Reviewerly/exHarmony huggingface dataset:
Self-taught Neural Agents in Clever Game Playing

In the Tic-Tac-Toe game community, brilliant children and some adults discover, through playing experience, exceptional game situations where the current player declares him/herself a winner, no matter what the opponent does in the next moves. This paper proposes a Tic-Tac-Toe learning environment based on a self-motivated neural agent that learns by itself these exceptional game situations and then use this knowledge in real world tournaments, where it mimics a Markov model. The used reinforcement learning method involves a reward gaining strategy where indexed sub networks are noise balanced trained, as to clearly point toward found rewards, thus endorsing a successful future search for maximal recompenses. During training the agent explores far ahead movements based on the Bellman equation and memorizes game patterns that assure winning-ahead situations. During the operating phase the neural agent receives advising from the trained networks and realizes aperture moves that mimic the abilities of clever human players.
Complex of morphological analysis of images for side-scan sonar on basis of Griffon hardware platform

The article describes the developed computational complex of morphological image analysis for interferometric side-scan sonars. Examples of modeling the operation of the side-scan sonar in cases where the structure of bottom sediments is not known in advance are given. The results of the analysis of experimental data are considered. A description is given of the structure for constructing algorithms for morphological image analysis used in the operation of the complex. Recommendations are given on the effective use of the computing capabilities of the Griffon hardware platform for organizing parallel-conveyor data processing of side-scan interference sonars in real time. Hardware and software modeling has been performed to assess the most important performance characteristics of a computing complex and determine the optimal organization of data processing. The obtained results of processing sonar images streams showed that the use of the Griffon hardware platform provides advantages in terms of organizing the computing process, including parallel operation of different sections of the computing pipeline, reducing the load on the transport bus and pipeline delay, as well as reducing the load and freeing up CPU resources for morphological processing.
Web application dashboards as a tool for data visualization and enrichment

Working place of an individual that manages data analysis and data processing can involve one person or a whole team. The tools that can make their job easier, reduce the work hours and ultimately reduce the number of people needed in the team are dashboards of web applications. With the help of dashboards one can manage, analyze and visually monitor their key metrics related to their business or to an individual. There is no universal dashboard to suit every form of business. Equally, there is no universal interface that can be declared as the only suitable solution for all businesses, therefore different dashboards are being developed in different interfaces. Individuals or teams are the users of dashboards. There is a number of dashboard templates for website interfaces that can help developers create their own dashboards for accessible data more easily and quickly. The aim of the paper is to analyze and evaluate various dashboard templates according to the defined criteria in order to demonstrate which template is the most appropriate for a certain domain and field of work. It is expected that the results will help developers with the choice of a dashboard template for their new projects. This paper gives basic guidelines regarding responsive web design and some of the frameworks that can be used to build a responsive web design page. It also briefly describes the CSS Grid. Furthermore, this paper analyzes five different dashboard templates created within four different frameworks and CSS Grid. They were analyzed with five given criteria: the 5 second rule, the inverted pyramid, data visualization, less is more and interactive elements. The results would emphasize the templates created within Bootstrap and Semantic UI because they were not evaluated by any criterion with a score less than 4 out of 5.
All of the Fairness for Edge Prediction with Optimal Transport

Machine learning and data mining algorithms have been increasingly used recently to support decision-making systems in many areas of high societal importance such as healthcare, education, or security. While being very efficient in their predictive abilities, the deployed algorithms sometimes tend to learn an inductive model with a discriminative bias due to the presence of this latter in the learning sample. This problem gave rise to a new field of algorithmic fairness where the goal is to correct the discriminative bias introduced by a certain attribute in order to decorrelate it from the model's output. In this paper, we study the problem of fairness for the task of edge prediction in graphs, a largely underinvestigated scenario compared to a more popular setting of fair classification. To this end, we formulate the problem of fair edge prediction, analyze it theoretically, and propose an embedding-agnostic repairing procedure for the adjacency matrix of an arbitrary graph with a trade-off between the group and individual fairness. We experimentally show the versatility of our approach and its capacity to provide explicit control over different notions of fairness and prediction accuracy.
Capturing Knowledge of Emerging Entities From Extended Search Snippets

Google and other search engines feature the entity search by representing a knowledge card summarizing related facts about the user-supplied entity. However, the knowledge card is limited to certain entities that have a Wiki page or an entry in encyclopedias such as Freebase. The current encyclopedias are limited to highly popular entities, which are far fewer compared with the emerging entities. Despite the availability of knowledge about the emerging entities on the search results, yet there are no approaches to capture, abstract, summerize, fuse, and validate fragmented pieces of knowledge about them. Thus, in this paper, we develop approaches to capture two types of knowledge about the emerging entities from a corpus extended from top-n search snippets of a given emerging entity. The first kind of knowledge identifies the role(s) of the emerging entity as, e.g., who is s/he? The second kind captures the entities closely associated with the emerging entity. As the testbed, we considered a collection of 20 emerging entities and 20 popular entities as the ground truth. Our approach is an unsupervised approach based on text analysis and entity embeddings. Our experimental studies show promising results as the accuracy of more than $87\%$ for recognizing entities and $75\%$ for ranking them. Besides $87\%$ of the entailed types were recognizable. Our testbed and source code is available on Github https://github.com/sunnyUD/research_source_code.
Weighted Information Filtering, Smoothing, and Out-of-Sequence Measurement Processing

We consider the problem of state estimation in dynamical systems and propose a different mechanism for handling unmodeled system uncertainties. Instead of injecting random process noise, we assign different weights to measurements so that more recent measurements are assigned more weight. A specific choice of exponentially decaying weight function results in an algorithm with essentially the same recursive structure as the Kalman filter. It differs, however, in the manner in which old and new data are combined. While in the classical KF, the uncertainty associated with the previous estimate is inflated by adding the process noise covariance, in the present case, the uncertainty inflation is done by multiplying the previous covariance matrix by an exponential factor. This difference allows us to solve a larger variety of problems using essentially the same algorithm. We thus propose a unified and optimal, in the least-squares sense, method for filtering, prediction, smoothing and general out-of-sequence updates, all of which require different Kalman-like algorithms.
Image Captioning with Data Augmentation Using Cropping and Mask Based on Attention Image

Automatic image captioning has various important applications such as the depiction of contents for the visually impaired. Most approaches use Deep Learning and have achieved remarkable results. However there are still some unresolved issues. One of them is the overfitting of the trained model to specific images, usually caused by limited training dataset sizes. In order to augment the training dataset size in such scenarios, previous researches proposed data augmentation using random cropping or mask. However, those do not specifically target overfitted regions in images and, therefore, may remove areas in images that are needed to generate captions and lower performance. In this study, we propose a novel data augmentation method that targets specifically regions in images subject to overfitting by using attention. Experimental results show that the proposed method allows generation of better image captions.
LibGuides: Streaming Video Access & Support: Documentary & Nonfiction

This guide highlights frequently used streaming video sources and available support for instructional use.
An Analysis on Task Migration Strategy of Big Data Streaming Storm Computing Framework for Distributed Processing

In this modern era, a large volume of data is generated regularly, which needs to be processed for gaining profits of latent information. The processing of big data is composed of a challenge termed as communication overhead. In order to minimize communication overhead on the premise of various resource constraints, a task migration strategy under heterogeneous storm environment is proposed. The proposed strategy is based on the establishment and demonstration of storm resource constraint model, optimal communication overhead model, and task migration model. Where the source node selection algorithm adds the nodes beyond the threshold to the source node set according to the load of CPU, memory, and network bandwidth of each working node in the cluster and the priority order of various resources. The experiment shows that compared with the existing research, computing storm can effectively reduce the delay and communication overhead between nodes, and the execution overhead is small.
ON THE QUESTION OF EVALUATING THE EFFICIENCY OF THE ORGANIZATION OF COOPERATION OF PARTICIPANTS OF SCIENTIFIC RESEARCH

The article is devoted to the issues of evaluating the effectiveness of scientific cooperation, including the factors that determine it. Four categories of factors — external, internal, factors of cooperation and teamwork management. The idea of Diane H. Sonnenwald on scientific collaboration as a process. The features of each stage of this process are systematized, which determine the value of scientific cooperation for its participants. Based on a survey of twenty leaders of scientific organizations using analysis of variance, the perception of the value of cooperation in the field of scientific research was evaluated. As the most valuable partners, the respondents named industry organizations that have units that conduct research and development. Higher education organizations do not yet have enough experience in research cooperation. Using the proposed approach to building the profile of each of the partners in research cooperation, the scientific organization is able to respond to factors affecting the effectiveness of these processes.
Methods for teaching activities of Literature at High school levelbased on the new general educational programme

Managing Literature teaching activities particularly and teaching activities generally by following the requirements of the upper secondary curriculum is an issue which education managers are concerned with. To meet the requirements and objectives of the new upper secondary curriculum, the article starts with the practice of research and Literature teaching activities management at high schools nowadays so as to suggest some measures in managing Literature teaching activities, such as determining objectives suitable for the new curriculum, choosing the appropriate teaching contents, training teachers for teaching method innovation and Literature teaching activities implementation; with the view to improving the efficiency in managing educational activities as well as meet the objectives of the new upper secondary curriculum.
Conventional implicatures of the 2019 Presidential Tribunal: implications for the Nigerian leadership, media and users

Conventional implicature deals with non-truth conditional content of the expression in any given situation. Nigerians went into the general election in the year 2019, which various positions were contested and so many conditions that took place. Political parties were at the helm of the affairs doing what they know how best to do, through manifestos, campaign, propaganda and so on. Later, there was outcome of the presidential tribunal, especially among the first and topmost two contenders in the election and actual implicature transpired in the judgement. The implication lies at what the majority sees as a rape of democracy and the court ruling. Language of affidavit became awash on the social media. So many non-truth conditions were generated on the media after the ruling. To ascertain this implicature, this study applied the qualitative research by using purposive sampling technique in gathering the data and its analysis. The study reveals the social vices in this situation against dwindling areas in our leadership and how it affects those using the language.&#x0D; Keywords: Conventional, Implicature, Language, Affidavit, leadership, Presidential, Social Media
Research on the Explicit and Implicit Instruction of English-Speaking Countries’ Culture for English Majors with the Assistance of Network Platform

Abstract With the rapid development of modern society, students majoring in English not only need to strengthen their listening, speaking, reading and writing skills, but also have to learn more about the explicit and implicit culture of English-speaking countries to better improve the level of cultural communication. Against the backdrop of the fast development of the modern Internet technology, teachers are required to make full use of the Internet platform to improve students’ awareness of the explicit and implicit cultures of English-speaking countries. Based on the current situation of explicit and implicit teaching in English culture, this paper gives relevant teaching suggestions in order to guarantee the improvement of oral communication skills of English majors in the future.
Philosophical and psychological considerations when choosing educational development for students to adjust to studies

According to earlier research, majority of first-year undergraduates have some difficulties adapting to the study pattern in universities. A series of papers state that this process is greatly affected by the regional realities where they live, therefore the researchers designed and tested a number of educational developments leading to better adapting. Herewith, the analysis of findings does not suggest that a combination of educational developments identified by theory and verified experimentally could be described as necessary and sufficient. It necessitates further research in this area and it’s efficiency will depend on choosing the right methodology for the research. Based on the four-level methodology of scientific analysis by I.V. Blauberg and E.G. Yudin there is rationale to incorporate the provisions of the activity theory when analyzing a package of educational developments. It’s implementation in physical education would ensure first-year undergraduates adapt to the study pattern in a university as effectively as possible.The main methods of research included the analysis and consolidation of scientific and methodological data, summative experiment to determine the functional state of students and correlation of the findings with their biological age. Experimentation was carried out by the Department of Physical Education, Chuvash State Agricultural Academy, Russia. The research resulted in supplementing the earlier proposed set of educational developments with other two. The first of them is to objectify the need for physical exercise as an effective way to enhance adaptation of firstyear students. The second one is to have monitoring and corrective measures in place as part of the activity. Together with the earlier recommended framework, these educational developments ensure all functionalities of the activity to improve the studied process of first-year students adaptation to the study pattern in a university. Whereby a set of educational developments could be termed as a coherent organic system
Next Level Odd-One-Out Puzzles

Abstract A commonly occurring task in intelligence tests or recreational riddles is to “find the odd one out”, that is, to determine a unique element of a set of objects that is somehow special. It is somewhat arbitrary what exactly the relevant feature is that makes one object different. But once that is settled, the answer becomes obvious. Not so with a puzzle popularized by Tanya Khovanova to express her dislike for this type of puzzle. Here, it is a more complicated relation between the objects and the features that determines the odd object, because there is only one object that does not have a unique feature expression. This puzzle inspired me to look for even more complicated relations between objects, features and feature expressions that appear to be even more symmetric, but actually still single out a “special object”. This paper provides useful definitions, a theoretical basis, solution algorithms, and several examples for this kind of puzzle.
USE OF MECHANICAL PUZZLES IN THE COURSE OF ROBOTICS FOR GRADES 2–4

The article discusses approaches to the implementation of an informatics course and highlights the special place of robotics in modern informatics teaching programs. The importance of early training in the basics of mechanics, algorithmization and robotics is noted. The main characteristics of the author's program are given, the use of mechanical puzzles in it is highlighted. On the example of several tasks, the features and tasks of mechanical puzzles in the Robotics program for grades 2–4 are explained. The tasks of mechanical puzzles are used both to consolidate the material and to activate the thinking process of students, which makes them important in the study of robotics. The article provides examples of crown, canon, and belt gear using the LEGO WeDo 2.0 Education Set.
Sentence Level Estimation of Psycholinguistic Norms Using Joint Multidimensional Annotations

Psycholinguistic normatives represent various affective and mental constructs using numeric scores and are used in a variety of applications in natural language processing. They are commonly used at the sentence level, the scores of which are estimated by extrapolating word level scores using simple aggregation strategies, which may not always be optimal. In this work, we present a novel approach to estimate the psycholinguistic norms at sentence level. We apply a multidimensional annotation fusion model on annotations at the word level to estimate a parameter which captures relationships between different norms. We then use this parameter at sentence level to estimate the norms. We evaluate our approach by predicting sentence level scores for various normative dimensions and compare with standard word aggregation schemes.
Recognition-Synthesis Based Non-Parallel Voice Conversion with Adversarial Learning

This paper presents an adversarial learning method for recognition-synthesis based non-parallel voice conversion. A recognizer is used to transform acoustic features into linguistic representations while a synthesizer recovers output features from the recognizer outputs together with the speaker identity. By separating the speaker characteristics from the linguistic representations, voice conversion can be achieved by replacing the speaker identity with the target one. In our proposed method, a speaker adversarial loss is adopted in order to obtain speaker-independent linguistic representations using the recognizer. Furthermore, discriminators are introduced and a generative adversarial network (GAN) loss is used to prevent the predicted features from being over-smoothed. For training model parameters, a strategy of pre-training on a multi-speaker dataset and then fine-tuning on the source-target speaker pair is designed. Our method achieved higher similarity than the baseline model that obtained the best performance in Voice Conversion Challenge 2018.
Necessary and Sufficient Explanations in Abstract Argumentation

In this paper, we discuss necessary and sufficient explanations for formal argumentation - the question whether and why a certain argument can be accepted (or not) under various extension-based semantics. Given a framework with which explanations for argumentation-based conclusions can be derived, we study necessity and sufficiency: what (sets of) arguments are necessary or sufficient for the (non-)acceptance of an argument?
LibGuides: Library & Technology Services: Safe Return to Campus Plan

A resource guide with information about accessing electronic resources and Law Library and Technology services.
Multi-Dimensional Anonymization for Participatory Sensing Systems

Participatory sensing systems are designed to enable community people to collect, analyze, and share information for their mutual benefit in a cost-effective way.The apparently insensitive information transmitted in plaintext through the inexpensive infrastructure can be used by an eavesdropper to infer some sensitive information and threaten the privacy of the participating users.Participation of users cannot be ensured without assuring the privacy of the participants.Existing techniques add some uncertainty to the actual observation to achieve anonymity which, however, diminishes data quality/utility to an unacceptable extent.The subset-coding based anonymization technique, DGAS [LCN 16] provides the desired level of privacy.In this research, our objective is to overcome this limitation and design a scheme with broader applicability.We have developed a computationally efficient subset-coding scheme and also present a multi-dimensional anonymization technique that anonymizes multiple properties of user observation, e.g. both location and product association of an observer in the context of consumer price sharing application.To the best of our knowledge, it is the first work which supports multi-dimensional anonymization in PSS.This paper also presents an in-depth analysis of adversary threats considering collusion of adversaries and different report interception patterns.Theoretical analysis, comprehensive simulation, and Android prototype based experiments are carried out to establish the applicability of the proposed scheme.Also, the adversary capability is simulated to prove our scheme's effectiveness against privacy risk.