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Prompt-Based Approach for Czech Sentiment Analysis

Prompt-Based Approach for Czech Sentiment Analysis

引用

2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023

作者： Šmíd, Jakub Přibáň, Pavel Department of Computer Science and Engineering University of West Bohemia Faculty of Applied Sciences Czech Republic NTIS - New Technologies for the Information Society University of West Bohemia Faculty of Applied Sciences Czech Republic

ISBN: (纸本)9789544520922

This paper introduces the first prompt-based methods for aspect-based sentiment analysis and sentiment classification in Czech. We employ the sequence-to-sequence models to solve the aspect-based tasks simultaneously and demonstrate the superiority of our promptbased approach over traditional fine-tuning. In addition, we conduct zero-shot and few-shot learning experiments for sentiment classification and show that prompting yields significantly better results with limited training examples compared to traditional fine-tuning. We also demonstrate that pre-training on data from the target domain can lead to significant improvements in a zero-shot scenario. © 2023 Incoma Ltd. All rights reserved.

关键词： Sentiment analysis

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Detecting Parkinson's Disease using High-Dimensional Feature-Based Support Vector Regression

Detecting Parkinson's Disease using High-Dimensional Feature...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence systems, IACIS 2024

作者： Aarthi, E. Jagadeesh, S. Lingineni, Sree Lakshmi Dharani, N.P. Pushparaj, T. Satyanarayana, A.N. Srm Institute of Science and Technology Faculty of Science and Humanities Department of Computer Science Kattankulathur India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Cse Chennai India R&d Institute of Science and Technology Vel Tech Rangarajan Dr Sagunthala Department of Mathematics Chennai India Department of Ece Tirupati India The American College Department of Information Technology Madurai India Sreenidhi Institute of Science and Technology Department of Ece Hyderabad India

ISBN: (纸本)9798350360660

Parkinson's disease (PD) is a progressive neurological disorder that significantly impacts patients' quality of life. Accurate and early detection of PD is crucial for effective management and treatment. A study proposes a High-Dimensional Feature-Based Support Vector Regression (HDFSVR) method for enhancing the accuracy of PD detection. This method integrates advanced normalization and feature selection techniques to improve diagnostic accuracy. Sensitivity analysis shows a prediction accuracy of 83%, while precision analysis indicates an accuracy of 87%. Recall analysis further highlights the method's efficacy with a prediction accuracy of 92.78%. The HDFSVR method achieves an accuracy of 94.78%, outperforming contemporary approaches such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Adaptive Genetic Weighted Optimizer (AGWO). It represents a substantial advancement in PD diagnostics, addressing the limitations of traditional support vector machines in handling high-dimensional data. The method can be seamlessly adopted in clinical settings, enhancing early and accurate detection of Parkinson's disease. © 2024 IEEE.

关键词： Support vector regression

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Performance LoRa Technology for Autonomous Vehicles 24

Performance LoRa Technology for Autonomous Vehicles

引用

24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

作者： Adi, Puput Dani Prasetyo Purnama, Iwan Siregar, Arfanda Anugrah Juledi, Angga Putra Edi, Firman Karim, Abdul Wahyu, Yuyu Maulana, Fairuz Iqbal Susilo, Suhardi Atmoko Budi Toni Harahap, Ilham Maliki Patria, Lintang Bandung Indonesia Labuhanbatu University Faculty of Science and Technology North Sumatra Labuhanbatu Indonesia Politeknik Negeri Medan Telecommunication Engineering Study Program North Sumatra Medan City Indonesia Batam Institute of Technology Faculty of Industrial Technology Department of Engineering Management Riau Islands Batam City Indonesia Budi Darma University Department of Information Technology North Sumatra Medan Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia University Labuhanbatu Faculty of Teacher Training and Education North Sumatra Labuhanbatu Indonesia Universitas Terbuka Faculty of Engineering Jakarta Indonesia

ISBN: (纸本)9798350313956

This research is an approach to intelligent vehicles with a LoRa communication system, LoRaWAN compatible for Long-Range and Outdoor Communication, but in this paper, we will test the ability of LoRa to handle autonomous vehicles, a Blind-Adaptive Data method will be tried on vehicles systems. The system offered is when the vehicle is running, the sensor will send position data in real-time, the number of vehicles is four, and GPS data is sent alternately using the Blind Adaptive Data Rate principle to avoid data collisions, and when the GPS shows a certain position, the value of SF is ascertained, which affects the data rate and ToA, and the output given is RSSI on each car, speed of a car, power consumption of sensor nodes, signal power, SNR, Bit rate or data rate, and sensitivity. With the Blind ADR principle, it is ensured that there is no data collision between car position data that causes packet loss. © 2023 IEEE.

关键词： Autonomous vehicles

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Improving Software Defects Detection: An In-Depth Analysis of Machine Learning Methods and Static Analysis Tools for Greater Accuracy

Improving Software Defects Detection: An In-Depth Analysis o...

引用

Machine Learning and Autonomous systems (ICMLAS), International Conference on

作者： Veer Bobade Chetan Puri Department of Artificial Intelligence and Machine Learning Faculty of Engineering And Technology DMIHER Wardha Maharashtra India Department of Computer Science and Engineering Faculty of Engineering And Technology DMIHER Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Enhancing software quality and reducing testing expenses requires better software fault detection. To guarantee the dependability and usability of software, Software Defect Prediction (SDP) uses machine learning approaches to anticipate and address any problems early in the development cycle. Numerous machine learning techniques, such as Support Vector Machines (SVM), Random Forest (RF), Logistic Regression, Naïve Bayes, Multilayer Perceptron (MLP), Decision Stump, J48, Lazy IBK, and ZeroR, are thoroughly examined in this work. With an emphasis on feature selection as a crucial step in increasing prediction accuracy and lower computing costs, these approaches are assessed for their capacity to detect software flaws precisely. This research study demonstrates how these models may improve fault identification and prevention procedures using historical records from sources such as the PROMISE repository. The study highlights the best practices for machine learning-based software quality assurance, lowering expenses while guaranteeing superior results.

关键词： Support vector machines Accuracy Machine learning algorithms Software algorithms Machine learning Software quality Multilayer perceptrons Feature extraction Prediction algorithms Random forests

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Leveraging Artificial Intelligence in Online Educational Platforms to Enhance Continuous Professional Development in the Teaching Profession 2

Leveraging Artificial Intelligence in Online Educational Pla...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Amankulovich, Khujakulov Sherzod Tokhirovich, Mannabov Javlon Bakhridinovich, Kadirov Tulkin Kuvandikovich, Taylakov Umid Ananth, Christo Kumar, T. Ananth Samarkand Regional National Center for Training Teachers in New Methods Uzbekistan Uzbekistan Samarkand Agroinnovations and Research University Uzbekistan Samarkand State University Uzbekistan&Adjunct Faculty Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Chennai India Ifet College of Engineering Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350364699

To keep up with the dynamic nature of the modern classroom, teachers must have access to online learning platforms for professional development purposes. This investigation looks at how these platforms can be used to improve teachers' skills, make information sharing easier, and open doors to CPD opportunities. This study examines the relationship between online resources and methods to demonstrate how digital tools have an impact on teacher training and professional development by leveraging AI. It also draws attention to the fact that these platforms are helping with information-methodological support for teachers as well as the ongoing reforms in our nation. Incorporating empirical observations and case studies, the analysis demonstrates how effective online platforms are in fostering a collaborative and innovative environment for professional learning. Digital transformation has the potential to radically alter how teachers obtain professional development, and this study emphasizes that fact. This will enable teachers to effectively address the demands of modern classrooms. © 2024 IEEE.

关键词： Contrastive Learning

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Direct Metric Optimization for Imbalanced Classification 11

Direct Metric Optimization for Imbalanced Classification

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11th IEEE International Conference on Healthcare Informatics, ICHI 2023

作者： Travadi, Yash Peng, Le Cui, Ying Sun, Ju University of Minnesota School of Statistics MinneapolisMN55455 United States University of Minnesota Department of Computer Science & Engineering MinneapolisMN55455 United States University of Minnesota Department of Industrial and Systems Engineering MinneapolisMN55455 United States

ISBN: (纸本)9798350302639

Imbalanced classification (IC) involves training models in datasets with uneven label distribution. It has been widely stuided [1] , [2] , with various applications such as object detection [3] , [4] , image retrieval [5] , [6] , and image segmentation [7] , [8] , [9]. In clinical research, data imbalance is also a prominent issue, where negative control usually significantly outweighs positive cases due to the innate low prevalence rate of certain diseases. Especially for tasks related to rare diseases (e.g., amyotrophic lateral sclerosis, where the prevalence rate can be as low as 0.001% [10] ), the imbalance problem is severe and almost inevitable. © 2023 IEEE.

关键词： Clinical research

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Multi-Representation Convolutional Neural Network to Recognize Human Emotion 8

Multi-Representation Convolutional Neural Network to Recogni...

引用

8th International Conference on Information Technology and Digital Applications, ICITDA 2023

作者： Prabowo, Dwi Wahyu Setiawan, Noor Akhmad Debayle, Johan Nugroho, Hanung Adi Universitas Gadjah Mada Faculty of Engineering Department of Electrical and Information Engineering Yogyakarta Indonesia Universitas Darwan Ali Faculty of Computer Science Department of Information System Sampit Indonesia Centre Spin Mines Saint-Etienne Cnrs Umr 5307 Lgf Saint-Etienne France

ISBN: (纸本)9798350344691

Emotion recognition using brain-computer interfaces (BCIs) is an emerging field with the ongoing challenge of developing robust and efficient classification methods. This study introduces an original approach, the multi-representation convolutional neural network (MrCNN), designed to exploit the complementary features of recurrence plots (RP) and spectrograms. The efficacy of this new method was empirically evaluated and compared with the standalone RP and spectrogram methods in emotion classification tasks using electroencephalogram (EEG) signals. Two widely used datasets, DEAP and SEED, were used as the experimental platforms for this study. Employing a multifaceted analytical approach involving descriptive statistics, data visualization, and hypothesis tests, this study revealed that MrCNN significantly outperforms the standalone RP and spectrogram methods in terms of key performance metrics. Specifically, MrCNN achieved an accuracy of 0.9663, sensitivity of 0.9848, specificity of 0.9825, kappa score of 0.9511, and F1 score of 0.9656 for the DEAP dataset. For the SEED dataset, the accuracy was 0.9695, with sensitivity, specificity, kappa scores, and F1 score of 0.9844, 0.9842, 0.9543, and 0.9695, respectively. These results were statistically validated, and a comparative analysis with existing literature substantiated the robustness and superiority of the MrCNN method. This study sets new performance benchmarks and offers a reliable and effective approach for emotion classification using EEG signals. © 2023 IEEE.

关键词： Emotion Recognition

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On the Approximation of the Quotient of Two Gaussian Densities for Multiple-Model Smoothing 26

On the Approximation of the Quotient of Two Gaussian Densiti...

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26th International Conference on Information Fusion, FUSION 2023

作者： Liu, Yi Li, Xi Yang, Le Mihaylova, Lyudmila Xue, Yanbo University of Canterbury Department of Electrical and Computer Engineering Christchurch New Zealand National University of Defense Technology State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems Changsha China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Career Science Lab Beijing China

ISBN: (纸本)9798890344854

The quotient of two multivariate Gaussian densities can be written as an unnormalized Gaussian density, which has been applied in some recently developed multiple-model fixed-interval smoothing algorithms. However, this expression is invalid if instead of being positive definite, the covariance of the unnormalized Gaussian density is indefinite (i.e., it has both positive and negative eigenvalues) or undefined (i.e., computing it requires inverting a singular matrix). This paper considers approximating the quotient of two Gaussian densities in this case using two different approaches to mitigate the caused numerical problems. The first approach directly replaces the indefinite covariance of the unnormalized Gaussian density with a positive definite matrix nearest to it. The second approach computes the approximation through solving, using the natural gradient, an optimization problem with a Kullback-Leibler divergence-based cost function. This paper illustrates the application of the theoretical results by incorporating them into an existing smoothing method for jump Markov systems and utilizing the obtained smoothers to track a maneuvering target. © 2023 International Society of Information Fusion.

关键词： Cost functions

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Improving Software Defects Detection: An In-Depth Analysis of Machine Learning Methods and Static Analysis Tools for Greater Accuracy 2

Improving Software Defects Detection: An In-Depth Analysis o...

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Bobade, Veer Puri, Chetan DMIHER Faculty of Engineering And Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India DMIHER Faculty of Engineering And Technology Department of Computer Science and Engineering Maharashtra Wardha India

ISBN: (纸本)9798331505745

关键词： Support vector machines

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Sleep Monitoring systems based on Edge Computing and Microservices Caching

Sleep Monitoring Systems based on Edge Computing and Microse...

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Artificial Intelligence of Things (AIoT), IEEE Annual Congress on

作者： Nico Surantha David Jayaatmaja Sani Muhamad Isa Faculty of Science and Engineering Department of Electrical Electronics and Communication Engineering Tokyo City University Tokyo Japan Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350392296

ISBN: (纸本)9798350392302

The rapid development of Internet of Things (IoT) technology has enabled the widespread deployment of health monitoring systems. Traditionally, the health monitoring system has been limited by centralized processing and storage in the cloud, leading to latency issues and potential data loss. This paper introduces a smart sleep monitoring system based on edge computing, utilizing microservices architecture and caching techniques. The proposed system employs edge computing to enable data processing closer to the source, reducing latency and improving real-time monitoring capabilities. Caching is employed to reduce database load and optimize random access memory (RAM) usage. This research addresses latency and response time challenges on IoT health monitoring platforms in environments with poor network quality while optimizing database load and resource usage on Jetson Nano as the edge computing device. Using Electrocardiogram (ECG) data as input, the proposed system yields impressive performance metrics. The research results indicate that the proposed system can increase throughput by 26.92 KB/s, reduce response time by 18.8 ms, and decrease latency by 20.86 ms compared to the previous work. Message Queuing Telemetry Transport (MQTT) integration reduces CPU usage by approximately 40% and RAM usage by about 81.24%.

关键词： Cloud computing Databases Random access memory Microservice architectures Real-time systems Nanoscale devices Internet of Things

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