Reviewerly/exHarmony huggingface dataset:
conomic Science in Ukraine: Challenges, Problems and Ways of their Solving

Economic Science in Ukraine: Challenges, Problems and Ways of their SolvingWorld experience shows that an effective activity of a country largely depends on the correct choice of priorities to support the development of its science, the choice of ways to finance it.Although many publications of scientists partially provide solutions to this problem, methodological and practical justification for a comprehensive study of reserves for the development of domestic economic science under current conditions of the life of society remains relevant.The aim of this article is to summarize the prerequisites for updating the theoretical and methodological support for the formation of the economic basis of Ukraine and identify some reserves for the development of this process.In the course of the research, it was found out that the sources of problems of economic science are: lack of systematization of a significant amount of available economic knowledge, which often creates problems with their practical implementation; still weak efforts on introducing the behavioral models of the economy.It is urgent to update the methodological basis for the accumulation of knowledge at all its levels: philosophical, general scientific, at the level of specific disciplines.There is no systematic procedure for diagnosing the cultural, socially reflective, humanitarian, environmental, moral functions of science.There is a need for a conceptual definition of the essence and content of fundamental and applied research, the choice of their optimal combination in solving various problems of economic theory and practice.The traditional picture of the world in studying economic problems urgently needs to be rationally supplemented with the recommendations of synergetics, capable of finding constructive technologies for establishing order from chaos.Diagnosing the truth of research results should be provided through an understandable professional, qualified expert examination, which can ensure creating a system of criteria to assess a specific subject and obtain a particular result.
Penerapan Metode Rapid Application Development (RAD) Dalam Perancangan Media Pembelajaran Multimedia

Multimedia learning media is one of the learning media used in the teaching-learning process. Based on the results of observations made on simulation subjects and digital communication shows several obstacles in the learning process such as the level of student understanding of subject matter is still low, the level of student activity and the media used are still less varied because the learning process is still fixated with books and not available media multimedia learning in simulation subjects and digital communication. The method used is the RAD (Rapid Application Development) method with object oriented approach and uses UML as a tool for media development. Based on the results of the trial, the media designed can attract the attention of students when studying, and in hrapakan can increase student grades in simdig subjects.
The effects of cooperative learning on reading comprehension

This study aimed to determine the influence of cooperative learning in reading comprehension of high school students at Unidad Educativa “Riobamba” during 2017-2018 school period. For the assessment of the validity and effectiveness of this research, a pre-test and a post-test were applied based on the Cambridge PET (Preliminary English Test) exam, reading section. That exam was composed of two parts: the first was carried out through a cooperative work in pairs and the second part, taking into consideration the cooperative work in groups of four students. A didactic guide for the teacher was designed and applied in the classroom. After that, the analysis of data was made before and after applying the intervention. The teacher's guide was designed to put cooperative learning strategies such as jig-saw, think-pair-share, and reciprocal questioning into action. Three stages of reading were used namely before reading, during reading, and after reading. After the implementation of the proposal, the data analysis was performed using the T-student mathematical test. It is concluded that the students improved their reading comprehension through cooperative work in pairs and in groups as well. It was recommended that teachers practice cooperative learning in the classroom to improve students' reading comprehension in order to optimize their development in English language learning.
Automatic identification of writers’ intentions: Comparing different methods for predicting relationship goals in online dating profile texts

Psychologically motivated, lexicon-based text analysis methods such as LIWC (Pennebaker et al., 2015) have been criticized by computational linguists for their lack of adaptability, but they have not often been systematically compared with either human evaluations or machine learning approaches.The goal of the current study was to assess the effectiveness and predictive ability of LIWC on a relationship goal classification task.In this paper, we compared the outcomes of (1) LIWC, (2) machine learning, and (3) a human baseline.A newly collected corpus of online dating profile texts (a genre not explored before in the ACL anthology) was used, accompanied by the profile writers' self-selected relationship goal (long-term versus date).These three approaches were tested by comparing their performance on identifying both the intended relationship goal and content-related text labels.Results show that LIWC and machine learning models both correlate with humans in terms of content-related label assignment.Furthermore, LIWC's content-related labels corresponded more strongly to humans than those of the machine learning model.Moreover, all approaches were similarly accurate in predicting the relationship goal.
Analisa Algoritma SHA-256 Untuk Mendeteksi Orisinalitas Citra Digital

In the digital era, changing human habits, one of the changing habits is in applying for someone's job. Before the digital era, applying for a job by sending an application to the address of the destination company. In the digital era, as now, applying for jobs can be done boldly, and sent in softcopy. This softcopy file forms a digital image that can be easily manipulated. How would it be difficult for the receiving committee to determine the original file with the manipulated a file. With the existing questions, we need a technique that can be used to request the originality of the job application file. One technique that can be used is the hash function that exists in cryptography. The hash function is a one-way function that can be used to find a message. If applied to digital images, the originality of the image can be recognized. The algorithm in the hash function used is Secure Hash Algorithm 256 (SHA256) until now this algorithm cannot be solved by cryptanalysts. By applying the Secure Hash Algorithm 256 algorithm, it will make it easier and faster to access the originality of digital images.
Generating Sentential Arguments from Diverse Perspectives on Controversial Topic

Considering diverse aspects of an argumentative issue is an essential step for mitigating a biased opinion and making reasonable decisions.A related generation model can produce flexible results that cover a wide range of topics, compared to the retrieval-based method that may show unstable performance for unseen data.In this paper, we study the problem of generating sentential arguments from multiple perspectives, and propose a neural method to address this problem.Our model, ArgDiver (Argument generation model from Diverse perspectives), in a way a conversational system, successfully generates high-quality sentential arguments.At the same time, the automatically generated arguments by our model show a higher diversity than those generated by any other baseline models.We believe that our work provides evidence for the potential of a good generation model in providing diverse perspectives on a controversial topic.
Fast Domain Adaptation of Semantic Parsers via Paraphrase Attention

Semantic parsers are used to convert user's natural language commands to executable logical form in intelligent personal agents.Labeled datasets required to train such parsers are expensive to collect, and are never comprehensive.As a result, for effective postdeployment domain adaptation and personalization, semantic parsers are continuously retrained to learn new user vocabulary and paraphrase variety.However, state-of-the art attention based neural parsers are slow to retrain which inhibits real time domain adaptation.Secondly, these parsers do not leverage numerous paraphrases already present in the training dataset.Designing parsers which can simultaneously maintain high accuracy and fast retraining time is challenging.In this paper, we present novel paraphrase attention based sequence-to-sequence/tree parsers which support fast near real time retraining.In addition, our parsers often boost accuracy by jointly modeling the semantic dependencies of paraphrases.We evaluate our model on benchmark datasets to demonstrate upto 9X speedup in retraining time compared to existing parsers, as well as achieving state-of-the-art accuracy.
Deconstructing Supertagging into Multi-Task Sequence Prediction

Supertagging is a sequence prediction task where each word is assigned a piece of complex syntactic structure called a supertag.We provide a novel approach to multi-task learning for Tree Adjoining Grammar (TAG) supertagging by deconstructing these complex supertags in order to define a set of related but auxiliary sequence prediction tasks.Our multi-task prediction framework is trained over the exactly same training data used to train the original supertagger where each auxiliary task provides an alternative view on the original prediction task.Our experimental results show that our multi-task approach significantly improves TAG supertagging with a new state-of-the-art accuracy score of 91.39% on the Penn treebank supertagging dataset.
Twitter Sentiment Analysis using Rapid Miner Tool

Since last few years social networking and Micro-blogging sites have become a largest platform for sharing users' personal feelings, marketing or social liking.Especially in product based company where success of a company depends on the opinion of different customers.These opinions can be use to analyze the user's sentiments, feelings and assessment of product.In this paper tweets about government schemes has been fetched from twitter with the help of scraper written in python language.Tweets are divided into two data sets, one is of 50 tweets length and another data set is of 200 tweets length.An experiment has been performed in Rapid Miner tool to find accuracy of sentiments polarity using Naive Bayes and k-NN techniques, also comparison between these techniques is observed to find out the best performing one.
Estimation of Global Solar Radiation on Horizontal Surface from Sun Shine Hours and Temperature at Kombolcha, Amhara Region, Ethiopia

The detailed knowledge of the local solar radiation is important but, measuring instruments are not readily available in many places, particularly in Ethiopia.Therefore, this work focused on the application of climate data to compare sunshine and temperature based empirical equation of the global solar radiation in Kombolcha sites, Amhara region, Ethiopia.For data obtained in a period of 2008 to 2017 from the Kombolcha meteorological agency correlations be tested for errors using: Mean Bias error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), and correlation Coefficient (r).The latest computing MATLAB soft ware and excel spread sheet has been used for the entire analysis.The empirical equation having the least error was selected for Kombolcha sites.Therefore, according to accuracy the Hargreaves and Samani empirical equation is suitable for Kombolcha sites than Angstrom-Prescott empirical equation.The abundant average global solar radiation of 6.19 KWh/m 2 /day and 7.09 KWh/m 2 /day was estimated using sun shine and temperature based empirical equation respectively.This result showed that there is good solar potential in Kombolcha, Amhara region, Ethiopia.
USB Artifact Analysis Using Windows Event Viewer, Registry and File System Logs

A USB mass storage device yields a lot of artifacts when connected to a system. These artifacts are persistent in nature and are retained even after the system has been shut down and the information they contain may assist in carrying out forensic analysis on a suspect system. In this paper, we demonstrate how Windows Event Viewer can be used to find forensic artifacts in a suspect system for investigative purposes. We also discuss the potential that Windows registry holds to identify USB devices’ information that have been connected to the system, to corroborate our findings from Windows Event Viewer. Finally, we use the Windows 10 file system to extract log details that contain the setup information of a USB device that was connected to the system the very first time, and obtain the necessary identifiers and time stamp details.
An Eye-Tracking Dataset for Visual Attention Modelling in a Virtual Museum Context

Predicting the user’s visual attention enables a virtual reality (VR) environment to provide a context-aware and interactive user experience. Researchers have attempted to understand visual attention using eye-tracking data in a 2D plane. In this poster, we propose the first 3D eye-tracking dataset for visual attention modelling in the context of a virtual museum. It comprises about 7 million records and may facilitate visual attention modelling in a 3D VR space.
Application Programming Interface for Flood Forecasting from Geospatial Big Data and Crowdsourcing Data

Nowadays, natural disasters tend to increase and become more severe. They do affect life and belongings of great numbers of people. One kind of such disasters that hap-pen frequently almost every year is floods in all regions across the world. A prepara-tion measure to cope with upcoming floods is flood forecasting in each particular area in order to use acquired data for monitoring and warning to people and involved per-sons, resulting in the reduction of damage. With advanced computer technology and remote sensing technology, large amounts of applicable data from various sources are provided for flood forecasting. Current flood forecasting is done through computer processing by different techniques. The famous one is machine learning, of which the limitation is to acquire a large amount big data. The one currently used still requires manpower to download and record data, causing delays and failures in real-time flood forecasting. This research, therefore, proposed the development of an automatic big data downloading system from various sources through the development of applica-tion programming interface (API) for flood forecasting by machine learning. This research relied on 4 techniques, i.e., maximum likelihood classification (MLC), fuzzy logic, self-organization map (SOM), and artificial neural network with RBF Kernel. According to accuracy assessment of flood forecasting, the most accurate technique was MLC (99.2%), followed by fuzzy logic, SOM, and RBF (97.8%, 96.6%, and 83.3%), respectively.
Empirical White Noise Processes and the Subjective Probabilistic Approaches

The paper discusses the identification of the empirical white noise processes generated by deterministic numerical algorithms.The introduced fuzzy-random complementary approach can identify the inner hidden correlational patterns of the empirical white noise process if the process has a real hidden structure of this kind. We have shown how the characteristics of auto-correlated white noise processes change as the order of autocorrelation increases. Although in this paper we rely on random number generators to get approximate white noise processes, in our upcoming research we are planning to turn the focus on physical white noise processes in order to validate our hypothesis.
Multi-font Devanagari Text Recognition Using LSTM Neural Networks

Current research in OCR is focusing on the effect of multi-font and multi-size text on OCR accuracy. To the best of our knowledge, no study has been carried out to study the effect of multi-fonts and multi-size text on the accuracy of Devanagari OCRs. The most popular Devanagari OCRs in the market today are Tesseract OCR, Indsenz OCR and eAksharayan OCR. In this research work, we have studied the effect of font styles, namely Nakula, Baloo, Dekko, Biryani and Aparajita on these three OCRs. It has been observed that the accuracy of the Devanagari OCRs is dependent on the type of font style in text document images. Hence, we have proposed a multi-font Devanagari OCR (MFD_OCR), text line recognition model using long short-term memory (LSTM) neural networks. We have created training dataset Multi_Font_Train, which consists of text document images and its corresponding text file. This consists of each text line in five different font styles, namely Nakula, Baloo, Dekko, Biryani and Aparajita. The test dataset is created using the text from benchmark dataset [] for each of the font styles as mentioned above, and they are named as BMT_Nakula, BMT_Baloo, BMT_Dekko, BMT_Biryani and BMT_Aparajita test dataset. On the evaluation of all OCRs, the MFD_OCR showed consistent accuracy across all these test datasets. It obtained comparatively good accuracy for BMT_Dekko and BMT_Biryani test datasets. On performing detailed error analysis, we noticed that compared to other Devanagari OCRs, the MFD_OCR has consistent, insertion and deletion type of errors, across all test dataset for each font style. The deletion errors are negligible, ranging from 0.8 to 1.4%.
Introduction: BEYOND HEVC

The internet was not designed for the transportation of video content. However, over the past ten years, video traffic on the internet has increased dramatically, rising from one-third of all traffic in 2008 to roughly 82% in 2020. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> This growth, in part, reflects the effects of the transition from analog to digital television broadcasting in 2009 and in the shift of media and entertainment transmission from serial digital interface (SDI), standardized by SMPTE in 1989, to video over IP, standardized by SMPTE in ST 2110 in 2013. Video over IP enables the widespread streaming of video content, exemplified for most consumers in the form of video-on-demand (VOD) systems, such as Netflix, Hulu, and Amazon, and in the creation of video channels on the internet by YouTube, BuzzFeed Video, and others. At the same time, video content has become more data-intensive with greater reliance on the display of content in high dynamic range (HDR), high frame rate (HFR), and wide color gamut (WCG), not to mention the growth of a consumer market for 4K content and experimental forays into the streaming of augmented reality (AR) and virtual reality (VR) material. All of these developments amount to more and more data that need to be squeezed through the existing internet infrastructure. Cisco predicts that, by 2021, video over IP will be the equivalent of the transmission of 7.2 billion DVDs a month.
Adversarial Structured Neural Network Pruning

In recent years, convolutional neural networks (CNN) have been successfully employed for performing various tasks due to their high capacity. However, just like a double-edged sword, high capacity results from millions of parameters, which also brings a huge amount of redundancy and dramatically increases the computational complexity. The task of pruning a pretrained network to make it thinner and easier to deploy on resource-limited devices is still challenging. In this paper, we employ the idea of adversarial examples to sparsify a CNN. Adversarial examples were originally designed to fool a network. Rather than adjusting the input image, we view any layer as an input to the layers afterwards. By performing an adversarial attack algorithm, the sensitivity information of the network components could be observed. With this information, we perform pruning in a structured manner to retain only the most critical channels. Empirical evaluations show that our proposed approach obtains the state-of-the-art structured pruning performance.
Computing Quality of Structure of a Web-Site

Information dissemination is the key.WEB sites play a vital role in this area.Most of the businesses are being conducted world over using websites.The effectiveness of the businesses conducted is dependent on the quality of web sites.The quality of a web site is dependent on many factors.The computation of the quality of a WEB site is rather complex.The quality of each factor computed individually, and then the total quality of a web site is to be computed.The quality of a web site is dependent on the quality of the structure using which the content displayed to the user.In this paper, a data mining approach has been presented that determines all the hidden structures within the WEB site, and then a method is presented using which quality of the structure of the website computed.The method presented tested on a live website and weaknesses of the site found and forwarded the same to the site owners for their site correction.
Simplifying the Analysis of Software Design Variants with a Colorful Alloy

Formal modeling and automatic analysis are essential to achieve a trustworthy software design prior to its implementation. Alloy and its Analyzer are a popular language and tool for this task. Frequently, rather than a single software artifact, the goal is to develop a full software product line (SPL) with many variants supporting different features. Ideally, software design languages and tools should provide support for analyzing all such variants (e.g., by helping pinpoint combinations of features that could break a property), but that is not currently the case. Even when developing a single artifact, support for multi-variant analysis is desirable to explore design alternatives. Several techniques have been proposed to simplify the implementation of SPLs. One such technique is to use background colors to identify the fragments of code associated with each feature. In this paper we propose to use that same technique for formal design, showing how to add support for features and background colors to Alloy and its Analyzer, thus easing the analysis of software design variants. Some illustrative examples and evaluation results are presented, showing the benefits and efficiency of the implemented technique.
Analysis of Cyberterrorism and Online Social Media

Cyberterrorism is terrorism in form of non-conventional security threats. In this paper, firstly, the definition of Cyberterrorism is elaborated, then the reasons of carrying on terrorism activities based on social media on the network in the recent years are be deeply analyzed, and finally the ways about anti-terrorism on the network is explored concisely.