Instance Selection using Genetic Algorithms for an Intelligent Ensemble Trading System

Instance selection is a way to remove unnecessary data that can adversely affect the prediction model, thereby selecting representative and relevant data from the original data set that is expected to improve predictive performance. Instance selection plays an important role in improving the scalability of data mining algorithms and has also proven to be successful over a wide range of classification problems. However, instance selection using an evolutionary approach, as proposed in this study, is different from previous methods that have focused on improving accuracy performance in the stock market (i.e., Up or Down forecast). In fact, we propose a new approach to instance selection that uses genetic algorithms (GAs) to define a set of target labels that can identify the buying and selling signals and then select instances according to three performance measures of the trading system (i.e., the winning ratio, the payoff ratio, and the profit factor). An intelligent ensemble trading system with instance selection using GAs is then developed for investors in the stock market. An empirical study of the proposed model is conducted using 35 companies from the Dow Jones Industrial Average, the New York Stock Exchange, and the Nasdaq Stock Market from January, 2006 to December, 2016

Using Neural Networks to Forecast Volatility for an Asset Allocation Strategy Based on the Target Volatility

The objective of this study is to use artificial neural networks for volatility forecasting to enhance the ability of an asset allocation strategy based on the target volatility. The target volatility level is achieved by dynamically allocating between a risky asset and a risk-free cash position. However, a challenge to data-driven approaches is the limited availability of data since periods of high volatility, such as during financial crises, are relatively rare. To resolve this issue, we apply a stability-oriented approach to compare data for the current period to a past set of data for a period of low volatility, providing a much more abundant source of data for comparison. In order to explore the impact of the proposed model, the results of this approach will be compared to different volatility forecast methodologies, such as the volatility index, the historical volatility, the exponentially weighted moving average (EWMA), and the generalized autoregressive conditional heteroskedasticity (GARCH) model. Trading measures are used to evaluate the performance of the models for forecasting volatility. An empirical study of the proposed model is conducted using the Korea Composite Stock Price Index 200 (KOSPI 200) and certificate of deposit interest rates from January, 2006 to February, 2016

Optimal inverter logic gate using 10-nm double gate-all-around (DGAA) transistor with asymmetric channel width

We investigate the electrical characteristics of a double-gate-all-around (DGAA) transistor with an asymmetric channel width using three-dimensional device simulation. The DGAA structure creates a siliconnanotube field-effect transistor (NTFET) with a core-shell gate architecture, which can solve the problem of loss of gate controllability of the channel and provides improved short-channel behavior. The channel width asymmetry is analyzed on both sides of the terminals of the transistors, i.e., source and drain. In addition, we consider both n-type and p-type DGAA FETs, which are essential to forming a unit logic cell, the inverter. Simulation results reveal that, according to the carrier types, the location of the asymmetry has a different effect on the electrical properties of the devices. Thus, we propose the N/P DGAA FET structure with an asymmetric channel width to form the optimal inverter. Various electrical metrics are analyzed to investigate the benefits of the optimal inverter structure over the conventional inverter structure. Simulation results show that 27% delay and 15% leakage power improvement are enabled in the optimum structure.ope

A Case Study for Technical and Vocational Education and Training Professional Development Training in Untact Era: Focusing on Official Development Assistance Project in Botswana

The prolonged COVID-19 pandemic has disrupted the TVET professional development training, which was planned to be carried out in a face-to-face manner. This study aims to examine TVET professional development training based on the ADDIE model and provide implications for ODA and TVET professional development training in the post-COVID era. In this case study, a TVET training program for Botswana was developed based on the ADDIE model in an untact environment. Accordingly, the performance activities for each phase of analysis, design, development, implementation, and evaluation were analyzed, and based on this, implications for ODA and TVET were provided. This study identified the applicability of non-face-to-face education in the ODA project. Also, this study explored how to develop systematic and dimensional programs based on the ADDIE model in a non-face-to-face manner. In addition, sustainability could be secured through cooperative partnerships between donor and beneficiary countries from the perspective of SDGs Goal

Optimal Schedules in Multitask Motor Learning

Although scheduling multiple tasks in motor learning to maximize long-term retention of performance is of great practical importance in sports training and motor rehabilitation after brain injury, it is unclear how to do so. We propose here a novel theoretical approach that uses optimal control theory and computational models of motor adaptation to determine schedules that maximize long-term retention predictively. Using Pontryagin’s maximum principle, we derived a control law that determines the trial-by-trial task choice that maximizes overall delayed retention for all tasks, as predicted by the state-space model. Simulations of a single session of adaptation with two tasks show that when task interference is high, there exists a threshold in relative task difficulty below which the alternating schedule is optimal. Only for large differences in task difficulties do optimal schedules assign more trials to the harder task. However, over the parameter range tested, alternating schedules yield long-term retention performance that is only slightly inferior to performance given by the true optimal schedules. Our results thus predict that in a large number of learning situations wherein tasks interfere, intermixing tasks with an equal number of trials is an effective strategy in enhancing long-term retention

Saliency-guided Graphics and Visualization

In this dissertation, we show how we can use principles of saliency to enhance depiction, manage visual attention, and increase interactivity for 3D graphics and visualization. Current mesh saliency approaches are inspired by low-level human visual cues, but have not yet been validated. Our eye-tracking-based user study shows that the current computational model of mesh saliency can well approximate human eye movements. Artists, illustrators, photographers, and cinematographers have long used the principles of contrast and composition to guide visual attention. We present a visual-saliency-based operator to draw visual attention to selected regions of interest. We have observed that it is more successful at eliciting viewer attention than the traditional Gaussian enhancement operator for visualizing both volume datasets and 3D meshes. Mesh saliency can be measured in various ways. The previous model of saliency computes saliency by identifying the uniqueness of curvature. Another way to identify uniqueness is to look for non-repeating structure in the middle of repeating structure. We have developed a system to detect repeating patterns in 3D point datasets. We introduce the idea of creating vertex and transformation streams that represent large point datasets via their interaction. This dramatically improves arithmetic intensity and addresses the input geometry bandwidth bottleneck for interactive 3D graphics applications. Fast-previewing of time-varing datasets is important for the purpose of summarization and abstraction. We compute the salient frames in molecular dynamics simulations through the subspace analysis of the protein's residue orientations. We first compute an affinity matrix for each frame i of the simulation based on the similarity of the orientation of the protein's backbone residues. Eigenanalysis of the affinity matrix gives us the subspace that best represents the conformation of the current frame i. We use this subspace to represent the frames ahead and behind frame i. The more accurately we can use the subspace of frame i to represent its neighbors, the less salient it is. Taken together, the tools and techniques developed in this dissertation are likely to provide the building blocks for the next generation visual analysis, reasoning, and discovery environments