Reviewerly/exHarmony huggingface dataset:
A Scientometric Analysis of “Water” from 2006-2019

Zhang, L., 2019. A Scientometric analysis of “water” from 2006-2019. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 1–8. Coconut Creek (Florida), ISSN 0749-0208.Water is an important component of nature and the most active element in the environment, it plays a significant role on the earth and attracts many authors to study it. Therefore, this paper explored the hot topics and new trends of through the statistics of the journal between 2009 and 2019 from the perspective of scientometric based on the keyword, “water”. 1160 records were downloaded from the SSCI and SCIE database from the category of economics. Correspondingly, the data were analyzed from the perspective of analysis of year distribution of water publications, major journals analysis, influential institutions analysis, productive countries analysis, core authors analysis, research clusters analysis, most cited references analysis, burst detection analysis. Ultimately, the situation and the analysis results of water publications were presented to readers, authors and scholars who are interested in the topic and it could provide a reference for them to explore further in this field.
Broadcasting Correlated Gaussians and Asymmetric Data Transmission

We consider the broadcasting of a memoryless bivariate Gaussian source over a two-user Gaussian broadcast channel where in addition to the source broadcasting a message is conveyed reliably to the “higher quality” receiver. Receiver 1 reconstructs the first source component while Receiver 2 reconstructs the second source component. Given the message rate, we characterize the set of attainable mean squared-error distortion pairs at the two receivers.
Google Cloud Dataprep

Google Cloud Dataprep is a managed cloud service for quick data exploration and transformation. Dataprep makes it easy to clean and transform large datasets for analysis. It is auto-scalable as it takes advantage of the distributed processing capabilities of Google Cloud Dataflow.
Asymmetric Encryption: Public/Private Keys

Asymmetric encryption is one of the most important advances in cryptographic security ever made. It underpins all of the security on the Web, in your Wi-Fi connections, and in secure email and other communication of all kinds. It is ubiquitous, but it is also subtle and easy to implement or use incorrectly, and a lack of correctness means a sometimes drastic reduction in security.
The platform approach to research and development using high-performance computing

In this paper, we analyze the prerequisites and substantiate the relevance for creating an open Internet platform that employs big data technologies, highperformance computing, and multilateral markets in a unified way. Conceived as an ecosystem for the development and use of applied software (including in the field of design and scientific research), the platform should reduce time/costs and improve the quality of software development for solving analytical problems arising in industrial enterprises, scientific research organizations, state bodies and private individuals. The article presents a working prototype of the platform using supercomputer technologies and desktop virtualization systems.
Optimization for Machine Learning: Gradient Descent

Gradient descent is an optimization algorithm that is used to minimize the cost function of a machine learning algorithm. Gradient descent is called an iterative optimization algorithm because, in a stepwise looping fashion, it tries to find an approximate solution by basing the next step off its present step until a terminating condition is reached that ends the loop.
Text recognition in radiographic weld images

Automatic recognition of text characters on radiographic images based on computer vision would be a very useful step forward as it could improve and simplify the file handling of digitised radiographs. Text recognition in radiographic weld images is challenging since there is no uniform font or character size and each character may tilt in different directions and by different amounts. Deep learning approaches for text recognition have recently achieved breakthrough performance using convolutional neural networks (CNNs). CNNs can recognise normalised characters in different fonts. However, the tilt of a character still has a strong influence on the accuracy of recognition. In this paper, a new improved algorithm is proposed based on the Radon transform, which is very effective at character rectification. The improved algorithm increases the accuracy of character recognition from 86.25% to 98.48% in the current experiments. The CNN is used to recognise the rectified characters, which achieves good accuracy and improves character recognition in radiographic weld images. A CNN greatly improves the efficiency of digital scanning and filing of radiographic film. The method proposed in this paper is also compared with other methods that are commonly used in other fields and the results show that the proposed method is better than state-of-the-art methods.
Heterogeneous real-time multi-channel time-domain feature extraction using parallel sum reduction on GPU

Online BCI has become a fascinating field of research nowadays. One of the main challenges in this field is to reduce the latency caused by the computational complexity of the signal processing algorithms. This issue leads to difficulty in processing real-time data. Usually, a trade-off needs to be considered between the number of input samples and precision of the processing algorithms. In this paper, heterogeneous computing concept is investigated to alleviate the computational complexity occurred in real-time processing. An OpenCL was utilized to implement signal processing algorithms in parallel. Feature extraction methods including band power and statistical moments were selected to examine the power of heterogeneous computing using parallel sum reduction. As a result, varying the number of work-group sizes which is an essential parameter of parallel processing provided dissimilar computing times. Also, running at a higher sampling rate yielded a higher benchmark ratio between sequential and parallel. However, system optimization is still necessary when processing BCI in real time.
Quantum Phase Transition of a Spin Coupled with an Engineered Bosonic Reservoir

A scheme to realize a subohmic bosonic reservoir is proposed. The reservoir can be coupled to a spin or two-level system, demonstrating an intriguing quantum phase transition.
Phase shifting method for non-sinusoidal interference fringes with phase shift error

When applying phase shifting interferometry to practical phase measurement, the wave may become non-sinusoidal, and could include some phase shift errors. Prior phase shift algorithms are primarily used for sinusoidal cases, and can cause system errors when used for these non-sinusoidal interference. We propose two phase recovery methods for non-sinusoidal interference fringes: specifically, a least-squares-based fitting algorithm that eliminates system errors using a sufficient number of measured images, and a Fourier transform-based method that effectively suppresses system errors using a lower (or insufficient) number of images. Simulation and experimental results verify the efficacy of the proposed methods.
THE IMPACT OF PLEASE STRATEGY ON DEVELOPING PARAGRAPH WRITING SKILLS

This study aimed at investigating the effect of PLEASE (Pick, List, Evaluate, Activate, Supply, and End) strategy on developing paragraph writing skills of first-year students at Al-Balqa Applied University. It attempted to investigate the effect of PLEASE on paragraph writing achievement, compared to conventional method. Eighty-one EFL learners participated in the study at Ajloun College and were distributed on experimental and control groups. To fulfil the purpose of the study, the researchers applied pre- and post-test on both groups. After analysing the obtained data, the results indicated that PLEASE strategy affected paragraph writing positively. The study suggested several implications and recommendations for the future research.DOI: 10.24071/llt.2019.220211
Optimizing Self-Organizing Maps Parameters Using Genetic Algorithm: A Simple Case Study

A Self-Organizing Map (SOM) is a powerful tool for data analysis, clustering, and dimensionality reduction. It is an unsupervised artificial neural network that maps a set of n-dimensional vectors to a two-dimensional topographic map. Being unsupervised, SOMs need little input to be successfully deployed. The only inputs needed by a SOM are its own parameters such as its size, number of iterations, and its initial learning rate. The quality and accuracy of the solution offered by a SOM depend on choosing the right values for such parameters. Different attempts have been made to use the genetic algorithm to optimize these parameters for random inputs or for specific applications such as the traveling salesman problem. To the best knowledge of the authors, no roadmaps for selecting these parameters were presented in the literature. In this paper, we present the first results of a proposed roadmap for optimizing these parameters using the genetic algorithm and we show its effectiveness by applying it on the classical color clustering problem as a case study.
Machine Learning to Identify Gender via Hair Elements

Currently, the gender is mainly inferred through bone and dental analyses. However, such sort of analysis is useless when bones are not available. Hair is a stable substance that, depending on its length, is capable of retaining years of information. In this paper we propose a machine learning based approach aimed to identify gender through hair elements. Preliminary results have even indicated that the method is promising also for forensics analysis since it is able to identify the gender with a high accuracy.
Paraphrase Generation with Collaboration between the Forward and the Backward Decoder

Automatic paraphrase generation plays a key role in many natural language applications. The dominant paraphrase generation models are the encoder-decoder neural networks with attention, where the decoder uses the information of the source text while predicting target text. However, the outputs of these paraphrase models often suffer the semantic error problem. This problem is caused by the inadequate information of the decoder. In this work, we introduce a novel neural model to solve this problem, called Collaboration between the Forward and the Backward Decoder. Specifically, the hidden states of the backward decoder are used as supplementary information of the forward decoder. Therefore, the forward decoder can generate more reasonable paraphrase text using the target-side future contextual. Conversely, the backward decoder employs the hidden states of the forward decoder to prevent the semantic error problem. As two experimental examples show, the proposed model can generate the high-quality paraphrase through this collaboration mechanism. The empirical study on two benchmark datasets demonstrates that our model outperforms some baselines and achieves the state-of-the-art performance.
Generating Natural Video Descriptions using Semantic Gate

Video captioning task aims to generate a textual description of the situation in a video. It is challenging because of the nature of modality-difference between video and language. We present a novel method to bridge the gap between them by utilizing the semantic gate in two ways. First, we develop an activation mechanism to make a video description that captures the concept of the video. Next, we design a network that evaluates the similarity between visual and sentence feature. Semantic gate is used to transform sentence into a semantic embedding. We also conduct experiments to show that image and action classification task performance is transferred to video captioning task. Experimental results show that our proposed method has gained promising improvements compared to the baseline model. Consequently, our model demonstrated the effectiveness by achieving new best record on MSRVTT and MSVD dataset.
Modulation of Activation Function in Triangular Recurrent Neural Networks for Time Series Modeling

This paper introduces a novel method to dynamically vary the activation function slopes in recently developed upper and lower triangular recurrent neural networks (ULTRNN) to enhance their modeling capability. The ULTRNN employs a pair of triangular feedback weight matrices with block diagonal elements whose eigenvalues are constrained to lie on or close to the unit circle in the complex z-plane to maintain network and training stability. The activation function slopes of the ULTRNN state variables are dynamically varied by a second modulating network. The inputs to the modulating network are the state variables of the principal ULTRNNs and their inputs. The modulating network is trained simultaneously with the principal ULTRNN to compute the activation function slope for the latter’s each state variable at each time step. Such dynamic variation of the activation function slopes selectively enhances the contribution of certain states while suppressing that of the others. A larger slope results in a longer time contribution of the corresponding state and helps model long-term dependencies. Conversely, a smaller slope results in a shorter time contribution and may be used to model controlled forgetting. The proposed modulation technique enhances the ULTRNN’s ability to effectively incorporate short-term memory and long-term dependencies. Simulation results show that with activation function modulation the ULTRNNs are able to autonomously replicate the outputs of sample chaotic dynamic system with good accuracy. This capability can be highly effective in modeling or characterizing the inherent process that generates the time series.
Comparison of Optical Flow Methods under Stereomatching with Short Baselines

This article studies the effectiveness of optical flow methods applied to short baseline image pairs under different noise levels.New metrics have been developed to analyze the results because the usual metrics are inadequate in a subpixel context.We have used the implementation of some standard optical flow methods adapted to the stereo problem.Our experiments show that the Brox et al. method produces the least errors, with a 60% success rate and a relative precision at 1/100th of a pixel.On the other hand, our comparison shows that a discontinuity preserving method, derived from Brox et al., also provides competitive results at the same time that it yields disparities with more details and correct contours.
Correlated Components Analysis - Extracting Reliable Dimensions in Multivariate Data.

How does one find dimensions in multivariate data that are reliably expressed across repetitions? For example, in a brain imaging study one may want to identify combinations of neural signals that are reliably expressed across multiple trials or subjects. For a behavioral assessment with multiple ratings, one may want to identify an aggregate score that is reliably reproduced across raters. Correlated Components Analysis (CorrCA) addresses this problem by identifying components that are maximally correlated between repetitions (e.g. trials, subjects, raters). Here we formalize this as the maximization of the ratio of between-repetition to within-repetition covariance. We show that this criterion maximizes repeat-reliability, defined as mean over variance across repeats, and that it leads to CorrCA or to multi-set Canonical Correlation Analysis, depending on the constraints. Surprisingly, we also find that CorrCA is equivalent to Linear Discriminant Analysis for zero-mean signals, which provides an unexpected link between classic concepts of multivariate analysis. We present an exact parametric test of statistical significance based on the F-statistic for normally distributed independent samples, and present and validate shuffle statistics for the case of dependent samples. Regularization and extension to non-linear mappings using kernels are also presented. The algorithms are demonstrated on a series of data analysis applications, and we provide all code and data required to reproduce the results.
Weibo Recommendation Algorithm Based on Tag Clustering and User Preference

The microblog recommendation algorithm has the problems of cold start, sparse data and low accuracy. To solve this problem, this paper proposes a microblog recommendation algorithm based on label clustering and user preference. Firstly, the user tag is obtained as the user's preference. The user's preference is divided by the tag clustering algorithm to obtain the similarity between the users. Secondly, the important user of the user is obtained by the PageRank algorithm, and the user is obtained according to the relationship between the users. The important user preference similarity, in order to obtain user preferences indirectly, the combination of the two to calculate the similarity of user preferences. The experimental results show that the improved algorithm can mine user preferences and improve the recommendation quality.
Decoupling the programming model from resource management in throughput processors

This chapter introduces a new resource virtualization framework, Zorua, that decouples the graphics processing unit (GPU) programming model from the management of key on-chip resources in hardware to enhance programming ease, portability, and performance. The application resource specification-a static specification of several parameters such as the number of threads and the scratchpad memory usage per thread block-forms a critical component of the existing GPU programming models. This specification determines the parallelism, and, hence, performance of the application during execution because the corresponding on-chip hardware resources are allocated and managed purely based on this specification. This tight coupling between the software-provided resource specification and resource management in hardware leads to significant challenges in programming ease, portability, and performance, as we demonstrate in this chapter using real data obtained on state-of-the-art GPU systems. Our goal in this work is to reduce the dependence of performance on the software-provided static resource specification to simultaneously alleviate the above challenges. To this end, we introduce Zorua, a new resource virtualization framework, that decouples the programmer-specified resource usage of a GPU application from the actual allocation in the on-chip hardware resources. Zorua enables this decoupling by virtualizing each resource transparently to the programmer. The virtualization provided by Zorua builds on two key concepts-dynamic allocation of the on-chip resources and their oversubscription using a swap space in memory. Zorua provides a holistic GPU resource virtualization strategy designed to (i) adaptively control the extent of oversubscription and (ii) coordinate the dynamic management of multiple on-chip resources to maximize the effectiveness of virtualization.We demonstrate that by providing the illusion of more resources than physically available via controlled and coordinated virtualization, Zorua offers several important benefits: (i) Programming ease. It eases the burden on the programmer to provide code that is tuned to efficiently utilize the physically available on-chip resources. (ii) Portability. It alleviates the necessity of retuning an application&apos;s resource usage when porting the application across GPU generations. (iii) Performance. By dynamically allocating resources and carefully oversubscribing them when necessary, Zorua improves or retains the performance of applications that are already highly tuned to best utilize the resources. The holistic virtualization provided by Zorua has many other potential uses, e.g., fine-grained resource sharing among multiple kernels, low latency preemption of GPU programs, and support for dynamic parallelism, which we describe in this chapter.
Installing a Docker Host on an Amazon EC2 Instance

In this chapter, I will show you how to install Docker on an Amazon Elastic Compute Cloud (EC2) instance. Amazon EC2 provides a computing infrastructure that is scalable and allows you to run all kinds of workloads including Docker containers.
Building argumentative adpositional trees : Towards a high-precision method for reconstructing arguments in natural language

There is a need for a tool for reconstructing arguments that describes their linguistic elements with high precision and at the same time identifies their type. In this paper, we prepare the ground for developing such a tool by introducing the notion of ‘argumentative adpositional tree’. The notion is based on a combination of the linguistic representation framework of Constructive Adpositional Grammars (CxAdGrams) and the argument classification framework of the Periodic Table of Arguments (PTA).
FPGA Implementation of Moving Object Segmentation using ABPD and Background Model

Hardware Implementation of an efficient technique for moving object segmentation in video frame plays important role in real-time applications such as Video Surveillance system, traffic monitoring system, robotics vision, etc. This paper presents a hardware implementation of moving object segmentation using Adaptive Block Partition Decision Technique (ABPD) with a fixed threshold and pixel- based background model. The pixel-based background model is included to make background subtraction method insensitive to surrounding environment illumination variations and noise, also it makes an algorithm useful for the case where no background image exist or hard to generate. Behavior of proposed method is improved in terms of FP rate, Precision, Similarity, and F-measure. The architecture for proposed algorithm is implemented on a vertex6 xc6vlx75t device with 5 pipeline stages. This architecture achieves high throughput with less resource utilization compared to existing architectures.
Factors Influencing Consumers Adoption of Internet of Things (IoTs): An Empirical Study from Indian Context

This study aims to find out the impact of safety, status, cost, and convenience on the consumer's awareness regarding internet of things (IoTs) as well as the consumer's adoption of IoTs. The population of the study is the total number of students of Aligarh Muslim University in which the sample size of 310 students has been taken for the study. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) is applied for testing reliability and validity. Also, structural equation modelling (SEM) is used for testing the hypotheses of the study. The results reveal that the students have a positive attitude of awareness in the case of the cost of IoTs devices while they have a negative attitude towards awareness regarding safety, convenience and status. Besides, the results show students' positive attitude of adoption in case of convenience, and status.
Dropping Activations in Convolutional Neural Networks with Visual Attention Maps

The introduction of visual attention models in data selection and features selection in CNNs for the task of image classification is an intensive and interesting research topic. In CNNs, the strategy of dropping activations, after features extraction layers, shown an increase in the generalization gap in large-scale datasets and avoiding over-fitting. Dropout has been studied in the literature in a fully-randomized manner to take down activations during training. In this paper, we introduce a saliency-based dropping strategy to take down activations in our AlexNet-like architecture. Our experiments are conducted for the specific task of specific Mexican architectural recognition, in 67 categories. The results are promising: the proposed approach outperformed other models reducing training time and reaching a higher accuracy.