Reviewerly/exHarmony huggingface dataset:
Fourier Transform Approach to Machine Learning.

We propose a supervised learning algorithm for machine learning applications. Contrary to the model developing in the classical methods, which treat training, validation, and test as separate steps, in the presented approach, there is a unified training and evaluating procedure based on an iterative band filtering by the use of a fast Fourier transform. The presented approach does not apply the method of least squares, thus, basically typical ill-conditioned matrices do not occur at all. The optimal model results from the convergence of the performance metric, which automatically prevents the usual underfitting and overfitting problems. The algorithm capability is investigated for noisy data, and the obtained result demonstrates a reliable and powerful machine learning approach beyond the typical limits of the classical methods.
Exploring the Link Between Socially Responsible HRM and Affective Commitment of Employees in Ethiopian Context

This investigation focuses on the impacts of socially responsible human resource management (SR-HRM) methods on affective commitment (AC) as well as the intervening impact of perceived organizational support (POS) for this association.Primary data depending on 555 respondents owned by various socially responsible companies within in Ethiopia were gathered.The data were analyzed with Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS to determine the hypothesized connections concerning socially responsible -HRM and affective commitment.The outcomes have uncovered that the employees' perceptions of socially responsible human resource management (SR-HRM) practice impact affective commitment indirectly via perceived organizational support in Ethiopian setting.This research enhances the literature by analyzing the causal association concerning socially responsible HRM practices and employees' affective commitment in developing country context.
Learning Factored Markov Decision Processes with Unawareness

Methods for learning and planning in sequential decision problems often assume the learner is fully aware of all possible states and actions in advance. This assumption is sometimes untenable: evidence gathered via domain exploration or external advice may reveal not just information about which of the currently known states are probable, but that entirely new states or actions are possible. This paper provides a model-based method for learning factored markov decision problems from both domain exploration and contextually relevant expert corrections in a way which guarantees convergence to near-optimal behaviour, even when the agent is initially unaware of actions or belief variables that are critical to achieving success. Our experiments show that our agent converges quickly on the optimal policy for both large and small decision problems. We also explore how an expert's tolerance towards the agent's mistakes affects the agent's ability to achieve optimal behaviour.
Learning Low-Rank Representations

We investigate the effect of the dimensionality of the representations learned in Deep Neural Networks (DNNs) on their robustness to input perturbations, both adversarial and random. To achieve low dimensionality of learned representations, we propose an easy-to-use, end-to-end trainable, low-rank regularizer (LR) that can be applied to any intermediate layer representation of a DNN. This regularizer forces the feature representations to (mostly) lie in a low-dimensional linear subspace. We perform a wide range of experiments that demonstrate that the LR indeed induces low rank on the representations, while providing modest improvements to accuracy as an added benefit. Furthermore, the learned features make the trained model significantly more robust to input perturbations such as Gaussian and adversarial noise (even without adversarial training). Lastly, the low-dimensionality means that the learned features are highly compressible; thus discriminative features of the data can be stored using very little memory. Our experiments indicate that models trained using the LR learn robust classifiers by discovering subspaces that avoid non-robust features. Algorithmically, the LR is scalable, generic, and straightforward to implement into existing deep learning frameworks.
RETROSPECTIVE ANALYSIS OF ACCESSIBILITY OF THE EDUCATIONAL WEB PORTALS: THE ECUADORIAN CASE

1 Catholic University of Cuenca, Academic Unit of Information and Communication Technologies (ECUADOR)2 University of Alicante, Department of Software and Computing Systems (SPAIN)3 Universitat de les Illes Balears, Departament de Matemàtiques i Informàtica (SPAIN)
Very Short-Term Photovoltaic Power Forecasting Considering the Stochastic Factors

This paper proposes photovoltaic (PV) power forecasting model, which is applied Gaussian blur algorithm filtering technique, for estimating power output and create stochastic forecasting model. As a result, affected power can be forecasted from stochastic factors with machine learning and artificial neural network. This work focuses on a very short-term forecast for 5 minutes ahead. As it uses only endogenous data, no exogenous data is needed. To evaluate the model, the result has been compared to the persistence model, which has a good accuracy for a short-term forecasting. It has been shown that the proposed PV forecasting considering the stochastic factors gained higher accuracy than the persistence model.
THE EFFECT OF FOREIGN DIRECT INVESTMENT ON FINANCIAL DEVELOPMENT: EMPIRICAL EVIDENCE FROM BAHRAIN

The aim of this paper is to analyze the relationship between foreign direct investments and financial development in Bahrain. The estimation financial development effects was performed for the period 1978 to 2015, which covers the political conflicts that recently happened in Middle East area (Arab Spring). On the other hand, the paper sought to examine the causality relationship between foreign direct investments and financial development. The study empirically investigates the short and long run equilibrium relationship between the variables by applied co-integration and Autoregressive Distributed Lags Approach (ARDL). The Granger causality test was employed to capture causality relationship. The obtained results show that there is a significant positive relationship between FDI and financial development in short and long run, while, a significant negative relationship between Arab Spring and financial development. However, the results also revealed bidirectional causality relationship between FDI and financial development.
Probability Learning by Perceptrons and People

.article-title, .h1-h2-after, .h1, .h1-references, .bib-doi { color:#2d7db5; } .bib-doi { white-space:nowrap; } .h1, .h1-h2-after, .h1-references { color:#2d7db5; font-size:1.6rem; line-height:1.5em; font-weight:700; margin:0 0 0.5em 0; } .
Deep learning for pose-invariant face detection in unconstrained environment

&lt;span lang="EN-US"&gt;In the recent past, convolutional neural networks (CNNs) have seen resurgence and have performed extremely well on vision tasks. Visually the model resembles a series of layers each of which is processed by a function to form a next layer. It is argued that CNN first models the low level features such as edges and joints and then expresses higher level features as a composition of these low level features. The aim of this paper is to detect multi-view faces using deep convolutional neural network (DCNN). Implementation, detection and retrieval of faces will be obtained with the help of direct visual matching technology. Further, the probabilistic measure of the similarity of the face images will be done using Bayesian analysis. Experiment detects faces with ±90 degree out of plane rotations. Fine tuned AlexNet is used to detect pose invariant faces. For this work, we extracted examples of training from AFLW (Annotated Facial Landmarks in the Wild) dataset that involve 21K images with 24K annotations of the face.&lt;/span&gt;
Stochastic Methods for Image Analysis

These lectures about stochastic methods for image analysis contain three parts. The first part is about visual perception and the non-accidentalness principle. It starts with an introduction to the Gestalt theory, that is a psychophysiological theory of human visual perception. It can be translated into a mathematical framework thanks to a perception principle called the non-accidentalness principle, that roughly says that “we immediately perceive in an image what has a low probability of coming from an accidental arrangement”. The second part of these lectures is about the so-called “a contrario method” for the detection of geometric structures in images. The a contrario method is a generic method, based on the non-accidentalness principle, to detect meaningful geometric structures in images. We first show in details how it works in the case of the detection of straight segments. Then, we show some other detection problems (curves, vanishing points, etc.) The third part of these lectures is about stochastic models of images for the problem of modeling and synthesizing texture images. It gives an overview of some methods of texture synthesis. We also discuss two models of texture images: stationary Gaussian random fields and shot noise random fields.
A survey on predicting breast cancer survivability and its challenges

Data mining is a powerful technology that can be used in all domains in order to detect hidden patterns from a large volume of data. A huge amount of medical data gives opportunities to health research community to extract new knowledge in different parts of medicine such as diagnosis, prognosis, and treatment by using data mining applications in order to improve the quality of patient care and reduce healthcare costs. Breast cancer is the most common cancer in women worldwide and it is the leading cause of death among women. Data mining can be used as a decision support system to predict survival of new patients. In this study, related works in the field of breast cancer survival prediction are reviewed and by compromising these works challenging issues are presented. Pages:37-42
Unreal for VR: Basic Ingredients for an Oculus Go Project Setup

In this chapter, we look at the core concepts of creating a VR experience using the Unreal Engine. We’ll take a quick look at how VR components are used across platforms, and then go straight into setting up an Oculus Go project right from the beginning. We will start with the prerequisites and initial setup followed by creating a project for testing different locomotion, teleportation, and object interaction concepts. After that we are going to build our scene using a pawn Blueprint with its components and event graphs, a level Blueprint for object interaction, and various material Blueprints to complete the scene. The basic concepts in this chapter are valid across all VR platforms and focus on the most common use cases for VR experiences.
Eye Tracking Methodology for Research in Consumer Psychology

Marketing provides rich natural contexts in which consumers engage with many complex stimuli, through a variety of tasks, and eye tracking research has yielded a range of fundamental insights into how people perform these tasks. For most natural visual tasks, eye movements are tightly coupled with visual attention and reliable and valid indicators of attention, reflecting information uptake during such common tasks as exploration, search, and choice. Therefore, video-based eye tracking solutions provide lower spatiotemporal precision than infrared eye trackers and so they cannot yet be used to detect and record specific eye movements such as fixations and saccades. Eye trackers also provide other information about the participants’ visual behaviors, including head–stimulus distance, eye blinks, and dilation of the pupil. In eye tracking experiments, a researcher or research assistant needs to be present to do the calibration and sometimes to explain the procedures and monitor data collection.
ELECTRONIC DOCUMENT MANAGEMENT SYSTEM IMPLEMENTING INTERNET OF THINGS (IOT)

The system that we’re about to improve will give remedies on the troubles that our customer encountered. Shifting on Electronic Document Management System Implementing Internet of Things (IoT) will gather benefits and ease to a business technique such as decreasing working hours, storage space and this will expand the efficiency of the business enterprise plant’s operation and aiming for paperless documentation that will guide the Human Resource (HR) and Quality Assurance (QA) Departments. All archives about product quality, non-conformity reports and employee’s data compiled in the gadget with the aid of the usage of Electronic Document Management System Implementing Internet of Things (IoT). We gathered the data, summarized, assigned approved personnel, uploaded a file for viewing, modifying and authorization. Prevents the information redundancy, loss of data, cope up with customer’s documentation request in a limited time, the file sharing and organized facts base for history monitoring and got here up with digital filing.
Multiphase Level-Set Loss for Semi-Supervised and Unsupervised Segmentation with Deep Learning

Recent state-of-the-art image segmentation algorithms are mostly based ondeep neural network, thanks to its high performance and fast computation time.However, these methods are usually trained in a supervised manner, whichrequires large number of high quality ground-truth segmentation masks. On theother hand, classical image segmentation approaches such as level-set methodsare still useful to help generation of segmentation masks without labels, butthese algorithms are usually computationally expensive and often havelimitation in semantic segmentation. In this paper, we propose a novelmultiphase level-set loss function for deep learning-based semantic imagesegmentation without or with small labeled data. This loss function is based onthe observation that the softmax layer of deep neural networks has strikingsimilarity to the characteristic function in the classical multiphase level-setalgorithms. We show that the multiphase level-set loss function enablessemi-supervised or even unsupervised semantic segmentation. In addition, ourloss function can be also used as a regularized function to enhance supervisedsemantic segmentation algorithms. Experimental results on multiple datasetsdemonstrate the effectiveness of the proposed method.
Fundamental Flowers: Evolutionary Discovery of Coresets for Classification

In an optimization problem, a coreset can be defined as a subset of the input points, such that a good approximation to the optimization problem can be obtained by solving it directly on the coreset, instead of using the whole original input. In machine learning, coresets are exploited for applications ranging from speeding up training time, to helping humans understand the fundamental properties of a class, by considering only a few meaningful samples. The problem of discovering coresets, starting from a dataset and an application, can be defined as identifying the minimal amount of samples that do not significantly lower performance with respect to the performance on the whole dataset. Specialized literature offers several approaches to finding coresets, but such algorithms often disregard the application, or explicitly ask the user for the desired number of points. Starting from the consideration that finding coresets is an intuitively multi-objective problem, as minimizing the number of points goes against maintaining the original performance, in this paper we propose a multi-objective evolutionary approach to identifying coresets for classification. The proposed approach is tested on classical machine learning classification benchmarks, using 6 state-of-the-art classifiers, comparing against 7 algorithms for coreset discovery. Results show that not only the proposed approach is able to find coresets representing different compromises between compactness and performance, but that different coresets are identified for different classifiers, reinforcing the assumption that coresets might be closely linked to the specific application.
Multidirectional harmony search algorithm for solving integer programming and minimax problems

Integer programming and minimax problems are essential tools in solving various problems that arise in data mining and machine learning such as multi-class data classification and feature selection problems. In this paper, we propose a new hybrid harmony search algorithm by combining the harmony search algorithm with the multidirectional search method in order to solve the integer programming and minimax problems. The proposed algorithm is called multidirectional harmony search algorithm (MDHSA). MDHSA starts the search by applying the standard harmony search for numbers of iteration then the best-obtained solution is passing to the multidirectional search method as an intensification process in order to accelerate the search and overcome the slow convergence of the standard harmony search algorithm. The proposed algorithm is balancing between the global exploration of the harmony search algorithm and the deep exploitation of the multidirectional search method. MDHSA algorithm is tested on seven integer programming problems and 15 minimax problems and compared against 12 algorithms for solving integer programming problems and 11 algorithms for solving minimax problems. The experiments results show the efficiency of the proposed algorithm and its ability to solve integer programming and minimax problems in reasonable time.
Novelty Detection with Self-Organizing Maps for Autonomous Extraction of Salient Tracking Features

In the image processing field, many tracking algorithms rely on prior knowledge like color, shape or even need a database of the objects to be tracked. This may be a problem for some real world applications that cannot fill those prerequisite. Based on image compression techniques, we propose to use Self-Organizing Maps to robustly detect novelty in the input video stream and to produce a saliency map which will outline unusual objects in the visual environment. This saliency map is then processed by a Dynamic Neural Field to extract a robust and continuous tracking of the position of the object. Our approach is solely based on unsupervised neural networks and does not need any prior knowledge, therefore it has a high adaptability to different inputs and a strong robustness to noisy environments.
A Conversational Analysis of Grice’s Maxims Theories on Cooperative Principles at Undergraduate English Students

This study was aimed to analyze students’ conversation based on Grice’s maxim theory at the English department of Muhammadiyah University of Mataram. The method of this research was qualitative. The result of this research showed that students were followed the maxim of quantity is 60 utterances, followed the maxim of quality is 16 utterances, followed the maxim of relevant is 24 utterances, and followed the maxim of manner is 6 utterances. Violated maxim of quantity is 47 utterances, violated maxim of quality is 27 utterances, violated maxim of relevant is 50 utterances and violated manner is 89 utterances. Violated maxims often happen than followed the maxims, total of violated whole the maxims are 213 and total of followed whole the maxims are 106 because the speaker and participants are come from different cultures, using informal language and their purpose in communication to maintain their social harmony.
The benefits of prosody for music educators and students: Unpacking prosody and songwriting strategies in the classroom

Literary and linguistic prosody principles can provide music educators and students with constructive, time honoured, deeply creative songwriting strategies. This article is an opportunity to share a direct and unique classroom experience of teaching prosody within songwriting. Through discussing a personal songwriting and teaching practice grounded in prosody, this article will present recommendations for a specific pedagogy underpinned by prosody for use in the songwriting classroom. Within this article the principles of literary and linguistic prosody will be outlined and discussed alongside songwriting strategies such as writing from a title, matching stressed syllables to stressed beats, metre, song structure and rhyme. The principle of prosody will also be expanded to involve the idea of unity and will be explained in relation to songwriting areas including rhythm, harmony, melody and performance.
Soft Subspace Topological Clustering over Evolving Data Stream

Subspace clustering has been successfully applied in many domains and its goal is to simultaneously detect both clusters and subspaces of the original feature space where these clusters exist. A Data stream is a massive sequences of data coming continuously. Clustering this type of data requires some restrictions in time and memory. In this paper we propose a new method named S2G-Stream based on clustering data streams and soft subspace clustering. Experiments on public datasets showed the ability of S2G-Stream to detect simultaneously the best features, subspaces and the best clustering.
Wearable Device for EMG and EOG acquisition

This article presents a wearable device capable of acquiring both facial EMG and EOG biopotentials. The circuit developed has amplification and filtering stages that eliminate higher frequency noises and low frequency biopotentials to use a band of frequency for acquisition of contractions from Frontalis muscle above the eyebrow and ocular movements on the vertical and horizontal axis. The output signal has an adjustment of gain and an offset that permits the application in embedded systems and digitally processing to eliminate 60 Hz noises. The wearable device is a mask with five electrodes allocated to make the acquisitions according to the positioning. The circuit is capable of acquiring both facial EMG and EOG simultaneously, but in this article they were acquired individually. Improvements in the whole system are being made and other ocular movements, like eye focus and involuntary movements for example, may be acquired in future works involving this system.
Addressing class imbalance in trust and stereotype assessment

Trust, reputation and stereotypes enable agents to identify reliable interaction partners based on past interactions. However, such methods can cause agents to choose the same known partners instead of unknown, but potentially better, alternatives, giving rise to a class imbalance in their interaction histories. In this paper, we present a Class Imbalance Modification (CIM) method, to improve agents' initial assessments of others by becoming aware of the bias towards known agents. CIM enables an agent to determine whether data-driven trust, reputation and stereotypes are appropriate to assess a target agent, depending on how representative the agent's past interaction data is of the target. We also present a technique, Direct Comparative Stereotypes (DCS), which does not use past interaction data to make a stereotypical assessment, and so can be used if CIM concludes the data is inappropriate. Finally, CIM determines whether data-driven models have been rendered inappropriate by dynamic agent behaviour, where old interactions may no longer reflect current behaviour. Our results show that CIM significantly reduces error in a priori estimates, which improves partner selection and increases average utility.