Reviewerly/exHarmony huggingface dataset:
COLLISION WITH THE SOVIET REALITY: WAYS OF ADAPTATION OF PROFESSOR OF MATHEMATICS OF KAZAN UNIVERSITY N.N. PARFENTJEV

The article raises the problems of professional adaptation of university intelligentsia of the Tatar province/Tatar ASSR in 1920-1930s. In particular, the social practices of Professor of Mathematics of Kazan University N.N. Parfentjev are studied. Involvement of the complex of archival documents in the research allowed revealing the ambiguous position of the university intelligentsia in relation to the social changes that took place in the professional sphere. During the period under study, there is a rich palette of adaptive practices used by scientists, ranging from demonstrating full loyalty to the new government and ending with a hostile attitude to all Soviet initiatives. Of particular interest is the position of the scientists who occupied a marginal position. These include N. N. Parfentjev. The scientist, often demonstrating loyalty to the new power, was able to use the Soviet structures and realize his scientific and professional luggage in the new conditions. The article proves that the adaptation practices used by Professor Parfentjev were characteristic of a number of scientists, which led to changes in their social and professional space and contributed to the formation of a new identity.
Analytical solutions of differential equations based on genetic meta-heuristic algorithm and ant colony optimization

Many issues are expressed in terms of various applied sciences such as physics, chemistry, and economics, which are concerned with the examination of variations of one or more variables, by differential equations. The prediction of climate, quantum mechanics, wave propagation and dynamics of the stock market is some of these examples, whose quick and accurate solution will have tremendous effects on human life, and therefore several methods have been proposed for solving differential equations.The main objective of this study was to investigate the applicability of the antler colony genetic algorithm to the production of experimental solutions and improve them to produce numerical analytic-numerical solutions of various types of ordinary differential equations. An antler colony optimization algorithm (ACO) has an appropriate algorithm with high convergence accuracy and speed for finding approximate solutions for solving optimization problems using probability function dependent on the amount of residual effect of anti-movement. Genetic algorithm is also an optimization method based on mutated and intersect operators with a wide search area that prevents the algorithm from trapping in the local response. The combination of these two algorithms creates an algorithm with maximum efficiency. Examining various examples in the final section of the article will highlight the speed and accuracy of the proposed method.
A Prospective Comparative Study of Peripheral Blood Smear, Modified Centrifuged Buffy Coat Smear and Rapid Malaria Antigen Detection Test in Diagnosing Malaria at a Tertiary Care Hospital in Western Maharashtra

Malaria is a life-threatening parasitic disease & is a major public health problem.Considering the prevalence of malaria infection in India, there is an urgent need to look for tests that are simple, inexpensive and can be used to improve diagnosis, so that accurate treatment and management can be done.Present study was undertaken to compare and study the efficacy of peripheral blood smear (PBS), modified centrifuged buffy coat smear (CBCS) & rapid malaria antigen detection test (RDT) in malarial diagnosis.The study included all cases with fever for >24 hours with symptoms suggestive of malaria.Patients who came for follow up visit of an earlier episode of malaria or within 4 weeks of post treatment were excluded.For statistical analysis of results, RDT was used as gold standard.Highest number of cases were detected by RDTs (97.83%) followed by CBCS (91.30%).PBS was found to be a better test to diagnose malaria than RDT.CBCS were found to be of less utility in diagnosing malaria as compared to RDT.CBCS would be especially useful with negative or low parasitic index (PI) which may be missed by the conventional PBS.Hence, present study concludes that, the CBCS method can be adopted in the routine as it just needs an additional centrifugation procedure to the conventional method making PBS.
LibGuides: ESL 91 - Controversial: Home

This guide serves as a tool for students who will be writing research papers in their ESL 91 class.
Effects of Perceived Career Barriers on Career Decision Level and Career Preparation Behavior : Focused on Female College Students majoring in Aviation Tourism

Entering the era of the 4th industrial revolution, the labor market shrinks, and the problem of youth unemployment is on the rise as a serious problem in our society due to economic recession. Especially, before entering society after graduating from university, female students suffer difficulty in getting a job more than male students due to social and environmental barriers. Thus, this study would investigate the effects of perceived career barriers in female college students majoring in aviation tourism on their career decision level and career preparation behavior. The subjects of the study were 600 female college students majoring in aviation tourism, and for statistical analysis, SPSS WIN 21.0 was utilized. As a result of the analysis, first, of career barriers, lack of job information, lack of interest, conflict with parents, anxiety about the future, interpersonal difficulties, and age issues had significant negative effects on their career decision level. Second, of career barriers, economic difficulties, lack of interest, lack of self-clarity, lack of job information, anxiety about the future, and interpersonal difficulties had significant negative effects on career preparation behavior. Third, career decision level had significant positive effects on career preparation behavior. As an implication drawn based on the analysis results, it is necessary to make efforts to enhance the students’ career competency, improving career counseling, operating a career program and developing a career support plan in order to lower the perceived career barriers in female college students majoring in aviation tourism and improve their career decision level and career preparation behavior.
The Universal Neural Network Model for Solving the Navier-Stokes Equation

Considered the solving of Navier-Stokes equation with neural network with the same architecture as for Laplace and Poisson equations. Developed neuroequations, proved assertions about the output values of the neurons.
Zero-Shot Generation of Human-Object Interaction Videos

Generation of videos of complex scenes is an important open problem in computer vision research. Human activity videos are a good example of such complex scenes. Human activities are typically formed as compositions of actions applied to objects -- modeling interactions between people and the physical world are a core part of visual understanding. In this paper, we introduce the task of generating human-object interaction videos in a zero-shot compositional setting, i.e., generating videos for action-object compositions that are unseen during training, having seen the target action and target object independently. To generate human-object interaction videos, we propose a novel adversarial framework HOI-GAN which includes multiple discriminators focusing on different aspects of a video. To demonstrate the effectiveness of our proposed framework, we perform extensive quantitative and qualitative evaluation on two challenging datasets: EPIC-Kitchens and 20BN-Something-Something v2.
Eliminating the redundant data for side channel in cloud

In recent years, the amount of data stored at the cloud storage is increasing rapidly due to the prevalence of data outsourcing. In order to be cost-effective and to reduce the bandwidth consumption, cloud stores use cross-user client-side data deduplication which eliminates the need to store redundant copies by keeping only a single copy of the data at the cloud storage. Cloud storage providers generally adopt data deduplication, a technique for eliminating redundant data by keeping only a single copy of a file, thus saving a considerable amount of storage and bandwidth. Even then, the attackers use many deduplication protocols to steal information. For example, an attacker can perform the duplicate check to verify whether a file (e.g., a pay slip, with a specific name and salary amount) is already stored (by someone else), hence breaching the user privacy.
Long Distance Relationships without Time Travel: Boosting the Performance of a Sparse Predictive Autoencoder in Sequence Modeling

In sequence learning tasks such as language modelling, Recurrent Neural Networks must learn relationships between input features separated by time. State of the art models such as LSTM and Transformer are trained by backpropagation of losses into prior hidden states and inputs held in memory. This allows gradients to flow from present to past and effectively learn with perfect hindsight, but at a significant memory cost. In this paper we show that it is possible to train high performance recurrent networks using information that is local in time, and thereby achieve a significantly reduced memory footprint. We describe a predictive autoencoder called bRSM featuring recurrent connections, sparse activations, and a boosting rule for improved cell utilization. The architecture demonstrates near optimal performance on a non-deterministic (stochastic) partially-observable sequence learning task consisting of high-Markov-order sequences of MNIST digits. We find that this model learns these sequences faster and more completely than an LSTM, and offer several possible explanations why the LSTM architecture might struggle with the partially observable sequence structure in this task. We also apply our model to a next word prediction task on the Penn Treebank (PTB) dataset. We show that a 'flattened' RSM network, when paired with a modern semantic word embedding and the addition of boosting, achieves 103.5 PPL (a 20-point improvement over the best N-gram models), beating ordinary RNNs trained with BPTT and approaching the scores of early LSTM implementations. This work provides encouraging evidence that strong results on challenging tasks such as language modelling may be possible using less memory intensive, biologically-plausible training regimes.
The Effect Of Contextual Teaching And Learning (Ctl) Approach Toward The Reading Achievement Of Class V Students At Sapiria Elementary School, Barombong District, Gowa Regency

Contextual Learning Teaching and Learning (CTL) on the reading achievement of fifth grade students of SD Inpres Sapiria, Barombong District, Gowa Regency.This type of research uses experimental research with research in this study are all fifth grade students of SD Inpres Sapiria District Barombong Gowa Regency with a total population of 56 students.The results of the research analysis showed the average reading achievement of students taught by using learning Contextual Teaching and Learning (CTL) was 84,931 of the 29 people who used conventional averaging 77,370 of the 27 students.Based on the results of the analysis obtained from CTL (Contextual Teaching and Learning) a positive and significant effect on student achievement in reading and learning is more effective than conventional methods of learning Indonesian reading skills at SD Inpres Sapiria in Barombong District, Gowa Regency.
Organizing Joint Activities of Teachers and Students in the Process of Preparation of Culture and Creative Projects

The article outlines principal directions in preparation of culture projects by future music teachers during their studies at Taras Shevchenko National University "Chernihiv Colehium". The author presents some academic views on the essence of such concepts as "project" and "project activity". Applying the project method in the study of academic disciplines is described as one which allows all students to identify their own abilities in accordance with their interests, gain practical experience in the particular field, promote self-realization, and also supports interest in studying in the chosen subject area. In future music teachers training at Taras Shevchenko National University "Chernihiv colehium", the directions following in creating culture projects are as follows. The first one provides the involvement of students in the project activity, aimed at mastering specific training material. The next one stipulates the preparation of individual projects, aimed at helping students express themselves, their scriptwriting and directing skills, and their own vision. Another direction is connected with the participation of students and teachers in concerts. This direction helps to identify creative abilities of future teachers in the following areas: scriptwriting, casting, production of performances (individual or group). The next one characterizes vocational guidance work, which covers, in addition to traditional concert performances in secondary schools, such forms of work as: flash mobs held at schools, organizing games with students, making trainings for students.It is important to organize joint activities for teachers and students when creating these projects. Their features are: the teacher’s own example, generating an atmosphere of trust and partnership, support for initiatives by future music teachers.
133 Factor’s Contrubuting for Non-Compliance Physiotherapy Sesions among Geriatric Patient at Physiotherapy Fall Intervention Program

Abstract Introduction Current world is going through an increase in ageing population with one in eight people in the world are aged 60 or over. By 2025, 15 % of Malaysia population will be aged above 60 years old. Physiotherapy playing a major role in facilitates healthy ageing process. However, compliance to physiotherapy session is still low among geriatric population. A defaulter was defined as a patient who presented for less than 6 visits during the whole period. Method A Retrospective study will calling patient after patient did not turn up the physiotherapy appointment and interview was done on appointment with the doctor together. This study was conducted at Geriatric Clinic, Hospital Kuala Lumpur. Physiotherapy session records were tracked to get there number of defaulters in Physiotherapy Geriatric clinic during 2017 &amp; 2018. Result Reasons for defaulting the therapy session was gained via phone call interview and during Geriatrician appointment. Total of 223 samples were tracked and interviewed in this study. The result reveals 73% of the patients defaulted in 2017 and 51% in 2018 respectively. Lack of facilities comprises 22% of factors for non-compliance. Meanwhile other factors such as human resource and functional capabilities contribute 14% and 19% respectively for patient’s non-compliance. Conclusion As conclusion, clinic need to take steps such as call to remind their appointment and provide proper home education to increase patient’s compliance. Together with in poor compliance which was successfully brought down by taking into consideration patient factors. This may be the best compromise until which time where special parking lots, improved transportation services or adequate skilled facilities in the periphery are made available to the patients.
PROBLEMS OF DIGITAL HOLOGRAMS SYNTHESIS DUE TO MASS DATA

The problems of digital synthesis of holograms due to big data and integral calculations of mass data are analyzed. Methods and algorithms are discussed and some examples of how to overcome these problems are given. A method of using symmetry in the cores of the Fresnel integral transform to reduce the amount of computation is proposed. Examples of the appearance of bands in a hologram arising from the discrete representation of the frame are given, and limitations that prevent their appearance are proposed.
One-Stage Inpainting with Bilateral Attention and Pyramid Filling Block

Recent deep learning based image inpainting methods which utilize contextual information and two-stage architecture have exhibited remarkable performance. However, the two-stage architecture is time-consuming, the contextual information lack high-level semantics and ignores both the semantic relevance and distance information of hole's feature patches, these limitations result in blurry textures and distorted structures of final result. Motivated by these observations, we propose a new deep generative model-based approach, which trains a shared network twice with different targets and utilizes a single network during the testing phase, so that we can effectively save inference time. Specifically, the targets of two training steps are structure reconstruction and texture generation respectively. During the second training, we first propose a Pyramid Filling Block (PF-block) to utilize the high-level features that the hole regions has been filled to guide the filling process of low-level features progressively, the missing content can be filled from deep to shallow in a pyramid fashion. Then, inspired by the classical bilateral filter [30], we propose the Bilateral Attention layer (BA-layer) to optimize filled feature map, which synthesizes feature patches at each position by computing weighted sums of the surrounding feature patches, these weights are derived by considering both distance and value relationships between feature patches, thus making the visually plausible inpainting results. Finally, experiments on multiple publicly available datasets show the superior performance of our approach.
Expanding Label Sets for Graph Convolutional Networks

In recent years, Graph Convolutional Networks (GCNs) and their variants have been widely utilized in learning tasks that involve graphs. These tasks include recommendation systems, node classification, among many others. In node classification problem, the input is a graph in which the edges represent the association between pairs of nodes, multi-dimensional feature vectors are associated with the nodes, and some of the nodes in the graph have known labels. The objective is to predict the labels of the nodes that are not labeled, using the nodes features, in conjunction with graph topology. While GCNs have been successfully applied to this problem, the caveats that they inherit from traditional deep learning models pose significant challenges to broad utilization of GCNs in node classification. One such caveat is that training a GCN requires a large number of labeled training instances, which is often not the case in realistic settings. To remedy this requirement, state-of-the-art methods leverage network diffusion-based approaches to propagate labels across the network before training GCNs. However, these approaches ignore the tendency of the network diffusion methods in biasing proximity with centrality, resulting in the propagation of labels to the nodes that are well-connected in the graph. To address this problem, here we present an alternate approach to extrapolating node labels in GCNs in the following three steps: (i) clustering of the network to identify communities, (ii) use of network diffusion algorithms to quantify the proximity of each node to the communities, thereby obtaining a low-dimensional topological profile for each node, (iii) comparing these topological profiles to identify nodes that are most similar to the labeled nodes.
ACCOMPANYING PROGRAM OF UNIVERSITY GALLERIES AS A TOOL FOR NON-FORMAL AND INFORMAL EDUCATION

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 2151-2158ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.0594Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
On privacy preserving data release of linear dynamic networks

Distributed data sharing in dynamic networks is ubiquitous. It raises the concern that the private information of dynamic networks could be leaked when data receivers are malicious or communication channels are insecure. In this paper, we propose to intentionally perturb the inputs and outputs of a linear dynamic system to protect the privacy of target initial states and inputs from released outputs. We formulate the problem of perturbation design as an optimization problem which minimizes the cost caused by the added perturbations while maintaining system controllability and ensuring the privacy. We analyze the computational complexity of the formulated optimization problem. To minimize the $\ell_0$ and $\ell_2$ norms of the added perturbations, we derive their convex relaxations which can be efficiently solved. The efficacy of the proposed techniques is verified by a case study on a heating, ventilation, and air conditioning system.
TEACHING COMPUTER SCIENCE METHODOLOGY WORKSHOP

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 482-490ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.0156Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
Integrating Lexical Knowledge in Word Embeddings using Sprinkling and Retrofitting

Neural network based word embeddings, such as Word2Vec and GloVe, are purely data driven in that they capture the distributional information about words from the training corpus. Past works have attempted to improve these embeddings by incorporating semantic knowledge from lexical resources like WordNet. Some techniques like retrofitting modify word embeddings in the post-processing stage while some others use a joint learning approach by modifying the objective function of neural networks. In this paper, we discuss two novel approaches for incorporating semantic knowledge into word embeddings. In the first approach, we take advantage of Levy et al's work which showed that using SVD based methods on co-occurrence matrix provide similar performance to neural network based embeddings. We propose a 'sprinkling' technique to add semantic relations to the co-occurrence matrix directly before factorization. In the second approach, WordNet similarity scores are used to improve the retrofitting method. We evaluate the proposed methods in both intrinsic and extrinsic tasks and observe significant improvements over the baselines in many of the datasets.