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2021 25th International conference on Methods and Models in Automation and Robotics, MMAR 2021

2021 25th International Conference on Methods and Models in ...

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25th International conference on Methods and Models in Automation and Robotics, MMAR 2021

ISBN: (纸本)9781728173801

The proceedings contain 67 papers. The topics discussed include: the use of counting peaks method for the purpose of smoothing filtering efficiency assessment in analysis of electroencephalography data;incremental 3D maps server based on feature matching for multi-robot systems;internal model-based lateral and longitudinal control for autonomous electric vehicles;analysis of microphone use for perception of autonomous vehicles;traffic lane-changing modeling and scheduling with game theoretical strategy;continuous-time model predictive control with disturbances compensation for a glass forehearth;accuracy analysis of the fractional order, positive, state space model of heat transfer process;and generalized proportional integral observer approach for trajectory tracking control of autonomous underwater vehicles.

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Investigation on Human-Centric Security Based on Understanding the Role of User Behavior in Information Security

Investigation on Human-Centric Security Based on Understandi...

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Electronics and Devices, Computational Science (ICEDCS), International conference on

作者： Lihua Wang Dongying Vocational Institute Dongying China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

Human Health Information Exchange (HHIE) provides significant benefits to patient assistance, including faster coordinated treatment and higher-quality healthcare. The Office of the National Coordinator (ONC) for Health Information Technology is looking for patient-centric HHIE ideas that can assist shift ownership of information from suppliers to patient. The current system confronts significant challenges to patient-centric HHIE, including privacy and security issues, inaccurate data, and delayed access to relevant records across several medical centres. To ensure accountability and integrity, we suggested a decentralised EMR management (DPEM) Structure based on methods of blockchain. With the following objectives in mind, we suggest a four-layer layout for DPEM: data preparation, data exchange, data storage, and access control/security. (i) We suggest a new elliptic curve-based content extraction signature (EC-CES) that allows patients to filter out sensitive EMR data, hence eliminating privacy information leakage during the data sharing process and ensuring privacy in DPEM. (ii) Blockchain smart contracts are utilised to specify the patients' specified Permissions for access, enabling safe data sharing. (iii) In order to provide secure storage, we store the actual EMRs in a cloud facility. The corresponding indexes of the EMRs are stored on consortium blockchain, which simultaneously optimises data leakage from the EMRs and ensures the integrity of the EMRs. (iv) We created a ciphertext-policy attribute-based encryption (CP-ABE) access control strategy to allow data owners to protect cloud storage while granting access to authorised people via an encrypted link to the cloud for storage with restricted access rules disabled. This enabled control of entry in data sharing. Finally, the security study demonstrates that DPEM is the most effective method of achieving safe data exchange for EMR. We carried out a thorough simulation of this patient-centered HHIE procedu

关键词： data privacy Cloud computing Technological innovation Smart contracts Medical services Stability analysis Robustness data models Blockchains Secure storage

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Leveraging BERT Models for Toxicity Detection in Moroccan Dialect

Leveraging BERT Models for Toxicity Detection in Moroccan Di...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Rabia Rachidi Mohamed Amine Ouassil Mouaad Errami Mounir Omari Bouchaib Cherradi Hassan Silkan LaROSERI Laboratory Faculty of Science Chouaib Doukali University El Jadida Morocco GENIUS Laboratory SUPMTI Rabat Morocco EEIS Laboratory ENSET of Mohammedia Hassan II University of Casablanca Mohammedia Morocco DATA Science et AI Team GENIUS Laboratory SUPMTI Rabat Morocco STIE Team CRMEF Casablanca-Settat. EEIS Laboratory ENSET of Mohammedia Hassan II University of Casablanca Mohammedia Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

The widespread use of social media and increased internet accessibility have led to the generation of vast amounts of data. Much of the online textual content consists of informal expressions of opinions by individuals, which often include hateful language. The advent of transformer-based models has greatly enhanced addressing such challenges in textual data. With their ability to process context bidirectionally and handle large-scale data efficiently, transformers have revolutionized natural language processing, particularly in tasks such as classifying online comments. From this perspective, this study has been conducted to deploy transformer-based models to identify harmful statements written in the Moroccan dialect (Darija), which poses unique challenges due to its informal and diverse linguistic nature. The Moroccan Arabic Toxic Comments dataset (MATCD), gathered using Selenium, serves as the foundation for this study, enabling the evaluation of transformer-based models in detecting harmful comments in the Moroccan dialect. As the interest in the BERT (Bidirectional Encoder Representations from Transformers) model increased, several model variations were developed to adjust to different languages, including Arabic, to enhance the ability to handle language-specific tasks effectively. The experimental results exhibit the strengths and weaknesses of each approach, with the model achieving high accuracy, reaching up to 95%, providing valuable insight into the suitability and effectiveness of transformer-based models in this domain.

关键词： Training Adaptation models Sentiment analysis Toxicology Social networking (online) Bidirectional control Linguistics Transformers Encoding Selenium

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A Hierarchical Regression Chain Framework for Affective Vocal Burst Recognition

A Hierarchical Regression Chain Framework for Affective Voca...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jinchao Li Xixin Wu Kaitao Song Dongsheng Li Xunying Liu Helen Meng The Chinese University of Hong Kong Hong Kong SAR China Microsoft Research Asia Shanghai China

As a common way of emotion signaling via non-linguistic vocalizations, vocal burst (VB) plays an important role in daily social interaction. Understanding and modeling human vocal bursts are indispensable for developing robust and general artificial intelligence. Exploring computational approaches for understanding vocal bursts is attracting increasing research attention. In this work, we propose a hierarchical framework, based on chain regression models, for affective recognition from VBs, that explicitly considers multiple relationships: (i) between emotional states and diverse cultures; (ii) between low-dimensional (arousal & valence) and high-dimensional (10 emotion classes) emotion spaces; and (iii) between various emotion classes within the high-dimensional space. To address the challenge of data sparsity, we also use self-supervised learning (SSL) representations with layer-wise and temporal aggregation modules. The proposed systems participated in the ACii Affective Vocal Burst (A-VB) Challenge 2022 and ranked first in the "TWO" and "CULTURE" tasks. Experimental results based on the ACii Challenge 2022 dataset demonstrate the superior performance of the proposed system and the effectiveness of considering multiple relationships using hierarchical regression chain models.

关键词： Emotion recognition data analysis Computational modeling Self-supervised learning Bidirectional control Signal processing data models

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Code generation technology and reliability analysis based on structure flow chart 2

Code generation technology and reliability analysis based on...

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2021 2nd International conference on Machine Learning and Computer Application, ICMLCA 2021

作者： Zhang, Tao Sun, Xuyang Shao, Zhong School of Electrical Engineering Dalian University of Technology Dalian China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Software College Shenyang University of Technology Liaoning Shenyang China

ISBN: (纸本)9783800757398

With the rapid iterative development of software development technology, the increasing scale of software and the increasing demand for reliability, the research on automatic code generation and reliability prediction technology has increasingly become a hot issue in the field of software engineering. The purpose of this paper is to obtain data from the user modeling process and store it, and then use software metrics to analyze and judge the corresponding data, and obtain a method for optimizing the model, so as to prevent some errors made by users in the modeling phase. And incomplete information, and then iteratively improve and repair the model. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Reliability analysis

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A Model for Selecting Relevant Topics in Documents Aimed at Compliance processes

A Model for Selecting Relevant Topics in Documents Aimed at ...

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IEEE Latin American conference on Computational Intelligence (LA-CCI)

作者： da Silva Amaral, Joao Alberto de Lima Neto, Fernando Buarque Univ Pernambuco UPE Polytech Sch Pernambuco Recife PE Brazil

ISBN: (纸本)9781728188645

This paper proposes a semantic Natural Language processing (NLP) approach used to assist in the automated characterization of information relevant to compliance activities. In this context, the proposed model combines two topic modeling techniques: Latent Semantic analysis (LSA) and Latent Dirichlet Allocation (LDA), the first used to assist in the dimensionality reduction process, while the second, used to identify the number of relevant topics addressed in the processed data. Interesting results were achieved when three large databases tested the model, namely: database of European Laws (period 1952 to 1990), database of Audit reports issued by the State General Secretariat of Management of Pernambuco (period 2010 to 2019), and database of Appellate Decisions issued by the Brazilian Federal Accountability Office (year of 2019). We compared the performance of three machine learning methods: K-means, LSA and LDA. In our experiments, we observed that (i) pre-processing techniques have a marked influence on the result of topic extraction;that (ii) Silhouette techniques and topic coherence produced the best value for the quantitative topics in all databases;that LSA associated with LDA presented the best performance in all three databases, regarding the identification of relevant themes (topics) identified. Overall, the best results were obtained using the database in English (European Union Laws).

关键词： Dimensionality reduction Clustering Topic modeling Latent semantic analysis (LSA) Natural language processing Text mining

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An iterative feature selection procedure for a classification problem based on the method of logical analysis of data 3

An iterative feature selection procedure for a classificatio...

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3rd International Scientific conference on Applied Physics, Information Technologies and Engineering, APITECH-iiI 2021

作者： Kuzmich, R.I. Stupina, A.A. Zhirnova, I.S. Slinitsyna, O.V. Boubriak, I.I. Department of Business Informatics and Business Process Modeling Siberian Federal University Krasnoyarsk Russia Department of Digital Control Technologies Siberian Federal University Krasnoyarsk Russia Department of System Analysis and Operation Research Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Department of International Management Krasnoyarsk State Agrarian University Krasnoyarsk Russia Department of Economics and Business Process Management Siberian Federal University Krasnoyarsk Russia Department of Biochemistry Oxford University Oxford United Kingdom

An iterative procedure for selecting features for classifying observations is proposed. The main principles of the proposed iterative procedure are ranking and selection of features according to the frequency of their use when constructing logical patterns based on the method of logical analysis of data. The empirical confirmation of the expediency of this procedure is given. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Classification (of information)

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Ensemble Sparse Approach to Smart Meter Forecasting from Different Horizons

Ensemble Sparse Approach to Smart Meter Forecasting from Dif...

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International conference on Computer Engineering and Applications (ICCEA)

作者： Fan Wang Yanyu Zhang Caijia Lei Yuxin Lu Yun Zhao Wei Pan Guangzhou Power Supply Bureau Guangdong Power Grid Co. Ltd. Guangdong China Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid CSG Electric Power Research Institute Guangdong China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

With the widespread installation of smart meters globally, opportunities arise to reduce energy consumption and improve consumer awareness and control over electricity usage. At the core of offering these advanced services lies the capability to precisely predict the electricity demand of individual households. Contrasted with load forecasting at broader scales such as city-level or larger system aggregates, forecasting for individual households poses a significantly greater challenge due to the increased stochasticity and volatility of their loads. In this paper, a simple yet effective ensembled machine learning method is proposed for modeling and forecasting these individual household electricity loads at various time horizons and resolutions. The methodology is based on sparse Gaussian processing (SGP) and sparse coding (SC) theories. The ensemble sparse approach can help reduce the dimension of the input data, making it easier to process and analyze the data efficiently. It can help select relevant features or patterns from the vast number of smart meters, also enhance the accuracy of forecasting models. The proposed method is tested on a publicly available dataset and outperforms alternative approaches in every case examined in terms of deterministic forecast accuracy. The forecast horizons covered range from one minute to one year, demonstrating the significant advantage that comes with probabilistic forecasting.

关键词： Accuracy Load forecasting Electricity Computational modeling Time series analysis Predictive models Probabilistic logic

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Area-wise Augmentation on Segmentation datasets from 3D Scanned data Used for Visual Navigation

Area-wise Augmentation on Segmentation Datasets from 3D Scan...

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International conference on control, Decision and Information Technologies (CoDIT)

作者： Marin Wada Yuriko Ueda Miho Adachi Ryusuke Miyamoto Dept. of Computer Science Graduate School of Science and Technology Meiji University Kawasaki Japan Dept. of Computer Science School of Science and Technology Meiji University Kawasaki Japan

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Visual navigation rely heavily on semantic segmentation outcomes, which is invaluable for practical applications. However, the efficacy of this navigation method is compromised when the accuracy of semantic segmentation falls short. Crucially, the availability of an appropriate dataset containing pixel-wise class labels is imperative for constructing a robust classifier. To alleviate the burden of manual annotation, the authors have endeavored attempt to implement a semi-automatic process for generating a training dataset from 3D scanned data. To enhance the versatility of the approach, the present study introduces augmentation techniques that consider the semantic attributes of images within the target scenario: DMIT and ToD are employed to address color variations caused by seasonal changes lawn growth and fluctuations on the sun’s height, respectively. Experimental results based on images captured during the Tsukuba Challenge, a competition featuring autonomous moving robots in Japan, showed that the proposed methodology substantially enhances classification accuracy, particularly for images taken under conditions different from those during the creation of the 3D model.

关键词： Training Visualization Solid modeling Three-dimensional displays Accuracy Navigation Image color analysis Semantic segmentation Semantics Robots

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An Improved ARIMA Based Anomaly Detection Method for Time Series data 8

An Improved ARIMA Based Anomaly Detection Method for Time Se...

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8th IEEE conference on Energy Internet and Energy System Integration, EI2 2024

作者： Sun, Guangwei Yin, Chaofan Xia, Tian Lu, Yunpeng Mao, Jiawei Research Center for Tobacco Cultivation and Modulation Hubei Provincial Tobacco Research Institute Wuhan China Wuhan Vocational College of Science and Technology Department of Information Engineering Wuhan China Inspection and Testing Center China Electric Power Research Institute Wuhan China Huazhong University of Science and Technology School of Mechanical Science and Engineering Wuhan China

ISBN: (纸本)9798331523527

Currently, IoT data processing standards in China are somewhat vague, and monitoring data is typically used in subsequent stages for analysis, prediction modeling, and applications. However, monitoring and control data are often affected by various factors that can introduce anomalies, which in turn impacts intelligent control systems. To improve the quality of monitoring data, this paper addresses the limitations of traditional anomaly detection methods, which often require high similarity in data and face challenges with highly diverse monitoring data. Additionally, traditional ARIMA models are limited by difficulties in parameter selection, threshold determination, and high false-positive rates. Thus, we propose an improved ARIMA-based anomaly detection method for time-series data. This approach encompasses the complete process of monitoring data fitting and prediction, anomaly detection, and anomaly validation. The method has three main features:*** Parameter Selection: For varied batches of monitoring data, this method combines ACF and PACF plots with AIC and BIC heatmaps to determine optimal ARIMA parameters for each data *** of a Sliding Window: A sliding window is used to analyze residual data characteristics within the window, enabling precise anomaly detection for individual data *** Validation: A K-means clustering algorithm is incorporated to validate initial anomaly detection *** method was verified in a real-world cigar leaf curing barn, and experimental results demonstrate that it effectively detects anomalies in the IoT monitoring system data, contributing to improved IoT data quality. © 2024 IEEE.

关键词： Merging

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