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IEEE International Conference on Robotic Computing (IRC)

作者： Olcay Kursun Beiimbet Sarsekeyev Mahdi Hasanzadeh Ahmad Patooghy Oleg V. Favorov Dept. of Computer Science Auburn University at Montgomery Montgomery Alabama Dept. of Computer Science University of Central Arkansas Conway Arkansas Dept. of Computer Systems Technology North Carolina A&T State University Greensboro North Carolina Dept. of Biomedical Engineering University of North Carolina at Chapel Hill Chapel Hill North Carolina

Texture classification plays a crucial role in applications ranging from object recognition and product design to surface exploration. Utilizing deep learning methods with sensors, such as accelerometers, offers a way to identify key surface features without the need to precisely replicate human touch. A Contextually Guided Convolutional Neural Network (CG-CNN) employs contextual guidance by developing auxiliary tasks during its training. These tasks offer implicit, yet rigorous, internal supervision signals. When trained with these subtasks, CG-CNN learns to represent the innate structure and patterns within the data, resulting in robust, transferrable, and local/contextual-neighborhood-preserving domain representations. This paper extends the CG-CNN framework for texture classification by integrating semisupervised learning. Empirical evaluations on the VibTac-12 texture dataset reveal that CG-CNN effectively generalizes to novel and unfamiliar textures, even when trained with scarce labeled examples. By harnessing vast amounts of unlabeled, contextually relevant data alongside the labeled samples, CG-CNN ensures robust and precise texture classification. Such advancements hold promise for applications in robotics, prosthetics, and haptic interfaces.

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Demosaicing of Multispectral Imaging Camera data using Neural Networks

Demosaicing of Multispectral Imaging Camera data using Neura...

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BIT Conference (BITCON), International

作者： Sumita Gupta Sapna Gambhir Saru Dhir Simran Sapui Rana Majumdar Deepsikha Naskar Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University UP Department of Computer Science College of Engineering and Computing George Mason University USA Dept. of Information Technology Amity School of Engineering & Technology Amity University UP India Department of Computer Science Sister Nivedita University

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

In most digital cameras, the sensor uses a Bayer filter array to capture the image. This array records only one colour, either blue or green or red for every image pixel, resulting in a mosaic image. To retrieve the missing colour information in the captured image making use of cross-channel interpolation is referred to as demosaicing. Demosaicing, especially in the context of colour demosaicing (CDM), plays a vital role as the first step in obtaining high-quality colour images with single-chip cameras. Traditional demosaicing methods, used to reconstruct colour images from raw sensor data in digital cameras, have several drawbacks. They often result in a loss of spatial resolution, introduce artifacts, lack robustness in challenging conditions, and may require manual tuning for optimal performance. In contrast, neural networks offer advantages like end-to-end learning, improved image quality, robustness, flexibility, and state-of-the-art performance. They learn complex mappings directly from raw data, adapt to various conditions, and produce visually pleasing, high-resolution images. However, they require substantial training data and computational resources, making them less suitable for resource-constrained applications. In this paper, conventional interpolation methods are compared to deep-learning based approaches for image demosaicing. To validate the approach to image demosaicing, aerial images captured from the Mars Colour Camera have been used.

关键词： Image quality Interpolation Visualization Image color analysis Neural networks Training data Robustness Digital cameras Spatial resolution Tuning

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Real-Time Sign Language Recognition using Machine Learning and CNN-based EfficientNetB3 for Accessible Communication

Real-Time Sign Language Recognition using Machine Learning a...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Zabir Ibna Mizan Farzam Aidelkhani Khandaker Tasfia Reza Tanbir Hasan Bhuiyan Munzirul Alom Saiful Islam Tuhin Dept. of Electrical and Computer Engineering (ECE) North South University (NSU) Dhaka Bangladesh Dept. of Electrical and Electronic Engineering (EEE) Independent University Bangladesh (IUB) Dhaka Bangladesh Dept. of Computer Science Engineering Engineering (CSE) Bangladesh University of Business and Technology (BUBT) Dhaka Bangladesh Dept. of Electrical and Electronic Engineering (EEE) Bangladesh University of Business and Technology (BUBT) Dhaka Bangladesh

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

This research analyzes the challenges faced by Sign Language Recognition (SLR) systems by evaluating the performance of EfficientNetB3, Inception-v3, and GoogLeNet models. Using a dataset of 27,000 American Sign Language (ASL) images across 27 classes, EfficientNetB3 emerged as the top performer. After fine-tuning the model with the full dataset, it achieved a test accuracy of 99.53%. We then integrated this optimized model into a real-time, user-friendly Android app for sign language recognition, offering a valuable tool for enhancing communication accessibility, particularly for the deaf and hard-of-hearing. This research work contributes to advancing SLR and promoting inclusive communication.

关键词： Training Sign language Renewable energy sources Accuracy Computational modeling Face recognition Machine learning Programming Real-time systems Robustness

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Dual trustworthy mechanism for illness classification with multi-modality data

Dual trustworthy mechanism for illness classification with m...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Qing Wang Jia Zhu Changfan Pan Jianyang Shi Chaojun Meng Hanghui Guo Dept. Computer Science and Technology Zhejiang Normal University Jinhua China Dept. Education Zhejiang Normal University Jinhua China

Multi-modality classification has flourished in recent years. Traditional methods mainly focus on advancing deep neural networks (DNN) to meet high performance. However, the interpretability of these methods remains blind due to the complexity and ambiguity of DNN, which also causes distrust. This problem is enlarged in sensitive areas, such as biomedical computing. Hence, we propose a novel dual trustworthy mechanism for multi-modality classification (DTMC), which can make the process and results of DNN more credible and interpretable while increasing performance. Specifically, a confidence attention mechanism is performed from local and global views to improve the process’ confidence by evaluating the attention scores and distinguishing the abnormal information. A confidence probability mechanism from local and global perspectives is conducted in the prediction stage to enhance the results’ confidence. Extensive experiments on multi-modality medical classification datasets show superior performance with the interpretability of the proposed method compared to the state-of-the-art (SOTA) methods. Our resources are open at https://***/ghh1125/data.

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JARVIS - Smart AI Support System

JARVIS - Smart AI Support System

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Electrical Power Engineering, Communication and Computing technology(ODICON), Odisha International Conference on

作者： V. Ramya Manaswi Meduri Saketh Mandumula Sai Ravi Tejha Atluri Sai Karthikeya Dharani Chandra Prakash Ahmad Alkhayyat Dept. of Computer Science Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Dept. of computer science engineering with specialisation of cybersecurity GITAM university Hyderabad India Dept. of Electronics & Communication Engineering K.G Reddy College of Engineering and Technology Hyderabad Telangana India Dept. of computers Techniques engineering College of technical engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

The JARVIS AI Support System represents a remarkable fusion of modern technology, blending a sophisticated GUI design, seamless voice control, and inventive features like the captivating “Air Canvas” facilitated by OpenCV. This AI-driven virtual assistant offers users a natural and intuitive experience, allowing them to effortlessly perform tasks such as browsing the web, interacting with a chatbot, and executing dynamic voice- controlled actions. Moreover, the system showcases advanced capabilities including motion detection and facial recognition with an accuracy of 95% in multiple runs. Leveraging the power of computer vision, the Air Canvas feature empowers users to express creativity through fluid hand gestures, while voice commands effortlessly manage diverse tasks. This innovative project presents an approachable way to interact with technology in the world of AI.

关键词： Emotion recognition Video on demand Speech recognition Chatbots Motion detection Internet of Things Web sites Security Artificial intelligence Graphical user interfaces

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Wildfire Spread Prediction using Machine Learning Algorithms

Wildfire Spread Prediction using Machine Learning Algorithms

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International Conference on Computing and Networking technology (ICCNT)

作者： Aman Singh Rakesh Yadav Gadug Sudhamshu Aryan Basnet Rahmat Ali Dept. of Computer Science and Engineering Faculty of Engineering and Technology Jain (Deemed-To-Be) University Bangalore India

Wildfires are one of the most destructive natural disasters that cause significant harm to both humans and the environment. Predicting their spread is critical for disaster management and preparedness. In this study, we have utilized machine learning algorithms, including Decision Tree Regression, XG Boost Regression, and Artificial Neural Networks, to predict the spread of wildfires using the Next Day Wildfire dataset. The dataset includes satellite images, weather, and geography conditions aggregated across the United States from 2012 to 2020. We preprocessed and engineered the dataset which includes the features such as elevation, wind direction and speed, temperature, humidity, precipitation, drought index, vegetation index, energy release component, and population density. We evaluated the models using the Root Mean Squared Error (RMSE) metric and found that the Decision Tree Regression algorithm performed the best with the lowest RMSE score. Our study highlights the potential of machine learning algorithms in predicting the spread of wildfires, which can aid in better disaster management and preparedness efforts.

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LLM4CVE: Enabling Iterative Automated Vulnerability Repair with Large Language Models

arXiv

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arXiv 2025年

作者： Fakih, Mohamad Dharmaji, Rahul Bouzidi, Halima Araya, Gustavo Quiros Ogundare, Oluwatosin Al Faruque, Mohammad Abdullah Dept. of Electrical Engineering and Computer Science University of California IrvineCA United States Siemens Technology PrincetonNJ United States

Software vulnerabilities continue to be ubiquitous, even in the era of AI-powered code assistants, advanced static analysis tools, and the adoption of extensive testing frameworks. It has become apparent that we must not simply prevent these bugs, but also eliminate them in a quick, efficient manner. Yet, human code intervention is slow, costly, and can often lead to further security vulnerabilities, especially in legacy codebases. The advent of highly advanced Large Language Models (LLM) has opened up the possibility for many software defects to be patched automatically. We propose LLM4CVE - an LLM-based iterative pipeline that robustly fixes vulnerable functions in real-world code with high accuracy. We examine our pipeline with State-of-the-Art LLMs, such as GPT-3.5, GPT-4o, Llama 3 8B, and Llama 3 70B. We achieve a human-verified quality score of 8.51/10 and an increase in ground-truth code similarity of 20% with Llama 3 70B. To promote further research in the area of LLM-based vulnerability repair, we publish our testing apparatus, fine-tuned weights, and experimental data on our website. © 2025, CC BY-NC-SA.

关键词： Software testing

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A Data-Driven Approach to Time-Domain Electromagnetic Modeling Based on Dynamic Mode Decomposition

A Data-Driven Approach to Time-Domain Electromagnetic Modeli...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Yanming Zhang Steven Gao Lijun Jiang Dept. of Electronic Engineering Chinese University of Hong Kong Shatin Hong Kong Dept. of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA

ISBN: (数字)9798350355581

ISBN: (纸本)9798350355598

This paper presents a data-driven methodology that utilizes Dynamic Mode Decomposition (DMD) for the time-domain (TD) electromagnetic (EM) modeling of microwave devices. As an unsupervised machine learning technique, DMD leverages a limited set of unlabeled spatio-temporal electromagnetic (EM) data to determine DMD eigenvalues and eigenmodes. Then, the obtained DMD model reconstructs the dynamics as a series of exponential terms based on linear assumptions. The effectiveness of this approach is demonstrated through the TD EM modeling of photonic crystal waveguides. Comparative analysis with the finite-difference time-domain (FDTD) method shows that the DMD model not only achieves precise modeling but also facilitates robust short-term forecasting.

关键词： Analytical models PSNR Photonic crystals Predictive models Mathematical models Indexes Time-domain analysis Electromagnetics Optical ring resonators Finite difference methods

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Integer Wavelet Transform based Secured Image Steganography using LSB and Coefficient Value Differencing 2

Integer Wavelet Transform based Secured Image Steganography ...

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2nd International Conference on Secure Cyber Computing and Communications, ICSCCC 2021

作者： Mandal, Pratap Chandra Mukherjee, Imon Indian Institute of Information Technology Kalyani B.P. Poddar Institute of Management and Technology Dept. of Computer Science and Engineering Kolkata India Indian Institute of Information Technology Kalyani Dept. of Computer Science and Engineering India

ISBN: (纸本)9781665444156

A steganography technique based on the Integer Wavelet Transform (IWT) is proposed. High-frequency coefficients are exploited more to embed the confidential message since they are less sensitive to the human eye. Based on this notion, the 2-LSB method is employed in the wavelet coefficients of the approximation subband which contains most of the signal energy and the 4-LSB method is applied in the wavelet coefficients of the diagonal subband. The Coefficient Value Differencing (CVD) method has been introduced in the horizontal and vertical subbands. To obtain the highest embedding capacity with an acceptable distortion, a threshold value has been applied in the CVD method. The proposed technique achieves an average embedding capacity of 2.28 bpp for all the stego images while maintaining a lower distortion. It shows strength against the standard tool, StirMark Benchmark 4.0, and withstands the steganalysis attacks. © 2021 IEEE.

关键词： Steganography

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Deepfake Detection of Singing Voices With Whisper Encodings

arXiv

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arXiv 2025年

作者： Sharma, Falguni Gupta, Priyanka Centre for Artificial Intelligence Banasthali Vidyapith Jaipur India Dept. of Communication and Computer Engineering The LNM Institute of Information Technology Jaipur India

The deepfake generation of singing vocals is a concerning issue for artists in the music industry. In this work, we propose a singing voice deepfake detection (SVDD) system, which uses noise-variant encodings of open-AI’s Whisper model. As counter-intuitive as it may sound, even though the Whisper model is known to be noise-robust, the encodings are rich in non-speech information, and are noise-variant. This leads us to evaluate Whisper encodings as feature representations for the SVDD task. Therefore, in this work, the SVDD task is performed on vocals and mixtures, and the performance is evaluated in %EER over varying Whisper model sizes and two classifiers-CNN and ResNet34, under different testing conditions. © 2025, CC BY-NC-ND.

关键词： Signal encoding

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