Reviewerly/exHarmony huggingface dataset:
PREPARATION OF FUTURE TEACHERS OF ART AND ART DISCIPLINES WITH THE HELP OF INNOVATIVE TECHNOLOGIES IN EDUCATION

During the writing of the article, we analyzed the domestic and foreign experience in implementing STEAM approaches in the educational process of educational institutions. The article considers the issues of modern STEAM strategy - education, which is successfully developing in our time, which is the result of the integration of science and art, as a result of which scientific technologies are born. Some historical and pedagogical aspects of this problem are analyzed. Attention is drawn to the five components of STEAM-education: S - science (natural sciences), T - technology (technology), E - engineering (engineering art), A - art (creativity) and M - mathematics (mathematics) and emphasis on the importance of the artistic component in the interdisciplinary formation of a holistic personality. The results of separate researches of domestic and foreign scientists are covered. Approaches to the implementation of such educational standards in the United States and Austria, other EU countries are analyzed. It is determined that for pedagogical representatives, in particular, future teachers of art disciplines, the introduction of STEAM helps to increase their own level of professionalism, improve the quality of the educational process, reduce preparation time, allows to make a radically new job. Artistic and creative activity is an important indicator of the formation of the creative personality of the student, a special role in relation to artistic activity is played by the development of artistic perception when acquainted with works of painting, graphics, sculpture, arts and crafts. This acquaintance can take various forms, including the use of modern information technology and STEAM-approaches. We concluded that a comprehensive understanding of scientific problems, creative and engineering approach to their solution, critical thinking, the ability to process data representing figurative and symbolic objects; the ability to analyze several data streams simultaneously, the integrated application of scientific and artistic methods of cognition contributes to the development of all important competencies of future professionals.
Handwriting Perusal Using Gan

Many studies concerning author identification are done on totally different written documents however less number of researches has been according on Marathi or syllabary documents. Here, during this technique, we have a tendency to settle for handwriting as texture image and have extraction done from preprocessed documents image. Considerably, the feature of conferred methodology uses feature extraction that is appropriate for structure of Devnagari written texts. And a brand new feature extraction technique is additionally conferred that is predicated on classifier style. Firstly, we have a tendency to study totally different ways for feature extraction from output of preprocessing. This enforced system represents performance of mimicree estimation techniques on syllabary written documents. Mimicree could be a distinctive feature; it acts as an important role in author identification, GAN. Further to enhance the performance of those techniques, the extracted pictures area unit reconstructed victimisation GAN algorithmic program for Devnagri datasets area unit used. The experimental result shows that mimicree calculable for pictures reconstructed victimisation GAN algorithmic program provides higher results.
SOS Rules for Equivalences of Reaction Systems

Reaction Systems (RSs) are a successful computational framework inspired by biological systems. A RS combines a set of entities with a set of reactions over them. Entities can be provided by an external context, used to enable or inhibit each reaction, and also produced by reactions. RS semantics is defined in terms of an (unlabelled) rewrite system: given the current set of entities, a rewrite step consists of the application of all and only the enabled reactions. In this paper we define, for the first time, a compositional labelled transition system for RSs with recursive and nondeterministic contexts, in the structural operational semantics (SOS) style. This is achieved by distilling a signature whose operators directly correspond to the ingredients of RSs and by defining some simple SOS inference rules for any such operator. The rich information recorded in the labels allows us to define an assertion language to tailor behavioural equivalences on some specific properties or entities. The SOS approach is suited to drive additional enhancements of RSs along features such as quantitative measurements of entities and communication between RSs. The SOS rules have been also exploited to design a prototype implementation in logic programming.
Tonal optimization of bells utilizing evolutionary shape optimization

This study proposes a robust and effective evolutionary shape optimization methodology for bell design. Axisymmetric bell profiles are defined within a 2D-half-cross-sectional design space, from which a 3D finite element model is constructed. An objective function is formulated in terms of a desired tuning spectrum and evaluated through a modal analysis. A pseudo-velocity of the boundary profile is derived via a sensitivity analysis and is used to iteratively evolve the design and minimize the tuning error. A series of computational design examples are provided demonstrating effective tuning of up to twelve partial frequencies simultaneously with tuning accuracy equal to or improved with respect to hand-tuned bell data in current literature. Finally, the proposed method is experimentally validated through fabrication of a prototype bell which exhibits close correlation of partial frequencies with its computational model.
On Selective-Opening Security of Deterministic Primitives

Classically, selective-opening attack (SOA) has been studied for randomized primitives, like randomized encryption schemes and commitments. The study of SOA for deterministic primitives, which presents some unique challenges, was initiated by Bellare et al. (PKC 2015), who showed negative results. Subsequently, Hoang et al. (ASIACRYPT 2016) showed positive results in the non-programmable random oracle model. Here we show the first positive results for SOA security of deterministic primitives in the standard (RO devoid) model. Our results are: Any 2t-wise independent hash function is SOA secure for an unbounded number of “t-correlated” messages, meaning any group of up to t messages are arbitrarily correlated. A construction of a deterministic encryption scheme with analogous security, combining a regular lossy trapdoor function with a 2t-wise independent hash function. The one-more-RSA problem of Bellare et al. (J. Cryptology 2003), which can be seen as a form of SOA, is hard under the \(\varPhi \)-Hiding Assumption with large enough encryption exponent. Somewhat surprisingly, the last result yields the first proof of RSA-based Chaum’s blind signature scheme (CRYPTO 1982), albeit for large exponent e, based on a “standard” computational assumption. Notably, it avoids the impossibility result of Pass (STOC 2011) because lossiness of RSA endows the scheme with non-unique signatures.
On generic complexity of the problem of searching of isomorphism for finite semigroups

Abstract This article is devoted to the investigation of generic complexity of the problem of searching of isomorphism for finite semigroups. That problem can be formulated as follows. For any pair of isomorphic finite semigroup Si and S 2 with elements {1, 2,…, n}, represented by its multiplication tables, we need to find a bijection (permutation) of the set {1, 2,…, n}, which implements an isomorphism between S 1 and S 2 . As for the classical isomorphism problem, any polynomial (even probabilistic) algorithms are unknown for the problem of searching of isomorphism for finite semigroups. We construct natural subproblems of this problem, for which there is no polynomial generic algorithm, provided that this problem is hard in the classical case. The study was carried out within the framework of the state contract of the Sobolev Institute of Mathematics (project no. 0314-2019-0004).
Semantic Compositional Network with Top-Down Attention for Product Title Generation from Image

E-commerce is currently one of the most widely used forms of transactions. C2C e-commerce is one type of e-commerce where the seller can be anyone, thus the product data input is done manually. Product title generation from image system will be very useful for sellers in C2C e-commerce. In this work, we propose the usage of image captioning method for generating product title from the image, by combining two recent works: Semantic Compositional Networks and top-down attention from Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering. Applying this approach, our result improves ROUGE-L and BLEU-1 - BLEU-4 score by 1.5887% -9.6275% against the Semantic Compositional Networks and by 0.5593% -4.9356% against the image captioning with top-down attention from the second work except for the BLEU-2.
Sub-centimeter 3D gaze vector accuracy on real-world tasks: an investigation of eye and motion capture calibration routines

Measuring where people look in real-world tasks has never been easier but analyzing the resulting data remains laborious. One solution integrates head-mounted eye tracking with motion capture but no best practice exists regarding what calibration data to collect. Here, we compared four ~1 min calibration routines used to train linear regression gaze vector models and examined how the coordinate system, eye data used and location of fixation changed gaze vector accuracy on three trial types: calibration, validation (static fixation to task relevant locations), and task (naturally occurring fixations during object interaction). Impressively, predicted gaze vectors show ~1 cm of error when looking straight ahead toward objects during natural arms-length interaction. This result was achieved predicting fixations in a Spherical coordinate frame, from the best monocular data, and, surprisingly, depends little on the calibration routine.
Eye-GUAna: Higher Gaze-Based Entropy and Increased Password Space in Graphical User Authentication Through Gamification

Graphical user authentication (GUA) is a common alternative to text-based user authentication, where people are required to draw graphical passwords on background images. Recent research provides evidence that gamification of the graphical password creation process influences people to make less predictable choices. Aiming to understand the underlying reasons from a visual behavior perspective, in this paper, we report a small-scale eye-tracking study that compares the visual behavior developed by people who follow a gamified approach and people who follow a non-gamified approach to make their graphical password choices. The results show that people who follow a gamified approach have higher gaze-based entropy, as they fixate on more image areas and for longer periods, and thus, they have an increased effective password space, which could lead to better and less predictable password choices.
T-Net: Learning Feature Representation With Task-Specific Supervision For Biomedical Image Analysis

The encoder-decoder network is widely used to learn deep feature representations from pixel-wise annotations in biomedical image analysis. Under this structure, the performance profoundly relies on the effectiveness of feature extraction achieved by the encoding network. However, few models have considered adapting the attention of the feature extractor even in different kinds of tasks. In this paper, we propose a novel training strategy by adapting the attention of the feature extractor according to different tasks for effective representation learning. Specifically, the framework, named T-Net, consists of an encoding network supervised by task-specific attention maps and a posterior network that takes in the learned features to predict the corresponding results. The attention map is obtained by the transformation from pixel-wise annotations according to the specific task, which is used as the supervision to regularize the feature extractor to focus on different locations of the recognition object. To show the effectiveness of our method, we evaluate T-Net on two different tasks, i.e., segmentation and localization. Extensive results on three public datasets (BraTS-17, MoNuSeg and IDRiD) have indicated the effectiveness and efficiency of our proposed supervision method, especially over the conventional encoding-decoding network.
Designing Service Environments

Operations management involves utilizing given resources as efficiently as possible to deliver services to customers and meet business goals. Developing efficient business operations requires a hospitality organization to design efficient service environments, as part of its mission. This chapter articulates the key design and planning strategies for the development of a successful hospitality organization. The first section covers the process of location selection, as the most important factor leading to the success of a hospitality organization. The second section discusses strategies for estimating the number of users (service employees and customers) during peak and idle times to justify a costly financial investment. The third section concerns the readers with the topic of layout planning, with the goal of service optimization for a targeted number of customers. The fourth section deliberates workflow conditions, and finally, the last section addresses the ambience and design of the physical hospitality environment, which is crucial for customer evaluation of a hospitality organization as it creates a first impression.
«… é chësta mat y mot gioca ein palla» (I bambini giocano a palla). Dinamiche di apprendimento in contesti multilingue

What kind of dynamics in language learning can we find in a historically multilingual context where the languages of the newly migrated speakers have been added? The paper presents a brief overview of the characteristics that could influence plurilingual learning for students in the Ladin area (Gardena, Badia and Fassa valleys) and in the Italian or German school systems in the province of Bolzano, Italy. Examples of transfer, translanguaging with two and more languages, reflections on the influence of prestige varieties and the challenges in the school system to promote effective multilingualism will be proposed.
ENHANCING VOCABULARY MASTERY USING A COMPUTER-BASED GAME FOR TEACHING ENGLISH TO YOUNG LEARNERS (TEYL)

In learning English, the students have to master the four English skills. There are speaking, listening, writing, and reading. All these language skills are related to each other. However, vocabulary is considered as one of component of a language. Vocabulary is important for students in learning English in order to make a sentence. And according the preliminary study there are several problems face by studentsâ?? in learning vocabulary. Such as lack of vocabulary, need an interactive, fun and approproate media in learning vocabulary. In other aspect teacher still uses the traditional ways in teaching English especially in teaching vocabulary. Based on the background above, the researcher is interested in conducting a research using computer-based game. Especially computer-based game as media in teaching English to young learners (TEYL). Computer-based game is used in this research, since this game is equipped with appropriate visual and context. This game is given to students as a media to learn vocabularies. This research focuses on the implementation and solving studentsâ?? problem in learning English vocabulary using computer-based game. This computer-based game focused in learning English vocabulary items. It uses text and images. The idea of the game is to move the image with the suitable andÂ  matching text, so that student matchs up the image and vocabulary item using installed application.
CRITERIA FOR SELECTION OF TRAINING TOOLS USING REMOTE EDUCATIONAL TECHNOLOGIES

The criteria for choosing the means of organizing training in educational institutions &#x0D; of higher education with the use of distance learning technologies are considered
Optimal stratification of survival data via Bayesian nonparametric mixtures

The stratified proportional hazards model represents a simple solution to take into account heterogeneity within the data while keeping the multiplicative effect of the predictors on the hazard function. Strata are typically defined a priori by resorting to the values of a categorical covariate. A general framework is proposed, which allows the stratification of a generic accelerated lifetime model, including, as a special case, the Weibull proportional hazards model. The stratification is determined a posteriori, taking into account that strata might be characterized by different baseline survivals, and also by different effects of the predictors. This is achieved by considering a Bayesian nonparametric mixture model and the posterior distribution it induces on the space of data partitions. An optimal stratification is then identified following a decision theoretic approach. In turn, stratum-specific inference is carried out. The performance of this method and its robustness to the presence of right-censored observations are investigated through an extensive simulation study. Further illustration is provided analysing a data set from the University of Massachusetts AIDS Research Unit IMPACT Study.
Big-data driven approaches in materials science on cloud storage API through optimal homomorphic encryption

Its size and complexity, cloud externalization is considered for enhancing security in API. Nonetheless, cloud storage API has two key problems that allows user usability to be effectively functioning and that securely update cipher text in cloud through a new access policy for data owner. This paper recommends an effective secure access update algorithm based on an optimum cryptographic algorithm for API in cloud. Proposed method uses an optimal homomorphic Encryption (OHE) algorithm for secure storage. With Adaptive Salp Swarm Optimizations (ASSO), key values for the OHE algorithm are optimally selected. The proposed project also allows cloud server to efficiently update cipher text and monitor updates against cloud misbehaviors when a new access policy has been mentioned by data owner. It enables data proprietor and skilled users to effectively monitor legitimacy user data access and allow the user to verify that information provided by others is correctly recovered in plaintext. For that, the suggested method utilizes the attribute based access control model. Its analysis reveals how the policy reform program is formulated accurately, completely, safely and efficiently. The performance of the method proposed is measured by differentiating data and the cluster size due to its memory and execution times. Unlike the existing systems with a secure update in the access policies, this approach consumes minimum memory and time. The proposed process is introduced in cloud handset to Hadoop Map Reduction Framework.
The Multi-Watermarks Attack of DNN Watermarking

Deep learning models are widely used in business scenarios and have achieved some success. It is usually time or computing consuming to build a production-level deep learning model. As a result, such models require copyright protection by watermarks. So the security of watermarks is important. In this paper, multi-watermarks attack, which can prevent the model owner from declaring his ownership of the model, is proposed. In special cases, it can completely remove the watermarks which are based on the output of models and it can decrease the watermark accuracy down to less than 15% with only 5 rounds of retraining. Besides, it can also be used to remove the backdoor in models or decrease the task accuracy of models.
Features of the formation of the concept of educational work in an educational institution of civil aviation

The paper discusses the current problem of organizing and forming the concept of educational work in civil aviation educational institutions, which is a special branch of the national economy. It makes increased demands not only on the professional qualities of specialists, but also on their spiritual and moral image, as well as the general degree of culture and harmonious development of the personality. The features of the concept of educational work are shown - the focus on the development of professionalism and social responsibility to ensure safety in the industry. The sections of the concept of educational work and its key areas in educational institutions of civil aviation are considered, the need for digitalization and its consideration in the formation of a specialist's personality is emphasized.
Short Answer Marking Agent for GCSE Computer Science

Teachers often spend a long time assessing work and examinations for students; automatic short answer grading dramatically reduces any time spent grading. Also is can be tough for teachers to be entirely unbiased when assessing work. Towards addressing this, a short answer automatic grading agent utilizing answer segmentation for GCSE Computer Science with semantic matching was designed and implemented using a four stage pipeline. The short answer marking agent has over a 96% accuracy rating and marks over 55% of student answers with the rest being referred for human grading. The human graded answers are then used to further automatically improve the system. In the future the agent could easily be extended to other subjects and used for staff training in a wide variety of areas or possibly even to form the basis for active tutoring systems.
IACN: Influence-Aware and Attention-Based Co-evolutionary Network for Recommendation

Recommending relevant items to users is a crucial task on online communities such as Reddit and Twitter. For recommendation system, representation learning presents a powerful technique that learns embeddings to represent user behaviors and capture item properties. However, learning embeddings on online communities is a challenging task because the user interest keep evolving. This evolution can be captured from 1) interaction between user and item, 2) influence from other users in the community. The existing dynamic embedding models only consider either of the factors to update user embeddings. However, at a given time, user interest evolves due to a combination of the two factors. To this end, we propose Influence-aware and Attention-based Co-evolutionary Network (IACN). Essentially, IACN consists of two key components: interaction modeling and influence modeling layer. The interaction modeling layer is responsible for updating the embedding of a user and an item when the user interacts with the item. The influence modeling layer captures the temporal excitation caused by interactions of other users. To integrate the signals obtained from the two layers, we design a novel fusion layer that effectively combines interaction-based and influence-based embeddings to predict final user embedding. Our model outperforms the existing state-of-the-art models from various domains.