Reviewerly/exHarmony huggingface dataset:
Project activity as a means of improving the quality of professional training of future philologists

In the context of modernization of education and digitalization of society, one of the socially significant areas implemented in educational institutions of higher education is pedagogical design.The urgency of the problem is due to the social order of the society for the training of highly qualified specialists with critical design thinking, ready to solve professional problems of any level of complexity; the increased interest of scientists in the development of theoretical and methodological foundations of pedagogical design, on the one hand, and the lack of development of mechanisms for its practical implementation in higher education, methodological support tools for its implementation on the other.Students should select texts based on the language of their future specialty, make assignments for the consolidation of the passed terminology (Erofeeva & Yusupova, 2018).The design background goes back to antiquity.Plato in the works "State" and "Laws" proposes and discusses the project "ideal state".Speaking as a practitioner of state building, Plato proposes to first build a state mentally, which is one of the first prerequisites for design.Scientific understanding of the process and results of design as a special activity began at the turn of 20-21 st centuries, which was characterized by the presence of reflection and design activities, the emergence of project terminology.
Reducing Traverse Space in Path Planning using Snake Model for Robots

Path planning for robots is one of the important aspects of robots where they need to get an optimal and obstacle-free path from source to destination. Getting an optimal and obstacle-free path, while traversing is the core research issue in robotics. To address this research issue, several techniques were proposed and implemented in the past, and still, a good amount of work is being carried on. Some well-known algorithms for path planning are ${A}^{*}$, Probabilistic Roadmap Planner (PRM), Rapidly Exploring Random Tree (RRT) and RRT Smooth. Fundamentally, to find an optimal and obstacle-free path, any path planning algorithms needs to explore the whole configuration space, which increases traversing time and efforts. However, the exploration of the whole configuration space can be done efficiently, which result in improving the performance of the path planning algorithms. This paper addresses the use of Snake Model as a preliminary step to path planning algorithms to find optimal and obstacle-free paths for robots efficiently by reducing the traversing in configuration space. Several experiments have been carried out to show the effectiveness of the proposed setup. In the experiments, the Snake model has been applied along with some standard algorithms such as ${A}^{*}$, PRM, RRT and RRT Smooth, where the parameters such as path length, No. of Moves and Time taken are used to record the performance.
Image Classification Methods Applied in Immersive Environments for Fine Motor Skills Training in Early Education

Fine motor skills allow to carry out the execution of crucial tasks in people's daily lives, increasing their independence and self-esteem.Among the alternatives for working these skills, immersive environments are found providing a set of elements arranged to have a haptic experience through gestural control devices.However, generally, these environments do not have a mechanism for evaluation and feedback of the exercise performed, which does not easily identify the objective's fulfillment.For this reason, this study aims to carry out a comparison of image recognition methods such as Convolutional Neural Network (CNN), K-Nearest Neighbor (K-NN), Support Vector Machine (SVM) and Decision Tree (DT), for the purpose of performing an evaluation and feedback of exercises.The assessment of the techniques is carried out using images captured from an immersive environment, calculating metrics such as confusion matrix, cross validation and classification report.As a result of this process, it was obtained that the CNN model has a better supported performance in 82.5% accuracy, showing an increase of 23.5% compared to SVM, 30% compared to K-NN and 25% compared to DT.Finally, it is concluded that in order to implement a method of evaluation and feedback in an immersive environment for academic training in the first school years, a low margin of error must be taken in the percentage of successes of the image recognition technique implemented, to ensure the proper development of these skills considering their great importance in childhood.
A Study on the Effect of Capstone Design Class on Creative Activities

This study examined the impact and level of variables(collaboration, convergence thinking, creativity problem solving) on the capstone design class. 540 students among engineering colleges in P, K, D and U were sampled. For the statistical analysis, analysis of covariance structure by AMOS 22.0 was applied and the difference between groups in capstone design class by SPSS was analyzed(independent sample t-est). Results from structural equation modeling analyses indicated that a conceptual model shows the following results. Firstly, on the basis of the conceptualized model, capstone design class effected to directly collaboration and capstone design class effected to indirectly convergence thinking and creative problem solving by collaboration. The Collaboration effected to directly convergence thinking and creative problem solving, collaboration effected to indirectly creative problem solving by convergence thinking. Secondly, the average of the experience group in capstone design class was statistically high in collaboration, convergence thinking and creative problem solving.
I’m tired of demos: an adaptive MR remote collaborative platform

In AR/MR remote collaboration, a remote expert often has to demonstrate tasks for a local worker. To make this easier, we have developed a new adaptive MR remote collaborative architecture which enables a remote expert to guide a local user in physical assembly and training tasks. A remote user can activate the instructions through simple and intuitive interaction, then clear instructions are shown in both AR (local) and VR (remote) views, enabling a local worker to operate the tool following the instructions. In the demonstration we use a hammer operation as a physical task, showing the benefits of the adaptive MR remote collaborative platform.
Bayesian Inference over the Stiefel Manifold via the Givens Representation

We introduce an approach based on the Givens representation for posterior inference in statistical models with orthogonal matrix parameters, such as factor models and probabilistic principal component analysis (PPCA). We show how the Givens representation can be used to develop practical methods for transforming densities over the Stiefel manifold into densities over subsets of Euclidean space. We show how to deal with issues arising from the topology of the Stiefel manifold and how to inexpensively compute the change-of-measure terms. We introduce an auxiliary parameter approach that limits the impact of topological issues. We provide both analysis of our methods and numerical examples demonstrating the effectiveness of the approach. We also discuss how our Givens representation can be used to define general classes of distributions over the space of orthogonal matrices. We then give demonstrations on several examples showing how the Givens approach performs in practice in comparison with other methods.
Image Processing Operators Based on the Gyrator Transform: Generalized Shift, Convolution and Correlation

The gyrator transform (GT) is used for images processing in applications of light propagation. We propose new image processing operators based on the GT, these operators are: Generalized shift, convolution and correlation. The generalized shift is given by a simultaneous application of a spatial shift and a modulation by a pure linear phase term. The new operators of convolution and correlation are defined using the GT. All these image processing operators can be used in order to design and implement new optical image processing systems based on the GT. The sampling theorem for images whose resulting GT has finite support is developed and presented using the previously defined operators. Finally, we describe and show the results for an optical image encryption system using a nonlinear joint transform correlator and the proposed image processing operators based on the GT.
An Enhanced Multiclass Support Vector Machine Model and its Application to Classifying File Systems Affected by a Digital Crime

The digital revolution we are witnessing nowadays goes hand in hand with a revolution in cybercrime. This irrefutable fact has been a major reason for making digital forensic (DF) a pressing and timely topic to investigate. Thanks to the file system which is a rich source of digital evidence that may prove or deny a digital crime. Yet, although there are many tools that can be used to extract potentially conclusive evidence from the file system, there is still a need to develop effective techniques for evaluating the extracted evidence and link it directly to a digital crime. Machine learning can be posed as a possible solution looming in the horizon. This article proposes an Enhanced Multiclass Support Vector Machine (EMSVM) model that aims to improve the classification performance. The EMSVM suggests a new technique in selecting the most effective set of parameters when building a SVM model. In addition, since the DF is considered a multiclass classification problem duo to the fact that a file system might be accecced by more than one application, the EMSVM enhances the class assignment mechanism by supporting multi-class classification. The article then investigates the applicability of the proposed model in analysing incriminating digital evidence by inspecting the historical activities of file systems to realize if a malicious program manipulated them. The results obtained from the proposed model were promising when compared to several machine-learning algorithms.
Learning to be Relevant

We present the evolution of a large-scale content recommendation platform for LinkedIn Learning, serving 645M+ LinkedIn users across several different channels (e.g., desktop, mobile). We address challenges and complexities from both algorithms and infrastructure perspectives. We describe the progression from unsupervised models that exploit member similarity with course content, to supervised learning models leveraging member interactions with courses, and finally to hyper-personalized mixed-effects models with several million coefficients. For all the experiments, we include metric lifts achieved via online A/B tests and illustrate the trade-offs between computation and storage requirements.
Unsupervised Multi-Document Opinion Summarization as Copycat-Review Generation.

Opinion summarization is the task of automatically creating summaries that reflect subjective information expressed in multiple documents, such as product reviews. While the majority of previous work has focused on the extractive setting, i.e., selecting fragments from input reviews to produce a summary, we let the model generate novel sentences and hence produce abstractive summaries. Recent progress in summarization has seen the development of supervised models which rely on large quantities of document-summary pairs. Since such training data is expensive to acquire, we instead consider the unsupervised setting, in other words, we do not use any summaries in training. We define a generative model for a review collection which capitalizes on the intuition that when generating a new review given a set of other reviews of a product, we should be able to control the amount of going into the new review or, equivalently, vary the extent to which it deviates from the input. At test time, when generating summaries, we force the novelty to be minimal, and produce a text reflecting consensus opinions. We capture this intuition by defining a hierarchical variational autoencoder model. Both individual reviews and the products they correspond to are associated with stochastic latent codes, and the review generator (decoder) has direct access to the text of input reviews through the pointer-generator mechanism. Experiments on Amazon and Yelp datasets, show that setting at test time the review's latent code to its mean, allows the model to produce fluent and coherent summaries reflecting common opinions.
A (Zero-Knowledge) Vector Commitment with Sum Binding and its Applications

Abstract Vector commitment (VC) schemes allow committing to an ordered sequence of ${q}$ values ${(m_1,\cdots ,m_q)}$ in such a way that one can later open the commitment at specific positions. However, the existing VC schemes suffer from two substantial shortcomings that limit their use: (i) the commitments cannot be opened except at some specific positions, and (ii) their security only captures position-binding but offers no privacy: the client may learn additional information about the committed sequence through the proofs and the commitments. To resolve these problems, we first extend VC to a more expressive primitive called VC with sum binding (VCS), in which the commitment can also be opened to the sum of all elements in the committed sequence. VCS additionally satisfies the security of sum binding, which guarantees that the commitment cannot be opened to different sums. To enhance its privacy, we extend VCS to zero-knowledge VCS (ZKVCS), in which commitments and proofs constructed during the protocol execution leak nothing about the committed sequence. We formalize this new property by a standard real/ideal experiment. Meanwhile, the detailed performance analyses and simulations show that our proposed schemes are more practical. Finally, we introduce a novel notion of (zero-knowledge) verifiable database supporting sum and show how to construct it from our (ZK)VCS scheme.
Emotion Evoking Art Exhibition in VR

Many museums today lack an aspect of technology that will attract younger visitors to visit the art. By implementing Virtual Reality into art museum solves this problem. Virtual Reality is a popular phenomenon that attracts many viewers and is growing every day. Art museums want to express emotion through their art and Virtual Reality can evoke that emotion more. By creating a virtual museum that not only has all the art on display but also is set an outdoor environment such as a garden or a dark forest will further enhance the emotion. If a piece of art is supposed to show warmth or positive feelings, why not place it in a garden? If the art is supposed to show darkness or cold why not place it in a dead forest? Using Virtual Reality allows us to place art in these environments so further museum goal of expressing emotion.
A Comparative Study of Planar Surface and Spherical Surface for 3D Pointing Using Direct Touch

We investigated the performance of 3D pointing using direct touch in a planar surface condition (PC) and a spherical surface condition (SC). In addition, we examined the performance in terms of Fitts’ law. Although the results showed that the performance in SC was slightly worse than PC, SC was higher conformed to Fitts’ law than PC without the conditions involving head rotation (PC’s and SC’s R2 is 0.945 and 0.971, respectively).
A Personalized Music Recommender System Using User Contents, Music Contents and Preference Ratings

Recently, the advances in communication technologies have made music retrieval easier. Without downloading the music, the users can listen to music through online music websites. This incurs a challenging issue of how to provide the users with an effective online listening service. Although a number of past studies paid attention to this issue, the problems of new user, new item and rating sparsity are not easy to solve. To deal with these problems, in this paper, we propose a novel music recommender system that fuses user contents, music contents and preference ratings to enhance the music recommendation. For dealing with problem of new user, the user similarities are calculated by user profiles instead of traditional ratings. By the user similarities, the unknown ratings can be predicted using user-based Collaborative Filtering (CF). For dealing with problems of rating sparsity and new items, the unknown ratings are initialized by acoustic features and music genre ratings. Because the unknown ratings are initially imputed, the rating data will be enriched. Thereupon, the user preference can be predicted effectively by item-based CF. The evaluation results show that our proposed music recommender system performs better than the state-of-the-arts methods in terms of Root Mean Squared Error.
Security and time insertion

Insertion functions as a tool to guarantee security of processes is discussed and studied. We work with security properties opacity and processes opacity and we investigate how they can be enforced. The insertion function can alter behaviour of the original systems by inserting some actions to guarantee its security. Here we propose time insertion functions which insert time delays to prevent timing attacks.
Procedural Content Generation using Artificial Intelligence for Unique Virtual Reality Game Experiences

The aim of this study is to define and develop a procedural content generation method based on Artificial Intelligence for immersive games. We investigated the use of procedural content generation techniques based on agents and decision trees, applying it to a virtual reality game design using Unreal. In this context, it is possible to offer a unique game experience for each user, considering personal decisions during the interaction process.
On Handling Missing Values in Data Stream Mining Algorithms Based on the Restricted Boltzmann Machine

This paper addresses the issue of data stream mining using the Restricted Boltzmann Machine (RBM). Recently, it was demonstrated that the RBM can be useful as a concept drift detector in data streams with time-changing probability density. In this paper, we consider another problem which often occurs in real-life data streams, i.e. incomplete data. We propose two modifications of the RBM learning algorithms to make them able to handle missing values. The first one inserts an additional procedure before the positive phase of the Contrastive Divergence. This procedure aims at inferring the missing values in the visible layer by performing a fixed number of Gibbs steps. The second modification introduces dimension-dependent sizes of minibatches in the stochastic gradient descent method. The proposed methods are verified experimentally, demonstrating their usability for concept drift detection in data streams with incomplete data.
Wasserstein GAN Can Perform PCA

Generative Adversarial Networks (GANs) have become a powerful framework to learn generative models that arise across a wide variety of domains. While there has been a recent surge in the development of numerous GAN architectures with distinct optimization metrics, we are still lacking in our understanding on how far away such GANs are from optimality. In this paper, we make progress on a theoretical understanding of the GANs under a simple linear-generator Gaussian-data setting where the optimal maximum-likelihood generator is known to perform Principal Component Analysis (PCA). We find that the original GAN by Goodfellow et. at. fails to recover the optimal PCA solution. On the other hand, we show that Wasserstein GAN can approach the PCA solution in the limit of sample size, and hence it may serve as a basis for an optimal GAN architecture that yields the optimal generator for a wide range of data settings.
Reduction and Cancellation of the RCS of Cylindrical Surfaces Using Conformal Digital 1-bit Meta-covers

The following topics are dealt with: intelligent transportation systems; convolutional neural nets; collision avoidance; road vehicles; traffic engineering computing; road traffic control; mobile robots; image segmentation; security of data; road safety.
EEIRI: Efficient Encrypted Image Retrieval in IoT-Cloud

One of the best means to safeguard the confidentiality, security, and privacy of an image within the IoT-Cloud is through encryption.However, looking through encrypted data is a difficult process.Several techniques for searching encrypted data have been devised, but certain security solutions may not be used in IoT-Cloud because such solutions are not lightweight.We propose a lightweight scheme that can perform a content-based search of encrypted images, namely EEIRI.In this scheme, the images are represented using local features.We develop and validate a secure scheme for measuring the Euclidean distance between two descriptor sets.To improve the search efficiency, we employ the k-means clustering technique to construct a searchable tree-based index.Our index construction process ensures the privacy of the stored data and search requests.When compared with more familiar techniques of searching images over plaintexts, EEIRI is considered to be more efficient, demonstrating a higher search cost of 7% and a decrease in search accuracy of 1.7%.Numerous empirical investigations are carried out in relation to real image collections so as to evidence our work.
Practical Calibration Method for Aerial Mapping Camera Based on Multiple Pinhole Collimator

Calibration of aerial mapping camera has an important influence on the applications of earth observation. However, the traditional aerial mapping cameras calibrations depend on large-scale calibration target or collimated light, moreover, it is difficult to build the large-scale calibration targets and the collimated light method requires an accurate turntable and a high-precision goniometer, which is a kind of expensive instrument. To solve this problem, this paper proposes a novel high-precision calibration method which is not restricted by the rigorous conditions, such as small aperture, unevenly energy distribution and expensive equipment. Specifically, a collimator and an elaborately designed multiple pinhole mask are firstly used to generate the collimated light of a large aperture with known directions to simulate the calibration targets at infinity. Then, the camera takes pictures for the aperture of the multiple pinhole collimator at multiple angles to ensure that the image points cover the entire detector. Thirdly, the final calibrated results are obtained by solving the data acquired from multiple angles. Finally, the proposed method is verified by Monte-Carlo simulation and real experimental data, whose results indicate that our method can reach the same accuracy performance as the existing methods at lower cost and faster speed, and thus is practical for engineering application.
Emerging trends and technologies for digital transformation of libraries

Emerging trends and technologies for digital transformation of libraries - IJLSIT- Print ISSN No: - 2582-1555 Online ISSN No:- 2456-9623 Article DOI No:- 10.18231/j.ijlsit.2019.011, IP Indian Journal of Library Science and Information Technology-IP Indian J Libr Sci Inf Technol