Reviewerly/exHarmony huggingface dataset:
Electronic Payment Systems (E-payments) and Nigeria Economic Growth

The electronic payment system is a platform that settles financial transactions between the buyer and seller.Payment systems are meant to ease the stress of both parties making an easy exchange or flow of money in a safe and secure environment.This study statistically estimated the relationship between electronic (e-payment) systems and economic growth in Nigeria.Monthly available Data for Nigeria on values of various payments systems were analyzed using Autoregressive Distributed Lagged regression (ARDL) method covering the period of (2012)(2013)(2014)(2015)(2016)(2017).The result indicates a significant positive relationship between the electronic payment system and economic growth in terms of real gross domestic product (GDP) growth.Automated teller machines have a positive significant impact on economic growth, based on a probability of (0.06), but its contribution to the real GDP growth is negative (-5.0 percent).This means the ATM based transaction encourages more cash, possessions and may not yield the required goal of low cash based transactions within Nigeria's banking populace.POS contributes 17 percent growth to the real GDP growth in Nigeria, web based transactions (WBT), contributes 2.3 percent to the real GDP growth, but INTERBANK transactions, has an insignificant impact on GDP growth while MOP has a negative contribution to the impact on real GDP growth.Point of Sales (POS) transactions is also the most patronized electronic banking tool and this is seen from the descriptive analysis, followed by web base transactions (WBT).POS and WBT have the highest average amongst all other variables.This implies that POS and WBT are significantly part of the major determining factors influencing and contributing to the real GDP growth output in Nigeria, while other variables such as INTERBANK transactions are although relevant but contributes minimally and drive real GDP output negatively down, as reflected in the results.Since the successful implementation of the e-payment systems which has much to do with internet connectivity and mobile banking, efforts should be made to design or improve on the internet security framework to check online fraud.There should be adequate legislation on all aspects of the operations of the e-banking and cashless system so that both the operators of the system and the public can be adequately protected.
A Fuzzy MLP Approach for Identiﬁcation of Nonlinear Systems

In case of decision making problems, identiﬁcation of non-linear systems is an important issue. Identiﬁcation of non-linear systems using a multilayer perceptron (MLP) trained with back propagation becomes much complex with an increase in number of input data, number of layers, number of nodes, and number of iterations in computation. In this paper, an attempt has been made to use fuzzy MLP and its learning algorithm for identiﬁcation of non-linear system. The fuzzy MLP and its training algorithm which allows to accelerate a process of training, which exceeds in comparing with classical MLP is proposed. Results show a sharp reduction in search for optimal parameters of a neuro fuzzy model as compared to the classical MLP. A training performance comparison has been carried out between MLP and the proposed fuzzy-MLP model. The time and space complexities of the algorithms have been analyzed. It is observed, that number of epochs has sharply reduced and performance increased compared with classical MLP.
An Approximately Optimal Relative Value Learning Algorithm for Averaged MDPs with Continuous States and Actions

It has long been a challenging problem to design algorithms for Markov decision processes (MDPs) with continuous states and actions that are provably approximately optimal and can provide arbitrarily good approximation for any MDP. In this paper, we propose an empirical value learning algorithm for average MDPs with continuous states and actions that combines empirical value iteration with n function-parametric approximation and approximation of transition probability distribution with kernel density estimation. We view each iteration as operation of random operator and argue convergence using the probabilistic contraction analysis method that the authors (along with others) have recently developed.
A Simple but Effective Way to Improve the Performance of RNN-Based Encoder in Neural Machine Translation Task

Armed with attention mechanism, the recurrent neural network-based encoder-decoder model (or sequence to sequence model) has become the standard architecture to tackle many sequence Nature Language Processing (NLP) tasks. With regard to the neural machine translation (NMT) tasks, our paper proposed a new architecture to proficiently mines the ability of attention mechanism and stacked recurrent neural networks. As a lot of work has given proved that each layer of the stacked recurrent neural networks learns different aspects of a sequence. That means the information represented by each layer is important in terms of the translation task. However, usually, most work just simply adopt stacked recurrent neural networks as a whole part as the encoder or decoder and then combined them with the attention mechanism. While our work creatively uses the attention mechanism to explore each layer of the encoder. In this way, many aspects of the sequence could be learned. For example, linguistic features and semantics information of a word in a sentence could be clearly captured and finally influences the generation of the current translation word. Experiments have shown the effectiveness of our model and an average of 5.67 points BLEU scores were promoted.
Surface Defect Classification of Steels Based on Ensemble of Extreme Learning Machines

In recent years, iron and steel industry of China has developed rapidly, and steel surface defects recognition has attracted wide attention in the field of industrial inspection. Aiming at the problems of poor precision and low speed of traditional surface defect detection methods, we propose to use a fully learnable ensemble of Extreme Learning Machines (ELMs), which is ELM-IN-ELM, for defect classification. The Local Binary Pattern is adopted as the basic feature extraction method. The ELM-IN-ELM determines the final classification decision by automatically learning the output of M independent ELM sub-models. To further illustrate the superiority of the ELM-IN-ELM algorithm for classification, the Northeastern University (NEU) surface defect database is used to evaluate its classification effect. The experimental results demonstrate that this method works remarkably well for surface defects classification. Compared with other methods, the proposed method can identify the types of defects more accurately, which is of practical significance to steel surface defect detection.
Impact of corporate social responsibility on reputation, trust, loyalty of the customers in the banking sector – Evidence in dalat city

The theoretical model illustrates the relationship between the implementation of corporate social responsibility (CSR) on customer loyalty in the banking industry through intermediary variables that are bank reputation and customer trust. The research was conducted in two stages. The first one is preliminary research that seeks to adjust and modify observed variables used in previous studies to fit the context of banking products in Da Lat City. Preliminary qualitative method was carried out via direct interviews with bank managers and customers in Da Lat City and preliminary quantitative research was conducted via a structured survey with a sample size of 150. The purpose of this stage is to evaluate the reliability and the monotonicity of the scale using SPSS 20 software. The main research was conducted via a detail structured survey with 350 customers in Da Lat City to evaluate the reliability and validity of the scale and test the hypotheses of the research model. The Cronbach’s Alpha reliability analysis, exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and structural equation modeling (SEM) was performed on AMOS 20 software. The results indicate that three CSR activities of banks have a direct impact on their reputation which affects trust and trust, in turn, affects customer loyalty. Some suggestions for the bank managers were also proposed.&#x0D;
Urban scene semantic segmentation with insufficient labeled data

Semantic segmentation of urban scenes is an enabling factor for a wide range of applications. With the development of deep learning in recent years, semantic segmentation tasks using high-capacity models have achieved considerable successes on large datasets. However, the pixel-level annotation process, especially for urban scene images with various objects, is tedious and labor intensive. Meanwhile, the scale of the unlabeled data, which is currently easy to collect, is often much larger than labeled data. Thus, using the abundant unlabeled data to make up the loss of the segmentation model from insufficient labeled data is of great interest. In this paper, we propose a semi-supervised method based on reinforcement learning to capture the contextual information from the unlabeled data to improve the model trained on the small scale labeled data. Both quantitative and qualitative experiments have shown the effectiveness of the proposed method.
The digital learning movement: how should Irish schools respond?

There is a significant movement towards digital learning internationally. However, policymaking in Ireland regarding the use of ICT in education, specifically second-level education, has been lacking. Students’ experience of technology at school varies hugely. The current debate has become device focussed rather than content focussed. This paper examines how the lack of clear policy by the Department of Education and Skills has led to resistance by many stakeholders in embedding the use of technology into schools, and particularly teaching and learning.
Implementing Scaled-Agile Frameworks at Non-Digital Born Companies - A Multiple Case Study

For traditional enterprises to harness the advantages of organizational agility, scaled-agile frameworks seem to be more appropriate to adopt agile practices at large scale. However, the adoption of agile practices often creates trade-offs between the implementation of an ideal theoretical framework and company-specific necessities. While extant research has covered the implications and challenges when adopting agile structures, our research focuses on the how and why of such trade-offs using Socio-Technical Systems Theory. Drawing on the results of an exploratory multiple case study, we reveal that companies either choose a top-down or bottom-up approach for implementation. While the first often is triggered by the need to increase customer centricity, the latter is mostly triggered by the need to increase the number of releases. Moreover, we found that the selected implementation approach has significant impact on the key design parameters for and the content of the implementation of scaled-agile frameworks.
Maximum Likelihood Estimation of Factored Regular Deterministic Stochastic Languages

This paper proves that for every class C of stochastic languages defined with the coemission product of finitely many probabilistic, deterministic finite-state acceptors (PDFA) and for every data sequence D of finitely many strings drawn i.i.d.from some stochastic language, the Maximum Likelihood Estimate of D with respect to C can be found efficiently by locally optimizing the parameter values.We show that a consequence of the co-emission product is that each PDFA behaves like an independent factor in a joint distribution.Thus, the likelihood function decomposes in a natural way.We also show that the negative log likelihood function is convex.These results are motivated by the study of Strictly k-Piecewise (SP k ) Stochastic Languages, which form a class of stochastic languages which is both linguistically motivated and naturally understood in terms of the coemission product of certain PDFAs.
The Augmented Reality Technologies in Tourism: A State of Art

In tourism, as in other economic sectors, technological innovation plays a very important role in achieving the companies' objectives and, also, in meeting consumers' desires as much as possible.In this context, the concrete benefits of the augmented reality in the field of tourism have been identified in the paper, starting from the concrete examples of its application provided by the literature.At the same time, the main expectations of its potential users were determined, namely, on the one hand, the companies operating in the field of tourism and on the other side the tourists
Secure Data Encryption Through Combination of RSA Cryptography Random Key Algorithm and Quadratic Congruential Generator

Abstract A cryptographic algorithm is used to enhance the security of the data. Algorithms whose security level is not too strong will pose a risk to data security. To safeguard the security of data, the key on the cryptographic algorithm is made as strong as possible, in order to avoid from irresponsibility parties. This paper uses the random keys for the Rivest Shamir Adleman Algorithm. The random keys that used to encrypt each plaintext letter are always different. The random key was originally formed from the first value of iteration, and it was called as seed. The seed for forming the value of p and q which generated by the Quadratic Congruential Generator Algorithm. The combination of the RSA Random Key Algorithm and the Quadratic Congruential Generator algorithm shows that each plaintext letter has own unique value of p, q, public key, and private key. This value is really always different for each encryption process. The result from this research can measure from the resistance of the cryptanalyst attack. The resistance of the RSA Random Key Algorithm toward the cryptanalyst attacks is 64 %.
Energy-saving framework for data center from reduce, reuse and recycle perspectives

Recently, there is a heightened level of awareness towards energy efficiency in high-performance data centers both to reduce environmental pollution and save cost. Such data centers consume massive amount of energy for processing huge computational requirements from users. These supercomputers demand a constant supply of electricity to be available 24/7 for both its core computing functions as well as cooling the data center. Previously, researchers had introduced various strategies for achieving energy efficiency. However, in order to achieve a truly effective energy management, factors that influence energy usages must also be taken into consideration. The failure to manage such factors leads to excessive energy consumption. In this work, we shall focus on factors relevant to running the operation of high-performance data centers. We reconstructed and analyzed such factors or attributes based on the universally accepted Reduce, Reuse and Recycle Concept (3R). We recategorized energy attributes of the existing Energy Efficient Data Center Frameworks (EEDCFs) to be aligned with 3R. Then, we developed energy-saving algorithms in response to the concept. Our framework was then measured according to the performance metrics namely power usage effectiveness (PUE), energy reuse effectiveness (ERE) and carbon usage effectiveness (CUE) against variability size of data center. The simulation results of our EEDCF showed that better energy saving is achieved in comparison to the existing EEDCFs. This signifies that the application of the 3R concept in energy consumption yielded a promising result as a platform for other researchers to explore more on energy renewal initiatives and embrace it for future application.
Image tamper detection and multi-scale self-recovery using reference embedding with multi-rate data protection

This paper proposes a multi-scale self-recovery (MSSR) approach to protect images against content forgery. The main idea is to provide more resistance against image tampering while enabling the recovery process in a multi-scale quality manner. In the proposed approach, the reference data composed of several parts and each part is protected by a channel coding rate according to its importance. The first part, which is used to reconstruct a rough approximation of the original image, is highly protected in order to resist against higher tampering rates. Other parts are protected with lower rates according to their importance leading to lower tolerable tampering rate (TTR), but the higher quality of the recovered images. The proposed MSSR approach is an efficient solution for the main disadvantage of the current methods, which either recover a tampered image in low tampering rates or fails when tampering rate is above the TTR value. The simulation results on 10000 test images represent the efficiency of the multi-scale self-recovery feature of the proposed approach in comparison with the existing methods.
Joint Learning of Generative Translator and Classifier for Visually Similar Classes

In this paper, we propose a Generative Translation Classification Network (GTCN) for improving visual classification accuracy in settings where classes are visually similar and data is scarce.For this purpose, we propose joint learning from a scratch to train a classifier and a generative stochastic translation network end-to-end.The translation network is used to perform on-line data augmentation across classes, whereas previous works have mostly involved domain adaptation.To help the model further benefit from this data-augmentation, we introduce an adaptive fade-in loss and a quadruplet loss.We perform experiments on multiple datasets to demonstrate the proposed method's performance in varied settings.Of particular interest, training on 40% of the dataset is enough for our model to surpass the performance of baselines trained on the full dataset.When our architecture is trained on the full dataset, we achieve comparable performance with state-of-the-art methods despite using a light-weight architecture.
Dependency Parsing as Sequence Labeling with Head-Based Encoding and Multi-Task Learning

Dependency parsing as sequence labeling has recently proved to be a relevant alternative to the traditional transition-and graph-based approaches.It offers a good trade-off between parsing accuracy and speed.However, recent work on dependency parsing as sequence labeling ignore the pre-processing time of Part-of-Speech tagging -which is required for this task -in the evaluation of speed while other studies showed that Part-of-Speech tags are not essential to achieve state-ofthe-art parsing scores.In this paper, we compare the accuracy and speed of shared and stacked multi-task learning strategies -as well as a strategy that combines both -to learn Part-of-Speech tagging and dependency parsing in a single sequence labeling pipeline.In addition, we propose an alternative encoding of the dependencies as labels which does not use Part-of-Speech tags and improves dependency parsing accuracy for most of the languages we evaluate.
Eliminating Extremism: A Legal Analysis of Hate Crime and Terrorism Laws in the United States

The distinction between hate crime and terrorism is a contentious legal issue that impacts how respective crimes are labeled and prosecuted. Using a legal content analysis, this study examines what central legal elements comprise hate crimes and terrorism, respectively, for each state to discern their legal differences. The analysis along with exegesis of terrorism and hate crime definitions enumerated in state law demonstrates that these crimes are too similar to be different legal constructs, given their definitional elements and real-world applications. The study concludes that these “crimes” need to either be eliminated from criminal law or altered so as to make the adjudication of so-called extremism more parsimonious, equitable, and consistent. Recommendations are proffered on how this can be accomplished.
Paving the way towards counterfactual generation in argumentative conversational agents

Counterfactual explanations present an effective way to interpret predictions of black-box machine learning algorithms.Whereas there is a significant body of research on counterfactual reasoning in philosophy and theoretical computer science, little attention has been paid to counterfactuals in regard to their explanatory capacity.In this paper, we review methods of argumentation theory and natural language generation that counterfactual explanation generation could benefit from most and discuss prospective directions for further research on counterfactual generation in explainable Artificial Intelligence.
Policy Search by Target Distribution Learning for Continuous Control

It is known that existing policy gradient methods (such as vanilla policy gradient, PPO, A2C) may suffer from overly large gradients when the current policy is close to deterministic, leading to an unstable training process. We show that such instability can happen even in a very simple environment. To address this issue, we propose a new method, called target distribution learning (TDL), for policy improvement in reinforcement learning. TDL alternates between proposing a target distribution and training the policy network to approach the target distribution. TDL is more effective in constraining the KL divergence between updated policies, and hence leads to more stable policy improvements over iterations. Our experiments show that TDL algorithms perform comparably to (or better than) state-of-the-art algorithms for most continuous control tasks in the MuJoCo environment while being more stable in training.
Real Time Human Computer Interaction Using Facial Gestures

The main focus of this paper is to advance the study of controlling a personal computer using various face gestures and movements captured using RGB webcam in real time. This is highly useful for physically challenged people. Recent state-of-the-art face landmark detection algorithm is used to localize facial features which helped in estimating various parameters for computer control with high precision. Position of nose tip is tracked for cursor control and smile detection is carried out for scrolling of pages. A unique mathematical expression is introduced which is used to detect the state of the eye (blinking or not). The algorithm developed was found to be robust against different head orientation and lighting condition. This algorithm also outperforms many other related works both in terms of accuracy and computation time. The relevant pipeline architecture and results are demonstrated in this paper.
Integrating the Musical, the Natural, and the Improvised: David Rothenberg and Multispecies Musicking

It is in musical performance, suggests Ingram (2010), that our relationships with environments most cogently sustain and enrich ecology (p. 11). Ecology informs and inspires music, but it does not govern music (Boyle & Waterman, 2016). Composers have long incorporated the sounds of birds and animals into their music, and in recent years, technology has played a role in the study and development of more-than-human “musicality.”1 However when jazz musicians improvise in or out of sync with the acoustic presence of wildlife, creativity remains in flux.2 This baffling anti-institutional practice furnishes new combinations of sound, but it remains subject to philosophical questions that deflect attention away from the music's inherent artistic worth. For the purposes of this reflective essay, there exists a gap in the literature around fresh approaches to jazzing within—and with—nature. Likewise, a pedagogy is needed to explain the benefits of multispecies musicking.