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46th Annual Conference on European Association for computer Graphics, EUROGRAPHICS 2025

作者： Nikolov, I. Computer Graphics Group Department of Architecture Design and Media Technology Aalborg University Denmark

ISBN: (纸本)9783038682684

Correct lighting and shading are vital for pixel art design. Automating texture generation, such as normal, depth, and occlusion maps, has been a long-standing focus. We extend this by proposing a deep learning model that generates point and directional light maps from RGB pixel art sprites and specified light vectors. Our approach modifies a UNet architecture with CIN layers to incorporate positional and directional information, using ZoeDepth for training depth data. Testing on a popular pixel art dataset shows that the generated light maps closely match those from depth or normal maps, as well as from manual programs. The model effectively relights complex sprites across styles and functions in real time, enhancing artist workflows. The code and dataset are here - https://***/IvanNik17/light-sprite. © 2025 The Authors.

关键词： Mapping

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Effects of non-ideal seabed topography on low frequency sound propagation in shallow sea

Effects of non-ideal seabed topography on low frequency soun...

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2022 International Conference on Information Processing in Ocean Science and technology, ICIPOST 2022

作者： Chai, Z.G. Wang, Q.L. Zhu, H.H. Cui, Z.Q. Tu, H.W. School of Naval Architecture and Maritime Zhejiang Ocean University Zhoushan China Institute of Marine Science and Technology Zhejiang Ocean University Zhoushan China Department of Computer Science and Technology National University of Defense Technology Changsha China

In order to study the influence of seabed topography on sound propagation under non-ideal conditions, a simulation model of low frequency sound propagation of whole waveguide is established by using finite element method (FEM). The action mechanism of the topography with seamounts on the components of the seismic wave field is studied in time domain. The simulation results show that the propagation of various seismic wave components can be seen intuitively from the time domain. Scholte waves can be excited easily in a suitable range from the seabed, and can propagate along the seabed for a long distance with small attenuation and strong stability. The presence of a seamount allows Scholte waves to scatter, and Scholte waves can travel along the surface of the seamount to the back of the seamount. The path and energy intensity of sound wave change with the change of terrain. © Published under licence by IOP Publishing Ltd.

关键词： Seismic waves

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A Novel Hybrid architecture for Superior IoT Threat Detection through Real IoT Environments

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computers, Materials & Continua 2024年第11期81卷 2299-2316页

作者： Bassam Mohammad Elzaghmouri Yosef Hasan Fayez Jbara Said Elaiwat Nisreen Innab Ahmed Abdelgader Fadol Osman Mohammed Awad Mohammed Ataelfadiel Farah H.Zawaideh Mouiad Fadeil Alawneh Asef Al-Khateeb Marwan Abu-Zanona Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26150Jordan Computer Engineering Department College of Engineering&Information Technology Buraydah CollegesBuraydah51418Saudi Arabia Faculty of Architecture and Design Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjlun26810Jordan Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia

As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense *** paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world *** proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive *** integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT *** evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated *** research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive *** model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security *** our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of *** model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat *** achievemen

关键词： A hybrid deep learning model IoT threat detection real IoT environments cybersecurity attention mechanism

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Early Execution for Soft Error Detection

Early Execution for Soft Error Detection

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International Conference on VLSI Design

作者： Raj Kumar Choudhary Janeel Patel Virendra Singh Computer Architecture and Dependable Systems Laboratory Indian Institute of Technology Bombay India

Single event upsets have made modern integrated circuits more susceptible to soft errors, making their operation less reliable. In order to detect induced computational faults, we propose a fault-tolerant, low-overhead microarchitecture that detects soft errors using temporal redundancy. The idea of introducing an Early Execution Unit (EXU) at the processor frontend has been explored by previous proposals in order to execute dynamic instructions with short dependency chains expeditiously for performance improvement. However, we observe that the functional units in the EXU are idle for a significant portion of the program execution duration. Our proposed microarchitecture utilises these inactive frontend functional units in order to re-execute dynamic instructions to ascertain computational correctness. A low-overhead backend verifier leverages idle resources for redundant execution using the proposed priority-based scheduling algorithm that interleaves program execution with re-execution for error detection. Our proposal provides exhaustive transient fault coverage while improving performance by 7.5% over an existing restricted OoO microarchitecture, Freeflow Core, delivering IPC close to that of an out-of-order baseline, while being $1.78 \times $ more energy-efficient than the involuted, power-hungry out-of-order design.

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Classification of Arrhythmia ECG Signals using Convolutional Neural Network 30

Classification of Arrhythmia ECG Signals using Convolutional...

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30th International Conference on Systems, Signals and Image Processing, IWSSIP 2023

作者： Habijan, Marija Galic, Irena Pizurica, Aleksandra Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek31000 Croatia Ghent University TELIN-GAIM Faculty of Engineering and Architecture Ghent Belgium

ISBN: (纸本)9798350337297

The electrocardiogram (ECG) has been established as a reliable tool for monitoring cardiovascular health. Vast amount of ECG recordings can pose a challenge for its processing and analysis and seeking out experts to analyze such a large amount of ECG data can deplete valuable medical resources. Recently, there has been a growing interest in automatic methods for accurate heartbeat categorization. Our motivation behind this work is to improve efficiency and accuracy for arrhythmia classification based on convolutional neural networks (CNNs). We first pre-process the input signals by normalizing them using a z-score. Following the data segmentation, the under-represented classes are oversampled using random duplication. By randomly duplicating the samples of underrepresented categories and eliminating instances from the overrepresented categories, this approach minimizes the imbalance in the training data. We propose an efficient 12-layer 1D-CNN to classify cardiac arrhythmia into five classes. We demonstrate the oversampling technique's impact on the proposed CNN's performance by comparing its performance before and after its application. © 2023 IEEE.

关键词： Electrocardiograms

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Prediction of the Pore-Pressure Built-Up and Temperature of Fire-Loaded Concrete with Pix2Pix

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computers, Materials & Continua 2024年第5期79卷 2907-2922页

作者： Xueya Wang Yiming Zhang Qi Liu Huanran Wang Mechanics and Materials Science Research Center Ningbo UniversityNingbo315211China Jinyun Institute Zhejiang Sci-Tech UniversityLishui321400China School of Civil Engineering and Architecture Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China

Concrete subjected to fire loads is susceptible to explosive spalling, which can lead to the exposure of reinforcingsteel bars to the fire, substantially jeopardizing the structural safety and stability. The spalling of fire-loaded concreteis closely related to the evolution of pore pressure and temperature. Conventional analytical methods involve theresolution of complex, strongly coupled multifield equations, necessitating significant computational efforts. Torapidly and accurately obtain the distributions of pore-pressure and temperature, the Pix2Pix model is adoptedin this work, which is celebrated for its capabilities in image generation. The open-source dataset used hereinfeatures RGB images we generated using a sophisticated coupled model, while the grayscale images encapsulate the15 principal variables influencing spalling. After conducting a series of tests with different layers configurations,activation functions and loss functions, the Pix2Pix model suitable for assessing the spalling risk of fire-loadedconcrete has been meticulously designed and trained. The applicability and reliability of the Pix2Pix model inconcrete parameter prediction are verified by comparing its outcomes with those derived fromthe strong couplingTHC model. Notably, for the practical engineering applications, our findings indicate that utilizing monochromeimages as the initial target for analysis yields more dependable results. This work not only offers valuable insightsfor civil engineers specializing in concrete structures but also establishes a robust methodological approach forresearchers seeking to create similar predictive models.

关键词： Fire loaded concrete spalling risk pore pressure generative adversarial network(GAN) Pix2Pix

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Heart Sound Classification using Deep Learning 8

Heart Sound Classification using Deep Learning

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8th International Conference on Smart and Sustainable Technologies, SpliTech 2023

作者： Habijan, Marija Galić, Irena Pizurica, Aleksandra Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek31000 Croatia Ghent University TELIN-GAIM Faculty of Engineering and Architecture Ghent Belgium

ISBN: (纸本)9789532901283

Cardiac auscultation is the process of listening to the sounds of the heart with a stethoscope, which can provide important diagnostic information about a patient's heart function. It is a key component of a physical examination and can help doctors identify potential heart problems such as murmurs, arrhythmias, and valve disorders. Deep learning-based cardiac auscultation is of great interest to medical professionals as it can reduce the burden of manual auscultation by automatically detecting abnormal heartbeats. However, automatic cardiac auscultation is a challenging task due to the need for dependable and precise results, as well as the interference of background noise in heart sounds. In this work, we propose a heart sound classification method that could potentially accelerate automated cardiac auscultation. The method is based on Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN). We use heart sounds from the dataset Classifying Heart Sounds Challenge and extract input features using Mel Frequency Cepstral Coefficients (MFCCs). Further, we examine the use of a simple data augmentation technique and show that adding noise, stretching, and shifting of heart sound signals significantly improves the classification accuracy of the normal, murmur, and artifact heart sounds. © 2023 University of Split, FESB.

关键词： Heart

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Supporting Disaster Resilience Building through Cross-Community Knowledge Sharing 13

Supporting Disaster Resilience Building through Cross-Commun...

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13th Annual IEEE Global Humanitarian technology Conference, GHTC 2023

作者： Koga, Motoya Kaneko, Tetsuya Leleito, Emanuel Karashima, Kazuki Aharari, Ari Sojo University Department of Architecture Kumamoto Japan Nagoya University Graduate School of Engineering Nagoya Japan Maebashi Institute of Technology Dept.of Architecture Maebashi Japan Sojo University Department of Computer and Information Sciences Kumamoto Japan

ISBN: (纸本)9798350321326

Collaborative action in disaster mitigation efforts contributes to knowledge and experience sharing, resource sharing, improvement of response and coordination, and standardization of good practice. In this study, we propose a workshop model and develop a virtual collaboration tool to support and promote cross-community knowledge sharing and collaboration focusing on disaster mitigation efforts. The workshop model involves pre-fieldwork training, actual fieldwork, and post-fieldwork information exchange. The collaboration tool enables information collection, sharing and record keeping. The effectiveness of the workshop model and the collaboration tool is demonstrated through use in a citizen participatory disaster preparedness workshops linking two elementary schools located in the central and southern regions of Japan. © 2023 IEEE.

关键词： Disaster prevention

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Cloud-IoT Application for Scene Understanding in Assisted Living: Unleashing the Potential of Image Captioning and Large Language Model (ChatGPT) 16

Cloud-IoT Application for Scene Understanding in Assisted Li...

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16th International Conference on Developments in eSystems Engineering, DeSE 2023

作者： Hafeth, Deema Abdal Lal, Gokul Al-Khafajiy, Mohammed Baker, Thar Kollias, Stefanos University of Brighton School of Architecture Technology and Engineering Brighton United Kingdom University of Lincoln School of Computer Science Lincoln United Kingdom National Technical University of Athens School of Electrical and Computer Engineering Athens Greece

ISBN: (纸本)9798350381344

Vision is a vital sense that plays a pivotal role in our understanding of the world. The majority of our external information is acquired through our visual system, which significantly impacts various aspects of our lives, including mobility, cognitive abilities, access to information, and how we interact with both our surroundings and other individuals. Hence, individuals who need assisted living due to visual challenges are left behind and rely on human-driven image captioning services to make sense of their surroundings. In response to this challenge, we have developed a proof-of-concept system that integrates a large language model like ChatGPT to provide assistance to individuals with visual impairments in their daily lives through the utilisation of image captioning techniques. Our proposed model leverages the image captioning technique to describe the user's environment. It is a fusion of concepts from Deep Learning and the Internet of Things, enabling it to provide more informative and enriched image captions. In this process, ChatGPT is stimulated to generate increasingly detailed and informative descriptions of images, allowing users to gain a deeper understanding of their surroundings. Our findings show that the proposed system generates captions that are contextually relevant to the visual content. These captions can assist individuals in various day-today activities, contributing to an improved quality of life. © 2023 IEEE.

关键词： Internet of things

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Genetic Programming and Coevolution to Play the Bomberman™ Video Game 26th

Genetic Programming and Coevolution to Play the Bomberman...

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26th International Conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of EvoStar 2023

作者： Gold, Robert Branquinho, Henrique Hemberg, Erik O’Reilly, Una-May García-Sánchez, Pablo ALFA MIT CSAIL Cambridge United States Centre for Informatics and Systems of the University of Coimbra Department of Informatics Engineering University of Coimbra Coimbra Portugal Department of Computer Architecture and Computer Technology University of Granada Granada Spain

ISBN: (纸本)9783031302282

The field of video games is of great interest to researchers in computational intelligence due to the complex, rich and dynamic nature they provide. We propose using Genetic Programming with coevolution and lexicographic fitness to generate an agent that plays the Bomberman™game. We investigate two sets of Genetic Programming building blocks: one contains conditions relative to movement, and the other does not. We aim to see whether the benefits of these movement-related conditions outweigh the negatives caused by increased search space size. We show that the benefits gained do not outweigh the increase in search space size. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic programming

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