Reviewerly/exHarmony huggingface dataset:
Economic and Energy Feasibility using Photovoltaic Solar Energy Sources as a response to the energy consumption of the municipality of Barcarena

This article provides an economic and energy feasibility study for the implementation of renewable photovoltaic energy sources, in the municipality of Barcarena, in order to sustain electric energy consumption, taking into account technological advances, the expected population increase and the energy potential of the study area. The consumption of electricity of the municipality under study is analysed, as well as their distribution among the various types of existing electricity production. The economic and energy influence of this implementation is analysed through different case studies, in order to meet the targets defined by the Oeiras Council with regard to the Sustainable Energy for Oeiras Action Plan (PAESO).
Chapter 6. Resources

Chapter 6 of Library Technology Reports (vol. 57, no. 8), “Using the Toward Gigabit Libraries Toolkit,” lists a helpful article on the TGL project. It also lists a few of the publicly available webinars that Carson Block or Stephanie Stenberg have given to various organizations about how to utilize the toolkit for various purposes in libraries around the country.
LibGuides: LibGuides Website Tips: Remove the breadcrumbs

This guide accompanies PALNI's Website Style Guide and focuses on assisting you to create a custom website in LibGuides
Method and Style of Religious and Philosophical Criticism of Vasily Rozanov

espanolLa aparicion de una figura nueva y significativa en la critica no puede ser accidental. La critica como una forma especifica de actividad presupone una mentalidad igualmente especial para la cual las tareas algo diferentes son importantes como, por ejemplo, para un filosofo, un artista o un cientifico. El proposito del presente articulo es mostrar como el metodo especifico de trabajar con el texto y el estilo literario unico de Rozanov determinaron su camino de la filosofia a la critica. Los principios metodologicos y las caracteristicas estilisticas se descubren en la herencia literaria y critica de V. Rozanov, que luego se convierten en caracteristicas de atributos del pensamiento religioso y filosofico ruso. EnglishThe appearance of a significant and new figure in criticism cannot be accidental. Criticism as a specific form of activity presupposes an equally special mindset for which somewhat different tasks are important than, for example, for a philosopher, an artist or a scientist. The purpose of the present article is to show how the specific method of working with text and the unique literary style of Rozanov determined his way from philosophy to criticism. Methodological principles and stylistic features are discovered in V. Rozanov’s literary and critical heritage, which later become attribute features of Russian religious and philosophical thought.
Multi‐scale affinities with missing data: Estimation and applications

Abstract Many machine learning algorithms depend on weights that quantify row and column similarities of a data matrix. The choice of weights can dramatically impact the effectiveness of the algorithm. Nonetheless, the problem of choosing weights has arguably not been given enough study. When a data matrix is completely observed, Gaussian kernel affinities can be used to quantify the local similarity between pairs of rows and pairs of columns. Computing weights in the presence of missing data, however, becomes challenging. In this paper, we propose a new method to construct row and column affinities even when data are missing by building off a co‐clustering technique. This method takes advantage of solving the optimization problem for multiple pairs of cost parameters and filling in the missing values with increasingly smooth estimates. It exploits the coupled similarity structure among both the rows and columns of a data matrix. We show these affinities can be used to perform tasks such as data imputation, clustering, and matrix completion on graphs.
QUALITY ANALYSIS OF STUDENTS ' RESEARCH IN THE ENGINEERING FACULTY OF IBNU SINA BATAM UNIVERSITY

This study aims to obtain data about the quality of research conducted by students at the Faculty of Engineering, Ibnu Sina University over the past three years. The population used in this study were students of the Faculty of Engineering at the University of Ibnu Sina from two knowledge groups found at the Faculty of Engineering at the University of Ibnu Sina. Sampling was conducted based on purposive sampling, namely educational research thesis in 2016-2019. From the number of theses that existed during the period 2016-2019, random sampling was conducted for sampling. The research method used is descriptive qualitative by conducting content analysis systematically of documents as data sources. The research quality parameters measured included aspects: theme chosen, presentation and language, structure and logic of the writings, relevance between sub-chapters (Background, Identification, Formulation, Hypothesis, Depth and Extent of Theory, and Research Methods), determination of subjects and research objects , data collection technique; the validity of the instruments used, data analysis, drawing conclusions, and referral sources. The results showed that the quality of educational research conducted by students of the Faculty of Engineering showed that the topics studied did not yet show the presentity and innovation. The topic chosen is still limited to the topics discussed in the previous thesis. The topic of media and learning outcomes dominates as a topic of great interest. When viewed from the object of research the ability to prepare a research thesis for students needs to be improved, especially in methodology, topic selection, language, data collection techniques, instrument preparation, data analysis, and drawing conclusions.
Uncertainty-Aware Machine Translation Evaluation

Several neural-based metrics have been recently proposed to evaluate machine translation quality. However, all of them resort to point estimates, which provide limited information at segment level. This is made worse as they are trained on noisy, biased and scarce human judgements, often resulting in unreliable quality predictions. In this paper, we introduce uncertainty-aware MT evaluation and analyze the trustworthiness of the predicted quality. We combine the COMET framework with two uncertainty estimation methods, Monte Carlo dropout and deep ensembles, to obtain quality scores along with confidence intervals. We compare the performance of our uncertainty-aware MT evaluation methods across multiple language pairs from the QT21 dataset and the WMT20 metrics task, augmented with MQM annotations. We experiment with varying numbers of references and further discuss the usefulness of uncertainty-aware quality estimation (without references) to flag possibly critical translation mistakes.
Sparse is Enough in Scaling Transformers

Large Transformer models yield impressive results on many tasks, but are expensive to train, or even fine-tune, and so slow at decoding that their use and study becomes out of reach. We address this problem by leveraging sparsity. We study sparse variants for all layers in the Transformer and propose Scaling Transformers, a family of next generation Transformer models that use sparse layers to scale efficiently and perform unbatched decoding much faster than the standard Transformer as we scale up the model size. Surprisingly, the sparse layers are enough to obtain the same perplexity as the standard Transformer with the same number of parameters. We also integrate with prior sparsity approaches to attention and enable fast inference on long sequences even with limited memory. This results in performance competitive to the state-of-the-art on long text summarization.
LibGuides: Online Learning Resources: E-Books

This guide has been created in support of Monmouth University's commitment to online learning.
On the Generative Utility of Cyclic Conditionals

We study whether and how can we model a joint distribution $p(x,z)$ using two conditional models $p(x|z)$ and $q(z|x)$ that form a cycle. This is motivated by the observation that deep generative models, in addition to a likelihood model $p(x|z)$, often also use an inference model $q(z|x)$ for data representation, but they rely on a usually uninformative prior distribution $p(z)$ to define a joint distribution, which may render problems like posterior collapse and manifold mismatch. To explore the possibility to model a joint distribution using only $p(x|z)$ and $q(z|x)$, we study their compatibility and determinacy, corresponding to the existence and uniqueness of a joint distribution whose conditional distributions coincide with them. We develop a general theory for novel and operable equivalence criteria for compatibility, and sufficient conditions for determinacy. Based on the theory, we propose the CyGen framework for cyclic-conditional generative modeling, including methods to enforce compatibility and use the determined distribution to fit and generate data. With the prior constraint removed, CyGen better fits data and captures more representative features, supported by experiments showing better generation and downstream classification performance.
Pengaruh pH dan Konsentrasi Larutan Terhadap Penyerapan Zat Warna Rhodamin B Menggunakan Biosorben Kulit Pisang Kepok (Musa Balbisiana Colla)

Abstract ─ Pollution that occurs in the community is caused by solid or liquid waste. One of the pollutants that are discharged into the environment are dyes. The dye waste originates from the coloring and dyeing process which is discharged directly in large quantities into the environment without prior processing. While the environment has certain limits to degrade the incoming compounds or dyes. One alternative method used to remove Rhodamin B dyes is the biosorption method. This method is believed to be able to eliminate pollutants in the waters because of its absorption which is quite effective at a relatively low cost, easy workmanship and the materials used are easily obtained such as kepok banana peels. This research uses the Batch method by looking at the effect of pH and concentration of the solution on the absorption of Rhodamin B by kepok bananas (Musa balbisiana Colla). The results showed that the optimum conditions occurred at pH 3 with 58% absorption of Rhodamin-B, concentration of 120 mg / L with 96% absorption of Rhodamin-B. The interaction between adsorbent and adsorbate is determined by the study of adsorption isotherms including the Langmuir isotherm and the Freundlich isotherm. In order to obtain Langmuir equilibrium with qmax of 25.9067 mg/g. Characterization of kepok banana peel using FTIR to see functional groups after activation. Keywords : Biosorption, Rhodamin-B, kepok banana peel (Musa balbisiana Colla), Batch method
TYPICAL CLASS METHODS FOR SOLVING EQUATIONS AND INEQUALITIES WITH DIFFERENT STRUCTURES

The article discusses the ways of developing skills and abilities to effectively solve problems when describing methods for solving equations and inequalities, clarifying theoretical knowledge, the basics of forming skills for practical application. The formation of mathematical concepts through solving problems in teaching mathematics opens the way to the development of mathematical thinking, the application of knowledge in practice, and the development of search skills. To master a mathematical concept, along with its definition, it is necessary to know its features and properties. This can be achieved primarily through problem solving and exercise. Problem solving is based on the development of new methods, the creation of algorithms, ways of developing practical skills in the methods and techniques mastered with the help of tasks.In addition, transforming equations and inequalities through the development of thinking skills helps to identify common or special properties in order to draw correct conclusions. Solving various problems, it shows what operations should be used to determine the situation in which a solution was found, and what features of the solution allow choosing the most effective methods. Thanks to the theoretical substantiation of the general article, it is possible to master convenient methods for solving equations and inequalities of various structures.
A DEEP NEURAL NETWORK – BASED APPROACH FOR RECOGNIZING STATISTICAL PROBABILITY DISTRIBUTIONS

The probability distribution is extremely important in data analysis. Selection of the right distribution requires critical thinking that based on adequate mastery of the characteristics of the distribution. Classically, the probability distribution is identified by several methods, such as: Chi-square goodness of fit test, graph, normal plot and non-parametric goodness of fit test. To identify probability distributions, this article proposes Deep Neural Network (DNN) approaches. Particularly, this article discusses three DNN approaches including DNN using backpropagation with two hidden layers, Artificial Neural Network (ANN) using backpropagation with one hidden layer and Fuzzy Learning Vector Quantization (FLVQ). The implementation through a simulation to identify the probability distributions results show that the DNN approach outperforms the ANN and FLVQ approaches. Key Word: Artificial Neural Network, Deep Neural Network, Fuzzy Learning Vector Quantification, and Probability Distribution.
REVIEW AND ANALYSIS OF ADVANCED RESEARCH IN THE FIELD OF CYBER PROTECTION OF INFORMATION SPACE OF EDUCATIONAL INSTITUTIONS

Мақалада заманауи ЖОО-ның қауіпсіз ақпараттық білім беру ортасын қалыптастырудың алғы шарттары қарастырылған. Отандық және жетекші шетелдік зерттеулердің жарияланымдары талданды. Оқу орындарының ақпараттық кеңістігін кибер қорғау (КҚау) саласындағы алдыңғы зерттеулерге шолу жасалып, осы тақырып бойынша шыққан жарияланымдарға талдау жүргізілді. Жасалған талдау жоғары оқу орындарының КҚау жүйесін үздіксіз өзара инвестициялау міндеттерінде ШҚЖ үшін модельдерді одан әрі дамыту проблемасының өзектілігін растады. Ақпараттандыру нысандардың КҚау қатерлерінің моделін сипаттау үшін Петри желілерін қолдануға арналған зерттеулер нәтижелері бойынша жарияланымдарға талдау жасалды. Бұл жұмыстар осы міндетте айтарлықтай теориялық үлес қосса да, біздің ойымызша, авторлар ұсынған модельдерді, атап айтқанда АН-ді АҚ және КҚау бойынша ШҚИЖ-да және СЖда бағдарламалық жүзеге асыру біршама қиынға соғады. Бұл өз кезегінде қосымша зерттеулерді талап етеді. Жалпы мақалада ақпараттық жүктемесі көп жұмыс орындары үшін қауіпсіздікті қамтамасыз ететін және мәліметтердің бірегейлігін сақтайтын мүмкіндіктер жайлы жан жақты келтірілген. Осы саладағы мамандар үшін мақала қажетті ақпараттарды бере алады.
Improved Regularization and Robustness for Fine-tuning in Neural Networks

A widely used algorithm for transfer learning is fine-tuning, where a pre-trained model is fine-tuned on a target task with a small amount of labeled data. When the capacity of the pre-trained model is much larger than the size of the target data set, fine-tuning is prone to overfitting and memorizing the training labels. Hence, an important question is to regularize fine-tuning and ensure its robustness to noise. To address this question, we begin by analyzing the generalization properties of fine-tuning. We present a PAC-Bayes generalization bound that depends on the distance traveled in each layer during fine-tuning and the noise stability of the fine-tuned model. We empirically measure these quantities. Based on the analysis, we propose regularized self-labeling -- the interpolation between regularization and self-labeling methods, including (i) layer-wise regularization to constrain the distance traveled in each layer; (ii) self label-correction and label-reweighting to correct mislabeled data points (that the model is confident) and reweight less confident data points. We validate our approach on an extensive collection of image and text data sets using multiple pre-trained model architectures. Our approach improves baseline methods by 1.76% (on average) for seven image classification tasks and 0.75% for a few-shot classification task. When the target data set includes noisy labels, our approach outperforms baseline methods by 3.56% on average in two noisy settings.
Do UD Trees Match Mention Spans in Coreference Annotations

One can find dozens of data resources for various languages in which coreference - a relation between two or more expressions that refer to the same real-world entity - is manually annotated. One could also assume that such expressions usually constitute syntactically meaningful units; however, mention spans have been annotated simply by delimiting token intervals in most coreference projects, i.e., independently of any syntactic representation. We argue that it could be advantageous to make syntactic and coreference annotations convergent in the long term. We present a pilot empirical study focused on matches and mismatches between hand-annotated linear mention spans and automatically parsed syntactic trees that follow Universal Dependencies conventions. The study covers 9 datasets for 8 different languages.
Campus Autonomous Robot Tours

Understanding how humans and machines interact and designing autonomous systems based on this interaction is crucial to enable more complex robotic exploration missions. In this paper, we present our ongoing research in Campus Autonomous Robot Tours at the Autonomy Research Center for STEAHM (ARCS) as our effort to investigate human-robot remote interaction design in real-world scenarios. The campus autonomous robot tours system is designed to provide guests with an interactive tour of the California State University, Northridge (CSUN) campus through the eyes of a mobile robot. The human team is assigned with different roles including robot supervisor, navigator, campus ambassador, and observer. We present the result of the robot remote deployment on campus, analysis of the interactions between the human team and the mobile robot, and provide recommendations for further research and development.
Quora: Finding The Most Common Question Pairs Similarity

Data mining is the ability to extract the most relevant information from raw data and process the relevant information into a separate list. In general, the word ontology is termed an alternate name for data mining, in which extracting useful information from the big data sources is the most common task present in the ontology domain. In current days the semantic web is becoming a more trending topic for a lot of new research inventions. One of the recent research inventions is finding the most common question pair similarity from several questions and answers which are published on certain topics on the semantic web. This is becoming a challenge for the developers to prove this task and hence this is the main motivation for me to design this current work in which one will get a lot of useful information from this research topic. For testing this current model we assume QUORA as the sample data source in which a group of several question-and-answers is asked, answered, edited, and organized by its community of users. On this site lot of end, users can collaborate with each other for certain questions, share their suggestions, and edit the answers which are already submitted by others. This collaboration is taken as a thread on a single question with a list of similar/related questions so that users would not have to answer similar questions once again. By using the several various Natural Language Processing (NLP) concepts from the given dataset and applying several ML algorithms on that input features, we try to fund our most distinct question and answers and remove the duplicates which are present for a certain topic.
A Survey on Ontology Operations Techniques

Ontologies are increasingly seen as key factors in enabling interoperability between heterogeneous systems and semantic web applications and are emerging as representative techniques for overlapping complementary context domains. A single ontology is no longer sufficient to support the tasks predicted by a distributed environment such as the Semantic Web; several ontologies for many applications are necessary. Different ontological tools have different representations of data and concept operations with respect to their input. Its functions and informational structures also differ depending on its tools and processes. Currently very few investigation documents provide an in-depth discussion of these technologies and their applications. In this article, we discuss various sophisticated ontological tools with their various internal processes and algorithms. Mapping, aligning, or merging ontologies creates an identification layer that allows different applications to access the resulting ontology and then share its information, of course, while preserving the semantics it contains. For integrating large ontologies, automatic matches become an essential solution. However, the large ontology matching process presents high spatial and temporal complexities. Therefore, for a tool to efficiently and accurately match this large ontology within limited computing resources, it must have techniques that can significantly reduce the high spatio-temporal complexities associated with the existential matching process. These processes provide an important basis for many other processes such as translation, reconciliation, coordination and negotiation between ontologies.
Face Mask Detection with Automated Door Entry Control Using Convolutional Neural Network

COVID-19, the coronavirus infection of 2019, has infected over 172 million people globally and killed over 3.7 million. One of the most prevalent ways for people to protect themselves in public areas is to wear masks. Face mask detection has become a critical computer vision task to control the infection, yet mask detection research is limited. So, real-time detection of the mask is proposed using a deep learning approach with an automated door entry control system. Deep learning plays a major role in Natural Language Processing, object identification, and facial recognition. The proposed system with the face net model incorporates mask detection efficiently and accurately. To detect the presence of a face mask, the trained face mask classifier is applied to the live video stream. The data from the detection phase is passed to an automated door entry control mechanism to control the entry of the people. The experiment is conducted, and the results are obtained. From the results, it is inferred that the system recognizes the presence of a mask and ensures safety through the automated door entry mechanism.
ФЕНОМЕН ГРАФИЧЕСКОЙ СУБСТИТУЦИИ В ИНТЕРНЕТ-ДИСКУРСЕ

Elena A. Kalinovskaya, Anna S. Kobysheva (Stavropol, Russian Federation)The article is devoted to the study of graphic substitutes in the language of Internet communication. The authors identified and described the main ways of graphical transformation of language phenomena at the level of supragraphics, syngraphics and changing the letter composition of a word. The following means of graphic substitution in virtual discourse are distinguished: font variation, punctuation marks, alphabetic and «nonletter» symbols.Key words: graphical substitution, graphic substitute, virtual communication, Internet communication.DOI 10.23683/1995-0640-2019-1-91-98