Reviewerly/exHarmony huggingface dataset:
Profit Maximization of GENCO’s Using an Elephant Herding Optimization Algorithm

When electrical power systems are restructured managers look for satisfying several objectives that include: the working cost minimization and the profit for the unit commitment problems maximization.Power generating companies that comply with the two above mentioned objectives are able to provide good quality and reliable power at a cheaper cost.The individual power producers, schedule their generating units in such a way that they gain maximum profit.This is known as Profit Based Unit Commitment (PBUC).The target of this proposed work is to obtain an optimum generating schedule of the Power Generating Companies (GENCO's) and maximize the profit of power generating companies when the system is under various constraints like forecasted demand, minimum start-up /shutdown time, spot price, forecasted reserve and ramp rate limits.In order to address this problem, a new meta-heuristic approach called Elephant Herding Optimization (EHO) algorithm is presented.The method may help to solve the complex PBUC problems in the deregulated open market.The effectiveness of projected EHO is tested on various systems with various market conditions.The comparison of the test results with other optimization methods are presented and discussed taking into account their convergence characteristics, solution superiority and reliability.
Spectral graph clustering via the expectation-solution algorithm

The stochastic blockmodel (SBM) models the connectivity within and between disjoint subsets of nodes in networks. Prior work demonstrated that the rows of an SBM’s adjacency spectral embedding (ASE) and Laplacian spectral embedding (LSE) both converge in law to Gaussian mixtures where the components are curved exponential families. Maximum likelihood estimation via the Expectation-Maximization (EM) algorithm for a full Gaussian mixture model (GMM) can then perform the task of clustering graph nodes, albeit without appealing to the components’ curvature. Noting that EM is a special case of the Expectation-Solution (ES) algorithm, we propose two ES algorithms that allow us to take full advantage of these curved structures. After presenting the ES algorithm for the general curved-Gaussian mixture, we develop those corresponding to the ASE and LSE limiting distributions. Simulating from artificial SBMs and a brain connectome SBM reveals that clustering graph nodes via our ES algorithms can improve upon that of EM for a full GMM for a wide range of settings.
THE CHILDREN’S CONGRESS: CREATIVE COMPUTATIONAL THINKING &amp; STEM-EDUCATION

S. Hinterplattner, B. Sabitzer, M. Rottenhofer, H. Demarle-MeuselJohannes Kepler Universität Linz, Department of STEM-Education (AUSTRIA)
Investigating the Hand Ownership Illusion With Two Views Merged in

Researchers investigating virtual/augmented reality have shown humans’ marked adaptability, especially regarding our sense of body ownership; their cumulative findings have expanded the concept of what it means to have a body. Herein, we report the hand ownership illusion during “two views merged in”. In our experiment, participants were presented two first-person perspective views of their arm overlapped, one was the live feed from a camera and the other was a playback video of the same situation, slightly shifted towards one side. The relative visibility of these two views and synchrony of tactile stimulation were manipulated. Participants’ level of embodiment was evaluated using a questionnaire and proprioceptive drift. The results show that the likelihood of embodying the virtual hand is affected by the relative visibility of the two views and synchrony of the tactile events. We observed especially strong hand ownership of the virtual hand in the context of high virtual hand visibility with synchronous tactile stimulation.
THE USE OF LITERATURE-BASED INSTRUCTION TO IMPROVE READING COMPREHENSION AND WRITING ACHIEVEMENTS OF THE EIGHTH GRADE STUDENTS

Abstract: This research aimed to investigate whether or not there were significant differences in reading comprehension and writing achievement between the eighth grade students of SMP Islam Az-Zahra 2 Palembang who were taught by using Literature-Based instruction and those who were not. This study used one of the quasi experimental designs: pretest-posttest design. The sample was selected purposively from the whole population based on their reading comprehension scores. Forty six eighth graders were selected as the sample and equally divided into experimental and control groups. Pretest and posttest were given to both groups. Using paired sample statistics, the results of the experimental group showed that the students’ reading comprehension and writing achievement ? significantly improved. Furthermore, the result of the independent t-test showed that with mean difference of reading comprehension was 8.609, t value 11.111(p&lt;0.05). Moreover, the mean difference of writing achievement was 6.8043, t value 10.478 (p&lt;0.05). Keywords: Literature-based instruction, reading comprehension and writing achievement.
How to Find a Point in the Convex Hull Privately

We study the question of how to compute a point in the convex hull of an input set $S$ of $n$ points in ${\mathbb R}^d$ in a differentially private manner. This question, which is trivial non-privately, turns out to be quite deep when imposing differential privacy. In particular, it is known that the input points must reside on a fixed finite subset $G\subseteq{\mathbb R}^d$, and furthermore, the size of $S$ must grow with the size of $G$. Previous works focused on understanding how $n$ needs to grow with $|G|$, and showed that $n=O\left(d^{2.5}\cdot8^{\log^*|G|}\right)$ suffices (so $n$ does not have to grow significantly with $|G|$). However, the available constructions exhibit running time at least $|G|^{d^2}$, where typically $|G|=X^d$ for some (large) discretization parameter $X$, so the running time is in fact $Ω(X^{d^3})$. In this paper we give a differentially private algorithm that runs in $O(n^d)$ time, assuming that $n=Ω(d^4\log X)$. To get this result we study and exploit some structural properties of the Tukey levels (the regions $D_{\ge k}$ consisting of points whose Tukey depth is at least $k$, for $k=0,1,...$). In particular, we derive lower bounds on their volumes for point sets $S$ in general position, and develop a rather subtle mechanism for handling point sets $S$ in degenerate position (where the deep Tukey regions have zero volume). A naive approach to the construction of the Tukey regions requires $n^{O(d^2)}$ time. To reduce the cost to $O(n^d)$, we use an approximation scheme for estimating the volumes of the Tukey regions (within their affine spans in case of degeneracy), and for sampling a point from such a region, a scheme that is based on the volume estimation framework of Lovász and Vempala (FOCS 2003) and of Cousins and Vempala (STOC 2015). Making this framework differentially private raises a set of technical challenges that we address.
Adversarially Robust Hypothesis Testing

In this paper, we investigate the adversarial robustness of classification problems. In the considered model, after a sample is generated, it will be modified by an adversary before being observed by the classifier. The classifier needs to decide the underlying hypothesis that generates the sample from the adversarially modified data. We formulate this problem as a minimax hypothesis testing problem, in which the goal of the adversary is to design attack strategy to maximize the error probability while the decision maker aims to design decision rule so as to minimize the error probability. We solve this minimax problem and characterize the corresponding optimal strategies.
A Study of the Landscape of Internet Censorship and Anti-Censorship in Middle East

The power of technology is one which supersedes any other tool of communication ever formulated and implemented by human beings. The internet has been long cited by scholars and practitioners alike to be an empowerment tool that allows individuals to either seek, receive or dole out information and ideas without any basis being drawn on boundaries or geographical locations. This, therefore, means that online communication has to be protected in lieu with the international dictums and pretensions that call for the right to freedom of expression.
MOODLE PLUG-INS FOR DESIGN AND DEVELOPMENT OF GAMIFIED COURSES

The current generation of learners desires fast, attractive, quality and effective training that utilizes the latest information and communication technologies and tools. This naturally leads to the need for new or changed pedagogical methods, approaches and strategies. The article examines the use of serious games (in particular, gamification) as a suitable approach for attaining effective education for modern learners. The authors present how the implementation of games and game elements and techniques influences student motivation. This paper reviews software systems which use gaming and in particular existing gamification plugins in the Moodle e-learning environment.The game elements of electronic games that can be applied in training are explored - story/history, level, badge, leader board, bonus, reward, combo, challenge/quest, hidden treasure, game rules, game progress, team and time frame. An appropriate interpretation of these game elements is offered in a learning context, as well as their possible implementation through elements of the Moodle environment, which is the main e-learning environment of the University of Plovdiv “Paisii Hilendarski”, Bulgaria.The authors’ main goal is to design and develop software for implementing structural gamification of learning, which integrates game elements and techniques with the e-learning process, without any changes to the learning content of the courses. The article introduces the two plug-ins developed to assist the authors of the Moodle courses in the creation of gamified courses. The first plug-in changes the design of a new or already existing training course in a gamified way - based on game levels (open or locked with input requirements), and offers support for how to apply existing Moodle elements in a game context. The second plug-in allows for adding specific game elements to standard e-courses that do not exist in Moodle or cannot be implemented with standard Moodle elements. This plugin implements the game elements leaderboard, avatar and game progress.
A Convolutional Fuzzy Min-Max Neural Network for Image Classification

Convolutional neural network (CNN) is a well established practice for image classification. In order to learn new classes without forgetting learned ones, CNN models are trained in offline manner which involves re-training of a network considering seen as well as unseen data samples. However, such training takes too much time. This problem is addressed using proposed convolutional fuzzy min-max neural network (CFMNN) avoiding the re-training process. In CFMNN, the online learning ability is added to network by introducing the idea of hyperbox fuzzy sets for CNNs. To evaluate the performance of CFMNN, benchmark datasets such as MNIST, Caltech-101 and CIFAR-100 are used. The experimental results show that drastic reduction in the training time is achieved for online learning of CFMNN. Moreover, compared to existing methods, the proposed CFMNN has compatible or better accuracy.
INFORMATION LITERACY IN THE FRAMEWORK OF HIGHER EDUCATION: FOCUS ON INTELLECTUAL PROPERTY TRAINING

With the rapid development of new technologies, when information becomes one of the most valuable resources, Information Literacy (IL) plays a leading role. With the global penetration of the Internet, social networks, the digitalization of information arrays and the emergence of new tools for managing and using information resources, people's expectations for access to information have increased significantly. New technologies and modes of communication have broken many barriers to access, calling into question the efficiency of the search, selection, evaluation, correct, ethical and legal use of information, which also necessitated the acquisition of new competences and skills. Information literacy is one of the key competences of today's society. Her upbringing is the initial step in the ability and ability to seek and use the huge information flow. Intellectual Property (IP) can be seen as an element of information literacy in the higher education framework. In order to successfully develop students at university and in life, they must learn to use efficiently and effectively the wide variety of information and communication technologies to search, find, organize, analyze and evaluate the information they need. In addition, they must understand the ethics of the use of the information, including in violation of subjective intellectual property rights such as plagiarism, use without permission of the author of works of literature, art, science, as well as patented inventions, industrial design, designations (trademarks, geographical indications, domain names, companies). Finally, they must be able to systematize all this knowledge into one in order to create an effective end product. This requires them to assemble the full suite of basic research, technological, critical thinking and evaluation skills. In today's society, experts who are able to interpret IP issues, such as library and information professionals, play an important role. It is their responsibility to create policies to promote the understanding and resolution of legal disputes and conflicts that are unique to this aspect of the information society. One way to achieve this is through the educational impact of information literacy programs that incorporate IP issues as part of the curriculum. The aim of this paper is to present an overview of the current state of intellectual property as an element of information literacy of the students at the university. To achieve the set objective are defined the basic concepts and is made an analytical overview of their essence. Finally are made, some major conclusions and recommendations.
An Improved Image Compression Technique using Large Adaptive DCT Psychovisual Thresholds

High quality multimedia requires high bandwidth and data transfer rate to transmit multimedia data in communication networks. Image compression is one of solutions to reduce the storage of multimedia data which in turn allows an efficient transmission through networks. An adaptive image compression technique through customized quantization tables based on user preference has been widely used in many applications. Scaling quantization table can significantly influence the reconstruction error and compression rate. This paper proposes an adaptive psychovisual threshold for customizing large quantization tables to improve image compression. An adaptive psychovisual threshold is computed based on a smooth curve of the absolute reconstruction error by incrementing the DCT frequency order. Experimental results show that the performance of adaptive large DCT psychovisual threshold achieves high image quality and minimum average bit length of Huffman code. The proposed method also demonstrates that boundary effects do not appear when the compressed image is zoomed in to 400%.
Cost-Sensitive BERT for Generalisable Sentence Classification with Imbalanced Data

The automatic identification of propaganda has gained significance in recent years due to technological and social changes in the way news is generated and consumed. That this task can be addressed effectively using BERT, a powerful new architecture which can be fine-tuned for text classification tasks, is not surprising. However, propaganda detection, like other tasks that deal with news documents and other forms of decontextualized social communication (e.g. sentiment analysis), inherently deals with data whose categories are simultaneously imbalanced and dissimilar. We show that BERT, while capable of handling imbalanced classes with no additional data augmentation, does not generalise well when the training and test data are sufficiently dissimilar (as is often the case with news sources, whose topics evolve over time). We show how to address this problem by providing a statistical measure of similarity between datasets and a method of incorporating cost-weighting into BERT when the training and test sets are dissimilar. We test these methods on the Propaganda Techniques Corpus (PTC) and achieve the second-highest score on sentence-level propaganda classification.
Matrix Smoothing: A Regularization for DNN with Transition Matrix under Noisy Labels

Training deep neural networks (DNNs) in the presence of noisy labels is an important and challenging task. Probabilistic modeling, which consists of a classifier and a transition matrix, depicts the transformation from true labels to noisy labels and is a promising approach. However, recent probabilistic methods directly apply transition matrix to DNN, neglect DNN's susceptibility to overfitting, and achieve unsatisfactory performance, especially under the uniform noise. In this paper, inspired by label smoothing, we proposed a novel method, in which a smoothed transition matrix is used for updating DNN, to restrict the overfitting of DNN in probabilistic modeling. Our method is termed Matrix Smoothing. We also empirically demonstrate that our method not only improves the robustness of probabilistic modeling significantly, but also even obtains a better estimation of the transition matrix.
Vigi Flood: a serious game for understanding the challenges of crisis communication.

Emergency managers receive communication training about the importance of being 'first, right and credible', which is not easy. For instance, in October 2018, the Aude department in the SouthWest of France was hit by intense rain. Flash floods were hard to forecast and only the 'orange' level of vigilance could be raised initially, but the population dismissed this very usual warning in that season. The 'red' level was then raised too late, leading to high criticism. The main problem here is the loss of trust induced by too many 'false alarms'. In this paper we propose a serious game called VigiFlood for raising awareness in the population about the difficulty of crisis communication and their own responsibility for reacting to the alerts. The implemented game still has limited functionality but already shows interesting results in helping the user to visualise and understand the trust dynamics.
[Papers] Multi-view video synchronization using motion rhythms of human joints

This paper presents a novel method of estimating temporal offsets between multi-view unsynchronized videos. When synchronizing multiple cameras scattered in a large area with a wide baseline (e.g., a sports stadium, an event hall, etc.), conventional epipolar-based approaches sometimes fail due to the difficulty of robust point correspondences. For such cases, 2D projections of human joints can be robustly associated with each other even in wide baseline videos and can be utilized as corresponding points. However, the detected 2D poses include detection errors in general that cause estimation failures. To address these problems, we introduce the motion rhythm of 2D human joints as a cue for synchronization. The proposed method detects motion rhythms from videos and estimates temporal offsets with the best harmonized motion rhythms. Moreover, we propose a hybrid synchronization algorithm to get sub-frame precision. We demonstrate our method's performance with indoor and outdoor data.
ON THE USEFULNESS OF HTML META ELEMENTS FOR WEB RETRIEVAL

Web retrieval studies have mostly used URL, title, body, and anchor text fields to represent Web documents. On the other hand, HTML standards provide a rich set of elements to define different parts of a Web page. For example, meta elements are used to provide structured metadata about a Web page not to end users, but instead to browsers or crawlers. However, it is unclear whether meta tags are or are not useful for Web retrieval, as most of the previous studies leveraged URL, title, body, and anchor text fields. In this work, we examine the usefulness of two meta tags, namely keywords and description, based on ad-hoc tasks of previous TREC studies. Through experiments on the standard TREC Web datasets and several query sets, our results using the state-of-the-art term-weighting models show that the utilization of description field systematically increases the retrieval effectiveness, to a statistically significant degree most of the time. By contrast, the employment of keywords field may cause a significant deterioration in retrieval effectiveness for certain term-weighting models.
GANcoder: robust feature point matching using conditional adversarial auto-encoder

Objects without prominent textures pose challenges for an automatic 3D model reconstruction and feature point matching. Such objects are common in many industrial applications such as metal defect detection, archeological applications of photogrammetry, and 3D object reconstruction from infrared imagery. Most of the common feature point descriptors fail to match local patches in featureless regions of an object. Various kind of textures requires different feature descriptors for high-quality image matching. Hence, automatic low-textured 3D object reconstruction using Structure from Motion (SfM) methods is challenging. Nevertheless, such reconstruction is possible with the aid of a human operator. Deep learning-based descriptors have outperformed most of common feature point descriptors recently. This paper is focused on the development of a new conditional generative adversarial auto-encoder (GANcoder) based on the deep learning. We use a coder-decoder architecture with four convolutional and four deconvolutional layers as a staring point for our research. Our main contribution is a generative adversarial framework GANcoder for training the auto-encoder on the textureless data. Traditional training approaches using an L1 norm tend to converge to the mean image on the low-textured images. In contrast, we use an adversarial discriminator to provided an additional loss function that is focused on distinguishing real images from the training dataset from the auto-encoder reconstruction. We collected a large GANPatches dataset of feature points from nearly textureless objects to train and evaluate our model and baselines. The dataset includes 16k pairs of image patches. We performed qualitative evaluation of our GANcoder and baselines for two tasks. Firstly, we compare the matching score of the our GANcoder and baselines. Secondly, we evaluate the accuracy of 3D reconstruction of low-textured objects using an SfM pipeline with stereo-matching provided by our GANcoder. The results of the evaluation are encouraging and demonstrate that our model achieves and surpasses the state of the art in the feature matching on low-textured objects.
Model of Accumulation and Loss of Knowledge in Computerization Systems of Education with Remote Access

The article discusses the approach to the methods of creating expert assessments to determine the level of knowledge of students in education computerization systems with remote access.This is achieved through the use of the probabilistic approach and Bayesian processes in comparison with the approach based on the theory of fuzzy sets.A model for the accumulation and loss of knowledge is proposed.A life-cycle model of a student's knowledge system has been developed.The model of finding the optimal periodicity of the learning process with forced interruptions is investigated.