Reviewerly/exHarmony huggingface dataset:
Visual Saliency Prediction Using Deep Learning

Visual saliency prediction is one of the most trending technology that may also leads to the future. The object detection is one of the main concepts behind this saliency prediction. So high level visual features are augmented with contextual information such as text, images etc. One method of prediction is by using the convolution neural network with the encoder-decoder architecture. This method achieves accurate, efficient and competitive result. The visual saliency prediction is used as a highlighter for the meaningful regions in the scenes. The other method is done using the cues which can reduce the weight of the network and this network takes 224*224 images. The other identification is done using Human Visual System with the usage of the architecture of fully connected convolution network. The other way of prediction is through the Deep Neural Network which has more trainable data and so some difficulties faced through this process can be removed. Another approach is done using neuroscience principles which includes a data driven approach by Convnet. Euclidian distance is measured for the performance of the network. All these methods are done using the publicly available datasets and some of them are MIT1003, MIT300, CAT2000, DUTOMRON etc.
OPTrack: A Novel Online People Tracking System

Multi-camera people tracking problem presents a crucial step for intelligent video surveillance systems in a large camera network. In this paper, we present a novel Online People Tracking system (OPTrack) that tracks people in a camera network with non-overlapping filed of views under uncontrolled acquisition conditions. It represents a challenging task as people appearance is largely affected by the different acquisition conditions in terms of viewpoint and posture variations, lighting conditions, etc. The proposed method aims to establish the correspondence between the persons, detected in the different cameras, based on spatio-temporal and appearance features. Particularly, a new Multi-level Semantic Appearance Representation descriptor (MLSAR) is proposed to encode two complementary appearance characteristics, i.e. the low-level features and the semantic attributes. The proposed system has been experimentally validated, quantitatively and qualitatively, based on the HDA+ and the VIPeR datasets. The outcomes of this evaluation show promising results and demonstrate the effectiveness of our system.
Solar Radiation Change

Global solar radiation data were analysed at 10 actinometric stations located in different regions of Poland for the period from 19 (Sosnowiec) to 125 years (Kraków; data partially reconstructed). Only series with the data gaps not exceeding 5% were considered. The most important results of the study of changes in solar radiation in Poland are as follows: (1) the average long-term totals of global solar radiation range in Poland from approximately 3750 MJ/m2 to 4070 MJ/m2 throughout the year; (2) the year-to-year variability of solar radiation expressed by coefficient of variability is rather small and ranges from 3.5 to 7% in Poland; the highest values of variability are observed in autumn and winter; (3) long-term trends of global solar radiation in Poland are in most cases statistically insignificant; a few significant tendencies show different trend directions; (4) relative trends of global solar radiation in the area of Poland do not exceed ±10%/10 years; (5) in Kraków, for a 125-year series of global solar radiation values, about 60 years periodicity of radiation changes can be seen, with three periods of relatively high values (1880–1900, 1940–1960, 1990–2018), separated by periods of relatively low values: 1910–1930 and 1970–1990. The global solar radiation course in Kraków largely corresponds to the periods of “global dimming” and “global brightening” described in different parts of the world as a result of urbanisation, industrialisation and the increase in aerosols related to them. A decrease in values was observed until the end of the 1970s or 1980s, depending on the season of the year, and then there was an increase until the end of the twentieth century.
Enhanced Image Reconstruction with Quality-Weighted Iterative Deconvolution (QWID)

Multi-frame blind deconvolution (MFBD) algorithms are able to produce high-resolution image reconstructions from severely degraded inputs. Often these algorithms are designed with a number of assumptions about the observing scenario and the data quality, and when these assumptions are violated the reconstruction quality can suffer. However, it can be challenging to automatically assign quality scores to input images for data rejection, especially while observing Low Earth Orbit (LEO) satellites through the turbulent atmosphere as they transit across a large patch of the sky. We report on an algorithm that uses a convolutional neural network (CNN) to assign quality scores to images prior to MFBD processing in a system titled Quality-Weighted Iterative Deconvolution (QWID). This quality assessment represents the likelihood that each input frame can contribute meaningful signal to the frame reconstruction process. The neural network is trained on a simulated dataset of ground-based observations of LEO satellites, where true quality is known. Improvements in performance over the same MFBD implementation in the absence of quality scores are demonstrated on a subset of these simulated observations.
SARIMA model for compiling the planting calendar

Abstract Rainfall patterns uncertainty in Southeast Sulawesi-Indonesia is causing many paddy crop failures. There needs to be an adjustment when the appropriate farming time. One of the adaptation efforts in dealing with the influence of climate shift is to establish a planting calendar. Paddy planting calendar is to determine the success of the harvest. The SARIMA model (0,0,0)(0,1,3) 12 is the best model for predicting rainfall data. Prediction results show that the highest rainfall occurs in January-July and November-December. Planting time can be done in January-July and November-December by avoiding August, September, and October.
Crowd Intelligence Empowered Video Transmission in Ultra-low-bandwidth Constrained Circumstances

The performance of video transmission depends on their ability to efficiently improve in jitter, latency and bit rate. However, emerging video transmission needs create new difficulties owing to the devices in ultra-low-bandwidth constrained circumstances, such as no man's land, underwater acoustic communication, etc. And the existing adaptive bit rate algorithms can not apply to the extreme circumstances well. In this work, a adaptive problem of video transmission is studied in which video frames are transmitted through extremely constrained network. The objective is to maximize the fluency and network efficiency. Furthermore, a crowd intelligence empowered dynamic adaptive video transmission method in extreme circumstances is presented. First, several typical ultra-low-bandwidth video transmission scenes are selected for network data collection. Second, working status data of modules internal devices are processed and evaluated. The evaluation method is empowered by crowd intelligence. Third, the reward function of Q-Learning is reconstructed to be dynamically updated according to device evaluation results and network status. Then, it can be corrected by exception handling strategies to adapt to the scenes better. Experiments on different scenes demonstrated the proposed method was effective, thereby verifying feasibility of the proposed method.
Application of Pretrained Models for Machine Translation

In recent years, the pre-training model represented by BERT has achieved significant improvement in many NLP tasks. However, because the MLM pre-training task used by the pre-training language model is different from the auto-regressive language model, it is difficult to apply it in the field of machine translation. This article introduces the basic concepts of machine translation, pre-training language models and BERT. In addition, it introduces the current research on pre-trained language models that can be used for machine translation and the prospects for future research in detail.
Research on the Development and Operation of Pre-School Dance Online Course Based on "Internet +"

Under the background of "Internet+Education", with the extensive and in-depth application of information technology in higher vocational education, dance courses for preschool education majors in higher vocational colleges are also facing the new requirements of kindergarten teachers and the problem of improving the quality of personnel training. With the popularity of smart phones, the traditional offline education has become increasingly information-based. At the same time, online education developed by relying on the Internet is in the process of constant adjustment and development. Because the theory of preschool education has not yet formed an independent system, it has been in the process of exploration and research in education. Many preschool teachers of preschool education major can't find a balanced position in theory and practice, and can't enter the working state immediately after arriving at the post. With the rapid development of the Internet, all walks of life are positively influenced by the Internet. In this paper, the online dance course under the environment of "Internet +" is analyzed. By analyzing the influence of Internet on preschool dance teaching, the innovative strategies of preschool dance teaching are finally put forward.
Excavation of Time sliced and Cost based KDD for the lead generation and promotion on B2C/B2B Sales

Fast advances in information gathering and capacity have empowered associations to collect huge measures of information and hence extracting the only relevant and useful information from large volumes of data is a challenging task. The conventional techniques for data analysis and evaluation cannot be utilized subsequently on the huge measure of the data-set and-so new methods were developed for mining data. Frequent pattern mining algorithms like Apriori, FP-Growth, etc. may sometimes miss the rare but important patterns of a database since they are dealing only with the number of times an item appears in the database. In business environments like retail shops or online markets, identifying the profit-generating items is more important than finding the items which are sold many times. The high utility item set mining with the time cube concept helps us to find the relevant, profit-generating item sets from the transactional data set by performing the calculations using the quantity that is purchased, total cost and unit gain of each item in the database. The concept of time cubes implemented here helps to efficiently deal with the temporal parts of the transactional data set. Considering total cost decides the frequent customer of particular product set. The proposed system is mainly applicable in online markets, FMCG Sectors, large retail stores, and manufacturing plants for improving the revenue by promoting the profit-generating item sets.
An Empirical Study of Deep Learning Models for Abstractive Text Summarization

With the development of the Internet and social media era, there is an immense amount of existing information. But humans have become busier in this modern era to interpret all that information. Text summarization, a method of summarizing a text into its shorter version, has been a research area for many years. It includes two categories, namely extractive and abstractive. Though the former method is comparatively easy to implement and the latter method generates more human-like summaries, there are many promising results with the advent of deep learning approaches. In this article, an empirical analysis was conducted for the three renowned deep learning models used along with the natural language processing. Deep learning approaches outperform the traditional methods such as predicate argument or semantic graph methods and also have some limitations. Lack of structured and usable datasets contributing is the major problem in this research.
HDBSCAN: Evaluating the Performance of Hierarchical Clustering for Big Data

Fast technology development in recent years has led to a rapid increase in data generation. Most of the data are raw and need to be classified and processed into clean, usable data. However, classifying a huge volume of data is not an easy task, and it requires massive human efforts to label the data. To assist humans and shorten this labelling process time, data clustering is one of the most used methods in the first step of data cleaning. Among popular data clustering algorithms, Hierarchical Clustering is one of the best that can help to separate noise to recognize data patterns. In this paper, we conducted intensive experiments to verify the effectiveness of Hierarchical Clustering on multiple datasets using Apache Spark. We evaluated Hierarchical Clustering by comparing its performance in different sizes. Their volumes are provided in the experiment section.
Cyber Bullying Detection on Social Media using Machine Learning

From the day internet came into existence, the era of social networking sprouted. In the beginning, no one may have thought internet would be a host of numerous amazing services like the social networking. Today we can say that online applications and social networking websites have become a non-separable part of one’s life. Many people from diverse age groups spend hours daily on such websites. Despite the fact that people are emotionally connected together through social media, these facilities bring along big threats with them such as cyber-attacks, which includes cyberbullying.
Enhanced Quality of Adaptive Video Streaming in HTTP-Based Protocol Using FESTIVE

For altering the network prerequisites most of the video participants fully relies upon logic i;e data rate adjustment which will modify data rate. In this paper there are three key things to do when in contrast to previous measurements i;e efficiency, fairness, stability. There is a problem known as bottleneck link which is identified when multiple data rate adaptive players watch a video. we have also mentioned about bitrate adaptation approach and three essential factors speed of transmission range, data rate choice and the process of chunk distribution. By this, everyone can discover the troubles of various countless negative interactions that occur as an effect of protecting the bitrate adaptation over HTTP. Based on appreciation of these techniques, we guide how to balance between fairness, stability and effectivity that will lead to robust video adaptation.
MULTI-FORUM INSTITUTIONS, THE POWER OF PLATFORMS, AND DISINVITING SPEAKERS FROM UNIVERSITY CAMPUSES

Abstract Much attention has been devoted recently to cases where a controversial speaker is invited to speak on campus, and subsequently, some members of the university seek to have that speaker disinvited. Debates about such scenarios often blur together legal, normative, and empirical considerations. I seek to help clarify issues by separating key legal, normative, and empirical questions. Central to my examination is the idea of the university as a multi-forum institution—that is, a complex public institution whose parts contain different types of forums. I conclude that it is sometimes legally and normatively permissible (1) for universities to disinvite speakers, and (2) for students to seek to get speakers they consider unacceptable disinvited. I also suggest that my arguments sometimes extend to shouting down speakers.
Crowd phenomenon as text and discourse

The article is devoted to the research of crowd as a text and discourse object and the definition of discourse parameters of crowd. The author compares the formulation of conception about crowd as text and discourse with the formation of anthropocentric paradigm in linguistics that was developing from text and linguistic identity to discourse and discourse identity/community. Projecting the results of scientific researches of XIX-XX centuries (works by G. Le Bon, E. Canetti, T. Tarde, S. Freud ect.) onto linguistic sphere, the author makes a conclusion that the crowd phenomenon was reviewed in the text dimension with the following characteristics: hyper- and intertextuality, inculcation of ideas through such factors as race, beliefs, traditions, time, education and upbringing: the transmission of the key codes by word-concepts that have conclusive sense (FREEDOM, EQUAILITY, FAMILY VALUES ect.); the usage of suggestive means to encourage crowd for some specific actions, ect. The transformation of text-crowd to discourse-crowd begins with the globalization of mass movements. For researching the crowd as a discourse, the pressing approaches of critical discourse analysis are the research of "authority in discourse and authority over discourse"; discourse is ideologically colored and built into specific culture, ideology or history; discourse is a form of social conduct. The research of the crowd as a discourse has to be carried out in marginal dimension in the junction of sciences – linguistics, psychology, sociology and jurisprudence. Critical discourse analysis as a scientific approach combines the approaches of different sciences and makes it possible not only to study crowd in discourse dimension but also to foresee possible proactive means to define narratives, messages, concepts that allow to organize social conduct to the crowd peacefully.
A Novel Hierarchical Threshold Secret Image Sharing Scheme

In this paper, we propose a novel hierarchical visual cryptography scheme to protect secret image by designing secret sharing and recover procedures. The polynomial coefficients are generated using the projection matrix method, and shadows are generated for different levels of participants based on the Birkhoff interpolation method. The proposed hierarchical secret sharing processes and recover processes are proposed in detail by mathematical design, utilizing the random matrix to generate the polynomial coefficients and generating quite different secret shadow image to each participant. Security analysis and experiments prove that the proposed scheme is effective and can defend fake shadow attacks and collusion attacks.
Project Activities of the Libraries of Germany to Promote Reading

The study of practical experience of the project activities of the libraries of the Federal Republic of Germany in the field of promoting library as a reading centre is relevant at the present stage of formation of digitalization society. Problems associated with the reduced motivation to read are manifested in one way or another in social and economic spheres, therefore the importance of promotion of reading at the national level is recognized in many countries. It resulted in the development of national programmes on support of reading. Despite the fact that all libraries in the world perform one common function — the development of reader competence and sustained interest in obtaining set of knowledge through reading, the experience of using the forms and methods of this activity is extremely wide. The purpose of the article is to study the trends in the promotion of books and reading in the libraries of Germany for the possible adaptation of examples of specific activities in the libraries of our country. Getting acquainted with international experience allows identifying common and special features in the practice of library activities in Russian Federation and Germany, to determine the achievements and to point out the problems in a broader context. The system of librarianship management in Germany and the typology of German libraries are similar in many respects to the structure of this sector in the Russian Federation, which is an important reason for the possibility of adapting the experience in the domestic librarianship. The author analyzes the primary sources in German, presented on the websites of libraries and on the pages of professional library journals of the Federal States of Germany. The article considers the experience of working with the target groups of children, young adults, families with children, refugees and migrants. The author describes the national reading program Lesestart 1-2-3, popular forms of promoting reading: “Knowledge Box” (Wissensbox), “Book Cinema” (Bilderbuchkino) and Kamishibai. The article emphasizes that reading aloud is a unique tool, undeservedly forgotten by our libraries. Research by German librarians, psychologists and educators testifies that reading aloud not only promotes imagination, creativity and empathy, but also influences educational opportunities. The author concludes that book and reading promotion events held in the libraries of the Federal Republic of Germany can be used as experience in a number of Russian libraries, considering the specific conditions.
On the Enlightenment of Ancient Chinese Za Zi Textbooks to Chinese Character Education

Chinese character education is an important part of Chinese traditional language education. With the continuous refinement of the social division of labor, a popular literacy textbook that is different from traditional textbooks, that is, Za Zi textbooks appeared in the Tang Dynasty of China. Because of its classification and compilation method, convenience and practicality, and easy-to-learn characteristics, it gradually became popular after the Tang Dynasty. Za Zi textbooks not only inherit the excellent experience of traditional classic literacy textbooks, but also have their own characteristics, which are inspiring to the current Chinese character education.
Research on the Application and Effect of Information Tools in Wisdom Teaching

Wisdom teaching is a hot issue in higher education at present. The application of information technology means to carry out pre-class learning, in-class interaction and after-class consolidation can effectively promote the construction and cultivation of learners' autonomous learning ability. By analyzing the function of rain classroom and comparing it with traditional teaching mode, this paper points out the advantages and disadvantages of rain classroom. According to the questionnaire, the effect research is carried out from the perspectives of teachers and students, and the improvement strategies for improving the teaching effect of wisdom classroom are given, which has theoretical and practical significance for teaching practice.
An attempt of applying the Lagrange-type 1-step-ahead numerical differentiation method to optimize the SGD algorithm in deep learning

The form of the original stochastic gradient descent (SGD) algorithm accords with the definition of forward Euler method, which has some inherent defects. Thus, in order to improve the original SGD algorithm, a Lagrange-type 1-step-ahead numerical differentiation method based parameter update algorithm is presented and validated. Instinctively, the new algorithm can fix some inherent flaws of the SGD algorithm. However, a series of experimental results show that the Lagrange-type 1-step-ahead numerical differentiation method cannot be applied to reduce the computational error of SGD. In addition, this method makes the model appear the phenomenon of non-convergence. Finally, on the basis of comparative experiments, the divergence phenomenon is analyzed and explained.