Reviewerly/exHarmony huggingface dataset:
Study protocol of sleep disorders in cancer patients of a reference hospital in Minas Gerais – cross-sectional observational study

Background: Sleep disorders are increasingly recognized as an important cause of morbidity and mortality. The poor quality of sleep is among the most frequent problems which patients with cancer have during treatment. Objectives: To verify the prevalence of sleep disorders in cancer patients attended at a reference hospital in the Center-West of Minas Gerais, Brazil. Methods: This research will be a cross-sectional observational study carried out by teachers and students from University of the State of Minas Gerais – UEMG, Divinópolis Unit, with subjects recruited from a reference hospital in the Center-West of Minas Gerais from March 2018 and February 2019. These patients will be evaluated clinically and will respond to Pittsburgh Sleep Quality Index (PSQI), Insonnia Severity Index (ISI), Epworth Sleepiness Scale (ESS), Berlin Questionnaire, European Organization for Research and Treatment of Cancer core Quality of life Questionnaire (EORTHC QLQ-30) and Beck Anxiety and Depression Inventory, respectively, assessing sleep quality, presence of sleep disorders, severity and presence of insomnia, excessive daytime sleepiness, risk for obstructive sleep apnea, quality of life and levels of anxiety and depression. Discussion: Currently, subjective instruments are used to evaluate the sleep pattern in cancer patients, since there is little information investigating the sleep disorders in these subjects. Recognizing these changes is fundamental as these symptoms can negatively affect the quality of life and their daytime performance.
PEA265: Perceptual Assessment of Video Compression Artifacts

The most widely used video encoders share a common hybrid coding framework that includes block-based motion estimation/compensation and block-based transform coding. Despite their high coding efficiency, the encoded videos often exhibit visually annoying artifacts, denoted as Perceivable Encoding Artifacts (PEAs), which significantly degrade the visual Qualityof- Experience (QoE) of end users. To monitor and improve visual QoE, it is crucial to develop subjective and objective measures that can identify and quantify various types of PEAs. In this work, we make the first attempt to build a large-scale subjectlabelled database composed of H.265/HEVC compressed videos containing various PEAs. The database, namely the PEA265 database, includes 4 types of spatial PEAs (i.e. blurring, blocking, ringing and color bleeding) and 2 types of temporal PEAs (i.e. flickering and floating). Each containing at least 60,000 image or video patches with positive and negative labels. To objectively identify these PEAs, we train Convolutional Neural Networks (CNNs) using the PEA265 database. It appears that state-of-theart ResNeXt is capable of identifying each type of PEAs with high accuracy. Furthermore, we define PEA pattern and PEA intensity measures to quantify PEA levels of compressed video sequence. We believe that the PEA265 database and our findings will benefit the future development of video quality assessment methods and perceptually motivated video encoders.
Plato's “Republic”: an example of proto-project mental activity and primary experience in design of new social forms

This article is dedicated to the analysis of Plato's work &amp;ldquo;The Republic&amp;rdquo; in the context of the genesis of project mentality&amp;nbsp; and project activity as the anthropological outcome that goes back to the early cultural states and primary personality-oriented ancient practices. The author shifts to the plane of historical-cultural retrospective of the origin of the phenomenon of project mentality with the objective vicissitudes of its genesis. An attempt is made to reflect on this process, examine and clarify certain moments associated with the prelude and dynamics of the development of project approach towards organizing modern reality. The author substantiates the original perspective on interpretation of Plato's &amp;ldquo;Republic&amp;rdquo; as the early typical example of proto-project mentality formed in the depths of ancient cultural practices. Project mentality is a promising vector of the existence of culture, the so-called matrix of personal fulfillment of a significant part of our contemporaries. The author believes that Plato's work &amp;ldquo;The Republic&amp;rdquo; should be recognized as one of the very first examples of proto-project due to the presence of a wide range of typological attributes of design. The conclusion is made that in accordance to most parameters, Plato's work &amp;ldquo;The Republic&amp;rdquo; falls under the concept of &amp;ldquo;project activity&amp;rdquo;, as it features majority of the aspects of sociocultural design.
Named Entity Recognition for Characteristic of Medical Herbs Using Modified HMM Approach

The amount of articles in medicinal herbs is very huge. It is performed with unstructured format so that it takes time to get information as reader’s need. Therefore, this research purposes to recognize the name entity of article from internet in order to increase information retrieval or other analysis data purposes. Named entity recognition is one of the goals of information extraction which is to identify the name and characteristics of the herbs. This paper is propose the modified method of Hidden Marcov Model (HMM) with Viterbi algorithm. In this method, it is enclosed gazetteer list for labeling name and location of data training to construct HMM. The data sets are taken from three web sites including: miliaton, aliweb, and plants. As a result, the performance is achieved at average precision value of 0.93, recall of 0.83 and f-measure of 0.85.
Design of Virtual Instrument Interface Based on Embedded Linux+QT

This paper mainly summarizes the technical scheme of replacing traditional pointer automotive instrument with full liquid crystal by choosing hardware platform and software platform. Emphasis is laid on the application of QT graphical development environment in embedded Linux operating system. The full liquid crystal can solve the display and instruction of various monitoring information on automotive instrument. Qt Quick tool is used in the dynamic pointer design of full liquid crystal instrument, and how to use QML (descriptive scripting language) and C++ language mixed programming technology. A practical and mature instrument system can be designed for users, and a set of reasonable construction ideas and methods can be provided.
Visualization of Multivariate Data on Surfaces

This research builds upon ideas introduced and discussed many years ago that focus on the problem of visualizing multiple attributes on surfaces in a single view. Here we present a new perspective to this problem as well as a solution that allows us to design, visualize and interact with multivariate data on surfaces. Building upon multidisciplinary aspects, we present a new way to visualize multivariate data on surfaces by exploiting the concept of layering. First, we introduce a new real-time rendering technique and the concept of Decal-Maps, which fills a gap in the literature and allow us to create 2D visual representations such as glyphs that follow the surface geometry. Building on this technique, we propose the layering framework to facilitate the multivariate visualization design on surfaces. The use of this concept and framework allows us to connect and generalize concepts established in flat space, such as 2D maps, to arbitrary surfaces. Decal-maps opens up other new possibilities such as the use of interaction techniques. Here we demonstrate this potential by introducing a new interaction technique that allows us to explore multivariate data and to create customized focus+context visualizations on surfaces. This is achieved by introducing a new category of lenses, Decal-Lenses, which extends the concept of magic-lenses from flat space to general surfaces. Finally, this thesis showcases the process of multivariate visual design and data exploration through a series of examples from several domains such as Medicine and Geology.
On ore-bearing capacity of Chuya anticlinorium granitoids (Baikal fold mountain area)

The article presents the results of a petrochemical recalculation study of ore-bearing capacity of the Chuya anticlinorium granitoids in Baikal fold mountain area (BFMT). It is shown that the early Proterozoic granitoids of Kuanda and Chuya-Kodar complexes composing the anticlinorium core are not sufficiently studied in mineralogical terms and, according to the geological survey data (GSD), are identified as barren formations, while the geological maps of the research area mark the presence of different ore mineral resources, the contiguous zones being Bodaibo province (gold-bearing) and Mama province (muscovite-bearing). Based on the available chemical tests data, petrochemical ore-bearing capacity modules have been defined using the known techniques and original charts by Z.G. Karajeva and B.N. Permyakov. In the chart by Z.G. Karajeva, most of the test samples fall into the field of the modified leukocratonic granitoids being mother rocks for the molybdenum, wolfram and tin deposits of the silicate and polymetallic formations as well as for the mica-bearing and partially, rare metal pegmatites. In the classification by B.N. Permyakov, the favorable combination of the petrochemical modules observed for most of the test samples allows referring them to various types of the granitoid fields with tin-wolfram-molybdenum mineralization. The conclusion is that further study of the granitoids is necessary to evaluate the geological position of the potentially ore-bearing facies and their fraction in the examined complexes.
Research on Evaluation Method of Scanner Imaging Quality

Scanner is an important tool for digitizing color graphics, and the imaging quality directly determines the accuracy and quality of digital image digitization. This article addressed the need for color image reproduction, and also developed and designed a set of scanner imaging quality evaluation process and their evaluation parameters. And it established a scanner imaging quality evaluation method, and achieved a scientific accurate quantitative assessment of the scanner. The results of research have played a very important guiding role in the evaluation and application of scanners.
Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets

Data sharing has become of primary importance in many domains such as big-data analytics, economics and medical research, but remains difficult to achieve when the data are sensitive. In fact, sharing personal information requires individuals' unconditional consent or is often simply forbidden for privacy and security reasons. In this paper, we propose Drynx, a decentralized system for privacy-conscious statistical analysis on distributed datasets. Drynx relies on a set of computing nodes to enable the computation of statistics such as standard deviation or extrema, and the training and evaluation of machine-learning models on sensitive and distributed data. To ensure data confidentiality and the privacy of the data providers, Drynx combines interactive protocols, homomorphic encryption, zero-knowledge proofs of correctness, and differential privacy. It enables an efficient and decentralized verification of the input data and of all the system's computations thus provides auditability in a strong adversarial model in which no entity has to be individually trusted. Drynx is highly modular, dynamic and parallelizable. Our evaluation shows that it enables the training of a logistic regression model on a dataset (12 features and 600,000 records) distributed among 12 data providers in less than 2 seconds. The computations are distributed among 6 computing nodes, and Drynx enables the verification of the query execution's correctness in less than 22 seconds.
Multiple Kernel Learning from $U$-Statistics of Empirical Measures in the Feature Space

We propose a novel data-driven method to learn multiple kernels in kernel methods of statistical machine learning from training samples. The proposed kernel learning algorithm is based on a $U$-statistics of the empirical marginal distributions of features in the feature space given their class labels. We prove the consistency of the $U$-statistic estimate using the empirical distributions for kernel learning. In particular, we show that the empirical estimate of $U$-statistic converges to its population value with respect to all admissible distributions as the number of the training samples increase. We also prove the sample optimality of the estimate by establishing a minimax lower bound via Fano's method. In addition, we establish the generalization bounds of the proposed kernel learning approach by computing novel upper bounds on the Rademacher and Gaussian complexities using the concentration of measures for the quadratic matrix forms.We apply the proposed kernel learning approach to classification of the real-world data-sets using the kernel SVM and compare the results with $5$-fold cross-validation for the kernel model selection problem. We also apply the proposed kernel learning approach to devise novel architectures for the semantic segmentation of biomedical images. The proposed segmentation networks are suited for training on small data-sets and employ new mechanisms to generate representations from input images.
LeARning—An AR Approach

A remote, instruction-based approach allows the students and teachers to interact in an AR environment that does not require their physical presence in the classroom, nor the physical presence of an object of study. In many laboratory environments, students are present in the same physical location and are instructed to interact with real devices. The aim of this research is to create a general development software tool that allows importing 3D models of laboratory devices; set up specific script actions that students must perform; and allow AR 3D interaction and communication with the instructor. The system allows one-to-one or one-to-many AR communication that will eliminate the barriers of audio calls or manual video-instructed actions. The application has a natural extension in any industrial environment that requires remote assistance with visual interpretation of problems and solutions.
DTC hybrid by different techniques of observation with Artificial Neuronal Network (ANN) for induction machine drives

&lt;p&gt;In this article, we present the construction of observers of rotor flux and mechanical speed needed for robust control of the induction machine. Two observers will be developed for comparison. The first is based on the techniques MRAS and the second is observer of KUBOTA, with the enhanced DTC-ANN (by artificial intelligence), "sensorless DTC-ANN". The validity of the proposed methods is confirmed by the simulation results. Through this comparative study we examine each observer in terms of characteristics that distinguish him from the other through these estimated value and error of estimation (flux and the speed of rotation) on the one hand, and on the other hand we study a property of great importance in the control and the response and robustness of the observer for very low speeds.&lt;br /&gt;In my work I concentrate this on the observer of KUBOTA because I noticed that during my research in the previous research and research there is very little and no detail even in terms of mathematical model in addition to the form of comparative study between him and the observer of MRAS in terms of the principle of control, accuracy, sensitivity and special response to low speeds and error In observation.The THD (Total Harmonic Distortion) of stator current, torque ripple and stator flux ripple are determined and compared with conventional DTC control scheme using Matlab/Simulink environment.&lt;/p&gt;
Fuzzy Method for Online Learning of Bayesian Network Parameters

In learning problems, there are situations where training data is not fully available at the learning time.They are incrementally generated by time, defining a type of domain called online that has among its characteristics the possibility of data failure or even missing data.In Bayesian networks, learning is divided into two categories: structure (related to the graph of conditional relations) and parameters (related to the strength of conditional relations).In this work we present an online parameter learning method that quickly adapts to changes in the environment aiming not only the reproduction of the probability distribution (generative learning) but also the increase of accuracy in the network (discriminatory learning).Our approach is compared with the Adaptative Voting EM method considering two simulation conditions: when distributions are unknown and when distributions undergo abrupt changes.The proposed method achieves good results in both situations by adjusting to environment changes more quickly and by simplifying the parameterization of the traditional approach.
Framework to Evaluate Emerging Systems Designed to Health Field

In recent years, several information and communication technology systems have emerged as tools to improve sleep quality. Research reveals that poor sleep quality may produce irritability and deficits in performance, concentration, and learning ability in the short term, and is associated with chronic disease in the long term. ICT proposals range from the old Polysomnography (PSG) to innovative systems, such as wearable devices, smartphone applications, and suites of sensors embedded in the users’ environment. Since these technological developments concern a health issue, they have raised important questions regarding their reliability and the level of rigor of the evaluations to which they are submitted. We found that some of the emerging systems that we studied, do not meet the requirements that health science demands to be accepted as clinical tools. The rationale behind this apparent weakness is explained with arguments from the field of evaluations for health interventions and evaluation of technological developments. We propose a framework to evaluate this kind of systems through appropriate scientific methods that provide valuable information to the research. These methods must be performed while designs mature and the feasibility of rigorous evaluations became appropriate.
GeoFencing: Location based services

The geofence has come out to be the application which helps in the protection of the machine and the jobs that need to be carried out both inside the fence or backyard the fence. This surely helps the employer to hold music of the gadget and the jobs that are being carried out from the devices, which increases the effectivity in the experience that the system is utilized in a suitable manner. One of the features of the product is Fencing where all the units will be given sure jobs which can only be performed when interior the fence and some jobs which will be active only backyard the fence. These points were already present in the Android Application side, now it needs to be applied in the Linux side. The approach that we have used is the geofencing API in the android side, however getting the geolocation of the laptop alongside with the latitude and longitude differs with the precise location. Therefore, we are going to use location-based offerings in order to get a specific area of the device
Experience of Blended Learning in the Basics of Digital Electronics

The article considers the practical experience of blended learning of students in the basics of digital electronics based on the use of the instrumental distance learning system DL.GSU.BY developed at the FranciskSkorinaGomelStateUniversity. There are described specialized tools for designing, modeling, debugging, and researching digital electronics devices developed specifically for learning the basics of digital electronics.
PROFESSIONAL ROLES OF THE HIGHER SCHOOL TEACHER IN THE FUTURE SPECIALISTS TRAINING

The article discusses the features of professional roles of a higher education teacher in the training of future specialists (using the example of a teacher of biology and human health). The features of the individual work of a teacher-tutor with a student are given; the functions of the moderator and the facilitator in the group work; working with future specialists of the teacher-curator during extracurricular time. Types of tutoring and structural elements of tutor support, moderation, facilitation, and curator functions are proposed. It is emphasized that the teacher-tutor is responsible for the level of knowledge of the student, and for the formation of his personality. The teacher-moderator carries out the process of managing the interaction in a group of students, combines the solution of issues of development of the educational group as a team, and organizes the interaction and cooperation of all participants. The teacher-facilitator stimulates the independence and responsibility of students when choosing courses, goals, and ways to achieve them when evaluating the results of their work. The teacher-curator creates external conditions and accompanies the actualization of the student’s internal reserves for full-fledged creative self-realization in all spheres of life. For the effective work of the curator it is proposed to combine the informative, organizational, communicative, administrative, creative functions. The survey data on the effectiveness of the use of new professional roles by a teacher among future specialists is indicated.
User Stories and Algorithms as Programming and Design Tools

Abstract Title: User Stories and Algorithms as Programming and Design Tools In this evidenced-based practice paper the results of a process are presented that find useful connections between the qualitative goals of a project and quantitative design results through the incorporation of the “agile method” borrowed from the computer software industry. For buildings with a high performance need, the success of the project is often dependent on the design professional’s experience within a particular building type to assure success. Professional intuition results from years of experience during the creation of work and the subsequent understanding of projects‘ successes and failures. One of the core goals of the academy is to develop professional intuition within its students as well as give them the tools or processes necessary to build that intuition into the future. In two years of a capstone design studio students were tasked to utilize the agile method to establish qualitative project design goals by developing highly empathetic and well-focused user stories when designing a theater audience chamber in the first year and a working food pantry in the second. By segmenting the project into the singular aspects defined within the user stories, quantitative objectives were able to be assigned to each goal, simulated through computational methods, and tangible results collected which helped the student understand how well the goals were reached within each of their design schemes. Each design scheme was evaluated by the students through simulations formulated to determine how well specific aspects performed. As a result, immediate and automated feedback provided students with a clear understanding of how well each design performed in each aspect and offered possibilities of where improvements needed to be made to advance their design. Through this design-evaluation feedback loop each student’s professional intuition was advanced more quickly and the design performance of their projects improved. Through the adoption of user stories, a well-focused design agenda can be established and computational methods can be derived to both simulate and evaluate the design’s success and in the process facilitate the development of students’ design intuition.