Reviewerly/exHarmony huggingface dataset:
USE OF DIKW METHODOLOGY FOR EDUCATIONAL PROPOSALS IN THE FRAMEWORK OF INNOVATIVE LEARNING IMPLEMENTATION

The article analyzes the possibilities of using the DIKW-model in relation to educational proposals. The analysis brings us back to understanding the concepts of "data", "information", "knowledge" and wisdom, where the goal of teaching is to obtain knowledge, and the learning process itself leads to wisdom. The definitions of these basic concepts are considered, which will prevent their misinterpretation and substitution. It will give an opportunity to develop a model of training, which in the future will form the required specialist. The structure of this model is considered, the connections between its components are highlighted, which are of paramount importance for the development of an effective educational proposal within the framework of innovative education and increase of independent work of the higher education seeker, where the role of the teacher changes. The preparedness for real life, forming the necessary competences and teaching the student as a specialist and personality become one of the primary tasks. First, to move to the concept of "learning", it is necessary to specify the definitions of "information" and "knowledge" to understand in which case the presentation of information on the subject will lead to the formation of knowledge and in which it will remain information. We understand the need to form a future specialist’s "knowledge" and opportunities related to the category of "wisdom" under the introduction of the DIKW methodology in the educational environment. "Knowledge" cannot be replaced by an information flow or a data flow. This is a process of multiple processing of information, data, related knowledge and the copyright of the studied information is not an indicator of effectiveness. The indicator of effectiveness was defining as the ability to make the decision that is most appropriate in a given situation.
LCD Codes from tridiagonal Toeplitz matrice

Double Toeplitz (DT) codes are codes with a generator matrix of the form $(I,T)$ with $T$ a Toeplitz matrix, that is to say constant on the diagonals parallel to the main. When $T$ is tridiagonal and symmetric we determine its spectrum explicitly by using Dickson polynomials, and deduce from there conditions for the code to be LCD. Using a special concatenation process, we construct optimal or quasi-optimal examples of binary and ternary LCD codes from DT codes over extension fields.
Analysis of the modern Russian market of educational services: challenges and development trends

The article has been devoted to analyzing some problems related to the Russian market of educational services functioning in the context of the rapid development of digital technologies, digitalization of the economy, education and society. This actualizes the issue of training highly qualified personnel, whose activities should comply with new requirements of the digital civilization, as well as new challenges and risks. The aim of the article has been to study the transformation of areas, peculiarities of providing educational services, educational technologies, and competencies in the main educational programs of higher education in the modern Russia in the context of digital realities, as well as to develop some solutions for updating the educational services market, improving the quality of education, and erudition of the society. Websites of the largest higher educational establishments of the country, including those located in the Orenburg region, have been analytically monitored for identifying the focus of educational programs and services on the consumer needs and preferences. The authors have come to the conclusion that it is necessary for all specialists, including humanitarians, to acquire digital competence.
RatioNet: Ratio Prediction Network for Object Detection

In object detection of remote sensing images, anchor-free detectors often suffer from false boxes and sample imbalance, due to the use of single oriented features and the key point-based boxing strategy. This paper presents a simple and effective anchor-free approach-RatioNet with less parameters and higher accuracy for sensing images, which assigns all points in ground-truth boxes as positive samples to alleviate the problem of sample imbalance. In dealing with false boxes from single oriented features, global features of objects is investigated to build a novel regression to predict boxes by predicting width and height of objects and corresponding ratios of l_ratio and t_ratio, which reflect the location of objects. Besides, we introduce ratio-center to assign different weights to pixels, which successfully preserves high-quality boxes and effectively facilitates the performance. On the MS-COCO test–dev set, the proposed RatioNet achieves 49.7% AP.
Content-Preserving Unpaired Translation from Simulated to Realistic Ultrasound Images

Interactive simulation of ultrasound imaging greatly facilitates sonography training. Although ray-tracing based methods have shown promising results, obtaining realistic images requires substantial modeling effort and manual parameter tuning. In addition, current techniques still result in a significant appearance gap between simulated images and real clinical scans. In this work we introduce a novel image translation framework to bridge this appearance gap, while preserving the anatomical layout of the simulated scenes. We achieve this goal by leveraging both simulated images with semantic segmentations and unpaired in-vivo ultrasound scans. Our framework is based on recent contrastive unpaired translation techniques and we propose a regularization approach by learning an auxiliary segmentation-to-real image translation task, which encourages the disentanglement of content and style. In addition, we extend the generator to be class-conditional, which enables the incorporation of additional losses, in particular a cyclic consistency loss, to further improve the translation quality. Qualitative and quantitative comparisons against state-of-the-art unpaired translation methods demonstrate the superiority of our proposed framework.
THE USE OF ACTIVE WORK FORMS IN THE INCULCATING THE LIFE SKILLS AMONG YOUNGER STUDENTS

The experience of using active forms of work in inculcating life skills in students in the primary grades of secondary schools was widely analyzed in the article. The types of lessons are classified, in which the essence of the types of active lessons is clarified. It is noted that it is possible to achieve the formation of students' life skills through the use of active forms of work in the learning process organized for all types of modern lessons. For this, it is necessary to create motivation in the course of an active lesson, to use problem situations. We know that training mainly motivates thinking. Setting a task for students motivates them to think and creates conditions for the formation of their thinking. By solving the problem, children develop positive emotions and enthusiasm for learning. The use of the active work forms in the inculcation of life skills also creates the basis for enhancing students' cognitive activity. In this regard, it is also important to perform problematic tasks. Thus, creating the basis for the acquisition of new knowledge and creating conditions for active understanding of knowledge plays a key role in the implementation of problematic tasks. The article summarizes the use of interactive teaching methods, and also focuses on the main aspects of the activity process of the primary school teachers. Thus, the article reflects the use of active forms of work in the process of teaching the subject "life knowledge" in the disclosure of life skills of students.
Risk Prediction with Imperfect Survival Outcome Information from Electronic Health Records

Readily available proxies for time of disease onset such as time of the first diagnostic code can lead to substantial risk prediction error if performing analyses based on poor proxies. Due to the lack of detailed documentation and labor intensiveness of manual annotation, it is often only feasible to ascertain for a small subset the current status of the disease by a follow up time rather than the exact time. In this paper, we aim to develop risk prediction models for the onset time efficiently leveraging both a small number of labels on current status and a large number of unlabeled observations on imperfect proxies. Under a semiparametric transformation model for onset and a highly flexible measurement error models for proxy onset time, we propose the semisupervised risk prediction method by combining information from proxies and limited labels efficiently. From an initial estimator solely based on the labelled subset, we perform a one-step correction with the full data augmenting against a mean zero rank correlation score derived from the proxies. We establish the consistency and asymptotic normality of the proposed semi-supervised estimator and provide a resampling procedure for interval estimation. Simulation studies demonstrate that the proposed estimator performs well in finite sample. We illustrate the proposed estimator by developing a genetic risk prediction model for obesity using data from Partners Biobank Electronic Health Records (EHR).
Contrastive Separative Coding for Self-supervised Representation Learning

To extract robust deep representations from long sequential modeling of speech data, we propose a self-supervised learning approach, namely Contrastive Separative Coding (CSC). Our key finding is to learn such representations by separating the target signal from contrastive interfering signals. First, a multi-task separative encoder is built to extract shared separable and discriminative embedding; secondly, we propose a powerful cross-attention mechanism performed over speaker representations across various interfering conditions, allowing the model to focus on and globally aggregate the most critical information to answer the "query" (current bottom-up embedding) while paying less attention to interfering, noisy, or irrelevant parts; lastly, we form a new probabilistic contrastive loss which estimates and maximizes the mutual information between the representations and the global speaker vector. While most prior unsupervised methods have focused on predicting the future, neighboring, or missing samples, we take a different perspective of predicting the interfered samples. Moreover, our contrastive separative loss is free from negative sampling. The experiment demonstrates that our approach can learn useful representations achieving a strong speaker verification performance in adverse conditions.
Aplikasi Virtual Tour Islamic Center Berbasis Android

developers Smartphone software have been expanding variety of technologies, one example is thetechnology Virtual Tour (VR).Technology Virtual Tour or also called panoramic a tour is a simulation of a place that actually exists, consisting of a collection of 360 photos, panoramic photos, a collection of images connected by hyperlinks, 3-dimensional models or videos.Virtual Tour has been widely used as an effective promotional tool, especially in tourism through interactive media.Currently the Islamic center has not been able to maximize visitor interest because it still uses conventional media such as brochures and banner advertisements as well as locations that are far from urban areas, so there has not been a significant increase in the local area. The research conducted aims to build an android mobile based application by utilizing technology virtual The tour is expected to increase the promotional value and attraction of visitors, as well as introduce the Islamic Center from outside the West Tulang Bawang district in the form of a 360̊ panoramic video object. The development method in this study uses Luther's model of Multimedia Development Life Cycle (MDLC).As for testing the system, the author uses thequality standard International Organization of Standardization (ISO) 25010, the aspects to be tested areaspects.functional Suitability, aspects Operability, andaspects Transferability.This application has been tested using ISO 25010, the aspects tested areaspects functional Suitability gets a score of 100%, theaspect Operability gets a score of 94% and theaspect Transferability gets a score of 100% and the average execution time is 1.0364 seconds.From the overall testing of ISO 25010, we get an average total score of 95% or have a very good scale.
Crimeengineering:An Analysiswith Referencetocrimes And Cyber Crimes Against Women Inperson

It is significant to revisit the criminal justice administration in the backdrop of emergence of new principles and values. It is also significant to re-examine the definition of crimes and crime prone activities. Law grows with the growth of other disciplines. Crimes and cyber crimes are rising against the women in person. Criminal Justice administration only bases its premise on the preventive approach. Precautionary approach in the criminal justice administration is minimal. Analogy of environmental engineering with the foundational principle of precautionary approach can also be engineered in the criminal justice administration. Researchers have attempted in this paper to focus how at the threshold crime prone activities can be engineered with precautionary model? How in the society at the threshold there are social deviations and how it takes form in the grave and heinous crimes?
Hetero-Modal Learning and Expansive Consistency Constraints for Semi-Supervised Detection from Multi-Sequence Data

Lesion detection serves a critical role in early diagnosis and has been well explored in recent years due to methodological advancesand increased data availability. However, the high costs of annotations hinder the collection of large and completely labeled datasets, motivating semi-supervised detection approaches. In this paper, we introduce mean teacher hetero-modal detection (MTHD), which addresses two important gaps in current semi-supervised detection. First, it is not obvious how to enforce unlabeled consistency constraints across the very different outputs of various detectors, which has resulted in various compromises being used in the state of the art. Using an anchor-free framework, MTHD formulates a mean teacher approach without such compromises, enforcing consistency on the soft-output of object centers and size. Second, multi-sequence data is often critical, e.g., for abdominal lesion detection, but unlabeled data is often missing sequences. To deal with this, MTHD incorporates hetero-modal learning in its framework. Unlike prior art, MTHD is able to incorporate an expansive set of consistency constraints that include geometric transforms and random sequence combinations. We train and evaluate MTHD on liver lesion detection using the largest MR lesion dataset to date (1099 patients with >5000 volumes). MTHD surpasses the best fully-supervised and semi-supervised competitors by 10.1% and 3.5%, respectively, in average sensitivity.
Multicultural Competence as a Factor in Updating the Cognitive Potential of a Teacher at a Higher School

Purpose: The purpose of the research is to analyze the current problem associated with increasing the professional competence of pedagogical personnel and those competencies, the importance of the formation of which is dictated by the realities of our time. In turn, this will provide an opportunity to improve the professionalism of the personality and activities of modern teachers, update their potential, improve the quality of the educational process and train competitive graduates. Design/Methodology/Approach: The article considers theoretical studies of the personality and activity of the teacher; synthesis of androgological and competency approaches based on conceptual provisions of pedagogy, psychology and the concept of continuing education, which focuses the person on increasing the available knowledge, improving skills and skills; an analysis of ideas about multicultural education, about the cognitive potential of the individual, about ways of its actualization and development. Findings: The use of theoretical analysis of primary sources, conversations, questionnaires made it possible to conclude about the peculiarities of the teacher's activities in the context of the implementation of new paradigms, concepts, a competent approach and the modernization of educational systems that acquire a multicultural character. This actualized the nomination of the task related to the formation of competencies due to the realities of the modern world. And at present, the multicultural competence of teachers is of particular importance, the development of which, first of all, contributes to the actualization of cognitive potential. This can be done by including teachers in additional vocational education. Originality/Value: As a result of the research, it was found that the development and implementation of a program for the formation and subsequent development of multicultural competence provides for the expansion of the corresponding range of knowledge, transformation of everyday knowledge of teachers into scientific knowledge, improvement of knowledge about multicultural personality, developing a willingness to apply the knowledge gained and effectively solve the problems of higher education, successfully functioning in a multicultural environment, which makes it possible to recommend it for introduction into the system of advanced training of teachers, when training graduate students, residents and students.
No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks

There has been increasing interest in building deep hierarchy-aware classifiers that aim to quantify and reduce the severity of mistakes, and not just reduce the number of errors. The idea is to exploit the label hierarchy (e.g., the WordNet ontology) and consider graph distances as a proxy for mistake severity. Surprisingly, on examining mistake-severity distributions of the top-1 prediction, we find that current state-of-the-art hierarchy-aware deep classifiers do not always show practical improvement over the standard cross-entropy baseline in making better mistakes. The reason for the reduction in average mistake-severity can be attributed to the increase in low-severity mistakes, which may also explain the noticeable drop in their accuracy. To this end, we use the classical Conditional Risk Minimization (CRM) framework for hierarchy-aware classification. Given a cost matrix and a reliable estimate of likelihoods (obtained from a trained network), CRM simply amends mistakes at inference time; it needs no extra hyperparameters and requires adding just a few lines of code to the standard cross-entropy baseline. It significantly outperforms the state-of-the-art and consistently obtains large reductions in the average hierarchical distance of top-$k$ predictions across datasets, with very little loss in accuracy. CRM, because of its simplicity, can be used with any off-the-shelf trained model that provides reliable likelihood estimates.
The application of research teaching mode in the teaching reform of computer composition and structure

Computer composition and structure is the core course for students to understand the working principle of computer and master the hardware characteristics of computer science and technology. Students feel boring and difficult to learn. In order to improve students' interest in learning and achieve the goal of cultivating innovative talents, the research-oriented teaching mode is introduced into the teaching reform of computer composition and structure, and the teaching method, teaching organization form and assessment method are comprehensively reformed to improve the teaching effect and teaching quality.
BP Neural Network-Based Evaluation on University Teachers' Teaching Quality

Because of the one-sidedness in most universities by using the single teaching evaluation mechanism to evaluate the teachers' teaching quality, this paper presents a comprehensive teaching quality evaluation system. 7 indicators under the 2 level systems were constructed. Furtherly 2-level neural network is established. The training results show that the network can fit the training data well and the effect is significant, which indicates that the teaching quality evaluation model based on neural network is reasonable and feasible.
Graph Signal Processing: A Signal Representation Approach to Convolution and Sampling

The paper presents sampling in GSP as 1) linear operations (change of bases) between signal representations and 2) downsampling as linear shift invariant filtering and reconstruction (interpolation) as filtering, both in the spectral domain. To achieve this, it considers a spectral shift $M$ that leads to a spectral graph signal processing theory, $\text{GSP}_{\textrm{sp}}$, dual to GSP but that starts from the spectral domain and $M$. The paper introduces alternative signal representations, convolution of graph signals for these alternative representations, presenting a $\textit{fast}$ GSP convolution that uses the DSP FFT algorithm, and sampling as solutions of algebraic linear systems of equations.
Effectiveness Of Archive Management by Digitizing Documents

ABSTRACT The organization's expectation of implementing Information Systems (IS) is the presentation of fast information, precise, and accurate in supporting decision making so there is an increase in company performance. But in reality, not all applications of information systems in an organization can provide an increase in the operational activities of the organization / company. There is one method which is taken as one of the efforts to increase the application of information systems in an organization so it can be accessed or processed quickly by its employees is with apply the digitizing documents. Digitizing documents can facilitate the processing and distribution of documents in an organization, where processing and distributing documents is the most carried out activity in an organization. Therefore, it is important to observe the Effectiveness of Document Digitalization Implementation in Organization. This article aims to see how effective the application of documents digitalization in Organization. The method used is direct observation and using Total Quality Management to measure the effectiveness of the application of digitizing documents.
Agile Development A Revolutionary Paradigm

Software engineering practices have been used for years to build software products.Choosing a software development methodology suitable for a specific project and harmonizing the methodology for clear requirements has been controversial ever since the discipline of the software development organization.Currently, the goal of most software companies is to produce software in minimal time at an insignificant cost and to produce software in an unbalanced and changing environment as Agile grows.Finding and addressing mechanisms to address and consider improving successful agile implementation, use, and delivery.In this paper, we will discuss some configurations that confirm why Agile is preferred over waterfall models.As the appeal of Agile methods grows, many software organizations are moving away from traditional systems to adopt Agile development methodologies.As an alternative to extrapolation, Agile is highly adaptable and people-focused.Support those who work closely and for joint teams.However, team size is limited by the workforce factor.This white paper provides a multidimensional observation of performance factors in agile software development projects.Then, evaluate the performance factors published in research papers, articles and technical reports related to real agile projects.To promote the indulgence of success factors, researchers organize them into five categories: organization, people, process, design, and technology.This is why in this white paper we will analyze some of the success factors of agile practices that are manipulated at the organizational level.