Reviewerly/exHarmony huggingface dataset:
Applying Visible Strong Equivalence in Answer-Set Program Transformations

Strong equivalence is one of the basic notions of equivalence that have been proposed for logic programs subject to the answer-set semantics. In this article, we propose a new generalization of strong equivalence (SE) that takes the visibility of atoms into account and we characterize it in terms of appropriately revised SE-models. Our design resembles (relativized) strong equivalence but is substantially different due to adopting a strict one-to-one correspondence of models from the notion of visible equivalence. We additionally tailor the characterization for more convenient use with positive programs and provide formal tools to exploit the tailored version also in the case of some programs that use negation. We illustrate the use of visible strong equivalence and the characterizations in showing the correctness of program transformations that make use of atom visibility. Moreover, we present a translation that enables us to automate the task of verifying visible strong equivalence for particular fragments of answer-set programs. We experimentally study the efficiency of verification when the goal is to check whether an extended rule is visibly strongly equivalent to its normalization, i.e., a subprogram expressing the original rule in terms of normal rules only. In the process, we verify the outputs of several real implementations of normalization schemes on a considerable number of input rules.
The Vis Te Project: Visualization For Improved Technological And Scientific Literacy

Visualization in Technology Education (VisTE) is a standards-based initiative designed to promote the use of graphic visualization among grade 8-12 students to improve their higher order thinking, communication skills, and understanding of science, technology, engineering, and mathematics (STEM) topics.By using simple and complex visualization tools, students can conduct research, analyze, solve problems, and communicate major topics identified in the new ITEA Standards for Technological Literacy (STL) as well as topics aligned with national mathematics and science standards.Unfortunately, rigorously tested instructional materials to support such activities do not yet exist.Over three years, the project team will develop 12 discrete units.Units will encompass areas such as biotechnology, medical technology, communications, transportation, power & energy, and construction.While the curriculum is not developed for the post-secondary level, it is expected to have an impact on the students' understanding of key technologies and their ability to apply graphic communication tools in the exploration and conveyance of technology and engineering concepts.This presentation will provide an overview of the project. I. Background
Decision Making Styles as Deviation from Rational Action: A Super Mario Case Study

In this paper we describe a method of modeling play styles as deviations from approximations of game theoretically rational actions. These deviations are interpreted as containing information about player skill and player decision making style. We hypothesize that this information is useful for differentiating between players and for understanding why human player behavior is attributed intentionality which we argue is a prerequisite for believability. To investigate these hypotheses we describe an experiment comparing 400 games in the Mario AI Benchmark testbed, played by humans, with equivalent games played by an approximately game theoretically rationally playing AI agent. The player actions’ deviations from the rational agent’s actions are subjected to feature extraction, and the resulting features are used to cluster play sessions into expressions of different play styles. We discuss how these styles differ, and how believable agent behavior might be approached by using these styles as an outset for a planning agent. Finally, we discuss the implications of making assumptions about rational game play and the problematic aspects of inferring player intentions from behavior.
Analytics-Driven Dynamic Game Adaption for Player Retention in a 2-Dimensional Adventure Game

This paper shows how game analytics can be used to dynamically adapt a casual, 2-D adventure game named Sidequest: The Game (SQ:TG) in order to increase session-level retention. Our technique involves using game analytics to create an abstracted game analytic space to make the problem tractable. We then model player retention in this space and move through this space in accordance to a target distribution of game states in order to influence player behavior. Experiments performed show that the adaptive version of SQ:TG is able to better fit a target distribution of game states while also significantly reducing the quitting rate compared to the non-adaptive version of the game.
Unsupervised Real-Time Company Name Disambiguation in Twitter

This paper presents a new approach to disambiguate company names in the Twitter social network. We have focused on making lighter the processing of comparing company profiles with tweets in order to obtain a competitive real-time system. With this aim, we only use the home page of each company as information source to create a unique profile. On the other hand, we compute the similarity of a tweet in connection to a profile by comparing the content of the tweet with the profile. Both steps do not use any other external information source and all the process is developed in an unsupervised way. We have tested our application with the test WePS-3 CLEF ORM corpus obtaining encouraging results.
Avoiding Communication in Primal and Dual Block Coordinate Descent Methods

Primal and dual block coordinate descent methods are iterative methods for solving regularized and unregularized optimization problems. Distributed-memory parallel implementations of these methods have become popular in analyzing large machine learning datasets. However, existing implementations communicate at every iteration, which, on modern data center and supercomputing architectures, often dominates the cost of floating-point computation. Recent results on communication-avoiding Krylov subspace methods suggest that large speedups are possible by re-organizing iterative algorithms to avoid communication. We show how applying similar algorithmic transformations can lead to primal and dual block coordinate descent methods that only communicate every $s$ iterations---where $s$ is a tuning parameter---instead of every iteration for the regularized least-squares problem. We show that the communication-avoiding variants reduce the number of synchronizations by a factor of $s$ on distributed-memory parallel machines without altering the convergence rate and attain strong scaling speedups of up to $6.1\times$ over the “standard algorithm" on a Cray XC30 supercomputer.
Hypertext Transfer Protocol (HTTP) over multicast QUIC

This document specifies a profile of the QUIC protocol and the HTTP/3mapping that facilitates the transfer of HTTP resources over multicastIP using the QUIC transport as its framing and packetisation layer.Compatibility with the QUIC protocol's syntax and semantics ismaintained as far as practical and additional features are specifiedwhere this is not possible.
The Impact of Knowledge Management Strategy on Service Innovation Performance in Private and Public Hospitals

The main objective of this research is to investigate the comparative effect of knowledge management strategy on the service innovation performance in public and private hospitals through the mediating role of knowledge management practices. Drawing in knowledge management theory, all the organizations require a suitable knowledge management strategy to perform successfully. Therefore, this research focuses on this under-researched subject. This research benefits from quantitative and hypothetical-deductive approach. The sample of the research includes 852 therapy sections of both public and private hospitals in Tabriz. The data was collected via research questionnaire. The data has been analyzed using structural equation modeling (SEM) methodology. The results indicate a significant positive relationship between the knowledge management strategy and the knowledge management practices. Moreover, the mediating role of knowledge management practices was confirmed in the relationship between knowledge management strategy and service innovation performance in public and private hospitals.
Elliptic Curve Cryptosystems-based Date-constrained Hierarchical Key Management Scheme in Internet of Things

In this paper, we propose a new mechanism to improve the disadvantage of the security mechanism proposed by a scholar and then fulfill the demands of Internet of Things (IoT) to go through the decentralized environment access control functions.We also propose the date-constrained hierarchical key management scheme for mobile agents.With elliptic curve cryptosystems (ECCs) and discrete logarithms, the proposed scheme is flexible.Moreover, the duration of access for each security class is restricted with a certain authorized discrete time period.We demonstrate the mathematical derivation and arguments for our scheme and further conduct a numerical trial.The constructed scheme could meet security needs and be more space-efficient.
Efficient Hardware-Software Codesigns of AES Encryptor and RS-BCH Encoder

The modern real time embedded applications are implemented as a mixture software-hardware designs. In the most of the hardware-software codesigns, the main processor is used to perform the part of the operation in software and to send/retrieve data to/from the hardware or co-processor. This paper proposes efficient hardware-software codesigns for AES encryptor and RS-BCH concatenated encoder, where the latency and hardware cost lie in between the fully hardware and software based designs. The synthesis results show that our proposed hardware-software codesigns of 128-bit AES and RS(255,239)-BCH(2184,2040) serial concatenated error correction encoder achieve $$85\%$$ and $$40\%$$ of reduction in switching power dissipation over the conventional folded AES design and [8] using Artix-7 FPGA implementation respectively.
Automatic Color Sketch Generation Using Deep Style Transfer

Recent advances in deep learning based algorithms have made it feasible to transfer image styles from an example image to other images. However, it is still hard to transfer the style of color sketches due to their unique texture statistics. In this paper, an automatic color sketch generation system is developed from existing real-time style transfer methods. We choose a suitable image from a set of carefully selected color sketch examples as the style target for every content image during training. We also propose a novel style transfer convolutional neural network with spatial refinement to realize high-resolution style transfer. Finally, gouache color is introduced to the generated images via a linear color transform followed by a guided filtering operation. Experimental results illustrate that our system can produce vivid color sketch images and greatly reduce artifacts compared to previous state-of-the-art methods.
Impact of economic diversification on graduates employment

Economic diversification is defined as the diversity of goods and services produced at any economy.The economic diversification is a significant issue for many developing countries, particularly for those countries which traditionally relied heavily on the production of primary commodities.Economic diversification is also looked as a means to reduce economic risks.The present article can be seen in the analysis of the effects of economic diversification on the diversification of job opportunities, especially for university graduates.The aim of this paper is to study the impact of economic diversification on the graduate's employment rate in Saudi Arabia during the period 2005-2016.The results show that successful economic diversification contributes positively to job creation.Moreover, The Saudi Arabia case study shows that the actual economic diversification level of the kingdom is low regarding the creation of new job opportunities, especially for the bachelor's degree holders.Accordingly, the Saudi government needs to raise its level of economic diversification so that it can create graduates' opportunities for jobs.
A Stochastic Trust Region Method for Unconstrained Optimization Problems

In this paper, a stochastic trust region method is proposed to solve unconstrained minimization problems with stochastic objectives. Particularly, this method can be used to deal with nonconvex problems. At each iteration, we construct a quadratic model of the objective function. In the model, stochastic gradient is used to take the place of deterministic gradient for both the determination of descent directions and the approximation of the Hessians of the objective function. The behavior and the convergence properties of the proposed method are discussed under some reasonable conditions. Some preliminary numerical results show that our method is potentially efficient.
The Problem of Redundant Variables in Random Forests

Random forests are currently one of the most preferable methods of supervised learning among practitioners. Their popularity is influenced by the possibility of applying this method without a time consuming pre‑processing step. Random forests can be used for mixed types of features, irrespectively of their distributions. The method is robust to outliers, and feature selection is built into the learning algorithm. However, a decrease of classification accuracy can be observed in the presence of redundant variables. In this paper, we discuss two approaches to the problem of redundant variables. We consider two strategies of searching for best feature subset as well as two formulas of aggregating the features in the clusters. In the empirical experiment, we generate collinear predictors and include them in the real datasets. Dimensionality reduction methods usually improve the accuracy of random forests, but none of them clearly outperforms the others.
Fast Minimization of Fixed Polarity Reed-Muller Expressions

Logic minimization has recently attracted significant attention because in many applications it is important to have a compact representation as possible. In this paper, we propose a fast minimization algorithm (FMA) of fixed polarity Reed-Muller expressions (FPRMs). The main idea behind the FMA is to search the minimum FPRM with the fewest products by using the proposed binary differential evolution algorithm (BDE). The BDE can efficiently maintain population diversity and achieve a better tradeoff between the exploration and exploitation capabilities by use of proposed binary random mutation operator and improved selection operator. The experimental results on 24 MCNC benchmark circuits demonstrate that the FMA outperforms the genetic algorithm-based and simulated annealing genetic algorithm-based FPRMs minimization algorithms in terms of accuracy of solutions and solving efficiency. To the best of our knowledge, we are the first to use differential evolution algorithm to minimize FPRMs. The FMA can be extended to derive a minimum mixed polarity Reed-Muller expression.
Mechanism for correlation in a coherent electron beam

The correlation of electron counts from two detectors illuminated by a coherent electron beam is analyzed by associating the path of the electron beam through the lenses with the direct Coulomb interaction between two individual electrons. This is shown to lead to a full statistical description of the electron counts. The dominant contribution to the correlation is found to be due to the trajectory displacement caused by the repulsive Coulomb interaction between the first anode and the cathode tip, and the correlation of electron counts is found to depend on the amount of defocusing on the shift of the virtual source for two electrons within the correlation time. The Coulomb scatterings, which altered the direction of two neighbor electrons, occur during the acceleration, leading to a significant decrease in the electron density. The Coulomb potential with no screening will then cause large-angle scattering of nearest-neighbor electrons within the correlation time. These results are consistent with those obtained by a previous experiment.
Línea base de indicadores de apropiación de TIC en instituciones educativas

Línea base de indicadores de apropiación de TIC en instituciones educativasLinha base de indicadores de apropriação TIC em instituições educativasThis article presents the results of applying a methodology for collegiate self-assessment of comprehensive ICT adoption to two public Educational Institutions (EI). Forty surveys and eight interviews were conducted with teachers; their resources (lesson plans and blogs) were observed; three administrative staff members were interviewed; and a checklist was completed, which included the institutional educational project, school rules and regulations, and the information of the planning of spaces, equipment, ICT resources, etc. Data systematization and analysis were carried out using our own software: BaseTIC. The results of ICT adoption show advances at the macro- and meso-curricular level, while at the micro-curricular level, technologies are still given a basic use in the educational processes and the gap between teachers’ being/knowledge and practice remains. The research process assessment itself allows to conclude that this type of project requires that teachers have conceptual and pedagogical ICT training and educational research training and that the EI administrative management ensures the consolidation and commitment of a research team that represents the interests of the entire academic community. The absence of these conditions makes clear that ICT adoption self-assessment is made from the perceptions and imaginary of a few, which do not necessarily represent the institutional reality and has effects on the empowerment of the proposed methodology.Para citar este artículo / To reference this article / Para citar este artigoÁngel-Uribe, I. C. y Patiño-Lemos, M. R. (2018). Línea base de indicadores de apropiación de TIC en instituciones educativas. Educación y Educadores, 21(3), 435-457. DOI: 10.5294/edu.2018.21.3.4Recibido: 26/04/2018Aprobado: 15/11/2018
Indonesian students’ perceptions of mnemonics strategies to recognize Japanese kanji characters

ABSTRACTThe purpose of this study is to explore university students’ perceptions of the effectiveness of recognizing Japanese Kanji characters by using mnemonic strategies and of understanding Kanji’s meanings. Fifty Indonesian university students majoring in Japanese Language Education participated in this study. Data were collected through an online questionnaire survey and an in-depth interview with Japanese as a foreign language (JFL) students. Drawing on the analysis of questionnaire and interview data, students reported that the use of mnemonic strategies successfully enhanced their comprehension of Japanese Kanji characters lexically and semantically. The ﬁndings also showed that the mnemonic strategy was so applicable that students could recognize Japanese Kanji characters. The use of technology also mediated the adoption of the mnemonic strategy. Thus, the implication of the study is that by using different mnemonic strategies along with the use of technology, Japanese teachers could teach Japanese Kanji characters to students whose writing systems background is other than Latin/Roman alphabet systems.
EKSTRAKSI FITUR PADA PENGENALAN KARAKTER AKSARA JAWA BERBASIS HISTOGRAM OF ORIENTED GRADIENT

Ancient Javanese books have Indonesian intellectual property content such as religion, linguistics, philosophy, myths, moral lessons, customary laws and norms, kingdoms, folklore, history, and so on.Not many people studied the work because it was written in Javanese script and not many understood it.To help translation of Javanese documents, translation system automation is carried out.The translation stage consists of segmentation to get characters from the written image in the Javanese script.Then each character is recognized as alphabet.And the last is to combine alphabets that have been recognized as meaningful words.Research about recognition of Javanese scripts has been carried out, such as the focus on character segmentation and the introduction of Javanese script.In the previous study, improvements were made to the segmentation method but still obtained the same results in terms of accuracy.In this study we propose Histogram of Oriented Gradient (HOG) method for extracting feature.The HOG method is widely used in object recognition detection on image data.The HOG method has also been used in handwriting recognition of English and Bengali characters with optimal result.In this study the accuracy of the character recognition of Javanese script is 93.3%.
Non-Subsampled Shearlet Transform and Log-Transform Methods for Despeckling of Medical Ultrasound Images

Ultrasound is a diagnostic imaging technique based on the application of ultrasound in various branches of medical sciences.It can facilitate the observation of structures of internal body, such as tendons, muscles, vessels and internal organs such as male and female reproductive system.However, these images usually degrade by a special kind of multiplicative noise called speckle.The main effects of speckle noise in the ultrasound images appear in the edges and fine details which lead to reduce their resolution and consequently make difficulties in medical diagnosing.Therefore, reducing of speckle noise seriously plays an important role in image diagnosing.Among the various methods that have been proposed to reduce the speckle noise, there exists a class of approaches that firstly convert multiplicative speckle noise into additive noise via log-transform and secondly perform the despeckling process via a directional filter.Usually, the additive noises are mutually uncorrelated and obey a Gaussian distribution.On the other hand, non-subsampled shearlet transform (NSST), as a multi scale method, is one of the effective methods in image processing, specially, denoising.Since NSST is shift invariant, it diminishes the effect of pseudo-Gibbs phenomena in the denoising.In this paper, we describe a simple image despeckling algorithm which combines the log-transform as a pre-processing step with the non-subsampled shearlet transform for strong numerical and visual performance on a broad class of images.To illustrate the efficiency of the proposed approach, it is applied on a sample image and two real ultrasound images.Numerical results illustrate that the proposed approach can obtain better performance in term of peak signal to noise ratio (PSNR) and structural similarity (SSIM) index rather than existing state-of-the-art methods.
A comparative study of author gender identification

In recent years, author gender identification has gained considerable attention in the fields of information retrieval and computational linguistics. In this paper, we employ and evaluate different learning approaches based on machine learning (ML) and neural network language models to address the problem of author gender identification. First, several ML classifiers are applied to the features obtained by bag-of-words. Secondly, datasets are represented by a low-dimensional real-valued vector using Word2vec, GloVe, and Doc2vec, which are on par with ML classifiers in terms of accuracy. Lastly, neural networks architectures, the convolution neural network and recurrent neural network, are trained and their associated performances are assessed. A variety of experiments are successfully conducted. Different issues, such as the effects of the number of dimensions, training architecture type, and corpus size, are considered. The main contribution of the study is to identify author gender by applying word embeddings and deep learning architectures to the Turkish language.