Reviewerly/exHarmony huggingface dataset:
Texture Mixing by Interpolating Deep Statistics via Gaussian Models

Recently, enthusiastic studies have devoted to texture synthesis using deep neural networks, because these networks excel at handling complex patterns in images. In these models, second-order statistics, such as Gram matrix, are used to describe textures. Although these models have achieved promising results, the structure of their parametric space is still unclear. Consequently, it is difficult to use them to mix textures. This paper addresses the texture mixing problem by using a Gaussian scheme to interpolate deep statistics computed from deep neural networks. More precisely, we first reveal that the statistics used in existing deep models can be unified using a stationary Gaussian scheme. We then present a novel algorithm to mix these statistics by interpolating between Gaussian models using optimal transport. We further apply our scheme to Neural Style Transfer, where we can create mixed styles. The experiments demonstrate that our method outperforms a number of baselines. Because all the computations are implemented in closed forms, our mixing algorithm adds only negligible time to the original texture synthesis procedure.
Deep Learning Method to Estimate the Focus Time of Paragraph

There are a lot of reasons that helps to create a text document that one them is time.Time is an important aspect of texts which shows the value of text and it helps searching for various topics easier.We have different type of documents that some of them has time stamp or temporal documents and some of them are without time stamp which are atemporal documents.In this paper we explain problem of paragraph focus time using convolutional neural network (CNN) and natural language processing (NLP) to show the differences between publication time and focus time of document.We implement paragraph focus time which is explains the time period that document content refers and considered to document creation time.We defined a specific time period for this process that document data is related to that and it contains the publication time and also we evaluate our method on various text documents related to historical events in defined lines.
Augmented Reality with Multi-view Merging for Robot Teleoperation

This paper proposes a user-friendly teleoperation interface for manipulation. We provide the user with a view of the scene augmented with depth information from local cameras to provide visibility in occluded areas during manipulation tasks. This gives an improved sense of the 3D environment which results in better task performance. Further, we monitor the pose of the robot's end effector in real-time so that we are able to superimpose a virtual representation into the scene when parts are occluded. The integration of these features enables the user to perform difficult manipulation tasks when the action environment is normally occluded in the main camera view.
Direct estimation of minimum gate fidelity

With the current interest in building quantum computers, there is a strong need for accurate and efficient characterization of the noise in quantum gate implementations. A key measure of the performance of a quantum gate is the minimum gate fidelity, i.e., the fidelity of the gate, minimized over all input states. Conventionally, the minimum fidelity is estimated by first accurately reconstructing the full gate process matrix using the experimental procedure of quantum process tomography (QPT). Then, a numerical minimization is carried out to find the minimum fidelity. QPT is, however, well known to be costly, and it might appear that we can do better if the goal is only to estimate one single number. In this work, we propose a hybrid numerical-experimental scheme that employs a numerical gradient-free minimization (GFM) and an experimental target-fidelity estimation procedure to directly estimate the minimum fidelity without reconstructing the process matrix. We compare this to an alternative scheme, referred to as QPT fidelity estimation, that does use QPT, but directly employs the minimum gate fidelity as the termination criterion. Both approaches can thus be considered as direct estimation schemes. General resource estimates suggest a significant resource savings for the GFM scheme over QPT fidelity estimation; numerical simulations for specific classes of noise, however, show that both schemes have similar performance, reminding us of the need for caution when using general bounds for specific examples. The GFM scheme, however, presents potential for future improvements in resource cost, with the development of even more efficient GFM algorithms.
Student Perceptions of Online Learning

The development of technology has indirectly influenced the world of education. These influences include online learning. This study aims to determine the effect of student perceptions on the application of online learning. This research is a descriptive verification study with an ex post facto and survey approach. The results of this study reveal that although there were some students who were less satisfied with online learning, the majority of students already knew that online learning had a good perception of the application of such learning.
The Characteristics of Literacy Management in School Literacy Movement (SLM) at Junior High School in Malang - Indonesia

This study aims to evaluate the implementation of the school literacy movement (SLM) program in junior high schools in Malang, East Java, Indonesia. The evaluation includes three stages of SLM: the stage of habituation, development, and learning. This study was an evaluative study using the CIPP model (context, input, process, and product) and used a qualitative descriptive approach. The sample unit consisted of 12 schools, with a category of nine public schools and three private schools. The schools were selected using a purposive random sampling technique. The research sample consisted of teachers, principals, and education personnel. Methods of data collection through observation, interviews, and examination of program implementation documents. Data analysis uses the Miles and Huberman model which consists of data reduction, data display, and conclusion drawing, while the formulation of strategic steps uses the calculation of internal strategic factors (IFAS) and external strategic factors (EFAS). The results showed that the implementation of new literacy at the habituation and development stage had not yet reached the learning stage. However, IFAS and EFAS values are 3.34 and 3.39, respectively. These results indicate that the development of the literacy program still has many weaknesses (&amp;lt;5.00) and needs intensive efforts in order to increase student literacy.
Getting Model of MVVM Pattern from UML Profile

&lt;p&gt;The rejuvenation of applications to harmonize with technological watch is the major challenge for all computer boxes, frameworks and languages are constantly proliferating by offering a range of improvements in terms of security and performance, which pushes all applications to invest in order to align oneself, to orient oneself towards another perspective of application implementation has become a primacy. MVW is considered the new concept of application models where the developer can choose according to his needs, which component, for example, it can be a controller, a directive or a unit test for applications where we use the AngularJS framework, modeling an application is one of the basic steps to reach it , the emergence of new patterns press IT companies to think to renew their application architecture for more security and performance, moving from an old to a new model meets this need. AngularJS is one of the widely used frameworks for modern single-page web application development which is designed to support dynamic views in the applications.&lt;/p&gt;&lt;p&gt;We propose an UML profile for AngularJS for building a model of an AngularJS web application, and a set of transformations that transform the model into a code template.&lt;/p&gt;
Brain Extraction Using Active Contour Neighborhood-Based Graph Cuts Model

The extraction of brain tissue from brain MRI images is an important pre-procedure for the neuroimaging analyses. The brain is bilaterally symmetric both in coronal plane and transverse plane, but is usually asymmetric in sagittal plane. To address the over-smoothness, boundary leakage, local convergence and asymmetry problems in many popular methods, we developed a brain extraction method using an active contour neighborhood-based graph cuts model. The method defined a new asymmetric assignment of edge weights in graph cuts for brain MRI images. The new graph cuts model was performed iteratively in the neighborhood of brain boundary named the active contour neighborhood (ACN), and was effective to eliminate boundary leakage and avoid local convergence. The method was compared with other popular methods on the Internet Brain Segmentation Repository (IBSR) and OASIS data sets. In testing cross IBSR data set (18 scans with 1.5 mm thickness), IBSR data set (20 scans with 3.1 mm thickness) and OASIS data set (77 scans with 1 mm thickness), the mean Dice similarity coefficients obtained by the proposed method were 0.957 ± 0.013, 0.960 ± 0.009 and 0.936 ± 0.018 respectively. The result obtained by the proposed method is very similar with manual segmentation and achieved the best mean Dice similarity coefficient on IBSR data. Our experiments indicate that the proposed method can provide competitively accurate results and may obtain brain tissues with sharp brain boundary from brain MRI images.
Sentiment Analysis of Twitter Data for Saudi Universities

With the tremendous increase in web technologies, many people are conveying and expressing their feelings through social media.These data collections, called big data, can be of high value to institutions, governments, and researchers if analyzed and interpreted as well.Most of the studies on sentiment analysis have been performed for the English language and few of these works focus on Arabic.Further, these studies rarely focus on colloquialisms, and no research was carried out on Twitter data related to Saudi universities.Therefore, this paper aims to develop a sentiment analysis system for analyzing Tweets generated by Saudi Twitter users about Saudi universities.There are two proposed different models used for Twitter-based sentiment analysis.The first model begins with Tweet collection followed by preprocessing.Latent Dirichlet Allocation, used here, eliminated Tweets that were not expressing sentiments; therefore, the document will be reduced, which affects system accuracy.Finally, Support-Vector Machine (SVM) chosen to perform Tweet classification.The second model is based on using different classifier models, such as Naïve Bayes (NB), Stochastic Gradient Descent (SGD), Sequential Minimal Optimization (SMO), K-nearest neighbors Classifier (KNN), Random Forest, Multilayer Perceptrons using Deeplearning, (MPD), and Sentiment Score Calculation (SSC).
Multi-Scale Spatio-Temporal Feature Extraction and Depth Estimation from Sequences by Ordinal Classification

Depth estimation is a key problem in 3D computer vision and has a wide variety of applications. In this paper we explore whether deep learning network can predict depth map accurately by learning multi-scale spatio-temporal features from sequences and recasting the depth estimation from a regression task to an ordinal classification task. We design an encoder-decoder network with several multi-scale strategies to improve its performance and extract spatio-temporal features with ConvLSTM. The results of our experiments show that the proposed method has an improvement of almost 10% in error metrics and up to 2% in accuracy metrics. The results also tell us that extracting spatio-temporal features can dramatically improve the performance in depth estimation task. We consider to extend this work to a self-supervised manner to get rid of the dependence on large-scale labeled data.
On Editing Dictionaries for Uralic Languages in an Online Environment

We present an open online infrastructure for editing and visualization of dictionaries of different Uralic languages (e.g.Erzya, Moksha, Skolt Sami and Komi-Zyrian).Our infrastructure integrates fully into the existing Giellatekno one in terms of XML dictionaries and FST morphology.Our code is open source, and the system is being actively used in editing a Skolt Sami dictionary set to be published in 2020.
Meta-design knowledge for Clinical Decision Support Systems

Knowledge gained from a Decision Support Systems (DSS) design should ideally be reusable by DSS designers and researchers. The majority of existing DSS research has mainly focused on empirical problem solving rather than on developing principles that could inform solution approaches for other user contexts. Design Science Research (DSR) has contributed to effective development of various innovative DSS artefacts and associated knowledge development, but there has been limited progress on new knowledge development from a practical problem context, going beyond product and process descriptions. For DSS applications such as Clinical Decision Support Systems (CDSS) design and development, relevant reusable prescriptive knowledge is of significance not only to understand mutability but also to extend application of theory across domains. In this paper, we develop new design knowledge abstracted from the approach taken in a representative case of innovative CDSS development, specified as an architecture and six design principles. The CDSS design artefact was initially designed for a specific clinical need is shown to be flexible for meeting demands of knowledge production both for diagnosis and treatment. It is argued that the proposed general strategy is applicable to designing CDSS artefacts in similar problem domains representing an important contribution of design knowledge both in DSS and DSR fields.
Decentralized identity authentication and key management scheme

SM9 is an identity-based cryptography and constitutes an important part of Chinese commercial cryptography system. It can avoid the complicated PKI system certificate management problems and provides new solution for IoT (Internet of Things) security applications. But SM9 needs the trusted third party KGC (Key Generator Center) to generate and manage the key for the user. So, key update is very troublesome. In order to solve the problem of SM9 key update, this paper proposes a decentralized identity authentication and key management scheme using block chain technology. In the scenario, the user only needs the IGC (Identity Generator Center) to generate its identifier and the key for the first time. Then, the user can automatically update the key. In the process, identifier remains the same as public key and only the private key and parameters update, so it is quite convenient for identity authentication. This paper redefines data structure of the transaction and each key update process is recorded in the chain blocks in the form of transaction. The block chain can guarantee the security and reliability of key update process since it can't be tampered with. The research in this paper is quite valuable for the identity authentication and key management in decentralized IoT application scenarios.
Joint Knowledge Graph and User Preference for Explainable Recommendation

Collaborative Filtering is recognized as an effective recommendation method, but it suffers from sparsity and cold start problems. To this end, this paper uses knowledge graph as a kind of auxiliary information to make more accurate recommendations, and fully considers the heterogeneity of entities and relations. A Joint Knowledge graph and user Preference model JKP is proposed, which combines user preferences and knowledge graph effectively for explainable recommendation. The model uses Multi-Layer Perceptron for knowledge graph embedding representations and item recommendation, and the task of knowledge graph embedding representation acts as a role to assist in recommendation tasks. At the same time, the model takes into account the preferences of the user while selecting the item. As for the different user preferences, the model adopts a soft mechanism to represent the user preference while interacting. The train process of JKP uses a joint method, in which the vector representation of the entity and the collaborative representation of the item in the recommendation algorithm can influence each other. The proposed model is validated on public dataset from real-world, and the results show the improvements not only in CTR tasks but also Top-N recommendations.
Focusing on Attention: Prosody Transfer and Adaptative Optimization Strategy for Multi-Speaker End-to-End Speech Synthesis

End-to-end speech synthesis can generate high-quality synthetic speech and achieve high similarity scores with low-resource adaptation data. However, the generalization of out-domain texts is still a challenging task. The limited adaptation data leads to unacceptable errors and the poor prosody performance of the synthetic speech. In this paper, we present two novel methods to handle the above problems by focusing on the attention. Firstly, compared with the conventional methods that extract prosody embeddings for conditioning input, a duration controller with feedback mechanism is proposed, which can control the states transition in the sequence-to-sequence model more directly and precisely. Secondly, to alleviate the unmatching text-audio pairs' impact on model, an adaptative optimization strategy which would consider the matching degree of the training sample is also proposed. Experimental results on Mandarin dataset show that proposed methods lead to an improvement on both robustness and overall naturalness.
Correction to: Underwater image enhancement using an edge-preserving filtering Retinex algorithm

In the original publication, Equations were incorrectly presented. The original article has been corrected.
Automatic Coverage Based Neighbour Estimation System: A Cloud-Based Implementation

Over the years, mobile networks have grown exponentially due to rising demand. These networks mix different types of cells, which makes manual configuration difficult, costly and tedious. Furthermore, inefficiencies stemming from these problems can cause problems in Handover performance, since a mobile device may not always connect to the optimal cell upon switching from one to the other, potentially harming service quality and increasing Operational Costs (OPEX). Existing solutions, like Automatic Neighbour Relations (ANR), while they are valuable in estimating the best neighbouring cells through the rate of successful Handovers, fail to take into account topological coverage factors and fictional cells, therefore inefficiencies lie hidden and it isn't suited to calculate relations between planned cells and active cells. In this article, a proposal of a cloud-based, on-demand automatic coverage based neighbour estimation system is proposed, which utilises topological signal coverage data from each cell provided by the network's operations support system, in order to mitigate the aforementioned issues and provide a reliable and convenient coverage analysis paradigm.
QR code based two-factor authentication to verify paper-based documents

Important paper-based documents Exposed to forgery such as: official certificates, birth, marriage, death certificates, selling and buying documents and other legal documents is more and more serious and sophisticated. With the purposes of fraud, appropriation of property, job application and assignment in order to swindle public authorities, this forgery has led to material loss, belief deterioration as well as social instability. There are many techniques has been proposed to overcome this issues such as: ink stamps, live signatures, documented the transaction in third party like the court or notary. In this paper, it’s proposed a feasible solution for forgery prevention for paper-based documents using cloud computing application. With the application of Quick Response bidirectional barcode and the usage of hash algorithm. The study aims at developing an electronic verification system for official and issued books (documents, endorsements, and other official books) to/from different sections of the Institute using QR technology.
What Can Task Teach Us About Query Reformulations?

A significant amount of prior research has been devoted to understanding query reformulations. The majority of these works rely on time-based sessions which are sequences of contiguous queries segmented using time threshold on users' activities. However, queries are generally issued by users having in mind a particular task, and time-based sessions unfortunately fail in revealing such tasks. In this paper, we are interested in revealing in which extent time-based sessions vs. task-based sessions represent significantly different background contexts to be used in the perspective of better understanding users' query reformulations. Using insights from large-scale search logs, our findings clearly show that task is an additional relevant search unit that helps better understanding user's query reformulation patterns and predicting the next user's query. The findings from our analyses provide potential implications for model design of task-based search engines.
Improving Children’s E-Safety Skills through an Interactive Learning Environment: A Quasi-Experimental Study

There is a worldwide concern for young children’s online safety and a growing necessity for e-safety skills to be taught to children from a young age as part of formal schooling. The purpose of this study was to design and evaluate the effectiveness and motivational capacity of an interactive web-based learning environment for improving children’s e-safety skills. A quasi-experimental pre-test post-test control group design was used with an experimental group of 48 sixth-grade primary school students, who used the web-based learning environment over two 80-min lessons, and a control group of 25 students who did not. Findings revealed a statistically significant difference (t(47) = −14.06, p &lt; 0.01) in the experimental group students’ e-safety performance, when students’ pre-test scores (mean (Μ) = 41.13, SD = 10.47) were compared to their post-test scores (Μ = 56.69, SD = 9.38). The analysis of an attitudes questionnaire and of student interviews documented the experimental group students’ positive attitudes toward the learning environment. Findings provide evidence of the effectiveness and motivational capacity of the web-based learning environment, which can be used in either formal education or informal learning settings, for improving children’s e-safety skills.