Reviewerly/exHarmony huggingface dataset:
Research on the Algorithm of Art Style Transfer of Xin'an Painting School

Xin‘an Painting School plays an important role in the history of Chinese painting. It takes Huizhou landscape as the creative theme and has a unique artistic style. However, the current art style transfer field does not concern about this very regional characteristics of painting school. Therefore, we propose an improved CycleGAN to realize the transfer of Xin'an painting style. Firstly, DenseNet is introduced to alleviate the gradient vanishing problem and optimize the content and style features transfer between the layers of neural network. Secondly, group normalization is used to reduce the calculation error and keep the network training process stable. Finally, the least square loss is introduced in the adversarial losses, and the identity loss is introduced to obtain the feature of the target image as much as possible, which constrains the arbitrary transformation of the feature of the input image. The experiment shows that the generated pictures have a good artistic style of Xin’ an Painting School.
A Classification for Managing Software Engineering Knowledge

This taxonomy paper presents a novel way of knowledge engineering in the software engineering research community. Till now, research papers are organized digitally as documents, mostly in PDF files. Not much effort is spent on effective knowledge classification, retrieval, storage, and representation. In contrast to the current paper-based approach for knowledge documentation, we present a statement-based approach, where each statement is linked to arguments and data of its evidence as well as to related statements. We argue that in this way, knowledge will be easier to retrieve, compare, and evaluate in contrast to current paper-based knowledge engineering in scientific search engines and digital libraries. Therefore, we present as a first step a novel multi-dimensional classification for statements in software engineering research. Statements are classified according to their research object, their kind (e.g., relevance), and their underlying evidence. This classification is validated and extended with a first systematic literature review. Additionally, we provide an example for illustration purpose.
Mineral resources ethics

To what extent can a generation be held responsible for keeping sufficient mineral resources available for future generations? Would the current poor not have the right to get more attention in this respect than the future rich? Different ethical views and sustainability paradigms are presented. Applicable normative principles are discussed. Existing international agreements are investigated in this respect. Sustainability goals in connection with the geological availability of scarce raw materials are analyzed. We formulate a definition for the sustainable production of mineral resources. Based on this definition we calculate the sustainable extraction rates of the thirteen scarcest mineral resources for three sustainability periods: 200 years, 500 years and 1000 years.
Research of psychological stability of the employees of the penal system to the influence of destructive factors of professional activity

The article presents the results of an empirical study of employees of the penitentiary system, the purpose of which was to study their psychological resistance to the impact of destructive factors of professional activity and the development of a psychocorrectional program. The professional activity of employees of the penal system is associated with the impact of various kinds of stress factors, the main of which is interaction with convicts with different levels of psychological and pedagogical neglect and criminal infection, which leaves an imprint on the employee's personality and destructive changes, and therefore the study the psychological stability of the penitentiary system employees to the impact of destructive factors of professional activity is of particular relevance for the penitentiary department. Having studied the psychological characteristics of employees and determined the destructive factors of professional activity, the authors checked the presence of a statistical relationship between the studied phenomena using Spearman's correlation coefficient, which made it possible to develop a psychocorrectional program for psychological support of this category of employees of the penal system. Also, within the framework of the developed psychocorrectional program, the main directions of work on the prevention and correction of destructive behavior are given, which in turn will increase the efficiency of the professional activities of penitentiary officials, improve the social and psychological climate within the team, and prevent discipline among the employees of the penal system.
Efficient Workflow for Social Big Data Processing

Now a days social media is very popular among people and its data can be used for analysis of market strategy, getting insights etc. Social big data processing involves multiple phases and processes like gathering data from multiple sources, pre-processing, feature selection, feature extraction, evaluation logic for processing etc. Here data is gathered from multiple sources so this should be aggregated and managed in efficient manner. If all involved steps are performed through efficient workflow this overcomes the complexity of further processing steps. As part of this paper, we suggest efficient workflow schedule for one task which helps to follow further step by step processes involved in social big data processing. An efficient automated workflow overcomes manual efforts for submission of respective task jobs and select appropriate jobs out of multiple jobs. As future work, this can be added for further tasks with intelligent decision making workflows which behave on the basis of nature of the data.
Semantic Text Segment Classification of Structured Technical Content

Semantic tagging in technical documentation is an important but error-prone process, with the objective to produce highly structured content for automated processing and standardized information delivery. Benefits thereof are consistent and didactically optimized documents, supported by professional and automatic styling for multiple target media. Using machine learning to automate the validation of the tagging process is a novel approach, for which a new, high-quality dataset is provided in ready-to-use training, validation and test sets. In a series of experiments, we classified ten different semantic text segment types using both traditional and deep learning models. The experiments show partial success, with a high accuracy but relatively low macro-average performance. This can be attributed to a mix of a strong class imbalance, and high semantic and linguistic similarity among certain text types. By creating a set of context features, the model performances increased significantly. Although the data was collected to serve a specific use case, further valuable research can be performed in the areas of document engineering, class imbalance reduction, and semantic text classification.
Sentiment Classification for Moroccan Arabic Using the Lexicon-Based Approach

Social networks users contribute in producing huge quantity of data due to their increasing online activity. However, extracting useful knowledge from social networks can be a difficult process, especially when conducting sentiment analysis on Arabic texts. The challenging aspect of Arabic language resides primarily in the mutual use of standard and dialectal forms in expressing opinions. This work focuses on sentiment analysis using the semantic orientation approach that rely on a lexicon of opinion words and a rule-based classifier to achieve sentiment classification. In this methodology, we build a large sentiment analysis lexicon that covers modern standard Arabic and Moroccan dialectal Arabic. Then, we have developed several rules and tested them in the classification process. The proposed methodology showed promising results.
Information Exchange Platform Based on a Q&amp;A Model

Information exchange systems are the basis on which the current Information age operates. It is therefore important to understand how these systems work and how we can use them to our benefit. This study aims to analyze information exchange platforms found on the internet, how they operate, what benefits they offer, and what disadvantages they pose. To more deeply understand their operation, we created our own questions-and-answers system. In later chapters, we discuss how this system is created, how users can interact while on the platform, and what best practices we used based on our previous analysis of other similar platforms. Based on our research and the system created, we can conclude that while creating a Q&A platform is with the right knowledge easy to implement, and it acts as a very useful tool to support the free exchange of information. Especially in the field of business, such a system may be a major advantage in the field of marketing, recruiting and customer support or when used internally to support the exchange of ideas and experiences between employees.
Generalized threshold secret sharing and finite geometry

Abstract In the history of secret sharing schemes many constructions are based on geometric objects. In this paper we investigate generalizations of threshold schemes and related finite geometric structures. In particular, we analyse compartmented and hierarchical schemes, and deduce some more general results, especially bounds for special arcs and novel constructions for conjunctive 2-level and 3-level hierarchical schemes.
Reader for Unique 2D Circular Marks

The production of metal rods in Třinecké Železárny, a.s. is followed by multiple movements across the production plant mainly due to various quality controls, that are performed in different places. The products stack before each control. To increase effectivity of identifying an individual rod, a unique mark is engraved onto its cross-section. This article is devoted to developing a reader device, that can read and decode these unique marks.
16‐2: Machine‐Anomaly Sound Detection Using Convolutional Recurrent Neural Network with Prediction Loss

Automatic machine anomaly sound detection is important for machine maintenance in display manufacturing factory. Recently, unsupervised anomaly detection approach based on autoencoder and reconstruction error has been proposed, which has assumption that the network trained only with the normal data produces higher reconstruction errors for the anomalies than the normal inputs. However, most approaches based on autoencoder consist of Convolutional Neural Network (CNN) which deals with the spectrogram of the sound like an image. In this paper, we propose Convolutional Recurrent Neural Network to use the characteristics of the spectrogram. To effectively integrate time and frequency information, we apply a 1‐dimensional convolutional layers and recurrent neural network to the network structure. Also, we introduce a prediction loss to prevent the network from learning to reconstruct anomalies well. Experimental results on the ToyADMOS and MIMII datasets demonstrate that the proposed approach achieves promising performance.
Reviewing Minimalist Theories of Control and a Brief Look at Romanian Control

"Reviewing Minimalist Theories of Control and a Brief Look at Romanian Control. The phenomenon of control is a long-discussed topic within the enterprise of generative grammar. Multiple theories were composed and dismissed along with the advancement of the module, and with the development of the Minimalist program, more recent theories on control came to surface. The present article provides a review of two minimalist theories of control: the Movement Theory of Control and the Agree Model of Obligatory Control. A synopsis of one applied model of the MTC on Romanian data is also part of the paper, as is a brief commentary on the structure of Romanian control, namely an exploration of the tension between subjunctive and infinitive control complements. Keywords: control theories, minimalist program, Romanian control, Agree Model of Control, Movement Theory of Control "
A Sustainable Smart Technique for Generating Water from Atmosphere for Future Cities - A Thermal Electric Cooling Approach

Worldwide poor management of the available water sources in urban cities has been affecting the water quantity and quality for decades. In the future, water scarcity will result in a hike in the price of drinking water. Consequently, we might have limited access to it. There has been an extensive research in the production of water, which predominantly requires an external power, additional maintenance cost, and complex operations. This research provides an experimental approach for a sustainable and economical production of water from condensation of atmospheric moisture based on the principle of heat flow and Peltier effect and secondarily generating electricity. Research suggests that voltage thus developed isnâ€™t substantial enough which can be amplified further. The results show that an ample amount of water has been produced, which can be increased by installing multiple units that can lead to mitigating water scarcity in future cities.
Digital environment as effective pedagogical tool of socialization in the context of modern education

Today, the modern educational paradigm is based on the organization of new forms of social education and training with new digital technologies introduction. In the article, the author sproceed from the main conceptual provisions of domestic and foreign the ories, which allow us as serting that the pedagogically justified use of the digital environment, aimed at solving the problems of social education, can contribute to the adaptation of students to life in the modern in formation society. The purpose of the study is to identify the problems and perspectives of the students’positive socialization formation under the conditions of modern education based on the theoretic alanalys is of domestic and English-speaking (the USA and Canada) scientific works that reflect the content of the digital technologies implementation. This article at tempts to: reveal the essence of the digital environment concept as a pedagogical tool for students’ positive socialization, where the digital environment represents an element of the information and educational environment, with in which socialization is revealed, where the cybers pacesocial process espotential comprehension is carried out; determine the social and educational effects, the main conceptual provisions of the digital environment use as the pedagogical tool for the students’ positives ocialization; based on the analysis, expand knowledge in the context of the theoretical and methodological aspects for domestic sciencein view of the research of scientific works in modern English-speaking countries (the USA, Canada).
Research on Flow Classification Model Based on Similarity and Machine Learning Algorithm

In recent years, with the rapid development of the Internet, complex and diverse applications and network traffic have been generated. At the same time, network encryption technologies and various new network traffic have emerged, which affects the efficiency of the original traffic classification technology. In order to improve the efficiency of traffic classification and reduce the classification time, this paper proposes a network traffic classification model (Cosine similarity and decision tree classification model, CSDT) based on cosine similarity and decision tree algorithm to identify and classify traffic. First, the cosine similarity algorithm is used to judge the similarity of adjacent network traffic, and the network traffic with higher similarity is labeled with a known classification and forwarded. For network traffic with low similarity, the decision tree algorithm is used to classify the related feature values. This model utilizes the characteristics of high similarity in adjacent data streams, and uses similarity algorithms to preprocess network traffic to reduce classification time. The Moore data set publicly available in the field of network traffic classification is used for training and testing, and the results are compared with various machine learning algorithms on the Weka platform. The experimental results show that the model has a good classification accuracy, which greatly reduces the classification time and improves the classification efficiency of network traffic is improved.
Hierarchical Encoder-Decoder Summary Model with an Instructor for Long Academic Papers

Summary models, whether extractive or abstractive, have achieved great success recently. For long academic papers, the abstractive model with the encoder-decoder architecture mainly only relies on the attentional context vector for generation, unlike humans who have already mastered the salient information of the source text to have full control over what to write. While the extracted sentences always contain the correct and salient information which can be used to control the abstraction process. Therefore, based on a hierarchical encoder-decoder architecture specifically for academic papers, we proposed a summary model with an Instructor, an encoder in essence by taking the guiding sentences as the input to further control the generating process. In the encoder part, the final hidden state from Instructor is directly added to the basic hierarchical hidden state from the encoder. Experimental results on arXiv/PubMed show that the only encoder-improved model can generate better abstract. In the decoder part, the context vector from Instructor is integrated with the original discourse-aware context vector for the generation. The results show that Instructor is effective for control and our model can generate a more accurate and fluent abstract with significantly higher ROUGE values.
The Nuts and Bolts of the India-Abusive Fake Government of Telangana: Cyberpolicing Against Online Sedition

Nowadays, cybercrimes are galore everywhere. The impact of cybercrimes is appalling on human ecosystems, digital societies and life. Our work considers the cybercrimes in JNTU Hyderabad academic environment, a premier university in India, for study from a multidimensional perspective. Particularly, here, we present the case as a big data crime. The enormity of the crime is evaluated as the degree of cybercrime. We find that policing reforms are a necessity in view of the dissatisfactory performance of the Cyberabad Police in handling cybercrime. Cybercrimes researchers should track the cybercrime lifespan/cycle by field studies in police stations, courts and the documents of the running cases so as to ensure a better role play in the diffusion of cybercrimes and the resultant deleterious effects to the individuals, society and, in particular, the nation. IT professionals and researchers should work in tandem with the cyber police and the cyber law practitioners for collaboratively synergistic efforts to defuse the cybercriminal organizations effectively for a progressive society and Swachch digital Bharat. Our work serves this purpose.
YOGA POSE DETECTION.

An approach to precisely determine various Yoga pose Assessment using deep learning algorithms has been demonstrated in this work. In this system, we propose a yoga pose assessment system using pose detection to help the self-training of yoga. The system first identifies the yoga pose by detecting multiple parts of humans, such as joints only with the help of pc camera. In this system, we also propose an improved algorithm to calculate scores that can be applied to all poses. Our application is analyzed on different Yoga poses under different scenarios, and its robustness is also. An in-depth hybrid learning model is suggested for the use of the convolutional neural network (CNN) and long short-term memory (LSTM) for identification of yoga in real-time videos where the CNN layer is used to extract features from key points for each found frame from an open pose and made by LSTM to give a temporary forecast.
Knowledge Transfer for Semantic Segmentation Based on Feature Aggregation

In recent years, deep neural network has achieved high accuracy in the field of image recognition, but the consumption of resource is high. Inspired by collaborative learning approaches and the knowledge distillation technique, this study proposes a knowledge transfer method for semantic segmentation based on feature aggregation. In this research, the original student network is applied to generate the auxiliary teacher network, and share the information learned from the network by establishing dense feature connections between the two networks, which are trained simultaneously. Any one of these networks has access to information that is not available to the individual networks. In addition, in order to increase the degree of collaboration, this paper proposes two methods for establishing connections between the teacher network and the student network. The first method is a dense feature connection between networks of the same layer, and the second method is a dense feature connections between multi-layer networks. The approaches proposed above are validated on the train of Unet, and the experimental results show that the knowledge transfer approach of shared feature aggregation has better performance than the traditional single network.
문법화에 의한 품사 통용 교육을 위한 시론

The purpose of this study is to propose the contents of word class conversions by grammaticalization.Word class conversions in grammar education are presented as synchronic phenomenon in which words of the same form are used as different part of speech. Language is a representation of the process and consequences of cognitive thinking by language speakers, and these changes are attributed to comprehensive aspects of history, culture, and cognitive working of language users. Grammaticalization theory is an understanding of linguistic phenomena from a panchronic perspective that encompasses synchronic and diachronic aspects, and it is a more useful methodology to grasp the original characteristics of language.Therefore, as an example of word class conversions in grammar textbooks of 2015 curriculum, the process of grammaticalization was discussed, focusing on the frequently appearing ''mankeum'', ''Oneul''. When explained by the process of grammaticalization, learners can clearly identify the property of the part of speech and relate it to the speaker''s intention. In addition, the learners can discover that word class conversions are also occur throughout the life of a language and identify the cause and aspect by grammaticalization.
Proposal and Development of Anonymization Dictionary Using Public Information Disclosed by Anonymously Processed Information Handling Business Operators

To increase the number of anonymously processed information handling business operators, support for business operators to produce anonymously processed information is indispensable. In this study, as one of the supports, an “anonymization dictionary,” which focuses on public information disclosed by anonymously processing information handling business operators is proposed and developed. In Japan, in compliance with the Act on the Protection of Personal Information, a personal information handling business operator, when producing anonymously processed information and providing it to a third party, shall disclose to the public the categories of information relating to an individual contained in the information and the method of providing the information. Most operators disclose them on their webpages. In several cases, the disclosures also contain information other than that imposed by the act, such as the anonymization techniques used and the purpose of its use. Herein, the disclosures are collected from webpages, and an anonymization dictionary is developed using the disclosures. Evaluating the effectiveness of the dictionary through scenario-based evaluation, we demonstrate that the dictionary provides useful information for anonymously processed information production.