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Time-Series in Hyper-parameter Initialization of Machine learning Techniques 22nd

Time-Series in Hyper-parameter Initialization of Machine Lea...

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22nd international conference on intelligent data engineering and automated learning

作者： Horvath, Tomas Mantovani, Rafael G. de Carvalho, Andre C. P. L. F. Eotvos Lorand Univ Fac Informat Pazmany Peter Setany 1-C H-1117 Budapest Hungary Fed Technol Univ Parana Campus ApucaranaRua Marano Dia 635 BR-86812460 Apucarana PR Brazil Univ Sao Paulo Inst Math & Comp Sci Ave Trabalhador Sao Carlense 400 BR-13566590 Sao Carlos SP Brazil Pavol Jozef Safarik Univ Fac Sci Inst Comp Sci Jesenna 5 Kosice 04001 Slovakia

ISBN: (纸本)9783030916077;9783030916084

Initializing the hyper-parameters (HPs) of machine learning (ML) techniques became an important step in the area of automated ML (AutoML). The main premise in HP initialization is that a HP setting that performs well for a certain dataset(s) will also be suitable for a similar dataset. Thus, evaluation of similarities of datasets based on their characteristics, named meta-features (MFs), is one of the basic tasks in meta-learning (MtL), a subfield of AutoML. Several types of MFs were developed from which those based on principal component analysis (PCA) are, despite their good descriptive characteristics and relatively easy computation, utilized only marginally. A novel approach to HP initialization combining dynamic time warping (DTW), a wellknown similarity measure for time series, with PCA MFs is proposed in this paper which does not need any further settings. Exhaustive experiments, conducted for the use-cases of HP initialization of decision trees and support vector machines show the potential of the proposed approach and encourage further investigation in this direction.

关键词： automated ML Metalearning PCA DTW

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Persuasive System Design for Climate Change Awareness 22nd

Persuasive System Design for Climate Change Awareness

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22nd IFIP TC 14 international conference on Entertainment Computing (ICEC)

作者： Adib, Ashfaq Orji, Rita Dalhousie Univ Halifax NS B3H 4R2 Canada

ISBN: (纸本)9789819982479;9789819982486

We have been experiencing warmer years than before especially during the last decade, which has raised concerns among researchers of all discipline about climate change. In our attempt to raise awareness and promote positive behavior change to mitigate climate change, we designed an intervention consisting of three design paradigms that are not commonly used together in the area of climate change: data visualization, interactive narrative, and persuasive system design. By conducting a user study with 100 participants, followed by interview of 20 participants, we found evidence showing that all the persuasive strategies implemented in our system were significantly persuasive. Moreover, our qualitative analysis provided insight on the effectiveness of the strategies, such as, Customization, Simulation, Social learning, Social Comparison and Cooperation in increasing likeness towards the system, and motivating positive behavior change about climate change by providing a sense of togetherness among the participants. Based on our findings, we offer design recommendations for implementing persuasive strategies in such systems to increase their effectiveness.

关键词： Climate Change Interactive Narrative data Visualization Persuasive System Design

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A Comparative Study on COSMIC FP Approximation with Deep learning and Conventional Machine learning

A Comparative Study on COSMIC FP Approximation with Deep Lea...

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international conference on Software engineering Research, Management and Applications (SERA)

作者： Kaoru Yokogawa Masashi Hiroishi Sousuke Arnasaki Hirohisa Aman Tomoyuki Yokogawa Department of Systems Engineering Okayama Prefectural University Soja Japan Center for Information Technology Ehime University Matsuyama Japan

ISBN: (数字)9798350391343

ISBN: (纸本)9798350391350

CONTEXT: Functional size measurement (FSM) is a basis of project planning. FSM methods usually need experts and well-specified requirements. To relax these constraints, automated size approximation models have been developed. A recent study proposed a deep learning based model named DEEP-COSMIC-UC for COSMIC FP approximation based on use cases. Performance comparison in that study did not consider conventional machine learning techniques and contextualized word embeddings. OBJECTIVE: To evaluate the effectiveness of the deep learning based COSMIC approximation. METHOD: Empirical experiments were conducted with actual project data. Random Forests and Support Vector Regression were used as representative conventional machine learning techniques. BERT-based model was used as a representative contextualized word embedding model. RESULTS: No clear superiority of DEEP-COSMIC-UC was observed. CONCLUSION: Simple off-the-shelf models are enough at least for the dataset we used.

关键词： Deep learning Support vector machines Size measurement Planning Random forests Context modeling Software engineering

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EEG Signal Classification using Deep Transfer learning Technique in an Internet of Medical Things Environment 2

EEG Signal Classification using Deep Transfer Learning Techn...

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2nd IEEE international conference on Advanced Technologies in intelligent Control, Environment, Computing and Communication engineering, ICATIECE 2022

作者： Saxena, Pulkit Raj Chauhan, Deepika Teerthanker Mahaveer University Department of Computing Science and It Moradabad India Jaipur National University Department of Engineering and Technology Jaipur India

ISBN: (纸本)9781665493963

Successful categorization of epileptic electroencephalogram (EEG) signals is a crucial part in making a correct diagnosis of epilepsy. The findings of an electroencephalogram (EEG) test are also used in this categorization to assess whether or not the EEG signal represents an epileptic signal (healthy). There have been many suggested automated algorithms to help neurologists with the classification of these signals. Although these methods have often resulted in high average accuracy during classification, there is still room for advancement in terms of performance. We thus present a multi-segment EEG-based convolutional neural network for epilepsy-related EEG data classification. To train and test the neural network convolutional thoroughly, the spectrogram picture is fed into it. Each EEG signal segment on each test channel is first classified using the convolutional neural network model based on the training results, and then the results are combined in an ensemble for a final classification. © 2022 IEEE.

关键词： Convolutional neural networks

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Benchmarking Large Language Models for Multi-agent Systems: A Comparative Analysis of AutoGen, CrewAI, and TaskWeaver 22nd

Benchmarking Large Language Models for Multi-agent Systems: ...

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22nd international conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2024

作者： Barbarroxa, Rafael Gomes, Luis Vale, Zita GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI – Intelligent Systems Associate Laboratory Polytechnic of Porto R. Dr. António Bernardino de Almeida 431 Porto4249-015 Portugal

ISBN: (纸本)9783031704147

This paper presents the benchmarking of three multi-agent systems powered by large language models. The paper presents a comparative analysis of AutoGen, CrewAI, and TaskWeaver. Nowadays, large language models have emerged as powerful tools able to assist users in various areas. The integration of large language models into multi-agent systems increases their potential for collaborative problem-solving. This study focuses on a case study involving a machine learning code generation task which is used to evaluate the framework’s performance. To assess the performance of the solutions, it is requested to create energy forecasting models using the same dataset as the base. After producing the code, a new dataset is used to test the model performance using the root mean square error. The three solutions were able to provide results using multiple large language models. The best result was achieved by TaskWeaver using GPT-3.5, with an error of 25.04. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multi agent systems

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Facial Expression Recognition based on ArcFace Features and TinySiamese Network 22

Facial Expression Recognition based on ArcFace Features and ...

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22nd international conference on Cyberworlds, CW 2023

作者： Altaha, Mohammed A. Jarraya, Islem Hamdani, Tarek M. Alimi, Adel M. Sfax Tunisia Sfax Tunisia University of Monastir Tunisia University of Johannesburg Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment South Africa

ISBN: (纸本)9798350315653

Facial Expressions Recognition (FER) has become an active area of research. To accomplish emotion recognition, several of machine learning algorithms have been employed. However, these models require a significant amount of data, a large training memory and an important time for training. This paper presents a Facial Expression Recognition method based on ArcFace features and TinySiamese network to solve these problems. The proposed method consists of two parts: the first part is for feature extraction and the second is for feature recognition. In the first part, a feature extractor was used to extract features from the embedded image. The feature extractor was based on a ResNet-50 network and ArcFace loss function. In the second part, the TinySiamese network was used for feature classification and verification. The proposed method was evaluated on three popular datasets: FER2013, RAF-DS, and ExpW. The experimental part shows that the proposed method achieved competitive results compared to related works with classification rates equal to 60.43%, 85.14%, 65.13% and verification rates equal to 73.31%, 84.29%, 76.08% on the FER2013, RAF-DS and ExpW datasets respectively. © 2023 IEEE.

关键词： Emotion Recognition

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Ensemble Synthetic Oversampling with Manhattan Distance for Unbalanced Hyperspectral data 22nd

Ensemble Synthetic Oversampling with Manhattan Distance for ...

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22nd international conference on intelligent data engineering and automated learning

作者： Miftahushudur, Tajul Grieve, Bruce Yin, Hujun Univ Manchester Dept Elect & Elect Engn Manchester M13 9PL Lancs England Indonesian Inst Sci LIPI Res Ctr Elect & Telecommun Bandung Indonesia

ISBN: (纸本)9783030916077;9783030916084

Hyperspectral imaging is a spectroscopic imaging technique that can cover a broad range of electromagnetic wavelengths and subdivide those into spectral bands. As a consequence, it may distinguish specific features more effectively than conventional colour cameras. This technology has been increasingly used in agriculture for various applications such as crop leaf area index, plant classification and disease monitoring. However, the abundance of information in hyperspectral imagery may cause high dimensionality problem, leading to computational complexity and storage issues. Furthermore, data availability is another major issue. In agriculture application, typically, it is difficult to collect equal number of samples as some classes or diseases are rare while others are abundant and easy to collect. This may give rise to an imbalanced data problem that can severely reduce machine learning performance and introduce bias in performance measurement. In this paper, an oversampling method is proposed based on Safe-Level synthetic minority oversampling technique (Safe-Level SMOTE), which is modified in terms of its k-nearest neighbours (KNN) function to make it fit better with high dimensional data. Using convolutional neural networks (CNN) as the classifier combined with ensemble bagging with differentiated sampling rate (DSR), the approach demonstrates better performances than the other state-of-the-art methods in handling imbalance situations.

关键词： Imbalanced data Hyperspectral imaging Plant analysis Safe-level SMOTE CNN Ensemble

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Towards Detecting and Quantifying Identity-Based Polarization in Online Content: A Deep-learning Approach 22

Towards Detecting and Quantifying Identity-Based Polarizatio...

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22nd IEEE/WIC international conference on Web Intelligence and intelligent Agent Technology (WI-IAT)

作者： Thota, Hima Moh, Melody Moh, Teng-Sheng Harker Sch San Jose CA 95129 USA San Jose State Univ Comp Sci Dept San Jose CA USA

ISBN: (纸本)9798350309188

Identity-based polarization is prevalent across many news outlets, and yet goes unnoticed by most readers. This separate form of polarization differs greatly from the typical ideology-based polarization, which is seen when two separate political parties solely disagree due to a policy difference. In this paper, we focus on a distinct kind of polarization that we call identity-based polarization. This is the act of refusing to focus on differences in ideology and instead focusing on individual or group identities such as race, sexuality and gender to form extreme opinions. The never-ending stream of online content through social media and news outlets has posed significant challenges to accurately understand, detect, and quantify identity-based polarization. If one cannot quantify, one cannot manage, and the health of our society, especially for the younger generation, suffers greatly. In the past, research has been done in detecting polarization in areas such as political bias, but such studies solely relied on sentiment analysis. In this paper, we take a vastly different approach as we first implement an entity-recognition system to detect identities in the article, and then as a follow up, we attempt to attribute polarization to the recognized entities using sentiment analysis. In addition, this paper leverages BERT (Bidirectional Encoder Representations from Transformers) and NLP (Natural Language Processing) techniques through the combination of sentiment analysis and a customized NER (Named-Entity-Recognition) system. In this way, this paper takes a novel and more scientific approach to accurately identifying and quantifying identity-based polarization. By utilizing news article data from five major news outlets, the final developed NER model achieved an F1 score of over 83%, and we uncovered a strong correlation between identity density in texts and extreme sentiment expressed in the text, with the correlation ranging from +0.48 to +0.68. This work is a first

关键词： BERT NLP identity-based polarization sentiment analysis correlation media bias news articles

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learning-Based Optimization of the Exchange Price in a Heterogeneous Market

Learning-Based Optimization of the Exchange Price in a Heter...

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international conference on intelligent Systems Applications to Power Systems (ISAP)

作者： Guénolé Chérot Roman Le Goff Latimier Benjamin Cajna Hamid Ben Ahmed SATIE UMR CNRS 8029 UniR – ENS Rennes Rennes France

ISBN: (数字)9798331531751

ISBN: (纸本)9798331531768

In coming years, energy systems are likely to be organized as a heterogeneous system, where independent energy communities coexist with a main market. The price of electricity exchanges between communities and the main system should then reflect not only production costs, but also network constraints, potential distribution congestion and incentives towards local flexibilities. This price would necessarily be local, and setting it optimaly would require an all-knowing operator. The present contribution aims to investigate the potential of reinforcement learning to predict this exchange price. A minimalist case study is introduced to improve the interpretability and generalizability of the results obtained. In particular, learning speeds will be studied in order to discuss the volume of data required to guarantee a given level of performance. The transfer of trained algorithms from one case study to another will also be discussed.

关键词： Electric potential Costs Electricity Reinforcement learning Production Prediction algorithms Power systems intelligent systems Optimization

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Reinforcement learning-Enhanced Active Disturbance Rejection Control for Mobile Robot Trajectory Tracking

Reinforcement Learning-Enhanced Active Disturbance Rejection...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： H. Usama M. Nasir R. Fareh J. Ghommam S. Khadraoui M. Bettayeb Research Institute of Sciences and Engineering University of Sharjah Sharjah UAE Department of Electrical Engineering University of Sharjah Sharjah UAE Department of Electrical and Computer Engineering Sultan Qaboos University Oman Control and Energy Management Laboratory (CEM Lab) Ecole National d'Ingenieur de Sfax Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

In recent years, the application of robotics has significantly advanced many fields, providing robust performance and efficiency in complex tasks without the need for human intervention. Robot control is a key area of robotics that has received a lot of interest and technological development. Active Disturbance Rejection Controllers (ADRCs) are widely adopted due to several features, including their ability to maintain robust performance and effectively reject disturbances. The manual tuning of controller parameters is time-consuming and highlights the need for automation. This paper presents an innovative approach to tune the parameters of ADRC using Reinforcement learning (RL) based on Deep Deterministic Policy Gradient (DDPG) for a Differential Drive Mobile Robot (DDMR). The RL agent learns the ideal parameters of the ADRC through real-time, iterative interactions with the simulated environment, improving ADRC's performance without manual tuning. Simulation results demonstrate the effectiveness of the proposed idea in improving the trajectory tracking performance. Combining RL and ADRC provides a promising automated controller tuning solution, opening the door to more intelligent and adaptive robotic systems. 10.0pt.

关键词： Robust control Trajectory tracking Simulation Reinforcement learning Manuals Scattering parameters Mobile robots Artificial intelligence State estimation Tuning

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