Reviewerly/exHarmony huggingface dataset:
Subgroup Preference Neural Network

Subgroup label ranking aims to rank groups of labels using a single ranking model, is a new problem faced in preference learning. This paper introduces the Subgroup Preference Neural Network (SGPNN) that combines multiple networks have different activation function, learning rate, and output layer into one artificial neural network (ANN) to discover the hidden relation between the subgroups' multi-labels. The SGPNN is a feedforward (FF), partially connected network that has a single middle layer and uses stairstep (SS) multi-valued activation function to enhance the prediction's probability and accelerate the ranking convergence. The novel structure of the proposed SGPNN consists of a multi-activation function neuron (MAFN) in the middle layer to rank each subgroup independently. The SGPNN uses gradient ascent to maximize the Spearman ranking correlation between the groups of labels. Each label is represented by an output neuron that has a single SS function. The proposed SGPNN using conjoint dataset outperforms the other label ranking methods which uses each dataset individually. The proposed SGPNN achieves an average accuracy of 91.4% using the conjoint dataset compared to supervised clustering, decision tree, multilayer perceptron label ranking and label ranking forests that achieve an average accuracy of 60%, 84.8%, 69.2% and 73%, respectively, using the individual dataset.
Media literacy: a necessity in basic and university education

The purpose of the research is to explain the current trends and importance of media literacy in basic and university education, where the analysis, interpretation, understanding of the messages transmitted to us by various communication media is promoted, reading and interpreting them effectively and efficiently for optimal decision making. The research corresponds to the bibliographic review methodology. Different databases were used, these were: Scopus, Wos, Dialnet, Redalyc, Scielo, ResearchGate, RIDU (UPC Journals) and RevCom, researches from 2000 to 2020, forty were reviewed Research validated by the importance of the information, articles from different countries, English and Spanish languages were considered. The study concludes that media literacy is fundamental in basic and higher education who have the responsibility of providing media education or media education, in which media knowledge is inserted in students from the interpretation that they make of the information, necessary because they spend most of their time exposed to social networks and interacting with multiple media.
A Trajectory Abnormal Detection Method Based on Segmentation and Clustering

Abstract The early warning of abnormal behaviour is helpful to explore the potential risk and avoid the occurrence of accidents. Therefore, it is of great significance to excavate abnormal trajectory patterns from a large number of trajectories. From the perspective of unsupervised learning, a two-level trajectory abnormal detection method based on segmentation and clustering based on segmentation and clustering is proposed in this paper. Firstly, the method considers the influence of a time factor in the stages of segmentation, and segments by the constraint of trajectory state. Then, it redefines the distance between the trajectory segments according to the temporal and spatial distance to cluster after segmentation. Finally, abnormal detection is carried out according to clustering results. In the abnormal detection phase, a two-level detection method is adopted. Firstly, the abnormal trajectory segment is found by first-level coarse-grained abnormal detection. Then, the abnormal subtrajectory segment is detected by second-level fine-grained abnormal detection. The real Atlantic Ocean hurricane data is used for the simulation experiments. Compared with the classical TRAOD detection method, proposed method is more consistent with the actual situation.
Evaluating Elements of Web-Based Data Enrichment for Pseudo-relevance Feedback Retrieval

In this work, we analyze a pseudo-relevance retrieval method based on the results of web search engines. By enriching topics with text data from web search engine result pages and linked contents, we train topic-specific and cost-efficient classifiers that can be used to search test collections for relevant documents. Building upon attempts initially made at TREC Common Core 2018 by Grossman and Cormack, we address questions of system performance over time considering different search engines, queries, and test collections. Our experimental results show how and to which extent the considered components affect the retrieval performance. Overall, the analyzed method is robust in terms of average retrieval performance and a promising way to use web content for the data enrichment of relevance feedback methods.
Deceptive Tactics Used in Online Shopping

The debate on deceptive advertising is getting more critical alongside the proliferation of the Internet and global e-commerce. This study aims to portray the nature and variances of deceptive tactics employed in online shopping sites by utilising an original database and a content analysis. Findings verified that the use of online deceptive advertising practices is common among Turkish online-shopping sites. First and foremost, the study displayed the high propensity of advertisers to omit or obscure information within the online ad content. The findings also provided empirical evidence on the correlational nature of involvement level and price with the deception levels.
Recommendations and Results Organization in Netflix Search

Share on Recommendations and Results Organization in Netflix Search Authors: Sudarshan Dnyaneshwar Lamkhede Netflix Research Netflix, United States Netflix Research Netflix, United StatesView Profile , Christoph Kofler Netflix, United States Netflix, United StatesView Profile Authors Info & Claims RecSys '21: Fifteenth ACM Conference on Recommender SystemsSeptember 2021 Pages 577–579https://doi.org/10.1145/3460231.3474602Online:13 September 2021Publication History 0citation381DownloadsMetricsTotal Citations0Total Downloads381Last 12 Months381Last 6 weeks33 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Cluster States from Gaussian States: Essential Diagnostic Tools for Continuous-Variable One-Way Quantum Computing

Continuous-variable (CV) cluster states are a universal quantum computing platform that has experimentally outscaled qubit platforms by orders of magnitude. Room-temperature implementation of CV cluster states has been achieved with quantum optics by using multimode squeezed Gaussian states. It has also been proven that fault tolerance thresholds for CV quantum computing can be reached at realistic squeezing levels. In this paper, we show that standard approaches to design and characterize CV cluster states can miss entanglement present in the system. Such hidden entanglement may be used to increase the power of a quantum computer but it can also, if undetected, hinder the successful implementation of a quantum algorithm. By a detailed analysis of the structure of Gaussian states, we derive an algorithm that reveals hidden entanglement in an arbitrary Gaussian state and optimizes its use for one-way quantum computing.Received 13 December 2019Revised 22 July 2021Accepted 9 August 2021DOI:https://doi.org/10.1103/PRXQuantum.2.030343Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMeasurement-based quantum computingQuantum entanglementQuantum information processing with continuous variablesQuantum opticsAtomic, Molecular & OpticalQuantum Information
The Impact of Culture on Visual Design Perception

This paper questions the concept of universal users, taking a first step in looking at design perception differences between cultures, opening a discussion about culture in the context of HCI and more specifically interface interaction. This is done through a short study assessing participants from 3 cultures and their perception of interface by looking at 3 mobile payment applications with the help of remote eye-tracking technology (iMotions), and AI predictive analysis software (EyeQuant). Results show considerable differences between the two software solutions as well as between cultures, diverging in preference over text vs icons, with little consistent patterns as ground for speculation further than what the data shows.
Evaluation of the effectiveness of competitive educational activities: analysis of the experience of using the methodology of structural equations modeling

Introduction. The problem and the objective of scientific and methodological research of the issues of civil-patriotic education of young people are determined by the insufficiency of the used descriptive assessments of the results of educational work and the need to determine the possibilities of quantitative approaches in measuring the effectiveness of the educational process. Materials and methods. The SEM (Structural Equation Modeling) was used to study the data obtained as a result of the content analysis of essays submitted to the competition "The feat of a teacher during the Great Patriotic War", dedicated to the 75th anniversary of the Victory in the Great Patriotic War and 90th anniversary of the Federal State Budgetary Educational Institution "Ural State Pedagogical University". These essays were understood as a narrative, i.e., a literary text combining a description and explanation of events, actions in a person's life. The method of content analysis and SEM models of narratives that were used to process and present information are original and are subject to the intellectual property of the authors. The Results. An objective assessment of the result of educational work with young people in the context of the teaching profession is presented. A statistically significant relationship between educational results "Patriotism" and "Civic identity" is found, and the most significant educational result is identified – "Civic identity" and the least significant – "Subjective assessment", and the relationship between educational results "Professional activity" and "Civic identity" is identified. The high efficiency of the use of the competition for forming the civil identity of the participants was proved, as well as the insufficient one – for forming the ability to formulate their own (subjective) assessment; educational work on the formation of patriotic feelings at the same time allows the formation of the civic identity of students and vice versa); it is required to conduct purposeful educational work on the formation of civic identity in the professional activity of a teacher. Conclusion. It was concluded that the competitive event "The feat of a teacher during the Great Patriotic War" was an effective tool for preserving the historical memory and patriotic education of young people.
Context Reasoning for Predicting Gestational Diabetes Mellitus Using CA-RETE Algorithm

Today, IoT-related applications play an important role in scientific world development. Context reasoning emphasizes the perception of various contexts by means of collection of IoT data which includes context-aware decision making. Context-aware computing is used to improve the abilities of smart devices and is increased by smart applications. In this paper, context-aware for the internet of things middleware (CAIM) architecture is used for developing a rule-based system using CA-RETE algorithm. The objective of context-aware systems are concentrated on 1) context reasoning methodologies and analyzing how the technologies will involve enhancing the high-level context data, 2) framework of context reasoning system, 3) implementation of CA-RETE algorithm for predicting gestational diabetes mellitus in healthcare applications.
HISTORY AND OPERATION OF THE DOCUMENTALISTICS COMMISSION

The article deals with the history of the creation and activity of the Documentalistics Commission which functioned between the 1960s and 1980s under the Scientific Council on the Complex Problem of Cybernetics. The Commission’s activities are related to the formation of documentalistics as an independent scientific discipline, its object is presented by large documentary systems, the use of new technologies in the processes of collecting, processing, searching, storing and using information. The author focuses on the transformation of the Commission’s research directions throughout the entire period of its existence. In the first years of the Commission’s work, the issues of the information classification and the use of new technological solutions in working with documents (punch cards and microphotoreproduction) were prevalent. The heyday of the Commission’s activities is associated with the work on the creation of the unified documentation systems and all-Union classifiers of information for the automated control systems. The interdisciplinary nature of the Commission’s activities influenced the subject of documentalistics and the organizational form of its existence. Despite the fact that by the end of the 1980s the Documentalistics Commission had virtually ceased to exist, documentalistics as a scientific direction had a huge impact on the development of records management and archival studies in Russia.
Cryptocurrency and Bitcoin: Future Works, Opportunities, and Challenges

Rapid expansion of the world's population has been facilitated by technology, which has made knowledge and business readily available to its users at the touch of a button. New technologies like the internet have disrupted the traditional methods of communication and information exchange for ideas, money, or commerce. As a result, the internet is seen as a disruptive technology that has shook the world to its core. Using e-commerce platforms, we can order meals, groceries, and other necessities without leaving our homes or offices. Aside from using our debit or credit cards, we also use alternative wallets like PayTM. Additionally, a huge section of the globe transacts money using virtual currencies, often known as cryptocurrencies. As a safety measure, these tokens and coins have been encrypted for usage on the internet. They're a means of exchange that relies on encryption to verify the legitimacy of new currencies. These virtual currencies are kept in E-Wallets and sent safely and anonymously via the internet on a peer-to-peer basis within seconds. This article performs an analytical study of various cryptocurrencies. It also discusses opportunities and challenges in the vibrant field of cryptocurrencies.
Student’s Perception towards Mobile learning using Interned Enabled Mobile devices during COVID-19

INTRODUCTION:The novel corona disease disrupted education all around the world.This shifted people to mobile learning in real time wireless classroom from the physical face-to-face classroom.OBJECTIVE: Mobile learning has been present for years but the use of mobile learning is more in the current scenario due to COVID-19.However, people's acceptance of mobile learning education at institutions is still low.Thus, this research seeks to understand the student's perspective by analysing constructs hypothesized in the proposed hybrid model.METHOD: Data is collected using a survey from an Indian institute of the Meerut region with a total of 1022 students.RESULT: Data analysis and research findings showed that Random Forest and K-Nearest Neighbour Algorithms outperforms than other classifiers in predicting the dependent variables with better accuracy rate, precision, and recall value in this study.CONCLUSION: The research findings will help the designers and software development to design learning applications considering the perspective of students with respect to 5G technology.
Continuation-Passing Style, Defunctionalization, Accumulations, and Associativity

Context: Reynolds showed us how to use continuation-passing style and defunctionalization to transform a recursive interpreter for a language into an abstract machine for programs in that language. The same techniques explain other programming tricks, including zippers and accumulating parameters. Inquiry: Buried within all those applications there is usually a hidden appeal to the algebraic property of associativity. Approach: Our purpose in this paper is to entice associativity out of the shadows and into the limelight. Knowledge: We revisit some well-known applications (factorial, fast reverse, tree flattening, and a compiler for a simple expression language) to spotlight their dependence on associativity. Grounding: We replay developments of these programs through a series of program transformations and data refinements, justified by equational reasoning. Importance: Understanding the crucial role played by associativity clarifies when continuation-passing style and defunctionalization can help and when they cannot, and may prompt other applications of these techniques.
Don’t be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

Consistency Identification has obtained remarkable success on open-domain dialogue, which can be used for preventing inconsistent response generation.However, in contrast to the rapid development in open-domain dialogue, few efforts have been made to the task-oriented dialogue direction.In this paper, we argue that consistency problem is more urgent in task-oriented domain.To facilitate the research, we introduce CI-ToD, a novel dataset for Consistency Identification in Taskoriented Dialog system.In addition, we not only annotate the single label to enable the model to judge whether the system response is contradictory, but also provide more finegrained labels (i.e., Dialogue History Inconsistency, User Query Inconsistency and Knowledge Base Inconsistency) to encourage model to know what inconsistent sources lead to it.Empirical results show that state-of-the-art methods only achieve 51.3%, which is far behind the human performance of 93.2%, indicating that there is ample room for improving consistency identification ability.Finally, we conduct exhaustive experiments and qualitative analysis to comprehend key challenges and provide guidance for future directions.All datasets and models are publicly available at https://github.com/yizhen20133868/CI-ToD.* Email corresponding.User: Give me directions to the closest grocery store.System: There is a whole foods 2 miles away and their address is 880_ames_ct.User: I need a route that avoids all heavy traffic.
SOCIAL SCIENCES’ TRANSFORMATION IN LIGHT OF COVID-19

Transformations in social sciences accelerated by the COVID-19 pandemic remain under-explored. The aim of the article is to analyse scientific literature on social sciences’ transformation in light of COVID-19 underpinning the elaboration of implications for teaching a Research Methodology course to the higher education students. The present research work made use of theoretical and empirical methods. The exploratory study was of qualitative nature. The study carried out in April 2021 was based on the analysis of scientific publications selected from the Web of Science and google search by indicating the key words “COVID-19” and “Social sciences’ transformation”. The conclusion is drawn that social sciences’ transformation to the remote conduct is “a new normal” in the conditions of uncertainty. Virtual fieldwork is proposed to be a new qualitative method in social sciences’ research as some populations live mostly in digital environments. Another conclusion is that there is a need for a balance, when composing the empirical study’s sample, between respondents in the digitally divided populations, namely inclusions of both groups’ - digital and non-digital - respondents. Implications for teaching a Research Methodology course to the higher education students are presented. Research limitations are identified. Directions of further research are proposed.
Efficient Detection of Abrupt Transitions Using Statistical Methods

The rapid increment of technological advancements in the field of multimedia stream has piled up data production and consumption in cyberspace in the past two decades. It has prompted a swift increase in data transmission volume and repository size. Video is the most consumed data type on the internet. So, retrieval of selected video clips from the extra-large database of videos is extremely complex. The video shot boundary detection (SBD) is used for retrieval of desired video clips. It also is the fundamental step in video processing, which also was found important for applications like video browsing and indexing. The SBD handles the identification of abrupt and gradual transitions in the video database. In this paper, the statistical methods have implemented to detect the abrupt cuts. This benefitted the advantage of less complexity. The experiments are performed on TRECVID 2007 dataset, and the results obtained with high accuracy in less computational time.
ADVERSARIAL OPEN DOMAIN ADAPTION FRAMEWORK (AODA): SKETCH-TO-PHOTO SYNTHESIS

This paper aims to demonstrate the efficiency of the Adversarial Open Domain Adaption framework for sketch-to-photo synthesis. The unsupervised open domain adaption for generating realistic photos from a hand-drawn sketch is challenging as there is no such sketch of that class for training data. The absence of learning supervision and the huge domain gap between both the freehand drawing and picture domains make it hard. We present an approach that learns both sketch-to-photo and photo-to-sketch generation to synthesise the missing freehand drawings from pictures. Due to the domain gap between synthetic sketches and genuine ones, the generator trained on false drawings may produce unsatisfactory results when dealing with drawings of lacking classes. To address this problem, we offer a simple but effective open-domain sampling and optimization method that “tricks” the generator into considering false drawings as genuine. Our approach generalises the learnt sketch-to-photo and photo-to-sketch mappings from in-domain input to open-domain categories. On the Scribble and SketchyCOCO datasets, we compared our technique to the most current competing methods. For many types of open-domain drawings, our model outperforms impressive results in synthesising accurate colour, substance, and retaining the structural layout.
An Adaptive Control System of a Polymerizer Based on Fuzzy Supervisor

article is devoted to solving the problem of controlling weakly deterministic chemical-technological systems using the example of a polymer synthesis reactor. There is developed a block diagram of an adaptive regulator with a fuzzy-logic supervisor, which changes the regulator settings during the process according to a certain rule base. The membership functions and rulebase of the supervisor is given. The results of the operation of such a control system are shown in comparison with the classical one. As a result of a comparative analysis, a significant increase in the quality of the transition process in the proposed system is shown.