Reviewerly/exHarmony huggingface dataset:
Visualization of Blood Erythrocytes Sedimentation Process on Transitive Layer ”Erythrocytes - Plasma”

Actuality of the presented problem is caused by widespread usage of laboratory blood tests. The purpose of this work is to prove specific character of this analysis. In modern medical practice it is considered that the blood sedimentation rate does not represent an opportunity to diagnose diseases. Results of visualization for erythrocytes sedimentation process and dynamics on border ”erythrocytes - plasma” convince different. Process is registered by devices for visualization of blood cells deposition with high degree of space and time resolution in real time. The research of dynamic changing of border and transitional layer ”erythrocytes - plasma” character gives chance to considerably enlarge the content of the diagnostic indicator of erythrocytes sedimentation rate (ESR).
Deep Reinforcement Learning with Hierarchical Structures

Hierarchical reinforcement learning (HRL), which enables control at multiple time scales, is a promising paradigm to solve challenging and long-horizon tasks. In this paper, we briefly introduce our work in bottom-up and top-down HRL and outline the directions for future work.
LEARNING SITUATIONS AND ICT SKILLS IN MATHEMATICS IN THE FIRST YEAR OF THE SECONDARY SCHOOL FOR ECONOMICS

This research presents one of many possibilities of how to check and repeat knowledge at the end of the school year and at the same time provide an answer to questions often asked by students who are disinterested in learning mathematics: "Where will we need it?" or "Why do we learn this?" To provide them with an answer and motivate them at the same time, this research focused on actively encouraging students to find the answers themselves and thus find the importance of learning mathematics. With a changed way of repeating and consolidating the material at the end of the school year, the aim of this research is to reduce the fear of mathematics and increase the motivation of students in the following year. Students, divided into groups, choose the topic or examples of the use of mathematical knowledge of the first year of secondary technical and professional education in everyday life, and thus shape the learning situation (LS). The goal of preparing the LS is for students to make sense of the subject matter with examples from everyday life. For the selected LS, they prepare a short story with tasks that they solve by calculation, prepare presentations and also, present the LS to the classmates. During the formation of the LS and the preparation of presentations, students are active (active learning methods: task search, knowledge of the subject matter, interviews, problem solving, use of mathematical applications, teamwork, problem solving…) and cooperate with each other. They are constantly developing more 21st century competencies (self-regulation, collaboration, problem solving) and digital competencies. With the formation of the LS, the world of mathematical knowledge gets a little closer to students, they lose their fear of mathematics and become more motivated. Keywords: learning situations, collaborative work, active learning methods, mathematics in everyday life, deviation from the established
La regulación de la publicidad encubierta en el marketing de influencers para la Generación Z: ¿Cumplirán los/as influencers el nuevo código de conducta de Autocontrol?

The use of influencers by brands in their strategies, mainly digital, is currently a consolidated practice. However, from a legal point of view, the nature of the message is not always identified, inducing or may induce an error to the recipient. This behavior is also favored by the influencers themselves in their ways of presenting and communicating the message, since they appear to be third parties not unrelated to the advertisers. These practices where is not identified are considered in themselves misleading by Law 3/1991 on Unfair Competition. The situation has led to finally Autocontrol and AEA have signed with the Ministry of Economic Affairs and Digital Transformation and Consumer Affairs, a of conduct on the use of influencers in advertising (Autocontrol, 2020). In this paper, the presence of a brand in a sample of influencers targeting young people on Instagram is analyzed during a period of 6 months (4T2020-1T2021) in order to study the level of legal identification of said brands before and after entry into force of the Code of Conduct on January 1, 2021, as well as the type of brand presence (verbal or written), its degree of visibility or its presence in shared content.
Bucketed PCA Neural Networks with Neurons Mirroring Signals

The bucketed PCA neural network (PCA-NN) with transforms is developed here in an effort to benchmark deep neural networks (DNN's), for problems on supervised classification. Most classical PCA models apply PCA to the entire training data set to establish a reductive representation and then employ non-network tools such as high-order polynomial classifiers. In contrast, the bucketed PCA-NN applies PCA to individual buckets which are constructed in two consecutive phases, as well as retains a genuine architecture of a neural network. This facilitates a fair apple-to-apple comparison to DNN's, esp. to reveal that a major chunk of accuracy achieved by many impressive DNN's could possibly be explained by the bucketed PCA-NN (e.g., 96% out of 98% for the MNIST data set as an example). Compared with most DNN's, the three building blocks of the bucketed PCA-NN are easier to comprehend conceptually - PCA, transforms, and bucketing for error correction. Furthermore, unlike the somewhat quasi-random neurons ubiquitously observed in DNN's, the PCA neurons resemble or mirror the input signals and are more straightforward to decipher as a result.
Copula-Based Normalizing Flows

Normalizing flows, which learn a distribution by transforming the data to samples from a Gaussian base distribution, have proven powerful density approximations. But their expressive power is limited by this choice of the base distribution. We, therefore, propose to generalize the base distribution to a more elaborate copula distribution to capture the properties of the target distribution more accurately. In a first empirical analysis, we demonstrate that this replacement can dramatically improve the vanilla normalizing flows in terms of flexibility, stability, and effectivity for heavy-tailed data. Our results suggest that the improvements are related to an increased local Lipschitz-stability of the learned flow.
Individual action and group activity recognition in soccer videos. Master's thesis

Data and statistics are key to soccer analytics and have important roles in player evaluation and fan engagement. Automatic recognition of soccer events - such as passes and corners - would ease the data gathering process, potentially opening up the market for non-professional soccer analytics. We propose a novel method for the automatic recognition of soccer events from video. To the best of our knowledge, it is the first method that infers both individual actions and group activities simultaneously from soccer videos. Three key contributions in the proposed method are (1) the use of player-centric snippets as model input, (2) per-player feature extraction with an I3D CNN - based on RGB video and optical flow - and (3) the use of feature suppression and zero-padding in graph attention networks for feature contextualisation. The results show that the proposed method performs better than an alternative state-of-the-art method, designed for action and activity recognition in volleyball. Our method gains 98.7% accuracy for the recognition of eight actions and 75.2% for eleven activities.
Embedded Librarians to Support Data Management Needs of a Multidisciplinary Research Program

&lt;p&gt;This presentation describes the establishment of a partnership between the Libraries and a multidisciplinary research program, and some of the products and outcomes from immersive and embedded roles within that program. Several factors contributed to the development of this partnership: outreach efforts by the Engineering Library and the Data Services Librarian to faculty, staff, students, and research administrators; a research program director who has a history of engagement with the Libraries; and the funder’s data management and sharing mandates in the funding opportunity announcement for the research program.&lt;/p&gt;
ASSESSMENT AND COMPARISON OF MACHINE LEARNING ALGORITHM CAPABILITY IN SPATIAL MODELING OF DENGUE FEVER VULNERABILITY BASED ON LANDSAT IMAGE 8 OLI/TIRS

The spread of dengue fever in Indonesia has become a major health problem. Spatial modeling for the distribution of dengue fever vulnerability is an important step to support the planning and mitigation of dengue fever in Indonesia. This study aims to assess and compare the capability of two machine learning algorithms to create a spatial model of dengue fever vulnerability. The research was conducted in Baubau City, Southeast Sulawesi Province by taking 129 cases that occurred from 2015 to February 2016. In this study, the model was created using R software and machine learning algorithms including support vector machine (SVM) and random forest (RF). The six modeling variables involved include land use/cover, BLFEI, NDVI, LST, rainfall and humidity extracted from Landsat 8 OLI/TIRS imagery as well as BMKG (Meteorological, Climatological, and Geophysical Agency of Indonesia) and BWS climate data. The model's capability was assessed using the Area Under Curve-Receiver Operating Characteristic (AUC-ROC) curve. The results of the research show that both algorithms provide excellent model accuracy with AUC values of 1 for SVM and 0.997 for RF with SVM as the best algorithm for modeling dengue fever in Baubau City.Keywords: Machine Learning, Vulnerability, Dengue Fever, Landsat 8 Image
Application (Gym Fit) As a Fitness Center Marketing Tool in Digital Era

This study aims to produce an application that functions as a marketing tool for fitness centers in the digital era. This study uses the research and development (R&D) method, which consists of (1) collecting research results and information or often referred to as a preliminary study, (2) planning or planning, (3) developing an initial product or developing a preliminary form of product, ( 4) initial test or preliminary field testing, (5) revision of the first product preparation or main product revision, (6) main field testing of main field testing, (7) revision of operational product preparation or operational product revision, (8) trial operational product or operational field testing, (9) final product revision or final product revision. This application is called Gym Fit which consists of 4 main menus, namely education, profile of fitness center, personal trainer, and contact person. The results of the large-scale trial assessment of the developed product stated a total score of 70 with a percentage (4.8%) of the sample stating ENOUGH, then a total score of 452 with a percentage (31.4%) of the sample saying GOOD, and a total score of 918 with a percentage (63.75%) of the sample stating very good .
TAXOGAN: Hierarchical Network Representation Learning via Taxonomy Guided Generative Adversarial Networks (Extended Abstract)

Network representation learning aims at transferring node proximity in networks into distributed vectors, which can be leveraged in various downstream applications. Recent research has shown that nodes in a network can often be organized in latent hierarchical structures, but without a particular underlying taxonomy, the learned node embedding is less useful nor interpretable. In this work, we aim to improve network embedding by modeling the conditional node proximity in networks indicated by node labels residing in real taxonomies. In the meantime, we also aim to model the hierarchical label proximity in the given taxonomies, which is too coarse by solely looking at the hierarchical topologies. Comprehensive experiments and case studies demonstrate the utility of TAXOGAN.
Formation of methodological competencies of future mathematics teachers in the field of quality assessment of education

In the context of modernization of education, instability of educational standards and curricula, mathematics teachers are forced to independently compose tasks and systems of tasks. To correctly evaluate the ready-made systems of problems, the teacher must be competent in theory, methodology and have experience in drawing up problems. The demand in education for a modern mathematics teacher who is ready to carry out professional activities in the context of modernization of education, and the insufficient focus on this of the existing system of professional training in pedagogical universities.
SecondSight: Demonstrating Cross-Device Augmented Reality

SecondSight is a framework for rapid prototyping of cross-device interfaces that combine an optical see-through Augmented Reality (AR) head-mounted display (HMD) with a smartphone. SecondSight contains code for simulating AR HMDs with different Fields of View (FOV), providing gesture and head pointing input, and allowing virtual content to be placed in different coordinate frames. Overall, this gives AR researchers flexibility to explore different types of cross-device interfaces and interaction techniques with different types of display, input, and content placement.
AMAZING RACE: ENHANCING SOFT SKILLS AND LANGUAGE LEARNING VIA COLLABORATIVE LEARNING AMONG PRE-DIPLOMA STUDENTS AT KEDAH

Universities in Malaysia are aggressively preparing their students to face thechallenges of the Industrial Revolution (IR 4.0) which requires them to master thesoft skills and English language. Owing to the pressure and demands of theworkforce, University Technology of MARA has introduced the Week WithoutWall (WWW) as an approach to enrich students learning experience throughan alternative method of teaching and learning delivery. Therefore, theresearchers employ a new and innovative English language teaching andlearning method - The Amazing Race. The objective of this study was theidentification of the skills developed and applied by students during theoutdoor adventure program. There were 25 full-time students enrolled in Pre-Diploma programme at UiTM Kedah Branch in northern Malaysia, and theyhad participated in this innovative teaching and learning opportunity, whichincorporated the necessary language skills and also soft skills outcomes. For thestudy, these students aged 18 years, voluntarily completed three open-endedquestions regarding the skills they applied and developed during the eventparticipation. Qualitative techniques were utilised to analyse the datainductively and rendered 121 meaning units, grouped into 11 categories.Students identified more soft skills than language learning skills. Therefore,tertiary institutions could employ this outdoor innovative teaching and learningmethod to develop studentsâ€™ soft skills and acquire English in the process.
Deblurring Turbulent Images via Maximizing L1 Regularization

Atmospheric turbulence significantly degrades image quality. A blind image deblurring algorithm is needed, and a favorable image prior is the key to solving this problem. However, the general sparse priors support blurry images instead of explicit images, so the details of the restored images are lost. The recently developed priors are non-convex, resulting in complex and heuristic optimization. To handle these problems, we first propose a convex image prior; namely, maximizing L1 regularization (ML1). Benefiting from the symmetrybetween ML1 and L1 regularization, the ML1 supports clear images and preserves the image edges better. Then, a novel soft suppression strategy is designed for the deblurring algorithm to inhibit artifacts. A coarse-to-fine scheme and a non-blind algorithm are also constructed. For qualitative comparison, a turbulent blur dataset is built. Experiments on this dataset and real images demonstrate that the proposed method is superior to other state-of-the-art methods in blindly recovering turbulent images.
Detecting framerate-oriented cyber attacks on user experience in virtual reality

Virtual Reality (VR) is expected to become an enabling technology for training in realistic conditions, data visualisation, education and many other applications. However, there is still limited research on cyber threats to VR environments and even less on technical protections against them. We are currently developing a VR testbed specifically designed for assessing different cyber threats, their impact to user experience and corresponding defences. In this work in progress, we demonstrate two novel approaches by which a cyber attack can potentially cause VR sickness on demand based on frame rate manipulation by taking advantage of GPU and network vulnerabilities. We further show that a simple unsupervised machine learning method using Isolation Forest can provide early warning of such attacks likely before they have significant impact on the VR system and its user.
Implementation of different image edge detection algorithms on a real embedded ADAS platform

Advanced Driver-Assistance Systems (ADASs) are becoming more and more popular in modern vehicles in the last years. A number of them are based on processing the images captured by in-vehicle cameras. Furthermore, in image processing-based ADAS algorithms one of the most important steps often is edge detection. Therefore, it is important to properly choose the edge detection method, to achieve high performance and low processing time. Due to limited hardware resources available when using real ADAS platforms, the trade-off is needed. In this paper, the implementation of four different edge detection operators (Sobel, Prewitt, Laplace and Canny) onto a real ADAS Alpha board is performed. The implementation is performed using the Vision Software Development Kit (SDK), which is a multi-processor software platform specifically optimized to work with Texas Instruments (TI) Systems-On-Chip (SoCs) that Alpha board consists of. To test the operators’ performance in a real ADAS operational environment, the Berkeley dataset is used. The output results of each edge detector are compared to available ground truth labeled images from the Berkeley dataset to check which detector achieves the highest performance in terms of edge detection accuracy. Furthermore, the operators are compared in terms of execution time and memory usage. It was shown that Canny operator requires the longest execution time and the highest amount of memory, but it also achieves the highest edge detection accuracy. It is also shown that the trade-off between detector accuracy and its requirements can be achieved in certain situations where it is acceptable.
Emerging Learning Technologies in Next Generation Learning Spaces: Implications for Learning and Cognition

Current growth and development points in the direction of classroom technologies beyond their previous roles as tools. This book presents discussions and studies on issues and possibilities in next generation education with a focus on the place of learning technologies within next-generation learning spaces. We aim to offer a holistic view of next generation learning technologies as all-encompassing, and in various educational roles as tools, as well as content, and environment. The central role of education in previous industrial revolutions and human society places a demand for education that counterparts the next change that will extend the status of current digitization to critical changes in the role of technology in human life. The chapters highlight perspectives on educational technology. Focusing mostly on developing and transition nations, they present pointers to the future, deriving from the experiences of educational technology instructors and researchers. Perspectives are from various backgrounds and classrooms, including mainstream classrooms and classrooms of special needs learners. Issues on how current global eLearning situation reflects on the Sustainable Development Goals (SDGs), especially to “ensure inclusive and equitable quality education and promote lifelong learning opportunities for all” in line with SDG4 was also discussed. The issues addressed are intended to provoke further inquiry and studies within the field.
On Weak Stubborn Sets in Classical Planning

Stubborn sets are a pruning technique for state-space search which is well established in optimal classical planning. In this paper, we show that weak stubborn sets introduced in recent work in planning are actually not weak stubborn sets in Valmari's original sense. Based on this finding, we introduce weak stubborn sets in the original sense for planning by providing a generalized definition analogously to generalized strong stubborn sets in previous work. We discuss the relationship of strong, weak and the previously called weak stubborn sets, thus providing a further step in getting an overall picture of the stubborn set approach in planning.
The effect of effective governance and quality of regulations on financial development in the current economic conditions of Iran

The present study investigates effective governance and quality of regula-tions on financial development in Iran's current economic conditions. For this purpose, the model is estimated based on the annual data of 1996-2018 using Smooth Transition Autoregressive (STAR). The results of estimating the linear part of the model (first regime) show that the variables of GDP, role or the rule of law, quality of regulations, and government size have a significant and positive impact on Iran's financial development at 95% confi-dence level. Also, the variables of devaluation of the national currency and financial crises have a negative impact on financial development in the Irani-an economy. Besides, the results of the non-linear part of the model (second regime) show the existence of a positive relationship between the variables of role or the rule of law and GDP with financial development. The sign of the variables of quality of regulations, government budget deficit, govern-ment effectiveness, devaluation of the national currency, nuclear sanctions, and financial crises are negative that is expected because Iran is developing and growing. The positive sign of the lag of the dependent variable of the financial development index shows the country's attention to the issue of financial development and the use of solutions and attention to infrastructure to increase financial development over time, which needs more attention from government officials.
Deep nonlinear optical neural networks using physics-aware training

We experimentally demonstrate deep nonlinear optical neural networks using a universal algorithm for backpropagating through arbitrary physical input-output transformations. Ultrafast second harmonic generation and other diverse processes are trained to perform image and audio classification.