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A new skeletal representation based on gait for depression detection 22

A new skeletal representation based on gait for depression d...

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22nd IEEE international conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Lu, Haifeng Shao, Wei Ngai, Edith Hu, Xiping Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou730000 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong

ISBN: (纸本)9781728162676

As the challenge of depression problems increases today, it is important to effective and timely detect for patients' treatments and depression prevention. Current methods of automated depression diagnosis depend almost entirely on audio, video, and Electroencephalogram (EEG) etc. In this paper, we propose a novel method to detect depression using gait data that is collected by Kinect camera. Its key components include a camera rectification method and a rigid-body representation of the human body. The camera rectification method achieves the purpose of improving data processing accuracy by changing the camera angle. The rigid-body representation can not only improve the robustness of detecting depression patients with noisy input, but also can reduce the classification time. We evaluate our method on the depression gait dataset in postgraduate students. The proposed method has an outstanding performance in classic machine learning algorithms, and the best accuracy can achieve 88.89%. Our solution provides a new method for automatic depression detection (ADD) that has exciting implications in clinical theory and practice, and has the advantages of high accuracy, inexpensive, low time cost, and no-contact. © 2021 IEEE.

关键词： Cameras

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Authentic e-learning Perspectives from Online Facilitators in a Developing Country 2nd

Authentic e-Learning Perspectives from Online Facilitators i...

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22nd international conference on Interactive Collaborative learning (ICL) / 48th IGIP international conference on engineering Pedagogy - Impact of the 4th Industrial Revolution on engineering Education

作者： Wong, Amy Ooi Mei Yuen, Kevin Kam Fung Singapore Univ Social Sci Sch Business 463 Clementi Rd Singapore 599494 Singapore

ISBN: (纸本)9783030402716;9783030402709

Although the implementation of e-learning has reached advanced stages in developed countries, it is still in its infancy in many developing countries. This paper examined the benefits and challenges of e-learning as well as the concept of authentic e-learning. To better understand authentic learning in online environments in developing countries, data was collected from 52 faculty members of an institution in India. As participants of a Faculty Online Facilitation course, the faculty members were asked to share their views on how they would go about establishing presence and facilitating an online class in their institution. They were also asked to share their preferred choice of communication tools and elaborate on the reasons for selecting the tools. The data was analysed using the text mining approach. The findings of the study provide pedagogical implications on how to design and facilitate authentic e-learning courses for higher education institutions in developing countries.

关键词： e-learning Authentic learning Online facilitators Higher education Developing countries

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Byzantine-Robust Federated learning through Dynamic Clustering

Byzantine-Robust Federated Learning through Dynamic Clusteri...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Hanyu Wang Liming Wang Hongjia Li Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Federated learning enables distributed and collaborative learning among multiple participants while protecting their privacy. However, due to its distributed nature, federated learning is vulnerable to Byzantine attacks. These attacks can poison the data or directly modify the model parameters, making the global model performance degrade or even leaving a backdoor. Existing strategies for mitigating Byzantine attacks require a priori information about the number of attackers or require additional validation datasets. However, prior knowledge of the number of Byzantine clients or the collection of representative validation datasets is not always feasible in practice. Moreover, recent research has shown that well-designed attacks can make malicious updates indistinguishable from benign ones by making them highly similar, thus bypassing existing defense methods that rely on these *** tackle these problems, we propose Dynamic Clustering based Federated learning (DCFL), a novel Byzantine robust FL approach without any additional validation datasets. The main idea behind DCFL is to rigorously constrain the magnitude and direction of local updates through norms and signs. To achieve this, we propose a novel metric that can effectively distinguish malicious updates from benign updates in terms of direction, which can help the server eliminate malicious updates before final aggregation. Our experiments on three datasets demonstrate the effectiveness of DCFL in mitigating various popular Byzantine attacks. Remarkably, the accuracy of the global model learned in the adversarial setting is even close to that of FedAVG under no attack.

关键词： Measurement Privacy Toxicology Federated learning Robustness data models Servers

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JUST-Studio: A Platform for Spatio-Temporal data Map Designing and Application Building 22nd

JUST-Studio: A Platform for Spatio-Temporal Data Map Designi...

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22nd international conference on Web Information Systems engineering (WISE)

作者： Sui, Yuan Li, Ruiyuan Wang, Xu Liu, Jun Tang, Juncheng JD Intelligent Cities Res Beijing Peoples R China Chongqing Univ Chongqing Peoples R China

ISBN: (纸本)9783030915605;9783030915599

With the proliferation of GPS (Global Position System) and other space sensors, there emerged many spatial-temporal (a.k.a. ST) urban applications, such as trajectory visualization and geofence analysis. It is time-consuming and tedious to build these applications from scratch by writing codes only. This paper introduces JUST-Studio, a holistic platform that analyzes spatio-temporal data and builds urban applications with minor efforts. JUST-Studio consists of two main components: service manager and ST-App designer. By this platform, users could: 1) upload their own ST data to the built-in data store, or connect to an existing ST data store and register it as a data source;2) create ST models using ST data and publish ST services;3) make ST base map and build ST applications based on ST services. JUST-Studio is not only capable of making commonly used static map scenes, but also good at rendering dynamic data. In this paper, we will use vehicle trajectories with geofence analysis to build an application for monitoring restricted areas, which is very common in urban traffic applications.

关键词： data virtualization Web mapping Location analysis Application builder

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MODEL-BASED data AUGMENTATION TO IMPROVE THE PERFORMANCE OF MACHINE-learning DIAGNOSTIC SYSTEMS 22

MODEL-BASED DATA AUGMENTATION TO IMPROVE THE PERFORMANCE OF ...

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22nd international Symposium on High Voltage engineering, ISH 2021

作者： Kahlen, Jannis N. Würde, Andre Andres, Michael Moser, Albert Fraunhofer Institute for Applied Information Technology FIT Digital Energy Schloss Birlinghoven 1 Sankt Augustin Germany RWTH Aachen University Schinkelstraße 6 Aachen52062 Germany

ISBN: (纸本)9781839536052

Machine-learning diagnostic systems are already being used to detect abnormal conditions in electrical equipment. The challenge in machine-learning diagnostic approaches lies in dealing with small training databases of abnormal-condition samples available as the performance of the machine-learning diagnostic systems relies on the quality and quantity of the training data. One possible solution to this problem are data augmentation techniques generating synthetic data from the available data to augment the training data. However, these generic methods do not consider domain-specific knowledge about the diagnosis task. In this paper, a model-based data augmentation approach using computer-implementable, electromechanical models is presented. This approach uses statistical information extracted from the available data to sample model parameters and input variables to generate synthetic normal- and abnormal-condition data. The model-based data augmentation is showcased for detecting a distribution transformer fault. Machine-learning diagnostic systems are trained utilizing the model-based data augmentation and benchmarked with state-of-the-art diagnostic systems. The number of abnormal-condition measurements for training is limited and the performance of all diagnostic systems is analysed. It is shown that the model-based data augmentation is capable of improving the diagnosis accuracy for the analysed diagnostic task especially when only a very small amount of abnormal-condition data is available. © 2021 The Institution of engineering and Technology.

关键词： Fault detection

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Automatic Lane Change System with Double Loop PID Algorithm

Automatic Lane Change System with Double Loop PID Algorithm

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international conference on Control, Automation and Systems ( ICCAS)

作者： Junki Hong Jaehwan Choi Sooho Woo Jaehong Lee Soon-Geul Lee Jaehyeong Kim Kyungsu Cha Department of Mechanical Engineering Integrated Education Institute for Frontier Science and Technology (BK21 Four) Kyung Hee University 1732 Deogyeong-daero Giheung-gu Yongin-si Gyeonggi-do Korea Intelligent Automotive System E Intelligence 703-8 Yeoksam Gangnam-gu Seoul Korea

For automatic lane change of the advanced driver assistance system, it is necessary to compensate the sensor limitations and also to provide a stable and accurate steering controller. In this paper, a look-ahead model that uses the future position data of the vehicle to supplement the limitations of the sensor mounted in the vehicle is used. In addition, to make smooth the lateral control of the vehicle body, a double loop PID is used to control the heading angle rate. Simulink-based program is used for the automatic lane change by manually operating the turn indicator in the vehicle. The program was simulated with IPG’s CarMaker and ran in a real-world environment with MicroAutoBox II on Rav4 of Toyota. Experimental results showed that both the real vehicle test and the simulation completed the lane change stably with an error within 50 cm, despite the influence of the disturbance in the real environment.

关键词： Automation Roads Control systems data models Trajectory Advanced driver assistance systems

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Fine-Tuned BERT Model for Hate Speech Detection in Political Discourse

Fine-Tuned BERT Model for Hate Speech Detection in Political...

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international conference on ICT and Knowledge engineering

作者： Steve Nwaiwu Nipat Jongsawat Anucha Tungkasthan Jarinya Thaloey Data and Information Science Faculty of Science and Technology RMUTT Pathum Thani Thailand Big Data Analytics and Management Faculty of Science and Technology RMUTT Pathum Thani Thailand

ISBN: (数字)9798350391435

ISBN: (纸本)9798350391442

This research tackles the challenge of detecting hate speech and offensive content in political discussions on social media. By employing natural language processing (NLP) techniques, the study aims to contribute to creating healthier online communities. The researchers started with a manually labeled dataset of Reddit comments centered on political discourse. To address the common issue of class imbalance in classification tasks, they used an oversampling strategy to ensure equal representation of each class. Additionally, they applied data augmentation, specifically synonym replacement, to increase the diversity of the training data, which helps improve the model's generalization. For text preprocessing, the Bidirectional Encoder Represen- tations from Transformers (BERT) tokenizer was used. This tokenizer converts text into subwords, which is especially useful for handling out-of-vocabulary terms. The model used in this study is a fine-tuned version of BERT base, trained with specific hyperparameters: a learning rate of 5e-5, a batch size of 24, and a weight decay of 0.01. Training was carried out over seven epochs using the Hugging Face Transformers library and NVIDIA Tesla P100 GPUs available on Kaggle. The model's performance was assessed using 5-fold cross- validation, resulting in impressive metrics: an average accuracy of 99.34 percent, precision of 99.36 percent, recall of 99.34 percent, F1-score of 99.34 percent, and an ROC-AUC score of 99.96 percent. These results highlight the model's effectiveness in identifying hate speech and offensive content across different data splits. Compared to earlier research, this approach shows significant improvement in performance metrics. It demonstrates the potential of combining BERT with data augmentation and oversampling techniques for detecting hate speech in political discourse on Reddit. This study lays the groundwork for future research into real-time detection systems and cross-lingual models.

关键词： Analytical models Social networking (online) Hate speech Training data Bidirectional control Transformers data augmentation Encoding Natural language processing data models

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Adequate Basis for the data-Driven and Machine-learning-Based Identification 1

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22nd international conference on Artificial Intelligence and Soft Computing, ICAISC 2023

作者： Rojahn, Marcel Ambros, Maximilian Biru, Tibebu Krallmann, Hermann Gronau, Norbert Grum, Marcus University of Potsdam Potsdam Germany Krallmann AG Berlin Germany

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

Process mining (PM) has established itself in recent years as a main method for visualizing and analyzing processes. However, the identification of knowledge has not been addressed adequately because PM aims solely at data-driven discovering, monitoring, and improving real-world processes from event logs available in various information systems. The following paper, therefore, outlines a novel systematic analysis view on tools for data-driven and machine learning (ML)-based identification of knowledge-intensive target processes. To support the effectiveness of the identification process, the main contributions of this study are (1) to design a procedure for a systematic review and analysis for the selection of relevant dimensions, (2) to identify different categories of dimensions as evaluation metrics to select source systems, algorithms, and tools for PM and ML as well as include them in a multi-dimensional grid box model, (3) to select and assess the most relevant dimensions of the model, (4) to identify and assess source systems, algorithms, and tools in order to find evidence for the selected dimensions, and (5) to assess the relevance and applicability of the conceptualization and design procedure for tool selection in data-driven and ML-based process mining research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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An A3C Deep Reinforcement learning FPGA Accelerator based on Heterogeneous Compute Units

An A3C Deep Reinforcement Learning FPGA Accelerator based on...

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international conference on Communication Technology (ICCT)

作者： Ziyu Li Fen Ge Fang Zhou Ning Wu College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

Deep reinforcement leaning (DRL) has a great performance in intelligent decision-making question. As one of well-performance DRL algorithms, Asynchronous Advantage Actor-Critic (A3C) can make distributed agents work simultaneously to infer and train without experience pool. However, the multiple agents also bring complex deep neural network (DNN) computations in each agent which costs the almost time in A3C algorithm. In this paper, we propose a DRL FPGA accelerator based on heterogeneous compute units for A3C algorithm to reduce the DNN computing periods. According to A3C algorithm computing flow, we implement both inference and training steps on accelerator and pipeline their computations. In DNN forward propagation (FP) and backward propagation (BP) computing, the Multiply-ADDer-tree (MADD) structure with high input parallelism and Multiply-Accumulation (MAC) structure with high output parallelism are proposed to be employed, respectively. Heterogeneous computing method makes the weight parameters layout of inference and training consistent, and improves the access efficiency of on-chip memory. Experimental results show that our design achieves 40-times speedup in inference-training speedup compared to CPU, and 54-times speedup compared to GPU. Moreover, our heterogeneous computing method lead to better efficiency in memory accessing compared to exiting approaches.

关键词： Training Deep learning Accelerated aging Memory management Graphics processing units Parallel processing Inference algorithms

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TRANSFORMERS FAULT IDENTIFICATION BY FREQUENCY RESPONSE ANALYSIS USING intelligent CLASSIFIERS 22

TRANSFORMERS FAULT IDENTIFICATION BY FREQUENCY RESPONSE ANAL...

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22nd international Symposium on High Voltage engineering, ISH 2021

作者： Ferreira, Regelii S.A. Ezzaidi, Hassan Fofana, Issouf Picher, Patrick Université du Québec à Chicoutimi Saguenay Canada Varennes Canada

ISBN: (纸本)9781839536052

Failures in power transformers generates high financial loss inherent to equipment cost and power interruption. Thus, the industry has been working into developing efficient and reliable monitoring techniques with the objective of detecting incipient faults. Frequency Response Analysis (FRA) is among well-known methods to detect faults in transformers' active parts. FRA is very sensitive to changes in the winding geometry and a comparison between healthy and faulty measurements can present deviations that leads to the fault identification. However, until now, FRA interpretation is performed with the aid of experts. The development of FRA and the spreading of its application in transformers' diagnostics, have led studies to focus on the advancement of objective interpretation techniques. To contribute to this matter, intelligent classifiers are evaluated over its capabilities to classify transformers faults. For this purpose, a database of measurements performed on a laboratory winding model including different faults is used. Numerical indices are used as input for machine learning classifiers. This analysis indicated that the use of one individual index is appropriate for a good classification performance using Radial Basis Function neural network. Meanwhile Support-vector machine and Backpropagation neural network performed better with a combination of indices. © 2021 The Institution of engineering and Technology.

关键词： Condition monitoring

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