Reviewerly/exHarmony huggingface dataset:
Consideration of Quality Attribute Tradeoffs of the Blockchain Pattern in the Software Development Process

The Blockchain (BC) design pattern has many variations and is a concept that is anticipated to lead many implementations in the years to come. The number of choices for a BC implementation continues to increase since new design and implementation patterns and applications are emerging. This increasing number of design patterns enables correspondingly increasing tradeoff opportunities at every evolutionary round of architecture elaboration. Key components of a BC include network nodes, blocks, and consensus methodologies. These components all possess critical characteristics that can be designed and implemented in a variety of different ways. A central thesis here is that the choice of the design methodologies has direct and varying impact with regard to resulting quality attributes such as performance, security, and availability. We describe the use of a tradeoff matrix during the initial design phase of a development cycle that identifies the quality attributes to be evaluated when designing software systems comprising a BC. We hypothesize that consideration of the quality attributes at this initial design stage via the use of the proposed tradeoff matrix enables designers to meet requirements more efficiently and accurately. This hypothesis is tested and the use of the tradeoff matrix is demonstrated by creating a consensus algorithm whose performance is evaluated through a simulation that compares the behaviour in a “bare-metal” versus a Cloud-based environment. This simulation approach drives the usage of one of the quality tradeoff parameters in achieving a more optimal solution.
On Categorial Grammatical Inference and Logical Information Systems

We consider several classes of categorial grammars and discuss their learnability. We review results where learning is viewed as a symbolic issue in an unsupervised setting, from raw or from structured data, for some variants of Lambek grammars and of categorial dependency grammars. In that perspective, we discuss for these frameworks different type connectives and structures, some limitations (negative results) but also some algorithms (positive results) under some hypothesis. On the experimental side, we also consider the Logical Information Systems approach, that allows for navigation, querying, updating, and analysis of heterogeneous data collections where data are given (logical) descriptors. Categorial grammars can be seen as a particular case of Logical Information System.
PREDICTION OF READING AND WRITING DIFFICULTIES OF FIRST GRADERS USING DYSLEXIA RISK QUESTIONNAIRE (DRQ) IN PRESCHOOL AGE / PIRMOKŲ SKAITYMO IR RAŠYMO SUNKUMŲ NUMATYMAS TAIKANT DISLEKSIJOS RIZIKOS KLAUSIMYNĄ (DRK) PRIEŠMOKYKLINIAME AMŽIUJE

&#x0D; This article examines relationships between the scores of Dyslexia Risk Questionnaire (DRQ) in preschool age and the abilities, necessary for acquisition of reading and writing skills, as well as reading and writing skills in the 1st Grade. The results of the study showed that the scores of Dyslexia Risk Questionnaire (DRQ), completed by pre-primary teachers and parents of children attending pre-primary group, are related to prognostic indicators of reading and writing difficulties (phonological abilities: rapid naming, phonological awareness, short-term auditory memory; vocabulary; visual-motor skills), as well as to 1st graders’ reading skills and their reading and writing ratings provided by parents and teachers.&#x0D;
Diagnosis Penyakit Diabetes pada Masyarakat Awam Menggunakan Metode Backward Chaining

Expert system is a system that seeks to adopt human knowledge into computers, this research was conducted to diagnose diabetes early. Disease data that were processed were sourced from the South Solok District General Hospital. The method used is Backward Chaining by using a goal-driven approach. The result of testing this method is that the user understands the danger of diabetes and knows how to revent it. Trials conducted on an expert system for diagnosing diabetes, most people understand the characteristics and symptoms of diabetes and understand how to prevent it. So that this expert system can be recommended for ordinary people
Real-time air-writing recognition in motion stream

The main contribution of this work is developing an end-to-end air-writing recognition technique for a real-time application. We assume the user performs the air-writing naturally and intuitively without doing any explicit signal. For avoiding the spotting process, this work considers the segmentation free technique using the LSTM network with CTC loss. The fusion scheme models the writing trajectory with the spatial and temporal features. To extract the writing information from the finger motion, we utilize a window-based technique for segmenting stream data for generating the training features. We deploy two features: the hand position and the path signature, to train the proposed network. For evaluating the performance of the proposed technique, we conduct the experiments the public dataset namely the finger writing. From the result, it confirms the fusion scheme can improve the recognition accuracy. The appropriate size of the sliding window for the proposed structure is 0.25 second while the skip size equals 83 milliseconds. The proposed network can recognize the air-writing word 75.81% without the language model. When considering the processing time of the recognition technique, the air-writing could predict the written word within 6.37 milliseconds. It confirms the proposed algorithm can deploy for a real-time application.
Discovering Students’ Personalised Uses of a Paediatrics Acute Care E-book – a Qualitative study

<ns4:p>This article was migrated. The article was marked as recommended. Background: Medical educators are increasingly developing and recommending e-books to supplement students' learning. Despite this, there is a gap in the literature surrounding E-book developers' understanding of how students can personalize an e-book for multiple different purposes. This qualitative research project aimed to explore students' experiences of using a pediatric acute care E-book (pRRAPID) within a spiral, multi-method teaching package. The researchers aimed to get an insight into the ways that learners used E-books outside of the classroom. Methods: Three focus groups were conducted with 12 fourth-year medical students who had completed a pediatric acute care course. Students were invited to discuss their use of the E-book during the course. Thematic analysis was conducted on the transcripts to identify codes in the data; these were grouped to determine the overarching themes. Results: Students outlined five different ways that they personalized the use of the E-book. Three of these were related to uses of the E-book within the context of the pediatric acute care course; students using the E-book to prepare for a formal teaching session ("Flipped classroom"),students using the E-book as a workbook (going from start to end) and students using the E-book to prepare for an exam. The other two themes related to the use of the E-book outside of the acute care course; students using the E-book as a resource "on the go" and students using the E-book within clinical practice. Conclusions This qualitative study outlines how an educational resource can be utilized in numerous different ways in order to satisfy the needs of the student. Focus-group participants outlined how important it was to engage with the students, as the primary stakeholder, when developing an E-book.</ns4:p>
Temporal Convolutional Networks

In this chapter, you will learn about temporal convolutional networks (TCNs). You will also learn how TCNs work and how they can be used to detect anomalies and how you can implement anomaly detection using a TCN.
Motivating factors for university students to use digital technologies in educational process

The study considers the formation of human capital in the transition to a digital economy that places new demands on the competences of future specialists working in new conditions.Educational programs of universities need to be modernized; in particular, this means adopting a broad range of information and communication technologies in the educational process.We have carried out a survey of students from 6 universities of the Northwestern and Southern Federal Districts.We have confirmed that forming a digital infrastructure in the educational environment of universities is a factor of key importance.Analyzing hygiene factors, we have found they were at a sufficient level.We have identified the motivating factors for using digital technologies in higher education and developed a set of recommendations aimed at improving the use of information and communication technologies by students.
MODERN STUDENT VALUE-SEMANTIC ATTITUDE FORMATION TOWARDS CHOSEN OCCUPATION IN UNIVERSITY EDUCATIONAL ENVIRONMENT

Purpose of the study: The purpose of the article is to substantiate the need for students’ value-semantic attitude formation towards the chosen occupation based on the University educational environment resources activation. This attitude, as practical experience proves, allows more successfully carry out occupational activities, strive for self-improvement of labor skills, and permanently extrapolates this attitude to the profession in one’s own life. Methodology: Research on testing models of value-semantic attitude’s formation to the profession among the students was conducted in the period from 2009 to 2018. Experimental research covered 395 people, including 339 students and 56 teachers. The following techniques and tests contributing to diagnostics were used for the study: the personality's strong-willed organization, the training motivation, educational achievements and need for them, valuable orientation-based unity of the group, the attitude to specific types of occupational activity. Results: The article clarifies the concept of value-semantic attitude to the profession, characterizes the educational environment of the University in the context of this problem, presents the functions and components of the axiological approach aimed at the development of the University educational environment; defines the pedagogical conditions under which students’ value-semantic attitude to the chosen profession will be qualitatively formed. The article reveals the possibilities of simulating such an educational environment. The article describes the results of the interrelated components’ implementation of the developed model for value-semantic relations’ formation, implemented for many years in educational institutions of higher education. Applications of this study: The possibility of educational courses, methods, forms and training technologies adaptation to the educational environment of universities, regardless of their profile orientation is described. Novelty/Originality of this study: The article proposes adapted and developed diagnostic methods; presents the developed criteria and levels to identify the formation of value-semantic attitude to the profession among the students.
Ultimate Power of Inference Attacks: Privacy Risks of Learning High-Dimensional Graphical Models

Models leak information about their training data. This enables attackers to infer sensitive information about their training sets, notably determine if a data sample was part of the model's training set. The existing works empirically show the possibility of these tracing (membership inference) attacks against complex models with a large number of parameters. However, the attack results are dependent on the specific training data, can be obtained only after the tedious process of training the model and performing the attack, and are missing any measure of the confidence and unused potential power of the attack. A model designer is interested in identifying which model structures leak more information, how adding new parameters to the model increases its privacy risk, and what is the gain of adding new data points to decrease the overall information leakage. The privacy analysis should also enable designing the most powerful inference attack. In this paper, we design a theoretical framework to analyze the maximum power of tracing attacks against high-dimensional models, with the focus on probabilistic graphical models. We provide a tight upper-bound on the power (true positive rate) of these attacks, with respect to their error (false positive rate). The bound, as it should be, is independent of the knowledge and algorithm of any specific attack, as well as the values of particular samples in the training set. It provides a measure of the potential leakage of a model given its structure, as a function of the structure complexity and the size of training set.
Data Mining Menggunakan Rough Set dalam Menganalisa Modal Upah Produksi pada Industri Seragam Sekolah

In a fund industry is a very important factor, mismanagement or unavailability of funds can have a negative impact on the industry, the successful shop still uses internal capital that is capital from the sale of the store itself, the sales results are not always sufficient to pay the production wage money cause late payments which adversely affect the performance of workers and the industry itself, production wage data on successful stores can be utilized by using the rough set method to find solutions to predict future production wages, The results found 57 rules of 8 reducts from 11 Equivalence Classes that provide new information that is the cause factor of not achieving capital production wages, the main factor is income followed by sewing wages, cut wages
Novel Bi-directional Images Synthesis Based on WGAN-GP with GMM-Based Noise Generation

A novel WGAN-GP-based model is proposed in this study to fulfill bi-directional synthesis of medical images for the first time. GMM-based noise generated from the Glow model is newly incorporated into the WGAN-GP-based model to better reflect the characteristics of heterogeneity commonly seen in medical images, which is beneficial to produce high-quality synthesized medical images. Both the conventional “down-sampling”-like synthesis and the more challenging “up-sampling”-like synthesis are realized through the newly introduced model, which is thoroughly evaluated with comparisons towards several popular deep learning-based models both qualitatively and quantitatively. The superiority of the new model is substantiated based on a series of rigorous experiments using a multi-modal MRI database composed of 355 real demented patients in this study, from the statistical perspective.
Getting Found, Staying Found, Increasing Impact

Publishing a journal is about more than simply putting ink to paper (or pixels to screen). It is a collaboration between you and your readers. Two critical aspects of this relationship are, first, making your journal visible to your prospective audience. By putting your content online and making it freely available through open access, you can be reaching of millions of people around the world. But if they don’t know you are out there, they will not be able to become part of your scholarly community. Second, once you have your content in place, and have established an audience of dedicated readers, you will want to ensure that your journal is always available – 24 hours a day, 7 days a week, 365 days a year. Unlike paper publications, electronic journals can disappear rather easily, either temporarily or permanently. Regular downtime caused by an unreliable server environment, or worse, complete loss of your content due to a lack of any kind of backup or preservation strategy, can seriously undermine your credibility with your readers, or even totally wipe out all of the work you and your collaborators have done together.&#x0D; &#x0D; This revised edition of Getting Found, Staying Found highlights many aspects of the publishing process that are important for increasing your journal’s “findability” and building a wider audience. Moreover, it will also show you how to ensure reliable and ongoing access to your valuable content. Much of the information in this resource is intended to be generic and could be applied to any journal, using any software platform. However, the authors have opted to include additional information pertaining to the Open Journal Systems (OJS) software developed by the Public Knowledge Project to provide further illustrations of how to apply this information in a real world setting.&#x0D; &#x0D; This guide is designed to offer practical advice on various considerations for making Open Access journals more discoverable and sustainable, and is geared largely towards journals that use the Open Journal Systems (OJS) platform. This guide is intended largely for journal managers, editors, and those responsible for a journal’s operations. Some of PKP’s other documentation, such as the Learning OJS 3 guide and the PKP Administrators guide contains information pertinent to using and administrating OJS.&#x0D;
Vídeo Síncrono com Preservação Digital e Registro Distribuído como um Serviço para Telessaúde

This paper proposes a video as a service (VaaS) platform enabling synchronous video and confidential transmission based in an open and scalable architecture in order to simplify the integration of streaming features in telehealth systems. The platform will provide participant authentication, preservation and recovery of videos with proof of existence, integrity and authenticity through digital certificates and blockchain registration, and the association of the recorded media with the patient's electronic medical record. The goal is that users and health professionals can adopt synchronous video resources to carry out the activities of supervision, teleconsulting, telediagnosis and preceptory of resident doctors with focus on Primary Health Care in remote locations in a safe and reliable way.
GreenML : A methodology for fair evaluation of machine learning algorithms with respect to resource consumption

Impressive results can be achieved when stacking deep neural networks hierarchies together. Several machine learning papers claim state-of-the-art results when evaluating their models with differen ...
Sequential VAE-LSTM for Anomaly Detection on Time Series.

In order to support stable web-based applications and services, anomalies onthe IT performance status have to be detected timely. Moreover, the performancetrend across the time series should be predicted. In this paper, we proposeSeqVL (Sequential VAE-LSTM), a neural network model based on both VAE(Variational Auto-Encoder) and LSTM (Long Short-Term Memory). This work is thefirst attempt to integrate unsupervised anomaly detection and trend predictionunder one framework. Moreover, this model performs considerably better ondetection and prediction than VAE and LSTM work alone. On unsupervised anomalydetection, SeqVL achieves competitive experimental results compared with otherstate-of-the-art methods on public datasets. On trend prediction, SeqVLoutperforms several classic time series prediction models in the experiments ofthe public dataset.
Developing an Indonesian Reading Proficiency Test for BIPA Learners 

The use of Indonesian proficiency tests for non-native speakers of Bahasa Indonesia is still equated with tests for native speakers. This has become a point of debate for many teachers and experts of Indonesian for Foreigners (Bahasa Indonesia untuk Penutur Asing - BIPA). The crux of the debate focuses on whether the same proficiency test should be used for both native speakers (NS) and non-native speakers (NNS) alike, or whether separate tests should be developed. In accordance with the peculiarities of Bahasa Indonesia, Indonesian proficiency tests for NS and NNS should be differentiated. The underdevelopment of specialized proficiency tests for NNS can be explained by the fact that Bahasa Indonesia is not one of the dominant languages learned in the world today. This research aims to develop materials for an Indonesian proficiency test for NNS. The development of the test focuses on reading comprehension. To advance development of the test, discussions of the processes for defining the theoretical construct as well as empirical analysis of students' result were combined. The method used in this study involved expert review, text readability analysis, and item analysis. The findings show that the test items developed can be used to test students’ proficiency, particularly in reading comprehension.
A Machine Learning Approach to GNSS Functional Safety

Emerging applications in the automotive environment (ADAS, autonomous driving, etc.), are requiring high GNSS accuracy in conjunction with high reliability. For that reason, this kind of applications are demanding for GNSS receivers with enhanced safety, as specified by the automotive functional safety standards, and in particular by the ISO 26262 standard. In order to meet this requirement, an additional security, integrity and safety (SIS) layer has been added to the GNSS receiver to monitor, detect, estimate the risk, and switch to a safe state when integrity appears to be compromised. In this contribution, the evaluation and detection of the risk will be implemented using a two different machine learning approaches, i.e. using two different kind of deep neural network (DNN), the first using supervised learning and the second using unsupervised learning. Machine learning is used in many ADAS systems, and it is an enabling feature for the overall autonomous driving industry. Moreover, the complexity of the network for both approaches will be kept into account, because they have to be deployed on a GNSS receiver hardware, which has memory and computational capacity constraints. At the end, the performances of the two proposed approaches will be illustrated, and the results will be compared to a logistic regression solution used as baseline, to show the large improvement in accuracy provided by both solutions. Finally, the advantages and disadvantages of each approach and the future development of such approaches will be discussed.
Question Answering for Link Prediction and Verification

In this work we tackle the link prediction task in knowledge graphs. Following recent success of Question Answering systems in outperforming humans, we employ the developed tools to identify and verify new links. To identify the gaps in a knowledge graph, we use the existing techniques and combine them with Question Answering tools to extract concealed knowledge. We outline the overall procedure and discuss preliminary results.
Arterial Spin Labeling Images Synthesis via Locally-Constrained WGAN-GP Ensemble

Arterial spin labeling (ASL) images begin to receive much popularity in dementia diseases diagnosis recently, yet it is still not commonly seen in well-established image datasets for investigating dementia diseases. Hence, synthesizing ASL images from available data is worthy of investigations. In this study, a novel locally-constrained WGAN-GP model ensemble is proposed to realize ASL images synthesis from structural MRI for the first time. Technically, this new WGAN-GP model ensemble is unique in its constrained optimization task, in which diverse local constraints are incorporated. In this way, more details of synthesized ASL images can be obtained after incorporating local constraints in this new ensemble. The effectiveness of the new WGAN-GP model ensemble for synthesizing ASL images has been substantiated both qualitatively and quantitatively through rigorous experiments in this study. Comprehensive analyses reveal that, this new WGAN-GP model ensemble is superior to several state-of-the-art GAN-based models in synthesizing ASL images from structural MRI in this study.