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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Safira, Shaina Maulida Gunawan, Putu Harry Kurniawan, Wandi Yusuf Gede Karang Komala Putra, I. Reganata, Gde Palguna Kadek Winda Patrianingsih, Ni Gede Wahyu Surya Dharma, I. Kadek Arya Sugianta, I. Udhayana, Khadijah F. R. Telkom University School of Computing Bandung Indonesia Universitas Bali Internasional Informatics Department Denpasar Indonesia

ISBN: (纸本)9798350394559

Falling events occur every day, especially among elderly patients or patients in need of special care. To some extent, falling can cause serious injuries, both open and internal wounds. Therefore, fast and accurate technology is needed to detect whether someone has fallen, especially older adults. This research aims to find a solution for detecting fall movement using machine learning algorithms. In this research, the dataset was collected from 206 participants through the Phyphox application on smartphones. This app utilizes the accelerometer sensor to measure acceleration on each 3D axis (x, y, and z). Here, two proposed classification methods are used to detect the fall movement, i.e., K-Nearest Neighbor and Support Vector Machine. The accuracy of both models is calculated and compared in this paper. The research results show that the linear kernel Support Vector Machine (SVM) has a performance range of 77% to 95%. Meanwhile, the RBF kernel SVM demonstrates a performance range of 79% to 87%, and the polynomial order three of the SVM kernel displays a performance range of 51% to 87%. Moreover, the performance of the K-NN algorithm falls within the scope of 72% to 84%. Overall, the scenario employing the linear kernel Support Vector Machine (SVM) technique exhibits the highest level of efficacy, boasting an accuracy rate of up to 95%. © 2023 IEEE.

关键词： Support vector machines

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Implementation of a Mixed Triple Logistic Map and the BB84 Quantum Key Distribution for Secure Image Communication

Implementation of a Mixed Triple Logistic Map and the BB84 Q...

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2024 International of Seminar on Application for Technology of Information and Communication, iSemantic 2024

作者： Morissa, Virnika Shefira Gina Setiadi, De Rosal Ignatius Moses Universitas Dian Nuswantoro Informatics Engineering Department Semarang50131 Indonesia Universitas Dian Nuswantoro Research Center for Quantum Computing and Materials Informatics Faculty of Computer Science Semarang50131 Indonesia

ISBN: (纸本)9798350366648

In the digital era, data communication security is one of the main challenges. This research proposes a new method for secure image communication, which is a mixed triple logistic map and BB84 quantum key distribution. This approach combines the strengths of classical and quantum cryptographic systems to improve security. BB84 quantum key distribution protocol for securely distributing encryption keys. The resulting key is then processed into a hash value using the SHA-256 algorithm. These hash values are converted into ASCII values, which are used to generate three key sequences using the modified logistic map. The three key sequences are combined to form a keystream used in image encryption. Several security metrics are used to evaluate encryption security, including entropy, the Chi-Square test, and pixel correlation. The analysis results show that the encrypted image has an entropy close to eight. In addition, all chi-square values smaller than 293 indicate a uniform distribution. The correlation of nearby pixels in the encrypted image is also very low, where all values are less than 0.01, which indicates the loss of local patterns in the encrypted image. These results demonstrate that the proposed method effectively secures image communications, providing high security and resistance to conventional and quantum cryptographic attacks. © 2024 IEEE.

关键词： Quantum cryptography

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User Experience Research Play Card in Augmented Reality: A sensemaking case study on designing Visibility and Modality 10

User Experience Research Play Card in Augmented Reality: A s...

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10th International Conference on Virtual Reality, ICVR 2024

作者： Liang, Sha Dogan, Huseyin Giff, Stephen Barsoum, Renee Bournemouth University Department of Computing and Informatics Bournemouth United Kingdom Google United States UX Strategist and Lead Admiral United Kingdom

ISBN: (纸本)9798350364231

Despite the existence of high-level guidelines and frameworks in Human-Computer Interaction (HCI), there is a lack of specific user experience principles for designing augmented reality (AR). This paper proposed the creation of a series of user experience research (UXR) play cards aimed at providing UX practitioners concrete, AR-specific UX design based on The Point of view (POV) UXR playbook. Through the development of a series of 'Hidden Reality' and 'Modality' play cards, we propose a tool that not only enhances the design process but also enriches the overall AR experience for users, paving the way for more meaningful and engaging AR applications. At the heart of this process is the concept of sensemaking, a key method that clarifies the way forward, simplifies the upward journey of the UX's POV pyramid and offers profound insights into user needs and behaviours. © 2024 IEEE.

关键词： Augmented reality

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Grammar-Based Question Classification Using Ensemble Learning Algorithms 1

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18th International Conference on Web Information Systems and Technologies, WEBIST 2022

作者： Mohasseb, Alaa Kanavos, Andreas School of Computing University of Portsmouth Portsmouth United Kingdom Department of Informatics Ionian University Corfu Greece

ISBN: (数字)9783031430886

ISBN: (纸本)9783031430879

Question Classification is one of the important applications of information retrieval, as it plays a crucial role in improving the performance of question-answering systems. Differentiating between factoid and non-factoid questions is a particularly difficult task. Different methods have been suggested to improve the identification and classification of factoid questions. Most of these methods rely on semantic features and bag-of-words. This research paper explores the utilization of a Grammar-based framework for Questions Categorization and Classification (GQCC) to identify question types. This framework incorporates features such as grammatical features, domain-specific features, and patterns. These features leverage the question structure. By employing Ensemble Learning models, experimental findings demonstrate that the integration of question grammatical features with Ensemble Learning models contributes to achieving a good level of accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Insights into research on blockchain for smart contracts: a bibliometric analysis

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Multimedia Tools and Applications 2024年 1-25页

作者： Singh, Renu Gupta, Ashlesha Mittal, Poonam Faculty of Informatics and Computing J.C. Bose University of Science and Technology YMCA Faridabad India Department of Computer Science & amp Engineering Indraprastha Institute of Information Technology IIIT New Delhi India

Over the past few years, blockchain technology has gained significant attention. This surge in popularity can be attributed to the emergence of cryptocurrencies and the development of smart contracts. Cryptocurrency is a digital currency that eliminates the problem of double spending. Cryptocurrencies like Bitcoin, Ethereum, Litecoin, Stellar, Zcash, Maker, Aave, etc. become popular and are preferred for money transfers. Smart contracts are the next popular technology on the blockchain after cryptocurrency. It can be considered a piece of code that can execute automatically when the predefined conditions are fulfilled. Researchers believe that the potential of blockchain with smart contracts is only in its initial stages and that its true potential has yet to be fully discovered. Hence, an extensive bibliometric analysis is conducted to understand blockchain trends for smart contracts and to give future directions in this field. For this analysis, various steps are followed, starting with formulating the research question, defining the scope of our research, extracting and analyzing data, answering the research question, and finally, drawing a conclusion. This research paper will be fruitful for scholars and researchers, providing an extensive statistical and network analysis of extracted smart contracts publications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Bibliometric analysis Blockchain Crypto contract Scientometrics Scopus Smart contracts VOSViewer

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Advancing Color Perception: An IoT-driven Pedagogical System for Visually Impaired Children 1

Advancing Color Perception: An IoT-driven Pedagogical System...

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1st IEEE International Conference on Advances in Signal Processing, Power, Communication, and computing, ASPCC 2024

作者： Balage, Prasandika Jayawardene, Nuwan University of Westminster School of Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798350355093

Visual impairment is characterized by the loss of vision, encompassing both complete blindness and partial vision loss. Studies reveal a notable prevalence of visual impairment among school-aged children. The significance of colors in acquiring and discerning visual information is crucial, aiding children in effectively perceiving and comprehending their surroundings. This study presents an Internet of Things (IoT) system designed for teaching colors to visually impaired children. The system achieved an accuracy rate surpassing 77.8%, and it is operated effectively regardless of surrounding lighting conditions. Given that the target users of the system are visually impaired children, this research was conducted in close alignment with Human-Computer Interaction (HCI) principles. © 2024 IEEE.

关键词： Color vision

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Feature Extraction and Preprocessing Techniques on Open-Ended Concept Mapping Activity Log Data 7

Feature Extraction and Preprocessing Techniques on Open-Ende...

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7th International Conference on Vocational Education and Electrical Engineering, ICVEE 2024

作者： Prasetya, Didik Dwi Muhtadi, Muis Azizah, Hanifah Nur Zain, Azlan Mohd State University of Malang Department of Electrical Engineering and Informatics Malang Indonesia Universiti Teknologi Malaysia Faculty of Computing Johor Bahru Malaysia

ISBN: (纸本)9798331505103

Concept maps are graphic representations of unique ideas widely used in learning. Concept mapping activities are often stored in an activity log which contains a record of data based on a particular time. The high activity of concept mapping can form extensive data collection and result in abundant information. An appropriate initial processing action is needed to process the concept mapping log data properly. This study aims to reduce the concept of map activity log data to obtain a reliable and noise-free dataset. This research begins with the data preprocessing stage, the initial technique in data mining that converts raw data into cleaner information and can be used for further processing. The preprocessing phase could be done using the data reduction method or adding a filter example to observe missing data on an attribute by eliminating missing observation data. The principal component analysis algorithm was used to normalize the data to become stable, neither too high nor too low. This study emphasizes that the extracted data could produce high accuracy and shorter processing time. © 2024 IEEE.

关键词： Data assimilation

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Wav2pos: Exploring syntactic analysis from audio for Highland Puebla Nahuatl 4

Wav2pos: Exploring syntactic analysis from audio for Highlan...

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4th Workshop on Natural Language Processing for Indigenous Languages of the Americas, AmericasNLP 2024

作者： Pugh, Robert Sreedhar, Varun Tyers, Francis Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States Department of Linguistics Indiana University Bloomington United States

ISBN: (纸本)9798891761087

We describe an approach to part-of-speech tagging from audio with very little human-annotated data, for Highland Puebla Nahuatl, a low-resource language of Mexico.1 While automatic morphosyntactic analysis is typically trained on annotated textual data, large amounts of text is rarely available for low-resource, marginalized, and/or minority languages, and morphosyntactically-annotated data is even harder to come by. Much of the data from these languages may exist in the form of recordings, often only partially-transcribed or analyzed by field linguists working on language documentation projects. Given this relatively low-availability of text in the low-resource language scenario, we explore end-to-end automated morphosyntactic analysis directly from audio. The experiments described in this paper focus on one piece of morphosyntax, part-of-speech tagging, and builds on existing work in a high-resource setting. We use weak supervision to increase training volume, and explore a few techniques for generating word-level predictions from the acoustic features. Our experiments show promising results, despite less than 400 sentences of audio-aligned, manually-labeled text. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhancing IoT Security: A Comparative Study of CNN and RNN-Based Anomaly Detection Using the CICIoT2023 Dataset

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第5期52卷 1424-1441页

作者： Al-Ghamdi, Amal M. Alansari, Marwah M. at College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Information Technology Department College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The rapid growth of Internet of Things (IoT) devices has resulted in numerous security challenges. Anomaly detection has become a crucial aspect of IoT security, as it helps identify unusual behaviors and potential threats within IoT networks. Deep learning techniques have shown considerable potential in recognizing anomalies across various domains. However, the dynamic and continuously developing nature of cyberattacks complicates the process of monitoring and identifying among different types of attacks effectively. This study aims to enhance IoT security by conducting a comparative analysis of two deep learning algorithms: convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We deploy these models on the new CICIoT2023 dataset to detect large-scale attacks targeting IoT devices. Our findings reveal that the CNN model outperforms others with an accuracy of 98%, while the BiLSTM model achieves 85%, and the hybrid CNN-BiLSTM model reaches 94%. These results show that CNNs are better at extracting spatial features and that hybrid models might be able to use both spatial and temporal dependencies. By providing insights into model selection and optimization, this research contributes to the development of robust intrusion detection systems for IoT security. © (2025), (International Association of Engineers). All rights reserved.

关键词： Recurrent neural networks

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Analyzing the Impact of Context Representation and Scope in Code Infilling 24

Analyzing the Impact of Context Representation and Scope in ...

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46th International Conference on Software Engineering: Companion, ICSE-Companion 2024

作者： Heo, Jinseok Lee, Eunseok Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798400705021

Existing studies solve software engineering tasks using code infilling through LLMC. They utilize context information, which refers to data near the target code of infilling, as input prompts. Although prompts are essential for infilling the target code, current studies use them without analyzing the impact of the representation and scope of context on code infilling. In this study, we analyzed how context representation and scope affect the performance of code infilling. We used XLCost, which contains code, comments, and a function comment for various programming languages. The combination of code and a function comment for context representation yielded the best code infilling performance. Furthermore, we found that the context scope is proportional to performance. Our analysis results can be applied in various tasks that involve code infilling in the future. © 2024 IEEE Computer Society. All rights reserved.

关键词： Software engineering

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