Reviewerly/exHarmony huggingface dataset:
Analisis Kualitas Website Badan Pengembangan Sumber Daya Manusia Menggunakan Metode Webqual 4.0

This study aims to determine the quality of the website of the DKI Jakarta Province Human Resources Development Agency with the address https://www.bpsdm.jakarta.go.id as a media of information about activities and institutions related to the development of competencies for civil servants (PNS) within the Government. DKI Jakarta Province is based on the perceptions of end users by analyzing end users' perceptions of usability, informaton quality and interaction quality using the WebQual 4.0 method. Data collection was carried out by survey using a questionnaire via Geogle Form with 22 statements based on the WebQual dimension and one statement about the whole (overall) with 438 respondents from the entire population of civil servants of the DKI Jakarta Provincial Government as many as 60,691 people as of September 30, 2020, but who meet requirements as many as 398 respondents. The website quality analysis stage is by describing the survey data. The prerequisite for the test is to test the validity and reliability. Furthermore, data analysis by calculating the score and average on each indicator. The results of this test analysis indicate that the variables of usability, informaton quality and interaction quality on the website of the BPSDM DKI Jakarta Province have quality. The results of this study have implications for the need for BPSDM DKI Jakarta Province to evaluate the maintenance and development of the website that has been built used to improve service quality.&#x0D;
Using Board Game Through TTW Strategy in Descriptive Writing

This research aims to find out the effect of using board game through TTW strategy on seventh grade students’ writing skill in descriptive text. Quasi-experimental design was implemented to the seventh grade in one of junior high school. The subject of this study was the whole population of seventh grade students in junor high school, in total 81 students. The data collection was taken from pre-test and post-test. The average score of experimental group is 73.81, and the average score of control group is 57.46. Moreover, the hypothesis test shows that Ho is rejected because tcounting (5.71) is higher than ttable (2.31). In TTW strategy the students collaborate between three activities those are Think, Talk, and Write. TTW strategy is more effective becausethe students have enough time in learning process in a class. Additionally, the result of this study indicates that using board game through TTW strategy gives significant effect for developing English writing skill.
A Framework for Model and Verification of Safety-Critical Operating System Based on ARINC653

As the scale and complexity of safety-critical software continue to grow, it is necessary to ensure safety and reliability to avoid minor errors leading to catastrophic disasters. Meantime, the traditional method, such as testing and simulation alone is insufficient to ensure the correctness of systems. This leads to using formal methods to provide sufficient evidence for systems. However, design a high assurance safety-critical system by formal methods is challenging due to the complexity of operating systems. In addition, the traditional interactive theorem prover used in system verification requires hand-written proofs, which are more expensive. Therefore, the efforts of providing a standardized formal framework as well as safety proofs, are notable for the develop a safety-critical system. The purpose of this paper is to provide a safety framework to establish a highly reliable and safety-critical operating system based on the ARINC653 standard, a multilevel and standardized formal model. To verify the functional correctness of this model, we propose a context-based formal proof method for programs. To achieve this goal, we first model 57 core services of ARINC653 and define the high-level requirements as pre-and post-conditions. Then, we construct a set of specification statements a formal axiom system transformed into logical sentences, and the core service model is transformed into a logical sentence sequence to be proved. Finally, a context-based formal proof system for specification correctness is developed. We have verified the correctness of safety-critical operating system core services with this system. Experience shows that the verification system we developed can be achieved the functional correctness of a complete OS with a low implement burden, and that can simplify the difficulty of automated verification and increase the degree of automation of proof.
An Approach to Partial Observability in Games: Learning to Both Act and Observe

Reinforcement learning (RL) is successful at learning to play games where the entire environment is visible. However, RL approaches are challenged in complex games like Starcraft II and in real-world environments where the entire environment is not visible. In these more complex games with more limited visual information, agents must choose where to look and how to optimally use their limited visual information in order to succeed at the game. We verify that with a relatively simple model the agent can learn where to look in scenarios with a limited visual bandwidth. We develop a method for masking part of the environment in Atari games to force the RL agent to learn both where to look and how to play the game in order to study where the RL agent learns to look. In addition, we develop a neural network architecture and method for allowing the agent to choose where to look and what action to take in the Pong game. Further, we analyze the strategies the agent learns to better understand how the RL agent learns to play the game.
An Ensemble Random Forest Algorithm for Privacy Preserving Distributed Medical Data Mining

As the voluminous amount of data is generated because of inexorably widespread proliferation of electronic data maintained using the Electronic Health Records (EHRs). Medical health facilities have great potential to discern the patterns from this data and utilize them in diagnosing a specific disease or predicting outbreak of an epidemic etc. This discern of patterns might reveal sensitive information about individuals and this information is vulnerable to misuse. This is, however, a challenging task to share such sensitive data as it compromises the privacy of patients. In this paper, a random forest-based distributed data mining approach is proposed. Performance of the proposed model is evaluated using accuracy, f-measure and appa statistics analysis. Experimental results reveal that the proposed model is efficient and scalable enough in both performance and accuracy within the imbalanced data and also in maintaining the privacy by sharing only useful healthcare knowledge in the form of local models without revealing and sharing of sensitive data.
Purview for Data Governance

Numerous organizations are needing to establish data governance processes, standards, and methodologies and have been able to do this with on-premises SQL Server tools such as Master Data Services. However, there has been a major gap in the Azure space for such data governance products. Previously, Microsoft has attempted to bring data governance to Azure through hosting an MDS database on an Azure managed instance or through Azure Data Catalog, which hasn’t really been a full-fledged data governance product. Azure Purview is a cloud-based data governance product that is used to centrally manage data governance across your data estate, spanning both cloud and on-premises environments.
Linking Reversed and Dual Codes of Quasi-Cyclic Codes

It is known that quasi-cyclic (QC) codes over the finite field 𝔽q correspond to certain 𝔽q[x]-modules. A QC code C is specified by a generator polynomial matrix G whose rows generate C as an 𝔽q[x]-module. The reversed code of C, denoted by R, is the code obtained by reversing all codewords of C while the dual code of C is denoted by C⊥. We call C reversible, self-orthogonal, and self-dual if R = C, C⊥ ⊇ C, and C⊥ = C, respectively. In this study, for a given C, we find an explicit formula for a generator polynomial matrix of R. A necessary and sufficient condition for C to be reversible is derived from this formula. In addition, we reveal the relations among C, R, and C⊥. Specifically, we give conditions on G corresponding to C⊥ ⊇ R, C⊥ ⊆ R, and C = R = C⊥. As an application, we employ these theoretical results to the construction of QC codes with best parameters. Computer search is used to show that there exist various binary reversible self-orthogonal QC codes that achieve the upper bounds on the minimum distance of linear codes.
SphinxNet - A Lightweight Network for Single Image Deraining

Rain produces haze and streak noise in an image that destroys the intricate details, turning even the most effective computer vision algorithms obsolete. This calls for a good deraining algorithm to restore a rainy image to a rain-less image with natural sharpness, contrast and brightness. In this paper, we propose SphinxNet with a less number of parameters by utilizing autoencoders in an intelligent formation to derive maximum spatial awareness. Which helps it to achieve not only sharp but better derained results than other models with more number of trainable parameters. Autoencoders are made of linear sequence of convolutional layers with channel wise bottleneck and skip concatenation connections between encoder and decoder layers. We trained our network on popular synthetic datasets. In quantitative and qualitative analysis we found our method to perform not only better but more consistent on variety of images.
PERANCANGAN APLIKASI PENGGAJIAN KARYAWAN BERBASIS WEB MENGGUNAKAN FRAMEWORK CODEIGNITER PADA PO ARISTA TEHNIK JAKARTA

Arista Tehnik Jakarta is an individual company engaged in manufacturing services for AC ducting in buildings. Arista Tehnik Jakarta in processing employee payroll data still uses the manual / conventional method, which is taking notes on bookkeeping so that errors often occur in calculating employee salaries. Calculating employee salaries can have fatal consequences if something goes wrong and it can affect the preparation of payroll reports. The manual / conventional payroll system is not able to provide adequate benefits for calculating employee salaries. With a good payroll system, every company that already has many employees is very much needed. Companies that have many employees definitely need a payroll system that can make it easier to pay salaries appropriately and efficiently. The method used in this research is by making observations, interviews, literature studies, designing information systems followed by the development of information systems and then testing. The result of this research is to produce a computerized payroll information system that provides convenience in providing payroll data information such as employee data information, job data and payroll data. In the Payroll Data Information System using UML (Unified Modeling Language), programming using the CodeIgniter framework, and MySQL for database processing. After the researcher then created a Payroll Information System, the work procedure could be easier for Arista Tehnik Jakarta.
The Impacts of Social Media on Kurdish EFL Students' English Language Proficiency

Advances in technology have resulted in increasing the number of internet users among students. They use social Medias for several purposes. The Internet as a means of advanced technology has had an enormous influence on the English language in less than two decades changing it to a considerable extent. The crucial purpose for this is the effectiveness of the internet communication and easy accessibility of the world wide net. Nowadays, one of the key questions is with social Media commerce and its impact on students' language proficiency. Many researches have been implemented to find out how it impacts students' language and in most of the cases encouraging results are greater than negative ones. Thus, this study addressed the effects of social media on Kurdish students' English language competence, whether it touches their language knowledge in a negative or positive way. This study is implemented by conducting quantitative and qualitative research with bachelor degree students studying at Salahaddin University, Colleges of Education, Basic Education and Languages. Close-ended questionnaire and an interview are availed for adopting the study. This study focuses on grammar, vocabulary, and four key language skills: listening, speaking, reading and writing. It comes to the conclusion that a considerably positive consequence is the role of social media in improving student language skills. From the students' reports, it can be concluded that although writing (spelling particularly) has not improved to some extent, other language skills show significant enhancement in terms of listening, speaking skills and vocabulary enrichment with grammar point. Moreover, regarding reading skills, a mainly significant finding is the efficiency of practicing scanning skill while students read any text from the net.
General Invertible Transformations for Flow-based Generative Modeling

In this paper, we present a new class of invertible transformations with an application to flow-based generative models. We indicate that many well-known invertible transformations in reversible logic and reversible neural networks could be derived from our proposition. Next, we propose two new coupling layers that are important building blocks of flow-based generative models. In the experiments on digit data, we present how these new coupling layers could be used in Integer Discrete Flows (IDF), and that they achieve better results than standard coupling layers used in IDF and RealNVP.
INNOVATIVE PEDAGOGICAL METHODS AND COMPUTER TELECOMMUNICATIONS IN DISTANCE EDUCATION

Formulation of the problem. The problems of pedagogical system, innovative methods of education and training of specialists, use of new teaching methods based on computer information technologies and distance learning platforms, use of innovative methods and distance learning platforms in the implementation of online education are considered. According to statistical research, the effectiveness of the traditional pedagogical system is no more than sixty percent. Innovative methods of training specialists in education are the use of new methods that qualitatively increase the effectiveness of ways and means of presenting information compared to the traditional system, learning to find the necessary information independently, checking its adequacy and learnability. Such innovations and novelties can be the use of computer technology and the Internet, and, especially now, of distance learning and communication technology.&#x0D; Materials and methods. The methods include content analysis, synthesis, comparison, description of the obtained research results, generalization.&#x0D; Results. The existing solutions for remote learning (distance learning platforms), using computer telecommunications, were analyzed: Meet, iSpring, WebTutor, GetCourse, Google Classroom, Microsoft Teams, Moodle, Blackboard Learning System, LMS «Prometheus», The Virtual Laboratory, VirtuLab and ZILLION, video communication and conference programs: Zoom, YouTube, Viber, Skype and others. All these platforms have their own characteristics and features, and their use lies in the possibilities of application in educational institutions and personal preferences of teachers and students. All distance learning platforms are web-based and therefore the requirements for client computers are minimal. The analysis of the remote learning technologies used allows us to determine the basic necessary and sufficient set of software tools that need to be applied for the full operation of the platform in the application of distance learning technologies. Microsoft Teams is suggested as a possible platform for distance learning.&#x0D; Conclusion. Application of remote technologies and software in creation of hardware-distance scientific-educational systems will lead to low cost of software product both for developers and customers (reduction of expenses on additional software) and will provide attraction of a wide range of users due to low system requirements and possibility of use in mobile devices. And usage of Microsoft Teams distance learning platform allows implementing a wide range of capabilities, which this platform has: video communication, different types of communication, provision of all kinds of materials, demonstration of any participant's screen during broadcast for the whole group, list of participants and attendance control, polling, possibility to record events and their further viewing, administration of all processes from the learning side, etc.
Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedback

A promising approach to solving challenging long-horizon tasks has been to extract behavior priors (skills) by fitting generative models to large offline datasets of demonstrations. However, such generative models inherit the biases of the underlying data and result in poor and unusable skills when trained on imperfect demonstration data. To better align skill extraction with human intent we present Skill Preferences (SkiP), an algorithm that learns a model over human preferences and uses it to extract human-aligned skills from offline data. After extracting human-preferred skills, SkiP also utilizes human feedback to solve down-stream tasks with RL. We show that SkiP enables a simulated kitchen robot to solve complex multi-step manipulation tasks and substantially outperforms prior leading RL algorithms with human preferences as well as leading skill extraction algorithms without human preferences.
Kinerja Pendekatan Convolutional Neural Network dan Dense Network dalam Klasifikasi Citra Malaria

Indonesia is an archipelago, which three of its five main island consists mainly, or dense tropical rainforest. This rainforest is main breeding ground for malaria disease that mostly affect regions near said forest. In an effort to treat malaria disease, a diagnostic process is performed to correctly identify the disease. Several image pattern recognition technique been developed and have potential to be utilized as malaria diagnostic tool. In this research, a method is described on designing neural network to detect a blood cell parasitized by malaria. The method consists of utilizing a dense network, and a convolutional neural network, to be trained using publicly available training dataset. Both models’ performance is then compared and analyzed. Before the data is used, a process of padding is performed to resize the input image into 200 x 200 pixels. The resized input data is then used to train both models. From the training and testing, it is found that the dense network achiever 64.78% accuracy. On the other hand, model based on convolutional neural network achiever 94.32%. From analysis, it is found that the size of the model being used is not big enough to achieve better performance. Hence, it is suggested for future research to increase the model size in terms of network width and depth.
An Optimized Neural Network Model for Facial Expression Recognition over Traditional Deep Neural Networks

Emotions have a key role in Feedback analysis to provide a good customer service, the main seven emotions are Anger, Disgust, Fear, Happy, Neutral, Sad and Surprise.There are several advantages, an efficient Facial Emotion Recognition model can help us in self-discipline and control over the drivers, while they are driving the vehicle.Low resolution and Lowreliable images are main problems in this field.We proposed a new model which can efficiently perform on Low resolution and Low-reliable images.We created a low resolution facial expression dataset (LRFE) by collecting various images from different resources, which contains low resolution images.We also proposed a new hybrid filtering method, which is a combination of Gaussian, Bilateral, Non local means filtering techniques.Densenet-121 achieves 0.60 0.68 accuracy on fer2013 and LRFE respectively.When hybrid filtering method is combined with Densenet-121, it achieved 0.95 accuracy.Similarly Resnet-50, MobileNet, Xception models performed effectively when combined with the hybrid filtering method.The proposed convolutional neural network(CNN) model achieved 0.65 accuracy on fer2013 dataset, while the existing models like Resnet-50, MobileNet, Densenet-121 and Xception obtained 0.60 0.57 0.60 0.52 accuracies on fer2013 respectively.The proposed model when combined with hybrid filtering method achieved 0.85 accuracy.Clearly the proposed model outperforms the traditional methods.When the hybrid filtering method is combined with the CNN models, there is significant increase in the accuracy.
Effect of Term Weighting on Keyword Extraction in Hierarchical Category Structure

While there have been several studies related to the effect of term weighting on classification accuracy, relatively few works have been conducted on how term weighting affects the quality of keywords extracted for characterizing a document or a category (i.e., document collection). Moreover, many tasks require more complicated category structure, such as hierarchical and network category structure, rather than a flat category structure. This paper presents a qualitative and quantitative study on how term weighting affects keyword extraction in the hierarchical category structure, in comparison to the flat category structure. A hierarchical structure triggers special characteristic in assigning a set of keywords or tags to represent a document or a document collection, with support of statistics in a hierarchy, including category itself, its parent category, its child categories, and sibling categories. An enhancement of term weighting is proposed particularly in the form of a series of modified TFIDF's, for improving keyword extraction. A text collection of public-hearing opinions is used to evaluate variant TFs and IDFs to identify which types of information in hierarchical category structure are useful. By experiments, we found that the most effective IDF family, namely TF-IDFr, is identity&gt;sibling&gt;child&gt;parent in order. The TF-IDFr outperforms the vanilla version of TFIDF with a centroid-based classifier.
Feature Engineering Framework to detect Phishing Websites using URL Analysis

Phishing is a most popular and dangerous cyberattack in the world of internet.One of the most common attacks in cyber security is to access the personal information of internet users through "Phishing Website".The major element through which hacker can do this job is through URL.Hacker creates an almost replica of original URL in which there is a very small difference, generally not revealed without keen observation.By pipelining various machine learning algorithms, the proposed model aims to recognize the important features to classify the URL using a recursive feature elimination process.In this work the data set of various URL records has been collected with 112 features including one target value.In this work a Machine Learning based model is proposed to identify the significant features, used to classify a URL, the wrapper method recursive feature elimination compares different bagging and boosting machine learning approaches .Ensemble algorithms, Bootstrap Aggregation Algorithms, Boosting and stacking algorithms are used for feature selection.The proposed work has five sections: work on the pre-processing phase, finding the relation between the features of the dataset, automatic selection of number of features using Extra Tree Classifier, comparison of the various ensemble algorithm and finally generates the best features for URL analysis.This paper, designs meta learner with XG BOOST classifier as base classifier and achieved an accuracy of 93% Out of 112 features, this model has performed an extensive comparative study on feature selection and identified 29 features as core features by performing URL analysis.