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Secure and Efficient Face Recognition via Supervised Federated Learning

Secure and Efficient Face Recognition via Supervised Federat...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Enoch Solomon Abraham Woubie Eyael Solomon Emiru Ahmed F Abdelzaher Department of Computer Science Virginia State University Richmond Virginia Silo AI Helsinki Finland Dept. of Information Engineering and Computer Science University of Trento Italy

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

The state-of-the-art face recognition systems frequently rely on training with large image dataset acquired from several users on a single device. However, these databases usually contain sensitive information, which raises substantial privacy concerns. To address this issue, we propose the use of federated learning for a supervised face recognition. Federated learning enables model training while maintaining privacy by leveraging decentralized edge devices. In our method, each edge device trains a model individually, which is subsequently sent to a secure aggregator. We use GAN to introduce different data without sending actual data. The aggregator combines various models into a global one, which is subsequently disseminated to the edge devices. Experiments with CelebA datasets show that federated learning effectively protects privacy while maintaining performance levels.

关键词： Training Performance evaluation Data privacy Privacy Federated learning Databases Face recognition Image edge detection Servers Robots

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APOA based Multi-scale Parallel Convolution Blocks with Hybrid Deep Learning for Gastric Cancer Prediction from Endoscopic Images 1

APOA based Multi-scale Parallel Convolution Blocks with Hybr...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Padthe, Adithya Mathapati, Mahantesh Priyanka, M.S. Nandihal, Priya University of the Cumberlands Department of Information Technology United States Dept.of Cse India Nitte Meenakshi Institute of Technology Dept. of Aiml Bengaluru India Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350316469

In terms of mortality rates, gastric cancer is second only to lung cancer. Manual gastric slice pathology examination is labor-intensive and prone to observer bias. Endoscopy of the upper digestive tract is commonly used for the screening of gastric cancer. An object identification model, a kind of deep learning, was presented as a means of automating the diagnosis of early stomach cancer using endoscopic pictures. However, difficulties were encountered while attempting to reduce the sum of false positives in the identified findings. Tumour segmentation from the preprocessed pictures was carried out in this study, which is often more challenging and crucial. The research suggests a productive approach that makes use of multi-scale parallel convolution blocks (MPCs). Multi-scale parallel convolutions (MPCs) use filters of variable sizes to extract characteristics that are relevant across a range of tumour sizes. To further aid feature extraction with fewer parameters, residual connections and residual blocks can be used. In addition, the suggested study uses an Artificial plant optimisation algorithm (APOA) to fine-tune the segmentation model's parameters without resorting to post-processing approaches. Finally, gastric cancer detection from endoscopic pictures is accomplished using a hybrid classification strategy that incorporates Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Experiments employing 1208 photos from healthy people and 533 photographs from patients with stomach cancer examined detection performance using the 5-fold cross-validation approach. These findings show promise for the suggested method's application in automated early stomach cancer diagnosis using endoscopic images. © 2023 IEEE.

关键词： Artificial plant optimization algorithm Convolutional Neural Network Endoscopic images Gastric cancer Multi-scale parallel convolution blocks Recurrent Neural Network

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Real-Time Person Re-Identification to Improve Human-Robot Interaction

Real-Time Person Re-Identification to Improve Human-Robot In...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Carlsen, Per Antoine Taylor, Angelique M. Chan, Darren M. Uddin, Md Zia Riek, Laurel Torresen, Jim Logistics Oda Oslo Norway Cornell University College of Computing and Information Science Cornell Tech New York United States HRL Laboratories LLC MalibuCA United States Sustainable Communication Technologies SINTEF Digital Oslo Norway University of California Dept. of Computer Science and Engineering San Diego United States University of Oslo Dept. of Informatics and RITMO Oslo Norway

ISBN: (纸本)9798350372601

Mobile robots deployed in the homes of disabled people and older adults can play a vital role as companions and aid in activities of daily living, enhancing people's quality of life. For these robots to effectively assist, it can be helpful if they possess social and interactive skills to facilitate natural and personalized interactions. Personalization, including using proper names and remembering preferences, is crucial for building strong social connections and fostering trust in human-robot interaction. However, achieving personalized interactions necessitates the robot's ability to autonomously recognize and re-identify individuals without relying on specific constraints or prior knowledge. This work introduces two lightweight Dual convolutional neural networks, LuNet Light and LuNet Lightest, designed for person re-identification in a robotic context without the limitations of existing systems. We introduce a lightweight architecture, LuNet Lightest which achieves near state-of-the-art performance on the MARS dataset. It is well-suited for real-time applications on low-cost, hardware-constrained robots. This person-identification system enables assistive mobile robots to accurately identify users, fostering trust and facilitating natural, personalized interactions. © 2024 IEEE.

关键词： Convolutional neural networks

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3D-CAN: A 3D Convolution Attention Network for Feature Extraction and Classification of Hyperspectral Images

3D-CAN: A 3D Convolution Attention Network for Feature Extra...

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Network, Multimedia and information Technology (NMITCON) International Conference on

作者： Murali Kanthi K. Venkateshwara Rao L. Chandra Sekhar Reddy T. Hitendra Sarma Nuthanakanti Bhaskar Nam Vasundhara Dept. of Computer Science & Engineering (Data Science) CMR Technical Campus Hyderabad Telangana India Dept. of Computer Science & Engineering CMR College of Engineering & Technology Hyderabad Telangana India Dept. of Computer Science & Engineering (Data Science) CMR College of Engineering & Technology Engineering Hyderabad Telangana Dept. of Information Technology Vasavi College of Engineering Hyderabad Telangana India Dept. of Computer Science & Engineering CMR Technical Campus Hyderabad Telangana India

In recent years, the deep learning method has gained widespread popularity in hyperspectral image classification (HSI) tasks because of remarkable feature extraction capability. However, conventional deep learning approaches often yield suboptimal classification performance due to the limited discrimination ability of the extracted features. Moreover, the challenge of obtaining high classification performance with a limited number of samples in hyperspectral images (HSIs) has emerged as a prominent research area of interest. To address this concern, this letter provides a novel 3D convolution attention network (3D-CAN) for feature extraction and HSIs classification. The attention mechanism employed in this context enables capturing both the long-range spatial dependencies of HSIs in the horizontal and vertical directions and the varying significance levels across distinct spectral channels. The experiments are executed on both the new data sets and the publicly accessible benchmark data sets to expose the efficiency and durability of the proposed model. Empirically, it has been demonstrated that the presented model succeeds over the remaining state-of-the-art approaches in terms of classification accuracy.

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Instance Segmentation based Object Detection with Enhanced Path Aggregation Network

Instance Segmentation based Object Detection with Enhanced P...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Ade Indra Onthoni Prasan Kumar Sahoo Dept. of Computer Science and Information Engineering Chang Gung University Guishan Taoyuan Taiwan

Multi-scale networks rely heavily on the aggregation process to aggregate feature information into other feature maps. However, it has disadvantages in aggregating information, where the localization information becomes inconsistent. An Enhancing Path Aggregation Network through concatenation and attention mechanism is proposed here to improve the aggregation path for generating the feature maps in multi-scale network. This approach helps to keep information consistently during the process of aggregation. The proposed model is evaluated using the COCO dataset, which achieves significant improvements on several metrics.

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Fairness-Based Data Rate Control for Channel Congestion Mitigation in Vehicular Communications

Fairness-Based Data Rate Control for Channel Congestion Miti...

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IEEE International Conference on Communications Workshops, ICC

作者： Lan-Huong Nguyen Van-Linh Nguyen Jian-Jhih Kuo Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan

Rate control is one of the promising approaches to mitigate channel congestion in wireless vehicular networks once resource allocation-based techniques are overloaded due to too many requests. Recently, state-of-the-art rate control methods have been proposed to mitigate channel congestion by reducing the data rate from low-risk vehicles. However, there is a lack of fairness guarantees during the risk assessment. As a result, if many vehicles are at the same risk level, there are possibilities that several of them may not be able to access the wireless channels to transmit messages on time due to the long queue of channel occupation requests. In this work, we present a novel fairness risk-based transmission control model, namely F-RTC, to automatically adjust the message broadcasting rate of a group of vehicles under channel congestion. F-RTC works based on Double Deep Q-learning Networks with a centralized control center to measure Jain's fairness index for each vehicle. F-RTC can suggest cutting down or increasing the data rate of the vehicles based on their score results. The evaluation results show that F-RTC outperforms the state-of-the-art risk-based rate control methods in terms of congestion rate reduction and channel busy ratio while having no significant impact on the packet delivery rate. We release the source code of the learning model for interesting readers at https://***/lanvernon/FRTC.

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Real-time Arm Motion Tracking and Hand Gesture Recognition Based on a Single Inertial Measurement Unit

Real-time Arm Motion Tracking and Hand Gesture Recognition B...

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International Conference on Internet of Things: Systems, Management and Security

作者： Tien-Chiao Chang Yu-Chi Wu Chin-Chuan Han Chao-Shu Chang Dept. of Electrical Engineering National United University Miao-Li Taiwan Dept. of Computer Science and Information Engineering National United University Miao-Li Taiwan Dept. of Information Management National United University Miao-Li Taiwan

ISBN: (数字)9798350366501

ISBN: (纸本)9798350366518

With the development of virtual reality (VR) and augmented reality (AR) devices, handheld controllers and camera-based hand tracking are the most common methods for interacting with the virtual world. This paper proposes arm motion tracking and hand gesture recognition methods based on a single IMU (Inertial Measurement Unit) sensor embedded in a wristband. This approach is more convenient than handheld controllers and more energy-efficient than camera-based tracking. We utilize two deep learning models: one for arm motion tracking and the other for hand gesture recognition. When training the arm motion tracking model, we use two IMU sensors-one placed on the wrist to provide forearm pose data and the other placed above the elbow to provide upper arm pose data. We use the elbow’s IMU data as the reference for training the model and calculate the real-time upper arm pose vector based on the wrist’s pose data. We train the hand gesture recognition model using only the wrist-mounted IMU sensor. This model captures different vibrations in the IMU during various gestures (such as grab, release, snap, and tap) for training. We achieve real-time arm motion tracking and hand gesture recognition using only a single IMU sensor. The angle error for arm motion tracking is only 15 degrees, and the hand gesture recognition accuracy reaches 95%.

关键词： Training Wrist Solid modeling Measurement units Accuracy Tracking Gesture recognition Inertial navigation Real-time systems Data models

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Dynamic Asset Allocation Using Trading Agent

Dynamic Asset Allocation Using Trading Agent

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Shingchern D. You Chien-Hung Liu Ching-Cheng Yen Dept. of Computer Science and Information Engineering National Taipei Universiyt of Technology Taipei Taiwan

We study the use of PPO (Proximal Policy Optimization) algorithm for trading ETFs belonging to different asset classes. The studied asset classes include common stocks, bonds, REIT (Real Estate Investment Trust), gold, and future contracts on agricultural commodities. When properly training the PPO agent with the proposed ratio allocation strategy, the agent outperforms the static, periodical rebalancing approach.

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Latency of spatial audio plugins: a comparative study

Latency of spatial audio plugins: a comparative study

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Immersive and 3D Audio: from Architecture to Automotive (I3DA)

作者： Matteo Tomasetti Angelo Farina Luca Turchet Dept. of Information Engineering and Computer Science University of Trento Trento Italy Dept. of Engineering and Architecture University of Parma Parma Italy

The use of spatial audio plugins (SAPs) with Ambisonics processing and binaural rendering has become widespread in the last decade, thanks to their increased accessibility and usability. SAPs are particularly relevant in scenarios involving real-time music playing with headphones, such as networked music performance and individual recreational music-making using backing tracks. However, a crucial issue that has been largely overlooked thus far is the measurement of the processing latency introduced by currently available SAPs. Identifying which SAPs are the fastest is essential to enable designers, musicians, and researchers to create time-sensitive applications involving 3D audio. To bridge this gap, we compared nine systems formed by different SAPs that enable 3D audio management. We measured the latency of each system throughout the third-order Ambisonics plugins pipeline: encoding, room simulation, sound scene rotation, and binaural decoding. In particular, the measurements were performed utilizing different buffer sizes. Results showed that to achieve a minimization of the latency, it is necessary to use a combination of different SAPs from different systems. Based on our measurements, we propose two spatial audio systems that mix different SAPs. Considering a sampling rate of 48 kHz, a Dell Alienware x15 R2 laptop running the Windows 10 operating system, and an RME Fireface UFX sound card, the two systems achieved an overall latency of 0.33 ms and 0.94 ms respectively.

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An Ensemble Heart Disease Prediction Model Bagged with Logistic Regression, Naïve Bayes and K Nearest Neighbour.

An Ensemble Heart Disease Prediction Model Bagged with Logis...

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International Conference on Smart Technologies for Sustainable Development 2021, ICSTSD 2021

作者： Majumder, Annwesha Banerjee Gupta, Somsubhra Singh, Dharmpal Dept. of Information Technology JIS College of Engineering Nadia West Bengal Kalyani India Amity University West Bengal Kolkata India Dept. of Computer Science and Engineering JIS College of Engineering Nadia West Bengal Kalyani India

Cardiovascular disease is an immense threat in today's world. Every year lots of people died by this disease. Though medical science evolved and evaluated much extends still in many areas of the world there are lack of medical support. Early detection of cardiovascular can save lives where machine learning can help us. Advancement of artificial intelligence and machine learning are now playing an effective role in disease prediction and different health care applications. In this paper a heart disease prediction model has been proposed using ensemble learning. Single learning mechanism may not give a proper result so by combing multiple learners better performance from the model can be achieved. In this paper bagging with Logistic Regression, Gaussian Naïve Bayes and K Nearest Neighbour have applied. © Published under licence by IOP Publishing Ltd.

关键词： Cardiology

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