Reviewerly/exHarmony huggingface dataset:
Scientific journal in the educational space of the university

The article presents the authors ‘view on the activities of the scientific journal” Bulletin of the North Ossetian State University named after K. L. Khetagurov”. The relevance of the article is due to a significant increase in the role of research work of teachers and students of universities and the requirements for their publication activity; the importance for the university of having highly rated scientific journals and the increasing importance of the scientific publication of the university for improving the training of students. At the scientific and theoretical level, the changes that were determined by the modernization of education in the country and the need to improve the quality of scientific publications in accordance with the vector of development of international high-ranking publications and the desire of the university management and the editorial board to keep the journal in the trend of modern science are understood. Attention is paid to the problem of evaluating and reviewing scientific articles, the idea of the impact of reviews on the development of scientific knowledge is justified; the systematic work of the editorial board on the introduction of scientific research into the educational process of the university is shown. The new tasks of the editorial board are considered, the solution of which will contribute to improving the quality of the publication and the research activities of the teaching staff and students of the university. The purpose of the work is to substantiate the author’s approach to the current state, functioning and perspective view of the development of the university scientific publication and to determine its place in the modern scientific and educational space. The research methodology is based on systematic, activity-based and culturological approaches using such methods as systematization, generalization, analysis, description and comparison.
[Group psychological education film pathway for alcoholics and their families: a study on changes].

OBJECTIVE This research evaluates the individual traits and family system dysfunctional habits modification of participants in the multi-family psychological education group 'Primo Percorso' (GPP) of the alcohology service NOA Perini - ASST Fatebenefratelli - Sacco, Milano. This is constituted by a multi-discipline team (physicians, systemic psychologists psychotherapists, social worker and professional educator). The GPP was created to allow participants to work on themselves, on their own personal story and on their dysfunctional family relationships with tools that stem from reflecting on 12 topics, which are explained and developed by a therapist also through film watching. METHODS The sample we analysed is assembled by 46 patients. The following variables were measured: alexithymia, avoidance coping strategies, self-esteem, quality of life and psychological distress. RESULTS From the results it emerges that by the end of the treatment program alcoholic patients and their relatives show improved self-esteem and an increase in general and relational functioning. There is a decrease in perceived stress, in the use of avoidance coping strategies, and in symptoms of anxiety and depression, as well as physical ones. There is no evidence of significant changes in the alexithymia variable. The effect of the group factor (alcoholic patient with/without relative) was significant only for the avoidance variable. DISCUSSION AND CONCLUSIONS Both patients and relatives show an improvement in all the evaluated variables except for alexithymia by the end of the treatment. Relatives have a positive effect in the patient's change in relation to the coping strategies, that are poorer in the subjects with their own relatives in the group compared with those who are alone.
SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images

The detection of nuclei and cells in histology images is of great value in both clinical practice and pathological studies. However, multiple reasons such as morphological variations of nuclei or cells make it a challenging task where conventional object detection methods cannot obtain satisfactory performance in many cases. A detection task consists of two sub-tasks, classification and localization. Under the condition of dense object detection, classification is a key to boost the detection performance. Considering this, we propose similarity based region proposal networks (SRPN) for nuclei and cells detection in histology images. In particular, a customized convolution layer termed as embedding layer is designed for network building. The embedding layer is added into the region proposal networks, enabling the networks to learn discriminative features based on similarity learning. Features obtained by similarity learning can significantly boost the classification performance compared to conventional methods. SRPN can be easily integrated into standard convolutional neural networks architectures such as the Faster R-CNN and RetinaNet. We test the proposed approach on tasks of multi-organ nuclei detection and signet ring cells detection in histological images. Experimental results show that networks applying similarity learning achieved superior performance on both tasks when compared to their counterparts. In particular, the proposed SRPN achieve state-of-the-art performance on the MoNuSeg benchmark for nuclei segmentation and detection while compared to previous methods, and on the signet ring cell detection benchmark when compared with baselines. The sourcecode is publicly available at: https://github.com/sigma10010/nuclei_cells_det.
A REVIEW OF THE ONE-PARAMETER DIVISION UNDISTORTION MODEL

Abstract. The one-parameter division undistortion model by (Lenz, 1987) and (Fitzgibbon, 2001) is a simple radial distortion model with beneficial algebraic properties that allows to reason about some problems analytically that can only be handled numerically in other distortion models. One property of this distortion model is that straight lines in the undistorted image correspond to circles in the distorted image. These circles are fully described by their center point, as the radius can be calculated from the position of the center and the distortion parameter only. This publication collects the properties of this distortion model from several sources and reviews them. Moreover, we show in this publication that the space of this center is projectively isomorphic to the dual space of the undistorted image plane, i.e. its line space. Therefore, projective invariant measurements on the undistorted lines are possible by the according measurements on the centers of the distorted circles. As an example of application, we use this to find the metric distance of two parallel straight rails with known track gauge in a single uncalibrated camera image with significant radial distortion.
Experiencia de observación y registro de un plan de clase en la educación en línea por el COVID-19, en el cuarto semestre, asignatura de PLC “Controladores Lógicos Programables”

The COVID-19 emergency has generated that education rotates in a virtual environment. It is important to make observations in this process to improve this type of study. This focuses on technical education and technological work at the Higher Technological Institute Tsa'chila, Ecuador, through the monitoring and evaluation of a teacher and his PLC chair during a syllabus unit. An analysis has been made based on 11 essential pedagogical questions for the training process; obtaining conclusions regarding each aspect analyzed, achievements and recommendations. The methodology used is a case study. Obtained as results that the use of technology is essential for technical and technological training, it has been identified that virtual devices help the practical training process of the student in the subject of PLC. However, the implementation of remote laboratories with IoT technology (internet of things) should be considered; to guarantee the granting of the professional profile for graduation in technological and technical careers. Another fundamental aspect is the work strategies by the teacher for the monitoring and feedback of the teaching-learning process, considering that there is still no total access to the internet and / or technological devices for the entire academic population.
Research Guides: Foundations of Innovation: Searching the Libraries

Library research guide for the Foundations of Innovation course at Columbia Business School.
Denoising of MR images Using Reformed Structural Loss

Noise reduction or denoising of images will help in getting true images from noisy images.The differentiations of noise from other part of the images are difficult because edge and texture are also having high frequency as like noise. This work addresses the issue of denoising in Magnetic Resonance Imaging (MRI). This work proposes a new technique known as Reformed Structural Loss based 3D Multi-scale Deep Neural Network (RSLMDNN-3D)which is a variation of Generative Adversarial Network (GAN). A generator and a discriminator circuit help the work to reduce the noise. In order to preserve more structural information, a reformed model of multi scale 3D CNN model is proposed as Generator of this GAN framework. The proposed work gives good results when compared to Wasserstein Generative Adversarial Network (WGAN) and CNN based approach.
Prophecy on Programming Language using Machine Learning Algorithms

A programming language is a computer language developers use to develop software programs, scripts, or other sets of instruction for computers to execute. It is difficult to determine which programming language is widely used. In our work, I have analyzed and compared the classification results of various machine learning models and find out which programming language is widely used by developers. I have used Support Vector Machine (SVM), K neighbor classifier (KNN),Decision Tree Classifier(CART) for our comparative study. My task is to analyze different data and to classify them for the efficiency of each algorithm in terms of accuracy, precision, recall, and F1 Score. My best accuracy was 94.29% percent which was found using SVM. These techniques are coded in python and executed in Jupyter NoteBook, the Scientific Python Development Environment. Our experiments have shown that SVM is the best for predictive analysis and from our study that SVM is the well-suited algorithm for the prediction of the most widely used programming language.
Cascade Decoders-Based Autoencoders for Image Reconstruction

Autoencoders are composed of coding and decoding units, hence they hold the inherent potential of high-performance data compression and signal compressed sensing. The main disadvantages of current autoencoders comprise the following several aspects: the research objective is not data reconstruction but feature representation; the performance evaluation of data recovery is neglected; it is hard to achieve lossless data reconstruction by pure autoencoders, even by pure deep learning. This paper aims for image reconstruction of autoencoders, employs cascade decoders-based autoencoders, perfects the performance of image reconstruction, approaches gradually lossless image recovery, and provides solid theory and application basis for autoencoders-based image compression and compressed sensing. The proposed serial decoders-based autoencoders include the architectures of multi-level decoders and the related optimization algorithms. The cascade decoders consist of general decoders, residual decoders, adversarial decoders and their combinations. It is evaluated by the experimental results that the proposed autoencoders outperform the classical autoencoders in the performance of image reconstruction.
Adam in Private: Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation.

Privacy-preserving machine learning (PPML) aims at enabling machine learning (ML) algorithms to be used on sensitive data. We contribute to this line of research by proposing a framework that allows efficient and secure evaluation of full-fledged state-of-the-art ML algorithms via secure multi-party computation (MPC). This is in contrast to most prior works, which substitute ML algorithms with approximated MPC-friendly variants. A drawback of the latter approach is that fine-tuning of the combined ML and MPC algorithms is required, which might lead to less efficient algorithms or inferior quality ML. This is an issue for secure deep neural networks (DNN) training in particular, this involves arithmetic algorithms thought to be MPC-unfriendly, namely, integer division, exponentiation, inversion, and square root. In this work, we propose secure and efficient protocols for the above seemingly MPC-unfriendly computations. Our protocols are three-party protocols in the honest-majority setting, and we propose both passively secure and actively secure with abort variants. A notable feature of our protocols is that they simultaneously provide high accuracy and efficiency. This framework enables us to efficiently and securely compute modern ML algorithms such Adam and the softmax function as is, without resorting to approximations. As a result, we obtain secure DNN training that outperforms state-of-the-art three-party systems; our full training is up to 6.7 times faster than just the online phase of the recently proposed FALCON@PETS'21 on a standard benchmark network. We further perform measurements on real-world DNNs, AlexNet and VGG16. The performance of our framework is up to a factor of about 12-14 faster for AlexNet and 46-48 faster for VGG16 to achieve an accuracy of 70% and 75%, respectively, when compared to FALCON.
Pre-professional Socialization of High Schools as a Leading Aspect of Students Socialization in the Conditions of Transformation of Modern Society

The article deals with the issues of socialization and pre-professional socialization of the individual in the aspect of transformations of modern society. Pre-professional socialization is one of the tasks of the school, the formation of which will help the student to find his place in life and realize his opportunities as a person and fit into the modern labor market.
PHILOSOPHICAL ANALYSIS OF THE SYNTHESIS OF MORALITY AND AESTHETICS IN THE PERSONAL MIND

This article is devoted to the philosophical analysis of the role of morality and aesthetics in the spiritual existence of the individual, their synthesis. The problems of formation and development of the ideal in the mind of the person, structure, levels of moral and aesthetic consciousness of the person are covered. It is also analyzed that in the spiritual existence of the individual, morality and aesthetics come at the same time, inseparably. Moral consciousness, aesthetic activity are closely intertwined, aspects related to the aesthetic attitude to reality are widely analyzed.
Padlet-Based in German Learning as a Trigger for Learning Motivation During a Pandemic

The purpose of writing this best practice is to impelement the use of padlets and find out the results and impact of using padlets on student’s motivation learning in German during the Covid-19 pandemic. The strategy used is using padlets. Padlet is a free online whiteboard app that has many advantages. By using padlets, teachers and students can upload and download various documents in the form of videos, photos, voice recordings, texts or other documents and can be updated at any time. Padlet is an atrractive medium and very easy to use. The method used in this best practice research is qualitative descriptive. Padlet is implemented to deliver “Familie, Wohnung, Essen und Trinken”material for class XI MIPA 1 and “Freizeit, Reisen, Tagesablauf der Lieblingsfiguren” material for class XII IPS 1, XII IPS 2 in the odd semester of the 2020/2021 school year (August-November) at SMAN 2 Wates. The steps in this implementation consist of the preparation, implementation and evaluation. The end of this best practice activity is that author gives a questionnaire to students regarding the evaluation of using padlets, which are then analyzed and used as basis for making conclusions. From the questionnaire, it was found more than 80% of students felt happy and interested in participating in distance learning when using padlet. More than 80% of students do assigments on time and more than 75% students no longer feel reluctant to ask questions or discuss with teachers or other students. The four indicators than used as author to make conclusion that padlet can increase students’ motivation learning to learn German during the Covid-19 pandemic.
Advanced Lattice Sieving on GPUs, with Tensor Cores.

In this work, we study GPU implementations of various state-of-the-art sieving algorithms for lattices (Becker-Gama-Joux 2015, Becker-Ducas-Gama-Laarhoven 2016, Herold-Kirshanova 2017) inside the General Sieve Kernel (G6K, Albrecht et al. 2019). In particular, we extensively exploit the recently introduced Tensor Cores – originally designed for raytracing and machine learning – and demonstrate their fitness for the cryptanalytic task at hand. We also propose a new dual-hash technique for efficient detection of ‘lift-worthy’ pairs to accelerate a key ingredient of G6K: finding short lifted vectors.
Dec-SGTS: Decentralized Sub-Goal Tree Search for Multi-Agent Coordination

Multi-agent coordination tends to benefit from efficient communication, where cooperation often happens based on exchanging information about what the agents intend to do, i.e. intention sharing. It becomes a key problem to model the intention by some proper abstraction. Currently, it is either too coarse such as final goals or too fined as primitive steps, which is inefficient due to the lack of modularity and semantics. In this paper, we design a novel multi-agent coordination protocol based on subgoal intentions, defined as the probability distribution over feasible subgoal sequences. The subgoal intentions encode macro-action behaviors with modularity so as to facilitate joint decision making at higher abstraction. Built over the proposed protocol, we present Dec-SGTS (Decentralized Sub-Goal Tree Search) to solve decentralized online multi-agent planning hierarchically and efficiently. Each agent runs Dec-SGTS asynchronously by iteratively performing three phases including local sub-goal tree search, local subgoal intention update and global subgoal intention sharing. We conduct the experiments on courier dispatching problem, and the results show that Dec-SGTS achieves much better reward while enjoying a significant reduction of planning time and communication cost compared with Dec-MCTS (Decentralized Monte Carlo Tree Search).
Discovering Research Hypotheses in Social Science using Knowledge Graph Embeddings

In an era of ever-increasing scientific publications available, scientists struggle to keep pace with the literature, interpret research results and identify new research hypotheses to falsify. This is particularly in fields such as the social sciences, where automated support for scientific discovery is still widely unavailable and unimplemented. In this work, we introduce an automated system that supports social scientists in identifying new research hypotheses. With the idea that knowledge graphs help modeling domain-specific information, and that machine learning can be used to identify the most relevant facts therein, we frame the problem of hypothesis discovery as a link prediction task, where the ComplEx model is used to predict new relationships between entities of a knowledge graph representing scientific papers and their experimental details. The final output consists in fully formulated hypotheses including the newly discovered triples (hypothesis statement), along with supporting statements from the knowledge graph (hypothesis evidence and hypothesis history). A quantitative and qualitative evaluation is carried using experts in the field. Encouraging results show that a simple combination of machine learning and knowledge graph methods can serve as a basis for automated scientific discovery.
Recommendations of Insurance Products based on User Behaviour

The remarkable increase in competition within the insurance sector has resulted in an overwhelming number of insurance products being available in the market. With rapid development of recommendation system, how to accurately predict Insurance policies using user lifestyle choices has become more and more important. The problem with the traditional systems is data sparseness. This paper proposes a recommender system to predict insurance products for new and existing customers. The main goal of the proposed system is to generate personalized recommendations based on the user lifestyle practices. By providing accurate personalised recommendations, the customer experience with the insurers can be improved.
Teacher Strategies in Teaching English Vocabulary to Young Learner at SDN Percobaan Palangka Raya

The purpose of this study was to describe the teaching vocabulary conducted by a teacher of SDN Percobaan Palangkaraya in academic year 2019/2020. There three problems were going to be discussed  in this study: (1) What are the strategies used by the teacher in teaching English vocabulary to young learners at SDN Percobaan Palangka Raya, (2) How is the implementation of teacher strategies in teaching English Vocabulary to young learners at SDN Percobaan Palangka Raya, (3) What are the problems to the implementation of teacher strategies in teaching English Vocabulary to young learners at SDN Percobaan Palangka Raya. The researcher used descriptive qualitative research to address these problems. In collecting data, the researcher used interview, observation in teaching process, and documentation as the supporting data. The subject of this study was an English teacher at the third Grade of SDN Percobaan Palangka Raya. Based on the finding, the researcher found that the strategies and the implementation used by the teacher were explanation, translation, presentation, and memorization. The problem faced by the teacher derived into two aspects, there were internal came from the teacher and external came from young learners, environment, and the facility of the school.
Data Oblivious Algorithms for Multicores

As secure processors such as Intel SGX (with hyperthreading) become widely adopted, there is a growing appetite for private analytics on big data. Most prior works on data-oblivious algorithms adopt the classical PRAM model to capture parallelism. However, it is widely understood that PRAM does not best capture realistic multicore processors, nor does it reflect parallel programming models adopted in practice. In this paper, we initiate the study of parallel data oblivious algorithms on realistic multicores, best captured by the binary fork-join model of computation. We first show that data-oblivious sorting can be accomplished by a binary fork-join algorithm with optimal total work and optimal (cache-oblivious) cache complexity, and in O(log n log log n) span (i.e., parallel time) that matches the best-known insecure algorithm. Using our sorting algorithm as a core primitive, we show how to data-obliviously simulate general PRAM algorithms in the binary fork-join model with non-trivial efficiency. We also present results for several applications including list ranking, Euler tour, tree contraction, connected components, and minimum spanning forest. For a subset of these applications, our data-oblivious algorithms asymptotically outperform the best known insecure algorithms. For other applications, we show data oblivious algorithms whose performance bounds match the best known insecure algorithms. Complementing these asymptotically efficient results, we present a practical variant of our sorting algorithm that is self-contained and potentially implementable. It has optimal caching cost, and it is only a log log n factor off from optimal work and about a log n factor off in terms of span; moreover, it achieves small constant factors in its bounds.