Reviewerly/exHarmony huggingface dataset:
Gaussian Processes with State-Dependent Noise for Stochastic Control

This paper considers a stochastic control framework, in which the residual model uncertainty of a dynamical system is learned using a Gaussian Process (GP). In the proposed formulation, the residual model uncertainty consists of a nonlinear function and state-dependent noise. The proposed formulation uses a posterior-GP to approximate the residual model uncertainty and a prior-GP to account for state-dependent noise. The two GPs are interdependent and are thus learned jointly using an iterative algorithm. Theoretical properties of the iterative algorithm are established. Advantages of the state-dependent formulation include (i) faster convergence of the GP estimate to the unknown function as the GP learns which data samples are more trustworthy and (ii) an accurate estimate of state-dependent noise, which can, e.g., be useful for a controller or decision-maker to determine the uncertainty of an action. Simulation studies highlight these two advantages.
Applying Map Filters and Inversion Model for Gravity Data in Matlab-based Application

Abstract In general, there are two types of processes using the gravity method, that is the basic processes and the advanced processes. The basic process includes Gravity Meter reading conversion until the gravity corrections process. While the advanced process is a process to put an edge on the geological appearance of the research area that is already in the form of a map. The advanced process includes map filtering and inversion modeling. This advanced processing of gravity data is very useful, one of which is to separate regional anomalies and local anomalies, while inversion modeling is useful for knowing the approximate location of the anomaly depth. What will be applied to the software is advanced processing, because in its application this processing is required in every geophysical research and requires data processing support tools. However, for now, gravity data processing software, especially for the filtering and data modeling process is available as a licensed software. Where not everyone can access it because of limited costs and those who are still in the learning stage in lectures need practicing to process the geophysical data. The purpose of implementing this filtering process and inversion modeling is to provide access to gravity data processing for free and open source so that it can make it easier for students to process the data while practicing in the field, and in the future this software can be the basis for learning and development in the field of geophysical computing. This research was made using a descriptive method with a complete step and explanation of the creating and applying of the geophysical data processing into a software, so the detail processes inside it can be known. A literature study was also conducted to determine the basic formula that will be used as a reference in calculating gravity data in order to obtain the expected processing and modeling results. After that, the final step is to apply all of the components in a program to create a software based on MATLAB for the final result of this research. This software will containing map filtering and inverse modeling for gravity data which can be used for data processing.
Motion: A Mobile Application for Yoga Pose Accuracy and Consistency

Pre-recorded yoga tutorials have become more prevalent for the benefits it offers. The drawback of learning yoga using these tutorials is that practitioners do not receive feedback on their execution, which could lead to severe injuries. To eliminate this problem, software applications identify and correct improper executions; however, they do not provide a criteria-based evaluation. To address this issue, this study presents Motion, a mobile application that evaluates a yoga practitioner’s pose accuracy and consistency in real-time and provides a performance history of previous executions. The application utilizes MediaPipe Pose and ML Kit to perform Human Pose Estimation and Machine Learning to track and identify yoga execution. Angle data of essential body parts for each frame of a practitioner’s execution are stored and used to evaluate pose accuracy and consistency. The evaluation results are then stored in an SQLite database and displayed in the performance history. The application’s pose classifier achieved a 99.33% accuracy, demonstrating its effectiveness in the real-time classification of yoga poses, and achieved an average modified PSSUQ score of 1.99, indicating high usability. With these implications and Motion’s ability to provide real-time evaluation and progress tracking, the application can be used as a valuable tool in the fields of yoga, health, and education.
Comparing Deep Learning and Genetic Algorithms Techniques for Age and Gender Classification

Age and gender demographic attributes represent key components which are the basis of a wide range of practical applications including: biometric security and authentication, medical applications or facial recognition. Therefore, in this paper we want to compare the efficiency of both classical Deep Learning (DL) and Genetic Algorithms (GA) approaches to train a model for age and gender demographic attribute using the UTK-Face dataset. In addition, a preprocessing algorithm that approaches unbalanced labels is presented. We obtained competitive results in terms of overall accuracy with 89% for the DL approach as well as 82% for the GA approach.
Exploring the implementation of LSTM inference on FPGA

Long Short-Term Memory (LSTM) is a widely used deep neural network architecture in sequence modeling and temporal series forecasting. Although the main advantages that LSTM provides in terms of predictive capacity, it is well known in the literature that its performance in inference times and resource consumption is very high, so its application to low-resource environments is an issue. In this article, the performance of a High-Level Synthesis (HLS) implementation of the LSTM inference algorithm is analyzed. Two different LSTM implementations, decoupled-LSTM (d-LSTM) and coupled-LSTM (c-LSTM) are compared on the ZCU104 evaluation board, which contains a Xilinx <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Zynq <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> UltraScale+ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">™</sup> MPSoC device, and their performance and resource usage are evaluated for different neural network models with varying complexity. The results show that d-LSTM performs better than c-LSTM in terms of inference time, but is more resource-intensive. Furthermore, the FPGA implementation of LSTM outperforms other common devices like GPUs and CPUs. The scalability of d-LSTM concerning sequence length is also explored. A positive linear behavior has been observed in the two lighter models while in the heavier one, the growth is higher than linear. The study highlights the advantages of using FPGA for implementing LSTM inference algorithms and the importance of exploiting parallelism through libraries like the Basic Linear Algebra Subprograms for optimizing performance.
Analysis of the energy production performance of the Solar Photovoltaic System installed at ISEL

In recent years, the use of renewable sources to obtain electricity has been a big bet on the part of users of consumption installations, allowing greater independence and decentralization of consumption from the network. This paper aimed to analyse the performance of the photovoltaic solar production installation on the ISEL campus which came into operation since January of 2020. With the data taken from the system, several energy production and consumption scenarios were characterized throughout the year, as well as the calculation of the percentage of production and energy from the network in relation to monthly and annual consumption for 2020 and 2021. The functioning of the implemented system was simulated, using the PVsyst software, and an economic analysis was carried out for a solution with the optimization of the angle of inclination of the photovoltaic modules. With the analysis of the obtained results, it was verified a variation of percentage deviation in the less production in the order of 20% of the simulated system for the implemented system. With the implementation of the solution for the optimization the angles of inclination of the photovoltaic panels, there would be an additional gain in annual energy production of 4.5%, taking 11 years to recover the investment.
AIDETECT - AI-based Integratable Detection for Beyond 5G Networks

The integration of Artificial Intelligence (AI) technologies has revolutionized wireless communication in Multiple Input Multiple Output (MIMO) systems. However, signal detection remains a challenging task, and traditional algorithms have shown limited success in Massive MIMO communication systems. To address this, a lightweight AI-based integratable detection scheme called AIDETECT has been proposed in this paper. The AIDETECT model is trained and optimized to achieve superior performance in realistic MIMO and Massive MIMO use case scenarios, and extensive simulations have been conducted to evaluate its effectiveness of AIDETECT. The results of this evaluation demonstrate that the proposed AIDETECT scheme outperforms conventional detectors in terms of Symbol Error Rate (SER) for a certain range of SNR. Additionally, this study provides a detailed analysis of the optimal number of deep neural network hidden size layers for different SNR ranges, providing valuable insight into optimizing the performance of lightweight AI models for MIMO and Massive MIMO systems. This paper underscores the potential of deep learning techniques in addressing signal detection challenges and informs future research in this field.
A Trademarks Application, Payment, Objection, Registration and Reporting System Using Blockchain Smart Contract

The global economy is undergoing a remarkable transformation due to the witnessed scientific revolution, where new products and services are introduced. Consequently, a knowledge-based world economy emerged with Intellectual Property at its core. One of the substantial types of intellectual property is Trademark. It is a valuable intangible asset that opens doors for a wide span of opportunities that promote innovation and create jobs. Trademarks are tremendously important for countries’ economic development and global competitiveness. Therefore, effective trademark protection is a crucial issue. Countries set strong legal measures to protect trademark ownership and registration rights. Unfortunately, present trademark management systems have several problems including centralizations, lengthy procedures, amount of paperwork, cost and lack of secured authentication method. To overcome these shortcomings and develop an enhanced business model that is consistent with the level of importance of the trademark industry, trademark registration systems need to be significantly improved using the most advanced technologies. Blockchain is considered an ideal solution to address trademark management system issues and support efficient trademark management operations due to its unique characteristics of authenticity, traceability, decentralization and trust. This research developed a blockchain system that supports new trademark application submission, objection submission and decision, cryptocurrency payment, automated final registration and announcement and reporting processes. The deployment and testing of our Ethereum smart contract were completed successfully and demonstrated accurate and effective performance of system operations.
Face Recognition Method for Online Exams

Biometric systems have advanced greatly in this field of technology and can be used to many other uses. It is common practice to employ biometric systems for authentication, which involves identifying and analyzing unique human traits including fingerprints, retina, voice patterns, facial patterns, and more. Facial recognition technology is also being developed and used for a wide range of purposes, such as in security and attendance systems. In addition, any system for monitoring and recording people's physical presence during a certain task is a recurrent financial outlay. The presence of pupils is an integral aspect of a valid evaluation of instruction, making the attendance system crucial in the educational sector. The goal of this study is to create a proof-of-concept application using the Eigenface method for identifying absent students from live online exams.
Python-based Simulation and Analysis of Electrical Circuits through Transfer Functions

The objective of this article is to progressively instruct and present techniques for manipulating electrical circuit control systems through their transfer functions. This includes the manipulation of various magnitudes within these systems, such as voltage, current, inductance, and load, as well as their components. These components can be passive, such as resistors, inductors, and capacitors, or active, such as operational amplifiers. By manipulating control systems, circuits can be designed to exhibit specific desired characteristics, such as voltage or power stability. This is achieved through the integration of electrical modules, which can be interconnected to act as a block diagram, ultimately resulting in a balanced output voltage or power.
The Impact of Missing Data Handling Techniques on the Accuracy of COVID-19 Mortality Prediction

COVID-19, a novel disease, had introduced a new problem in the healthcare industry, where due to the lack of prior experience with the disease, healthcare professionals were unable to accurately estimate the Mortality Chance (MC) of a patient, thus challenging their ability to prioritize treatments and resources based on the urgency of a case. Enhancing the accuracy of Machine Learning (ML) models in predicting MC and using better predictions is a solution to the problem. The dataset used in this paper had missing values in 543444 data points (approximately 95.9% of the dataset), rendering most of the samples unusable unless treated using Missing Data Handling (MDH) techniques. Several MDH techniques were used in this paper to enhance the accuracy of ML models. These techniques were tested by creating an optimized ML Pipeline containing Dimensionality Reduction techniques, Oversampling methods, and various ML algorithms. The different MDH techniques were compared using Quantitative Analysis through Receiver Operating Characteristic (ROC) curves, Area Under ROC curve (AUROC) values, Accuracy scores and p-values obtained from Student’s T-Test and DeLong’s Test. This paper also compares the top two performing models using Shapley Additive Explanations (SHAP) to understand feature importance.
Exploring the Role of Extracted Features in Deep Learning-based 3D Face Reconstruction from Single 2D Images

In 3D face reconstruction, features extracted from a single image are crucial as they provide extra information beyond the image’s size and quality. These features, including facial landmarks and face parsing, texture and UV maps, depth maps, shading information, and albedo maps, can be used to compensate for the lack of prior knowledge provided by a single 2D image and to overcome the dimensional differences between 2D and 3D. By using these features, 3D face reconstruction neural networks can create more detailed and accurate 3D models of faces, even when the input image is of low quality or has extreme poses or occlusions. The paper investigates the definition, importance, extraction, and various applications of each feature, including their potential use in 3D face reconstruction and facial recognition. The paper will provide an analysis of the current state of the art in this field and discuss the potential for future research and development.
Smart Yoga Assistant: SVM-based Real-time Pose Detection and Correction System

SVM-based Real-time Pose Detection and Correction System refer to a computer system that uses machine learning techniques to detect and correct a person's yoga pose in real-time. This system can act as a virtual yoga assistant, helping people improve their yoga practice by providing immediate feedback on their form and helping to prevent injury. This paper presents a yoga tracker and correction system that uses computer vision and machine learning algorithms to track and correct yoga poses. The system comprises a camera and a computer vision module that captures images of the yoga practitioner and identifies the poses being performed. The machine learning module analyzes the images to provide feedback on the quality of the poses and recommends corrections to improve form and prevent injuries. This paper proposed a customized support vector machine (SVM) based real-time pose detection and correction system that suggests yoga practices based on specific health conditions or diseases. Paper aims to provide a reliable and accessible resource for individuals seeking to use yoga as a complementary approach to managing their health conditions. The system also includes a practitioner’s interface that enables practitioners to receive personalized recommendations for their yoga practice. The system is developed using Python and several open-source libraries, and was tested on a dataset of yoga poses. The hyper parameter gamma tuned to optimize the classification accuracy on our dataset produced 87% which is better than other approaches. The experiment results demonstrate the effectiveness of the system in tracking and correcting yoga poses, and its potential to enhance the quality of yoga practice.
Anomaly Detection Algorithm Based on Smart Contracts Behaviours in Ethereum Ecosystem

Blockchain technology has attracted significant attention in recent years. One of the reasons for this new trend is the introduction of on-chain smart contracts, enabling the implementation of decentralized applications in trustless environments. Given the high amounts handled, smart contracts have become the targets of attacks. Along with its adoption, attacks are perpetrated via criminal smart contracts and vulnerable smart contracts by exploiting vulnerabilities. To thwart these attacks and avoid the exploitation of flaws, several approaches have been explored, such as the identification of vulnerabilities or the verification of models using a formal verification. However, these approaches have failed in solving the blockchain crime problem. This article proposes a new anomaly detection approach based on the behavior of smart contracts in the Ethereum blockchain. This formalism will be based on mathematical principles such as state mutation and an approach to verify the behavior of smart contracts.
A CNN and LSTM-based Model for Creating Captions for Photos

Can a machine interpret an image's meaning with the same speed as the human brain when it is seen? This problem was heavily researched by computer vision specialists, who believed it to be unsolvable until recently. It is now possible to develop models that can generate captions for pictures because of advancements in deep learning techniques, accessibility to large datasets, and processing power. This will be accomplished by the Python-based implementation of the article's deep learning convolutional neural network technique and a particular kind of recurrent neural network. Here the proposed model uses CNN and LSTM methods to achieve desired task
Professional psychological well-being of educators and features of their support in the current crisis conditions

The article presents the essence, indicators, and factors of the professional psychological well-being of educators. The expediency of studying the phenomenon of professional psychological well-being of educators based on the eudanomic approach, according to which the phenomenon as a result of a person's experience of successes or achievements in the professional sphere is an important component of the psychological well-being of the individual as a whole, is substantiated. Based on the model of psychological well-being of K. Riff, modified taking into account the specifics of the professional activity of educators, indicators of their professional psychological well-being are determined (self-acceptance as a specialist in the field of education; professional and personal growth; goals of professional activity and development; positive relations with other subjects of educational activity; management of the educational environment; professional self-determination and autonomy, etc.). Three groups of factors of the professional psychological well-being of educators have been characterized at the macro- (levels of society), meso- (levels of the educational organization) and micro-levels (levels of individuals). Specific features of psychological support for the professional psychological well-being of educators in today's crisis conditions, caused by the state of war, at the level of management of an educational organization, the level of professional interaction, and the level of personality are determined. The prospects of further study of the professional psychological well-being of educators in the empirical plane are outlined.
Machine Learning Regression in Petrology

This chapter applies machine-learning regression to petrology. It explains how to calibrate machine-learning thermo-barometers based on orthopyroxene crystals in equilibrium with the melt in a volcanic plumbing system. It also describes the calibration of a thermo-barometer based on orthopyroxenes crystals.
Introduction to Machine Learning

This chapter introduces the basics of machine learning to geologists. Toward this end, it first provides fundamental definitions and introduces common terminology. It then discusses the learning process and defines the different types of learning paradigms (i.e., supervised, unsupervised, and semisupervised).
Unsupervised Machine Learning Methods

This chapter introduces unsupervised machine learning methods. It starts by describing the algorithms for dimensionality reduction, which include principal component analysis and manifold learning. It then describes clustering methods, such as hierarchical clustering, DBSCAN, mean shift, K-means, spectral clustering, and Gaussian-mixture models.
Introduction to Deep Learning

This chapter is about deep learning. It starts by introducing the basics of deep learning and then introduces PyTorch, a Python deep learning library. It also describes how to set up and train feedforward networks. Finally, it provides an example application dealing with deep learning potentials in the Earth Sciences.
Setting Up Your Python Environments for Machine Learning

This chapter details how to prepare a Python environment to start working with Machine Learning in Earth Sciences. First, it shows how to set up a local Python environment, and then how to create a remote Linux instance. Finally, it explains how to start working with cloud-based machine learning environments.
Clustering and Dimensionality Reduction in Petrology

Chapter 5 describes how to apply unsupervised machine learning methods in petrology. It focuses on analyzing the clinopyroxene erupted by Mt. Etna during the sequence of lava fountains that occurred between February and April of 2021. The application of clustering and dimensionality reduction techniques is described in detail.