Reviewerly/exHarmony huggingface dataset:
Toward a Computational Multidimensional Lexical Similarity Measure for Modeling Word Association Tasks in Psycholinguistics

Bruno Gaume, Lydia Mai Ho-Dac, Ludovic Tanguy, Cécile Fabre, Bénédicte Pierrejean, Nabil Hathout, Jérôme Farinas, Julien Pinquier, Lola Danet, Patrice Péran, Xavier De Boissezon, Mélanie Jucla. Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics. 2019.
ANDROID-BASED LECTURE ROOM SCHEDULING APPLICATION (CASE STUDY : STMIK PRINGSEWU CAMPUS)

Technological developments are increasingly rapid and fast, especially and communication technology. For all fields, starting the fields of buying and selling, education, health, and others. The communication and information technology very helpful in various ways from the word of lecturers, campus staff, and students. STMIK Pringsewu still use manual, student still have to see schedule of waiting room of STMIK Pringsewu. Application development using php script. The method used to develop this system is by System Development Life Cycle approach with stages and design using Data Flow Diagram modelling. Based on the results of system testing, Application and Scheduling Applications are designed in accordance with the expected, it can be concluded that the application design courses and lecture room based on Android is work and function.
A Perspective on Text Classification, Clustering, and Named-entity Recognition in Social Media

Knowledge Discovery is the process of extracting implied effective, and fresh valuable information from data (Fayyad ., 1996;Frawley ., 1992).Data Mining has used specif ic algorithms for inventive patterns from data.KDD objects at discovering concealed patterns and connections in the data.Knowledge discovery from the text (KDT or Text Mining) was f irst introduced by Feldman & Dagan (1995), refers to the process of extracting high quality of information from structured; such as RDBMS data (Akbari ., 2018;Chen ., 1996), semi-structured; such as XML and JSON, and unstructured text resources; such as word documents, videos, and images (Pouriyeh & Doroodchi, 2009;Pouriyeh , 2010).After the emergence of social media, an enormous amount of data started to generate.The researchers call this, the Big Data which has three key features, known by "3V": Volume, Variety, and Velocity.Some researchers add two other characteristics: Value and Veracity, and thus speak about "5V" (Jahanbin & Mehrjoo, 2017;Lomotey & Deters, 2014).Mukkamala .(2014) and Nguyen .(2015) use interchangeably the terms Big Social Data and Social Big Data to refer the overall data created by social media.To demonstrate the huge amount of data generated by social media aff irms that through Facebook, 10 million photos are uploaded every day.Marrin (2014) highlights the fact that more than 300 million tweets are sent to Twitter every day too, and 3000 photos are uploaded from Flickr every
Spider-Jerusalem at SemEval-2019 Task 4: Hyperpartisan News Detection

This paper describes our system for detecting hyperpartisan news articles, which was submitted for the shared task in SemEval 2019 on Hyperpartisan News Detection.We developed a Support Vector Machine (SVM) model that uses TF-IDF of tokens, Language Inquiry and Word Count (LIWC) features, and structural features such as number of paragraphs and hyperlink count in an article.The model was trained on 645 articles from two classes: mainstream and hyperpartisan.Our system was ranked seventeenth out of forty two participating teams in the binary classification task with an accuracy score of 0.742 on the blind test set (the accuracy of the top ranked system was 0.822).We provide a detailed description of our preprocessing steps, discussion of our experiments using different combinations of features, and analysis of our results and prediction errors.
A Unified Algebraic Framework for Non-Monotonicity

Tremendous research effort has been dedicated over the years to thoroughly investigate non-monotonic reasoning.With the abundance of non-monotonic logical formalisms, a unified theory that enables comparing the different approaches is much called for.In this paper, we present an algebraic graded logic we refer to as Log A G capable of encompassing a wide variety of non-monotonic formalisms.We build on Lin and Shoham's argument systems first developed to formalize non-monotonic commonsense reasoning.We show how to encode argument systems as Log A G theories, and prove that Log A G captures the notion of belief spaces in argument systems.Since argument systems capture default logic, autoepistemic logic, the principle of negation as failure, and circumscription, our results show that Log A G captures the before-mentioned non-monotonic logical formalisms as well.Previous results show that Log A G subsumes possibilistic logic and any non-monotonic inference relation satisfying Makinson's rationality postulates.In this way, Log A G provides a powerful unified framework for non-monotonicity.
Euclidean space and their functional application for computation analysis

on the courses are characterized as the unrelated focuses on the digression lines from guide sources toward obstructions raised.At that point, search the raster cells as per certain standards of defining
Visualization of the Strain-Rate State of a Data Cloud: Analysis of the Temporal Change of an Urban Multivariate Description

One challenging problem is the representation of three-dimensional datasets that vary with time. These datasets can be thought of as a cloud of points that gradually deforms. However, point-wise variations lack information about the overall deformation pattern, and, more importantly, about the extreme deformation locations inside the cloud. This present article applies a technique in computational mechanics to derive the strain-rate state of a time-dependent and three-dimensional data distribution, by which one can characterize its main trends of shift. Indeed, the tensorial analysis methodology is able to determine the global deformation rates in the entire dataset. With the use of this technique, one can characterize the significant fluctuations in a reduced multivariate description of an urban system and identify the possible causes of those changes: calculating the strain-rate state of a PCA-based multivariate description of an urban system, we are able to describe the clustering and divergence patterns between the districts of a city and to characterize the temporal rate in which those variations happen.
Local Engagement with the Election Administration and Voting Survey (EAVS)

The introduction of new data collection tools in election administration can create unique challenges for local election officials. This case presents one local election official's experience with the Election Administration and Voting Survey (EAVS) from its inception following the passage of the Help America Vote Act (HAVA) in 2002; also discussed is the important role that vendors play in ensuring that local jurisdictions provide the type of data required by the EAVS and other election administration data collection tools.
Name Entity Recognition with Policy-Value Networks

In this paper we propose a novel reinforcement learning based model for named entity recognition (NER), referred to as MM-NER. Inspired by the methodology of the AlphaGo Zero, MM-NER formalizes the problem of named entity recognition with a Monte-Carlo tree search (MCTS) enhanced Markov decision process (MDP) model, in which the time steps correspond to the positions of words in a sentence from left to right, and each action corresponds to assign an NER tag to a word. Two Gated Recurrent Units (GRU) are used to summarize the past tag assignments and words in the sentence. Based on the outputs of GRUs, the policy for guiding the tag assignment and the value for predicting the whole tagging accuracy of the whole sentence are produced. The policy and value are then strengthened with MCTS, which takes the produced raw policy and value as inputs, simulates and evaluates the possible tag assignments at the subsequent positions, and outputs a better search policy for assigning tags. A reinforcement learning algorithm is proposed to train the model parameters. Empirically, we show that MM-NER can accurately predict the tags thanks to the exploratory decision making mechanism introduced by MCTS. It outperformed the conventional sequence tagging baselines and performed equally well with the state-of-the-art baseline BLSTM-CRF.
UJI MODEL FISIK BANGUNAN PELIMPAH BENDUNGAN SAKA GILAS DENGAN SKALA 1:50

The physical hydraulic model carried out in the laboratory is intended to determine the hydraulic behavior of the flow in hydraulic structures. The aim of Hydraulic model test of Sakagilas Dam spillway is to determine the flow profile that occurs in spillway system, including flow depth, velocity, water flow pressure, and Froude numbers. Also to find out the hydraulic performance of side spillway design, transition channel, chute way channel, energy dissipator, and escape channel. Investigation of water flow pressure in the spillway system (side channel spillway, transition channel, and especially chute way channel) was measured and analyzed to determine the condition of structure safety against cavitation and vibration flow.The spillway of Saka Gilas Dam is a side channel spillway (Fixed+Gated) with an Ogee spillway. The shape of the side spillway is circular quarter circle (45⁰). Preliminary observations on the physical model test are carried out under the condition of Original Design, which if there is a poor flow hydraulic behavior, design modifications will be made. The design modification is intended to get the best flow hydraulic behavior, according to the standard design criteria for the side channel spillway system.
Learning Shared and Cluster-Specific Dictionaries for Single Image Super-Resolution

Recently, many multi-dictionary-based sparse representation methods have been proposed for single-image super-resolution (SISR) by learning a sub-dictionary for each specific type of visual content (i.e., a cluster). Although promising reconstruction results have been achieved for certain scenarios, due to the similarity shared among different types of visual content, each sub-dictionary is not learned as discriminatively as expected, which could compromise the visual quality of reconstructed high-resolution (HR) images. In this paper, we propose a novel shared and cluster-specific dictionaries learning method for SISR, where cluster-specific dictionaries can be learned as separate as possible and a dictionary shared among clusters is learned explicitly to explore similarity among clusters. First, low-resolution (LR) and HR image patches extracted from training images are jointly grouped into visual clusters. Then, a shared sub-dictionary and a set of cluster-specific sub-dictionaries are learned simultaneously under the sparse representation framework. In addition, the group sparsity constraint, the locality constraint, and the incoherence penalty term are incorporated into a unified framework to preserve the relationship and structure among the training data in dictionary learning. Finally, an anchored neighborhood regression method is devised to pre-calculate a projection matrix of each learned dictionary atom from LR to its HR space so that an HR image can be more efficiently recovered in the reconstruction stage. Comprehensive experimental results on two widely used benchmark datasets demonstrate the effectiveness and robustness of our method against a number of state-of-the-art methods. The reconstruction results of real-world maritime surveillance images further indicate that the proposed method is well suitable for practical applications.
Security Issues in Cloud-Based Businesses

Cloud-based Business is a Business running and relying on Cloud computing IT paradigm.Cloud computing is an emerging technology paradigm that transfers current technological and computing concepts into utility-like solutions similar to electricity and communication systems.It provides the full scalability, reliability, computing resources configurability and outsourcing, resource sharing, external data warehousing, and high performance and relatively low cost feasible solutions and services as compared to dedicated infrastructures.Cloud-based Businesses store, access, use, and manage their data and software applications over the internet on a set of servers in the cloud without the need to have them stored/installed locally on their local devices.The cloud technology is used daily by many businesses/people around the world from using web based email services to executing heavy complex business transactions.Like any other emerging technology, Cloud computing comes with a baggage of some pros and cons.It is very useful in business development as it brings amazing results in a timely manner; however, it comes with increasing security and privacy concerns and issues.In this paper we will investigate, analyse, classify, and discuss the new security concerns and issues introduced by cloud computing.In addition, we present some security requirements that address and may alleviate these concerns and issues.
PENGARUH METODE PEMBELAJARAN DENGAN TUTOR TEMAN SEBAYA UNTUK MATA KULIAH PROMOSI KESEHATAN

Learning method approach is one element of increasing student achievement index. In this study will be more focused on efforts to overcome the causes of the low learning outcomes of third semester students of the second semester in the Health Promotion course in the Department of Nursing Ambon. The method used in this research is Quasi Experimental. The population in this study were students of Maluku Health Ministry Polytechnic. The sample in this study was the third semester students of the Department of Nursing at the Maluku Ministry of Nursing. Based on the results of the research, Health Promotion learning outcomes using peer tutor learning models are more effective than learning using conventional learning models in terms of improving learning outcomes. This is shown from the results of the t test or the two average difference test where the difference in the average gain value of the experiment with the average gain control value is 0.47 - 0.32 = 0.15, and when interpreted the gain value is relatively low, then from the results of the t test obtained t count = 17.94&gt; t table (2.048) which means H1 is accepted. So it can be concluded that there are significant changes between student learning outcomes before and after using peer tutoring learning models in learning Health Promotion.&#x0D; Keywords: Learning methods, peer friend tutors, health promotion
THE USE OF "ABC-MODEL" FOR FORMING A VALUE COMPOSITION OF COGNITIVE COMPONENT OF PROFESSIONAL IDENTITY OF A SOCIAL PEDAGOGUE WORKING IN INCLUSIVE EDUCATION SYSTEM

The article deals with the peculiarities of using "ABC-model" to influence the value component of the professional identity of teachers working in inclusive education system. Change in cognitive assessments that the teacher gives to a complex situation leads to a decrease in the intensity of emotional experience. The article reveals the peculiarities of the professional activity of the social pedagogue who may cause difficulties in the regulation of unpleasant emotional states. The peculiarities of the use of the ABC-model for the influence on the value component of the cognitive part of the professional identity of the social pedagogue working in the system of inclusive education are explained; the essence of the professional identity of social educators is highlighted; the approaches of different scholars to understanding the value system and the peculiarities of its changes in the context of the professional activities of the social teacher. The characteristic of the ABC model in the interpretation of integrative cognitive behavioral therapy is proposed: sensations and emotions are the result of evaluations that are the product of the deep beliefs and values of a personal values system. The features of the use of the ABC-model for working with the value component are outlined. The scheme of work on changing irrational beliefs with the help of the Socratic Dialogue method is described, which consists in using different types of questions to ascertain the logic of the person's prevailing beliefs. The scheme of work with the value component of the cognitive component of the professional identity of a social teacher is described on the example of the use of the ABC model. It is substantiated how it is possible to approach the change of irrational interpretations and causes of the appearance of unpleasant emotional states on the example of analysis of life situations from social and psychological practice. Conclusions are made on the factors influencing the social teacher who works in the system of inclusive education and associated with the emergence of unpleasant emotional states. Integrative cognitive-behavioral therapy offers a number of techniques for successful work with such problems and one of the most effective is the ABC model. It is shown how the technique allows to influence the system of values of the social teacher related to the causal concepts of the formation and manifestation of emotions, which is an important component of the cognitive part of the professional identity and influences the effectiveness of the manifestation of professional identity in emotionally difficult situations. Using these psychological techniques, the teacher can form new rational beliefs and improve his level of adaptation to complex professional situations. Further research of the topic may be related to the identification of factors that stimulate or slow down the development of new beliefs in the cognitive component of professional identity.
A Proposition of Modifications and Extensions of Cloud Computing Standards for Trust Characteristics Measures

In recent years, we have witnessed a marked rise in the number of cloud service providers with each offering a plethora of cloud services with different objectives. Gaining confidence for cloud technology adoption as well as selecting a suitable cloud service provider, both require a proper evaluation of cloud service trust characteristics. Hence, the evaluation of cloud services before used by the customer is of utmost importance. In this article, we adapt the extracted trust characteristics from both system and software quality standards and cloud computing standards, for evaluating cloud services. Moreover, we derive measures for each trust characteristics to evaluate the trustworthiness of different cloud service providers, and generalize these trust measures for any type of cloud services (e.g. Software as a Service, Platform as a Service, and Infrastructure as a Service). Our work thereby demonstrates a way to apply generalized trust measures for cloud services and therefore contributes to a better understanding of cloud services to evaluate their quality characteristics. As part of our ongoing research, the results of this study will be used to develop a comprehensive cloud trust model.
Nanobob: a cubesat mission concept for quantum communication experiments in an uplink configuration

We present a ground-to-space quantum key distribution (QKD) mission concept and the accompanying feasibility study for the development of the low earth orbit CubeSat payload. The quantum information is carried by single photons with the binary codes represented by polarization states of the photons. Distribution of entangled photons between the ground and the satellite can be used to certify the quantum nature of the link: a guarantee that no eavesdropping can take place. The versatile space segment is compatible with a multiple of QKD protocols, as well as quantum physics experiments.
Geology and micro-structure analysis of the MCT zone along Khudi- Bahundanda area of Lamjung District, west-central Nepal

Geological mapping was carried out along the Marsyangdi Valley in the Khudi-Bahundanda area of west-central Nepal covering the Main Central Thrust (MCT) zone. The main objectives of the study were to draw a clear picture of lithology, geological structures and micro-tectonics in the rocks. A detail survey on stratigraphy and correlation with central Nepal reveals geological rock units such as the Benighat Slate, the Malekhu Formation and the Robang Formation of the Lesser Himalaya and the Formation I of the Higher Himalaya. Both regional and small-scale geological structures have been studied. The MCT zone has been mapped as a major regional structure in the region. The Bahundanda Thrust (BT), which has brought the older Malekhu Formation over the younger Robang Formation, is an another significant structure mapped. The BT is marked on the basis of fault breccia, slickensides as well as large deposits of debris mass at the fault zone.&#x0D; The study area has undergone poly-metamorphism and dynamic crystallization of minerals. The Lesser Himalayan rocks resemble the garnet zone while the Higher Himalaya rocks resemble to the kyanite grade of metamorphism. The present section clearly shows the inverted metamorphism in the MCT zone as in the other sections of the Himalaya. Microscopic features like ribbon-quartz, polygonization of quartz crystals, grain boundary reduction, mica-fish and rotated garnet grains indicates the ductile shearing in the MCT zone suggesting the dynamic recrystallization during thrust propagation. Numerous outcrop-scale structures like meso-scalefolds, quartz veins, boudinage and ptygmatic folds are abundant folds in the MCT zone and these are mostly E-W trending.
OntoFoCE: Herramienta para el Análisis Forense de Correos Electrónicos

espanolEste trabajo tiene como objetivo presentar una herramienta para el analisis forense de correos electronicos a partir de la cabecera de los mismos y la utiliza para instanciar una ontologia definida para responder a los puntos de pericia solicitados sobre el correo electronico. La herramienta consta de cuatro componentes que permiten la obtencion de las cabeceras de los correos a peritar, la instanciacion de la ontologia con los datos obtenidos de las cabeceras y la obtencion de respuestas a los puntos de pericia a partir de las preguntas de competencia definidas para la ontologia. Se describe cada componente y se ejemplifica el uso de la herramienta mediante un caso de estudio sobre analisis forense de un correo electronico. EnglishThe purpose of this work is to present a tool for the forensic analysis of emails from their headers. The tool is used to generate a defined ontology, in order to respond to the points of expertise requested about the email. It consists of four components which allow us to obtain the headers of the mails to be analyzed, to generate the ontology with the data acquired from those headers, and to give an answer to the points of expertise produced by the competency questions defined for the ontology. Each component is described and the use of the tool is exemplified through a case study on forensic analysis of an email.