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

作者： Bin Azhar, M. A. Hannan Meszaros, Zoltan Islam, Tasmina School of Engineering Technology and Design Canterbury Christ Church University Canterbury United Kingdom King's College London Department of Informatics London United Kingdom

ISBN: (纸本)9798331506490

This paper addresses the critical challenge of advancing autonomous vehicle control in off-road environments, where traditional driver assistance technologies often prove inadequate. While AI-powered systems in modern vehicles have become highly effective at navigating structured urban landscapes, adapting these technologies for rural and off-road settings remains a complex and necessary undertaking due to varied and unpredictable obstacles. Off-road scenarios present unique challenges, such as dense vegetation, rugged terrain, uneven surfaces, and water bodies, which demand robust detection and classification capabilities beyond those found in urban areas. This study explores the application of state-of-the-art machine learning models, particularly transformer-based architectures, to enhance feature recognition and classification in rural contexts. We evaluate several advanced models, including hybrid architectures that combine convolutional neural networks (CNNs) with transformers, to determine their effectiveness in identifying complex off-road features. Findings reveal that, although current data limitations restrict the development of fully autonomous systems for off-road navigation, meaningful progress can still be achieved to improve driver assistance functionalities. This paper emphasises the urgent need for broader, more diverse datasets to ensure model robustness and generalizability for autonomous navigation in unstructured, unpredictable environments. Ultimately, this work highlights a promising path toward safer, more effective driver assistance technologies tailored specifically for challenging off-road applications and scenarios. © 2024 IEEE.

关键词： Off road vehicles

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Real-Time Pattern Recognition in Augmented Reality Application Through the Integration of TensorFlow Lite and Unity

Real-Time Pattern Recognition in Augmented Reality Applicati...

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2024 International Conference on Computer engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Mulyo, Dwi Rante, Hestiasari Sukaridhoto, Sritrusta Arissabarno, Cahyo Politeknik Elektronika Negeri Surabaya Department of Creative Multimedia Technology Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350368802

The integration of machine learning with augmented reality (AR) is vital for traditional batik preservation, especially in real-time pattern recognition. Traditional AR applications often rely on external servers, increasing latency and reducing interaction fluidity. This paper presents real-time batik pattern recognition using quantized TensorFlow Lite (TFLite) in a mobile AR application. Using the Single Shot MultiBox Detector (SSD) architecture with quantization, model size is reduced by 75%, while maintaining 96% accuracy, 0.06 seconds inference time, and 46 frames per second (FPS). Compared to YOLO, which achieves 93% accuracy and 40 FPS, TFLite offers faster inference and reduced computational load, making it ideal for mobile AR. By embedding TFLite into an AR platform, users can interact with batik patterns in real-time without server reliance. This research shows that quantized TFLite enhances mobile AR performance, balancing speed, accuracy, and efficiency. © 2024 IEEE.

关键词： Object detection

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Development and Evaluation of an Integrated Vital Signs Simulator for Accurate Healthcare Measurements 4

Development and Evaluation of an Integrated Vital Signs Simu...

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4th International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2024

作者： Safitri, Meilia Nugraha, Septian Tri Surriani, Atikah Anggoro, Sotya Universitas Muhammadiyah Yogyakarta Department of Medical Electronics Technology Yogyakarta Indonesia Universitas Gadjah Mada Department of Electrical Engineering and Informatics Yogyakarta Indonesia Universitas Muhammadiyah Yogyakarta Department of Automotive Engineering Technology Yogyakarta Indonesia

ISBN: (纸本)9798350378368

Accurate monitoring of vital signs, including electrocardiogram (ECG) and non-invasive blood pressure (NIBP) measurements, is essential for assessing cardiovascular health in clinical settings. Regular calibration of the monitoring devices is crucial for maintaining their precision and reliability. This study presents the design and development of an integrated vital signs simulator capable of simulating ECG and NIBP parameters in a single device. The designed tool utilizes an Arduino Mega 2560 microcontroller as the control unit, IC MCP 4921 to convert digital values to analog signals to generate ECG waveforms, and the MPX5100 pressure sensor to measure pressure during NIBP assessments and control the motor pump. The designed device was evaluated across various settings, including heart rates of 30, 60, 120, and 180 beats per minute (BPM) and blood pressures of 60/30, 80/50, 120/80, and 150/100 mmHg. The results showed that the heart rate simulation function maintained a high degree of accuracy, with the highest error values at 30 and 60 BPM, and the lowest errors at 120 and 180 BPM. For the NIBP parameters, the highest error was observed in the systolic measurement at the 60/30 mmHg setting, whereas the lowest error was found in the systolic reading at the 80/50 mmHg setting. Overall, the percentage error for the heart rate parameters remained below 1% and for the NIBP parameters below 10%. These findings demonstrated the effectiveness and reliability of the designed device. © 2024 IEEE.

关键词： Blood pressure

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Transformer-based Models for Enhanced Amur Tiger Re-Identification 22

Transformer-based Models for Enhanced Amur Tiger Re-Identifi...

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2024 IEEE 22nd World Symposium on Applied Machine Intelligence and informatics, SAMI 2024

作者： Bai, Xufeng Islam, Tasmina Bin Azhar, M A Hannan King's College London Department of Informatics London United Kingdom Canterbury Christ Church University School of Engineering Technology and Design Canterbury United Kingdom

ISBN: (纸本)9798350317206

Rapid urban growth, with its profound impact on natural habitats, intensifies the global risk faced by many wildlife species, driving them closer to the brink of extinction due to factors like habitat destruction, illegal hunting, and the challenges posed by climate change. The urgency of this situation is highlighted by the current status of the Amur tigers, emphasising the need for continuous observation to ensure their survival. Within this context, re-identification (Re-ID) emerges as the method for recognising individual entities based on previously captured data. This study is dedicated to the re-identification of Amur tigers, employing the Amur Tiger Re-identification in the Wild (ATRW) dataset and placing a significant emphasis on assessing various deep learning architectures, particularly focusing on transformer-based models. Several neural network architectures, including Vision Transformer (ViT), Multiple Granularity Network (MGN), and Neighbor Transformer (NFormer), were explored. The results indicate that transformer-based methods hold substantial promise for further advancements in re-identification tasks. Notably, the ViT model achieved an impressive mAP score of 80.8, while the combination of ViT with MGN yielded an exceptional mAP of 83.4, surpassing the best benchmark method by an 9.3% in a single-camera scenario. Additionally, the NFormer architecture demonstrated comparable results, boasting a mAP score of 81.1. © 2024 IEEE.

关键词： Urban growth

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Covert Channel Detection Methods 20

Covert Channel Detection Methods

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20th IEEE International Conference on Emerging eLearning Technologies and Applications, ICETA 2022

作者： Ondov, Adrian Helebrandt, Pavol Institute of Computer Engineering and Applied Informatics Slovak University of Technology Bratislava Slovakia

ISBN: (数字)9798350320336

ISBN: (纸本)9798350320336

The modern networking world is being exposed to many risks more frequently every day. Most of systems strongly rely on remaining anonymous throughout the whole endpoint exploitation process. Covert channels represent risk since they ex-ploit legitimate communications and network protocols to evade typical filtering. This firewall avoidance sees covert channels frequently used for malicious communication of intruders with systems they compromised, and thus a real threat to network security. While there are commercial tools to safeguard computer networks, novel applications such as automotive connectivity and V2X present new challenges. This paper focuses on the analysis of the recent ways of using covert channels and detecting them, but also on the state-of-the-art possibilities of protection against them. We investigate observing the timing covert channels behavior simulated via injected ICMP traffic into standard network communications. Most importantly, we concentrate on enhancing firewall with detection and prevention of such attack built-in features. The main contribution of the paper is design for detection timing covert channel threats utilizing detection methods based on statistical analysis. These detection methods are combined and implemented in one program as a simple host-based intrusion detection system (HIDS). As a result, the proposed design can analyze and detect timing covert channels, with the addition of taking preventive measures to block any future attempts to breach the security of an end device. © 2022 IEEE.

关键词： Statistical methods

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Accounting for Non-Uniform Ambient Lighting in Ray Traced Ambient Occlusion

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica Electrical engineering and computer science 2022年第3期66卷 248-252页

作者： Fridvalszky, András Tóth, Balázs Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Muegyetem rkp. 3. BudapestH-1111 Hungary

Ambient occlusion is a popular method to enhance the visuals of real-time applications. The generally accepted equation assumes that incoming ambient lighting is uniform in the enclosing hemisphere around the surface point. This assumption does not always hold and can cause artifacts. With screen-space techniques the artifact may not be present, because of the lack of information, but it becomes visible with more accurate methods. This is especially true for ray tracing based implementations which are widely used to create the ground truth image to measure other techniques. With recent advancements of the graphics hardware, they also found their way into real-time applications where these shortcomings were neither discussed nor addressed. In this paper we analyze the problem and demonstrate its presence in popular game engines. We also propose a ray tracing based extension to eliminate these artifact in a physically plausible way. © 2022 Budapest University of technology and Economics. All rights reserved.

关键词： Ray tracing

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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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What Are the Points of Concern for Players about VR Games: An Empirical Study based on User Reviews in Different Languages

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Games: Research and Practice 2024年第4期2卷 1-18页

作者： Jiong Dong Kaoru Ota Mianxiong Dong Department of Sciences and Informatics Muroran Institute of Technology Muroran Japan School of Information Engineering Xuchang University Xuchang China Department of Sciences and Informatics Muroran Institute of Technology Muroran Japan

In recent years, the boom of Virtual Reality (VR) technology has signaled fruitful applications in various fields, such as education, industry, animation, entertainment, and game. The global VR games market is projected to grow to $53.44 billion in 2028 at a CAGR of 31.4% in the 2021–2028 period. Therefore, what players care about most about VR games is a topic worthy of research. In this article, we conduct an in-depth study of 1,362 VR games and 484,070 reviews on the Steam platform. Unlike other literature focusing only on monolingual reviews (e.g., English), we apply the topic clustering model (BERTopic) to comprehensively analyze multilingual user reviews, including English, Simplified Chinese, and French. We find that the three features people praise the most are the music, the gaming experience, and the gameplay. Meanwhile, gamers’ most common complaints are bug issues, insufficient content, and excessive pricing.

关键词： Virtual Reality (VR) VR games Steam platform user reviews multilingual reviews

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Whitening Consistently Improves Self-Supervised Learning 23

Whitening Consistently Improves Self-Supervised Learning

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

作者： Kalapos, András Gyires-Tóth, Bálint Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Telecommunications and Artificial Intelligence Muegyetem rkp. 3 BudapestH-1111 Hungary

ISBN: (纸本)9798350374889

Self-supervised learning (SSL) has been shown to be a powerful approach for learning visual representations. In this study, we propose incorporating ZCA whitening as the final layer of the encoder in self-supervised learning to enhance the quality of learned features by normalizing and decorrelating them. Although whitening has been utilized in SSL in previous works, its potential to universally improve any SSL model has not been explored. We demonstrate that adding whitening as the last layer of SSL pretrained encoders is independent of the self-supervised learning method and encoder architecture, thus improving performance for a wide range of SSL methods across multiple encoder architectures and datasets. Our experiments show that whitening is capable of improving linear and k-NN probing accuracy by 1-5 percentage points. Additionally, we incorporate metrics that allow for a comprehensive analysis of the learned features, provide insights into the quality of the representations, and help identify collapse patterns. © 2024 IEEE.

关键词： Self-supervised learning

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Towards Creating a Secure Framework for Building Mirror World Applications 17th

Towards Creating a Secure Framework for Building Mirror Wo...

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17th International Conference on Research Challenges in Information Sciences, RCIS 2023

作者： Mazarakis, Panos Privacy Engineering and Social Informatics Laboratory Department of Cultural Technology and Communication University of the Aegean University Hill Mytilene81100 Greece

ISBN: (纸本)9783031330797

The growing interest in Virtual Reality and Augmented Reality technologies has led to the development of the concept of Mirror Worlds, the Metaverse, and the AR Cloud. Different companies are creating their own version of the Metaverse and Mirror Worlds serve as a foundation for these experiences. As these technologies continue to grow, there is a need for the design of a systematic way for designing and implementing applications in virtual spaces. There are emerging technologies and standards that could form the basis of such a framework, however, privacy concerns like data privacy and location tracking need to be addressed while designing any potential framework or solution. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Augmented reality

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