Reviewerly/exHarmony huggingface dataset:
Developing Creative Activity Of Socio-Humanities Students: Faculty Members' Perspective

This paper analyzes how and to what extend socio-humanities’ faculty members in Russia develop students’ creative activity by the means of information educational environment (IEE). The relevance of this problem is proven due to the contradiction between current demands of the digital economy labour market and low digital competence of socio-humanities graduates. We argue that under the conditions of digital economy creativity can become a key competence for socio-humanities graduates; however in the domain of education we suggest the use of the term “creative activity”. To understand the level of awareness of this problem among socio-humanities faculty members, as well as their attitude towards developing students’ creativity through the interaction with the electronic means of the IEE, we conducted a pilot survey among 25 faculty members of two major St. Petersburg universities specializing in socio-humanistic studies. Our results demonstrated a positive outlook on work with the IEE, a high level of confidence in online activities being a means to develop students’ universal skills. We also learned about the tools that are currently used by the respondents to develop their students’ creative activity. In conclusion we suggest the importance of further pedagogical research to develop a systematic pedagogical approach to the problem.
Lightweight Classifier-Based Outlier Detection Algorithms from Multivariate Data Stream

Outlier detection is a preprocessing method that is useful in detecting unusual data if they are of interest to the users, or reducing irrelevant instances in machine learning if outliers are interpreted as noise. In the past decades, there are many outlier detection algorithms that have been developed and reported in the literature. Many have been implemented as software programs across a spectrum of various applications ranging from identifying special customers in marketing to fraud detection in security context. In chapter, a novel outlier detection method named lightweight analysis is proposed. Instead of using a full dataset to compute outlier values like the traditional outlier detection algorithms, the proposed concept of lightweight analysis works by examining only a certain “number of instances as a reference in order to calculate the outlier indicator, like cumulative analysis or lightweight analysis with sliding window, other than global analysis only.” These three mechanisms are then combined with the existing outlier measurements such as “interquartile, local outlier factor and Mahalanobis distance range.” In this study, the computer simulation experiments show encouraging results that support the fact that good classification accuracy is possible using reduced datasets. The outlier detection results are on par or more accurate than the existing state of the arts by using the “proposed classifier-based outlier detection (COD) method coupled with lightweight analysis.”
On the number of distinct roots of a lacunary polynomial over finite fields

We obtain new upper bounds on the number of distinct roots of lacunary polynomials over finite fields. Our focus will be on polynomials for which there is a large gap between consecutive exponents in the monomial expansion.
Privacy-Preserving Data Publishing via Mutual Cover

We study the anonymization technique of k-anonymity family for preserving privacy in the publication of microdata. Although existing approaches based on generalization can provide good enough protections, the generalized table always suffers from considerable information loss, mainly because the distributions of QI (Quasi-Identifier) values are barely preserved and the results of query statements are groups rather than specific tuples. To this end, we propose a novel technique, called the Mutual Cover (MuCo), to prevent the adversary from matching the combination of QI values in published microdata. The rationale is to replace some original QI values with random values according to random output tables, making similar tuples to cover for each other with the minimum cost. As a result, MuCo can prevent both identity disclosure and attribute disclosure while retaining the information utility more effectively than generalization. The effectiveness of MuCo is verified with extensive experiments.
PREDICTING THE AMOUNT OF PRODUCTION OF TERI USING FUZYY LOGIC TSUKAMOTO METHOD IN CV.MAHERA

Production is one of the activities carried out in a company, especially CV. Mahera engaged in the processing of export quality dried anchovy. Anchovy is one of the fisheries commodities that have high economic value and is a commodity in the processing of fishery products in CV. Mahera. But the system that is done on the CV. Mahera is still done manually, namely through several processes of weighing, cooking, drying, framing and sorting. so in determining the amount of anchovies exports take a long time and income the amount of production is uncertain in each process. Thus in every time exports sometimes can not meet the desired target by consumers. Therefore, the development of this system aims to apply the Tsukamoto fuzzy method to predict the amount of exported anchovy production based on the number of catches and the dry amount as input variables. The process is carried out by inputting the amount of catch and the amount of dry in the system will then display the results of production as output. The accuracy of the results of the assessment of this system between the original data (115 &amp; 101) with the calculated results (102.5 &amp; 99.25) the highest difference is 12.5 and the lowest difference is 1.75. Based on testing this application it can be seen that the prediction results from the application of the Tsukamoto fuzzy method meet the existing production amount. By using this application the company can predict production results faster than the manual process.
ANALISIS DAN PERANCANGAN DECISION SUPPORT SYSTEM PENYALURAN BANTUAN PENYANDANG MASALAH KESEJAHTERAAN SOSIAL (PMKS) MENGGUNAKAN METODE WEIGHTED PRODUCT (WP)

Abstract : Through Social Welfare Law No. 11 of 2009 and PERMENSOS 08 of 2012, the government made efforts to improve the social welfare of PMKS (People with Social Welfare Problems). So far, PMKS participants have still used manual identification, so there is a lack of acceptance or acceptance by PMKS participants. Participation of participants. Then an alternative solution using DSS. Then alternative solutions can use DSS. For reception to be targeted, it is necessary to use ranking methods or which order is more priority in receiving assistance. Before that, it is necessary to do an interview &amp; observation of related parties about what categories and criteria the community enters into PMKS members. Then to find out their priorities, in this study using the Weighted Product calculation method. The design of this DSS uses 12 PMKS Categories, each of which has criteria, and then is calculated. The results from the lowest to highest values ​​can be seen in each of the PMKS Categories. Later it will be seen who is more priority per category.&#x0D; The highest final acquisition value (V) is in the neglected children category 0.1503, the disabled children category 0.3610, the elderly abandoned category 0.1725, the disability category 0.1141, the socioeconomic-prone women category, and the socially difficult family category psychological 0,2301.&#x0D; &#x0D; Key words : Decision Support System, PMKS, Weighted Product (WP).&#x0D;
Development Of A Rural Teacher Training System In Russia

The article presents the results of a historical-pedagogical analysis of the theory and practice of professional activity and the training of teachers for a Russian rural school. One of the main features of a rural school is the leading “key” role of a rural teacher. The specifics of rural school and the pedagogical activity of a rural teacher reflect the trends in the development of society and education typical for the corresponding historical period - the innovative trends of its time. Currently, these trends include the ideas of lifelong education and pedagogical education. The periodization of the professional development of the rural teacher is built on the education representation and analysis of the way the need for purposeful, i.e. special, training was presented in the writings of Russian philosophers; the activities of a rural school and a rural teacher; professional training of teachers, in particular, rural teachers. Three elements of the structure of education are main indicators: the system of general educational institutions in rural areas, the system of professional pedagogical education that trains pedagogical staff for them, and the system of advanced training for rural teachers. Three main periods are distinguished: initial - until the end of the XVIII century, pre-system - from the end of the XVIII century until the middle of the XIX century, the system period - from the middle of the XIX century to the present.
Development of a Decision Support System for Urban Planning by Using K-means ++ Algorithm

This article depicts a decision support system (DSS) devoted to the coordinated administration of urban frameworks. This framework defines the information and related treatments normal to a few civil managers and characterizes the necessities and functionalities of the PC devices created to enhance the conveyance, execution, and coordination of metropolitan administrations to the populace. The cooperative framework called Decision Support System for Urban Planning (DSS-UP) is made out of a universal planning and coordination framework. So, it helps the decision-making process, a DSS was created as a learning-based framework gave derivation components that empower urban architect to settle on key decisions as far as specialized meditations on civil foundations. The learning-based framework stores experts_ information and additionally answers for past issues. Preparatory execution comes about demonstrate that DSS-UP viably and effectively underpins the decision-making process identified with overseeing urban foundations by using K-means++ data mining algorithm.
Practical Research on the Fusion of Classical Culture and Curriculum Construction

Curriculum is the main channel of the implementing cultural education. After practicing several years, we actively develop relevant courses and explore how to integrate classic culture into curriculum construction. Be clear that inheriting and innovating classic culture is the inherent requirement of curriculum construction; the characteristics of young students’ physical and mental development are the important basis of curriculum construction; the content arrangement of curriculum construction lays stress on the construction of classics, diversity and applicability based on the double line structure of historical time and theme education, and the construction of student aid system with ability practice as the capital mission, so that students’ self-learning and self-education can be promoted. Curriculum with distinct classics and vitality plays a significant role in laying the spiritual foundation for students’ growth.
Hubungan antara Sosialisasi dengan Pengetahuan Tentang Panas Bumi

Abstract — Geothermal energy or geothermal is a renewable energy that is environmentally friendly, this energy has the potential to produce the largest electricity because it can reduce the impact of global warming. PT Pertamina Geothermal Energy Area Kamojang, as the manager of geothermal energy, is conducting socialization efforts by using a video entitled Environmental Friendly PLTP. This socialization was carried out at the Geothermal Information Center. This study aims to determine the relationship between socialization and knowledge of geothermal on the aspects of Clarity of Message, User Friendly, Content Representation and Media Visualization. The method used is the quantitative method with a correlational approach. The population of this study were visitors to the Geothermal Information Center (GIC) from November to February using a stratified random sampling technique so as to get 79 respondents. With data collection techniques in the form of questionnaires, literature study, and interviews. This study uses the Information Processing Theory proposed by Robert Gagne. The results of the study show that there is a significant relationship between socialization and knowledge of geothermal energy. On aspects clarity of message has a weak relationship with knowledge. Then, theaspect user friendly has a moderate relationship with knowledge. Furthermore, the aspect of content representation has a moderate relationship with knowledge. And, in the aspect of media visualization there is a moderate relationship with knowledge. Keywords —Socialization, Geothermal, Knowledge, Pertamina, Geothermal Information Center.
Video quality vs. video usability in the era of surveillance

More than 100 years since the development of movies, film, and television, we have entered a new age of media, the surveillance era. Previously, media was used to entertain, or inform (news). Today, we are also increasingly collecting media to inform ourselves about who, what, and where. Public places from malls to airports to city streets are laced with cameras that watch us with hidden eyes. Home security systems now routinely embed cameras both in and around residences. Autonomous vehicles represent a massive new use of sensors, including RGB, infrared, and lidar. And there are eyes in the skies, not only over military battlespaces, but even over urban areas, as personal drones compete with package delivery robots. All of this surveillance video has a purpose (e.g., object detection, face recognition, object tracking, activity recognition), which is increasingly pursued by machines, not human observers. In the era of surveillance, what does the quality of video mean? One simple answer is: video quality is a measure of how well it supports the purpose. This is in an abstract sense the same as it is for entertainment video. But quantifying it requires new tools. In this paper, we address the case of surveillance, especially aerial surveillance. Our ideas apply equally to military and commercial applications like autonomous vehicles and package delivery.
Own Video Content of Libraries: Trends and Prospects for Use

Changing of media consumption in the digital age is the object of focus attention of both Russian and foreign researchers. Modern cultural studies note the increasing role of video content in the media environment. Taking into account current trends, libraries more often create their own video materials to implement various goals and objectives. Thus, creation of own video content is rapidly becoming an integral part of the work of modern library. However, despite some established experience of libraries in this area, there is still not enough research of general and theoretical nature on the content, guidelines and prospects for the development of activities related to the creation and use of library video content, and there is no its classification. The purpose of this work is to conduct analysis of the video content in libraries, identify the most common materials, as well as to determine the prospects for using this tool to reach their own goals. The article discusses definitions of the term “video content”. The author presents the main types of videos produced by libraries, their characteristics and features. Special attention is paid to video projects of libraries in Russia. The article reveals the experience of the Astrakhan Library for Youth named after B. Shakhovsky in using its own video content. The paper discusses the issue of classification of video materials produced by libraries. The author proposes the following classification of library videos: video review, virtual book exhibition, webinar (online seminar or web conference), interview, humorous video, webcast, event announcement, video report and booktrailer. The author indicates the main reasons hindering the demand for library video content among the wide range of Internet users and gives the rationale for the necessity and importance of this type of activity and proposes possible prospects for using own video content of libraries. Thanks to its presence, the library ceases to be a closed institution storing knowledge within itself that produces positive impact on its image. Using means of communication that are understandable to a person of visual culture, it changes stereotypes and demonstrates its modern capabilities.
LibGuides: First Year Experience: What The Library Does

This LibGuide was created to provide information on library resources for first-year students and their instructors.
Confidence-aware Adversarial Learning for Self-supervised Semantic Matching

In this paper, we aim to address the challenging task of semantic matching where matching ambiguity is difficult to resolve even with learned deep features. We tackle this problem by taking into account the confidence in predictions and develop a novel refinement strategy to correct partial matching errors. Specifically, we introduce a Confidence-Aware Semantic Matching Network (CAMNet) which instantiates two key ideas of our approach. First, we propose to estimate a dense confidence map for a matching prediction through self-supervised learning. Second, based on the estimated confidence, we refine initial predictions by propagating reliable matching to the rest of locations on the image plane. In addition, we develop a new hybrid loss in which we integrate a semantic alignment loss with a confidence loss, and an adversarial loss that measures the quality of semantic correspondence. We are the first that exploit confidence during refinement to improve semantic matching accuracy and develop an end-to-end self-supervised adversarial learning procedure for the entire matching network. We evaluate our method on two public benchmarks, on which we achieve top performance over the prior state of the art. We will release our source code at https://github.com/ShuaiyiHuang/CAMNet.
Frequency Diversity in Low-Altitude Target DOA Estimation

In the presence of sea-surface multipath, multipath distortion has been a challenge problem for low elevation target's direction of arrival (DOA) estimation in monopulse radar. Frequency diversity has been explored as a technique in communication to resist multipath and propagation effects. In this paper, approach to integrating frequency diversity into the DOA estimation, that is suitable for sea-surface multipath environment, is proposed. In particular, principles that seek for appropriate frequency selection with higher accuracy of angle measurement, is investigated. Compared with the general circumstances without frequency diversity, the advantage of this method in DOA estimation accuracy for low elevation targets is validated from a theoretical point of view as well as from numerical simulations.
Learn to Talk via Proactive Knowledge Transfer

Knowledge Transfer has been applied in solving a wide variety of problems. For example, knowledge can be transferred between tasks (e.g., learning to handle novel situations by leveraging prior knowledge) or between agents (e.g., learning from others without direct experience). Without loss of generality, we relate knowledge transfer to KL-divergence minimization, i.e., matching the (belief) distributions of learners and teachers. The equivalence gives us a new perspective in understanding variants of the KL-divergence by looking at how learners structure their interaction with teachers in order to acquire knowledge. In this paper, we provide an in-depth analysis of KL-divergence minimization in Forward and Backward orders, which shows that learners are reinforced via on-policy learning in Backward. In contrast, learners are supervised in Forward. Moreover, our analysis is gradient-based, so it can be generalized to arbitrary tasks and help to decide which order to minimize given the property of the task. By replacing Forward with Backward in Knowledge Distillation, we observed +0.7-1.1 BLEU gains on the WMT'17 De-En and IWSLT'15 Th-En machine translation tasks.
PColorSeg_Net: Investigating the impact of different color spaces for pathological image segmentation

Deep Learning (DL) shows the state-of-art performance in detection, classification and segmentation tasks compared to existing methods. As computational pathology becomes a very promising area of research, it is important to determine which color space provides better results for pathological image segmentation tasks. In this paper, we have considered six different color spaces, namely RGB, LAB, CIE, YCrCb, HSV and HSL for nuclei segmentation tasks where the Recurrent Residual based U-Net (R2U-Net) model is applied. The ISMI-2017 publicly available dataset is used for evaluating the model in this implementation. The Lab color space shows an F1-score of 0.9365, which is the highest segmentation performance when compared to the other color spaces. The Lab color space model shows around 0.38% better performance compared to the RGB color space for nuclei segmentation tasks. This investigation will provide a clear guidance in advance of pathological image segmentation and analysis tasks.
Learning latent expression labels of child facial expression images through data-limited domain adaptation and transfer learning

State-of-the-art deep learning models have demonstrated success in classifying facial expressions of adults by relying on large datasets of labeled images. Unfortunately, there is a scarcity of labeled images of child expressions. Deep learning models trained on adult data do not generalize well on child data due to the domain shift caused by morphological differences in their faces. Recent deep domain adaptation approaches align the data distribution of a target domain with the source domain using a few target domain samples. We propose that the domain adaptation may be improved by incorporating steps of deep transfer learning, such as initialization with pre-trained source weights and freezing early layers of the model. The knowledge of a few labeled examples from the child data (target domain) is incorporated into the adult data distribution (source domain) using a contrastive semantic alignment (CSA) loss. This work combines deep transfer learning and domain adaptation approaches to generate seven expression labels (‘happy’, ‘sad’, ‘anger’, ‘fear’, ‘surprise’, ‘disgust’, plus ‘neutral’) for facial images of children in reference to the source domain, adult facial expressions, using 10 or fewer samples per expression. Our hybrid approach outperforms the transfer learning model by 12% on mean accuracy using only 10 samples per expression class.
Position representation of single-mode Gaussian channels beyond the Gaussian functional form

Abstract We study one-mode Gaussian quantum channels in continuous-variable systems by performing a black-box characterization using complete positivity and trace preserving conditions, and report the existence of two subsets that do not have a functional Gaussian form. Our study covers as a particular limit the case of singular channels, thus connecting our results with their known classification scheme based on canonical forms. Our full characterization of Gaussian channels without Gaussian functional form is completed by showing how Gaussian states are transformed under these operations, and by deriving the conditions for the existence of master equations for the non-singular cases. We show that although every functional form can be found in the vicinity of the identity, one of them does not parametrize unitary channels.