Reviewerly/exHarmony huggingface dataset:
A Serious Game as an Auxiliary Tool for the Learning Process of Children With ASD

This chapter presents the game K-Hunters, a serious game with the purposes of decreasing the isolation time and helping the learning process of children with autistic spectrum disorder (ASD). The game uses geolocation, virtual reality, and augmented reality techniques to provide an environment to hunt and capture virtual monsters, holders of knowledge. These monsters are geographically spread throughout the real world and can be associated to learning objects. Through mobile devices, the users can go out hunting the monsters, capture them, and view their learning object-related content. Thus, the users are encouraged to get out of their isolation, to search for the virtual monsters, to obtain knowledge, as well as being inserted in scenarios favorable to interpersonal interaction.
A knowledge construction methodology to automate case‐based learning using clinical documents

Abstract The case‐based learning (CBL) approach has gained attention in medical education as an alternative to traditional learning methodology. However, current CBL systems do not facilitate and provide computer‐based domain knowledge to medical students for solving real‐world clinical cases during CBL practice. To automate CBL, clinical documents are beneficial for constructing domain knowledge. In the literature, most systems and methodologies require a knowledge engineer to construct machine‐readable knowledge. Keeping in view these facts, we present a knowledge construction methodology (KCM‐CD) to construct domain knowledge ontology (i.e., structured declarative knowledge) from unstructured text in a systematic way using artificial intelligence techniques, with minimum intervention from a knowledge engineer. To utilize the strength of humans and computers, and to realize the KCM‐CD methodology, an interactive case‐based learning system (iCBLS) was developed. Finally, the developed ontological model was evaluated to evaluate the quality of domain knowledge in terms of coherence measure. The results showed that the overall domain model has positive coherence values, indicating that all words in each branch of the domain ontology are correlated with each other and the quality of the developed model is acceptable.
Predicting method of monitoring information for telemonitoring system

According to the forecast problem of monitoring information, the predicting method of monitoring information is proposed based on PSO. In this method, the parameters of predicting model are given based on PSO, and the LS-SVM is adopted to predict monitoring information. Then, the prediction model of monitoring information based on LS-SVM is constructed, and the method of confirming parameters in model is proposed. Finally an experiment is carried out on PhysioNet database, which verifies the effectiveness of the proposed forecasting method.
Geochemistry of Minor and Trace Elements in Soils of Akuse Area, Southeastern Ghana

Statistical applications such as correlation, factor analyses and spatial distribution plots have been applied to soils in Akuse area, South-eastern Ghana, to determine minor and trace elements sources, distribution patterns and probable causes for such distributions. Composite soil samples taken on a grid of 1km×1km from the study area showed twenty-five minor and trace elements namely V, Cr, Co, Ni, Cu, Zn, Ga, Rb, Sr, Y, Zr, Nb, Mo, Sb, Cs, Ba, La, Ce, Hf, Ta, Pb, Bi, Th and U. From the statistical mean analyses, Chromium (Cr) showed the highest mean concentration, followed by Zircon (Zr), Strontium (Sr), Barium (Ba) and Vanadium (v). Elements such as Nickel, Copper, Zinc and Cerium showed average mean values ranging from 84.3ppm-47.9ppm. The rest of the elements had lower mean concentrations indicating lesser concentrations in the study area. Elemental association in the study environment were shown by the Pearson Correlation Coefficient. Element pairs such as Cr-Ni, Zn-Rb, Ni-Ba, Zn-Ba and Cu-Zn showed strongly positive correlation coefficient, indicating their occurrence together in the study area. Others such as Zn-Zr and Ga-Zr were negatively correlated as they do not occur together. The applied factor analyses categorized the sources of the elements into natural and anthropogenic with some elements originating from both sources. Plotted Geochemical contours showed the central portion mainly covered by pyroxenites lithology had high concentrations of arsenic, barium, cerium, cobalt, gallium and nickel whilst the northwest had higher concentrations of copper, zinc and rubidium. This distribution was attributed to topographic features, changes in the geology of the bedrock and anthropogenic factors. Arsenic, Bismuth and Thorium were relatively less in concentrations as compared to other elements. The distribution of Cr, Co, Ni, Rb, Th and Ba differentiated the geology of the area and hence distinguished between felsic, basic and ultra-basic precursors. The wide range in trace element characteristics of the soil is an indication for inefficient mixing of the various source rock lithologies during the soil formation processes. The basic gneisses which cover most parts of the survey area showed relatively low concentrations in minor and trace elements as compared to areas covered by pyroxenites intrusions.
Open challenges in visual analytics for security information and event management

Introduction: Visual analytics techniques support efficient analysis of the ever-growing amounts of data generated by securitysensors and facilitate a timely and reasonable response to the threats. Modern security information and event management systemspropose various solutions for processing large data streams and integrating heterogeneous sources which can be used as a framework to construct a visual analytics system for security tasks. Purpose: The analysis of visual analytics techniques implemented in securityinformation and event management systems and designed to support the studies on security incidents in the context of the main visualanalytics problems, including the validation of automatic analysis models. Results: A contradiction has been detected between thecapabilities of security information and event management systems in the visual analysis of security data and the implementation ofthese capabilities. Techniques for visual correlation of the data from different security sensors and for visual validation of automaticanalysis models which would allow you to evaluate their accuracy and adaptability to the changes in data streams are almost missing.A possible way to resolve this contradiction is using techniques which support a flexible mechanism for adjusting the analyzedattributes of the network device events. The article presents the main approaches to the development of such techniques, discussingtheir advantages and disadvantages. We propose a dashboard for monitoring the behavior of an automated network traffic analysismodel used in a cloud computing infrastructure. It allows you to monitor the analysis model behavior, perform a visual correlation ofthe analyzed parameters, and track changes in the network flows. Practical relevance: The results of the research can be used whendesigning security visual analytics tools for monitoring data flows and the behavior of automated analysis models.
Construction for a Nominative Signature Scheme from Lattice with Enhanced Security

The existing secure nominative signature schemes are all based on bilinear pairings and are secure only on classical machines. In this paper, we present the first lattice based nominative signature scheme. The security of our scheme relies on the hardness of short integer solution (SIS) and learning with error (LWE) problems for which no polynomial time quantum algorithms exist till now. Consequently, our scheme is the first nominative signature scheme that withstand quantum attacks. Furthermore, we propose stronger security models for unforgeability and invisibility and prove our construction achieve these enhanced security. Besides, our scheme exhibits impersonation and non-repudiation following standard security model. We emphasis that the security analysis against all the security attributes for our scheme are in standard model except the security against malicious nominator which uses random oracle.
Single Image Super-Resolution: From Sparse Coding to Deep Learning

Abstract Recently, deep learning has been successfully applied in numerous areas of computer vision, including low-level image restoration problems. For single image super-resolution (SR), which is an ill-posed problem that tries to recover a high-resolution image from its low-resolution observation, a number of models based on deep neural networks have been proposed and obtained superior performance that overshadows all previously handcrafted models. To regularize the solution of the problem, older methods have focused on using good priors from natural images such as sparse representation, or directly learning the priors from a large data set with models such as deep neural networks. In this chapter, we argue that domain expertise represented by the conventional sparse coding model is still valuable, and it can be combined with the key ingredients of deep learning to achieve further improvements. We demonstrate that a sparse coding model particularly designed for super-resolution can be incarnated as a neural network with the merit of end-to-end optimization over training data. The network has a cascaded structure which boosts the SR performance for both fixed and incremental scaling factors. The interpretation of the network based on sparse representation leads to much more efficient and effective training, as well as reduced model size. Moreover, we develop training and testing schemes that can be extended for robust handling of images with additional degradation such as noise and blurring. A subjective assessment is conducted and analyzed in order to thoroughly evaluate various SR techniques. Our proposed model is tested on several benchmark datasets, and it significantly outperforms existing state-of-the-art methods for various scaling factors both quantitatively and qualitatively. Furthermore, we propose the method of learning a mixture of SR inference modules in a unified framework to tackle the problem of single image SR. Specifically, a number of SR inference modules specialized in different image local patterns are first independently applied on the LR image to obtain various HR estimates, and the resultant HR estimates are adaptively aggregated to form the final HR image. By selecting neural networks as the SR inference module, the whole procedure can be incorporated into a unified network and optimized jointly. Extensive experiments are conducted to investigate the relation between restoration performance and various network architecture designs. Compared with other recent image SR approaches, this method consistently achieves superior restoration results on a wide range of images while allowing more flexible design choices.
Impact of Mobile Application on Student Enguagement and Public Consciousness Toward Computer Laboratory Use

The study was carried out to determine whether computer science students at Maejo university would prefer to use a checklist mobile application for computer laboratory care and maintenance and also whether their engagement and public consciousness could be boosted by the use the checklist application with an instructor repeatedly guidance. The study used a purposive sampling method to collect data from 81 sophomores who attended the 2018 database system class. Nonparametric methods were used to test hypotheses. The results revealed that student engagement and public consciousness toward computer laboratory care were significantly improved after using the checklist application. The students indicated that they satisfy with the application and suggested that the department should continue to use it.
The Impact of Human Resource Management Practices and Leader Member Exchange on Job Performance: a moderating Role of Job Satisfaction in Jordanian Public Universities

Objectives: The purpose of the present study is to examine the relationship between Human resource practices, leader member exchange, job satisfaction and job performance in the public Jordanian universities, this study examine the moderating effects of job satisfaction on the relationship between human resource practices, leader member exchange and job performance. Methods/Statistical Analysis: The analysis was conducted using Structural Equation Model (SEM) to validate the research model and test the data. The final structural model that uses maximum likelihood estimation analysis confirmed goodness of fit to the data. Findings: The result of this study indicated that job satisfaction have significant(positive) moderating effect on the relationship between leader member exchange and job performance and insignificant (negative) moderating effect on the relationship between human resource practices and job performance. Improvements: This study contribute to the body of knowledge by providing empirical evidences to support theories and previous literature related to the moderating roles of job satisfaction in the relationship between human resource practices, leader member exchange and job performance. The present study concludes with research contributions and recommendations for further studies Keywords: Human Resource Practices, Leader Member Exchange, Job Performance, Job Satisfaction, Public Jordanian Universities
Overcome Heterogeneity Impact in Modeled Fork-Join Queuing Networks for Tail Prediction

Inspired by the potential power of random scheduling at data centers, a novel approach for combining arbitrary dispatching policy and tail-latency prediction in heterogeneous fork-join network environments is proposed. Tail prediction is of practical importance in commercial data centers, where the need for sharing resources between many applications is desired at most, to ensure client satisfaction with guaranteed service level objectives (SLOs). Lots of research works in parallel scheduling were presented using event-based simulations, but none of them were able to implant dynamic variation of tasks numbers and maintain the determined load region using a precise, and reliable approach. In this paper, we propose extensive case studies for the presented prediction model in heterogeneous black-box using model-driven simulations. Experimental results show that by using random scheduling algorithm accompanied with inserted effects of different requests fan-out, tail latency can be predicted and stay consistent with relative errors of 10% at high load regions.
particleMDI: A Julia Package for the Integrative Cluster Analysis of Multiple Datasets

We present particleMDI, aCunningham, Nathan Julia package for performing integrative cluster analysis on multiple heterogeneousGriffin, Jim E. data sets, built within the framework ofWild, David L. multiple data integration (MDI). particleMDI updatesLee, Anthony cluster allocations using a particle Gibbs approach which offers better mixing of the MCMC chain—but at greater computational cost—than the original MDI algorithm. We outline approaches for improving computational performance, finding the potential for greater than an order-of-magnitude improvement. We demonstrate the capability of particleMDI to uncovering the ground truth in simulated and real datasets. All files are available at https://github.com/nathancunn/particleMDI.jl .
Generation of elementary gates and Bell’s states using controlled adiabatic evolutions

Fundamental quantum gates can be implemented effectively using adiabatic quantum computation or circuit model. Recently, Hen combined the two approaches to introduce a new model called controlled adiabatic evolutions [I. Hen, Phys. Rev. A, 91(2) (2015) 022309]. This model was specifically designed to implement one and two-qubit controlled gates. Later, Santos extended Hen’s work to implement [Formula: see text]-qubit controlled gates [A. C. Santos and M. S. Sarandy, Sci. Rep., 5 (2015) 15775]. In this paper, we discuss the implementation of each of the usual quantum gates, as well as demonstrate the possibility of preparing Bell’s states using the controlled adiabatic evolutions approach. We conclude by presenting the fidelity results of implementing single quantum gates and Bell’s states in open systems.
A unified scaling model in the era of big data analytics

As scale-out execution of big data analytics has become predominate datacenter workloads, it is of paramount importance to faithfully characterize the scaling properties for such workloads. To date, the most widely cited scaling laws for big data analytics is the traditional Amdahl's law, which was discovered well before the era of big data analytics. A key observation made in this paper is that both the system and workload models underlying the traditional scaling laws are too simplistic to fully characterize the scaling properties for big data analytics workloads. In this paper, we put forward a Unified Scaling model for Big data Analytics (USBA), based on a multi-stage system model and a discretized workload model. USBA allows for flexible workload scaling unifying the fixed-size and fixed-time workload models underlying Amdahl's and Gustafson's laws, respectively, and flexible system scaling in terms of both number of stages and degree of parallelism per stage. Moreover, to faithfully characterize the scaling properties for big data analytics workloads, USBA accounts for variabilities of task response times and barrier synchronization. Finally, application of USBA to the scaling analysis of four Spark-based data mining and graph benchmarks demonstrates that USBA is able to adequately characterize the scaling design space and predict the scaling properties of real-world big data analytics workloads. This makes it possible to use USBA as a useful tool to facilitate job resource provisioning for big data analytics in datacenters.
OCR correction for Indonesian historic newspapers using word repetition, stemmer and n-gram

Most digital archives of the old newspapers in Indonesia are only available as microfilm image file without their textual content. Manual transcription is certainly not effective and tiring for publishers that have large archives. Therefore, more automated transcription is required. As a part of that effort, this paper proposes OCR error correction of old spelling news articles utilizing new spelling databases. Spelling conversions which based on pattern are used to bridge spelling differences. The Error detection uses dictionary lookup and the phenomenon of word repetition and OCR errors that mostly are non-word errors. The Dictionary is built from KBBI and enriched with derivative words, English words, and entity names from validated news archives. Confix-stripping stemmer is used to validate derivative words while the English dictionary is used to validate English words in the news archive. The Error correction uses context-based methods by searching the phrase trigram/bigram word for each error in Google, then the Google Spelling suggestions are used as the correction. Experiments on 9 texts of OCR result of KOMPAS daily article between 1965 and 1966 is resulting the comparison of error rate before and after the correction (improvement ratio) of 193.01% compared to Hunspell spell checker of 61.47%.
Bayesian Analysis in Natural Language Processing, Second Edition

Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and data-driven techniques to analyze language. Since then, the u
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Strategies for Developing Mobile Location-Based Learning Activities by Teachers

This chapter analyses the educational activities created by teachers on an online game generator, Treasure-HIT. The analysis focused on two main components that exist in every game: (1) the location clue, which leads the players to the different stations of the game, and (2) the station task, which the players have to perform when they reach the right station. This qualitative research was based on content analysis of the activities accumulated on the system's server. A total of 112 games were analyzed, including 1272 clues, 1005 stations and 1862 station tasks. Five types of location-dependent tasks were identified: identifying information found on site, camera documentation, receiving an answer from someone on site, physical activities dependent on the location, measurements using a mobile phone, and leaving a personal mark.
Customizing fuzzy partitions for visual texture representation

Visual textures in images are usually described by humans using linguistic terms related to their perceptual properties, like "very coarse", "low directional", or "high contrasted". Thus, computational models with the ability of providing a perceptual texture characterization on the basis of these terms play a fundamental role in tasks where some interaction with subjects is needed. In this sense, fuzzy partitions defined on the domain of computational measures of the corresponding property have been proposed in the literature. However, the main drawback of these proposals is that they do not take into account the subjectivity associated to the human perception. For example, the perception of a texture property may change depending on the user, and in addition, the image context may influence the global perception of the properties. In this paper, we propose to solve these problems by means of a methodology that automatically adapts any generic fuzzy partition modeling a texture property to the particular perception of a user or to the image context. In this method, the membership functions associated to the fuzzy sets are automatically adapted by means of a functional transformation on the basis of the new perception. For this purpose, the information given by the user or extracted from the textures present in the image are employed.
Radial Loss for Learning Fine-grained Video Similarity Metric

In this paper, we propose the Radial Loss which utilizes category and sub-category labels to learn an order-preserving fine-grained video similarity metric. We propose an end-to-end quadlet-based Convolutional Neural Network (CNN) combined with Long Short-term Memory (LSTM) Unit to model video similarities by learning the pairwise distance relationships between samples in a quadlet generated using the category and sub-category labels. We showcase two novel applications of learning a video similarity metric - (i) fine-grained video retrieval, (ii) fine-grained event detection, along with simultaneous shot boundary detection, and correspondingly show promising results against those of the baselines on two new fine-grained video datasets.
Mobile Technologies for Making Meaning in Education

This chapter examines the use of mobile technologies to integrate technology into the classroom environment so that students can experience real-world learning. The use of augmented reality is emphasized to enhance the learning process and provide engaging and authentic student-centered experiences. Using augmented reality, personalized learning is possible that encourages a constructivist approach and where the learning process is the main focus. Students are able to construct knowledge through augmented experiences that support the curricular content and then share this newfound knowledge with others. The use of augmented reality as a cognitive tool can connect learning and allow students to work collaboratively with deeper and higher-level meaning as a result.