Reviewerly/exHarmony huggingface dataset:
Values of success in introducing the generation Z student youth

The authors determine the significance of the research of the value bases of the understanding of life activity by the representatives of the young generation for its productive inclusion in the system of social and industrial relations. The authors presented the results of the study of the axiological aspect of the idea of the success of university students belonging to the generation of centenials; The sample of the study was 591 students from three universities in Krasnoyarsk. The significance of success values was revealed by the method of S.A. Pakulina “The motivation for the success of university students.” The results of the study show that students’ perception of success contains a wide range of interrelated values. At the same time, the values of internalized (internally presented) success are of greater importance than the values of externalized (externally presented) success. The overall level of significance of the values of success is given by the cumulative effect of the values of the emotional state, personal success and the values of social significance. The authors describe the risks of such a structure of the axiological aspect of success for young people with a high focus on achievement.
Differentials and distances in probabilistic coherence spaces

In probabilistic coherence spaces, a denotational model of probabilistic functional languages, morphisms are analytic and therefore smooth. We explore two related applications of the corresponding derivatives. First we show how derivatives allow to compute the expectation of execution time in the weak head reduction of probabilistic PCF (pPCF). Next we apply a general notion of "local" differential of morphisms to the proof of a Lipschitz property of these morphisms allowing in turn to relate the observational distance on pPCF terms to a distance the model is naturally equipped with. This suggests that extending probabilistic programming languages with derivatives, in the spirit of the differential lambda-calculus, could be quite meaningful.
Wiener–Granger Causality Theory Supported by a Genetic Algorithm to Characterize Natural Scenery

Image recognition and classification have been widely used for research in computer vision systems. This paper aims to implement a new strategy called Wiener-Granger Causality theory for classifying natural scenery images. This strategy is based on self-content images extracted using a Content-Based Image Retrieval (CBIR) methodology (to obtain different texture features); later, a Genetic Algorithm (GA) is implemented to select the most relevant natural elements from the images which share similar causality patterns. The proposed method is comprised of a sequential feature extraction stage, a time series conformation task, a causality estimation phase, causality feature selection throughout the GA implementation (using the classification process into the fitness function). A classification stage was implemented and 700 images of natural scenery were used for validating the results. Tested in the distribution system implementation, the technical efficiency of the developed system is 100% and 96% for resubstitution and cross-validation methodologies, respectively. This proposal could help with recognizing natural scenarios in the navigation of an autonomous car or possibly a drone, being an important element in the safety of autonomous vehicles navigation.
Sign Language Recognition based on Key Frame

This paper presents a new real-time recognition system as for Chinese sign language. In order to decrease massive unnecessary data, including the body of model, the differential method is used to extract the arms of model. The sign language videos are stored in the different groups, where the number of key frames is same, to decrease matching process. Hu invariant moments models are implying to extract the image's feature and obtain the key frames.
Off-the-Shelf Connectors for Interdisciplinary Components

Introduction:Solutions based on software components, especially for heterogeneous constituents such as those pertaining to different disciplines suffer the interoperability burden. Adaptor technologies have been introduced before, as a potential remedy and utilized here through implementing them in component connectors. Objectives:The main objective of this research is to offer a consistent development environment that provides seamless development especially for the component heterogeneity cases. Method:A set of connectors are introduced to a component-based development environment where a variability model drives the configuration mechanisms in the flow of the application, components, and connectors. The offered set of connectors are the enabling technology incorporated through their selection and configuration. Conclusion:As demonstrated through an example in this article, academic experimentation revealed the practicality of the approach. Required adaptation can be achieved in connectors as the appropriate constituent, avoiding additional functional load on the domain components.
Geometric phase gates in dissipative quantum dynamics

Trapped ions are among the most promising candidates for performing quantum information processing tasks. Recently, it was demonstrated how the properties of geometric phases can be used to implement an entangling two-qubit phase gate with significantly reduced operation time while having a built-in resistance against certain types of errors [M. Palmero et al., Phys. Rev. A 95, 022328 (2017)]. We investigate the influence of both quantum and thermal fluctuations on the geometric phase in the Markov regime. We show that additional environmentally induced phases as well as a loss of coherence result from the nonunitary evolution, even at zero temperature. We connect these effects to the associated dynamical and geometrical phases. This suggests a strategy to compensate for the detrimental environmental influences and restore some of the properties of the ideal implementation. Our main result is a strategy for zero temperature to construct forces for the geometric phase gate which compensate for the dissipative effects and leave the produced phase as well as the final motional state identical to the isolated case. We show that the same strategy also helps at finite temperatures. Furthermore, we examine the effects of dissipation on the fidelity and the robustness of a two-qubit phase gate against certain error types.
Personalized Messaging Based on Dynamic Context Assessment: Application in An Informing Cyber-Physical System

Hazard-intense applications of cyber-physical systems (CPSs), such as the evacuation of a building on fire, require personalized informing on the basis of a real-time assessment of dynamic context. In this paper, a context-dependent message construction mechanism (CD-MCM) is proposed with the objec tives (i) to inform people about the emergence and development of a situation unsafe for them, and (ii) to instruct them what they have to do according to an adaptively computed action plan. To achieve this, the concepts of ‘situation’ and ‘impact indicator’ have been introduced in order to facilitate the computation of personalized action plans and to send messages about the level of danger and the requested actions. In both activities, the inferred implications of situations are used as the basis of informing the involved people. The messages are adapted to actual situations. In addition, the concept of ‘relevance indicator’ was utilized to assess the significance of the standing situations for the concerned people in a quasi-real-time manner. The level of danger was evaluated for each person by totaling the values of the situation-related relevance indicators. This was also used to select the proper message templates from the predefined alternatives in the process of message construction. The personalized messages were generated based on the chosen message template and the various message components describing concerned situations or providing instructions. The CD-MCM was validated in a simulated indoor fire evacuation guiding application. In the practical evaluation of the quality of the generated messages, a sample of test people was involved. The results of the evaluation show that the messages generated by the proposed CD-MCM lead to more effective messaging about the personal context and the expected actions than the messages constructed by using static context information only. The reason is that the proposed template-based message construction mechanism facilitates the appropriateness as well as the articulation of the contents of context-sensitive personalized messages.
Bayesian receiver operating characteristic metric for linear classifiers

We propose a novel classifier accuracy metric: the Bayesian Area Under the Receiver Operating Characteristic Curve (CBAUC). The method estimates the area under the ROC curve and is related to the recently proposed Bayesian Error Estimator. The metric can assess the quality of a classifier using only the training dataset without the need for computationally expensive cross-validation. We derive a closed-form solution of the proposed accuracy metric for any linear binary classifier under the Gaussianity assumption, and study the accuracy of the proposed estimator using simulated and real-world data. These experiments confirm that the closed-form CBAUC is both faster and more accurate than conventional AUC estimators.
Probabilistic Program Neurogenesis

We present a new method for addressing the challenge of continual learning wherein an agent must adapt to new tasks while maintaining high performance on previously learned tasks. To accomplish thi...
Multimodal Web Content Mining to Filter Non-learning Sites Using NLP

Today Internet is a rapidly growing field and it has become one of the huge sources of data. Internet plays a vital role in the educational field. Every student is using e-learning technology over internet to enhance their knowledge. Web mining is one of the data mining branch which helps user to filter and extract relevant data from web and avoid the hitting of the irritating sites. In this paper we have proposed an algorithm of filtering tool which can recognize and block all non learning sites by matching the multiple patterns like text, video and images of the web pages by web content mining. Html document of web page is processed using Natural Language Processing (NLP) and Word Sense Disambiguation (WSD) for recognizing the web content of learning sites.
A Literature Survey of Cognitive Complexity Metrics for Statechart Diagrams

Statechart diagrams have inherent complexity which keeps increasing every time the diagrams are modified. This complexity poses problems in comprehending statechart diagrams. The study of cognitive complexity has over the years provided valuable information for the design of improved software systems. Researchers have proposed numerous metrics that have been used to measure and therefore control the complexity of software. However, there is inadequate literature related to cognitive complexity metrics that can apply to measure statechart diagrams. In this study, a literature survey of statechart diagrams is conducted to investigate if there are any gaps in the literature. Initially, a description of UML and statechart diagrams is presented, followed by the complexities associated with statechart diagrams and finally an analysis of existing cognitive complexity metrics and metrics related to statechart diagrams. Findings indicate that metrics that employ cognitive weights to measure statechart diagrams are lacking.
Measuring Programming Competence by Assessing Chunk Structures in a Code Transcription Task.

In a simple transcription task in which sections of Java program code are copied by freehand writing, it is demonstrated that chunk related temporal signals are sufficiently robust to permit the measurement of programming competence. An experiment with 24 participants revealed that the number of views of the stimulus per trial and the duration of writing per stimulus view are both strongly correlated with independent measures of Java competence.
Does the Frequency of Using Emoticons in Computer-Mediated Communication Signal Creativity?

Abstract Nowadays many human interactions take place in the virtual environment. To express emotions and attitudes in computer-mediated communication (CMC) individuals use emoticons - graphic representations of emotions and ideas. Contemporary applications serving computer-mediated communication (CMC) are provided with a broad spectrum of emoticons which may be used in communication. Variety of emoticons gives users of CMC an opportunity to create unique messages and express emotions in a creative manner. This study involved 275 online respondents and aimed to verify whether the frequency of emoticons use may be predicted by the three characteristics of creativity (creative abilities, openness, independence). Bayesian regression analysis showed that creativity does not predict frequency of emoticons use in CMC. No correspondence between creativity and frequency of emoticons use may be explained by pragmatic function of emoticons as they are used to communicate efficiently with an emphasis on the sender-recipient shared understanding of the emoticons meaning. What is more, robust popularity of communication applications leads to widespread employment of emoticons by CMC users. Therefore, with growing number of emoticons users’ creative individuals may seek less common means of expressing own creativity.
Crawling and Classification Strategies for Generating a Multi-Language Corpus of Sign Language Video

Although there is considerable sign language content available online, it can be hard to locate content in a specific sign language on a particular topic. The Sign Language Digital Library (SLaDL) aims to improve access through the generation of a multi-language corpus of sign language video. SLaDL uses a combination of crawling to collect potential sign language content and applying multimodal sign language detection and identification classifiers to winnow the collected videos to those believed to be in a particular sign language. Here we compare the quantity and variety of sign language videos located via breadth-first, depth-first, and focused crawling strategies. Then we examine the accuracy of different approaches to combining textual metadata and video features for the 3-way classification task of identifying videos in American Sign Language (ASL), British Sign Language (BSL), and without-sign language. Finally, due to the high computational cost of generating the video features used for classification, we explore the tradeoffs when using a cascading classifier and when generating features based on motion in sampled frames on classifier accuracy.
Exploring the Relations Between Net Benefits of IT Projects and CIOs’ Perception of Quality of Software Development Disciplines

Software development enterprises are under consistent pressure to improve their management techniques and development processes. These are comprised of several software development methodology (SDM) disciplines such as requirements acquisition, design, coding, testing, etc. that must be continuously improved and individually tailored to suit specific software development projects. The paper proposes a methodology that enables the identification of SDM discipline quality categories and the evaluation of SDM disciplines’ net benefits. It advances the evaluation of software process quality from single quality category evaluation to multiple quality categories evaluation as proposed by the Kano model. An exploratory study was conducted to test the proposed methodology. The exploratory study results show that different types of Kano quality are present in individual SDM disciplines and that applications of individual SDM disciplines vary considerably in their relation to net benefits of IT projects. Consequently, software process quality evaluation models should start evaluating multiple categories of quality instead of just one and should not assume that the application of every individual SDM discipline has the same effect on the enterprise’s net benefits.
Verifying Bit-vector Invertibility Conditions in Coq (Extended Abstract)

This work is a part of an ongoing effort to prove the correctness of invertibility conditions for the theory of fixed-width bit-vectors, which are used to solve quantified bit-vector formulas in the Satisfiability Modulo Theories (SMT) solver CVC4.While many of these were proved in a completely automatic fashion for any bit-width, some were only proved for bit-widths up to 65, even though they are being used to solve formulas over arbitrary bit-widths.In this paper we describe our initial efforts in proving a subset of these invertibility conditions in the Coq proof assistant.We describe the Coq library that we use, as well as the extensions that we introduced to it.
Stability and Diversity in Generative Adversarial Networks

Generative Adversarial Networks (GANs) enable generating photo-realistic images more successfully compared to other generative models. However, when the resolutions of the generated images increase, the stability and the diversity problems that usually occur in GANs, cause important problems in generating images with high quality and variety. In this study, we empirically examined the state-of-the-art cost functions, regularization techniques and network architectures that have recently been proposed to deal with these problems, using CelebA dataset. In order to compare the numerical performances of the models, we used Frechet Inception Distance (FID) metric, which performs well in comparisons with the images in terms of blur, noise, distortion and diversity. As a result of improvements that are made based on the reference model, the FID score is reduced from 137 to 9.4.
Designing Android-Based Mobile Application for Language Learning (MALL) for the National Exam Simulation

This research aims at designing an Android-based mobile application for language learning (MALL) for the final exam simulation of the English subject for junior high school students. The method used in building the Android-based mobile application is the software development life cycle (SDLC). Based on the implementation of user design and testing, the user (teacher and students) can do a test by using an application that runs on the Android platform. The Android device will request (get / post) to the server via the internet. Then the web server (PHP) will process the request from Android and will query the database. The computer then sends data to the Android device via JavaScript Object Notation (JSON). It is as an intermediary between the server and the Android application. The name of this application is SOUN that stands for “Soal Ujian Nasional” or the National Examination Questions. The teacher can manage the questions and see the scores that the students have achieved. While the students can practice answering the questions of English exercises independently, anytime and anywhere both online and offline. They can also see the score immediately based on the questions that they answered. They also know the solution, tricks, and tips on doing the exercises by choosing the menu provided. Therefore, the use of MALL as the learning media can become one of the alternative English learning models in order to prepare the students for practicing the simulations of the national examination
LIASCRIPT: A DOMAIN-SPECIFIC-LANGUAGE FOR INTERACTIVE ONLINE COURSES

LiaScript is an attempt to enable everyone to create free and interactive online courses, without the need of being anexperienced programmer. Instead, it aims to bring both parties, software- and course-developers, closer together byintroducing
Link-aware semi-supervised hypergraph

Hypergraph learning has been widely applied to various learning tasks. To ensure learning accuracy, it is essential to construct an informative hypergraph structure that effectively modulates data correlations. However, existing hypergraph construction methods essentially resort to an unsupervised learning paradigm, which ignores supervisory information, such as pairwise links/non-links. In this article, to exploit the supervisory information, we propose a novel link-aware hypergraph learning model, which modulates high-order correlations of data samples in a semi-supervised manner. To construct a hypergraph, a coefficients matrix of the entire dataset is first calculated by solving a linear regression problem. Then, pairwise link constraints are exploited and propagated to the unconstrained samples, upon which the coefficients matrix is adjusted accordingly. Finally, the adjusted coefficients are used to generate a set of the hyperedges, as well as calculate the corresponding weights. We have validated the proposed link-aware semi-supervised hypergraph model on the problem of image clustering. Superior performance over the state-of-the-art methods demonstrates the effectiveness of the proposed hypergraph model.
Recognition and normalisation of temporal expressions using conditional random fields and cascade of partial rules

Abstract This article introduces the issue of recognition and normalisation of temporal expressions for the Polish language. We describe what temporal information is and we present TimeML specification, adapted to Polish as a model for the description of temporal expressions. Classes of temporal expressions are presented as well as guidelines for annotation, normalisation of these expressions and our approach to corpus annotation and temporal expressions recognition. The key aspect of the work is the description of the features used for the recognition, the use of the method for selection and creation of feature templates for the model of Conditional Random Fields. We demonstrate the experiments and conclusions drawn from them.
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling

We target modeling latent dynamics in high-dimension marked event sequences without any prior knowledge about marker relations. Such problem has been rarely studied by previous works which would have fundamental difficulty to handle the arisen challenges: 1) the high-dimensional markers and unknown relation network among them pose intractable obstacles for modeling the latent dynamic process; 2) one observed event sequence may concurrently contain several different chains of interdependent events; 3) it is hard to well define the distance between two high-dimension event sequences. To these ends, in this paper, we propose a seminal adversarial imitation learning framework for high-dimension event sequence generation which could be decomposed into: 1) a latent structural intensity model that estimates the adjacent nodes without explicit networks and learns to capture the temporal dynamics in the latent space of markers over observed sequence; 2) an efficient random walk based generation model that aims at imitating the generation process of high-dimension event sequences from a bottom-up view; 3) a discriminator specified as a seq2seq network optimizing the rewards to help the generator output event sequences as real as possible. Experimental results on both synthetic and real-world datasets demonstrate that the proposed method could effectively detect the hidden network among markers and make decent prediction for future marked events, even when the number of markers scales to million level.