Reviewerly/exHarmony huggingface dataset:
Automation of Purchase Order in Microsoft Dynamics 365 by Deploying Selenium

Regression testing is very important for dynamic verification.It helps to simulate a suite of test cases periodically and after major changes in the design or its environment, to check that no new bugs were introduced.Evidences regarding benefit of implementing automation testing which includes saves of time and cost as it can re-run test scripts again and again and hence is much quicker than manual testing, providing more confidence in the quality of the product and increasing the ability to meet schedules and significantly reducing the effort that automation requires from testers are provided on the basis of survey of 115 software professionals.In addition to this, automated regression suite has an ability to explore the whole software every day without requiring much of manual effort.Also, bug identification is easier after the incorrect changes have been made.Genius is going through continuous development and requires testing again and again to check if new feature implementation have affected the existing functionality.In addition to this, Erudite is facing issue in validation of the Genius installation at client site since it requires availability of testers to check the critical functionality of the software manually.Erudite wants to create an automated regression suite for Genius which can be executed at client site for checking the functionality of the software.In addition to this, this suite will also help the testing team to validate if the new features which have been added to the existing software are affecting the existing system or not.Visual studio, Selenium Webdriver, Visual SVN and Trello are the tools which have been used to achieve the creation of automation regression suite.The current research will provide guidelines to the future researchers on how to create an automated regression suite for any web application using open source tools.
Transform domain robust watermarking method using Riesz wavelet transform for medical data security and privacy

Rapid progress of digital multimedia content has tremendously improved different forms of information and its processing. New digital technologies help to store, transmit and process the information in a quick and accurate way. However, digital data in the form of videos and images can be effortlessly manipulated and redistributed using the computers. Violation of ownership rights and verifying the integrity of the digital content can be significantly improved by the watermarking approaches. Watermarking algorithms embed additional information for verifying the authenticity and trustworthiness. Security and privacy of medical data in the form of image or other 1-D signals are of prime importance and has become an emerging area in the field of biomedical information technology. Biomedical image watermarking algorithms enable transmission of medical records and patient history in a secure way. The aim of this book chapter is to propose a new watermarking approach using a transform domain for medical image security. Riesz wavelet transform (RWT) and singular value decomposition (SVD) is employed for embedding the watermarking in the cover medical image at the transmitter side. At the receiver, the embedded information is recovered successfully using the watermarking extraction algorithm. The RWT-SVD algorithm is tested on different types of medical images like X-ray, CT scan, MRI and retinal images. The watermark is extracted at the receiver without the original image. Imperceptibility evaluation using several metrics (SNR, PSNR, WPSNR, SSIM, MSSIM, SC) shows the improved performance of the proposed approach. In addition to this, robustness analysis is also carried in terms of correlation coefficient.
Pelatihan Aplikasi Kahoot di Lembaga Bimbingan Belajar Gama UI Cabang Duren 3 Raya Jakarta

Human resources are one of the important assets and have a direct impact on the organization or agency. An institution must be supported by competent human resources because human resources are very important in carrying out business or activities in the institution. One of the ways that can be done in employee performance improvement efforts is by providing training. The process of education underwent a rapid change by utilizing digital technology, where the technology is used to facilitate the access and for the benefit of service improvement as well as for the quality of education. The problems faced by the employees are not yet creative applications to attract students to the spirit to learn. To overcome the problems faced by the partners, training will be conducted on the application of Kahoot. The game design in a multiplayer kahoot allows children to collaborate while competing through interactive, creative games. In the training will be delivered strategies that must be done in order for students to be interested in learning and encouragement. The implementation of this activity is conducted in three phases, namely: preparation stage, implementation, and evaluation. The implementation of community service was completed with Gama UI's Bimbel. Employee enthusiasm in following this activity deserves to be appreciated. The activities of community dedication are necessary and directly felt by the benefits of Gama UI Bimbel.
An Efficient Multiauthority Attribute-Based Encryption Technique for Storing Personal Health Record by Compressing the Attributes

Personal health record system allows patient to share their health information in a remote location. Security of the data is an important criterion to be considered while outing the facts in the remote servers. Encryption is the common technique to provide the security for the data before outsourcing. This paper focus on multiauthority attribute-based encryption (MAABE) approaches, by compressing the least value attributes. In all the attribute-based encryption, the attributes are used to encode and decode the data. As the quantity of attributes used increases, the calculation time will be increased for the performance. So, in order to increase the system efficiency, least value attribute details should be compressed before encoding and decoding the information. Also, a binary tree structure is used to administer the attributes in various authority levels of the algorithm. This binary tree structure helps to decrease the encoding and decoding timing of the algorithm.
Classifying white blood cells from a peripheral blood smear image using a histogram of oriented gradient feature of nuclei shapes

Researchers developed various methods and algorithms to classify white blood cells (WBCs) from blood smear images to assist hematologists and to develop an automatic system. Furthermore, the pathological and hematological conditions of WBCs are related to diseases that can be analyzed accurately in a short time. In this work, we proposed a simple technique for WBC classification from a peripheral blood smear image based on the types of cell nuclei. The developed algorithms utilized a histogram of oriented gradient (HOG) feature typically known for application in human disease detection. The segmentation of WBC nuclei utilizes a YCbCr color space and K-means clustering techniques. The HOG feature contains information about the cell nuclei shapes, which then is classified using a support vector machine (SVM) and backpropagation artificial neural network (ANN). The results show that the proposed HOG feature is useful for WBC classification based on the shapes of nuclei. We are able to categorize the type of a WBC based on its nucleus shape with more than 95% accuracy.
Developing new mathematical model formats for estimating daily and monthly solar radiation: case study: Iran

Although a few numbers of studies are conducted for estimating in different cities of Iran, updating and developing new models based on more recent data are necessary. Six cities with different climate conditions were selected. Five newly developed daily and eight newly developed monthly model formats were fitted to measured data of Iranian cities. Seven statistical parameters were calculated to investigate the performances of models. To prove the excellence of the newly developed models compared to the performance of available models, four available daily models were fitted to daily measured data. Furthermore, to prove the generality of newly introduced formats in the present study, four foreign cities around the world were selected to use newly introduced model formats. Newly developed model formats have an extremely good and significant performance at 1% probability level. Furthermore, for Iranian cities, compared to available daily formats, newly introduced formats have shown to have better performances in most cases.
High-Speed Inversion Using $$x^{4^{n}}$$ Units

Inversion is a significant operation in ECC processors and is the most complex and time-consuming operation among operations like addition, subtraction and multiplication. Thus, proposing an algorithm and designing its architecture to compute inverse with minimum number of clock cycles are mandatory. In this brief, high-speed inversion of NIST recommended pentanomial $$\text {GF}(2^{163})$$, based on traditional Itoh–Tsujii inversion algorithm (ITIA) is proposed. This proposed inversion algorithm is then implemented on FPGA Virtex-5 platforms to analyze its performance. This design minimizes the latency and, thereby, improves speed. The developed high-speed Itoh–Tsujii inversion algorithm HS-ITIA computes inversions in 18 clock cycles with maximum clock frequency 64.5 MHz, which thereby yields a rise in performance by 56%.
Optimized Distribution of an Accelerated Convolutional Neural Network across Multiple FPGAs

Convolutional Neural Networks (CNN) have achieved a resounding success especially in computer vision and collaborative filtering. The general trend in CNN architectures has been to build deeper networks with a substantial number of convolution filters and several large feature maps. As a result, most of the current CNN inference routines are highly compute-intensive and have significant storage requirements. Field Programmable Gate Arrays (FPGAs) are among the most popular choices for accelerating CNN inference workloads as they can perform complex and massively parallel jobs. Recently, notable efforts have been made to distribute CNN inference workloads across multiple FPGAs [1]. These strategies, however, do not take into account variations in computational complexity across different layers of a CNN resulting in suboptimal performance gains. This work proposes an optimal distribution of CNN layers across different FPGA nodes while accounting for each layer’s performance to achieve maximum overall throughput.
Non-linear Regression Model Between Solar Irradiation and PV Power Generation by Using Gompertz Curve

With the opening of the small power brokerage business market in December 2018, the small power trading market has started in Korea. Operators must submit the day-ahead estimates of power output and receive incentives based on its accuracy. Therefore, the accuracy of power generation forecasts is directly affects profits of the operators. The forecasting process for power generation can be divided into two procedure. The first is to forecast solar irradiation and the second is to transform forecasted solar irradiation into power generation. There are two methods for transformation. One is to simulate with physical model, and another is to use regression model. In this study, we found the best-fit regression model by analyzing hourly data of PV output and solar irradiation data during three years for 242 PV plants in Korea. The best model was not a linear model, but a sigmoidal model and specifically a Gompertz model. The combined linear regression and Gompertz curve was proposed because a the curve has non-zero y-intercept. As the result, R2 and RMSE between observed data and the curve was significantly reduced.
Model Fusion with Kullback--Leibler Divergence

We propose a method to fuse posterior distributions learned from heterogeneous datasets. Our algorithm relies on a mean field assumption for both the fused model and the individual dataset posteriors and proceeds using a simple assign-and-average approach. The components of the dataset posteriors are assigned to the proposed global model components by solving a regularized variant of the assignment problem. The global components are then updated based on these assignments by their mean under a KL divergence. For exponential family variational distributions, our formulation leads to an efficient non-parametric algorithm for computing the fused model. Our algorithm is easy to describe and implement, efficient, and competitive with state-of-the-art on motion capture analysis, topic modeling, and federated learning of Bayesian neural networks.
Sentiment classification of hotel service review on traveloka sites using naïve bayes classifier (NBC) and binary logistic regression

Abstract Utilization of social media sites such as Traveloka can help hotel marketing by providing information as review related to search and hotel bookings online. The given review could be a feedback to the related hotel as well as to assist visitors in choosing the right hotel. Feedback information as a review is important data text, so we need to develop a method to classifiy it then the right method used for classifying text data is text mining. Sources of data are obtained from web scraping process which aims to obtain online data on website page by collecting visitor review’s of Favehotel and Gunawangsa Hotel from Traveloka sites. Text mining methods used in this study is Naïve Bayes Classifier (NBC) and Binary Logistic Regression which is the process of sentiment labeling based on Lexicon dictionary. Word cloud visualization shows that the highest keywords that lead to the both of the hotel with the largest positive sentiment are ‘clean’ and ‘comfortable’. A comparison methods between NBC and Binary Logistic Regression for Favehotel and Gunawangsa Hotel obtained a decision that Binary Logistic Regression method with SMOTE was better than NBC where AUC value in testing data for Favehotel is 0.84 and Hotel Gunawangsa is 0.82.
Post-Hoc Methods for Debiasing Neural Networks

As deep learning models become tasked with more and more decisions that impact human lives, such as hiring, criminal recidivism, and loan repayment, bias is becoming a growing concern. This has led to dozens of definitions of fairness and numerous algorithmic techniques to improve the fairness of neural networks. Most debiasing algorithms require retraining a neural network from scratch, however, this is not feasible in many applications, especially when the model takes days to train or when the full training dataset is no longer available. In this work, we present a study on post-hoc methods for debiasing neural networks. First we study the nature of the problem, showing that the difficulty of post-hoc debiasing is highly dependent on the initial conditions of the original model. Then we define three new fine-tuning techniques: random perturbation, layer-wise optimization, and adversarial fine-tuning. All three techniques work for any group fairness constraint. We give a comparison with six algorithms - three popular post-processing debiasing algorithms and our three proposed methods - across three datasets and three popular bias measures. We show that no post-hoc debiasing technique dominates all others, and we identify settings in which each algorithm performs the best. Our code is available at this https URL.
"The Structural Relationship among English Online Activities, Self-directedness, Attitudes toward International Community and English Proficiency of Korean EFL University Students"

This study investigates the structural relationship among use of English internet and online activities, self-directedness in English learning, attitudes toward international community and English proficiency. A total of 560 Korean EFL university students participated in this study. Results showed that students revealed a low degree of use of English online services and they were most likely to use English video services such as YouTube, that their self-directedness in learning English and attitudes toward international community were both at a medium level. The result also showed that all the variables were significantly different in English proficiency levels. The SEM analysis revealed that all of the paths in the model were statistically significant, where English online activities had direct paths to attitudes, self-directedness, and English proficiency. Attitudes had a direct path to self-directedness, which, in turn, had a direct path to English proficiency. (Geumgang University)
Research into cognition and numerical thinking in Colombia (Investigación sobre cognición y pensamiento numérico en Colombia)

The article presents the results of a literature review that covered various study types and contributions made on cognition and numerical thinking in Colombia. After conducting a search in different sources, 84 relevant documents were selected and analysed in detail. Three categories were created: (i) subjects and lines of research; (ii) teacher training; and (iii) contributions to teaching and learning processes in the classroom. The results of the study indicate that in Colombia there is a prevalence of studies that focus on proposals addressing needs in mathematics classes. There is also a significant amount of studies on cognitive, semiotic and sociocultural perspectives. A gap between these two types of studies is also presented. Some considerations for future research on the topic are outlined at the end of the article.
Improve Image Classification by Convolutional Network on Cambricon

Cambricon provides us with a complete intelligent application system, how to use this system for deep learning algorithms development is a challenging issue. In this paper, we exploit, evaluate and validate the performance of the ResNet101 image classification network on Cambricon with Cambricon Caffe framework, demonstrating the availability and ease of use of this system. Experiments with various operational modes and the processes of model inference show, the optimal running time of a common ResNet101 network that classifies the CIFAR-10 dataset on Cambricon is nearly three times faster than the baseline. We hope that this work will provide a simple baseline for further exploration of the performance of convolutional neural network on Cambricon.
Please Remember Me: Security Analysis of U2F Remember Me Implementations in The Wild

Users and service providers are increasingly aware of the security issues that arise because of password breaches. Recent studies show that password authentication can be made more secure by relying on second-factor authentication (2FA). Supported by leading web service providers, the FIDO Alliance defines the Universal 2nd Factor (U2F) protocols, an industrial standard that proposes a challenge-response 2FA solution. The U2F protocols have been thoughtfully designed to ensure high security. In particular, U2F solutions using dedicated hardware tokens fare well in term of security compared to other 2FA authentication systems. Thus, numerous service providers propose U2F in their authentication settings. Although much attention was paid to make U2F easy to use, many users express inconvenience because of the repeated extra step that it would take to log in. In order to address this, several service providers offer a remember me feature that removes the need for 2FA login on trusted devices. In this paper, we present the first systematic analysis of this undocumented feature, and we show that its security implications are not well understood. After introducing the corresponding threat models, we provide an experimental study of existing implementations of remember me. Here, we consider all the supporting websites considered by Yubico. The findings are worrisome: our analyses indicate how bad implementations can make U2F solutions vulnerable to multiple attacks. Moreover, we show that existing implementations do not correspond to the initial security analysis provided by U2F. We also implement two attacks using the identified design flaws. Finally, we discuss several countermeasures that make the remember me feature more secure. We end this work by disclosing a practical attack against Facebook in which an attacker can permanently deactivate the enabled 2FA options of a targeted victim without knowing their authentication credentials.
LSSL: Longitudinal Self-Supervised Learning.

Longitudinal neuroimaging or biomedical studies often acquire multiple observations from each individual over time, which entails repeated measures with highly interdependent variables. In this paper, we discuss the implication of repeated measures design on unsupervised learning by showing its tight conceptual connection to self-supervised learning and factor disentanglement. Leveraging the ability for `self-comparison' through repeated measures, we explicitly separate the definition of the factor space and the representation space enabling an exact disentanglement of time-related factors from the representations of the images. By formulating deterministic multivariate mapping functions between the two spaces, our model, named Longitudinal Self-Supervised Learning (LSSL), uses a standard autoencoding structure with a cosine loss to estimate the direction linked to the disentangled factor. We apply LSSL to two longitudinal neuroimaging studies to show its unique advantage in extracting the `brain-age' information from the data and in revealing informative characteristics associated with neurodegenerative and neuropsychological disorders. For a downstream task of supervised diagnosis classification, the representations learned by LSSL permit faster convergence and higher (or similar) prediction accuracy compared to several other representation learning techniques.
From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model

We consider PAC-learning a good item from k- subsetwise feedback information sampled from a Plackett-Luce probability model, with instancedependent sample complexity performance. In the setting where subsets of a fixed size can be tested and top-ranked feedback is made available to the learner, we give an algorithm with optimal instance-dependent sample complexity, for PAC best arm identification, of (â��k/k I£ni=2 max (1, 1/I�2i) ln k/I´ ( ln 1/I�i)), I�i being the Plackett-Luce parameter gap between the best and the ith best item, and â��k is the sum of the Plackett-Luce parameters for the top-k items. The algorithm is based on a wrapper around a PAC winner-finding algorithm with weaker performance guarantees to adapt to the hardness of the input instance. The sample complexity is also shown to be multiplicatively better depending on the length of rank-ordered feedback available in each subset-wise play. We show optimality of our algorithms with matching sample complexity lower bounds. We next address the winner-finding problem in Plackett-Luce models in the fixed-budget setting with instance dependent upper and lower bounds on the misidentification probability, of I© (exp(-2I�Q)) for a given budget Q, where I� is an explicit instancedependent problem complexity parameter. Numerical performance results are also reported. Copyright Â© 2020 by the Authors. All rights reserved.