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Survey on Emotion Recognition databases

Survey on Emotion Recognition Databases

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international conference on Control, Automation and Systems ( ICCAS)

作者： Juyoung Hong Yujin Hwang Gwangjin Lee Yukyung Choi Department of Intelligent Mechatronics Engineering Sejong University Seoul Korea

The social robots market has grown in recent years owing to advancements in robotics. Accordingly, there is a growing interest in emotion recognition technology, one of the fundamental technologies of social robots. Early research in emotion recognition predicted emotions into a Facial Action Coding System (FACS) that recognizes facial expressions by analyzing action units (AU), the fundamental action components of face muscles. However, with the advent of deep learning, models can now recognize facial emotions straight from images. For industry application of this deep learning based emotion recognition approach, supervised learning are usually applied, and this model training approach is highly dependent on training datasets. This survey gives an overview of datasets that can be used when the emotion recognition model is implemented. This paper focused on identifying the availability of datasets relevant to the elderly and children, the primary goals of social robots, as well as reviewing the data collection and annotation processes. This survey sheds insight on the current status of emotion datasets and presents suggestions for future development.

关键词： Deep learning Training Emotion recognition Face recognition Social robots Toy manufacturing industry Psychology

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Research on Task Scheduling Methods for intelligent Connected Vehicles with End-to-End Delay Constraints

Research on Task Scheduling Methods for Intelligent Connecte...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Shichun Yang Wenlong Chen Boao Zhang Qianlong Duan Bide Hao Xiaoyu Yan School of Transportation Science and Engineering Bei Hang University Beijing China Research Institute of Aero-Engine Bei Hang University Beijing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

With the rapidly growing intelligent Connect-ed Vehicle (ICV) industry, modern Advanced Driver Assistance Systems (ADAS) integrate critical and non-critical software functions on the same hardware platform. These functions have complex timing requirements and associated dependencies. To ensure the real-time safety of critical au-tomotive applications, complex task chains with dependencies generated by automotive applications (e.g. sensor data acquisition, data processing, and control execution) must meet the worst end-to-end latency. The resulting task sched-uling problem requires the assignment of tasks to available cores for execution and the generation of static schedules that satisfy the timing constraints of the tasks and the end-to-end latency of the task chain. In this paper, a task sched-uling and task mapping method based on an intelligent heu-ristic algorithm is proposed for the ADAS platform, and simulated scheduling based on the EDF algorithm. The op-timal task scheduling scheme is selected by calculating the fitness function by the intelligent heuristic algorithm, and the simulated annealing algorithm (SA) is selected as the benchmark for the experiment. The experimental results show that the grey wolf optimizer converges faster and finds a better scheduling scheme, which reduces the total time of scheduling tasks.

关键词： Schedules Job shop scheduling Processor scheduling Heuristic algorithms Optimal scheduling Transforms Simulated annealing Software Delays Advanced driver assistance systems

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Cognitive Radio Receiver Design Employing Pipeline Successive Approximation ADC

Cognitive Radio Receiver Design Employing Pipeline Successiv...

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learning and Technology conference (L&T)

作者： Muhammad Samee Baig Aziza I. Hussein Electrical and Computer Engineering Department Effat University Saudi Arabia College of Engineering Energy and Technology Research Center Effat University Saudi Arabia

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

Smart cities require better cellular communication, incorporating a high data rate that satisfies the Internet, cloud computing, and the Internet of Things (IoT) requirements. A high data rate demands higher bandwidth with low latency. However, the already saturated or underutilized frequency spectrum hinders this realization. 5G and cognitive radio technology can enhance spectrum utilization to meet the high data demands of future cellular communication. Cognitive radio uses spectrum sensing to identify underutilized frequencies without interfering with licensed users, addressing the scarcity of the electromagnetic spectrum. In this paper, a cognitive radio receiver design with a SAR analog-to-digital converter, which is recognized for having a modest resolution for high bandwidth signals, is implemented. Moreover, the pipeline technique in the SAR design is proposed to increase the resolution to be suitable for 5G applications. The high-level model of the system is simulated using MATLAB Simulink software. The quantization error observed in the regular SAR ADC design is compared to that in the proposed pipeline SAR ADC, highlighting the enhanced accuracy and efficiency of the pipeline SAR ADC, which makes it suitable for spectrum sensing in next-generation cognitive radio systems.

关键词： Quantization (signal) 5G mobile communication Software packages Smart cities Receivers Bandwidth Sensors Cognitive radio Signal resolution Pipeline processing

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An automated Safe Hybrid Energy Based On-Board Charging Station Using an intelligent Controller 2nd

An Automated Safe Hybrid Energy Based On-Board Charging Stat...

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2nd international conference on data Science, Machine learning and Applications, ICDSMLA 2020

作者： Jagadeesh, A. Sudarsana Reddy, K. Mahalakshmi, R. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9789811636899

Greener transportation is the motivation to shift from conventional vehicles to electrical vehicles (EV). EV’s being the present technology, charging of the EV’s battery with best charger in secured environment is the challenge. The EV’s fast charging DC has advantages in reducing charge time, but maintaining the optimal temperature for the battery and availability of charging stations have benefited on-board charging (through AC supply). The constant current constant voltage (CCCV) battery charger has reduced the peaking currents during initial stages and promotes charging of cells with different capacities at the final stage. The environment to charge must be safe, so as the battery never damages. In onboard charging, due to fault currents inducing in the supply might damage the battery, so an intelligent controller is required to detect theses faults and can open the circuit breaker associated with it. The idea of this work is to charge the EV’s battery with CCCV battery charger through on-board charging system where the supply is derived from grid and solar. The supply system has intelligent controllers operating to detect the faults and to work respectively. The backup generator has been provided during faults in grid. The intelligent controller used to generate the control signals for circuit breakers is Arduino Uno. The solar has been converted to AC by using a three-level neutral point clamped based State Vector Pulse Width Modulated (SVPWM) inverter. To generate the control signals from Arduino Uno, the Proteus Professional software has been utilized and to simulate the present work, MATLAB/Simulink software has been used respectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Charging (batteries)

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Masked Autoencoders for Unsupervised Anomaly Detection in Medical Images 27

Masked Autoencoders for Unsupervised Anomaly Detection in Me...

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27th international conference on Knowledge Based and intelligent Information and engineering Sytems, KES 2023

作者： Georgescu, Mariana-Iuliana Faculty of Mathematics and Computer Science University of Bucharest 14 Academiei Bucharest Romania

Pathological anomalies exhibit diverse appearances in medical imaging, making it difficult to collect and annotate a representative amount of data required to train deep learning models in a supervised setting. Therefore, in this work, we tackle anomaly detection in medical images training our framework using only healthy samples. We propose to use the Masked Autoencoder model to learn the structure of the normal samples, then train an anomaly classifier on top of the difference between the original image and the reconstruction provided by the masked autoencoder. We train the anomaly classifier in a supervised manner using as negative samples the reconstruction of the healthy scans, while as positive samples, we use pseudo-abnormal scans obtained via our novel pseudo-abnormal module. The pseudo-abnormal module alters the reconstruction of the normal samples by changing the intensity of several regions. We conduct experiments on two medical image data sets, namely BRATS2020 and LUNA16 and compare our method with four state-of-the-art anomaly detection frameworks, namely AST, RD4AD, AnoVAEGAN and f-AnoGAN. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-nd license (https://***/licenses/by-nc-nd/4.0)

关键词： anomaly detection deep learning masked autoencoders medical imaging self-supervised learning

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Dynamic Local Filters in Graph Convolutional Neural Networks 22nd

Dynamic Local Filters in Graph Convolutional Neural Networks

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22nd international conference on Image Analysis and Processing (ICIAP)

作者： Apicella, Andrea Isgro, Francesco Pollastro, Andrea Prevete, Roberto Univ Naples Federico II Dept Elect Engn & Informat Technol Naples Italy Lab Augmented Real Hlth Monitoring ARHeMLab Naples Italy Privacy Applicat AIPA Lab Lab Artificial Intelligence Naples Italy Lawrence Berkeley Natl Lab Berkeley CA 94720 USA

ISBN: (纸本)9783031431524;9783031431531

Over the last few years, we have seen increasing data generated from non-Euclidean domains, usually represented as graphs with complex relationships. Graph Neural Networks (GNN) have gained a high interest because of their potential in processing graph-structured data. In particular, there is a strong interest in performing convolution on graphs using specific GNN architectures, generally called Graph Convolutional Neural Networks (GCNN). This paper presents a novel method to adapt the behaviour of a GCNN using an input-based dynamically generated filter. Notice that the idea of adapting the network behaviour to the inputs they process to maximize the total performances has aroused much interest in the neural networks literature over the years. The experimental assessment confirms the capabilities of the proposed approach, achieving promising results using simple architectures with a low number of filters.

关键词： Graph Convolutional Neural Networks Deep learning Dynamic Neural Networks Programmable ANNs Graph Structure learning

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Multi-layer Intrusion Detection on the USB-IDS-1 dataset 22nd

Multi-layer Intrusion Detection on the USB-IDS-1 Dataset

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Dang, Quang-Vinh Industrial University of Ho Chi Minh City Ho Chi Minh City Viet Nam

ISBN: (纸本)9783031274084

Intrusion detection plays a key role in a modern cyber security system. In recent years, several research studies have utilized state-of-the-art machine learning algorithms to perform the task of intrusion detection. However, most of the published works focus on the problem of binary classification. In this work, we extend the intrusion detection system to multi-class classification. We use the recent intrusion dataset that reflects the modern attacks on computer systems. We show that we can efficiently classify the attacks to attack groups. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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A Deep learning Model for Five Stage Diabetic Retinopathy Detection 22

A Deep Learning Model for Five Stage Diabetic Retinopathy De...

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22nd OITS international conference on Information Technology, OCIT 2024

作者： Nukala, Sriya Setty Dalai, Asish Kumar Kar, Udit Narayana School of Computer Science and Engineering VIT-AP University India

ISBN: (纸本)9798331510404

Diabetic retinopathy (DR) is a common complication of diabetes that can result in vision loss if not diagnosed and treated in a timely manner. DR has several risk factors, including the duration of diabetes, poor control of blood sugar, hypertension, and high cholesterol levels. It is important for diabetics to undergo regular eye examinations in order to detect and manage this condition as early as possible. In order to slow down the progression of DR, healthcare professionals must be able to detect and classify the disease early in order to implement timely interventions and treatment strategies. Thus, it is possible to prevent the development of further visual impairments. This paper presents a novel approach for detecting and classifying DR stages based on image data. An array of retinal images obtained from diabetic patients was analyzed using state-of-the-art deep learning techniques. As a result, it aims to automate the detection and classification of Diabetic Retinopathy across its five stages. A pre-trained EfficientNetB3 model was applied with the transfer learning technique and achieved 90.03 percent accuracy. By employing this approach, this debilitating disease could be diagnosed and treated more effectively. © 2024 IEEE.

关键词： Ophthalmology

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On Corpus Construction and Joint Modeling Method for Clinical Pulse Feeling and Palpation Information Extraction and Lexical Analysis of Traditional Chinese Medicine 22

On Corpus Construction and Joint Modeling Method for Clinica...

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22nd Chinese National conference on Computational Linguistics, CCL 2023

作者： Wang, Yaqiang Jiang, Wen Jiang, Yongguang Shu, Hongping College of Software Engineering Chengdu University of Information Technology China Institute for Data Science and Engineering Chengdu University of Information Technology China Sichuan Key Laboratory of Software Automatic Generation and Intelligent Service China Department of Preclinical Medicine Chengdu University of Traditional Chinese Medicine China

ISBN: (纸本)9781713876229

Pulse feeling and palpation (PFP) are the most distinctive and representative clinical diagnostic methods of traditional Chinese medicine (TCM). They provide important evidence for TCM clinical practitioners to differentiate syndromes. Extracting PFP information and analyzing its lexical features have important clinical value. In this paper, we carried out research on corpus construction and joint modeling method for PFP information extraction (IE) and lexical analysis (LA) of TCM for the first time. Based on more than ten thousand TCM clinical records, we proposed a corpus construction framework, built annotation guidelines and constructed a labeled PFP IE and LA corpus to support multi-task learning. Labeling consistency evaluated by inter-annotator agreement value for the corpora achieve more than 0.94. Moreover, we attempted to jointly model PFP IE and LA tasks of TCM with a same-level-shared multi-task model, and experimental results verified effectiveness of the joint model. © 2023 China National conference on Computational Linguistics.

关键词： Information retrieval

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22nd international conference on Web engineering (ICWE)

作者： Qi, Zhiwei Yue, Kun Duan, Liang Liang, Zhihong Yunnan Univ Sch Informat Sci & Engn Kunming Yunnan Peoples R China Yunnan Univ Key Lab Intelligent Syst & Comp Yunnan Prov Kunming Yunnan Peoples R China Southwest Forestry Univ Sch Big Data & Intelligence Engn Kunming Yunnan Peoples R China

ISBN: (纸本)9783031099175;9783031099168

Similarity search on directed social networks (DSNs) could help users find the K nearest neighbors (KNNs). The graph index based similarity search does not have to compare query node against every node in DSNs, since the neighbor relationship of the nodes is captured by the edges. Nevertheless, the performance of similarity search is still unsatisfactory, such as not supporting the end-to-end search or taking unnecessary detours, etc. In this paper, we propose the method of Graph Index on Directed Social Network Embedding (GI-DSNE) to facilitate the approximate KNN search on DSNs. First, the DSNE is proposed to embed the DSN into a low-dimensional vector space to achieve the embeddings for efficient calculation of similarities on the search path. Then, the nearest neighbor descent algorithm is adopted to calculate the KNN graph. Subsequently, to construct the graph index efficiently, the direction guided strategy for edge selection, maximum out-degree of GI-DSNE and the depth-first-search tree for guaranteeing the connectivity of GI-DSNE are proposed. Experimental results show that our proposed method outperforms the state-of-the-art competitors on both execution time and precision.

关键词： Social network Similarity search Graph index Network embedding

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