Reviewerly/exHarmony huggingface dataset:
Constructive and object-oriented modeling text for detection of text borrowings

The scientific community is encouraged to use such models and data structures as arrays of LERP-RSA (the longest expected duplicate array of reduced suffix templates), tag classifier-a model based on Stanford NER's three-class, structures based on DN-sequences, graph representations, etc. The following algorithms are used: GreedyString-Tiling, ARPAD, shingle, statistical methods, genetic algorithms, and others. It should also be noted that much attention is paid to morphological analysis and lemmatization, pre-processing of texts. Models and algorithms only partly have program realization.The purpose of this work is to develop a text model to identify borrowings and bring it to program implementation. The task is to develop the object-oriented model and program implementation of a graph text model, with the application of the problem of detection of borrowing. As well as obtaining timeframes for program implementation work for further evaluation of the possibility of its use in the academic environment.The main idea of the graph model is to present the text as a weighted oriented graph. The vertex weight is a character or sequence of characters. Edge weight is the set of numbers of paths into which the edge enters. To formalize the model will use the apparatus of constructive-synthesizing modeling. To create graphs, a constructor and its components are defined: carrier, signature, multiple statements of information support for design. Transformations are made for the constructor: specialization, interpretation and concretization.On the basis of this model, the object-oriented model is constructed. it includes three classes: vertex, graph and work .The object of class Work presents the text as a set of objects of class Graph. The correspondences between the components of the presented models are established.The object-oriented model is implemented by software. Data are given about the execution time of graph construction and texts comparison.At this stage, software implementation of the model has shown acceptable time performance. Further research in this direction is promising. Directions for improving the model and program are proposed.
Leveraging User Profiling in Click-through Rate Prediction Based on Zhihu Data

With the advent of the Web 2.0 era, the prediction of Click-through Rate (CTR) has been essential to improve the user experience and loyalty for the newly emerged industry, Content Marketing. Additionally, an incisive understanding of online users is not only vital for many scientific disciplines, but also plays an important role in providing personalized products and recommendation services. In this paper, we propose a Profile-CTR model, which leverages user profiles and historical behavior data to predict CTR of certain items on Zhihu, a popular social Q&A platform. Specifically, we predict the user profiles, which include gender and occupation, using a CNN-based model on their textual data. Then, the user profiles and historical behavior records are applied to DeepFM simultaneously to predict CTR. We evaluate our method with extensive experiments and the result reveals that our approaches outperform baselines, showing that combining the user profiles with the historical behavior records can significantly improve the performance of the CTR prediction in the recommendation system.
Korelasi antara Minat Baca dengan Kemampuan Menulis Teks Prosedur Kompleks pada Siswa Kelas VII SMP Negeri Sumber Rejo Kabupaten Musi Rawas

The purpose of this study was to determine whether there is a positive and significant correlation between interest in reading with the ability to write complex procedure texts in Indonesian subjects in Grade VII students of SMP Negeri Sumber Rejo, Musi Rawas Regency. The research method uses a simple correlation method. Data collection techniques using questionnaires and tests. The population in this study was 81 students, sampling from the entire population divided by the number of students multiplied by the prediction set at 10%, then obtained 45 students. Analysis of the data used is the Pearson Product Moment Test. The results showed that there was a positive and significant relationship between interest in reading with the ability to write procedural texts in Grade VII students of SMP Negeri Sumber Rejo, Musi Rawas Regency. This is evidenced by the correlation coefficient of 0.01 smaller than 0.05 at a significant level of 5%. This proves that at a significant level ? = 0.05 or at the level of confidence of 95% there is a significant relationship between interest in reading and the ability to write complex procedural texts in SMP Negeri Sumber Rejo, Musi Rawas Regency.
Bilingual Low-Resource Neural Machine Translation with Round-Tripping: The Case of Persian-Spanish

The quality of Neural Machine Translation (NMT), as a data-driven approach, massively depends on quantity, quality and relevance of the training dataset.Such approaches have achieved promising results for bilingually high-resource scenarios but are inadequate for low-resource conditions.This paper describes a roundtrip training approach to bilingual lowresource NMT that takes advantage of monolingual datasets to address training data scarcity, thus augmenting translation quality.We conduct detailed experiments on Persian-Spanish as a bilingually low-resource scenario.Experimental results demonstrate that this competitive approach outperforms the baselines.
The Indonesian Syntactic Structure Interfere into English Sentences: An Interlanguage Study

The reason for conducting this research was based on consideration that university students majoring in English should be at the level where their English is considered good. Unfortunately, students still made errors in both spoken and written form. In this study, 20 students of English Department at Fajar University enrolled which were taken by total samplingmethod. This research is aimed to provide useful information particularly as referencefor English teachers, lecturers and curriculum developers about the extent of theIndonesian syntactic structure that interfere the students in making English sentence.A mix method was applied in which the data was analysed both qualitatively andquantitatively. The conclusion of the result shows that the students still have problemsin making English sentences. They still make errors in forming English sentences thatbased on the Indonesian form. Further, the most errors are found are the error of omissions. It is proven by the findings that the interference of Indonesian syntactic formis the main cause of student’s error in making English sentence.
On the Effectiveness of the Pooling Methods for Biomedical Relation Extraction with Deep Learning

Deep learning models have achieved state-ofthe-art performances on many relation extraction datasets.A common element in these deep learning models involves the pooling mechanisms where a sequence of hidden vectors is aggregated to generate a single representation vector, serving as the features to perform prediction for RE.Unfortunately, the models in the literature tend to employ different strategies to perform pooling for RE, leading to the challenge to determine the best pooling mechanism for this problem, especially in the biomedical domain.In order to answer this question, in this work, we conduct a comprehensive study to evaluate the effectiveness of different pooling mechanisms for the deep learning models in biomedical RE.The experimental results suggest that dependencybased pooling is the best pooling strategy for RE in the biomedical domain, yielding the state-of-the-art performance on two benchmark datasets for this problem.
Design Of Web-based Qr-code Absence At The Education Office

The need for fast and accurate data processing is needed by every government agency. The Education Office is an administrative administrator in the field of education that requires a fast system for processing data. The attendance system at the Tangerang Regency education office is already using a fingerprint. However, there are still many obstacles that arise due to lack of tools, and damage to the tool so that it often uses manual attendance. The research method used is the method of data collection, and the analytical method used is a form of Unified Modeling Language. The results and objectives of this study are the creation of a system that can facilitate employee and attendance managers to be more effective and efficient.
Necessity to Research Risks of Education Digitalization for Successful Training of Future Teachers

The relevance of the research resulting from the widespread introduction information space and computer technology into the educational process, which, along with indisputable advantages, is accompanied by risks, the least studied in terms of prediction. Due to the fact that by 2025, according to the project "Modern Digital Educational Environment in the Russian Federation", a digital educational space should be created, the aim of this research is to study the digital competence of future teachers and the attitude of parents to the implementation of this trend. During the research were used methods of surveys and interview: 258 first-year students (future teachers) and 365 parents of elementary school pupils were interviewed. The article substantiates necessity to study the behavior of the children using devices and gadgets, which should be taken into account designing the educational process of future teachers, as both teachers and students must have a reliable and objective notion about a degree of children immersion in the information space, digitalization risks and the degree of compliance of information and communication skills of pupils and future teachers by the time of their meeting at school. The article presents data on the degree of immersion in the Internet space of future teachers, children who have begun systematic training in rural and urban schools, as well as their parents. The article reflects the attitude of parents to online learning and the control of their children’ behavior in the Internet space. The results of the research can be used by teachers and researchers to design and monitor the digitalization of the educational process.
An Experimental Study to Investigate the Potential of Online Shopping in Immersive Virtual Realities Compared to Conventional Online Shops

Immersive virtual reality technology enables the experience of feeling ‘presence’, that is, to have the feeling of being in an immersive computer-supported room. Because these technologies are used increasingly in private households, retailers are very interested in using them as a new sales opportunity. In our experimental study we investigated whether VR-based online shops could be more profitable for them compared to conventional online shops. The following results were found: A VR-based online shop generated a more real perception of the objects, and more interest in buying them. In addition, the shopping experience in the VR-based online shop was perceived as very exciting. However, the amount of acquired knowledge and the influence on the attitude of the buyer seemed to depend on the object type. Despite the limitations of this experimental study due to its focus on internal validity, the results point to the potential of immersive VR-based online shops as a new distribution channel.
An Efficient Proxy Re-Signature Over Lattices

In 2008, Libert and Vergnaud constructed the first multi-use unidirectional proxy re-signature scheme. In this scheme, the proxy can translate the signatures several times but only in one direction. Thus, two problems remain open. That is, to construct a multi-use unidirectional proxy re-signature scheme based on classical hardness assumptions, and to design a multi-use unidirectional proxy re-signature scheme with the size of signatures and the verification cost growing sub-linearly with the number of translations. This paper solves the first problem and sharply reduces the verification costs. We use the preimage sampleable algorithm to develop a multi-use unidirectional proxy re-signature scheme based on lattices, namely, the hardness of the Small Integer Solution (SIS) problem. The verification cost does not grow with the number of translations and the size of signatures grows linearly with the number of translations in this scheme. Furthermore, the proposal is secure in quantum environment.
Node Embeddings for Graph Merging: Case of Knowledge Graph Construction

Combining two graphs requires merging the nodes which are counterparts of each other.In this process errors occur, resulting in incorrect merging or incorrect failure to merge.We find a high prevalence of such errors when using AskNET, an algorithm for building Knowledge Graphs from text corpora.AskNET node matching method uses string similarity, which we propose to replace with vector embedding similarity.We explore graph-based and wordbased embedding models and show an overall error reduction of from 56% to 23.6%, with a reduction of over a half in both types of incorrect node matching.
Formality Style Transfer for Noisy, User-generated Conversations: Extracting Labeled, Parallel Data from Unlabeled Corpora

Typical datasets used for style transfer in NLP contain aligned pairs of two opposite extremes of a style. As each existing dataset is sourced from a specific domain and context, most use cases will have a sizable mismatch from the vocabulary and sentence structures of any dataset available. This reduces the performance of the style transfer, and is particularly significant for noisy, user-generated text. To solve this problem, we show a technique to derive a dataset of aligned pairs (style-agnostic vs stylistic sentences) from an unlabeled corpus by using an auxiliary dataset, allowing for in-domain training. We test the technique with the Yahoo Formality Dataset and 6 novel datasets we produced, which consist of scripts from 5 popular TV-shows (Friends, Futurama, Seinfeld, Southpark, Stargate SG-1) and the Slate Star Codex online forum. We gather 1080 human evaluations, which show that our method produces a sizable change in formality while maintaining fluency and context; and that it considerably outperforms OpenNMT’s Seq2Seq model directly trained on the Yahoo Formality Dataset. Additionally, we publish the full pipeline code and our novel datasets.
EFFECT OF CORPORATE SOCIAL RESPONSIBILITY PROGRAM ON CORPORATE IMAGE IN JORDAN LOMBOK

The purpose of this study were (1) To determine the effect of Aspect Education Program on Corporate Image in Lombok Jordan Company.(2) To determine the effect on the Health Aspects Program on Corporate in Lombok Jordan Company.(3) To determine the effect of Environmental Aspects Program on Corporate Image in Lombok Jordan Company.Research type used is associative causal.Sampling technique in this research is purposive sampling technique of determining the sample with a certain consideration.In this case the 120 respondents who received the benefits of CSR Companies Jordan Lombok.Proving the hypothesis using a statistical test by way of Individual Test with SPSS.Version 21 for Windows.The conclusion that can be drawn is (1) There is a significant effect of education on Corporate ImageJordan Lombok, (2) There is no significant influence on the health of the Corporate Image Jordan Lombok, (3) There is a significant influence of the environment on the Company ImageJordan Lombok.
Person Re-identification with Neural Architecture Search

Most of the existing person re-identification (ReID) methods use a classification network pre-trained on external data as the backbone and then fine-tune it, which results in a network architecture that is fixed and dependent on pre-training of external data. There are also some methods that are specifically designed by human experts for ReID, but manual network design becomes more difficult as network requirements increase and often fails to achieve optimal settings. In this paper, we consider using emerging neural architecture search (NAS) technology as a tool to solve above problems. However, most of NAS methods deal with classification tasks, which causes NAS to not be directly extended to ReID. In order to coordinate the inconsistency between the two optimization goals, we propose to establish an objective function with the assistant of the triplet loss to guide the direction of architecture search. Finally, it is no longer dependent on external data to automatically generate a ReID network with excellent performance using NAS directly on the target dataset. The experimental results on three public datasets validate that our method can automatically and efficiently find the network architecture suitable for ReID.
The Challenge of Creating The Sarptical Dataset

The SARptical dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> consists of about 10,000 matching pairs of very high resolution optical and SAR image patches extracted from TerraSAR-X very high-resolution spotlight images and aerial UltraCAM optical images in dense urban areas of the city of Berlin, Germany. This dataset is distinct from any other existing SAR optical dataset, because the 3D location of the center pixels of the SAR and optical were explicitly matched. Still, creating such dataset poses a fundamental challenge. The reason is that a pixel level matching between SAR and optical images is generally impossible to achieve a without the assistance of a precise 3-D model. This is especially true in dense urban areas, because of the inevitable layover caused by the side-looking SAR imaging geometry. Such misalignment will affect applications like joint classification using SAR and optical images, and raise the difficulty in designing machine learning algorithms. This paper will discuss the challenge of jointly using SAR and optical images for remote sensing applications and propose possible methods to mitigate those misalignment errors.
Fusion CNN Based on Feature Selection for Crime Scene Investigation Image Classification

Crime Scene Investigation images have many semantic categories and complex image contents. The Convolution Neural Network (CNN) feature cannot express the uniformity of image content and high dimensional features can lead to redundancy of feature vectors in CNN. In the circumstance it is difficult to use CNN to process crime scene investigation images. To solve the above problems, we propose a fusion CNN algorithm based on feature selection for the classification of crime scene investigation images. In this paper, we build the fusion CNN features to enhance the ability of representation by fusing the convolutional layer with the fully connected layer. Then we select the fusion features with Laplacian score and label mutual information. Finally, we use the obtained features to train Support Vector Machine (SVM) classifier on the Crime Scene Investigation Images Database (CSID). Experiments show that the average classification accuracy of the proposed method can reach 93.67%.
Nimble Collection Development Policies: An Achievable Goal

This paper identifies and reviews some of the currently relevant components of collection development which contribute to the need for having a written collection development policy (CDP) in place. The requisite elements for a pertinent and usable CDP are identified, keeping in mind the need to customize these policies to each library’s unique needs. A literature review is presented to demonstrate the long-standing purposes of collection development policies (CDPs), quantitative studies of existing CDPs, and some of the inherent drawbacks in the creation and application of these policies. The author presents a case study which demonstrates the processes undertaken to create a CDP for a medium-sized academic library. This includes more current and relevant considerations for a modern day CDP. The paper also includes the best practices identified throughout the policy creation process and which has the potential to be applied to other similarly situated libraries.
MODIFIED CONJUGATE GRADIENT METHOD FOR TRAINING NEURAL NETWORKS BASED ON LOGISTING MAPPING

In this paper, we suggested a modified conjugate gradient method for training neural network which assurance the descent and the sufficient descent conditions. The global convergence of our proposed method has been studied. Finally, the test results present that, in general, the modified method is more superior and efficient when compared to other standard conjugate gradient methods
MICRON: Multigranular Interaction for Contextualizing RepresentatiON in Non-factoid Question Answering

This paper studies the problem of non-factoid question answering, where the answer may span over multiple sentences.Existing solutions can be categorized into representationand interaction-focused approaches.We combine their complementary strength, by a hybrid approach allowing multi-granular interactions, but represented at word level, enabling an easy integration with strong word-level signals.Specifically, we propose MICRON: Multigranular Interaction for Contextualizing RepresentatiON, a novel approach which derives contextualized uni-gram representation from n-grams.Our contributions are as follows: First, we enable multi-granular matches between question and answer n-grams.Second, by contextualizing word representation with surrounding n-grams, MICRON can naturally utilize word-based signals for query term weighting, known to be effective in information retrieval.We validate MICRON in two public non-factoid question answering datasets: WikiPassageQA and InsuranceQA, showing our model achieves the state of the art among baselines with reported performances on both datasets.
A Part-Based Deep Neural Network Cascade Model for Human Parsing

Human parsing is important for image-based human-centric and clothing analyses. With the development of deep neural networks, some deep human parsing methods were recently proposed, which substantially improve the parsing accuracy. However, some localized small regions (such as sunglasses) are not parsed well in these methods. In this paper, we propose a Part-based Human Parsing Cascade (PHPC) to segment human images, imitating the observational mechanism of how people, when first looking at a human image, quickly scan the entire photograph to first locate the face and then the body parts to see what clothing the person is wearing. The observational mechanism of human vision is used to establish a cascade relationship in designing our network, in which a head-parsing sub-network and a body-parsing sub-network are integrated to the cascade of human parsing networks. The head- and body-parsing sub-networks focus on the head and body classes, respectively, and add attention to the head and body in the final neural networks. Comprehensive evaluations on the ATR dataset have demonstrated the effectiveness of our method.
The Data Series – Solving The Data Privacy Problem Using Synthetic Data

WE ARE ALL BECOMING INCREASINGLY AWARE OF THE VALUE OF OUR DATA, and the desire to share it without the concept of a value exchange is dwindling. A true and widely accepted model for the value exch...
BEST PRACTICES AND RECOMMENDATIONS FOR WRITING GOOD SOFTWARE

Writing good software is not an easy task, it requires a lot of coding experience and skills. Therefore, inexperienced software developers or newbies suffer from this critical task. In this paper, we provide guidelines to help in this important context. It presents the most important best practices and recommendations of writing good software from software engineering perspective regardless of the software domain (whether for desktop, mobile, web, or embedded), software size, and software complexity. The best practices provided in this paper are organized in taxonomy of many categories to ease the process of considering them while developing software. Furthermore, many useful, practical, and actionable recommendations are given mostly in each category to be considered by software developers