Reviewerly/exHarmony huggingface dataset:
Text Mining Algorithms in Social Media

Abstract Social networks are wealthy in different sorts of substance, for example, texts and multimedia. The capacity to apply text mining calculations viably with regards to content information is basic for a wide assortment of uses. Interpersonal organizations require text mining calculations for a wide assortment of uses, for example, watchword search, characterization, and bunching. While search and characterization are notable applications for a wide assortment of situations, interpersonal organizations have a lot more extravagant structure both as far as content and connections. A great part of the work in the region utilizes either absolutely the content substance or simply the linkage structure. Be that as it may, numerous ongoing calculations utilize a mix of linkage and substance data for mining purposes. By and large, things being what they are, the utilization of a mix of linkage and substance data gives substantially more successful outcomes than a system which depends absolutely on both of the two. This paper gives an overview of social media, categories of social media, and challenges of text analysis in social media, six key tips for using text mining in social media and the algorithms that are used for text mining.
DyHCN: Dynamic Hypergraph Convolutional Networks

Hypergraph Convolutional Network (HCN) has become a default choice for capturing high-order relations among nodes, \emph{i.e., } encoding the structure of a hypergraph. However, existing HCN models ignore the dynamic evolution of hypergraphs in the real-world scenarios, \emph{i.e., } nodes and hyperedges in a hypergraph change dynamically over time. To capture the evolution of high-order relations and facilitate relevant analytic tasks, we formulate dynamic hypergraph and devise the Dynamic Hypergraph Convolution Networks (DyHCN). In general, DyHCN consists of a Hypergraph Convolution (HC) to encode the hypergraph structure at a time point and a Temporal Evolution module (TE) to capture the varying of the relations. The HC is delicately designed by equipping inner attention and outer attention, which adaptively aggregate nodes' features to hyperedge and estimate the importance of each hyperedge connected to the centroid node, respectively. Extensive experiments on the Tiigo and Stocktwits datasets show that DyHCN achieves superior performance over existing methods, which implies the effectiveness of capturing the property of dynamic hypergraphs by HC and TE modules.
Research Guides: Library Guide to SIP Research: Working with Statistics & Data

Each SIP is unique. These resources and suggestions do not replace guidance given by departments or advisors.
Artificial Intelligence and its application on the Istanbul Protocol

This work proposes the post-processing of photographic material accompanied by artificial intelligence algorithms as an alternative to strengthen the photography opinion within the Istanbul Protocol. The methods presented throughout this paper were applied to a judicialized case study in which the existence of injuries to the body anatomy of a defendant could be demonstrated five years after the fact reported as torture. Keywords: Neural Networks, Chronochromodiagnostic, Hopfield Network
Real-Time Semantic Segmentation via Multiply Spatial Fusion Network.

Real-time semantic segmentation plays a significant role in industry applications, such as autonomous driving, robotics and so on. It is a challenging task as both efficiency and performance need to be considered simultaneously. To address such a complex task, this paper proposes an efficient CNN called Multiply Spatial Fusion Network (MSFNet) to achieve fast and accurate perception. The proposed MSFNet uses Class Boundary Supervision to process the relevant boundary information based on our proposed Multi-features Fusion Module which can obtain spatial information and enlarge receptive field. Therefore, the final upsampling of the feature maps of 1/8 original image size can achieve impressive results while maintaining a high speed. Experiments on Cityscapes and Camvid datasets show an obvious advantage of the proposed approach compared with the existing approaches. Specifically, it achieves 77.1% Mean IOU on the Cityscapes test dataset with the speed of 41 FPS for a 1024*2048 input, and 75.4% Mean IOU with the speed of 91 FPS on the Camvid test dataset.
Design and performance evaluation of a solar tracking panel of single axis in Colombia

This paper presents the mechanical design of a single axis solar tracking system, as well as the electronic design of a system that to record in real time the electric power delivered by the solar tracker and to evaluate its performance. The interface was developed in Labview and it compares the power supplied by the tracker with the power supplied by static solar panel of the same characteristics. The performance is initially simulated using Pv-Syst software, and later validated with the data obtained by the interface. As a result, the use of the solar tracker increases the power delivered by a minimum of 19%, and it can go as high as 47.84%, with an average in increase in power of 19.5% in the monthly energy production. This experimental result was compared with the simulation by Pv-Syst software and shows a difference of only 2.5%, thus validating the reliability of the simulation. This behavior pattern coincides with previous studies carried out for equatorial latitudes.
Bayesian recurrent neural network as a tool for reconstructing dynamical systems from multidimensional data

&amp;lt;p&amp;gt;We suggest a new method for construction of data-driven dynamical models from observed multidimensional time series. The method is based on a recurrent neural network (RNN) with specific structure, which allows for the joint reconstruction of both a low-dimensional embedding for dynamical components in the data and an operator describing the low-dimensional evolution of the system. The key link of the method is a Bayesian optimization of both model structure and the hypothesis about the data generating law, which is needed for constructing the cost function for model learning. &amp;amp;#160;The form of the model we propose allows us to construct a stochastic dynamical system of moderate dimension that copies dynamical properties of the original high-dimensional system. An advantage of the proposed method is the data-adaptive properties of the RNN model: it is based on the adjustable nonlinear elements and has easily scalable structure. The combination of the RNN with the Bayesian optimization procedure efficiently provides the model with statistically significant nonlinearity and dimension.&amp;lt;br&amp;gt;The method developed for the model optimization aims to detect the long-term connections between system&amp;amp;#8217;s states &amp;amp;#8211; the memory of the system: the cost-function used for model learning is constructed taking into account this factor. In particular, in the case of absence of interaction between the dynamical component and noise, the method provides unbiased reconstruction of the hidden deterministic system. In the opposite case when the noise has strong impact on the dynamics, the method yield a model in the form of a nonlinear stochastic map determining the Markovian process with memory. Bayesian approach used for selecting both the optimal model&amp;amp;#8217;s structure and the appropriate cost function allows to obtain the statistically significant inferences about the dynamical signal in data as well as its interaction with the noise components.&amp;lt;br&amp;gt;Data driven model derived from the relatively short time series of the QG3 model &amp;amp;#8211; the high dimensional nonlinear system producing chaotic behavior &amp;amp;#8211; is shown be able to serve as a good simulator for the QG3 LFV components. The statistically significant recurrent states of the QG3 model, i.e. the well-known teleconnections in NH, are all reproduced by the model obtained. Moreover, statistics of the residence times of the model near these states is very close to the corresponding statistics of the original QG3 model. These results demonstrate that the method can be useful in modeling the variability of the real atmosphere.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The work was supported by the Russian Science Foundation (Grant No. 19-42-04121).&amp;lt;/p&amp;gt;
Availability of Hyperlinked Resources in Astrophysics Papers

Astrophysics papers often rely on software which may or may not be available, and URLs are often used as proxy citations for software and data. We extracted all URLs from two journals' 2015 research articles, removed those from certain long-term reliable domains, and tested the remainder to determine what percentage of these URLs were accessible in October 2018.
Annealing Genetic GAN for Minority Oversampling

The key to overcome class imbalance problems is to capture the distribution of minority class accurately. Generative Adversarial Networks (GANs) have shown some potentials to tackle class imbalance problems due to their capability of reproducing data distributions given ample training data samples. However, the scarce samples of one or more classes still pose a great challenge for GANs to learn accurate distributions for the minority classes. In this work, we propose an Annealing Genetic GAN (AGGAN) method, which aims to reproduce the distributions closest to the ones of the minority classes using only limited data samples. Our AGGAN renovates the training of GANs as an evolutionary process that incorporates the mechanism of simulated annealing. In particular, the generator uses different training strategies to generate multiple offspring and retain the best. Then, we use the Metropolis criterion in the simulated annealing to decide whether we should update the best offspring for the generator. As the Metropolis criterion allows a certain chance to accept the worse solutions, it enables our AGGAN steering away from the local optimum. According to both theoretical analysis and experimental studies on multiple imbalanced image datasets, we prove that the proposed training strategy can enable our AGGAN to reproduce the distributions of minority classes from scarce samples and provide an effective and robust solution for the class imbalance problem.
Some aspects of modeling in the study of COVID-19 data

Human society or civilization is constantly faced with various tests One such test is the COVID-19 pandemic, which is associated with the spread of a special type of coronavirus Therefore, humanity is forced to find adequate measures to protect against the COVID-19 pandemic For this purpose it is needed to know the reliable statistics Additionally, it is important to know the features of the spread of COVID-19, to be able to make effective forecasts This can be possible only by using various models Different models take into account with specific features for the future prediction of the spread of the pandemic These basic aspects are discussed in this work, which helps people for making better decisions for future strategies
TextGCN based on data enhancement

As the core of Internet text processing and information retrieval, text classification is often due to the scarcity of high-quality Chinese data sets and the diversity of Chinese vocabulary combinations in actual natural language scenarios. The trained Chinese language model usually has data Sparse problem. In response to this phenomenon, in this article we propose to amplify the text corpus used for training, that is, data enhancement, to solve the problem of data sparseness, and observe whether the data enhancement technique is useful for graph convolutional networks (GCN). After experiments, we found that data enhancement technology can improve the classification ability of the TextGCN model.
Sufficient and Disentangled Representation Learning

We propose a novel approach to representation learning called sufficient and disentangled representation learning (SDRL). With SDRL, we seek a data representation that maps the input data to a lower-dimensional space with two properties: sufficiency and disentanglement. First, the representation is sufficient in the sense that the original input data is conditionally independent of the response or label given the representation. Second, the representation is maximally disentangled with mutually independent components and rotation invariant in distribution. We show that such a representation always exists under mild conditions on the input data distribution based on optimal transport theory. We formulate an objective function characterizing conditional independence and disentanglement. This objective function is then used to train a sufficient and disentangled representation with deep neural networks. We provide strong statistical guarantees for the learned representation by establishing an upper bound on the excess error of the objective function and show that it reaches the nonparametric minimax rate under mild conditions. We also validate the proposed method via numerical experiments and real data analysis.
THE EFFECTIVENESS OF STRUCTURED NUMBER HEAD LEARNING MODELS ON CLASS V IPS LEARNING OUTCOMES

Based to the result, the observation and interview the low quality of education in Indonesia was caused by students' learning outcomes in some subjects were classified as low. This was supported by the data on social studies learning outcomes in the 5th grade students of elementary school of Kartini Cluster, Banjarnegara Regency, that were quite low. One of the reason was the teachers had not applied the appropriate learning model in accordance with educational regulations. The teachers used a teacher-centered learning model. The purpose of this study was to test the effectiveness of the Structured Numbered Heads model to result of social studies learning outcomes of 5th grade students of elementary school of Kartini Cluster, Banjarnegara Regency. This research used quantitative method with experiment design and nonequivalent control group design. The sample technique was cluster random sampling of 97 participantes. Data collection used test, observation and documentation. Hypotesis test result showed that tcount˃ttabel(5,8883&gt;2,0003) which mean structured numbered heads model is was more effective to result of social studies learning outcomes of 5th grade students. The experiment class n-gain test result 0,4611 belong to medium criteria while control class n-gaint test result 0.481 belong to low criteria. The conclusion of this research is structured numbered heads was effective to be used in social studies learning material of 5th grade students of elementary school of of Kartini Cluster, Banjarnegara Regency.
LibGuides: March is Reading Month: Listen to books

This LibGuide is for a public library's March is Reading Month reading challenge. The target audience is elementary school-aged library patrons and their parents. Listening to books is a great way to enjoy stories. Find some here.
Co-complexity: An Extended Perspective on Generalization Error

It is well known that the complexity of a classifier's function space controls its generalization gap, with two important examples being VC-dimension and Rademacher complexity (R-Complexity). We note that these traditional generalization error bounds consider the ground truth label generating function (LGF) to be fixed. However, if we consider a scenario where the LGF has no constraints at all, then the true generalization error can be large, irrespective of training performance, as the values of the LGF on unseen data points can be largely independent of the values on the training data. To account for this, in this work, we consider an extended characterization of the problem, where the ground truth labels are generated by a function within another function space, which we call the \textit{generator} space. We find that the generalization gap in this scenario depends on the R-Complexity of both the classifier and the generator function spaces. Thus, we find that, even if the R-Complexity of the classifier is low and it has a good training fit, a highly complex generator space could worsen generalization performance, in accordance with the no free lunch theorem. Furthermore, the characterization of a generator space allows us to model constraints, such as invariances (translation and scale in vision) or local smoothness. Subsequently, we propose a joint entropy-like measure of complexity between function spaces (classifier and generator), called co-complexity, which leads to tighter bounds on the generalization error in this setting. Co-complexity captures the similarities between the classifier and generator spaces. It can be decomposed into an invariance co-complexity term, which measures the extent to which the classifier respects the invariant transformations in the generator, and a dissociation co-complexity term, which measures the ability of the classifier to differentiate separate categories in the generator. Our major finding is that reducing the invariance co-complexity of a classifier, while maintaining its dissociation co-complexity, improves the training error and reduces the generalization gap. Furthermore, our results, when specialized to the previous setting where the LGF is fixed, lead to tighter generalization error bounds. Theoretical results are supported by empirical validation on the CNN architecture and its transformation-equivariant extensions. Co-complexity showcases a new side to the generalization abilities of classifiers and can potentially be used to improve their design.
PEMBELAJARAN MENULIS TEKS DRAMA DENGAN MENGGUNAKAN METODE PICTURE AND PICTURE

The learning of writing drama texts is generally less attractive to students because of the difficulty of students starting the writing process and developing words into appropriate sentences. This research was conducted to examine learning to write drama texts using the picture and picture method in class XI students at SMAN 1 MANIIS. The formulation of the problem in this study is the difficulty experienced by students in learning to write drama texts. This study aims to describe learning to write drama texts using the picture and picture method and to answer the problem formulation. This research method uses descriptive qualitative and uses data collection techniques in the form of writing skills tests. Based on the results of research conducted on students of class XI with a total of 20 students during the learning process, it shows that there is an increase in the average score obtained by students, the initial test gets an average score of 61 and the final test gets an average score of 75.5. These results indicate an increase in each test.
A brief overview of fuzzy logic theory

A brief overview of fuzzy logic theory - Uncertainty is a time period used in subtly one of a kind approach in a wide variety
국방 정신전력교육 평가지표의 탐색: 교육 만족도 지표의 개발과 활용방안

In response to the rapidly changing national security environments the Republic of Korean (ROK) Armed Forces have spent a significant amount of budgets on the troop education programs for soldiers. However very little scholarly attention has been paid to examining how much the ROK soldiers are satisfied with these education programs. To fill in this gap the study develops a new measure for troop education program satisfaction. Using a unique social survey data in which more than 5,000 ROK soldiers participated this study actually calculates the satisfaction scores by subject''s characteristics. This study finally discusses three practical applications with informative visualization for further utilization of this new measure--comparison across camp units evaluation of new educational contents and resource for the Integrated Data System in particular.
Thai EFL Students’ Perceptions of Development of English Reading Comprehension on Buddhist Contents through PSQ6R Technique

This research aimed to study the Thai EFL students’ perception of the development of English reading comprehension on Buddhist contents through PSQ6R technique.  The sample included 47 students enrolled in Dhamma in English course at Nakhon Lampang Buddhist College in the 1 st semester of academic year 2020. The samples were selected by the purposive selection method.  The instrument for collecting data was a questionnaire.   The data analysis was done using descriptive statistics which are percentage, mean score, and standard deviation. The research result revealed that the students strongly satisfied (=4.59) with the development of English reading comprehension on Buddhist contents through PSQ6R techniques.
Commutators for Stochastic Rewriting Systems: Theory and Implementation in Z3.

In the semantics of stochastic rewriting systems (SRSs) based on rule algebras, the evolution equations for average expected pattern counts are computed via so-called commutators counting the distinct sequential compositions of rules and observable patterns regarded as identity rules. In this paper, we consider the commutators for conditional SRS in the Sesqui-Pushout (SqPO) approach. However, graphs are often subject to constraints. To minimise the construction of spurious compositions prohibited by such constraints, we develop strategies for computing rule composition, both theoretically and using the SMT solver Z3 with its Python interface. Our two equivalent solutions for checking constraints include a straightforward generate-and-test approach based on forbidden graph patterns and a modular solution, where the patterns are decomposed as pushouts of monic spans into forbidden relation patterns. The implementation is based on a framework that allows a direct and modular representation of the categorical and logical theory in Python/Z3. For an example of SqPO rewriting of rigid multigraphs modelling polymer formation in organic chemistry, we assess and compare the performance of the two strategies.
Modelling Reachability in Transport Networks: Using Alternative Visual Representations in Interactive Linked‐Views to Gain Valuable Insights

Most people use maps for navigation. Geographic maps visually represent physical distance between locations. These maps sometimes provide a false impression of travel times. Two cities geographically close to each other might be “far apart” in terms of travel time because of slower connections, whereas two cities geographically distant might be “nearby” in terms of travel time because of faster connections. Under such circumstances, visualizing a transport network using time as a distance measure can make the transport network more understandable. This study integrates several (carto)graphic representations—a time line, a distance line, a time prism, a time cartogram, and a geographic map—in an interactive linked-views environment to model reachability in transport networks. A prototype is implemented in a web environment using D3.js. The implementation can be applied to any transport network. In this research, the approach is illustrated with railroad network data for the Dutch province of Overijssel. The solution provides an alternative and insightful perspective for analyzing the data. In addition to complementing a wide variety of methods to visualizing travel times, the approach could be applied in areas such as spatial analysis and transport planning.