Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of FOREX trading Strategies based in Random Forest and Support Vector Machines

The Foreign Exchange (Forex) is the largest market in the world and has a daily trading volume of approximately 3.2 trillion dollars. The price movements are influenced by many exogenous factors, thereby it is difficult to predict. As a result, modeling its movement would enable high profitable investment strategies. Aiming at setting up a model that helps the market practitioner make better decisions for trading on Forex, Machine Learning algorithms (Random Forest and SVM) are adopted in this work. Classic technical indicators, like moving averages, are used as features for these algorithms. In order to evaluate these approaches, several simulations were carried out on the pairs Euro/Dollar, Pound/Dollar, Dollar/Swiss Franc and Dollar/Japanese Yen, using three different metrics. For virtually all scenarios investigated, the proposed algorithms outperform the traditional technical indicators.
A New Color-Based Instrument Model For Basic Music Learning Eddy Nurraharjo

The development of applied hardware technology supports efforts on the education and creativity of users and has made microcontrollers a popular device among education practitioners and researchers. The focus of this article is learning for children in the introduction of activities in color-based creativity. A model design of a color-based musical learning instrument device, composed of 2 main elements. The TCS3200 color sensor and arduino microcontroller, used to convert system inputs in the form of unique pieces of color paper arranged on a plastic tape, will be the instrument's modeling keywords. The size of the color paper piece is the same width as the plastic tape, which is 5 centimeters, while the dimensions of length are 5 centimeters to represent the beat of the tone every second. The process begins with color selection of tone characters, color recognition and color classification. Meanwhile, the design of the system algorithm, has the ability to detect colors, determine the tone according to the choice of color categories, and adjust the stability of the speed of the DC motor 60 revolution per minutes. The final test results of the system successfully proved the color detection process of sensor input and tone audio selection according to the classification of specified categories while proving that the system is able to work properly.
Virtual Prototype Design of Flexible Exoskeleton based on Pattern Recognition Technology

the purpose of the research on flexible exoskeleton robot is to develop intelligent equipment suitable for human body to wear. This kind of exoskeleton can quickly provide additional energy compensation for human joints, so as to enhance joint motion function. The design of lower limb flexible exoskeleton should be combined with the characteristics of convenience of wearing, stability of use and rapidity of response. In this paper, according to the skeleton and muscle structure of human lower limbs under healthy condition, a kind of flexible exoskeleton which can be quickly worn in complex environment is designed. The exoskeleton is integrated into the shoes, and its main assistance points are ankle joint and knee joint, which greatly improves the wearing efficiency. After the design of flexible exoskeleton is completed, the simulation analysis of exoskeleton is carried out to verify that the design meets the use requirements.
Socially Compatible Control Design of Automated Vehicle in Mixed Traffic

In the car-following scenarios, automated vehicles (AVs) usually plan motions without considering the impacts of their actions on the following human drivers. This paper aims to leverage such impacts to plan more efficient and socially desirable AV behaviors in human-AV interactions. Specifically, we introduce a socially compatible control design for the AV that benefits mixed traffic in the car-following scenarios. The proposed design enables the altruistic AV in human-AV interaction by integrating the social value orientation from psychology into its decision-making process. The altruistic AV generates socially desirable behaviors by optimizing both its own reward and courtesy to the following human driver’s original plan in the longitudinal motion. The results show that as compared to the egoistic AV, the altruistic AV significantly avoids disrupting the following human driver’s initial plan and leads the following human driver to achieve considerably smaller car-following gap distance and time headway. Moreover, we investigated the impacts of the socially compatible control design with different altruism levels of the AV using statistical assessments. The results collectively demonstrate the significant improvement in traffic-level metrics as a result of the AV’s altruistic behaviors in human-AV interactions.
Efficient Scheduling in Space–Air–Ground-Integrated Localization Networks

High accuracy and seamless position information formulates the basis of many modern wireless applications, such as the Internet of Things (IoT) and intelligent transportation systems (ITSs). In this article, aiming at the ground user equipment (UE) those in the “blind spots,” where only limited navigation signals are provided, the temporary aerial-aided “anchors” such as the unmanned aerial vehicles (UAVs) are introduced as alternating solutions. We first give the general fundamental limits of the three-dimensional space–air–ground-integrated localization networks (SAGILNs) using both time and angle measurements. Unlike most existing investigations, we treat aerial nodes as “agents” whose positions are not known beforehand. We then try to formulate an efficient scheduling strategy, where proper network behaviors, including the resource optimization and UAV deployment, are provided. We find that the proposed scheduling problems could be formulated as standard semidefinite programming (SDP) problems and solved by off-the-shelf solvers. Numerical results are provided to validate our analysis. The proposed methods and analyses provide meaningful insights for performance benchmarks for the implementation of SAGILN.
Power Plant Production Equipment Sound Recognition Method Combined with Attention Mechanism

In the operation of production equipment of power plant, due to mechanical vibration and periodic action, it often makes a large sound, including many equipment information characteristics. To build an unattended power plant, perfect equipment protection and monitoring devices are indispensable. Firstly, this paper analyzes the sound spectrum energy of power plant production equipment under different operating states, and uses the characteristic fusion of Mel spectrum and constant Q-transform spectrum to form the operating sound characteristic matrix of power production equipment. Considering the sparsity of characteristic matrix, a convolutional neural network combined with attention mechanism is proposed, and the effectiveness of the proposed network is verified by experiments.
Permanent Magnet Synchronous Motor Parameter Identification Based on Improved Adaptive Extended Kalman Filter

The electromagnetic parameters of the permanent magnet synchronous motor (PMSM) will change influenced by certain factors like temperature and magnetic circuit saturation, which will reduce the motor control system performance, and even cause permanent damage to the motor. Therefore, accurate acquisition of motor parameters remarkably affects the high-performance operation of the motor. The paper adopts an improved adaptive extended Kalman filter (IAEKF) algorithm for identifying the parameters of the nonlinear permanent magnet synchronous motor (PMSM) system. This method performs Taylor expansion on the high-order nonlinear function of the system model, thereby converting the nonlinear problem into a linear problem for solving. Moreover, adaptive technology is adopted to the method. The process noise covariance is estimated in real time through an improved noise statistic estimator (NSE). The improved NSE is composed of a biased and an unbiased estimator, which can improve the accuracy of the noise parameter estimation, while ensuring the positive semi-definiteness of the process noise variance matrix to ensure the robustness of the algorithm. Finally, simulation analysis helps to verify the whether the algorithm is feasibly.
Identification of PMSM Moment of Inertia based on Model Reference Adaptive Algorithm

A method that can identify the rotational inertia in a timely manner is proposed because the performance of a permanent magnet synchronous motor servo system is easily affected by the rotational inertia. Firstly, principles of model-referenced adaptive recognition algorithm is introduced, and the influence of the adaptive gain on the identification results is analyzed. Then, design a suitable range of adaptive gain value, use the set error value to compare the identification error with the set value, and then select the appropriate gain for identification. Finally, through simulation verification, this method has fast identification convergence speed, high precision and strong anti-disturbance.
Finding Cost-Effective Re-Layouting Solutions in Modern Brownfield Facility Layout Planning

In facility layout planning (FLP) for shop floor optimization, the goal is to find an optimal arrangement for a number of machines in a given space. Despite the significance of machine selection for the FLP, the integration of arrangement and selection problems is neglected in the literature. This paper presents a methodology for solving an extended facility layout problem where not only the arrangement of machines is con-sidered, but also how many machines should be allocated and of which type. We assume the context of a modern brownfield planning project, where an initial layout already exists, which is to be re-purposed for manufacture of products with high variability. We apply our methodology in a case study where three objectives are to be minimized simultaneously. The results demonstrate that a standard NSGA-II introduces bias for this problem type that leads to an inadequate search space exploration. We test adapted mutation and crossover operators to overcome this challenge. While modified crossover had little impact, the new mutation operator was key to outperform the standard approach in all computed metrics. With this operator, we obtain diverse Pareto sets which strictly dominate up to 80% of the solutions found by the standard approach on average.
Dissipativity Theory for Discrete-Time Nonlinear Stochastic Dynamical Systems, Part II: Kalman-Yakubovich-Popov Conditions and Stability of Feedback Interconnections

In a companion paper (Dissipativity Theory for Discrete-Time Nonlinear Stochastic Dynamical Systems, Part I: Input-Output and State Properties) dissipativity theory was developed for discrete-time nonlinear stochastic dynamical systems. In this paper, we build on these results to develop extended Kalman–Yakubovich–Popov conditions for characterizing stochastic dissipativity in terms of the nonlinear system functions using continuous storage functions. In addition, feedback interconnection stability in probability results are developed generalizing the small gain and positivity theorems to discrete-time stochastic systems.
Application of Satellite Image in Substation Location

The selection of substation sites is an important part of power grid planning. The reasonable locations of substations not only affect the investment in the construction in the early stage, but also play a vital role in the operation of power grid in the later stage. A method of substation location based on satellite images is proposed in this paper. With the rapid development of technology, it has become possible to obtain high-definition satellite images. Satellite images are converted into corresponding data, and a wide-area geographic information database is established to build actual three-dimensional geographic models, which can save manpower, material resources and time consumption of on-site inspections. In the 3D model, a large amount of attribute data can be directly reflected, and the visualization degree is stronger. The basic process and advantages of this method are analyzed and summarized, and the feasibility is verified by a case.
Automated Machine Learning for Time Series Prediction

Automated Machine Learn (AutoML) process is target of large studies, both from academia and industry. AutoML reduces the demand for data scientists and makes specialists in specific fields able to use Machine Learn (ML) in their domains. An application of ML algorithms is over time-series forecasting, and about these, few works involve the application of AutoML. In this work, an AutoML approach that aggregates time-series forecasting models is proposed. Furthermore, a special focus is given to the optimization stage, which uses genetic algorithm to boost searching for hyper-parameters. In the end, results are compared with a recent time-series forecasting benchmark and we verify that the AutoML model proposed in this work surpasses the benchmark.
Multivariate generalizations of the proportional hazards model

A new approach to the analysis of bivariate survival data is presented. It involves the development of "a model for bivariate life-tables.with a single association parameter which is unaffected by monotone transformation of the marginal distributions. Methods for testing and estimating this parameter from right-censored sample pairs using only rank-order information are presented. The model is a generalization of the proportional hazards model and includes a random effect representing heterogeneity of frailty or proneness to failure. " A discussion on "the analysis of litter-matched and matched-pair failure-time data is [included]. Some uses of the methods in rank regression problems involving only one right-censored dependent variable are described and a test is proposed for proportional hazards against alternative error structures leading to converging hazards. Finally the methods are compared and validated by Monte-Carlo simulations. " Comments by several people and a discussion concerning the paper are included (pp. 108-17). (EXCERPT)
Designing Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification

In recent years, deep Convolutional Neural Networks (CNNs) have shown great potential in malware classification. CNNs, which are originally designed for image processing, identify malware binaries visualised as images. Despite offering promising performance, these human-designed networks are very large requiring more resources to train and deploy them. Evolutionary algorithms have been successfully used in designing deep neural networks automatically for different application domains. In this work, we use a Genetic Algorithm (GA) to optimise the CNN topology and hyperparameters for image-based malware classification. Computational experiments with two different malware datasets, Malimg and Microsoft Malware, show that the GA-evolved networks are very competitive to the networks designed by experts in classifying malware, yet they are also considerably smaller in size comparison.
Research on Non-contact Multi-person Heart Rate Measurement Method for Intelligent Education

Obtaining the learning status information of stu-dents in class is the premise of realizing intelligent education, and heart rate (HR) is important information reflecting the learning status of students. Imaging Photoplethysmography (iPPG) is a non-contact measuring technology of physiological indicators, which is beneficial to the promotion of intelligent education. In this paper, a non-contact multi-person heart rate measurement system based on video is designed for teaching scenarios. First, the raw signal with a high signal-to-noise ratio (SNR) is obtained through a new spatial averaging method that effectively uses the HR information contained in every part of the face. Next, unde-sirable various noises in the raw signal are removed by combining the color-distortion filtering and plane-orthogonal-to-skin based method. The experimental results show that this system has high HR measurement performance while the mean absolute error, root mean squared error, and Pearson correlation coeffi-cient is 0.90 bpm, 1.7 bpm, and 0.98, respectively.
ANALYSIS OF SECOND-GRADE FLUID FLOW IN POROUS CHANNEL WITH CATTANEO-CHRISTOV AND GENERALIZED FICK’S THEORIES

This attempt executes the combined heat mass transport features in steady MHD viscoelastic fluid flow through stretching walls of channel. The channel walls are considered to be porous. The analysis of heat transport is made by help Cattaneo-Christov heat diffusion formula while generalized Fick’s theory is developed for study of mass transport. The system of partial differential expressions is changed into set of ordinary differential by introducing suitable variables. The homotopic scheme is introduced for solving the resultant equations and then validity of results are verified by various graphs. An extensive analysis has been performed for the influence of involved constraints on liquid velocity, concentration and temperature profiles. It is noted that the normal component of velocity decreases by increasing Reynolds number while retards for increasing viscoelastic constraint. Both temperature and concentration profiles enhanced by increasing combined parameter and Reynolds number. The presence of thermal relaxation number and concentration relaxation number declines both temperature and concentration profiles, respectively.
Dynamic Multi-objective Optimisation Using Multi-guide Particle Swarm Optimisation

This study conducts a sensitivity analysis of the recently proposed multi-guide particle swarm optimisation (MG-PSO) algorithm for dynamic multi-objective optimisation problems (DMOPs). The MGPSO is a multi-swarm approach where each subswarm optimises one of the objectives. This paper further adapts the MGPSO algorithm to solve DMOPs by proposing alternative balance coefficient update strategies to allow efficient tracking of the changing Pareto-optimal front (POF). A total of twenty-nine benchmark functions and six performance measures were implemented to help with this task. The experiments were run against five different environment types to determine whether the MGPSO can solve problems with various spatial and temporal severities. The best control parameter update strategy was then compared with other state-of-the-art dynamic multi-objective optimisation algorithms (DMOAs). An extensive empirical analysis shows that MGPSO with the balance coefficient parameter re-initialized after the environment change achieves very competitive and oftentimes better performance when compared with the competing algorithms.
Sports Camera Pose Refinement U sing an Evolution Strategy

This paper presents a robust end-to-end method for sports cameras extrinsic parameters optimization using a novel evolution strategy. First, we developed a neural network ar-chitecture for an edge or area-based segmentation of a sports field. Secondly, we implemented the evolution strategy, which purpose is to refine extrinsic camera parameters given a single, segmented sports field image. Experimental comparison with state-of-the-art camera pose refinement methods on real-world data demonstrates the superiority of the proposed algorithm. We also perform an ablation study and propose a way to generalize the method to additionally refine the intrinsic camera matrix.
The Effect of Addiction, Perceived Values and Mobile Game Loyalty on Purchase Intention in Mobile Legends in Indonesia

The games business is quickly expanding both globally and in Indonesia, resulting in significant revenue, with Mobile Legends being one of the most popular. One way for a game to make money is through offering in-app features, which Mobile Legends does as well The goal of this study is to determine the impact of addiction, perceived value (which consist perceived playfulness, perceived flexibility, perceived connectedness, perceived reward, loyalty to Mobile Legends, and purchase intention in Mobile Legends), and loyalty to Mobile Legends on purchase intention. Based on respondent data acquired through questionnaires, this study is analyzed using PLS-SEM with SmartPLS. This was accomplished through purposive sample of Mobile Legends players that actively play the game nearly every day (every day or every two days), have never purchased in-game stuff, and intend to purchase in-game things. Addiction has a big impact on loyalty, but not on purchase intention. Apart from perceived flexibility, almost all criteria in perceived value have a major impact on mobile game loyalty. Only perceived playfulness and loyalty to mobile games have an impact on purchase intention.
An Adaptive Biased Random-key Genetic Algorithm for Rank Aggregation with Ties and Incomplete Rankings

The Rank Aggregation problem has applications in several fields of science like Information Retrieval, Social Sciences, Bioinformatics, among others. In this problem, the objective is to create a consensus ranking given a set of input rankings. This paper considers a variant of this problem, in which the set of input rankings may contain ties and may be incomplete. An Adaptive Biased Random-key Genetic Algorithm (A-BRKGA) is proposed to solve this problem, and the results are compared to an integer linear programming model previously introduced in the literature. Since the fitness evaluation of the A-BRKGA is its most time-consuming component, a partial fitness evaluation was derived and used in the local search component created for this problem. The partial evaluation reduces the time complexity from quadratic to linear. In the experiments, the efficiency of the A-BRKGA with the local search was evaluated against the A-BRKGA without the local search component and the integer linear programming introduced in the literature. The experimental results show that the proposed technique achieved superior results in terms of quality when compared to the A-BRKGA without the local search component, achieved similar results in terms of quality and better results in terms of computational time when compared to the integer linear programming model.
Human Nutrition and Metabolism Conversion of 5-Formyltetrahydrofolic Acid to 5-Methyltetrahydrofolic Acid Is Unimpaired in Folate-Adequate Persons Homozygous for the C 677 T Mutation in the Methylenetetrahydrofolate Reductase Gene 1

Methylenetetrahydrofolate reductase (MTHFR) catalyzes the synthesis of 5-methyltetrahydrofolic acid (5-CH3-H4 folic acid), the methyl donor for the formation of methionine from homocysteine. A common C677T transition in the MTHFR gene results in a variant with a lower specific activity and a greater sensitivity to heat than the normal enzyme, as measured in vitro. This study was undertaken to determine the capacity of homozygotes for the MTHFR C677T transition to convert 5-formyltetrahydrofolic acid (5-HCO-H4 folic acid) to 5-CH3-H4 folic acid, a process that requires the action of MTHFR. Six subjects homozygous for the C677T transition (T/T) and 6 subjects with wild-type MTHFR (C/C) were given a 5-mg oral dose of (6R,S)-5-HCO-H4 folic acid. Plasma and urine were analyzed for 5-CH3-H4 folic acid concentrations using affinity/HPLC coupled with fluorescence or UV detection. The mean areas under the curves created by the rise and fall of plasma 5-CH3-H4 folic acid after the oral dose did not differ between the two genotypes, 424.5 6 140.3 (T/T) vs. 424.16 202.4 hznmol/L (C/C). There also was no significant difference in the mean cumulative 7-h urinary excretion of 5-CH3-H4 folic acid between the T/T (2.5 6 1.4 mmol) and C/C (1.9 6 1.0 mmol) genotypes. Under the conditions employed, the conversion of oral 5-HCO-H4 folic acid to 5-CH3-H4 folic acid is not impaired in persons with the T/T MTHFR genotype. Possible reasons for these findings are discussed. J. Nutr. 130: 2238–2242, 2000.