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8th International Symposium on Intelligent Informatics, ISI 2023

作者： Shenbaga Moorthy, Rajalakshmi Arikumar, K.S. Prathiba, Sahaya Beni Pabitha, P. Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai600 116 India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai603 203 India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Computer Technology Anna University Madras Institute of Technology Campus Chennai600044 India

ISBN: (纸本)9789819721467

Accurate solutions are needed for a variety of computer vision applications, including medical imaging, object detection, and recognition. Such complicated challenges are beyond the capabilities of artificial intelligence and machine learning, which mainly rely on data and algorithms to learn. The path to Deep Learning (DL) is thus paved. Even though deep learning is incredibly effective at solving complex problems, it is invariant, meaning that it ignores the spatial relationship between the features and instead searches for features only. This can cause the model’s performance to suffer. Additionally, because the model is invariant, gathering a large volume of training data is a difficult undertaking. To address this, the Capsule Network (CapsNet), whose performance outperforms Deep Learning techniques, was introduced. Despite its success, applications of CapsNet and working remain a mystery. Thus, the performance of CapsNet across many applications and obstacles is examined in this study together with the predecessors of CapsNet. Additionally, this paper explores the use of Capsule Networks with an attention mechanism (CapsNet-ATM) for predicting potato leaf diseases. The performance of CapsNet-ATM in this crucial agricultural application is thoroughly assessed and contrasted with the well-known models VGG-16 and VGG-19, providing insight into the system’s efficacy and promise as a reliable tool for the detection and prediction of potato leaf disease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Revalidating the Encoder-Decoder Depths and Activation Function to Find Optimum Vanilla Transformer Model

Revalidating the Encoder-Decoder Depths and Activation Funct...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Fitria Murad, Dina Tho, Cuk Hashimoto, Kiyota Bina Nusantara University Graduate Program- Computer Science Computer Science Department BINUS Jakarta11480 Indonesia Bina Nusantara University Online Learning Computer Science Department BINUS Jakarta11480 Indonesia Bina Nusantara University BINUS Online Learning Information Systems Department Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

The transformer model has become a state-of-the-art model in Natural Language Processing. The initial transformer model, known as the vanilla transformer model, is designed to improve some prominent models in sequence modeling and transduction problems such as language modeling and machine translation. The initial transformer model has 6 stacks of identical encoder-decoder layers with an attention mechanism whose aim is to push limitations of common recurrent language models and encoder-decoder architectures. Its outstanding performance has inspired many researchers to extend the architecture to improve its performance and computation efficiency. Despite many extensions to the vanilla transformer, there is no clear explanation of the encoder-decoder set out depth in the vanilla transformer model. This paper presents exploration results on the effect of combination encoder-decoder layer depth and activation function in the feed-forward layer of the vanilla transformer model on its performance. The model is tested to address a downstream task: text translation from Bahasa Indonesia to the Sundanese language. Although the value difference is not significantly large, the empirical results show that the combination of depth = 2 with Sigmoid, Tanh, and ReLU activation function;and d = 6 with ReLU activation shows the highest average training accuracy. Interestingly, d = 6 and ReLU show the lowest average training and validation loss. However, statistically, there is no significant difference between depth and activation functions. © 2023 IEEE.

关键词： Neural machine translation

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Aggregate Signature with Single-Transaction Verification for Blockchain 9

Aggregate Signature with Single-Transaction Verification for...

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9th IEEE Smart World Congress, SWC 2023

作者： Wang, Chen Shen, Jian Ma, Qingru Dai, Hong-Ning Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou China Hong Kong Baptist University Department of Computer Science China

ISBN: (纸本)9798350319804

Blockchain has gradually become one of the important technologies concerned by many cross fields. Since the concept of blockchain has been proposed and implemented, a large number of new environments (such as smart cities, smart grids, electronic medicine, etc.) have been enabled by blockchain, while more security features, such as tamper resistance and traceability, have been realized. The verification of transaction records in the blockchain is particularly important for the implementation of the blockchain. Some researchers have achieved the recording and signing of transaction information on the blockchain through specific structures. However, the aggregation of transaction signatures and the verification of single transactions are still worthy of further study. An aggregate signature is proposed in this paper for blockchain, supporting single-transaction verification. This scheme can increase the flexibility of blockchain transaction information verification. When the committer deletes a single signature and only retains the aggregation result, it can effectively verify a single transaction. The analysis shows that the signature generated in this scheme is unforgeable and the verification of a single transaction is efficient. © 2023 IEEE.

关键词： Aggregate signature blockchain cybersecurity verification

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Safe Return Home Solution Using a Smart Drone Scout

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Journal of Institute of Control, Robotics and Systems 2024年第9期30卷 937-944页

作者： Heo, Hyeonjeong Lee, Hojun Lee, Kyuman Department of Robot and Smart System Engineering Kyungpook National University Korea Republic of School of Computer Science and Engineering Kyungpook National University Korea Republic of School of Space Engineering Sciences Kyungpook National University Korea Republic of

The women's safe return home service is short on operating hours and manpower. To address this social issue, this paper proposes an unmanned solution using a smart drone. When a user calls this system, it generates an initial safe path based on data from electronic tag wearers (i.e., obstacles) and map information. As the obstacles move, the safe path is re-planned to avoid them. The smart drone tracks and monitors the users returning home along the planned safe path at a certain distance and angle in real time. Since the raw GPS data from the user's app are unreliable, these are corrected to map-matching points for the drone to determine the points to track. Moreover, the smart drone identifies corners to avoid losing the monitored field of view at corners and adjusts its tracking accordingly. Flight test results validate the overall functionality and performance of the proposed solution, i.e., the proposed system improves user safety by effectively minimizing contact with electronic tag wearers while maintaining a certain distance and angle from the user by controlling the direction and speed of the drone. © ICROS 2024.

关键词： Drones

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ON DIFFUSION MODELING FOR ANOMALY DETECTION 12

ON DIFFUSION MODELING FOR ANOMALY DETECTION

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12th International Conference on Learning Representations, ICLR 2024

作者： Livernoche, Victor Jain, Vineet Hezaveh, Yashar Ravanbakhsh, Siamak School of Computer Science McGill University Canada Department of Physics University of Montreal Canada Mila-Quebec AI Institute Canada

Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detection. This paper investigates different variations of diffusion modeling for unsupervised and semi-supervised anomaly detection. In particular, we find that Denoising Diffusion Probability Models (DDPM) are performant on anomaly detection benchmarks yet computationally expensive. By simplifying DDPM in application to anomaly detection, we are naturally led to an alternative approach called Diffusion Time Estimation (DTE). DTE estimates the distribution over diffusion time for a given input and uses the mode or mean of this distribution as the anomaly score. We derive an analytical form for this density and leverage a deep neural network to improve inference efficiency. Through empirical evaluations on the ADBench benchmark, we demonstrate that all diffusion-based anomaly detection methods perform competitively for both semi-supervised and unsupervised settings. Notably, DTE achieves orders of magnitude faster inference time than DDPM, while outperforming it on this benchmark. These results establish diffusion-based anomaly detection as a scalable alternative to traditional methods and recent deep-learning techniques for standard unsupervised and semi-supervised anomaly detection settings. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Anomaly detection

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Evaluating Sentiment Analysis Algorithms with Varied datasets: A Machine Learning Approach 5

Evaluating Sentiment Analysis Algorithms with Varied Dataset...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Indhumathi, S. Mary Harin Fernandez, F. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

Sentiment analysis is essential for comprehending public opinion on a variety of platforms and offers insightful information to researchers, businesses, and legislators. This study evaluates five machine learning algorithms-Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB) -applied to two distinct datasets: IMDB movie reviews and Twitter Airline sentiments. To provide a thorough performance comparison, each model is evaluated using recall, accuracy, precision, and F1 score. SVM achieves 91.02% accuracy on the Twitter dataset, making it the best performance;on the IMDB dataset, Logistic Regression wins with 87.39% accuracy. Naive Bayes demonstrates high precision, particularly on the Twitter dataset, though its recall score is relatively lower, indicating it is more conservative in capturing true positives. Random Forest maintains a balanced performance across all metrics, whereas KNN underperforms, particularly in handling the complexities of the IMDB dataset with a lower accuracy of 71.08%. The analysis reveals that dataset-specific factors significantly influence the performance of each algorithm, highlighting the importance of data characteristics in determining the most suitable model. While SVM excels in high-dimensional data like Twitter, Logistic Regression proves more effective with the IMDB dataset's structured text. This reinforces the need for model selection in sentiment analysis, depending on the nuances of each dataset. By providing a detailed evaluation across different models and metrics, this study contributes to optimizing sentiment analysis techniques for diverse applications, ensuring more robust and accurate predictions. © 2024 IEEE.

关键词： Tweets

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An Improved Method for Class-specific Keyword Extraction: A Case Study in the German Business Registry 20

An Improved Method for Class-specific Keyword Extraction: A ...

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20th Conference on Natural Language Processing, KONVENS 2024

作者： Meisenbacher, Stephen Schopf, Tim Yan, Weixin Holl, Patrick Matthes, Florian Technical University of Munich School of Computation Information and Technology Department of Computer Science Garching Germany Fusionbase GmbH Munich Germany

The task of keyword extraction is often an important initial step in unsupervised information extraction, forming the basis for tasks such as topic modeling or document classification. While recent methods have proven to be quite effective in the extraction of keywords, the identification of class-specific keywords, or only those pertaining to a predefined class, remains challenging. In this work, we propose an improved method for class-specific keyword extraction, which builds upon the popular KEYBERT library to identify only keywords related to a class described by seed keywords. We test this method using a dataset of German business registry entries, where the goal is to classify each business according to an economic sector. Our results reveal that our method greatly improves upon previous approaches, setting a new standard for class-specific keyword extraction. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Initial development of an ontology for the semiconductor domain - SemicONTO 1

Initial development of an ontology for the semiconductor dom...

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1st International Workshop on Semantic Materials science: Harnessing the Power of Semantic Web Technologies in Materials science, SeMatS 2024

作者： Li, Huanyu Wang, Chuanfei Lambrix, Patrick Department of Computer and Information Science Linköping University Linköping Sweden Swedish e-Science Research Centre Linköping Sweden School of Materials Science and Engineering Ocean University of China Qingdao China Department of Science and Technology Linköping University Norrköping Sweden

Materials science domain is facing the fourth paradigm of science, i.e., data-driven science, which also encompasses the first three paradigms based on theory, experiment, and simulation. The semiconductor domain is one of many sub-domains of materials science, involving both mathematical models-based simulations and conventional experiments to study materials. A significant challenge in the semiconductor domain is the lack of interoperability between materials simulation data and experimental data. While there is existing work, such as the Materials Design Ontology, that enhances the interoperability of simulation data, there remains a need for representing experimental data with rich semantics. To improve the findability, accessibility, interoperability, and reusability of semiconductor experimental data, we present the initial steps in developing a semiconductor domain ontology, SemicONTO. © 2024 CEUR-WS. All rights reserved.

关键词： Reusability

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Indonesian-Sundanese Language Machine Translation using Bidirectional Long Short-term Memory Model

Indonesian-Sundanese Language Machine Translation using Bidi...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Fitria Murad, Dina Tho, Cuk Hashimoto, Kiyota BINUS Graduate Program - Doctor of Computer Science Computer Science Department Bina Nusantara University Jakarta11480 Indonesia BINUS Online Learning Bina Nusantara University Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Bina Nusantara University Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

Translating a language to another language has become instrumental when peoples interact with other people who speak a different language. However, the language translation is not an easy computation task when there is a language-resource gap. This paper presents empirical results on the performance of two models: the Long Short-term Memory and the Bidirectional Long Short-term Memory models as machine language translation models involving Bahasa Indonesia and the Sundanese language. The empiric results showed that the Bidirectional Long Short-term Memory model achieves higher performance as a language translator from the Sundanese language to Bahasa Indonesia and vice versa (0.95 and 0.95 average training accuracy respectively;and 0.90 and 0.89 average testing BLEU scores respectively) than the Long Short-term Memory model as a language translator from the Sundanese language to Bahasa Indonesia and vice versa (0.93 and 0.92 average training accuracy respectively;and 0.91 and 0.88 average testing BLEU scores). These results validate some previously reported studies that claim the Bidirectional Long Short-term Memory model potentially outperform the Long Short-term Memory model when it is used to process a sequence dataset. © 2023 IEEE.

关键词： Computational linguistics

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Towards Smart data Mining Support Based on Annotation Beneficial Grouping Method

Towards Smart Data Mining Support Based on Annotation Benefi...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Kavitha, S. Pushpavathi, K. Mahalakshmi, S. School of Arts and Science Department of Computer Science Vinayaka Missions Research Foundation DU AVIT Campus Chennai Tamil Nadu Paiyanoor India

ISBN: (纸本)9781665462631

A data mining (DM) system requires many steps, such as pre-processing the data, execution of the data mining algorithm, and extracting mining results. They can be a straightforward but traditional procedure. Each step has multiple choices;only a few variations provide promising results. Both beginners and specialists in data mining require assistance with the vast reach and non-trivial experiences throughout the composition and collection of DM methods. The intelligent exploration assistant prototype enables customers who have access to a systemic list of valid DM processes to raise the ranks of these valid processes in accordance with a variety of criteria. This helps customer's select DM processes that are appropriate and may be more efficient than other options, and it also ensures that appropriate alternatives are not overlooked. The prototypes demonstrate that a single IDA is still capable of having relevant lists and performing successful classification even in basic classification procedures. The potential for an IDA to play a significant role as a medium for the exchange of information within a data mining community is investigated. In conclusion, it has been shown that the claims have been proved by demonstrating that cost-sensitive classes have used a more nuanced procedure utilizing information gained from the KDDCUP competition in 1998. © 2023 IEEE.

关键词： Decision trees

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