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4th International Workshop on Knowledge-Infused Learning, KiL 2024

作者： Juraev, Firuz El-Sappagh, Shaker Abuhmed, Tamer College of Computing and Informatics Sungkyunkwan University Korea Republic of Faculty of Computer Science and Engineering Galala University Egypt

There has been a notable increase in research focusing on dynamic selection (DS) techniques within the field of ensemble learning. This leads to the development of various techniques for ensembling multiple classifiers for a specific instance or set of instances during the prediction phase. Despite this progress, the design and development of DS approaches with late fusion settings and their explainability remain unexplored. This work proposes an open-source Python library, Infodeslib, to address this gap. The library provides an implementation of several DS techniques, including four dynamic classifier selections and seven dynamic ensemble selection techniques, all of which are integrated with late data fusion settings and novel explainability features. Infodeslib offers flexibility and customization options, making it a versatile tool for various complex applications that require the fusion of multimodal data and various explainability features. Multimodal data, which integrates information from diverse sources or sensor modalities, is a common and essential setting for real-world problems, enhancing the robustness and depth of data analysis. These data can be fused in two main ways: early fusion, where different modalities are combined at the feature level before model training, and late fusion, where each modality is processed separately and the results are combined at the decision level. The library is fully documented following the Read the Docs standards. The documentation, code, and examples are available anonymously on GitHub at https://***/InfoLab-SKKU/infodeslib. © 2024 Copyright for this paper by its authors.

关键词： Python

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Learning to bag with a simulation‐free reinforcement learning framework for robots

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IET Cyber-Systems and Robotics 2024年第2期6卷 52-66页

作者： Francisco Munguia-Galeano Jihong Zhu Juan David Hernández Ze Ji Cooper Group University of LiverpoolLiverpoolUK School of Physics Engineering and Technology University of YorkYorkUK School of Computer Science and Informatics Cardiff UniversityCardiffUK School of Engineering Cardiff UniversityCardiffUK

Bagging is an essential skill that humans perform in their daily ***,deformable objects,such as bags,are complex for robots to manipulate.A learning-based framework that enables robots to learn bagging is *** novelty of this framework is its ability to learn and perform bagging without relying on *** learning process is accomplished through a reinforcement learning(RL)algorithm introduced and designed to find the best grasping points of the bag based on a set of compact state *** framework utilises a set of primitive actions and represents the task in five *** our experiments,the framework reached 60% and 80% success rates after around 3 h of training in the real world when starting the bagging task from folded and unfolded states,***,the authors test the trained RL model with eight more bags of different sizes to evaluate its generalisability.

关键词： reinforcement learning robot learning robotics

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A fusion of machine learning and cryptography for fast data encryption through the encoding of high and moderate plaintext information blocks

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Multimedia Tools and Applications 2025年第8期84卷 5349-5375页

作者： Shafique, Arslan Mehmood, Abid Alawida, Moatsum Elhadef, Mourad Rehman, Mujeeb Ur School of Biomedical Engineering University of Glasgow Glasgow United Kingdom Department of Computer Sciences Abu Dhabi University Abu Dhabi United Arab Emirates Cyber Technology Institute School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Within the domain of image encryption, an intrinsic trade-off emerges between computational complexity and the integrity of data transmission security. Protecting digital images often requires extensive mathematical operations for robust security. However, this computational burden makes real-time applications unfeasible. The proposed research addresses this challenge by leveraging machine learning algorithms to optimize efficiency while maintaining high security. This methodology involves categorizing image pixel blocks into three classes: high-information, moderate-information, and low-information blocks using a support vector machine (SVM). Encryption is selectively applied to high and moderate information blocks, leaving low-information blocks untouched, significantly reducing computational time. To evaluate the proposed methodology, parameters like precision, recall, and F1-score are used for the machine learning component, and security is assessed using metrics like correlation, peak signal-to-noise ratio, mean square error, entropy, energy, and contrast. The results are exceptional, with accuracy, entropy, correlation, and energy values all at 97.4%, 7.9991, 0.0001, and 0.0153, respectively. Furthermore, this encryption scheme is highly efficient, completed in less than one second, as validated by a MATLAB tool. These findings emphasize the potential for efficient and secure image encryption, crucial for secure data transmission in rea-time applications. © The Author(s) 2024.

关键词： Cryptography

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Designing a Digital Repository for the Cultural and Natural Heritage of the Island of Zakynthos 9

Designing a Digital Repository for the Cultural and Natural ...

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9th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Palamas, Stergios Kabassi, Katerina Mylonas, Phivos Ionian University Department of Informatics Corfu Greece Ionian University Department of Environment Zakynthos Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece

ISBN: (纸本)9798350342482

The present study discusses the design of a digital archive for the preservation and dissemination of the Cultural and Natural Heritage of Zakynthos Island. The proposed architecture suggests a Content Management System (CMS) to provide the core functionality, while a web application, interacting with the CMS through a RESTFul API, will provide an alternative front-end targeting mainly on-site end-users using mobile devices. Authors and contributors can submit multimedia content to the repository, which is reviewed and approved by system administrators before publishing, ensuring this way the information quality and *** system will also provide a RESTful API, allowing integration with 3rd party systems and web applications, thereby expanding the repository's reach and capabilities. Content delivery is personalized based on user profiles, location, and preferences, enhancing engagement and usability. By integrating these features, the repository effectively preserves and makes accessible the unique cultural and natural heritage of Zakynthos to both local and global audiences. © 2024 IEEE.

关键词： Augmented Reality Biodiversity Content Management Systems Cultural Heritage Multimedia Digital Repositories Natural Heritage Web Applications

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Neural End-to-End Coreference Resolution using Morphological Information 6

Neural End-to-End Coreference Resolution using Morphological...

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CRAC Shared Task on Multilingual Coreference Resolution at 6th Workshop on Computational Models of Reference, Anaphora and Coreference, CRAC 2023

作者： Arslan, Tugba Pamay Acar, Kutay Eryigit, Gulsen ITÜ NLP Research Group Department of AI&Data Engineering Faculty of Computer&Informatics Istanbul Technical University Turkey ITÜ NLP Research Group Department of Computer Engineering Faculty of Computer&Informatics Istanbul Technical University Turkey

ISBN: (纸本)9781955917025

In morphologically rich languages, words consist of morphemes containing deeper information in morphology, and thus such languages may necessitate the use of morpheme-level representations as well as word representations. This study introduces a neural multilingual end-to-end coreference resolution system by incorporating morphological information in transformer-based word embeddings on the baseline model. This proposed model participated in the Sixth Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC 2023). Including morphological information explicitly into the coreference resolution improves the performance, especially in morphologically rich languages (e.g., Catalan, Hungarian, and Turkish). The introduced model outperforms the baseline system by 2.57 percentage points on average by obtaining 59.53% CoNLL F-score. © CRAC 2023. All Rights Reserved.

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Improving SVM Performance in Determining Cost and Kernel Parameter Values Through Grid Search 2

Improving SVM Performance in Determining Cost and Kernel Par...

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2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Sylvia, Visca Lydia, Maya Silvi Nasution, Benny Benyamin Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia

ISBN: (纸本)9798350351613

The purpose of this study is to improve the performance of Support Vector Machine (SVM) algorithm in sentiment analysis of trainee reviews through parameter optimization using Grid Search. Trainee reviews were taken from the Vocational and Productivity Training Center (BBPVP) Medan, which is the context of this study. The method used includes collection and preprocessing of review data, followed by the application of SVM for sentiment analysis. Grid Search is used to optimize the cost and kernel parameters of SVM. The results show that after optimization, the resulting SVM model shows significant performance improvement compared to the model before optimization. Accuracy increased from 68.75% to 71.96%, with improvements in other metrics such as precision, recall, and F1 score. These findings suggest that Grid Search is an effective method to optimize SVM parameters in sentiment analysis, providing a more accurate evaluation of the effectiveness of training programs at BBPVP Medan. This research makes an important contribution by applying hyperparameter optimization to SVM and suggests the application of this method for similar analysis in the future. © 2024 IEEE.

关键词： Support vector machines

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Comparative Analysis of Edge Detection Algorithms with Particle Swarm Optimization for Enhanced Image Processing 11

Comparative Analysis of Edge Detection Algorithms with Parti...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Rahayu, Sri Nurtantio Andono, Pulung Pujiono Zainul Fanani, Ahmad Dian Nuswantoro University Faculty of Computer Science Department of Informatics Engineering Semarang Indonesia Institut Teknologi Garut Computer Science Department Garut Indonesia

ISBN: (纸本)9798350389654

Edge detection plays an important role in various fields by identifying object boundaries and supporting advanced image analysis, such as segmentation, recognition, and tracking. Many edge detection algorithms, such as Canny, Laplacian, and Prewitt, have been developed to address issues such as noise sensitivity, computational complexity, and detection accuracy. This research compares the optimized algorithms using Particle Swarm Optimization (PSO). The results of this study show that the optimized algorithm provides better performance based on the evaluation conducted using entropy, Mean Squared Error (MSE), and computation time, and has practical implications. This research also determines the most effective edge detection technique in various image processing scenarios, thus contributing to the optimization of image analysis workflows in real-world contexts. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Implementing a Digital Repository for the Cultural and Natural Heritage of the Island of Zakynthos 19

Implementing a Digital Repository for the Cultural and Natur...

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19th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2024

作者： Palamas, Stergios Voutos, Yorghos Kabassi, Katerina Mylonas, Phivos Ionian University Department of Informatics Corfu Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece Ionian University Department of Environment Zakynthos Greece

ISBN: (纸本)9798331504502

The present study discusses the development of a digital repository (ANTETI) for the preservation and dissemination of the Cultural and Natural Heritage of Zakynthos Island. The platform is based on a popular Content Management System (CMS) to provide the core functionality, extended with the use of the CMS's API, to provide additional personalisation functionality for the end-users. Additionally, the system features a web application, oriented mainly on users visiting the island of Zakynthos and developed exclusively with open web technologies and JavaScript frameworks. The web application is an alternative, map-centered, mobile optimised front-end, for the platform's content featured in the CMS. The system also provides a RESTful API, allowing integration with 3rd party systems and web applications, thereby expanding the repository's reach and capabilities. Content delivery is personalized based on user profiles, location, and preferences, enhancing engagement and usability. By integrating these features, the repository effectively preserves and makes accessible the unique cultural and natural heritage of Zakynthos to both local and global audiences. © 2024 IEEE.

关键词： Biodiversity Content Management Systems Cultural Heritage Multimedia Digital Repositories Natural Heritage Web Applications

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Mobile Crowd Sensing with Secure Coin Rewarding in Human Activity Recognition for Elderly People System 1

Mobile Crowd Sensing with Secure Coin Rewarding in Human Act...

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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Febrianti, Erita Cicilia Sudarsono, Amang Santoso, Tri Budi Politeknik Elektronika Negeri Surabaya Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350365191

Mobile Crowd Sensing (MCS) is a promising approach that harnesses the collective intelligence of mobile devices to gather data, particularly beneficial for monitoring the daily activities of elderly individuals. This paper proposes a system that integrates secure coin rewarding with MCS for Human Activity Recognition in elderly care. By addressing challenges such as data security, privacy attacks, and incentivizing healthy behaviors, the system aims to enhance the well-being of elderly individuals. Pairing-based cryptography is employed to ensure privacy preservation, while a reward system motivates active participation and data accuracy. The proposed system operates through a series of steps involving key generation, challenge authentication, receipt issuance, and reward claiming. Latency measurements demonstrate the efficiency of the system in issuing receipts and claiming rewards. Overall, this research contributes to the advancement of monitoring systems for elderly care by combining technology, community engagement, and personalized recommendations to promote healthy living among the elderly population. © 2024 IEEE.

关键词： Coin Rewarding Human Activity Recognition Mobile Crowd Sensing Pairing-Based Cryptography Privacy-preserving

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GAN-generated Synthetic Data and SVM-based Feature Selection for Improved Cardiovascular Disease Prediction

GAN-generated Synthetic Data and SVM-based Feature Selection...

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2023 Medical Technologies Congress, TIPTEKNO 2023

作者： Mandan, Amel Sulaiman Hazzaa, Nidhal Mohsin Yildiz, Oktay Informatics Institute Gazi University Computer Science Department Ankara Turkey Gazi University Faculty of Engineering Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350328967

Early diagnosis requires cardiovascular disease forecasting. Past patient interests can improve machine learning predictions. This study uses a Generative Adversarial Network (GAN) to augment data to address the issue of insufficient datasets. Our objective is to use the GAN and feature selection (FS) to tackle the issue of class imbalance. The study assesses the efficacy of three different machine learning (ML) algorithms in predicting coronary artery disease (CAD). The UCI repository provided the Z-Alizadeh sani dataset for our analysis. A GAN was used to balance an imbalanced dataset. We used GAN to augment data and synthesise data to improve the models. Support Vector Machine (SVM) as feature selection tool was used to extract more effective attributes. We trained and tested CAD prediction using machine learning algorithms using 5-fold cross validation. We compared the machine-learning algorithm's outcomes with and without GAN-based data enhancement and feature selection to evaluate its predicted accuracy. According to results GAN and feature selection improved accuracy, sensitivity, and specificity for all algorithms, suggesting that generated data and selected attributes can identify positive and negative cases. © 2023 IEEE.

关键词： Generative adversarial networks

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