Reviewerly/exHarmony huggingface dataset:
СПЕЦИФИКА ВИРТУАЛЬНОЙ КОММУНИКАЦИИ ЯПОНЦЕВ В СОЦИАЛЬНЫХ СЕТЯХ

The article considers the influence of virtual communication in social networks on the lexical and orthographic change of oral speech representation. The development of computer technologies creates groups of interest, forums and social networks in the Internet where the language of virtual communication becomes more variable moving from its being symbolic to a more creative one, when users get the opportunity not just to exchange short messages but express their opinion. A new sphere of communication originates a new lexical layer of transformers and promotes the convergence of written and oral types of speech interaction. The analysis of correspondence in social networks and examples from Japanese sources have showed that the language of Japanese politeness Keigo is not used in networks which can create certain difficulties in choosing the right forms in real communication. Conversely, simple or familiar forms allowed in written language are scarce and their automatic transfer to everyday life can become a hindrance to establishing social contacts. Actively integrating in the Japanese language neologisms most often conform to the norms of Japanese phonetics and grammar, but they cause significant deviations in a written fixation of the transmitted text. The lack of a visual contact in social networks forms a written oral speech, the analysis and consideration of which is important for an adequate perception of information and for a better orientation in the Japanese Internet space.
Toeplitz Inverse Covariance Based Robust Speaker Clustering for Naturalistic Audio Streams

Speaker diarization determines who spoke and when? in an audio stream. In this study, we propose a model-based approach for robust speaker clustering using i-vectors. The ivectors extracted from different segments of same speaker are correlated. We model this correlation with a Markov Random Field (MRF) network. Leveraging the advancements in MRF modeling, we used Toeplitz Inverse Covariance (TIC) matrix to represent the MRF correlation network for each speaker. This approaches captures the sequential structure of i-vectors (or equivalent speaker turns) belonging to same speaker in an audio stream. A variant of standard Expectation Maximization (EM) algorithm is adopted for deriving closed-form solution using dynamic programming (DP) and the alternating direction method of multiplier (ADMM). Our diarization system has four steps: (1) ground-truth segmentation; (2) i-vector extraction; (3) post-processing (mean subtraction, principal component analysis, and length-normalization) ; and (4) proposed speaker clustering. We employ cosine K-means and movMF speaker clustering as baseline approaches. Our evaluation data is derived from: (i) CRSS-PLTL corpus, and (ii) two meetings subset of the AMI corpus. Relative reduction in diarization error rate (DER) for CRSS-PLTL corpus is 43.22% using the proposed advancements as compared to baseline. For AMI meetings IS1000a and IS1003b, relative DER reduction is 29.37% and 9.21%, respectively.
A Study on Factors influencing ERM &amp; Trust among employees in Manufacturing Companies

This research investigates the effect of Factors influencing ERM &amp; Trust among employees in Machine tools Manufacturing Companies. A good relationship is necessary for the smooth running of any business. A good communication between an employer and its employees is imperative for building a positive work place culture. Gratitude &amp; Appreciations is very important. ERM main components are (Human Resources Practices, Communication, Trust, Shared Goals and Values, and Leadership Styles). Employee relationship management has emerged as one of the important tool for motivating employees. In this study statistical tools like weighted average &amp; Chi-Square test are used to find out the best factor &amp; Association.
FnnmOS-ELM: A Flexible Neural Network Mixed Online Sequential Elm

The learning speed of online sequential extreme learning machine (OS-ELM) algorithms is much higher than that of convolutional neural networks (CNNs) or recurrent neural network (RNNs) on regression and simple classification datasets. However, the general feature extraction of OS-ELM makes it difficult to conveniently and effectively perform classification on some large and complex datasets, e.g., CIFAR. In this paper, we propose a flexible OS-ELM-mixed neural network, termed as fnnmOS-ELM. In this mixed structure, the OS-ELM can replace a part of fully connected layers in CNNs or RNNs. Our framework not only exploits the strong feature representation of CNNs or RNNs, but also performs at a fast speed in terms of classification. Additionally, it avoids the problem of long training time and large parameter size of CNNs or RNNs to some extent. Further, we propose a method for optimizing network performance by splicing OS-ELM after CNN or RNN structures. Iris, IMDb, CIFAR-10, and CIFAR-100 datasets are employed to verify the performance of the fnnmOS-ELM. The relationship between hyper-parameters and the performance of the fnnmOS-ELM is explored, which sheds light on the optimization of network performance. Finally, the experimental results demonstrate that the fnnmOS-ELM has a stronger feature representation and higher classification performance than contemporary methods.
Hardware Reduction and Statistical Verification of Cryptographic Standard Cell Resistant to SCA

This paper presents results regarding the progress achieved in the design of a customized encrypted multiplexer cell. The focus of our interest is the no short-circuit dynamic differential logic method that demonstrates high immunity of a standard logic cell to the side channel attacks (SCAs). Three innovative solutions are examined and the best one is proposed for the encrypted multiplexer cell. Designed cells are realized to the layout masks level targeting the 350[Formula: see text]nm CMOS process. The post-layout simulations showed high resistivity to the SCA for all designed cells. The resistance to the SCA of the proposed design was statistically verified at the transistor level. The verification is performed using the Monte Carlo (MC) simulation in which widths and lengths of the transistor channel were varied according to the Gaussian distribution. The designed encrypted cell will be the part of a more complex crypto system for power consumption metering which should increase overall system’s data security.
INDONESIAN ISLAMIC JUNIOR HIGH SCHOOL STUDENTS’ SPEAKING ENGLISH AND ARABIC ANXIETY: A COMPARISON

This article reveals the phenomenon of speaking anxiety in two foreign languages, Arabic and English, simultaneously studied by students of Islamic junior high school in Indonesia.  It is aimed at analyzing the differences of level, causes as well as coping strategies of speaking anxiety in Arabic and English faced by the respondents.A survey inquiry was conducted by employing questionnaires to gather the data of respondents’level, causes and coping strategies of speaking anxiety.  The analysis indicates a similarity of speaking anxiety level of the two languages, yet some major differences regarding the prominent causes occur. Keywords : Speaking anxiety; Arabic; English; Indonesian students
Thoughts on Education and Teaching of Students Major in Music of Local Free Normal University and Colleges in Ethnic Minority Areas

In order to address the shortage of teaching staff in ethnic minorities in western China, western provinces, after the releasing of the tuition-free policy for the normal university undergraduates, have recruited government-funded normal university students.The policy aims to cultivate qualified primary and secondary school teachers for ethnic minority regions, but the outcome is not desirous.This paper takes the free normal university students major in music in ethnic minority regions as the research object, adopts interview, investigation and analysis to shed light on the student source of ethnic minority normal students at public expense, find the reason behind and offer countermeasures, in a bid to provide reference for education and teaching of ethnic minority free normal university students major in music.
CRUD Application with Flask (Part 1)

In the last chapter, we discussed about databases and implemented NoSQL- and SQL-based examples. In this chapter we'll be creating a RESTful Flask application from scratch. Here we'll maintain a database of Author objects alongside the books they have written. This application will have a user authentication mechanism to only allow logged-in users to execute certain functions. We'll now create the following API endpoints for our REST applications:
Optimized Search Method using Lazy Search Algorithm

A Lazy Search is process of the search method will produced optimized solution using Artificial Intelligence. For implementing Lazy Search Algorithm the five technologies are used. The following are technologies which is used for implementing the Lazy Search Algorithm. 1) Mechanize, 2) Beautiful Soup 4, 3) Web Scraping, 4) Python 5) Django. This project seeks to analyze the impact of SEO techniques on the effectiveness of SEO, to figure out which technique strategy is most effective, and furthermore, to test the possible influence of SEO techniques on Page Interest. In this proposed method, the search method is not based on the page ranking algorithm. Lazy search algorithm performance is fully based on the keyword search.Lazy search algorithm will produce the result based on the search keyword and using the key word the search method will produce the optimized solution.Search efforts are constrained by the confidentiality of the algorithm used by the search method themselves.
Evaluation of Strategies to Reduce Reperfusion Times in ST-Segment Elevation Acute Myocardial Infarction. Mobile Health Experience (mHealth)

espanolIntroduccion: La transmision de la foto del electrocardiograma via telefono movil, reduciria los tiempos de reperfusion en elinfarto agudo de miocardio con elevacion del segmento ST.Objetivo: Evaluar la eficacia de la tecnologia celular como estrategia para acortar los tiempos de reperfusion.Material y metodos: Estudio cuasi experimental, multicentrico, que incluyo pacientes del Gran San Miguel de Tucuman, desdeagosto de 2017 a abril de 2018. La estrategia consistio en la transmision de fotos del electrocardiograma via WhatsApp, parala activacion temprana del protocolo de infarto. Se midio tiempos de demora e impacto en la calidad de atencion. EnglishBackground: Mobile phone transmission of electrocardiogram photos would reduce time to reperfusion in ST-segment elevation acute myocardial infarction. Objective: The aim of this study was to evaluate the efficacy of mobile phone technology as a strategy to shorten times to reperfusion. Methods: A quasi-experimental, multicenter study, including patients from the Greater San Miguel de Tucuman, was carried out from August 2017 to April 2018. The strategy consisted in the transmission of electrocardiogram photos by WhatsApp, for the early activation of the infarction protocol. Delay times and impact on the quality of care were assessed
MENINGKATKAN KEMAMPUAN MEMAHAMI BACAAN DENGAN MODEL PEMBELAJARAN KOOPERATIF TIPE TEAMS GAME TURNAMENT (TGT) PADA SISWA KELAS IV SDN 010 LANGGINI

This research is motivated by the low ability of grade IV SDN 010 Langgini students to understand reading, out of 18 students there are 4 (22%) good people, 1 (6%) enough people, 4 (22%) less people and 9 (50%) ) people are very lacking. This research is a Class Action Research (CAR) which aims to improve the reading comprehension of Grade IV students at SDN 010 Langgini through cooperative learning models of team game tournament. This research was conducted in the even semester of the 2018/2019 school year for 2 weeks on January 14-28, 2019. The research subjects were Grade IV students of SDN 010 Langgni, as many as 18 students, consisting of 6 men and 12 women. The study was conducted in two cycles, each cycle consisting of four stages namely planning, implementing, observing and reflecting during the learning process using cooperative learning models of teams game tournament. Research results obtained before the action 45.3 and increased in cycle I as much as 56.9% with an average of 71.1 and increased again in the second cycle as much as 81.4% of the base score with an average of 82.2. based on the results of these improvements prove that the cooperative learning model team game tournament can improve the ability of fourth grade students SDN 010 Langgini in understanding reading
Functional Group Structure in a Novel Chaos Theory Diverging Non-Linear Significant Functions with Counters-Residues Pattern in Singular Integral – Derivative Applications

Different mathematical function gives divergence system in chaos theory functions. To all extinct in counter integration in HU evaluation theory A(H) and C(U) are the block functions with open and closed integral loop control in linear algebraic system at large expansion of system in nonlinear algebraic expression. It sets the point in H and u with combinational sequential order of values. Additive system function (z*, x, y, n) gives derivative product in individual terms where the subtractive function (-z*, -x, -y, -n) in integral form H-U vertical mathematical in k-1 divided by 2 is maxima and minima integral form in hu theory. Properties covering in novel system give diverging elements with covering section of reforms from all the data units in nonlinear algebraic system functional groups. Maxima functions lowest sequence terms with all the unit’s analytical minima function at highest enable elementary graph mode.
Digital Identity and Our Remembrance

The text password, which is a section of the secret credentials, is known to be too hard to manage. Secret credentials are absolutely necessary for digital identity in democratic societies. We could look for something other than the text password as the valid secret credential. Three big myths are rampant in the sphere of digital identity. These are ‘Higher security achieved by removal of password’, ‘Passwords killed by the biometrics that is dependent on passwords’ and ‘Passwords displaced by PIN that is no more than a weak form of numbers-only password’. Unraveling these myths, we come to the conclusions that we must look for something really valid in the sphere of ‘Non-Text Password’ and that the identity of ‘citizens’ cannot be separated from their volition and memory while the identity of ‘things’ can be handled only technologically. Our own autobiographic memory, especially episodic memory, enables us to come up with the most reliable digital identity platform, bidding farewell to the unsafe and torturous identity proofing.
Majorana bound state engineering via efficient real-space parameter optimization (Conference Presentation)

Recent progress toward the fabrication of Majorana-based qubits has sparked the need for systematic approaches to optimize experimentally relevant parameters for the realization of robust Majorana bound states. In this talk, I will introduce an efficient numerical method for the real-space optimization of tunable parameters, such as electrostatic potential profiles and magnetic field textures, in Majorana wires. Combining ideas from quantum control and quantum transport, our algorithm operates on a largely unexplored parameter space and opens new routes for Majorana bound states with enhanced robustness. I will conclude by commenting on the applicability of the algorithm towards the realization of Majorana modes in low g-factor materials.
Control directo del par de un motor de inducción utilizando redes neuronales

This paper deals with speed control for a squirrelcage induction motor by using direct torque control (DTC) through the Simulink® professional simulation program for later implementation. The control maintains the electromagnetic flux and torque of the motor constant, for which it estimates the flux and the torque measuring the voltages and currents in the stator. In addition it selects the suitable voltage vector according to the angle of the electromagnetic flux, and increase or decrease the torque or flux. In this work the selection of the appropriate voltage was made with the use of an artificial neural network, in order to determine if this contributes to decrease the torque and flux ripple, because the direct control of the torque is characterized by having a dynamic fast response but a high torque ripple.
Enemy item detection using data mining methods

Enemy items are any two items that should not appear on the same test form. These items may address the same material, or one may provide clues about the answer to another. Most enemy item pairs are identified before forms are published; subject matter experts (SMEs) manually review forms for enemy pairs, a process that can be both cognitively taxing and expensive. Some have suggested statistical approaches for identifying enemy item pairs; for instance, response data might show violations of local independence caused by clueing. One drawback, however, is that these are post hoc tests: the forms must have been administered to a sufficient number of examinees. This study proposed a method of identifying enemy item pairs that capitalized on two data mining approaches: latent Dirichlet allocation (LDA), an unsupervised topic model, and a random forest classifier, a supervised ensemble learning algorithm. Output from the LDA model was used to calculate the Jensen-Shannon distance (JSD) between items. Random forests were trained with and without the JSD, as well as several other item-level variables. Item pairs were scored using the resulting random forest classifiers, and SMEs evaluated the output. The random forest classifier was then retrained using input from the SMEs. This study suggests that random forest models can be useful in the identification of enemy item pairs; information derived from the LDA topic model improves the performance of the random forest classifier, and integrating feedback from SMEs further improves the performance.
Sketching with Hardware: A Course for Teaching Interactive Hardware Prototyping to Computer Science Students

Sketching with Hardware is an undergraduate university course with the goal to teach students how to build prototypes for tangible user interfaces. Goals of this course are to create awareness for tangible interaction among students and prepare them to realize advanced projects like bachelor's and master's theses in this field. In this paper, authors share their experience teaching the concepts of tangible interaction, electronics and prototyping to computer science students in a two week course. The course's content, structure and goals are explained, and needed material and infrastructure are described. The long-term effect of the course has been evaluated by conducting a survey among former participants.
Towards a Transparent Deep Ensemble Method Based on Multiagent Argumentation

Ensemble methods improve the machine learning results by combining different models. However, one of the major drawbacks of these approaches is their opacity, as they do not provide results explanation and they do not allow prior knowledge integration. As the use of machine learning increases in critical areas, the explanation of classification results and the ability to introduce domain knowledge inside the learned model have become a necessity. In this paper, we present a new deep ensemble method based on argumentation that combines machine learning algorithms with a multiagent system in order to explain the results of classification and to allow injecting prior knowledge. The idea is to extract arguments from classifiers and combine the classifiers using argumentation. This allows to exploit the internal knowledge of each classifier, to provide an explanation for the decisions and facilitate integration of domain knowledge. The results demonstrate that our method effectively improves deep learning performance in addition to providing explanations and transparency of the predictions.
Making Adaptation Decisions By Combining Deep Learning and Ontology

The advancement of on-board imaging of various modalities provides valuable information for plan adaptation. Retrospective assessment has demonstrated the severe suboptimality of online decision making, caused by inferior visual exam, and contouring quality under time pressure. We develop an online decision system using machine learning with embedded ontology priors. 7 pancreas patients treated with 5 fractions SBRT on a MR imaging system were retrospectively reviewed, with daily images recontoured and dose criterion reassessed to obtain the "ground-truth” adaptation decisions. We pose the adaptation decision system as a cascaded classifier consisting of a set of deep networks each infers the meet/fail behavior of a critical dose criterion and an ontology rule to make the clinical decision. Each deep network takes three volumetric inputs - planning image, planning dose, and daily image - and outputs a binary decision for a specific DVH criterion. For both data and computational efficiency, we chose to use 2D settings in this network. The 3D decision is simply replicated for each contributing slice. The output of the 2D network is fused with a simple logistic regression module to project the 2D decision vectors whose length is the number of slices to a single 3D decision. To address the extremely limited number of samples which often happen to be quite unbalanced, we have developed a simulation paradigm where each reference volume is deformed according to multiple realizations of physiologically feasible diffeomorphic motion vector fields. The decision corresponding to each simulated motion field is assessed by evaluating the planning dose map on the deformed planning contours. Upon training with this extensive simulation, testing is performed using both simulated instances and two clinical cases with both adaptation and nonadaptation decisions that were set aside for testing purpose. The developed simulation work turned out to be critical in achieving proper data augmentation required for the training of the network. The 2D network in itself achieved a mediocrite accuracy of 76.4% for 2D slice-wise decisions. This is expected given the sensitive decision boundaries for decision and the obscurity in 2D views. Fortunately, when combined with the fusion module, the accuracy improved to at least 92.5% with various runs and data splits. The test results on the two hold-off clinical cases were consistently correct. We have developed an approach by taking advantage of deep learning and ontology. Each module of the model is relatively simple, offers good interpretability and stability. The overall performance has a lower bounded accuracy of over 90%, providing confidence for clinical translation.