technology, INCITEST 2023

作者： Yulianto, Semi Soewito, Benfano Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350317862

The increasing threat of ransomware attacks in an interconnected and digital world presents important hurdles for cybersecurity, prompting further examination into how organizational security culture affects their ability to withstand these malicious threats. This study examines the impact and importance of organizational security culture in ransomware threat mitigation. We have surveyed five organizations, giving points from 1 to 5 for each question on cultural factors, leadership support, employee awareness and involvement (compliance), communication, behavior change or BBS practice incorporation, learning/training delivery methodology, and Size and industry impact on their security culture. The results indicate that a robust security culture, with leadership and effective communication, builds stronger resilience to ransomware. Organizations with jointly accountable cybersecurity and training practices are highly efficient. Suggestions range from establishing a secure environment to increasing management buy-in and integrating compliance. This work helps advance our understanding of ransomware resilience and highlights the key role of organizational security culture. The resulting learnings can help to inform organizations' security posture, ultimately helping them better protect their defenses against ransomware threats. © 2023 IEEE.

关键词： Cybersecurity

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Addressing the role and opportunities of machine learning utilization in brain tumor detection 9th

Addressing the role and opportunities of machine learning ut...

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

作者： Lesmana, Vallerie Delia Agustine, Holly Wairooy, Irma Kartika Makalew, Brilly Andro Computer Science Department School of Computer Science Binus University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department Binus University Jakarta11480 Indonesia

This research aims to develop a brain tumor detection model by utilizing the machine learning techniques and Convolutional Neural Network (CNN). A significant matter to address is revolving around early detection and the proper handling regarding the brain tumor. This research's methodology consists of collecting the dataset, identifying the tools and language to use, prepare and preprocessing the data, data augmentation, splitting and label encoding, building the model architecture, compiling the model, training, and evaluating the model, predicting the model, and comparing it with other models. Dataset consists of 7022 MRI images, divided into training and testing subsets;and four classes: glioma, meningioma, pituitary, and no tumor. There are four different CNN models that have been built and evaluated, namely VGG16, InceptionV3, ResNet50, and DenseNet121. The result gained shows VGG16 with the best performance achieving an accuracy rate of 96.43%, followed by DenseNet121 (94,96%), InceptionV3 (92,40%), and ResNet50 (78,69%). Although there is still room for improvement regarding overfitting and increasing the models' overall performance, this result is promising enough to enhance early diagnosis and offer an appropriate and effective treatment for patients. © 2024 The Authors.

关键词： Convolutional neural networks

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Advanced Design Pattern Selection Methodology through Weighted Semantic Ontologies for Microservices

Advanced Design Pattern Selection Methodology through Weight...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Attia, Rabab Atef Ghoniemy, Samy Hamdy, Abeer The British University in Egypt Faculty of Informatics and Computer Science Cairo Egypt Galala University Computer Science and Engineering Program Cairo Egypt

ISBN: (纸本)9798350367560

Seamless communication between authorities, people, and smart devices is crucial in today's globally interconnected world. Unprecedented demands on software design result from the advent of ubiquitous connectivity, which allows connections at any time and from any location. By structuring applications as separate services that can communicate both internally inside a platform and externally across platforms, microservices (MS) architecture satisfies these requirements. One of the most difficult parts, though, is still controlling communication between MS services, which calls for knowledge of the best design patterns. In order to solve this, we suggest a context-aware recommender system that is adapted to MS design patterns. This system not only makes recommendations for appropriate patterns, but it also gives statistical scores that show how relevant each solution is to the particular issue at hand. Other patterns with ratings indicating their possible feasibility are also displayed by our algorithm. On the other hand, sentence transformers handle intricate, human-like problem descriptions, while our method uses MS Domain Ontologies to distinguish pattern applications, constraints, and best practices. Our methodology provides a refined examination of solution suitability and recommendations through the use of weighing and correlation algorithms. Extensively tested with more than 237 developer-style scenarios, our framework offers qualitative and quantitative insights into its performance and efficacy, showing notable gains over current solutions in terms of offering pattern-specific, contextually accurate guidance for MS communication challenges. © 2024 IEEE.

关键词： Semantics

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