Reviewerly/exHarmony huggingface dataset:
SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution

With the development of Deep Neural Networks (DNNs), plenty of methods based on DNNs have been proposed for Single Image Super-Resolution (SISR). However, existing methods mostly train the DNNs on uniformly sampled LR-HR patch pairs, which makes them fail to fully exploit informative patches within the image. In this paper, we present a simple yet effective data augmentation method. We first devise a heuristic metric to evaluate the informative importance of each patch pair. In order to reduce the computational cost for all patch pairs, we further propose to optimize the calculation of our metric by integral image, achieving about two orders of magnitude speedup. The training patch pairs are sampled according to their informative importance with our method. Extensive experiments show our sampling augmentation can consistently improve the convergence and boost the performance of various SISR architectures, including EDSR, RCAN, RDN, SRCNN and ESPCN across different scaling factors (x2, x3, x4). Code is available at https://github.com/littlepure2333/SamplingAug
CULTURAL ASPECTS OF THE EDUCATIONAL PROCESS

The article deals with the interaction of culture and education, the influence of culture on the self-determination of the individual, its development as a person. The article describes the analysis of the study of the social experience of generations and its impact on the spiritual and moral development of the individual.
PHOTOVOICE ONLINE: LEARNING THE TECHNIQUE THROUGH THE REMOTE MODE

The pandemic caused by COVID-19 and the restrictions imposed around the world to limit its spread have led to significant changes in everyday life from a social and economic point of view. In particular, one of the dimensions that has suffered most from these changes is that linked to learning: schools and universities have had to adapt by relying on new learning strategies and through the use of online platforms. While on the one hand the timing dictated by urgency was a challenge in the school and academic world for both teachers and students, on the other hand they represented the necessary thrust for the search for new tools for distance learning.The use of online platforms, very widespread among the younger population, has favored the sudden adaptation to these new forms of distance learning during the lockdown, although not without difficulty.This work aims to highlight the strengths and criticalities of learning an action search tool that has been adapted to remote mode during lockdown: the Photovoice, participatory action research technique developed by Wang in the 1990s, which includes 4 phases: an introductory workshop, individual photographic activity, analysis of the photos of the participants through the SHOWeD method and the final event with suggestions for social action. As a visual method photovoice uses the photos taken by the participants to bring out their point of view and stimulate reflection. To this end, 130 young adults used this technique online during the period of the pandemic, using the Teams platform.The goal of this work is to return the individual and group reflections of the participants relating to the use of the online Photovoice and its learning through an online platform, bringing out its strengths, criticalities and challenges.The data collected were analyzed, on the basis of grounded theory, with the support of the NVivo 12 software. The results show that there are functional factors relating to learning the online Photovoice technique, such as the creation of a favorable group climate and the activation of a group decision-making process and factors to consider for its learning, such as those related to connection difficulties. In light of these results, it can be pointed out that, despite the distance and the particularly complex period, the online mode has proved to be a valid method for training and in particular for learning a participatory action research technique.
FRAMEWORK FOR STUDYING STUDENTS’ EXPERIENCES OF PRACTISING FOREIGN LANGUAGE SPEAKING SKILLS WITH CONVERSATIONAL AGENTS IN SPOKEN DIALOGUE SYSTEMS

Information about the paper titled "FRAMEWORK FOR STUDYING STUDENTS’ EXPERIENCES OF PRACTISING FOREIGN LANGUAGE SPEAKING SKILLS WITH CONVERSATIONAL AGENTS IN SPOKEN DIALOGUE SYSTEMS" at IATED Digital Library
Inovasi Perpustakaan Perguruan Tinggi Muhammadiyah–‘Aisyiyah (PTMA) Menghadapi Pandemi Covid-19: Sebuah Studi Literatur Bunga Rampai

Introduction. The COVID-19 pandemic has had an impact on education systems around the world, including Indonesia. The government carried out temporary closure of public services to reduce the number of additional cases. One of the community services affected is the library as a support for education. As a supporter of education, the perceived impact is the non-utilization of services in the library. Efforts made by the library to continue to provide services are by changing the system and management mechanism in the library. This paper aims to find out the innovations and strategies implemented in several PTMA libraries in the era of the covid-19 pandemic.Methodology. This study uses a literature review to collect data and documentation to support the analysis.Result. The result of this paper is that PTMA library librarians must be able to use facilities or technology to serve the academic community.Conclusion. The COVID-19 pandemic has not only hurt society. The other side of the Covid-19 pandemic is being able to develop competence through online seminars. In addition, the handling of the COVID-19 pandemic, which is gradually improving, has forced libraries to reopen services while still implementing health protocols to suppress the rate of transmission.
ReGroup: Recursive Neural Networks for Hierarchical Grouping of Vector Graphic Primitives

Selection functionality is as fundamental to vector graphics as it is for raster data. But vector selection is quite different: instead of pixel-level labeling, we make a binary decision to include or exclude each vector primitive. In the absence of intelligible metadata, this becomes a perceptual grouping problem. These have previously relied on heuristics derived from empirical principles like Gestalt Theory, but since these are ill-defined and subjective, they often result in ambiguity. Here we take a data-centric approach to the problem. By exploiting the recursive nature of perceptual grouping, we interpret the task as constructing a hierarchy over the primitives of a vector graphic, which is amenable to learning with recursive neural networks with few human annotations. We verify this by building a dataset of these hierarchies on which we train a hierarchical grouping network. We then demonstrate how this can underpin a prototype selection tool.
Priority search simulation for flood evacuation routes using fuzzy AHP approach

A flood is an event of an increase in water volume above the standard limit due to increased rainfall, rising sea levels, storms, and others that result in submerging an area. Floods are disasters that can cause damage and loss of property, disrupt community activities and even cause loss of life. The central defiance to rescue flood victims is choosing a safe route for flood victims to reach the evacuation site. To be able to choose a safe route for flood victims, a flood evacuation simulation is made. Flood evacuation simulation is part of the game that has been created and aims to provide education about the weight of the obstacle that needs to be considered in selecting routes for flood victims. In this flood evacuation simulation, each road has obstacles. The method proposed for choosing safe routes for flood victims is the Fuzzy-based Analytical Hierarchy Process (Fuzzy AHP). The calculation of road route weight using the Fuzzy AHP method will produce the weight for each route. The smallest weight route shows the priority route and the safe route for flood victims to pass. In this case, the Fuzzy AHP method's calculation produces the lowest weight of 0.02347, which is achieved by route 5, the route passing through S-a-b-d-D. This route is a priority route that is safe for flood victims to pass through.
An Investigation Into the Impacts of ICT in the Compacting of COVID-19: A Namibian Context

The study aimed to investigate into the impact of a National COVID-19 Health contact tracing and monitoring system for Namibia. The study used qualitative methods as are search strategy. Qualitative data was collected through zoom meeting and a Google form link was distributed to the participants. The findings of the study revealed that a total of 18 participants responded to the semi-structured questions of which 38.9% represents male while female 61.1%. The age group between 18-25 response rate were 22.2%, age group between 26-35 response rate were 55.6%, age group between 36-45 response rate were 16.7% and the age group between 46 and above response rate was 10% represented in green colour to represent participants who fall in the age group between 46 and above. Therefore, the present study will investigate into the impact of a National COVID-19 health contact tracing and monitoring system and proposed a system which will allow every public member who visits an enclosed public place by capturing their demographic information as well as the date and time the facility was visited. The system replaces the paper-based method of recording the information of people visiting public places with an entrance that allows the coming in and out of people. The proposed system will also allow for real-time monitoring of temperature changes of individuals.
Perceptual Evaluation of Driving Scene Segmentation.

Human visual perception forms different levels of abstractions expressing the essential semantic components in the scene at different scales. For real-world applications such as driving scene perception, abstractions of both coarse-level, such as the spatial presence of the lead vehicle, and the fine-level, such as the words on a traffic sign, serve as important signals for driver's decision making. However, the granularities of perception required for levels of abstractions are generally different. While current computer vision research makes significant progress in tasks of understanding the global scene (image classification), and dense scene (semantic segmentation), our work takes steps to explore the gap in between. In this paper, we propose Multi-class Probability Pyramid as a representation built on the top of pixel-level semantic scene labels. This representation forms region-level abstractions by controlling the granularity of local semantic information, and thus disentangles the variation of scene semantics at different resolutions. We further show how such representation can be effectively used for evaluation purposes, including interpretable evaluation of scene segmentation and unsupervised diagnosis of segmentation predictions.
Machine Learning-based analysis of metagenomic profiles for the stratification of patients affected by type I Diabetes

Although diabetes is known to be a disease that is closely linked to genetics and epigenetics, the mechanisms underlying the onset and/or progression of the disease have sometimes not been fully addressed in order to help patients. In recent years and due to a large number of recent studies, it is known that changes in the balance of the microbiota can cause a battery of diseases. Nowadays, massive sequencing techniques allow us to obtain the metagenomic profile of an individual, whether from a part of the body, organ or tissue, thus being able to identify the composition of a given microbiota. The use of Machine Learning (ML) techniques, which do not have any biological assumptions, are capable of identifying expression patterns and relationships between characteristics. We present a model based on ML techniques and a metagenomic signature capable of stratifying patients with Type I Diabetes (TID), to serve as a support tool for clinical decision making.
The Level Of Use And Acceptance Towards Online Learning In Covid-19 Pandemic (Case Study Of College Students In Pontianak)

This research to determine the perceptions of college students in Pontianak City on the level of adoption, use and acceptance of online learning during the COVID-19 pandemic. This research is a descriptive quantitative using non-probability sampling with purposive sampling and quota sampling techniques. The sample of this research is college students in Pontianak City with 180 respondents. The data analysis technique used is descriptive analysis with a focus on the elements of the average. The results showed the respondents 'responses regarding Attitude, Affect and Motivation towards online learning when the Covid-19 epidemic obtained the highest index value of 153, included in the high category, respondents' responses regarding Perceived Behavioral Control obtained an index value of 145.5 also included in the high category. Meanwhile, the respondents' responses regarding Perceived Behavioral Control have an index value of 120.5 in the medium category. The conclusion of research is that college students in Pontianak City prefer face-to-face learning activities compared to online learning. The ease of use of the online education platform and the acquisition of higher scores were also felt by students when participating in online learning. The majority of university students in Pontianak are still not satisfied with online learning
BACKGROUND AS FOREGROUND: FROM SUBTLE TECHNIQUES TO THE STRATEGY OF THE DISAPPEARANCE OF THE TEACHER IN THE POSTDIGITAL

With this communication, I am proposing a reflection on the education system and the role of the professor in the post-digital world. I seek a critical perspective based on emancipation from stultifying and reproductive learning models, drawing above all from the work of Jacques Rancière. I try to articulate this perspective with the distance learning plan, focusing on the pandemic period. While the pandemic has highlighted a series of problems and changes that already existed, these problems and changes have also accelerated without time to reflect on these changes. If it was necessary to adopt digital tools and models that were applied on a one-off basis, the need to make this use mandatory and systematic has revealed new possibilities and many obstacles as well. I, therefore, propose a reflection on these changes, and the virtues and obstacles associated to online teaching. To think about this framework, I use my own teaching experience during the pandemic in the curricular unit of History and Theory of Communication, as part of the Communication Design course.
Asymmetric Encryption Method Proposed for Arabic Letters Using Artificial Neural Networks

Asymmetric encryption algorithms suffer the problem of high execution time. This paper presents a proposed methodology to perform encryption and decryption of messages written in Arabic, using artificial intelligence represented by artificial neural networks based on the RSA algorithm. The method is based on dividing the message into partial messages. The arrays of weights and biases in the neural network layers are considered the encryption or decryption key, according to their function. All steps of the proposed method and how it works were shown and used in the MATLAB environment to design a system for encrypting and decrypting messages written in Arabic. The results proved the effectiveness of the proposed methodology and its superiority over the RSA algorithm in terms of execution time for both encryption and decryption. The encryption time in the proposed algorithm is close to the time of decryption, unlike what it was in the RSA algorithm, where the encryption time was relatively much greater than the decryption time. The proposed method was tested on four text files of 50KB, 100KB, 150KB, and 200KB, over two hundred iterations. The average improvement in execution time was 1,330ms and 4,497.5ms for encoding and decoding, respectively. KEYWORDS Data protection, decryption, execution time, security, RSA algorithm, performance
A comparison of streaming models and data augmentation methods for robust speech recognition

In this paper, we present a comparative study on the robustness of two different online streaming speech recognition models: Monotonic Chunkwise Attention (MoChA) and Recurrent Neural Network-Transducer (RNN-T). We explore three recently proposed data augmentation techniques, namely, multi-conditioned training using an acoustic simulator, Vocal Tract Length Perturbation (VTLP) for speaker variability, and SpecAugment. Experimental results show that unidirectional models are in general more sensitive to noisy examples in the training set. It is observed that the final performance of the model depends on the proportion of training examples processed by data augmentation techniques. MoChA models generally perform better than RNN-T models. However, we observe that training of MoChA models seems to be more sensitive to various factors such as the characteristics of training sets and the incorporation of additional augmentations techniques. On the other hand, RNN-T models perform better than MoChA models in terms of latency, inference time, and the stability of training. Additionally, RNN-T models are generally more robust against noise and reverberation. All these advantages make RNN-T models a better choice for streaming on-device speech recognition compared to MoChA models.
Accelerating Neural Architecture Search with Rank-Preserving Surrogate Models

Over the past years, deep learning has enabled significant progress in several tasks, such as image recognition, speech recognition and language modelling. Novel Neural architectures are behind this achievement. However, manually designing these architectures by human experts is time-consuming and error-prone. Neural architecture search (NAS) automates the design process by searching for the best architecture in a huge search space. This search process requires evaluating each sampled architecture via time-consuming training. To speed up NAS algorithms, several existing approaches use surrogate models that predict the neural architectures’ precision instead of training each sampled one. In this paper, we propose RS-NAS for Rank-preserving Surrogate model in NAS, a surrogate model trained with a rank-preserving loss function. We posit that the search algorithm doesn’t need to know the exact accuracy of a candidate architecture but instead needs to know if it is better or worse than others. We thoroughly experiment and validate our surrogate models with state-of-the-art search algorithms. Using the rank-preserving surrogate models, local search in DARTS finds a 2% more accurate architecture than using the NAS-Bench-301 surrogate model on the same search time. The code and models are available: https://github.com/IHIaadj/ranked_nas
PEDAGOGICAL-PSYCHOLOGICAL AND METHODOLOGICAL ASPECTS OF THE USE OF MOBILE TECHNOLOGIES

In the environment of e-learning, the effectiveness of education is often observed when there is a strong internal motivation of learners due to the lack of external motivation, which belongs to the form of full-time education, lack of control. Foreign experience in the development of e-learning environments shows that the standardization of e-learning environments is relevant for the development and implementation of the basic requirements for the organization of the educational process in our country. It should take into account its interactivity, flexibility, longterm use, practicality and economic feasibility.
Rancang Bangun Sistem E-Recruitment Karyawan pada PT Haleyora Powerindo

Information technology that can be used in this recruitment process is the recruitment of employees for web-based companies, smartphones and so on. e-Recruitment is a system that assists in the recruitment process of employees when selecting employees for recruitment at companies or at certain agencies. With an integrated employee recruitment system it will also make it easier for admins to control applicants who meet criteria and those who do not meet the criteria. Information technology-based e-recruitment can also be implemented for small companies to large companies. A website is a collection of pages that contain information stored on the internet that can be accessed or viewed via the internet network on devices that can access the internet itself such as computers. MySQL is one type of database server that is very popular, this is because MySQL uses SQL as the base language for accessing its database. MySQL is Open Source, this software is equipped with source code (code used to create MySQL), its executable form or code that can be run directly in the operating system UML (Unified Modeling Language) is a language for specification, visualization, building and documentation of an OO (Object-Oriented) based software system.
Scientific capacity of Russian universities: Problems and prospects

In the recent years, the contribution of Russian universities into science and technology has increased. The RF Government has been intensively developing and implementing instruments to support fundamental and applied sciences and to ensure implementation of their achievements into real economy and social sphere. Several Russian universities accomplish world-class studies and research, in particular, classic universities like Moscow State University, St. Petersburg State University, Ural Federal University, Tomsk State University, Moscow Institute of Physics and Technology (MIPT), Moscow Engineering Physics Institute (MEPhI), St. Petersburg Polytechnic University. Academic science has much to offer the real sector as the universities are much closer to and has got the tighter relations with the entrepreneurial sector than the state-financed (80-90% funding) research institutes. However, average universities fall short compared to those mentioned above, partially due to the lack of extrabudgetary financing by business organizations which makes a little bit over 30%, while in the developed countries this figure sometimes exceeds 70%.
Face Recognition System Using Local Features Fusion for Multi-Masks

Abstract Face recognition is a relatively novel research field, and its application is closely related to numerous other areas. Moreover, it is emerging as a critical research theme due to its broad range of applications. Thus, many face recognition methods use a variety of feature extraction approaches. Nonetheless, the issue continues to be challenging, particularly identifying non-biological entities. This paper proposes an extended descriptor for local features of an effectual facial recognition system using a local directional pattern operator. This technique combines the Frei-Chen and Robinson masks’ strengths by fusion of the directional features of LDP for these two masks; this elicits a robust feature extraction method for distinguishing faces. Experimental results using the Yale database show that the extended descriptor considerably improved recognition rate and better performance than traditional local feature descriptors.