Reviewerly/exHarmony huggingface dataset:
Negotiating new audiovisual rules for video sharing platforms: proposals for a responsive governance model of speech online

There is a unique upcoming policy momentum in European audiovisual media policy. This is the implementation of the new rules on Video Sharing Platform Providers (VSPs) with regard to the protection of minors against harmful content and of all users against incitement to violence and hatred. However, the application of the Country-of-Origin principle, the basic tenet of the Audiovisual Media Services Directive and audiovisual media regulation across the European Union, will inherently interfere with the stated policy aim of levelling the playing field vis-a-vis VSPs. This paper assesses the potential consequences of such regulatory dysfunctions in the context of hate speech regulation effective throughout Europe. It argues that Responsive Governance Model-type co-regulation could address and mitigate the loss of normative diversity, and outlines a blueprint for this. At stake is the (further) fragmentation of the internet. Hence, it is important and urgent to find adequate regulatory responses to ensure pluralistic and diverse media markets in Europe.
Research Guides: Python Resources: Free Resources for Learning Python

An overview of the capabilities of the Python programming language and resources for learning Python.
Research on information communication dispatching method of smart grid based on VR technology

In order to improve the dispatching efficiency of smart grid information communication, reduce the load of smart grid, and ensure the security of power grid operation, a smart grid information communication scheduling method based on VR technology is proposed. The 3D modeling of substations and towers in smart grid power equipment is completed by 3DMAX software, and the 3D modeling of overhead lines, cables and other ancillary simple equipment is completed by skyline software; The virtual environment of smart grid integrating viewing and listening is constructed by combining virtual reality 3D technology, and dynamic dispatching priority is assigned by task value characteristic model and task time characteristic model. This process realizes the information communication dispatching of smart grid. The experimental results show that the method can effectively realize the whole station and equipment simulation of smart grid, and ensure the safe operation of smart grid; the scheduling rate of important tasks of smart grid information communication is always around 0.95, and the load deviation is less than 0.25, and it has the advantage of high dispatching efficiency.
LibGuides: 53rd Annual CBHL 2021 Meeting: CBHL Annual Literature Award

Program and schedule the 53rd meeting of the Council on Botanical and Horticultural Libraries in Washington D.C., May 5-7, 2021
LibGuides: APA: In-Text Citations

This guide will introduce you to crafting a paper in APA style. This page details how to format in-text citations in MLA Style.
THE CONTRIBUTION OF REMOTE SENSING AND AEROMAGNETISM TO GOLD PROSPECTING: THE CASE OF THE MEIGANGA ZONE, CAMEROON

In order to optimize gold prospecting in the Meiganga zone located in the Adamaoua region of Cameroon, aeromagnetic and remote sensing prospecting was carried out in the eastern and southern parts. The remote sensing approach on a Landsat 8 OLI/TIRS image highlighted areas of maximum gold concentration. Thus, ferric ion bearing minerals are located in the North-West, silicate minerals bearing ferrous ions are in the Centre while clay minerals are in the North-East and East. The principal component analysis revealed important structural information. The PCA Spatial Map (PC1, PC2, PC3) showed the plutonic formations composed of anatexis and anatexis granites, vegetation cover (at the date of image acquisition: February 22, 2019), areas of permanent water circulation or accumulation, and metamorphic and sedimentary formations namely gneisses, quartzites, schists and superficial clay formations. A Landsat SRTM (Shuttle Radar Topography Mission) image was also used to enhance the lineaments through the Sobel filter to highlight the geomorphological (cliffs, valleys, ...) and topographic (river network, ridge and drainage segment) structures. The aeromagnetic approach was also important. The study of the modified magnetic field (CM) showed 4 ranges: very high, high, medium and low. The Total Magnetic Anomalies (TMI) of the area are subdivided into 2 ranges large positive anomalies (221.1-103.0 nT) located in the lower part of NE-SW orientation, small positive anomalies (103.0-(-)89.7 nT) located in the upper part of NE-SW orientation. The reduced total magnetic anomaly at the equator shows a fairly similar distribution to the total magnetic anomaly with the large positive anomalies in almost the entire lower part. Superimposed on the geological map, Neoproterozoic pre- to syn-tectonic granitoids (C) are superimposed on the large positive anomalies and Neoproterozoic conglomerates, quartzites, sedimentary shales and volcanosedimentary rocks (A) and Neoproterozoic syn-tectonic granitoids (B) are superimposed on the large and small positive anomalies. The grid of the reduced residual equatorial anomaly (ARRE) confirms that the local geology is strongly magnetic (gneiss and quartzite). The filters of the derivatives allowed to establish a map of magnetic lineaments of major orientation N045Ã‚Â° and minor orientation N130Ã‚Â°. The horizontal gadient superimposed on the local maxima showed the presence of deep structures oriented NE-SW. The analytical signal superimposed on the local maxima highlights the metamorphic basement consisting of rocks with strong magnetism. The application of Euler deconvolution localizes the depth of the sources of linear anomalies.
Method to obtain optimum number of clusters in geo-spatial data using stability analysis of cluster centres

&amp;lt;p&amp;gt;Recently the availability of large geo-spatial datasets has increased. These range from soil, quaternary, and geology maps to airborne geophysical and satellite remote sensing data. Such datasets may provide a means to spatially map hydrologically relevant properties (porosity, permeability, texture, depth etc), traditionally mapped via in-situ measurements. However, such datasets, and the relationship between them, is often complex.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Clustering of multidimensional data offers a means to simplify our understanding of the relationships in the data by dividing it into groups containing similar relationships. Clustering can be used as a form of initial exploratory analysis when little is known about how the data relates to the underlying processes in its creation. Such analysis can be useful for geo-spatial datasets whereby the resulting clusters can be re-projected back to the original spatial coordinates and the spatial distribution of the clusters can be visualised.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;There are various clustering algorithms available and the choice of algorithm is often as important as choosing the clustering parameters within it. This work shows a comparison between a more traditional K-Means clustering and a more modern machine learning technique known as Self-Organising Maps (SOMs). The choice of number of clusters for such an analysis is also ambiguous, requiring a priori knowledge of the result. This undermines the general idea behind unsupervised clustering, whereby the result should be driven by the data.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here, a method is proposed to allow the choice of clusters to be dependent on a metric derived from within the cluster analysis itself. The method presented uses the variation in (dataspace) distance between each data point and its cluster centre over 100 runs, for increasing number of clusters. The number of clusters at which this variation remains low is then determined to be the natural optimum number of clusters for a particular dataset.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This method is tested on a dataset where the cluster number is already known and a real-world example. Dataset 1 is the &amp;amp;#8220;Rice Grain Model&amp;amp;#8221; with four known clusters, which the method can accurately reconstruct. The real-world dataset is a combination of airborne radiometric geophysical data and a digital elevation model over peatland in the Republic of Ireland. The method outputs three as the optimum number of clusters and the result divides the peatland into three zones (confirmed with a ground geophysics survey) which are to be used in the creation of hydrological units within Soil and Water Assessment Tool (SWAT) modelling.&amp;lt;/p&amp;gt;
Publications Analysis of Energy Efficient Healthcare from SCOPUS Database: A Study

Healthcare is a field of research which improves the life time of human being. Day by day a lot of research is growing in this field. One of the main applications of Wireless Sensor Networks (WSN) is Healthcare. Body Sensor Network (BSN) is a type of WSN which is useful for patient monitoring. Because the sensor nodes are battery powered, energy is one of the main constraint takes to consider. So an energy efficient healthcare should be enabled. This study aims to analyze the Publications using the keywords Energy Efficient and Healthcare and the data for analysis is collected from the SCOPUS database. The growth rate of the publications regarding these keywords is analyzed. The publication data are collected for the years 2008 to 2018 which includes totally 60 publications in English language with different document types. This study analyzes the Source type of the publications, annual growth rate, Relative growth rate, Doubling time, keywords which is frequently occurred, Subject areas, and authors who published maximum publications in this area.
Medien und Mathematik

Unter Bezugnahme auf Philosophie und Mathematik schlägt dieser Artikel allgemeine Formeln für das Digitale und das Analoge vor, wobei das Digitale als das Verhältnis der diskreten Terme (a/b), das Analoge als eine Verhältnisgleichung (a/b = c/d) definiert sind. Mit diesen allgemeinen Formeln zur Hand werden wir in der Lage sein, zwei der häufigsten operativen Ontologien (Digitalität und das Analoge) zu erforschen und gleichzeitig ein ontologisches Szenario zu enthüllen, in dem keines der beiden zutrifft. Taking into account both philosophy and maths, this article suggests general formulas for both the digital sphere and the analogue, defining the digital sphere as a relation of discrete terms (a/b) whereas the analogue is described as a proportion (a/b = c/d). Falling back on those general formulas, one will be able to study two of the most frequently used operative ontologies (the digitality and the analogue) while at the same time unveiling an ontological scenario to which none of the aforementioned ontologies apply.
Modeling Hierarchical Structures with Continuous Recursive Neural Networks

Recursive Neural Networks (RvNNs), which compose sequences according to their underlying hierarchical syntactic structure, have performed well in several natural language processing tasks compared to similar models without structural biases. However, traditional RvNNs are incapable of inducing the latent structure in a plain text sequence on their own. Several extensions have been proposed to overcome this limitation. Nevertheless, these extensions tend to rely on surrogate gradients or reinforcement learning at the cost of higher bias or variance. In this work, we propose Continuous Recursive Neural Network (CRvNN) as a backpropagation-friendly alternative to address the aforementioned limitations. This is done by incorporating a continuous relaxation to the induced structure. We demonstrate that CRvNN achieves strong performance in challenging synthetic tasks such as logical inference and ListOps. We also show that CRvNN performs comparably or better than prior latent structure models on real-world tasks such as sentiment analysis and natural language inference.
Prehistoric Stone Image Tamil Character Recognition using Optimized Deep Neural Network using Zernike Moments and Simplex Method

Prehistoric Tamil character recognition is an important field of research in pattern recognition and it is a technical challenge than other languages in respect to the similarity and complexity of characters. The main focus of this paper is to determine characters from any given text of Tamil consonants and vowels taken from the stone images. The research challenge in recognizing Tamil character is mainly because of the characters consisting of the number of holes, loops and curves. Even though there are various approaches provided by the researchers, challenges and issues still prevail. The proposed system overcomes the issues behind the Tamil character recognition and provides an improved approach. This system takes a challenge to recognize prehistoric Tamil characters using Deep Neural Network. Pre-processing is done by Binarization, De-noising, character segmentation and size normalization for the stone images and then goes for Feature extraction which forms the basic underlying part of recognizing each character. Characters are then classified by Back Propagation deep Neural Network. The optimization of neural networks is done using simplex method during back propagation with improved data set.
PROBLEMS OF LINGUISTIC TIGHTENING OF UZBEK-ENGLISH PARALLEL CORPUS TEXTS

According to the article, the parallel corpus usually consists of texts on a particular topic. Parallel corpus of state and international official-administrative, diplomatic content make up the majority. The most widely used of these is the Corpus of European Documents. The fact that the translation of such texts is strictly and accurately requires a high quality of translation. Accuracy in translation makes it easier to equate the two language corpus units. Hence, in order for the parallel corpus text alignment to give good results, it is necessary to pay special attention to the quality of the translation when selecting the text for the parallel corpus.
Emotive Meaning in Political Argumentation

Donald Trump’s speeches and messages are characterized by terms that are commonly referred to as “thick” or “emotive”, meaning that they are characterized by a tendency to be used to generate emotive reactions. This paper investigates how emotive meaning is related to emotions, and how it is generated or manipulated. Emotive meaning is analyzed as an evaluative conclusion that results from inferences triggered by the use of a term, which can be represented and assessed using argumentation schemes.
외국인 학부 유학생의 전공 이수 과정의 어려움 및 요구 조사 - 포커스 그룹 인터뷰를 중심으로 -

The Purpose of this paper is to explore the difficulties and needs of international students studying in Korean universities. A total of 28 international students studying in Korea were interviewed in the manner of Focus Group Interview(FGI). Findings show that (1)international students had difficulties because of lack of understanding of major term and knowledge, lack of understanding of professors’ speech and the feeling of alienation as a foreign student, the uncaring attitude of professors. (2)And they hope to get supplementary materials or supplementary classes, receive evaluation separately from Korean students, and take easier major classes. These results show that the difficulty of international students is basically due to their lower Korean level than Korean students, but at the same time, there are difficulties due to difficulties in major learning itself. Therefore, diverse support have to be provided to help international students learn their major as well as improve their Korean language skills.(Pusan National University)
Regression Revisited: Using LP to Fit Linear Models

This chapter introduces concepts of norm-1 and infinity norm fitting, both in terms of their own merit as useful fitting techniques, apart from least squares, but also importantly to teach how optimization problems that seem hard to solve (such as by being non-differentiable) can be reformulated effectively into easier ones that can be handled by standard solution methods – in this case by LP solvers.
Reducing Unnecessary Wait Time At Traffic Light Using Opencv

Traffic Light Optimization uses Machine Learning to find an efficient solution in order to reduce the unnecessary waiting time on the traffic signals. It works by changing time which is a variable parameter relative to the traffic congestion on a traffic signal by using a computer software program. This project aims to minimize the time spent by people on traffic signals by adjusting the timer of green and red lights depending upon the density oftraffic at a particular time on that traffic signal.
Malay language as a mediator for idea generation among students in English language group activity / Noor ‘Izzati Ahmad Shafiai

Students’ participation in discussions encourages idea exploration. However, it is challenging for low-proficient students to participate in an English language (L2) classroom. Using Sociocultural Theory as theoretical framework, this qualitative paper aims to explore how students with low English proficiency utilize Malay language (L1) as a mediator for idea generation in a discussion. Three low-proficient students and three students of mixed proficiency were given a one-hour English writing task. The study revealed that Malay language functioned as a mediator when the students presumed that by speaking in Malay, it may ease their speech production as well as message delivery. The outcome of this study does not only provide initial insights to low-proficient learners’ option in getting their English tasks done, but also allows English instructors to reconsider strict restriction of the use of Malay in English learning
Hybrid Cloud Security Measures and Research Challenges

Cloud Computing is a cropping technology. There are many researches going under this cloud technology. There are many challenges in terms of security for data stored in cloud. Currently there are four cloud deployment models named as public cloud, private cloud, community cloud and hybrid cloud available in the market. According to the requirements customers can avail one among the four available cloud models. This paper especially discusses the security problems and stinks occurs for hybrid cloud. This paper also analyzes various factors that effects the security of the information stored in cloud. It provides a better understanding of the cloud computing research challenges, open issues and helps to find top security threats and solutions in hybrid cloud.
Safeguarding against spurious AI-based predictions: The case of automated verbal memory assessment

A growing amount of psychiatric research incorporates machine learning and natural language processing methods, however findings have yet to be translated into actual clinical decision support systems.Many of these studies are based on relatively small datasets in homogeneous populations, which has the associated risk that the models may not perform adequately on new data in real clinical practice.The nature of serious mental illness is that it is hard to define, hard to capture, and requires frequent monitoring, which leads to imperfect data where attribute and class noise are common.With the goal of an effective AI-mediated clinical decision support system, there must be computational safeguards placed on the models used in order to avoid spurious predictions and thus allow humans to review data in the settings where models are unstable or bound not to generalize.This paper describes two approaches to implementing safeguards: (1) the determination of cases in which models are unstable by means of attribute and class based outlier detection and (2) finding the extent to which models show inductive bias.These safeguards are illustrated in the automated scoring of a story recall task via natural language processing methods.With the integration of human-in-the-loop machine learning in the clinical implementation process, incorporating safeguards such as these into the models will offer patients increased protection from spurious predictions.
ESP Textbook Development for Vocational School in Indonesia

The role of textbooks for English language teaching (ELT) in a vocational school in Indonesia is indisputable. Theyfacilitate the teaching-learning process and source of skills development for the students. However, the ELT textbook forvocational school still received little attention since most ELT government-approved books still focus on general Englishwith little attention to the specialized skills development on the expertise. Vocational school varies in the expertise area;therefore, the students need intensive and extensive exposure to English for Specific Purposes (ESP). The ELT, then,should be based on the learners' needs. This paper discusses the literature review as a methodology for conductingresearch and offers ESP textbook development issues. The data were taken from some relevant references. The findingdiscussed a justification for developing ESP textbook in vocational school, and the criteria for textbook selection,material development, and development planning. Moreover, the materials reflect the authenticity of real-world languageand enable students to rehearse language outside the classroom, activate the acquisition process, focus on the linguisticsystem, and manipulate on a particular linguistic feature and also on exchange meaning. Those issues need to be coveredto informthe stakeholders on the significance ofESP textbooks, which will enable the English teacher to equip the studentswith the specific English skills that will be useful in their future careers.KeywordsEnglish language teaching (ELT), ELT textbook development, English for Specific Purposes (ESP) textbook.
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation

A graph generative model defines a distribution over graphs. One type of generative model is constructed by autoregressive neural networks, which sequentially add nodes and edges to generate a graph. However, the likelihood of a graph under the autoregressive model is intractable, as there are numerous sequences leading to the given graph; this makes maximum likelihood estimation challenging. Instead, in this work we derive the exact joint probability over the graph and the node ordering of the sequential process. From the joint, we approximately marginalize out the node orderings and compute a lower bound on the log-likelihood using variational inference. We train graph generative models by maximizing this bound, without using the ad-hoc node orderings of previous methods. Our experiments show that the log-likelihood bound is significantly tighter than the bound of previous schemes. Moreover, the models fitted with the proposed algorithm can generate high-quality graphs that match the structures of target graphs not seen during training. We have made our code publicly available at \hyperref[this https URL]{this https URL}.
Equivariant Networks for Pixelized Spheres

Pixelizations of Platonic solids such as the cube and icosahedron have been widely used to represent spherical data, from climate records to Cosmic Microwave Background maps. Platonic solids have well-known global symmetries. Once we pixelize each face of the solid, each face also possesses its own local symmetries in the form of Euclidean isometries. One way to combine these symmetries is through a hierarchy. However, this approach does not adequately model the interplay between the two levels of symmetry transformations. We show how to model this interplay using ideas from group theory, identify the equivariant linear maps, and introduce equivariant padding that respects these symmetries. Deep networks that use these maps as their building blocks generalize gauge equivariant CNNs on pixelized spheres. These deep networks achieve state-of-the-art results on semantic segmentation for climate data and omnidirectional image processing. Code is available at this https URL.