Reviewerly/exHarmony huggingface dataset:
FORMATION OF WAVELET - TRANSFORM SIGNALS TAKING INTO ACCOUNT THRESHOLD FUNCTIONS IN THE DIGITAL STREAM COMPRESSION PROBLEM

In order to reduce the digital stream, the problem of improving the quality of digital filtering based on Discrete Wavelet Transformation (DWT) was analyzed using different options for setting the threshold function and the choice of wavelet basis. Given the considerable interest in this problem and the numerous studies concerning the search for ways to optimize the suppression of interference present in signals and images, the topic continues to be relevant and important. For such tasks as image compression, operations in processing and synthesis of different signals, in the analysis of images of different nature, in reducing (compressing) large amounts of information, and to protect information is often used wavelet transform, implemented on the basis of mirror filters. For the formation of wavelet-transform signals taking into account the threshold functions in the problem of digital stream compression, a standard approach to solving the problem of signal purification from interference and random distortions using Daubechies wavelet and adjusting the signal decomposition coefficients based on wavelet functions using soft and rigid threshold task options. It is shown that the use of complex bases provides an advantage both in terms of threshold filtering error and in terms of reducing the risk of accidental distortion in the reconstruction of the useful signal by wavelet coefficients. Appropriate steps were taken for the test signal and experimental data. When using the threshold function, the large modulus (most significant) wavelet coefficients remain unchanged, and the small ones are reset to zero. The change in the amplitude of the recovered signal leads in the latter case to a decrease in the absolute values of all wavelet coefficients, including large modulo. For those applications where it is important to keep the amplitude characteristics constant, this approach is not suitable, but there are problems where it is more important to maintain the regularity of the signal than to accurately reproduce its amplitude. Rhis is the filtering of images from various obstacles, where the method of "soft" task of the threshold function is a widely used approach. When analyzing signals, the constant amplitude is also not always a mandatory requirement. An audio signal can be amplified after filtering, and pre-cleaning it from interference is more important than changing the amplitude characteristics. The use of complex wavelet transform allowed to obtain error reduction and lower threshold level when adjusting wavelet coefficients, and can be recommended for analysis of complex wavelet transform methods as an effective tool for cleaning signals and images of different nature for further research.
Approaches to digitalization of higher education

The Yearbook contains articles of a general theoretical, historical and applied nature concerning Russian education and science.The activities of world-class scientific and educational centers and the sectoral system of international scientific and educational cooperation, various problems of Russian educational organizations, including those related to their optimization, trends in socialization and professional orientation of young people are covered.A number of articles are devoted to international education and training of foreign students in Russia, the use and study of the Russian language in schools and universities of foreign countries.The Yearbook is written for employees of Russian educational and scientific organizations, students and graduate students of sociological and pedagogical faculties, as well as to anyone interested in trends in the development of domestic and international education and science.
Hybrid Encoder: Towards Efficient and Precise Native AdsRecommendation via Hybrid Transformer Encoding Networks

Transformer encoding networks have been proved to be a powerful tool of understanding natural languages. They are playing a critical role in native ads service, which facilitates the recommendation of appropriate ads based on user's web browsing history. For the sake of efficient recommendation, conventional methods would generate user and advertisement embeddings independently with a siamese transformer encoder, such that approximate nearest neighbour search (ANN) can be leveraged. Given that the underlying semantic about user and ad can be complicated, such independently generated embeddings are prone to information loss, which leads to inferior recommendation quality. Although another encoding strategy, the cross encoder, can be much more accurate, it will lead to huge running cost and become infeasible for realtime services, like native ads recommendation. In this work, we propose hybrid encoder, which makes efficient and precise native ads recommendation through two consecutive steps: retrieval and ranking. In the retrieval step, user and ad are encoded with a siamese component, which enables relevant candidates to be retrieved via ANN search. In the ranking step, it further represents each ad with disentangled embeddings and each user with ad-related embeddings, which contributes to the fine-grained selection of high-quality ads from the candidate set. Both steps are light-weighted, thanks to the pre-computed and cached intermedia results. To optimize the hybrid encoder's performance in this two-stage workflow, a progressive training pipeline is developed, which builds up the model's capability in the retrieval and ranking task step-by-step. The hybrid encoder's effectiveness is experimentally verified: with very little additional cost, it outperforms the siamese encoder significantly and achieves comparable recommendation quality as the cross encoder.
An optimization method for human-robot collaboration in a production unit in the context of intelligent manufacturing

With the increasing application of robots in manufacturing industry, the problem of collaboration between human and robot and the efficiency of collaboration between multiple robots becomes more and more prominent in complex production scenes. An optimization method of operators and robots using depth vision sensors and intelligence trajectory planning algorithm is proposed in human-robot system. The research is expected to improve the manufacturing efficiency by reducing the waste of time with better time-consuming stability performance to the maximum extent and guarantee the safety of operators.
DEVELOPMENT & ANALYSIS OF MOVING OBJECT FRAME CAPTURING THROUGH REAL TIME VIDEO.

Consistent moving article recognizable proof in unconstrained scenes is a problematic task in light of dynamic establishment, changing nearer see appearance and confined computational resource. At the present time, optical stream based moving thing area layout work is proposed to address this issue. Homography matrix's to online build up an establishment model as optical stream. When settling on a choice out moving bleeding edges from scenes, a twofold mode judge instrument is proposed to raise the system's acclimation to testing conditions. In attempt segment, two appraisal estimations are rethought for even more suitably reflecting the introduction of procedures. We quantitatively and emotionally support the ampleness and reasonableness of our procedure with accounts in various scene conditions. The exploratory results show that our system changes with different conditions and beats the forefront methodologies, exhibiting the advantages of optical stream based procedures.
Intelligent Secure Storage Mechanism for Big Data

The management of big data became more important due to the wide spread adoption of internet of things in various fields. The developments in technology, science, human habits, etc., generates massive amount of data, so it is increasingly important to store and protect these data from attacks. Big data analytics is now a hot topic. The data storage facility provided by the cloud computing enabled business organizations to overcome the burden of huge data storage and maintenance. Also, several distributed cloud applications supports them to analyze this data for taking appropriate decisions. The dynamic growth of data and data intensive applications demands an efficient intelligent storage mechanism for big data. The proposed system analyzes IP packets for vulnerabilities and classifies data nodes as reliable and unreliable nodes for the efficient data storage. The proposed Apriori algorithm based method automatically classifies the nodes for intelligent secure storage mechanism for the distributed big data storage.
Semantic Label Enhanced Named Entity Recognition with Incompletely Annotated Data

Named Entity Recognition (NER) is a fundamental task in natural language processing, and the output of which is usually utilized as the input for numerous downstream tasks, especially for dialogue system, question answering system, etc. Generally speaking, entities are assumed to have labels in most previous works. Nevertheless, it is unrealistic to obtain adequate labeled data in real circumstances. In this paper, a novel semantic label enhanced named entity recognition model is proposed to tackle with NER problems with incompletely annotated data. Significant improvements are achieved through extensive experiments. The proposed algorithm improves the F1 score on the Youku dataset by around 8% than the baseline model. Besides, our experiments prove the crucial role of semantic label in NER with incomplete annotations.
Indeks Keterpaparan Banjir dan Banjir Pasang di Kota dan Kabupaten Pekalongan

Sa’diyah H, Buchori I, Amirudin A.  2020.  Exposure index of flood and tidal flood in the city and regency of Pekalongan. In : Herlinda S et al. ( Eds .), Prosiding Seminar Nasional Lahan Suboptimal ke-8 Tahun 2020, Palembang 20 Oktober 2020. pp. xx.  Palembang: Penerbit & Percetakan Universitas Sriwijaya (UNSRI). Climate change can increase the threat of disasters, such as floods, prolonged drought, tidal waves, and others. Pekalongan City is one of the areas vulnerable to the impacts of climate change, especially in the form of floods and tidal floods. Exposure is the degree, duration and/or extent of the impact of a system or subject's contact with the disturbance. This study used a spatial model of the exposure index method. Data obtained from primary data (questionnaire surveys, field observations, and geospatial data analysis) and secondary data (statistical data from city and district governments and sub-district / village offices). The results of the exposure index modeling are classified into five classes with very high, high, medium, low, and very low. The purpose of this study is to analyze the spatial model of exposure index in the coastal areas of the city and regency of Pekalongan and the areas included in the Kupang River Basin. This study produces the distribution of the exposure index in the city and regency of Pekalongan which shows that the exposure index is dominated by high categories with a proportion value of 49%.
Speech Based Examination Android App

The main challenging task for the virtually and physically challenged students is to take their exams. Volunteers are required for writing their exams on behalf of these students. These volunteers must be confidential and should not involve themselves in any kind of malpractices. These students are in need of a technology that makes their task easier. We have developed an Android application that conducts the exam, evaluates it and displays the result. This application reads the given questions from the soft copy to the students, their answer in the form of speech is captured and converted into text. This text is evaluated and marks are calculated. In this paper, we have reduced the workload of the virtually and physically challenged students with the help of the Android application.
Resampled ensemble Kalman inversion for Bayesian parameter estimation with sequential data

&lt;p style='text-indent:20px;'&gt;Many real-world problems require to estimate parameters of interest in a Bayesian framework from data that are collected sequentially in time. Conventional methods to sample the posterior distributions, such as Markov Chain Monte Carlo methods can not efficiently deal with such problems as they do not take advantage of the sequential structure. To this end, the Ensemble Kalman inversion (EnKI), which updates the particles whenever a new collection of data arrive, becomes a popular tool to solve this type of problems. In this work we present a method to improve the performance of EnKI, which removes some particles that significantly deviate from the posterior distribution via a resampling procedure. Specifically we adopt an idea developed in the sequential Monte Carlo sampler, and simplify it to compute an approximate weight function. Finally we use the computed weights to identify and remove those particles seriously deviating from the target distribution. With numerical examples, we demonstrate that, without requiring any additional evaluations of the forward model, the proposed method can improve the performance of standard EnKI in certain class of problems.&lt;/p&gt;
The Era of Encryption - An Overview

Encryption is a technique that is used to convert original information, also called plain text, into unreadable ciphertext. This is done with the help of an encryption algorithm and key. It is meant to keep the original information secured from the wrong recipient. So only the right recipient can read the information by decrypting it using the decryption algorithm and key. Throughout history, encryption has been used to transfer secret information in the military. The most famous one is Caesar cipher. This method is named after Julius Cease, it was a very basic encryption technique, and it is a substitution cipher, in which letters were replaced by shifting by a fixed number. But nowadays encryption technique has advanced.
Research on Information Technology and Its Application in Supply Management

In recent years, with the rapid development of science and technology, especially the development of information technology represented by mobile Internet technology, blockchain technology, and cloud computing, information technology has been widely used in the business field. Therefore, it makes the traditional management mode into a new industrial form. Supply management is currently facing a serious trust problem. Traditional technologies cannot provide effective solutions, and the blockchain for commercial applications can effectively solve the trust problem. Therefore, this article mainly studies logistics management in supply management, researches and designs a logistics information platform based on blockchain, and aims to improve the core competitiveness of enterprises through informatization means, strive for customer resources, improve logistics efficiency, and provide logistics information about the construction of the platform provides reference.
Research on the AI and ML Applications to Structural Systems

Theses years, Artificial Intelligence and Machine Learning develop rapidly and related technologies are applied to many fields in nearly every aspect. The author explains the concepts of artificial intelligence and machine learning, and analyzes the basic components of structural systems in order to find accessible applications of AL and ML to these systems.
Model-independent light field reconstruction using a generic camera calibration

Abstract Sophisticated and highly specialized optical measuring devices are becoming increasingly important for high-precision manufacturing and environment perception. In particular, light field cameras are experiencing an ever-increasing interest in research and industry as they enable a variety of new measurement methods. Unfortunately, due to their complex structure, their calibration is very difficult and usually precisely tailored to the particular type of light field camera. To overcome these difficulties, we present a method that decodes a light field from the raw data of any light field imaging system without knowing and modeling the internal optical elements. We calibrate the camera using a precise generic calibration method and transform the obtained ray set into an equivalent light field representation. Finally, we reconstruct a rectified light field from the irregularly sampled data and in addition we derive the geometric ray properties as intrinsic camera parameters. Experimental results validate the method by showing that both the information of the observed scene and the geometric structure of the light field are preserved by an adequate rectification and calibration.
The development of emoji in the intelligent era

With the arrival of the era of big data, the development of intelligent networks makes it possible to realize many technologies. People can freely edit, operate some applications in the network, and realize a free human-computer interaction mode. The Internet has become a place for people to socialize. The way people communicating has gradually shifted from words to images, and images can express meaning more intuitively. This paper hopes to show some emotional changes in people's social networks through the change of emoticons and the form of online display, these changes also reflect the process of people using intelligent networks to play their own design and creation. The emoticon pack is the expression of emotion visualization. In the foundation of big data visualization, Emoticon is a medium for human-computer information interaction. Emoticon pack is also a way of visual expression. It expresses emotions, expresses information, and expresses this era. The development process of emoticons is actually very short, but in these short decades, there have been thousands or more changes and types. Its development can also witness the development of current popular culture. Through emoticons the rapid development of the modern intelligent network can also be seen. Many designs are professional, but the design of emoticons is for the masses. Through the creation of emoticons, ordinary people can express their thoughts on paper and express them visually. The current development of emoticons is not just a tool for chatting among the public. Through various media, it will also become a variety of novel expressions. Finally, it is hoped that through a emoji space study, people could explore the present possibilities of emoji applications and look forward to the future of visualization development.
Analysis on Database Testing Technology of Computer Software Development

The current development of computer technology is relatively fast. In the process of computer application and promotion, computer software development is an important part of it. Computer software development itself has a strong system and complexity. Therefore, carrying out effective database testing during the development of computer software is a prerequisite for ensuring stable and safe operation of computer software. As a result, we should pay attention to the effective application of database testing technology. In this way, we not only need to strengthen the research of database testing technology in the process of computer software development, but we also need to analyze the composition of database testing technology. Only in this way can the test efficiency and the accuracy of the test results be guaranteed.
Automatically Injecting Semantic Annotations into Online Articles

Extracting information from the Web is one of the most trending subjects for data analysts and scientists. Web scraping is one of the prominent means to do so by parsing HTML pages and extracting data from their embedded tags. To that end, software developers write customized scripts for each target Website. Crafting such scripts is a challenging task due to the different structures of Web sites and their dynamic rendering. Yet, a more challenging task is to infer semantic annotations from a news article. It is interesting, for example, to have a word such as the US in an already published article to be automatically annotated semantically by its population and area. We argue that these annotations should not be mere hyperlinks to be consumed by human readers but rather they can be machine readable using the Web standard RDF (Resource Description Framework). Embedding such RDF inside the HTML page of the news article should enrich it with automatically generated semantics that can boost its SEO (Search Engine Optimization) and are ready to be consumed by conventional Web scrapers. As a proof of concept, we built a prototype that focuses on the plain text of an article, without stipulating the existence of structured tags and attributes, to generate a new Web document that is augmented with semantic annotations using the RDF markup language, readable by humans and machine-consumable by Web scrapers.
Comparative analyses of skincare product advertisements in Georgian and English

Across the gamut of media formats – from television to the Internet – beauty product advertising influences consumers on a daily basis. Each advertisement seeks to persuade potential buyers of the product's value, or even its necessity for the buyer's well-being and self-image. These techniques, sometimes manipulative in nature, affect consumers’ self-concepts. One of the signature strengths of the beauty advertisement lies in its ability to transform seemingly mundane objects into highly desirable products. In some cases, the beauty industry uses buzzwords and scientific words to convince consumers of a product's value; these linguistic devices describe the product's apparent capabilities and appeal to the consumer's ego by suggesting that the product will enhance the assets the consumer already possesses. All things considered, the present paper deals with a comparative study of skincare product advertisements in English and Georgian and focuses on the use of persuasive strategies, buzzwords and scientific terminology in the advertisements that manipulate and influence potential consumers.
Innovative Educational Technologies for Teaching the "Philosophy" Discipline in a Hybrid Learning Model

The article discusses educational technologies used in teaching the discipline "Philosophy" in a hybrid learning model. Technologies of distance learning (video lectures, presentations, e-textbooks, online course, etc.) and face-to-face training (case technology, portfolio technology) are highlighted, and their implementation is shown.
A Study of Influences of Big Data on Machine Translation and Enlightenment for Translation Teaching in Cross-cultural Communication

Machine translation (MT) has been fiercely discussed and studied recently, especially neural machine translation (NMT). Most of the studies concentrated on MT working mechanism, translation systems, translation methodologies and chronological studies on the development of MT. However, there has been little work exploring the connection between big data and MT, or the influences of big data on MT. As the “Internet <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">†</sup> Era” comes, big data has so many influences on researches, work, studies, daily life etc. This paper examines how and from which aspects big data will affect MT, which shows that the following perspectives will be influenced: linguistic habit, translation of newly-born words, update rules of languages, English for special purpose (ESP), discourse systems with Chinese characteristics, hardware and software of computers, translation systems and storage ability, MT teaching, etc. With the research findings, this paper also proposes enlightenments for translation teaching so as to provide more qualified personnel to meet the demand of the market in cross-cultural communication.