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Design and Implementation of an EMG-based System for Monitoring and Help Request

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Design and Implementation of an EMG-based System for Monitor...

2022 International Conference on Smart Systems and Power Management, IC2SPM 2022

作者： Zeine, Carla Daou, Roy Abi Zeid Telawi, Samer Boercsoek, Josef Hayek, Ali Haidar, Gaby Abou University of Kassel Faculty of Electrical Engineering /Computer Science Department of Computer Architecture and System Programming Kassel Germany Lebanese German University Faculty of Public Health Jounieh Department of Computer Architecture and System Programming Biomedical Technologies Department Jounieh Lebanon Université la Sagesse Faculty of Engineering Furn El Chebbak Mechatronics Department Beirut Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Marker Allee 76-78 Hamm59063 Germany American University of Science and Technology Faculty of Engineering Zahle Lebanon

ISBN: (数字)9781665452915

ISBN: (纸本)9781665452915

Integrating electrophysiological signals in larger systems is getting wider due to the fact that such modalities facilitate the daily tasks of users. However, the high cost of monitoring systems (e.g., oscilloscopes) limits the testing and analysis of the acquired signal from such noisy systems (i. e., electrophysiological devices). The proposed system is a part of a larger proj ect that deals with the identification of sign language movements through the use of Muscle-computer Interfaces. On one hand, the user is able to display the signal acquired by the electrodes on an LCD screen. On the other hand, the system helps a mute patient to issue a special message to his surrounding environment that does not know the sign language to ask for help. The second feature is not unique, as it can be replaced by any other application based on the user's needs. The system was tested on several persons, and the results are promising. © 2022 IEEE.

关键词： Liquid crystal displays

User-Attention Based Product Aesthetics Evaluation with Image and Eye-Tracking Fusion Data Analysis

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User-Attention Based Product Aesthetics Evaluation with Imag...

International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Baixi Xing Xinjie Song Qimeng Chen Lei Shi Yanhong Pan Kaiqi Wang Mengyue Tang Industrial Design Institute Zhejiang University of Technology Hangzhou China School of Design and Architecture Zhejiang University of Technology Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Newcastle University Newcastle upon Tyne UK

Users' viewing behavior could affect their perception and evaluation of design works. Taking into account users' visual attention as a subjective cognition cue, we used eye-tracking evidence to identify users' focus areas for further analysis. We conducted experiments to extract the image features of design images and the reviewers' eye-tracking data, aiming to predict the product design ranking in the competition through fusion data analysis. In particular, we collected 1,504 product design images from a design competition. Four deep convolutional neural networks were selected to explore the best aesthetics computation model. The experimental results show that using design images and eye-tracking data fusion can improve the model prediction performance. Finally, MobileNet-V3 achieves the highest classification accuracy of 74.75%. This suggests the proposed method can provide useful insights into personalized aesthetics evaluation and user-centered design perception.

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A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection 3rd

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A Benchmark of Ocular Disease Intelligent Recognition: One S...

3rd BenchCouncil International Symposium on Benchmarking, Measuring, and Optimizing, Bench 2020

作者： Li, Ning Li, Tao Hu, Chunyu Wang, Kai Kang, Hong College of Computer Science Nankai university Tianjin300350 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Science Beijing100190 China Beijing Shanggong Medical Technology Co. Ltd. Beijing100176 China

ISBN: (纸本)9783030710576

In ophthalmology, early fundus screening is an economic and effective way to prevent blindness caused by ophthalmic diseases. Clinically, due to the lack of medical resources, manual diagnosis is time-consuming and may delay the condition. With the development of deep learning, some researches on ophthalmic diseases have achieved good results, however, most of them are just based on one disease. During fundus screening, ophthalmologists usually give diagnoses of multi-disease on binocular fundus image, so we release a dataset with 8 diseases to meet the real medical scene, which contains 10,000 fundus images from both eyes of 5,000 patients. We did some benchmark experiments on it through some state-of-the-art deep neural networks. We found simply increasing the scale of network cannot bring good results for multi-disease classification, and a well-structured feature fusion method combines characteristics of multi-disease is needed. Through this work, we hope to advance the research of related fields. © 2021, Springer Nature Switzerland AG.

关键词： computer aided diagnosis

SOCIO-SPATIAL SEGREGATION AND HUMAN MOBILITY: A REVIEW OF EMPIRICAL EVIDENCE

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arXiv 2024年

作者： Liao, Yuan Gil, Jorge Yeh, Sonia Pereira, Rafael H.M. Alessandretti, Laura Department of Space Earth and Environment Chalmers University of Technology Gothenburg Sweden Department of Architecture and Civil Engineering Chalmers University of Technology Gothenburg Sweden - Brazil Data Science Division Brazil Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark

Socio-spatial segregation is the physical separation of different social, economic, or demographic groups within a geographic space, often resulting in unequal access to resources, services, and opportunities. The literature has traditionally focused on residential segregation, examining how individuals' residential locations are distributed differently across neighborhoods based on various social attributes, e.g., race, ethnicity, and income. However, this approach overlooks the complexity of spatial segregation in people's daily activities, which often extend far beyond residential areas. Since the 2010s, emerging mobility data sources have enabled a new understanding of socio-spatial segregation by considering daily activities such as work, school, shopping, and leisure visits. From traditional surveys to GPS trajectories, diverse data sources reveal that daily mobility can result in spatial segregation levels that differ from those observed in residential segregation. This literature review focuses on three critical questions: (a) What are the strengths and limitations of segregation research incorporating extensive mobility data? (b) How do human mobility patterns relate to individuals' residential vs. experienced segregation levels? and (c) What key factors explain the relationship between one's mobility patterns and experienced segregation? Our literature review enhances the understanding of socio-spatial segregation at the individual level and clarifies core concepts and methodological challenges in the field. Our review explores studies of key themes: segregation, activity space, co-presence, and the built environment. By synthesizing their findings, we aim to offer actionable insights for reducing segregation. © 2024, CC BY-SA.

关键词： Leisure

BILINEAR-CONVOLUTIONAL NEURAL NETWORK USING A MATRIX SIMILARITY-BASED JOINT LOSS FUNCTION FOR SKIN DISEASE CLASSIFICATION

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arXiv 2024年

作者： Ahmad, Belal Usama, Mohd Ahmad, Tanvir Saeed, Adnan Khatoon, Shabnam Hu, Long School of Computer Science and Technology Huazhong University of Science and Technology China Departments of Diagnostics and Intervention and Biomedical Engineering Umea University Sweden School of Information and Control Engineering Xi’an University of Architecture and Technology China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology China School of Management Science and Engineering China University of Geosciences China

In this study, we proposed a model for skin disease classification using a Bilinear Convolutional Neural Network (BCNN) with a Constrained Triplet Network (CTN). BCNN can capture rich spatial interactions between features in image data. This computes the outer product of feature vectors from two different CNNs by a bilinear pooling. The resulting features encode second-order statistics, enabling the network to capture more complex relationships between different channels and spatial locations. The CTN employs the Triplet Loss Function (TLF) by using a new loss layer that is added at the end of the architecture called the Constrained Triplet Loss (CTL) layer. This is done to obtain two significant learning objectives: inter-class categorization and intra-class concentration with their deep features as often as possible, which can be effective for skin disease classification. The proposed model is trained to extract the intra-class features from a deep network and accordingly increases the distance between these features, improving the model’s performance. The model achieved a mean accuracy of 93.72%. © 2024, CC BY.

关键词： Convolutional neural networks

Scalable Infrastructure for Workload Characterization of Cluster Traces

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arXiv 2022年

作者： van Loo, Thomas Jindal, Anshul Benedict, Shajulin Chadha, Mohak Gerndt, Michael Computer Architecture and Parallel Systems Technical University Munich Germany Indian Institute of Information Technology Kerala Kottayam India

In the recent past, characterizing workloads has been attempted to gain a foothold in the emerging serverless cloud market, especially in the large production cloud clusters of Google, AWS, and so forth. While analyzing and characterizing real workloads from a large production cloud cluster benefits cloud providers, researchers, and daily users, analyzing the workload traces of these clusters has been an arduous task due to the heterogeneous nature of data. This article proposes a scalable infrastructure based on Google’s dataproc for analyzing the workload traces of cloud environments. We evaluated the functioning of the proposed infrastructure using the workload traces of Google cloud cluster-usage-traces-v3. We perform the workload characterization on this dataset, focusing on the heterogeneity of the workload, the variations in job durations, aspects of resources consumption, and the overall availability of resources provided by the cluster. The findings reported in the paper will be beneficial for cloud infrastructure providers and users while managing the cloud computing resources, especially serverless platforms. © 2022, CC BY.

关键词： Cluster computing

AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection

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arXiv 2022年

作者： Zhao, Yunpu Zhang, Rui Li, Xiaqing University of Science and Techonology of China China SKL of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

Automated machine learning has achieved remarkable technological developments in recent years, and building an automated machine learning pipeline is now an essential task. However, existing AutoML pipeline approaches adopt monotonous ensemble strategies across different machine learning classification tasks. They ignore the fact that no single ensemble strategy can perform best on all of the various classification tasks, and thus cannot meet the performance requirements in practice. To this end, we propose AutoDESS, an efficient AutoML approach to identifying the best ensemble strategy for machine learning classification tasks by combining different ensemble strategies' strengths. To our best knowledge, AutoDESS is the first trial in AutoML aiming at searching and optimizing ensemble strategies. In the comparison experiments, AutoDESS outperforms the state-of-the-art AutoML approaches while with the same CPU time in 42 classification datasets from the OpenML platform. In addition, ablation experiments also validate the effectiveness of AutoDESS. © 2022, CC BY-NC-ND.

关键词： Pipelines

Variation Enhanced Attacks Against RRAM-based Neuromorphic Computing System

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arXiv 2023年

作者： Lv, Hao Li, Bing Zhang, Lei Liu, Cheng Wang, Ying Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China State Key Laboratory of Computer Architecture China Capital Normal University China

The RRAM-based neuromorphic computing system (NCS) has amassed explosive interests for its superior data processing capability and energy efficiency than traditional architectures, and thus being widely used in many data-centric applications. The reliability and security issues of the NCS therefore become an essential problem. In this paper, we systematically investigated the adversarial threats to the RRAM-based NCS and observed that the RRAM hardware feature can be leveraged to strengthen the attack effect, which hasn't been granted sufficient attention by previous algorithmic attack methods. Thus, we proposed two types of hardware-aware attack methods with respect to different attack scenarios and objectives. The first is adversarial attack, VADER, which perturbs the input samples to mislead the prediction of neural networks. The second is fault injection attack, EFI, which perturbs the network parameter space such that a specified sample will be classified to a target label, while maintaining the prediction accuracy on other samples. Both attack methods leverage the RRAM properties to improve the performance compared with the conventional attack methods. Experimental results show that our hardware-aware attack methods can achieve nearly 100% attack success rate with extremely low operational cost, while maintaining the attack stealthiness. Copyright © 2023, The Authors. All rights reserved.

关键词： RRAM

Concept of an Optical Distance and Speed Sensor Using Novel Offsetless Spatial Frequency Filters in the Area of Functional Safety

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Concept of an Optical Distance and Speed Sensor Using Novel ...

IEEE International Symposium on Industrial Electronics (ISIE)

作者： Frank Wasinski Werner Bonath Ubbo Ricklefs Josef Börcsök Michael Schwarz Eike Hahn Dept. of Electrical Engineering and Information Technology THM University of Applied Sciences Giessen Germany ICAS Institute for Computer Architecture and System Programming University of Kassel Kassel Germany

Optical speed sensors based on the spatial frequency filter method are a proven technology that offer high measurement accuracy over a wide speed range. Newly developed variants also enable measurements at very low speeds and increase the robustness of the method [1]. This makes the sensor very suitable for use in the area of functional safety. A measurement chain up to safety integrity level 3 (SIL3) should be feasible via a combination with a safety system on a chip (SoC) for evaluating the measurement data and an extension of the sensor module to include safety-related elements [2], [3]. By integrating the sensor at IC level, a compact overall solution is to be made possible. After the first successful tests with the safety SoC, further examinations of the measuring method with regard to functional safety are now necessary.

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