Reviewerly/exHarmony huggingface dataset:
Equal Area Breaks: A Classification Scheme for Data to Obtain an Evenly-colored Choropleth Map.

An efficient algorithm for computing the choropleth map classification scheme known as equal area breaks or geographical quantiles is introduced. An equal area breaks classification aims to obtain a coloring for the map such that the area associated with each of the colors is approximately equal. This is meant to be an alternative to an approach that assigns an equal number of regions with a particular range of property values to each color, called quantiles, which could result in the mapped area being dominated by one or a few colors. Moreover, it is possible that the other colors are barely discernible. This is the case when some regions are much larger than others (e.g., compare Switzerland with Russia). A number of algorithms of varying computational complexity are presented to achieve an equal area assignment to regions. They include a pair of greedy algorithms, as well as an optimal algorithm that is based on dynamic programming. The classification obtained from the optimal equal area algorithm is compared with the quantiles and Jenks natural breaks algorithms and found to be superior from a visual standpoint by a user study. Finally, a modified approach is presented which enables users to vary the extent to which the coloring algorithm satisfies the conflicting goals of equal area for each color with that of assigning an equal number of regions to each color.
QoE Analysis of Real-Time Video Streaming over 4G-LTE for UAV-Based Surveillance Applications

Drones also known as Unmanned Aerial Vehicles (UAVs) perform an significant role in surveillance at a remote location by streaming real-time video with their attached cameras. A good architecture for such kind of surveillance is required that ensures real-time monitoring at targeted areas. As the streaming video is used in monitoring; it is much important to ensure its quality during transmission so that remote client can view clear insights and could take prompt action on time if required. In this paper, we have proposed a 4G-LTE architecture and examined the effects of different factors in such architecture. We have shown the comparative analysis between two latest codec schemes i.e. H.264 and H.265 (HEVC) in video streaming. Our study is an important step towards exploring the factors that influence the real-time video streaming and degrade the Quality of Experience (QoE) of video viewing in such architecture. To examine the received video quality, two objective metrics, Peak-Signal-to-Noise-Ratio (PSNR) and Structural-Similarity-Index (SSIM) have been considered in this paper. The simulation results are based on the most famous Network simulator in the research community i.e. NS-3. The results have shown that H.265 works better in comparison with H.264 under different circumstances.
Report on the 3rd International Joint Conference on Rules and Reasoning (RuleML+RR 2019)

The annual International Joint Conference on Rules and Reasoning (RuleML+RR) is an international conference on research, applications, languages and standards for rule technologies, rule-based programming and rule-based systems including production rules systems, logic programming rule engines, as well as business-rule engines and management systems; Semantic Web rule languages and rule standards, including RuleML (e.g., Datalog+ RuleML, Reaction RuleML and LegalRuleML), SWRL, RIF, Common Logic, PRR, decision rules and Decision Model and Notation (DMN), as well as Semantics of Business Vocabulary and Business Rules (SBVR); rule-based Event Processing Languages (EPLs) and technologies; and foundational research on inference rules, transformation rules, decision rules, production rules, and Event-Condition-Action (ECA) rules. In 2017, RuleML+RR joined the efforts of two wellestablished conference series: the International Web Rule Symposia (RuleML) and the Web Reasoning and Rule Systems (RR) conferences, and it is now the leading conference to build bridges between academia and industry in the field of Web rules and its applications, especially as part of the Semantic Technology stack. RuleML+RR is commonly listed together with and related to other major high-impact Artificial Intelligence conferences worldwide, starting with IJCAI in 2011 and 2016, ECAI in 2012, AAAI in 2013, ECAI in 2014, the AI Summit London in 2017, and GCAI in 2018 and 2019. The RuleML symposia and RR conferences have been held since 2002 and 2007, respectively. The RR conferences have been a forum for discussion and dissemination of new results on Web reasoning and rule systems, with an emphasis on rule-based approaches and languages. The RuleML symposia have been devoted to disseminating research, applications, languages, and standards for rule technologies, with attention to both theoretical and practical developments, to challenging new ideas, and to industrial applications. Building on the tradition of both, RuleML and RR, the joint conference series RuleML+RR aims at bridging academia and industry in the field of rules, and at fostering the cross-fertilization between the different communities focused on the research, development, and applications of rule-based systems. RuleML+RR aims at being the leading conference series for all subjects concerning theoretical advances, novel technologies, and innovative applications about knowledge representation and reasoning with rules.
ISSUES OF PROFESSIONAL TRAINING OF TEACHERS FOR PRESCHOOL EDUCATION INSTITUTIONS

This article discusses the need for reform in the field of preschool education to improve the management mechanisms of this process, that is, to abandon the traditional methods of this process and radically change the education to a new management paradigm.The author points out a number of methodological approaches related to the essence of the concept of management in management theory, the priority of a person-centered approach in the management of pedagogical processes, that management of preschool education has its own stages. The article also discusses the social order of preschool education in higher pedagogical education, the systematic development of scientific and methodological knowledge of future leaders by organizing their independent training, equipping students with the latest knowledge in higher pedagogical education, and their managerial skills. questions of the implementation of the pedagogical process aimed at teaching secrets.
Self-taught Learning: Image Classification Using Stacked Autoencoders

Availability of a large amountSingh, Upendra Pratap of unlabeledChavan, Swapnil dataHindwani, Sahil and practical challenges associated with annotating datasets for different domains has led to the development of models that can obtain knowledge from one domain (called source domain) andSingh, Krishna Pratap use this knowledge in a similar domain (called the target domain). This forms the core of transfer learning. Self-taught learning is one of the popular paradigms of transfer learning frequently used for classification tasks. In a typical self-taught learning setting, we have a source domain having a large amount of unlabeled data instances and a target domain having limited labeled data instances. With this setting, self-taught learning proceeds as follows: Given ample unlabeled data instances in the source domain, we try to obtain their optimal representation. Basically, we are learning the transformation that maps unlabeled data instances to their optimal representation.The transformation learnt in the source domain is then used to transform target domain instances; the transformed target domain instances along with their corresponding labels are then used in supervised classification tasks. In our work, we have applied self-taught learning in image classification task. For this, we have used stacked autoencoders (for grayscale images) and convolutional autoencoders (for colored images) to obtain an optimal representation of the images present in the source domain. The transformation function learnt in the source domain is then used to transform target domain images. The transformed target domain instances along with their labels are then used for building the supervised classifier (in our case, SVM). Rigorous experiments on MNIST, CIFAR10 and CIFAR100 dataset show that our self-taught learning approach is doing well against the baseline model (where no transfer learning has been used) even when there are limited number of labeled data instances in the target domain.
High Confidence Tracking with Offline Historical Learning and Online Correlation Filter Updating

Target tracking is one of the most challenging tasks in computer vision. In this paper, the high confidence tracking (HCT) algorithm is proposed by combining the offline historical learning network with online correlation filter updating model. First, the weighted historical targets are introduced into the offline learning network, which solves the problem of target loss caused by inaccurate tracking of the previous frame. Second, the targetâ€™s confidence detection mechanism is proposed, and added to the correlation filter tracking algorithm, so that the model drift is avoided. Finally, we form a new high confidence tracking algorithm with offline learning. Compared with the state-of-the-art tracking algorithm, our algorithm performs outstandingly on benchmark OTB13 and OTB15, while ensuring real-time performance.
Speeding-Up the Dynamic Programming Procedure for the Edit Distance of Two Strings.

We describe a way to compute the edit distance of two strings without having to fill the whole dynamic programming (DP) matrix, through a sequence of increasing guesses on the edit distance. If the strings share a certain degree of similarity, the edit distance can be quite smaller than the value of non-optimal solutions, and a large fraction (up to 80–90%) of the DP matrix cells do not need to be computed. Including the method’s overhead, this translates into a speedup factor from \(3\times \) up to \(30\times \) in the time needed to find the optimal solution for strings of length about 20,000.
A modified basic scale entropy computing method for one-dimensional signal complexity analysis in real time

Basic scale entropy (BSE) is one of the important indicators for evaluating the complexity of one-dimensional signal, while it is difficult to be employed to analyze the signal in microcontroller for its long time-consumption. In this study, a modified basic scale entropy (MBSE) computing method based on the theory of BSE and the process of data updating in buffer is proposed to reduce the computational complexity. The BSE and MBSE methods are engaged to compute the complexity of random noise, P-P intervals, and R-R intervals. The results indicate that the MBSE method saves more time and installed memory space than BSE method, especially in evaluating the long-term complexity of the signal (with longer sign vector and buffer space). Thus, the proposed can be used to compute the complexity of one-dimensional signal in real-time.
Regulation of AI and AI Crimes

According to McCarthy, artificial intelligence (AI) is “the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are highly biologically observable”. AI may play an imminent role in committing criminal acts in the future and these acts are called AI- crimes (AIC). AIC has posed a threat towards cybersecurity, a few of AIC are, a) theft of sensitive data from government and private organizations, b) hacked and weaponized autonomous weapons, c) social engineering attacks, d) disruption in the political arena, such as “misinformation” campaigns, etc.There is no regulatory framework at the moment which targets AI-crimes, majority of the stakeholder’s policy recommendations are targeted towards fairness, accountability, and transparency in machine learning or they target the R&D of robots, none of the legal texts cover the much-debated topic of criminal liability of an AI as the law is still undecided. The traditional method of regulation of AI has been by, product licensing, research and development oversight and tort liability. However, the current AI principles are not enough to determine the threat posed by AI acting autonomously in the future. The paper is going to be divided into four parts, the first part of the paper will be an introduction to the concept of Artificial Intelligence Crime (AIC) and plausible AI malicious actions. The second part of the paper will delve into the existing cybercrime framework and try to fathom whether the frameworks incorporate AI crime. The third part of the paper will be a study of different international organizations that have made progress pertaining to AI crimes and their success in the applicability of legal principles to AI crimes. Further, the author will recommend legal principles specifically targeting AI crimes which should be made applicable in the cybercrime framework. The fourth part of the paper will deal with the challenges of determining the jurisdiction of AI crimes as AI crimes are committed on computer systems and networks across the globe. In the conclusive part, the author will analyze how should the law deal with the above challenges along with recommendations of how the legal framework should specifically deal with the above challenges.
European experience in the economic analysis of university spatial networking

Subject. The paper deals with the economic analysis of university spatial networking. Objectives. The purpose is to work out methods for economic analysis of spatial network of higher education institutions based on the European experience and information sources of the space location of universities in the EU countries. Methods. The study rests on theoretical analysis of the higher education institutions network as a spatially localized system, economic and statistical techniques of sampling and grouping, methods for comprehensive economic indicators of distribution, indexing, and ranking. Results. We underpinned approaches to the university network development as part of the science-push and the local development driver model; developed information support and methods for economic analysis of spatial organization of university networks; presented and tested the methodology for assessing the university spatial networking. Conclusions. The offered methods enable to obtain the estimates of even distribution of universities across a country, which is achieved by the country policy. These methods may be recommended for sound management decisions and strategies for higher education institutions development, spatial and socio-economic growth, policies designed to equalize the level of social and economic development of subjects of the Russian Federation.
Institutional Pressures of CSR: Evidence from India

The aim of the present study is to identify the important pressures driving corporate social responsibility practices of companies in India. To achieve the objectives of the study, we conceptualise institutional pressures as a second order hierarchical components model (reflective formative type) and its multiple dimensions as first order reflective constructs forming second-order components. An exploratory study was conducted among top level and middle level CSR professionals of companies in BSE 500 index to empirically validate our constructs.  The model has its roots in Scott’s institutional theory as applied to CSR - linking regulative (government and industry association), normative (NGOs and media) and cognitive pressures (local communities and peers) to overall institutional pressure. The responses were collected through a structured questionnaire, and PLS-SEM was used for the purpose of analysis. We found support for measuring institutional pressures of CSR in a reflective-formative type hierarchical component model. Keywords : Institutional theory, Corporate social responsibility, Institutional pressures, PLS-SEM, Hierarchical Component Model.
Efficient Variational Inference for Hierarchical Models of Images, Text, and Networks

Author(s): Ji, Geng | Advisor(s): Sudderth, Erik B | Abstract: Variational inference provides a general optimization framework to approximate the posterior distributions of latent variables in probabilistic models. Although effective in simple scenarios, variational inference may be inaccurate or infeasible when the data is high-dimensional, the model structure is complicated, or variable relationships are non-conjugate. We propose solutions to these problems through the smart design and leverage of model structures, the rigorous derivation of variational bounds, and the creation of flexible algorithms for various models with rich, non-conjugate dependencies.Concretely, we first design an interpretable generative model for natural images, in which the hundreds of thousands of pixels per image are split into small patches represented by Gaussian mixture models. Through structured variational inference, the evidence lower bound of this model automatically recovers the popular expected patch log-likelihood method for image processing. A nonparametric extension using hierarchical Dirichlet processes further enables self-similarities to be captured and image-specific clusters created during inference, boosting image denoising and inpainting accuracy.Then we move on to text data, and design hierarchical topic graphs that generalize the bipartite noisy-OR models previously used for medical diagnosis. We derive auxiliary bounds to overcome the non-conjugacy of noisy-OR conditionals, and use stochastic variational inference to efficiently train on datasets with hundreds of thousands of documents. We dramatically increase the algorithm speed through a constrained family of variational bounds, so that only the ancestors of the sparse observed tokens of each document need to be considered.Finally, we propose a general-purpose Monte Carlo variational inference strategy that is directly applicable to any model with discrete variables. Compared to REINFORCE-style stochastic gradient updates, our coordinate-ascent updates have lower variance and converge much faster. Compared to auxiliary-variable bounds crafted for each individual model, our algorithm is simpler to derive and may be easily integrated into probabilistic programming languages for broader use. By avoiding auxiliary variables, we also tighten likelihood bounds and increase robustness to local optima. Extensive experiments on real-world models of images, text, and networks illustrate these appealing advantages.
Learning Implicit Text Generation via Feature Matching

Generative feature matching network (GFMN) is an approach for training implicit generative models for images by performing moment matching on features from pre-trained neural networks. In this paper, we present new GFMN formulations that are effective for sequential data. Our experimental results show the effectiveness of the proposed method, SeqGFMN, for three distinct generation tasks in English: unconditional text generation, class-conditional text generation, and unsupervised text style transfer. SeqGFMN is stable to train and outperforms various adversarial approaches for text generation and text style transfer.
A dynamic programming approach for distributing quantum circuits by bipartite graphs

Near-term large quantum computers are not able to operate as a single processing unit. It is therefore required to partition a quantum circuit into smaller parts, and then each part is executed on a small unit. This approach is known as distributed quantum computation. In this study, a dynamic programming algorithm is proposed to minimize the number of communications in a distributed quantum circuit (DQC). This algorithm consists of two steps: first, the quantum circuit is converted into a bipartite graph model, and then a dynamic programming approach (DP) is proposed to partition the model into low-capacity quantum circuits. The proposed approach is evaluated on some benchmark quantum circuits with remarkable reduction in the number of required teleportations.
Lesson on the theme "forms of recording algorithms" (6th grade)

The article provides an example of using the H5P online service at the informatics lesson on the theme "Forms of recording algorithms". The H5P service provides simple and convenient tools for creating interactive training materials based on templates. Interactive tasks allow you to organize individual work of students. The stages of the lesson are presented in the technological map. The content of the lesson corresponds to the textbook "Informatics" for the sixth grade L. L. Bosova, A. Yu. Bosova.
A COMPARATIVE STUDY BETWEEN GROUP INVESTIGATION AND NUMBERED HEAD TOGETHER METHODS ON STUDENTS ABILITY IN READING DESCRIPTIVE TEXTAbstract The aims of this study are to find out understanding of reading descriptive text-ability taught by using Group Inves

AbstractThe aims of this study are to find out understanding of reading descriptive text-ability taught by using Group Investigation (GI) and Numbered Head Together (NHT) method of the third-semester students at University of Graha Nusantara Padangsidimpuan and to know that in both teaching techniques, there are significant differences or no. This research is an experiment with a nonequivalent control group design. The researcher used cluster sampling, which is taken from the third-semester students of English Department at University of Graha Nusantara Padangsidimpuan in which the number of the whole sample are 20 students. The instruments used in this study were pretest-posttest questions. The research hypothesis test used T-test (independent sample t-test) through the SPSS 21 program. The results of the research that have been analyzed can be concluded that there is no significant difference in the ability to read the description text in students by applying the learning model of the Group Investigation and Numbered Head Together Methods.
Adaptive Neuro-Fuzzy Inference System Based Impulse Denoising

Removing impulse noise from images is a critical issue in image processing because it may occur frequently during acquisition or transmission of images. We propose an anfis based impulse denoising algorithm to preserve the intrinsic geometric details of an image. The main target of this project is to restore the features of an image without losing any information from the degraded image. This method is more suitable to preserve the features of an image with scale invariant properties of an image. Here we are performing the training the noisy image with ANFIS and testing the image to retain the features of an original image.
Applicability of Cloud Computing in University Libraries: A Feasibility Study

The study aimed at identifying the role of change approaches in achieving competitive advantage in Jordanian telecom companies through a case study on Umniah Company. The researcher used the questionnaire to identify the role of change approaches. The study sample consisted of (70) employees in Umniah company. Descriptive and analytical approach was used.The study concluded with a set of results that the arithmetic mean of the items of the dimension of organizational culture has reached (3.59) which is at a high level, and this indicates that there is a role for organizational culture as one of the change approaches in achieving competitive advantage.The results also showed that the arithmetic mean of the dimension of quality management items has reached (3.52) which is also located in the high level, and this also indicates that there is a role of quality management as one of the change approaches in achieving competitive advantage, and finally the results of the study was showed that there are no statistically significant differences at the level of significance (0.05) among theresponses of employees of Umniah company about the role of change approaches in achieving competitive advantage, attributed to study variables (job title, job department).