Reviewerly/exHarmony huggingface dataset:
Discriminative Convolutional Neural Network for Image Quality Assessment with Fixed Convolution Filters

Current image quality assessment (IQA) methods require the original images for evaluation. However, recently, IQA methods that use machine learning have been proposed. These methods learn the relationship between the distorted image and the image quality automatically. In this paper, we propose an IQA method based on deep learning that does not require a reference image. We show that a convolutional neural network with distortion prediction and fixed filters improves the IQA accuracy.
Discriminate and Reconstruct: Learning from Language Model to Answer Keyword Questions

We consider a new problem of question answering when the questions are in form of keywords, rather than natural language. While searching on machines or interacting with the robots, people usually prefer to raise a query by several keywords rather than a complete sentence. The new task of Keyword Question Answering (KQA) is challenging and significant because small variations to a question may completely change its semantical information, thus yields different answers. In this paper, we propose a simple but strong system for KQA composed of (1) a Keyword Question Discriminator to recognize the keyword questions, (2) a Question Reconstructor that learns from a language model to reconstruct the keyword questions and (3) a question answering model to produce answers. We further finetune the reconstructor via reinforcement learning by the quality of the answers to help generate answerable questions. Moreover, We also develop a semi-supervised learning method to build the keyword question datasets. Empirical results demonstrate the effectiveness of our method in comparison with various baselines, and we also find that the high layers of the language model are helpful in handling grammatical blunder and semantic fuzziness.
Path-Based Knowledge Graph Completion Combining Reinforcement Learning with Soft Rules

Knowledge graphs are useful resources for numerous applications, but they are often greatly incomplete. A popular method of knowledge graph completion is path-based reasoning that is to infer new relations by connecting other existing paths at a pair of entities. But this traditional method does not consider the reality of paths. In this paper, we propose a model that combines the reinforcement learning (RL) framework with soft rules to learn reasoning path. In our model, we adjust the partially observed Markov decision process and extract the soft rules with different confidence levels from datasets. In contrast to prior work, we modify the reward function to make RL-agent incline to choose the paths which conform to soft rules, and set the probability of reasoning paths. Meanwhile, we make some restrictions in order to extract soft rules with high confidence levels. We analyze the complexity of our algorithm and use an instance to evaluate the correctness of our model.
Twitter Users' Classification Based on Interest

Microblogging platforms, such as Twitter, have become a popular interaction media that are used widely for different daily purposes, such as communication and knowledge sharing. Understanding the behaviors and interests of these platforms' users become a challenge that can help in different areas such as recommendation and filtering. In this article, an approach is proposed for classifying Twitter users with respect to their interests based on their Arabic tweets. A Multinomial Naïve Bayes machine learning algorithm is used for such classification. The proposed approach has been developed as a web-based software system that is integrated with Twitter using Twitter API. An experimental study on Arabic tweets has been investigated on the proposed system as a case study.
Knowledge Graph Embedding Bi-vector Models for Symmetric Relation

Knowledge graph embedding (KGE) models focus only on the characteristics of the datasets, ignoring the relation semantics. Most of KGEs have no ability to handle dataset with large numbers of symmetric relations. Formalization and experimentation given in this paper clearly shows the existence of the aforementioned problem, since we propose KGE bi-vector models which represent each symmetric relation as vector pair. In the vector addition of entities and relations, the vector of bi-vector that is closer to the result is selected as the train/test vector. Symmetric information contained in the two symmetric edges is lossless in bi-vector models. We generate the benchmark datasets based on FB15k and WN18 by completing the symmetric relation triples to verify models. The experimental results of our models clearly affirm the effectiveness and superiority of our models against baseline.
Mining Expected Support-Based Frequent Itemsets by Sampling

Discovering Frequent itemsets over uncertain databases has become a hot topic in data mining community due to the wide application of uncertain data. Several algorithms are proposed to mine frequent itemsets over uncertain data. However, the existing mining algorithms generally suffer from extensive computation and prohibitively running time when the uncertain data is large or the frequent threshold is low. To address this issue, we propose a novel expected support-based frequent itemsets (E-FIs) mining method which discovers approximate E-FIs from a random sample of the original uncertain dataset. To obtain high quality approximation, we employ the solid theory of the Vapnik-Chervonenkis dimension. The mining result is thus a superset of the E-FIs with high probability guarantee. Finally, we conduct experiments on two public databases and evaluate our method in terms of accuracy, running time and scalability.
Graph Modeling for Identifying Breast Tumor Located in Dense Background of a Mammogram

Identifying breast tumor in a mammogram is a challenging task even for experienced radiologists if the tumor is located in a dense tissue. In this study, a novel superpixel based graph modeling technique is proposed to extract texture features from the computer identified suspicious regions of mammograms. Graph models are constructed from specific structured superpixel patterns and used to generate feature vectors used for classifications of regions of mammograms. Two mammographic datasets were used to evaluate the effectiveness of the proposed approach: the publicly available Digital Database for Screening Mammography (DDSM), and a local database of mammograms (BSSA). Using Linear Discriminant Analysis (LDA) classifier, an AUC score of 0.910 was achieved for DDSM and 0.893 for BSSA. The results indicate that graph models can capture texture features capable of identifying masses located in dense tissues, and help improve computer-aided detection systems.
A Short Term Cloud Tracking Model Based on the Bruhn Optical Flow Method

The recently developed Bruhn optical flow method has been found to improve imagery analysis results but has yet to be tested for cloud tracking purposes. We propose a cloud tracking model that is based on the Bruhn method and compare its performance with models based on more conventional optical flow methods. For lead-times spanning from 5s to 600s, the Bruhn based model has a comparable tracking accuracy when contrasted with current methods yet achieves a better than 30% improvement in computing processing time. We conclude that the Bruhn method represents a promising method for use in cloud tracking models.
Philosophy and the library and information studies: Vectors of interaction

The author analyzes the origins of interest towards philosophical issues the book and library scientists and researcher bibliographers take. The need for more close interaction between philosophy and the library and information science in the circumstances of transition from information society to knowledge society and digital economy is substantiated. The process of cognitive institutionalization of philosophical knowledge in the library and information science is examined. The author analyzes several works by Arkady V. Sokolov on the content and specific features of bibliosophy as a philosophical system focused on biblioshere within the historical context. The key functions of philosophical knowledge are discussed as related to library and information science which enables rationalizing and reliable, accurate and relevant assessment of multiple and unique processes and phenomena in this disciplines. The focus is made on the worldview, ontological, gnoseological, methodological and axiological functions. The authors emphasizes the role of philosophy in developing conceptual, theoretical, logical and methodological structures for serious study of bibliosphere and its rational perception as one of the fundamental cultural segments
Professional Identification of a Specialist According to the Factor of His Psychological Type of Personality

One of the factors determining the psychological comfort of the social and professional interaction of a specialist is his personality type. Modern psychology of personality considers sixteen basic types to identify a person. An individual personality type of a specialist largely either contributes to or hinders the achievement of the optimal level of his competitiveness in the professional environment. The method developed by the author, “The choice of the priority type of professional activity of a specialist on the factor of the psychological type of his personality” allows us to draw conclusions for each specialist: on the priority influence of the components of the psychological type of his personality on the formation of individual social and professional competitiveness; on the choice of this type of professional activity that would allow optimal use of the capabilities of the psychological type of his personality to increase competitiveness in the professional environment. Practical use of the technique is considered on the example of young specialists of machine-building profile who graduated from a professional college.
Backgrounds of Teacher Education System in South Korea

This chapter is related to the general background of the teacher education system in South Korea. It contains the information concerning educational institutions, teacher certification, and the teacher employment examination. Moreover, we discuss support for KLLs and teachers of KLLs, including Da-Mun-Wha education to provide our readers with practical background knowledge of this book.
Design of Algorithm Thinking-Based Software Basic Education for Nonmajors

Purpose:The purpose of this study is to design the curriculum of Basic College Software Programming to develop creative and logical-thinking.This course is guided by algorithmic thinking and logical thinking that can be solved by computing for problemsolving, and it helps to develop by software through basic programming education.Through the stage of problem analysis, abstraction, algorithm, data structure, and algorithm implementation, the curriculum is designed to help learners experience algorithm problem-solving in various areas to develop diffusion thinking.For Learners aim to achieve the balanced development of divergent and convergent-thinking needed in their creative problem-solving skills.Research design, data and methodology: This study is to design a basic software education for improving algorithm-thinking for non-major.The curriculum designed in this paper is necessary to non-majors students who have completed the 'Creative Thinking and Coding Course' Design Thinking based are targeted.For this, contents were extracted through advanced research analysis at home and abroad, and experts in computer education, computer engineering, SW education, and education were surveyed in the form of quasi-openness.Results: In this study, based on ADD Thinking's algorithm thinking, we divided the unit college majors into five groups so that students of each major could accomplish the goal of "the ability to internalize their own ideas into computing," and extracted and designed different content areas, content elements and sub-components from each group.Through three expert surveys, we established a strategy for characterization by demand analysis and major/textbook category and verified the appropriateness of the design direction to ensure that the subjects and contents of the curriculum are appropriate for each family in order to improve algorithm-thinking.Conclusions: This study helps develop software by enhancing the ability of students who practice various subjects and exercises to explore creative expressions in various areas, such as 'how to think like a computer' that can implement and execute their ideas in computing.And it helps increase the ability to think logical and algorithmic computing based on creative solutions, improving problem-solving ability based on computing thinking and fundamental understanding of computer coding and development of logical thinking ability through programming.
Non-Linear Polynomial Filters: Overview, Evolution and Proposed Mathematical Formulation

Last few decades have marked growing interest towards developments in signal processing techniques on account of the diverse spread in multimedia applications (which includes image and video processing).
Modification of ARMA (1, 1) model to simulate strictly stationary random series with uniform distribution

Abstract We consider a problem of simulating strictly stationary random series. A modification of an autoregressive–moving-average model of orders 1, 1 is proposed. Proposed algorithm allows to simulate stationary random series with uniform distribution. Properties of resulting random series are examined.
Memory-Based Multiagent One-Shot Learning: Extended Abstract

Learning correct behavior from one example (one-shot learning) is particularly difficult in multiagent systems where the pertinent information is potentially distributed across agents, and the emergent behavior of the system is dependent on inter-agent interactions. This paper introduces Distributed Modular Memory Units (DMMU), a distributed multiagent learning framework. DMMU uses a shared external memory to enable one-shot adaptive learning in multiagent systems. The external memory is accessed by agents acting independently and in parallel. Agents operate by processing their individual states and deciding when to interact with the shared external memory unit. This allows agents to identify, retain, and propagate information among the team. Subsequently, DMMU can rapidly assimilate task features from a group of distributed agents and successfully use the information to accomplish distributed one-shot learning. We compare the performance of the DMMU framework on a simulated cybersecurity task with traditional feed-forward ensembles, long short-term memory based agents, and a centralized learner. Results demonstrate that DMMU shows at least 100% performance improvement over the other methods, and exhibits distributed one-shot learning to effectively solve this complex task.
Web application for analyzing power electronic converter data

The goal is to provide fast and easy way for engineers to take a look at data oscillation, analyze the information gathered from every element of one convertor, filter the oscillation data by time to see voltage change and compare results. For the making of this web application we used several technologies for saving, collecting and representing data. All data is saved in a relational database from which we take all the information and represent it in our application. In the next part we will discuss about every separate technology.
Visual Navigation of Large Image Graphs

It is impossible to inspect or get an overview of image collections with millions of images. Users often start “exploring” images with a keyword or a similarity search. Both lead to long unstructured lists of result images. In this demo we present a graph-based system for visually exploring and navigating continuously changing sets of millions of images with a web browser. Subsets of images are successively retrieved from a image similarity graph and displayed as a visually sorted 2D image map, which can be zoomed and dragged to explore images from related concepts.
Monitoring of Material and Technical Potential of Modern Education As a Factor of Strategy of General Education Development and Implementation of Federal State Educational Standard

The article reveals the methodological foundations of the organization of monitoring of material, technical and information support of the Federal state educational standard, the role and place of means, environment and technological conditions of training, education and development in the educational infrastructure. The features of monitoring as a tool for assessing the state of infrastructure and the development of educational activities are shown. The essence of updating the content of education, technologies and teaching methods is determined and the priority directions of development of educational infrastructure are shown. The main groups of indicators and their requirements (criteria) for the implementation of modern monitoring of Material and technical base are identifi ed. Additions were made to the groups of indicators for monitoring the material and technical provision of the educational process. The necessity of normative regulation of infrastructure on the basis of integration of General requirements into the system of normative-methodical documents in the form of the standard of educational and material base of General secondary education is shown.
User-Aware Comfort in Retail Environments

A retail environment can be thought as an environment where customers can buy products, goods or services. The user-experience in physical retail environments is important not only for facilitating the selling of goods and services, but also for providing satisfaction and appealing to retain customers over the long term. The user-experience can be enhanced by adapting aspects of the physical environment such as music, colour, fragrance to the tastes of the customers. In this paper we propose a user-aware approach to adapt physical aspects of a retail environment in order to improve the perceived comfort level. We introduce a model of the user context, which can be used both for representing information about the customers and for driving the adaptation of the environment. The proposed decision system is based on a microservice architecture providing both modularity and flexibility. Real-world examples are also used to show applications of the approach.
FORMATION OF THE CONCEPT OF SELF-EDUCATION IN THE HISTORY OF RUSSIAN AND FOREIGN PEDAGOGY

Purpose of the study: The problems of the self-education of the person, the organization of his/her self-directed activity have deep historical roots and considered to be one of those pedagogical problems that have remained relevant regardless of certain historical conditions, being the focus of researchers for almost a century and a half. The authors have analyzed the concept of self-directed learning in Russian and foreign pedagogy, introduced the formation of self-education in domestic and foreign pedagogical science and identified the essential characteristics of self-education in different historical periods.&#x0D; Methodology: Our study is based on the theoretical research method – the analysis of foreign and national philosophical, historical, pedagogical literature on a problem; analysis and generalization of modern pedagogical experience; the method of the unity of historical and logical consideration of pedagogical phenomena.&#x0D; Results: The historical retrospective of processes of formation of self-education presented in the context of social development shows that essential characteristics of self-education are caused by a set of historical, economic, political and social factors including the entire logic of development of education in general. The author defines the essence of self-education in the context of a new paradigm of development of education – Life-Long Learning.&#x0D; Applications of this study: This research can be used for the universities, teachers, and students.&#x0D; Novelty/Originality of this study: In this research, the model of the Formation of the Concept of Self-Education in the History of Russian and Foreign Pedagogy is presented in a comprehensive and complete manner.
Application of multi-element geochemistry in the weathered environment: Controls, considerations and implications for exploration

SummaryUnderstanding the mobility of individual elements in the oxidised environment is important for the accurate interpretation of geochemical data from regolith and weathered bedrock samples. In the weathered environment, immobile element geochemistry can reflect primary lithological and in situ mineralisation signatures, whereas mobile elements can help establish the degree and extent of weathering. Three exploration case studies are presented here to demonstrate the application of multielement geochemistry in the weathered environment: An example from the Shorty Creek Project, Alaska (Freegold Ventures), highlights the importance of reviewing and understanding pathfinder elements in soil, weathered bedrock and fresh basement. Here, commodity elements Cu and Zn are highly mobile and relatively depleted in the soils and weathered zone over Cu-Au mineralisation, whereas Au, As, Bi and Sb are less mobile and highly anomalous in the oxidised bedrock and associated soils.The Hermosa Deposit, Arizona (South32), where downhole geochemistry can help map the oxide-fresh rock boundary at the deposit scale. Subtle depletion in mobile elements allows the weathered zone to be identified in altered rhyolites, and Zn:S and Pb:S ratios in the mineralised zone helps distinguish Zn and Pb oxides from Zn and Pb sulphides.A review of residual soils over a Ni-Cu-PGE prospect in Northern Queensland, where immobile elements are reliable discriminants of primary lithologies in highly weathered environments. Zirconium, Y, Th and Nb can be used to distinguish felsic from mafic bedrock, and variations in Cr, V, Al, Fe and Sc in soils confidently identify blind, compositionally distinct mafic-ultramafic bodies.This paper also highlights the importance of collecting high quality, multi-element geochemistry with low detection limits at every stage of exploration.
Direct Error Driven Learning for Classification in Applications Generating Big-Data

In this chapter, a comprehensive methodology is presented to address important data-driven challenges within the context of classification. First, it is demonstrated that challenges, such as heterogeneity and noise observed with big/large data-sets, affect the efficiency of a deep neural network (DNN)-based classifiers. To obviate these issues, a two-step classification framework is introduced where unwanted attributes (variables) are systematically removed through a preprocessing step and a DNN-based classifier is introduced to address heterogeneity in the learning process. Specifically, a multi-stage nonlinear dimensionality reduction (NDR) approach is described in this chapter to remove unwanted variables and a novel optimization framework is presented to address heterogeneity. In NDR, the dimensions are first divided into groups (grouping stage) and redundancies are then systematically removed in each group (transformation stage). The two-stage NDR procedure is repeated until a user-defined criterion controlling information loss is satisfied. The reduced dimensional data is finally used for classification with a DNN-based framework where direct error-driven learning regime is introduced. Within this framework, an approximation of generalization error is obtained by generating additional samples from the data. An overall error, which consists of learning and approximation of generalization error, is determined and utilized to derive a performance measure for each layer in the DNN. A novel layer-wise weight-tuning law is finally obtained through the gradient of this layer-wise performance measure where the overall error is directly utilized for learning. The efficiency of this two-step classification approach is demonstrated using various data-sets.