Reviewerly/exHarmony huggingface dataset:
Neural Video Coding using Multiscale Motion Compensation and Spatiotemporal Context Model

Over the past two decades, traditional block-based video coding has made remarkable progress and spawned a series of well-known standards such as MPEG-4, H.264/AVC and H.265/HEVC. On the other hand, deep neural networks (DNNs) have shown their powerful capacity for visual content understanding, feature extraction and compact representation. Some previous works have explored the learnt video coding algorithms in an end-to-end manner, which show the great potential compared with traditional methods. In this paper, we propose an end-to-end deep neural video coding framework (NVC), which uses variational autoencoders (VAEs) with joint spatial and temporal prior aggregation (PA) to exploit the correlations in intra-frame pixels, inter-frame motions and inter-frame compensation residuals, respectively. Novel features of NVC include: 1) To estimate and compensate motion over a large range of magnitudes, we propose an unsupervised multiscale motion compensation network (MS-MCN) together with a pyramid decoder in the VAE for coding motion features that generates multiscale flow fields, 2) we design a novel adaptive spatiotemporal context model for efficient entropy coding for motion information, 3) we adopt nonlocal attention modules (NLAM) at the bottlenecks of the VAEs for implicit adaptive feature extraction and activation, leveraging its high transformation capacity and unequal weighting with joint global and local information, and 4) we introduce multi-module optimization and a multi-frame training strategy to minimize the temporal error propagation among P-frames. NVC is evaluated for the low-delay causal settings and compared with H.265/HEVC, H.264/AVC and the other learnt video compression methods following the common test conditions, demonstrating consistent gains across all popular test sequences for both PSNR and MS-SSIM distortion metrics.
Video Classification Using Deep Learning

Video order has been comprehensively investigated in PC vision in view of its wide spread applications. In any case, it remains a surprising task by virtue of the mind boggling troubles in fruitful segment extraction and successful arrangement with high-dimensional video depictions. Video groupings present uncommon irregularity as a result of monster scope changes, point of view assortment, and camera development, which pose fantastic challenges for both video depictions and characterization. With the phenomenal accomplishment of significant learning, convolutional neural frameworks (CNNs) and their 3-D varieties have been considered in the video territory for an immense grouping of order assignments. Video depictions have accepted a basically noteworthy activity in video examination, which authentically impact a complete execution of video characterization. Both the spatial and brief information should be gotten and encoded for extensive and educational depiction of video progressions. Significant Learning feature level blend plans have exceptional capacity of video depictions for improving the introduction of video grouping. We have driven wide assessment on four going after for video arrangement including human movement affirmation and dynamic scene grouping.
Design of Reminder Information Last Contract (RILC) Applications Using Web-Based Sms Gateway

Currently informarsi system is needed by several large companies. To support some data processing, one of them is employee data processing and employee contract data. Given the existing problems in contract data pengelohan often occurs that the check is not maximal so some data left behind and even neglected impact on the increase in salaries of employees who are late due to contracts that are not handled according to schedule. This study aims to create a web-based RIL application using sms gateway for delivery of contract data reminder information. This research uses SDLC method with Extreme Programming model with stages of planning, designing, coding, testing, implementation and evaluation. SMS gateway is a gateway for information dissemination using SMS. With SMS gateway can spread the message of the serial number automatically and quickly which is directly connected with the database of mobile numbers that have been stored. This can be done by RIL applications in sending reminder information messages to the HRD division as well as employees receiving their contract expiration date information. The result of the research is the user can maximize the checking of contract data so that nothing is left behind and give the report that arranged well and clear
An Analysis of Trends and Connections in Google, Twitter, and Wikipedia

In this paper, we propose a system for extracting, storing, and analyzing the data provided by three well-known and widespread services available online. More specifically, the system can automatically collect a real-world dataset for a selected language and/or geographical region and match similar trends expressed through different keywords. Unlike previous studies in the same area, we avoided to focus on a specific aspect and explored which resonance different topics may have between one source and another, and how quickly each source generally reacts to external events.
A question of Mazurov on groups of exponent dividing 12

Mazurov asked whether a group of exponent dividing 12, which is generated by x, y and z subject to the relations x3=y2=z2=(xy)3=(yz)3=1, has order at most 12. We show that if such a group is finite, then the answer is yes.
Fuzzy Meets Privacy: A Short Overview

The amount of information currently available is a threat to individual privacy. Data privacy is the area that studies how to process data and propose methods so that disclosure does not take place. Fuzzy sets has been extensively used in all kind of applications. In this paper we review the areas in which fuzzy sets and systems can have an impact in the field of data privacy. We will review some of our contributions in the field and some open problems.
Fundamentals of Machine Learning

Kunstliche Intelligenz wird unser Leben nachhaltig verandern – sowohl im Job als auch im Privaten. Doch wie funktioniert maschinelles Lernen eigentlich genau? Dieser Frage gehen zwei Lubecker Professoren in ihrem englischsprachigen Lehrbuch nach. Definitionen sind im Buch hervorgehoben und Aufgaben laden die LeserInnen zum Mitdenken ein. Das Lehrbuch richtet sich an Studierende der Informatik, Technik und Naturwissenschaften, insbesondere aus den Bereichen Robotik, Artificial Intelligence und Mathematik. Artificial intelligence will change our lives forever - both at work and in our private lives. But how exactly does machine learning work? Two professors from Lubeck explore this question. In their English textbook they teach the necessary basics for the use of Support Vector Machines, for example, by explaining linear programming, the Lagrange multiplier, kernels and the SMO algorithm. They also deal with neural networks, evolutionary algorithms and Bayesian networks. Definitions are highlighted in the book and tasks invite readers to actively participate. The textbook is aimed at students of computer science, engineering and natural sciences, especially in the fields of robotics, artificial intelligence and mathematics.
Argomentazione e adesione dell'uditorio : Perelman e Aristotele

The aim of Perelman’s Treatise on Argumentation is to develop an argumentation theory to be used for value judgments, in which the tools of formal logic cannot be employed. Perelman defines his theory as a new rhetoric, and states he is referring to the ancient Greek and Latin authors, in particular to Aristotle. The essay analyses the similarities between Perelman’s argumentation theory and Aristotle’s rhetoric and dialectic, but also highlights their conflicting aspects. In par ticular, Perelman’s notion of ‘reasonable’, as opposed to that of ‘necessary’, typical of demonstrative logic, is compared to with Aristotle’s endoxon, that is, to what corresponds to commonly held opinions. The essay also examines the role assigned by both authors to the audience, since the purpose of argumentation is to obtain the agreement of listeners. In this regard, Perelman’s theory of universal audience is discussed in depth.
Detection of Cyberbullying using Machine Learning

Cyberbullying is a form of bullying in which technology is used as a medium to bully someone. As the new boom of the internet and other social media platforms are increasing, the number of users is also increasing and the main users of social media are mostly teens and young adults. As much as these social media platforms are used for getting new information and for entertainment, it is more prone for bullies to uses these networks as vulnerable to attacks against victims. Due to the increase in cyberbullying on victims, it is in need to develop a suitable method for the detection and prevention of cyberbullying. A growing body of work is emerging on automated approaches to cyberbullying detection. These approaches utilize machine learning and natural language processing techniques to identify the characteristics of a cyberbullying exchange and automatically detect cyberbullying by matching Textual data. The main objective of this project is to detect cyberbullying by matching both Image and Textual data. The test cases and are used to classify the dataset and detect the bullying. Machine learning techniques are used to efficiently predict and detect cyberbullying.
Analysis of the Function of Elder Education to Continue Socialization of the Elderly

In the city, more and more elderly people participate in the education of the elderly.This has become a social fact, and many elderly people are reluctant to quit studying once they participate.This shows that education for the elderly has become an important part in the life of the elderly.From the perspective of sociology, this study takes Elderly College as a case to investigate and analyze the functions of the elder college in terms of knowledge transfer, social adaptation, social participation, social interaction, intergenerational relationship, etc.The various studies and participation of the elderly in the geriatric university can avoid social discomfort caused by factors such as retirement and empty nest, and play an important role in continuing socialization of the elderly.
Experimental implementation of leakage elimination operators

Decoherence-induced leakage errors can potentially damage physical or logical qubits by coupling them to other system levels. Here we report the first experimental implementation of Leakage Elimination Operators (LEOs) that aims to reduce this undermining, and that can be applied alongside universal quantum computing. Using IBM's cloud quantum computer, we have studied three potentially applicable examples of subspaces in two- and three-qubit Hilbert spaces and found that the LEOs significantly suppress leakage.
Analysis of the Current Situation of Teaching Management in Colleges and Universities and the Countermeasures to Improve the Quality of Teaching Management in Colleges and Universities

With the continuous innovation and development of the teaching system, China's education system is also constantly improving.Teaching management has always been the focus of education and teaching.This paper will focus on the analysis of the problems existing in the teaching management of colleges and universities in China, put forward countermeasures and methods to improve the quality of teaching management in colleges and universities, elaborate on the significance of improving the quality of teaching management in colleges and universities, and thereby promoting the sustainable and healthy development of college education and teaching.
Improving Customer Experience for an Internet Service Provider: A Neural Networks Approach

Today one of the challenges of companies is to decrease call center costs while improving the customer experience. In this study, we make prediction and proactively take action in order to solve customer problems before they reach the customer call center. We use machine learning techniques and train models with a dataset of an internet service provider’s several different systems. We first use supervised techniques to classify the customers having slow internet connection problems and normal internet connection. We apply two classification approaches, multi perceptron neural networks and radial basis neural networks. Then, we cluster the same dataset using unsupervised techniques, namely Kohonnen’s neural networks and Adaptive Resonance Theory neural networks. We evaluate the classification and clustering results using measures such as recall, accuracy and Davies-Bouldin index, respectively.
Fuzzy Based Noise Removal, Age Group and Gender Prediction with CNN

Facial recognition systems, which are a type of biometric systems, use the person’s facial features to identify people, and determine the information in the position between the eyes, nose, cheekbones, jawline, chin and the like and reveal a personal numerical model. The main purpose of this study is to determine the gender and age group of a selected image. By making necessary studies on the image, it is aimed to separate the gender of the person and to extract child or adult information. This study is proposed as research that performs noise reduction using the fuzzy logic fire filter algorithm and classifies the result by gender using a convolutional neural network (CNN), matrix completion and deep learning techniques. CNN algorithm, which is one of the deep facial recognition algorithms that can be used in the recognition of facial images, is the algorithm used in the study. In this study, the data obtained after the cleaning of the noises in the photographs were classified. The fuzzy logic fire filter algorithm is used to remove the noise in the image, and the image is classified according to gender and age range with the help of CNN and TensorFlow algorithms. The system has been trained using certain learning and test data sets and these algorithms. The image entered into the system is converted into a matrix expression and compared with the matrix expressions on the trained system. According to the comparison result, the image is classified as male, female, boy or girl, which matrix is closer to the value group. The data we obtained as a result of our work and the success rates show the applicability of the study. The main purpose of the study is to make gender and age group estimations according to the image with CNN classifications.
ICT and Educational Inequalities in Logroño’s Schools (La Rioja, Spain)

From an exploratory point of view, it has been analyzed whether ITC can increase educational inequalities depending on the characteristics of the schools, the training of the teachers who compose them and the social characteristics of the students of the city of Logroño. Interviews have been conducted with reference personnel in the educational sphere of the city, completing the study with telematic surveys of active teachers and staff of the Management Teams that complete 20 educational centers in Logroño, making it easier to analyze the current approximate state of the matter. The data obtained allow us to affirm that there are educational inequalities in the use and development of ITC depending on the socio-cultural contexts of the schools.
Gaussian Temporal Awareness Networks for Action Localization

Temporally localizing actions in a video is a fundamental challenge in video understanding. Most existing approaches have often drawn inspiration from image object detection and extended the advances, e.g., SSD and Faster R-CNN, to produce temporal locations of an action in a 1D sequence. Nevertheless, the results can suffer from robustness problem due to the design of predetermined temporal scales, which overlooks the temporal structure of an action and limits the utility on detecting actions with complex variations. In this paper, we propose to address the problem by introducing Gaussian kernels to dynamically optimize temporal scale of each action proposal. Specifically, we present Gaussian Temporal Awareness Networks (GTAN) --- a new architecture that novelly integrates the exploitation of temporal structure into an one-stage action localization framework. Technically, GTAN models the temporal structure through learning a set of Gaussian kernels, each for a cell in the feature maps. Each Gaussian kernel corresponds to a particular interval of an action proposal and a mixture of Gaussian kernels could further characterize action proposals with various length. Moreover, the values in each Gaussian curve reflect the contextual contributions to the localization of an action proposal. Extensive experiments are conducted on both THUMOS14 and ActivityNet v1.3 datasets, and superior results are reported when comparing to state-of-the-art approaches. More remarkably, GTAN achieves 1.9% and 1.1% improvements in mAP on testing set of the two datasets.
Rupture parameters of dynamic source models compatible with NGA-West2 GMPEs

&amp;lt;p&amp;gt;Dynamic source inversions of individual earthquakes provide constraints on stress and frictional parameters, which are inherent to the studied event. However, general characteristics of both kinematic and dynamic rupture parameters are not well known, especially in terms of their variability. Here we constrain them by creating and analyzing a synthetic event database of dynamic rupture models that generate waveforms compatible with strong ground motions in a statistical sense.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We employ a framework that is similar to the Bayesian dynamic source inversion by Gallovi&amp;amp;#269; et al. (2019). Instead of waveforms of a single event, the data are represented by Ground Motion Prediction Equations (GMPEs), namely NGA-West2&amp;amp;#160; (Boore et al., 2014). The Markov chain Monte Carlo technique produces samples of the dynamic source parameters with heterogeneous distribution on a fault. For all simulations, we assume a vertical 36x20km strike-slip fault, which limits our maximum magnitude to Mw&amp;lt;7. For dynamic rupture calculations, we employ upgraded finite-difference code FD3D_TSN (Premus et al., 2020) with linear slip-weakening friction law. Seismograms are calculated on a regular grid of phantom stations assuming a 1D velocity model using precalculated full wavefield Green's functions. The procedure results in a database with those dynamic rupture models that generate ground motions compatible with the GMPEs (acceleration response spectra in period band 0.5-5s) in terms of both median and variability.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The events exhibit various magnitudes and degrees of complexity (e.g. one or more asperities). We inspect seismologically determinable parameters, such as duration, moment rate spectrum, stress drop, size of the ruptured area, and energy budget, including their variabilities.&amp;amp;#160; Comparison with empirically derived values and scaling relations suggests that the events are compatible with real earthquakes (Brune, 1970, Kanamori and Brodsky, 2004). Moreover, we investigate the stress and frictional parameters in terms of their scaling, power spectral densities, and possible correlations. The inferred statistical properties of the dynamic source parameters can be used for physics-based strong-motion modeling in seismic hazard assessment.&amp;lt;/p&amp;gt;
METHODOLOGICAL BASIS OF PRACTICE-ORIENTED EDUCATION AT THE UNIVERSITY

The article considers the problem of choosing the methodological basis of practice-oriented education, the actualisation of which in the vocational education system is caused by the innovative development of all spheres of public life and by the need to train a specialist who is capable and ready to independently solve problems in an interprofessional environment. Based on the analysis of the positions of researchers, the author’s defi nition of practice-oriented education is presented. The possibilities of axiological, culturological, personality-oriented, competency and activity-based methodological approaches to the implementation of all aspects (motivational, cognitive, activity-oriented and refl ective) of practice-oriented learning are considered. Based on this, as well as on the basis of the advantages of the competency approach for carrying out transprofessional activities, the authors substantiate the need to use a set of methodological approaches, a central place among which is given to the leading provisions of the competency approach.
Development of Guided Inquiry-Based Learning Materials in Acid-Base Topic, Enriched with Augmented Reality

Implementation of guided inquiry learning models can be helped by the use of learning materials.Learning materials that have been using today are less interactive and pay little attention to the interaction of three chemical representations, especially acid-base material.The alternative that can be used is using digital learning media that can visualize three representations, especially submicroscopic, for example, augmented reality technology.The learning materials were developed using Lee and Owens development models which consisted of five stages.However, in this study, the stages carried out were only analyzing, designing, and developing.The developed learning materials then were validated by one chemistry lecturer and one chemistry teacher.The results are 86.7% for the overall content, and 82.7% on media aspect.For the readability test, the learning materials developed obtained an average of 92.17% which was very valid.Therefore, based on these results this learning materials can be used in the learning process of chemistry, especially in the acid-base material.
Generic Source Parameter Determination for Earthquake Early Warning: Theory, Observations and Implications for the Mw 7.1 Ridgecrest Earthquake

&amp;lt;p&amp;gt;A generic approach for real-time magnitude and stress drop is introduced that is based on the omega-squared model (Brune, 1970) and results from Lior and Ziv (2018). This approach leads to approximate expressions for earthquake magnitude and stress drop as functions of epicentral distance and ground motion root-mean-squares (rms). Because the rms of the ground motion (acceleration, velocity and displacement) may be calculated directly from the seismogram in the time domain, the use of this approach for automated real-time processing is rather straightforward. Once the seismic moment and stress drop are known, they may be plugged in the ground motion prediction equations (GMPE) of Lior and Ziv (2018) to map the predicted peak shaking.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This method is generic in the sense that it is readily implementable in any tectonic environment, without having to go through a calibration phase. The potential of these results for automated early warning applications is demonstrated using a large dataset of about 6000 seismograms recorded by strong-motion and broadband velocity sensors from different tectonic environments. Optimal real-time performance is achieved by integrating magnitude and stress drop estimates into an evolutionary algorithm. The result of such an evolutionary calculation for the Mw 7.1 Ridgecrest earthquake indicates close agreement with the true magnitude.&amp;lt;/p&amp;gt;