Reviewerly/exHarmony huggingface dataset:
TEACHER´S DIDACTIC COMPETENCIES WHEN TEACHING NATURAL SCIENCE SUBJECTS AS A LEARNING ENVIRONMENT FACTOR

The relationship of primary and secondary schools pupils to natural science subjects is often discussed together with teacher´s didactic competencies that are implemented when teaching natural science subjects. The aim of the paper is to find out how pupils from chosen primary and secondary schools in the Slovak Republic evaluate didactic competencies of natural science subjects teachers in the framework of the overall learning environment evaluation of these subjects. The means to fulfil this objective is the analysis of corresponding questionnaire items oriented on the learning environment when teaching natural science subjects. A partial aim is to compare the evaluation of natural science teachers didactic competencies from several points of view: school type (at primary and secondary schools), gender issues (boys and girls), in relation to the overall learning environment and in relation to learning environment at primary and secondary schools. Article visualizations:
System Architecture Design for Scoring and Delivery

In this chapter we provide fundamentals around building automated scoring engines for production (versus research) use. The focus is not solely on architectural design but also on key considerations influencing that design because good architecture arises from identifying stakeholder concerns and realistic use cases and successful systems built on that architecture require strong software development processes. Specifically, we first outline common stakeholder contexts and use cases specific to automated scoring implementations. Next, we describe software considerations that impact the design of automated scoring software and provide recommendations regarding architecture in key areas. Finally, we close the chapter with recommendations regarding team roles and structure.
Residents' views on cyclist safety and cycling infrastructure in the City Municipality of Celje

Purpose:The paper presents the results of two research studies analysing the views of different target populations on cyclist safety and the adequacy of preventive measures in Slovenia. The purpose of both research studies was to identify the shortcomings of different approaches to ensuring cyclist safety, evaluate the adequacy of planned solutions and propose some improvements of preventive actions taken by various stakeholders.Design/Methods/Approach:A field survey was conducted among the residents of the City Municipality of Celje (n = 171) on their satisfaction with cyclist safety, while an online survey was carried out among internet users (n = 210) on the usefulness of an alternative approach to raise cyclists’ awareness by digitising cycling routes and safety risks.Findings:Results show that respondents are generally not satisfied with cyclist safety, as most believe that municipal efforts are insufficient to ensure it. It was observed that cycling infrastructure needs to be properly regulated and that a positive traffic culture should be promoted at the municipal level, including through the promotion of preventive activities. Both internet users and local residents recognise a strong need to digitise cycle paths by indicating safety risks. Therefore, it would be reasonable to upgrade conventional approaches to raising public awareness by introducing solutions that are useful for cyclists.Research Limitations / Implications:The limitation of the research study arises from the fact that its results cannot be generalised to all municipalities, since they apply different approaches to ensuring cyclist safety due to their autonomy and are facing different safety risks. Moreover, due to the use of a non-random sample, caution is necessary when generalising research results.Practical Implications:The results of the presented studies are primarily useful to decision-makers and infrastructure managers at national and local level when planning changes and safety measures in the field of cyclist safety.Originality/Value:The paper encompasses two studies, the findings of which complement one another substantially and provide a deeper insight into the issues of cyclist safety and preventive actions taken by different stakeholders. They serve as a basis for further exploring this issue in different local environments with a view of obtaining a more comprehensive insight into the key challenges of traffic safety from a broader perspective.
Basic Geophysics Wide - Angle Seismics

How deeply can applied seismics look into the Earth?Explanation of the branches of the travel time graphs, source and receiver arrangements, differences between vertical and wide-angle seismics, sample data and evaluation of the Transalp project.
Social Media and Hate Speech in India: A study on the use of YouTube as a tool of political propaganda

This study explores how does a political speech on YouTube shape the political ideology and behavior of social media users. Social media gives a much larger space for freedom of expression but within this new platform, hate can also be shared freely. The following paper also focuses on the impact and spreading of hate Speech delivered by politicians among social media users. The main purpose of the study is a) to analyze the content of speech used by politicians on YouTube, and b) to explore the use and impact of YouTube as a tool of political propaganda, c) whether speaker uses wrong facts and figures in their speech. For this research, the literature on hate speech in India, the theory of political propaganda, media content analysis and the use of social media are reviewed to make an effective correlation with its analysis. Moreover, this study employs a content analysis as a research design to analyze the political speech in videos of all national political parties. Videos are taken from their official YouTube channels. The duration has taken just before the state assembly elections in the five states (Chhattisgarh, Madhya Pradesh, Mizoram, Rajasthan and Telangana) during Nov-Dec 2018.
Environmental Wisdom: An Observation of Ecocriticism towards the Javanese Cyber Literature in 20th Century

This research was an attempt to explore the Quadriology of Ki Padmasusastra's Novel (hereinafter referred to as QKPN) in the perspective of ecocriticism, especially the JavanesePastoral image. This study used a qualitative research paradigm. The object of this study was called cyber literature, because the data was taken from an online site. The main data sources of the study included four novels by Ki Padmasusastra. The technical data analysis used content analysis technique that was based on cultural hermeneutic. The result showed that, QKPN was a reflection of the universality of Javanese literature wrapped in text symbolism of literature. Through the plot and character of figures, implicitly or explicitly showedan ethical sense of human towards nature. The description of community life was an ideal standard of living in accordance with the values ​​developed in Javanese culture. There was an attempt to transfer the value of environmental wisdom by Ki Padmasusastra to readers regarding the noble values ​​of Javanese culture. The main goal of environmental wisdom in the perspective of Javanese culture wasto memayuhayuningbawana 'to maintain the balance of nature', so as to create a harmony of life among beings in the universe. An important implication of the finding was human awareness to respect each other existing entities, was a foundation of strength that could guide human to seek fellowship with nature. Through QKPN, Ki Padmasusastra also stressed about preservation, empathy, and respect for non-human beings. Thus, QKPNwas involved in ecology in three ways, namely environmentalism ethic, scientific intellectual, and practical. Through its intrinsic reading, educational values ​​on QKPNcould be used as a platform for ethical environmental education. Keywords: Environmental Wisdom; KiPadmasusastra; Ecocriticism; Cyber Literature;Javanese Literature
Rethinking Stochasticity in Neural Networks for Reinforcement Learning with Continuous Actions*

In this paper, we reconsider the use of stochasticity in neural networks for reinforcement learning in continuous action spaces. We consider stochasticity from both a reinforcement learning perspective and a neural networks perspective, leading us to reconsider whether noise sampling for exploration should take place at the level of the synapse (S), unit (U), or network (N). To investigate this question, we introduce a superset of the venerable multiparameter REINFORCE algorithm that we call REINFORCE SUN because it allows for stochasticity at each of these levels, and compare these variants on multidimensional problem sets with either continuous or discrete states. Our results show that moving stochasticity from the unit level to the synapse level substantially improves performance across all instances considered. As placing stochasticity at the unit level is nearly ubiquitous within the discipline, our results suggest that this standard practice should be reexamined more broadly.
Utilizing a null class to restrict decision spaces and defend against neural network adversarial attacks

Despite recent progress, deep neural networks generally continue to be vulnerable to so-called adversarial examples--input images with small perturbations that can result in changes in the output classifications, despite no such change in the semantic meaning to human viewers. This is true even for seemingly simple challenges such as the MNIST digit classification task. In part, this suggests that these networks are not relying on the same set of object features as humans use to make these classifications. In this paper we examine an additional, and largely unexplored, cause behind this phenomenon--namely, the use of the conventional training paradigm in which the entire input space is parcellated among the training classes. Owing to this paradigm, learned decision spaces for individual classes span excessively large regions of the input space and include images that have no semantic similarity to images in the training set. In this study, we train models that include a null class. That is, models may opt-out of classifying an input image as one of the digit classes. During training, null images are created through a variety of methods, in an attempt to create tighter and more semantically meaningful decision spaces for the digit classes. The best performing models classify nearly all adversarial examples as nulls, rather than mistaking them as a member of an incorrect digit class, while simultaneously maintaining high accuracy on the unperturbed test set. The use of a null class and the training paradigm presented herein may provide an effective defense against adversarial attacks for some applications. Code for replicating this study will be made available at this https URL .
The Mertens Unrolled Network (MU-Net): A High Dynamic Range Fusion Neural Network for Through the Windshield Driver Recognition

Face recognition of vehicle occupants through windshields in unconstrained environments poses a number of unique challenges ranging from glare, poor illumination, driver pose and motion blur. In this paper, we further develop the hardware and software components of a custom vehicle imaging system to better overcome these challenges. After the build out of a physical prototype system that performs High Dynamic Range (HDR) imaging, we collect a small dataset of through-windshield image captures of known drivers. We then re-formulate the classical Mertens-Kautz-Van Reeth HDR fusion algorithm as a pre-initialized neural network, which we name the Mertens Unrolled Network (MU-Net), for the purpose of fine-tuning the HDR output of through-windshield images. Reconstructed faces from this novel HDR method are then evaluated and compared against other traditional and experimental HDR methods in a pre-trained state-of-the-art (SOTA) facial recognition pipeline, verifying the efficacy of our approach.
10: Algorithmen II, Vorlesung, WS 2019/20, 12.11.2019

10 | 0:00:00 Start0:00:05 Zusammenfassung letzter Vorlesung0:02:20 Highest Level Preflow Push0:04:14 Proof of Lemma 120:07:03 Claims0:20:04 MFIFO: Modified FIFO Selection Rule0:21:04 Heuristic Improvements0:28:20 Experimental Results0:29:51 Timings: Random Graphs0:33:04 Timings: CG10:34:10 Timings: CG20:35:07 Timings: AMO0:36:36 Asymptotics0:37:54 Recent AE Results on Max-Flow0:40:04 Zusammenfassung Flows und Matchings
Basic Geophysics - Earthquake Hazard

How to determine seismic hazard and seismic risk?4-step procedure to seismic hazard assessment using the example of Chile, interview with Andreas Schafer
i$$^*$$ and Enterprise Hierarchies

Distributed ontology integration deals with formal machinery that helps in establishing relations between concepts that belong to entirely different ontologies. Extensive use of multiple ontologies within the same enterprise often leads to inconsistencies, redundancies and anomalies. Thus, it becomes extremely important to accommodate these ontologies within the existing hierarchy. Defining correlations between model concepts belonging to different ontologies become mandatory for enabling such ontology integrations.
Modeling Graduates’ High Quality Employment Based on Support Vector Machine

In this study, the training data of graduate students of Xi'an University of Posts and Telecommunications for the past three years was used to construct a predictive model for graduate students' high-quality employment based on support vector machine through 13 feature attributes that may affect the high-quality employment of graduate students.Six important characteristics of high quality employment were studied, including gender, student origin, postgraduate score, innovation fund, book reading and graduation thesis score. After many experiments, the accuracy of the model was 87.41%.According to the calculation results of the model, it is concluded that the graduate students should make corresponding improvements in the postgraduate training sessions, encourage graduate students to engage in cutting-edge and groundbreaking research work; continuously expand the knowledge of students; strengthen the guidance of instructors, improve graduate students' ability to analyze and solve problems.
Implementing a More Challenging Parsons Problem Interface for Teaching Computer Science

Parsons Problems, in which students unscramble provided lines of code, are an exciting innovation for teaching introductory Computer Science. Prior work has shown that students can learn more efficiently by solving Parsons Problems compared to writing code and can learn more effectively by solving Parsons Problems compared to worked examples. Though Parsons Problems have been found effective, there is some evidence that students can use syntactic heuristics to help them solve these problems without full understanding, confirmed to be in use by mature CS1 students in our pilot study. To address this limitation, we propose Faded Parsons Problems, a variation of Parsons Problems where parts of the provided code are incomplete. We built a Flask app that supports Python programming exercises - traditional code writing, standard Parsons Problems, and Faded Parsons Problems - as well as survey and simple comprehension questions (multiple choice and short answer questions). The system can log anonymized data from participants, enforce time limits, and randomize treatment selection. Exercises support an autograder, implemented as a separate worker using RQ that safely executes arbitrary code. Students can run pre-configured tests as frequently as they want to receive detailed output from the test cases: function arguments, expected output, actual output, print statement output, raised exceptions, and custom messages configured by the instructor. Exercise descriptions, provided code lines (including blanks), initial code, and test cases may be configured from YAML files. The system has been used successfully with 50 simultaneous users. A demo can be viewed at https://codestyle.herokuapp.com/sigcse-demo
Model-Agnostic Structured Sparsification with Learnable Channel Shuffle.

Recent advances in convolutional neural networks (CNNs) usually come with the expense of considerable computational overhead and memory footprint. Network compression aims to alleviate this issue by training compact models with comparable performance. However, existing compression techniques either entail dedicated expert design or compromise with a moderate performance drop. To this end, we propose a model-agnostic structured sparsification method for efficient network compression. The proposed method automatically induces structurally sparse representations of the convolutional weights, thereby facilitating the implementation of the compressed model with the highly-optimized group convolution. We further address the problem of inter-group communication with a learnable channel shuffle mechanism. The proposed approach is model-agnostic and highly compressible with a negligible performance drop. Extensive experimental results and analysis demonstrate that our approach performs favorably against the state-of-the-art network pruning methods. The code will be publicly available after the review process.
A Deployment Framework for Ensuring Business Compliance Using Goal Models

Based on initial research to transform a sequence agnostic goal model into a finite state model (FSM) and then checking them against temporal properties (in CTL), researchers have come up with guidelines for generating compliant finite state models altogether. The proposed guidelines provide a formal approach to prune a non-compliant FSM (generated by the Semantic Implosion Algorithm) and generate FSM-alternatives that satisfy the given temporal property. This paper is an extension of the previous work that implements the proposed guidelines and builds a deployment interface called $$i^*$$ToNuSMV 3.0. The working of the framework is demonstrated with the help of some use cases. In the end, a comparative study of the performance between the previous and current versions of the Semantic Implosion Algorithm (SIA) with respect to the size of the solution space and the execution times, respectively, has also been presented.
Method of UAV route parameters when there are obstacles in megalopolis

İn recent years Unmanned Aerial Vehicle (UAV) more attract attention by researcher. This is due to the fact that they have many practice facility in the civil area, especially in the megalopolis. Despite enough work in this field, there are still many unsolved issues. Among issues, in the megalopolis more attract attention of route planning construction of UAVs’ when flight are prohibited (kindergartens, schools, hospitals e.t.c). Density of construction sites, relief complexity requires special scientific and practical approaches problems for ensuring automatically movement of UAVs’ by not passing through these prohibited territories. İn this article is proposed a simple algorithm for reschedule the required trajectory avoiding obstacles when there is relief map. Algorithm is based on intersection of UAVs' line segmented trajectory and boundary of no flight zone polygonal area. In this case, specifications of creating convex tetragon was used if a polygon's any side intersects with line segmented flight trajectory. Based on the proposed algorithm were conducted calculation operations. İt was assumed that UAV flying from Gazi University Facullity of Science to Gazi University Halkbank branch. Using Google Maps, we have identified areas where flights are prohibited from Ankara. Based on the proposed algorithm a program was developed in C# programming language. At the start of the program was entered determined based on the map coordinates of the prohibited territories into database. Based on the proposed algorithm determined a new flight trajectory. I would also like to point out that the algorithm can be applicable if prohibited territories are any geometric shapes. On the other hand, there are no restriction on the number of obstacles. İf the obstacles are very close to each other, then the algorithm can be applicable by combining them.
A neural network model of perception and reasoning.

How perception and reasoning arise from neuronal network activity is poorly understood. This is reflected in the fundamental limitations of connectionist artificial intelligence, typified by deep neural networks trained via gradient-based optimization. Despite success on many tasks, such networks remain unexplainable black boxes incapable of symbolic reasoning and concept generalization. Here we show that a simple set of biologically consistent organizing principles confer these capabilities to neuronal networks. To demonstrate, we implement these principles in a novel machine learning algorithm, based on concept construction instead of optimization, to design deep neural networks that reason with explainable neuron activity. On a range of tasks including NP-hard problems, their reasoning capabilities grant additional cognitive functions, like deliberating through self-analysis, tolerating adversarial attacks, and learning transferable rules from simple examples to solve problems of unencountered complexity. The networks also naturally display properties of biological nervous systems inherently absent in current deep neural networks, including sparsity, modularity, and both distributed and localized firing patterns. Because they do not sacrifice performance, compactness, or training time on standard learning tasks, these networks provide a new black-box-free approach to artificial intelligence. They likewise serve as a quantitative framework to understand the emergence of cognition from neuronal networks.
Emotion Recognition from Facial Images for Tamil Language Speakers

In the field of image processing, it is very interesting and challenging to recognize the human emotions in Tamil language for general life applications. Facial expressions are the visual methods to convey emotions. Facial expressions of a person at different instances are not the same. Emotions can be understood by text, vocal, verbal and facial expressions. Automatic recognition of facial expressions is important for human–machine interfaces. If a machine can identify human emotions, then it can understand human behavior better, thus improving the task efficiency. Nowadays, one of the challenging parts is face detection and recognition of wide variety of faces, complexity of noises and image backgrounds. To solve this problem, this paper aims to deal with facial emotion recognition using effective features and classifiers. Databases are created with large set of images and feature extraction is implemented. SVM classifier is used for classifying the emotions. The facial expression recognition rate is obtained by using a database especially created in Tamil language. Emotion recognition is analyzed for different emotions using facial expressions.
A magnetic resonance imaging denoising technique using non-local means and unsupervised learning

We propose a new non-local mean (NLM) algorithm using unsupervised learning and k-means clustering for denoising magnetic resonance (MR) images. Our technique improves image processing speeds with enhanced denoising performance on multiple types of images. The calculation of similarity weights at the cluster level improves computational efficiency. We conducted experiments with brain MR images of various sizes, including three T1- and T2-weighted images. Three quality metrics show that our algorithm achieves moderate improvements in denoising accuracy with significant reductions in execution time. The proposed method processed the sample data in one-fifth of the time of the original NLM method. Compared to several state-of-the-art methods, our method offers improved peak signal-to-noise ratios (PSNRs) for samples with large amounts of noise.