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Transfer Learning in sEMG-based Gesture Recognition 12

Transfer Learning in sEMG-based Gesture Recognition

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12th International Conference on Information, Intelligence, Systems and Applications, IISA 2021

作者： Tsinganos, Panagiotis Cornelis, Jan Cornelis, Bruno Jansen, Bart Skodras, Athanassios University of Patras Dept. of Electrical and Computer Engineering Patras Greece Vrije Universiteit Brussel Dept. of Electronics and Informatics Brussels Belgium Imec Leuven3001 Belgium

ISBN: (纸本)9781665400329

The latest advancements in the field of deep learning and biomedical engineering have allowed for the development of myoelectric interfaces based on deep neural networks. A longstanding problem of these interfaces is that the models cannot easily be applied to new users due to the high variability and stochastic nature of the electromyography signals. Further training a new model for every new subject requires the collection of large volumes of data. Therefore, this work proposes a transfer learning (TL) scheme which allows reusing the knowledge of a pre-existing model for a new user. Firstly, a convolutional neural network (CNN) is trained on an initial dataset using the data of multiple subjects. Then, the weights of this model are fine-tuned for a new target subject. The approach is evaluated on the Ninapro datasets DB2 and DB7. The experimentation included three different CNN models and eight preprocessing alternatives. The results showed that the success of the TL method depends on how the data are preprocessed. Specifically, the biggest accuracy improvement (+5.14%) is achieved when only the first 20% of the signal duration is used. © 2021 IEEE.

关键词： Convolutional neural networks

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Leveraging the Pre-Trained Whisper Model and Levenshtein Distance for Audio Buginesse Transcription

Leveraging the Pre-Trained Whisper Model and Levenshtein Dis...

引用

informatics International Conference (BTS-I2C), Beyond Technology Summit on

作者： Amirul Mukmin Adnan Abdul Jalil Mahabintang Pallawabonang Esa Prakasa Yuyun Dept. of informatics engineering Handayani University of Makassar Makassar Indonesia Dept. of computer system Handayani University of Makassar Makassar Indonesia Research center for data and information science National Research and Innovation Agency Bandung Indonesian

ISBN: (数字)9798331508579

ISBN: (纸本)9798331508586

This research aims to transcribe audio into text using the pre-trained Whisper model, focusing on Bugis, a low-resource language. A key challenge in transcribing local languages like Bugis is the limited availability of language model. To address this, we implemented an algorithmic correction approach that does not require extensive datasets or computationally demanding customizations. The Whisper model provides an initial transcription, serving as a foundation; however, the output often lacks accuracy due to the absence of specific adjustments for Bugis. To enhance accuracy, we applied the Levenshtein distance algorithm to correct word errors by comparing transcription results with entries in a Bugis language dataset based on similarity. Although this method is not fully precise and depends on the completeness of the reference dataset, evaluation results indicate that it effectively lowers the word error rate (WER) and improves transcription accuracy. This research produces SRT files containing transcripts that have been corrected using the Levenhstein distance algorithm, which not only improves accessibility but also contributes to the preservation of Bugis language through digital media. This approach is expected to provide a practical solution to improve transcription accuracy and support more accurate documentation in Bugis language preservation efforts.

关键词： Accuracy Error analysis Computational modeling Focusing Speech recognition Documentation Media Linguistics Synchronization informatics

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Causal Structure Learning from Imperfect Longitudinal Data in Healthcare

Causal Structure Learning from Imperfect Longitudinal Data i...

引用

IEEE International Conference on Healthcare informatics (ICHI)

作者： Haoyu Yang Roshan Tourani Jia Li Pedro Caraballo Michael Steinbach Vipin Kumar Gyorgy Simon Dept. of Computer Science and Engineering University of Minnesota Minneapolis USA Institute for Health Informatics University of Minnesota Minneapolis USA Dept. of Internal Medicine Mayo Clinic Rochester USA

Causal Structure Discovery (CSD) is the task of learning the set of underlying causal relationships from observational data. Due to their computational scalability and flexibility, a recently developed class of CSD methods, NOTEARS, based on a formulation allowing for continuous optimization, is gaining popularity. However, this formulation can lead to incorrect edge orientations when little/no likelihood advantage is conferred upon any edge orientation, e.g., a → b → c versus c → b → a. In longitudinal data, like electronic health records (EHRs), temporal relationships are observable among many pairs of variables. Such temporal relationship is imperfect but still suggest an orientation since causal effects cannot travel backwards in time. Following this idea, we propose methods to incorporate precedence constraints into continuous optimization-based CSD methods. Experiments on both a synthetic and two real-world datasets validate the effectiveness of the proposed precedence constraints.

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The Deep Learning-based Intelligent System for Extracting Information from e-KTP

The Deep Learning-based Intelligent System for Extracting In...

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Software engineering and Information Technology (ICoSEIT), International Conference on

作者： Zaki Fuadi Kahlil Muchtar Muhammad Subianto Hammam Riza Maulisa Oktiana Nizamuddin Department of Informatics Universitas Syiah Kuala Banda Aceh Indonesia Dept. of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia National Research and Innovation Agency (BRIN) Jakarta Indonesia

This research implements an Intelligent Information System for Text Recognition on Indonesian Identity Cards (eKTP) based on Deep Learning architecture. The aim of this research is to develop a system capable of accurately and efficiently detecting and recognizing text on e-KTP images. The first phase of this research involves selecting ideal images from the e-KTP dataset. Subsequently, pre-processing is conducted to crop the image sides and merge them with background images to create a more diverse dataset and obtain the e-KTP image masks as training data. After selecting the ideal images, the total training data amounts to 144 images. The U-Net architecture is chosen as the deep learning method for image segmentation in this research, while for text detection and recognition, two neural networks, CRAFT and TRBA (TPS-ResNet-BiLSTM-Attention), are employed. The testing, based on accuracy values, Dice coefficients, and IoU for the segmentation process, results in a U-Net model accuracy of 99.52%. Text detection and recognition follow confidence values.

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Polarization Converter Metasurface Using Machine Learning Methods for Bandwidth Enhancement

Polarization Converter Metasurface Using Machine Learning Me...

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International Conference on Modern Circuits and Systems Technologies (MOCAST)

作者： David Chatzichristodoulou Photos Vryonides Symeon Nikolaou Dimitra-Danai Varsou Maria Antoniou Dimitris Mintis Antreas Afantitis Frederick Research Center RF an Microwave Solutions LTD Nicosia Cyprus Dept. Electrical Computer Engineering and Informatics Frederick Research Center Frederick University Nicosia Cyprus NovaMechanics Ltd Nicosia Cyprus

ISBN: (数字)9798350385427

ISBN: (纸本)9798350385434

This manuscript presents the design of a polarization converter metasurface (MTS) where Machine Learning (ML) and Deep Learning (DL) methods were used, to enhance its bandwidth. The MTS is implemented with a periodic array of multi-layered cells with chiral geometry that supports asymmetric transmission. Any impingent x-polarized wave is transmitted as a y-polarized wave, and any incident y-polarized wave is effectively reflected. In order to increase the frequency range where the incident $\vert \mathbf{Ex} \vert$ field over the transmitted $\vert \mathbf{Ey} \vert$ field (Tyx) is higher than 90% and the associated Rxx which is the ratio of the incident $\vert \mathbf{Ex} \vert$ over the transmitted $\vert \mathbf{Ex} \vert$ is less than -10 dB, the Rxx plots vs frequency were used to train an artificial neural network (ANN). The trained ANN was used to optimize the cell's geometric dimensions to create a MTS response with more than 90% conversion rate (Tyx>90%), in the frequency range from 6.5 to 12.5 GHz, resulting in more than 60% fractional bandwidth.

关键词： Geometry Deep learning Circuits and systems Bandwidth Artificial neural networks Metasurfaces Frequency conversion

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Grammar Error Correction in Indonesian Sports News: Comparing the Performance of Pre-Trained T5 and BART Models

Grammar Error Correction in Indonesian Sports News: Comparin...

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informatics International Conference (BTS-I2C), Beyond Technology Summit on

作者： Muh Adnan Esa Prakasa Yuyun Najirah Umar Nasrullah Adi Sadli A. Edeth Fuari Anatasya Hazriani Mashur Razak dept. of informatics engineering Handayani University of Makassar Makassar Indonesian National Research and Innovation Agency research center for data and information science Bandung Indonesian dept. of computer system Handayani University Makassar Makassar Indonesian

ISBN: (数字)9798331508579

ISBN: (纸本)9798331508586

This research compares the performance of two pre-trained models based on the transformer, namely text-to-text transfer transformer (T5) and bidirectional and auto-regressive transformer (BART), through a fine-tuned for correcting grammar in Indonesian sports news texts. We used vennify/t5-base-grammar-correction and onionLad/grammar-correction-bart-base, was trained using a dataset of 10,075 sentences from Indonesian language sports news sites. We tested the model performance using bilingual evaluation understudy (BLEU), google's language evaluation understanding (GLEU), training loss, and validation loss. The results show that BART was more efficient in learning data patterns and has better generalization ability, supported by BLEU and GLEU scores of 0.8560 and 0.9655. Meanwhile, T5 maintains simpler sentence structures, supported by BLEU and GLEU scores of 0.8120 and 0.9564. BART excels at handling more complex contexts, making it more effective at accurately correcting errors in dynamic sports news. Both models have their respective advantages, with BART being better at correcting detailed grammatical errors, while T5 offers consistent correction of more simple sentence structures.

关键词： Training Measurement Analytical models Accuracy Computational modeling computer architecture Transformers Grammar Sports Context modeling

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Anomaly Detection With Quantified Explanations for Multivariate Time Series Networking Data

Anomaly Detection With Quantified Explanations for Multivari...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Panagiotis Marantis Kostas Ramantas Christos Verikoukis Industrial Systems Institute Patras Greece Iquadrat Informatica S.L Barcelona Spain Dept. of Computer Engineering and Informatics University of Patras Patras Greece Athena Research Center Athens Greece

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Next-generation telecommunication networks provide vast amounts of Multivariate Time Series monitoring data during their operation. Using these data to automatically detect anomalous system behavior is of critical importance to operators and Internet Service Providers, in order to analyze the network state and take mitigating actions to resolve possible faults. For the output of this Anomaly Detection process to be trusted, comprehensive and accurate explanations must be provided. These explanations are essential to make critical decisions that affect the network state. In this paper, we present a framework that utilizes reconstruction-based Deep Neural Network models to perform the anomaly detection task, while also using an explainability module that provides explanations of the results. The quality of the explanations is quantified through the use of interpretation labels. In addition, we provide a comparative analysis, performing Anomaly Detection on three different open-source datasets from the mobile networking domain, while testing multiple Deep Learning models and comparing their performance. The presented results show that our framework accurately detects various types of anomalies that can be present in mobile networks, while offering the flexibility to identify and select the model that offers the best performance for each dataset, in both the Anomaly Detection and explainability tasks.

关键词： Deep learning Time series analysis Web and internet services Data models Communications technology Global communication Anomaly detection Next generation networking Monitoring Testing

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Designing and Simulating a Smart Water Harvesting System Using PLC Techniques

Designing and Simulating a Smart Water Harvesting System Usi...

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Innovation and Intelligence for informatics, Computing, and Technologies (3ICT), International Conference o

作者： Hassaan Th. H. Thabet Aqeel A. Al-Hilali Haitham B. Qasim Thabit H. Thabit Firas M. Mahmood Dept. of Medical Instruments Techniques Engineering Al-Hadba University College Mosul Iraq Dept. of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Dept. of Computer Science Al-Esraa University College Baghdad Iraq Dept. of Computer Engineering and Information Ninevah University Mosul Iraq Dept. of Informatics and Software Engineering Cihan University-Erbil Iraq

The presence of water in human life is crucial, and God has blessed humans much with its availability. This paper tries to reduce the amount of water that people use for washing in all aspects of daily life and instead utilize it to irrigate gardens and maintain hygienic facilities. The importance of water as a fundamental ingredient and the range of social and age groups that regularly take advantage of the washing process made it an ideal location for water waste during the washing process. This is why the authors choose this region to conduct their research. The monitoring and management of water usage during the washing process are simulated using PLC techniques.

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Optimal MmWave Sensor Selection for Bearing-Only Localization in Smart Environments

Optimal MmWave Sensor Selection for Bearing-Only Localizatio...

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Mediterranean Conference on Control and Automation (MED)

作者： Evangelos Vlachos Evangelos D. Spyrou Chrysostomos Stylios Kostas Berberidis Industrial Systems Institute Athena Research and Innovation Centre Greece Dept. of Informatics and Telecommunications University of Ioannina Greece Dept. of Computer Engineering and Informatics University of Patras Greece

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

Nowdays, millimeter wave (mmWave) direction sensors are being used increasingly as general-purpose radars, since they can provide high-level of accuracy for a variety of situations at low-cost. Via mutliple mmWave sensors, bearing estimation can be derived to track the position of a target, while in smart environments several sensors can be deployed. In this work, we provide an optimal sensor selection technique, for choosing which sensors to activate for bearing estimation and which not. The proposed approach is decomposed into training phase, where sensor selection is performed, and operational phase, where bearing estimation is obtained. Via simulation results we evaluate the proposed approach compared with the conventional methodology of utilizing all available data streams.

关键词： Training Sensor placement Direction-of-arrival estimation Target tracking Simulation Optimization methods Radar tracking

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Design and Simulation of an Affordable Vehicle Speed Detection System

Design and Simulation of an Affordable Vehicle Speed Detecti...

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Modeling & E-Information Research, Artificial Learning and Digital Applications (ICMERALDA), International Conference on

作者： Hassaan Th. H. Thabet Aqeel A. Al-Hilali Mohammed G. S. Al-safi Thabit H. Thabit Firas M. Mahmood Dept. of Computer Technology Engineering Al-Hadba University College Mosul Iraq Dept. of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Dept. of Accounting Al-Esraa University College Baghdad Iraq Dept. of Computer Engineering and Information Ninevah University Mosul Iraq Dept. of Informatics and Software Engineering Cihan University-Erbil Kurdistan Region Iraq

The objective of this paper is to detect the over speed of vehicles on the roads. Although maximum speed limit signs exist on the roads, many accidents keep on because of high-speed driving. It is necessary to solve these problems through automation techniques. This paper describes a speed detection system for vehicles, the traffic police can easily check the speed of vehicles by using this system. This system consists of a PLC (Programmable Logic Controller) LOGO! V8.3, two Laser sensors, an HMI (Human Machine Interface), a speed camera, and a LOGO! CMR (Communication Module Radio). The detected speed is displayed on the HMI display, moreover, if a vehicle crosses the limited speed, this system displays the over-speed on HMI, and the speed camera is activated to capture an image of that vehicle. Also, an SMS (Short Messages Service) will be sent by the CMR to the traffic authority informing them about the violation to take the necessary legal actions. The system can also store the collected data for documentation purposes. The authors describe a simple, affordable technique for monitoring vehicles' speeds in this paper.

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