Reviewerly/exHarmony huggingface dataset:
Measuring and Estimating Overlap of Distributions: A Comparison of Approaches from Various Disciplines

In this work, we will compare three approaches on measuring the overlap of datasets. Different research areas lead to differing definitions and interpretations of overlap. We will discuss the differences, advantages and disadvantages of three methods which were all introduced in different research fields. Coming from a medical, a cryptographical and a statistical background, all three methods show interesting aspects of overlap. Even though quite differently defined, all three show reasonably interpretable results in simulations and data example.
Beyond lexical semantics: notes on pragmatic frames

Framenets as an incarnation of frame semantics have been set up to deal with lexicographic issues (cf. Fillmore and Baker 2010, among others). They are thus concerned with lexical units (LUs) and the conceptual structure which categorizes these together. These lexically-evoked frames, however, do not reflect pragmatic properties of constructions (LUs and other types of constructions), such as expressing illocutions or being considered polite or very informal. From the viewpoint of a multilingual annotation effort, the Global FrameNet Shared Annotation Task, we discuss two phenomena, greetings and tag questions, which highlight the necessity both to investigate the role between construction and frame annotation on the one hand and to develop pragmatic frames describing social interactions which are not explicitly lexicalized.
RUSSIAN LANGUAGE IN THE CIS COUNTRIES: CURRENT STATE OF SCIENTIFIC COMMUNICATIONS AND TRAINING OF HIGHLY QUALIFIED PERSONNEL

In the article the urgency of preservation of the Russian language in the former Soviet Union who became members of the CIS, the scientific and educational sphere. The authors point to the decrease in the sphere of influence of the Russian language in neighboring countries due to the trends of globalization and expansion of the English-speaking scientific and educational environment. At the same time, due to traditions and the presence of stable ties between representatives of the University environment, the Russian language continues to play a significant role in the system of scientific communications of certain CIS countries and in some of them is used as a basic language. Special attention is paid to the training of highly qualified personnel in the Russian language in the CIS and submissions for the degree of candidates and doctors of Sciences as well as issues of training in postgraduate study and doctoral studies and thesis in Russian universities, representatives of scientific and educational community of the CIS countries. The paper presents the results of a survey conducted among the teaching staff of the CIS countries on the issues of the degree of presence of the Russian language in scientific-educational sphere. The paper assesses the degree of presence of the Russian language in scientific communications in the CIS in the following areas: publication of scientific articles, organization and conduct of scientific and technical activities, R&D. Results of survey showed the existence of an interest in expanding scientific collaborations in various areas with Russian universities, which is one of the areas of promotion of the Russian language in the CIS.
Shape prior model via dual subspace segment projection learning

Background and Objective: Shape prior models play a vital role for segmentation in medical image analysis. These models are most effective when shape variations can be captured by a parametric distribution, and sufficient training data is available. However, in the absence of these conditions, results are invariably much poorer. In this paper, we propose a novel shape prior model, via dual subspace segment projection learning (DSSPL), to address these challenges. Methods: DSSPL serves to compose shapes from an ensemble of shape segments where each segment is formed using two subspaces: global shape subspace and segment-specific subspace, each necessary for extracting global shape patterns and local patterns, respectively. This ensures the proposed approach has general shape plausibility in regions of signal drop-out or missing boundary information, and also more localized flexibility. The learned projections are constrained with l2,1 sparse norm terms to extract the most distinguishable features, while the reconstructive properties of DSSPL reduces information loss and leverages the subspaces to provide contiguous shapes without any post-processing. Results: Extensive analysis is performed on three databases from different medical imaging systems across X-Ray, MRI, and ultrasound. DSSPL outperforms all compared benchmarks in terms of shape generalization ability and segmentation performance. Conclusions: We propose a new shape prior model for segmentation in medical image analysis to address the challenges of modelling complex organ shapes with low sample size training data.
Interactive and Scalable visualization framework for Version-aware XML documents

The Extensible Markup Language (XML) is widely used to store, retrieve, and share digital documents. Recently, a form of Version Control System has been applied to the language, resulting in Version-Aware XML and allowing for enhanced portability and scalability. While Version Control Systems are able to keep track of changes made to documents, we think that there is untapped potential in the technology. In this paper, we define a set of requirements for visualization tools in a modern version control system. We also present an interactive and scalable visualization framework to represent Version-Aware-related data that helps users visualize and understand version control data, delete specific revisions of a document, and access a comprehensive overview of the entire versioning history.
Language Models for Cloze Task Answer Generation in Russian

Linguistics predictability is the degree of confidence in which language unit (word, part of speech, etc.) will be the next in the sequence. Experiments have shown that the correct prediction simplifies the perception of a language unit and its integration into the context. As a result of an incorrect prediction, language processing slows down. Currently, to get a measure of the language unit predictability, a neurolinguistic experiment known as a cloze task has to be conducted on a large number of participants. Cloze tasks are resource-consuming and are criticized by some researchers as an insufficiently valid measure of predictability. In this paper, we compare different language models that attempt to simulate human respondents’ performance on the cloze task. Using a language model to create cloze task simulations would require significantly less time and conduct studies related to linguistic predictability.
THE INFLUENCE OF ENVIRONMENT TOWARDS JEAN LA’BARGE IN LOUIS L’AMOUR’S SITKA

This study is about the influence of the environments toward Jean LaBarge's in Sitka. The novel analyzed with qualitative descriptive research method and sociological approach. The analysis shows that character of Jean LaBarge gets influences from nature and other characters in his development. Jean is influenced by the great swamp and Alaska which makes him brave, independent, and ambitious. In addition, there is some social environment influencing him. His father, Captain Hutchins, Peter Hovey, Rob J. Walker, and Princess Helena Gagarin. Jean's characteristics were formed by both nature and social environment.
Power Big Data Distributed Retrieval System Based on Cloud Computing Technology

In order to improve the ability of power big data location identification and information fusion, and optimize the design of power big data retrieval system, a design method of power big data retrieval system based on cloud computing technology is proposed. Multi-regression analysis method is used to construct the storage structure model of power large database, analyze the characteristic of the distributed storage data structure of power big data, cluster the information of power big data in the cloud computing environment. The fuzzy feature grouping clustering method is used to detect power big data of different attributes extracts the association rule feature of power big data and realizes power big data distributed retrieval by cloud computing technology. The software of power big data distributed retrieval system is designed in an embedded environment. The simulation results show that the feature directivity of power big data retrieval is better and the data recall rate of data retrieval is higher.
Hierarchical Sparse Variational Autoencoder for Text Encoding.

In this paper we focus on unsupervised representation learning and propose a novel framework, Hierarchical Sparse Variational Autoencoder (HSVAE), that imposes sparsity on sentence representations via direct optimisation of Evidence Lower Bound (ELBO). Our experimental results illustrate that HSVAE is flexible and adapts nicely to the underlying characteristics of the corpus which is reflected by the level of sparsity and its distributional patterns.
Managing Virtual Networks

An Azure virtual network (aka a VNet) is a representation of your network in the cloud. It is a logical isolation of the cloud tied to your subscription. You can use VNets to provision and manage virtual private networks (VPNs) in Azure. You can also connect VNets with another VNets with a variety of techniques. Each VNet you create has its own Classless Internet Domain Routing (CIDR) block and can be linked with other VNets as long as the CIDR blocks do not overlap.
LINGKUNGAN PENDIDIKAN DAN MOTIVASI BELAJAR DI ERA GLOBALISASI

AbstractThis article presents an attempt to model the interrelationship between student motivation in an educational environment that utilizes several indicators of empirical evaluation. This study uses an empirical study research design. An in-depth study of qualitative research results is used to develop an understanding of the educational environment in relation to student motivation. Learn about the relationship between the educational environment with student motivation. The role of parents, sensitivity, communication and increased learning motivation.
Including the vacuum in the post-selected model of quantum illumination

In the paper by Weedbrook et. al,1 a post-selected model of Lloyd's original formulation of quantum illumination (QI)2 was used to show that quantum discord explains the underlying advantage of QI over conventional illumination. The same model was used by Ray et. al3 to show that the normalized Hilbert-Schmidt inner product (HSIP) is a valid distinguishability measure when analyzing quantum illumination. This post-selected model assumes that the detector always detects a photon whether it be from the signal or noise from the environment; thus, the vacuum is excluded from this model. In this paper, we include the vacuum back into these analyses. In the case of,1 we show that the conclusion is unaffected by the inclusion of the vacuum. We then analyze the effects of including the vacuum when distinguishing between a noisy signal and noise. In this analysis, we found that the normalized HSIP is not monotonic with respect to the parameter that controls the brightness of the noise. Because of this, the normalized Hilbert-Schmidt inner product cannot be used as a distinguishability as was seen in the post-selected model. To reconcile this problem, we used the proposed alternative fidelity measure defined in,4 and found that it is monotonic in the vacuum added model for all the examples considered.
Reference-frame-independent quantum key distribution with random atmospheric transmission efficiency

Reference-frame-independent quantum key distribution (RFI-QKD) has been proved to be tolerant against unknown reference frame misalignment, which reserves interesting prospects in implementing global quantum communication. However, few works have been addressed on the performance and feasibility for RFI-QKD in turbulent atmospheric channels. Here, we propose to implement RFI-QKD in practical free-space links with fluctuating transmission efficiency due to beam wandering and broadening. An improved model for estimating the probability distribution of single-photon receiving efficiency has been developed and we also simulated the Gaussian beam spot evolution and secure key rate based on that. Results show that the beam wandering model of probability distribution of transmission efficiency is reasonable to improve the performance of RFI-QKD in free-space channel.
Securing Our API

In this chapter we discuss how we can secure our API; specifically, we’ll add the “Bearer” authentication scheme into the mix that will allow only authorized clients to access our API resource through the use of Tokens.
Study of atmosphere dynamics in the event of very heavy rain causing flood in Supadio International Airport Pontianak using WRF-ARW Model and Himawari-8 Satellite Imagery (Case study: November 11, 2017)

Abstract On November 11, 2017, extreme weather in the form of very heavy rain has occurred in Pontianak. Based on surface observations data from Supadio Meteorological Station, the amount of rainfall reached 187.4 mm / day and caused flooding in several areas in Pontianak. As a result, a number of flights were cancelled on November 12, 2017 due to flooding in the airport runway. This study aims to examine the causes of very heavy rain using the WRF-ARW model and the results of Himawari-8 satellite images which are processed using Python Programming. Based on the output of the WRF-ARW model with the resolution 3 km, it shows some weather parameters that have potential for bad weather in Pontianak, namely the existence of shear line in West Kalimantan and the eddy circulation in this region which can trigger convective cloud accumulation in Pontianak, the wet RH in the 850-500 mb layer ranges from 70-90%, the CAPE ranges from 1000-2000 J/kg, and the air pressure decreases between 03.00 UTC until 06.00 UTC with a 1.7 mb tendency. In addition, the results of the Himawari-8 Satellite Image show that the cloud peak temperature is very low at -75.8°C at 08.33 UTC. Therefore, based on the WRF-ARW and Himawari-8 Satellite results, those support the occurrence of very heavy rain in Pontianak.
Taking advantage of images and texts in recommender systems: semantics and explainability

Traditionally, recommender systems are built on the users’ item consumption history. Many times, the users also make items reviews, giving us additional information in the form of text and images that are generally, not fully exploited. In this research we propose different approaches in the context of restaurants recommendation, where we take advantage of this information, by extracting a semantic meaning, in order to improve the traditional RS in terms of personalization and explainability.
Caching Video-on-Demand in Metro and Access Fog Data Centres

This paper examines the utilization of metro fog data centres and access fog datacentres with integrated solar cells and Energy Storage Devices (ESDs) to assist cloud data centres in caching Video-on-Demand content and hence, reduce the networking power consumption. A Mixed Integer Linear Programming (MILP) model is used to optimize the delivery of the content from cloud, metro fog, or access fog datacentres. The results for a range of data centre parameters show that savings by up to 38% in the transport network power consumption can be achieved when VoD is optimally served from fully renewable-powered cloud or metro fog data centres or from access fog data centres with 250 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> solar cells. Additional 8% savings can be achieved when using ESDs of 100 kWh capacity in the access fog data centres.
SENTIMENT ANALYSIS OFPRODUCT REVIEWS IN AMAZON USING MACHINE LEARNING

Sentiment Study is agrowing discipline of studies in the text searching field. Currently, the reviews extracted are increasing everyday on the web. It is almost impossible to investigate and extract reviewssuch a big variety of evaluations manually. One issue of studies which is measured on this document istocategoriesagiven internet article/section whether it's of Positive [False positive,True positive] or Negative [False Negative, True negative] sentiment. Sentiment evaluation is an expeditiously surface domain within the field of evaluation in the field of Natural Language Processing (NLP).
Autonomous resource management for high performance datacenters

Over the last decade, new applications such as data intensive workflows have hit an inflection point in wide spread use and influenced the compute paradigm of most scientific and industrial endeavo ...
Real-Time Safety and Surveillance System Using Facial Recognition Mechanism

Face recognition mechanisms can be used in many diverse applications such as Security, Retail, Healthcare, and Marketing. The webcams are often used as a security standard with the integration of the facial recognition technology which allows authorized users to unlock objects such as Computer, Laptop, and Mobile. Cameras have a major role in recognition technology to track human faces and keep a track of the number of people in a shot or a certain location such as in an entrance or public places. This technology can be further narrowed down to the recognition and tracking of eyes. In the proposed work, we used an existing Viola–Jones algorithm as a basis for face detection to create a surveillance mechanism by analyzing captured data. Besides, the e-complaint registration feature has been achieved by sending security breach email alerts to the nearby police station or registered users. The proposed system uses cloud storage along with cryptographic techniques to achieve confidentiality, availability and provides surveillance against any uncertain incidents.
Using Deep-Learning Approach to Detect Anomalous Vibrations of Press Working Machine

In recent years, there has been a demand for advanced maintenance in factories. Data collection from factory equipment is being carried out, and the collected sensor data is widely used for statistical analysis in quality control and failure prediction by machine learning. For example, if it is possible to detect an abnormality using vibration data obtained from an equipment, increase in the operation rate of the plant can be expected. In this research, we aim at early detection of equipment failure by finding signs of abnormality from vibration data, using a deep-learning technique, particularly an autoencoder. In this paper, the following two methods were tested. The first scheme is based on the reconstruction error in an autoencoder. An autoencoder is trained using normal data only. Looking at the difference between input data and reconstructed data, we can regard the data having higher difference as abnormal. In the second approach, given the input data, values of the middle layer of the autoencoder are extracted, and we calculate the degree of abnormality using a Gaussian Mixture Model (GMM), representing a data set by superposition of a mixture of Gaussian distributions. In this framework, regarding an autoencoder structure, we tested both full-connection networks and convolutional networks. In this work, we chose a press machine. Frequency characteristics were acquired from the data in production mode of a press machine. Then using each method, we evaluated whether abnormality could be found by calculating the degree of abnormality. We employed two-day data without failure as training data, and another data set was prepared as forecast data obtained on the following days; on one of the days the machine stopped due to a sudden abnormality. Similar to time-series signal processing, we applied framing processing so that we can analyze data even in the case we can only get a small amount of data. As a result, our method succeeded in finding the day when the abnormality occurred and the machine stopped. In addition, the degree of abnormality became higher before the abnormality occurs, indicating we can detect signs of abnormality. In conclusion, the degree of abnormality could be calculated using the reconstruction error using an autoencoder from the vibration data during production, and the method using GMM from the middle layer of autoencoder. We consequently conclude it is possible to detect a sudden abnormality in which the device stopped, from actual vibration data. These results provide new solutions for equipment failure estimation.
Cryptospatial Coordinate using the RPCA Based on a Point in Polygon Test for Cultural Heritage Tourism

This research was aimed to enhance the cryptospatial with geospatial blockchain based on a point in polygon test.Ripple Protocol Consensus Algorithm (RPCA) was used for developing a blockchain.The steps taken include: (1) Data from the surveyors were entered using application connected to the transaction set; (2) The transaction set sent data to the transaction proposal; (3) The transaction proposal will distribute to every connected validating of nodes for executing the smart contract with the point in a polygon test method; (4) If the process succeeded with the maximum fault tolerance of 20%, then the node records a new chain to the ledger.This method is faster than Practical Byzantine Fault Tolerance (PBFT) blockchain for approximately 26% to add a new chain in the ledger and for 52% to decrypt the blockchain with a mobile device.The result of this process is a cryptospatial coordinate for the cultural heritage tourism.
G&amp;#252;ralp recommendations for the installation of high-performance, observatory-grade seismic stations

&amp;lt;p&amp;gt;In order to monitor nuclear tests on a global scale, it is one of the IMS&amp;amp;#8217;s fundamental tasks to maintain a network of seismic stations with high data reliability. Installation is often the most critical aspect of a successful seismic station. A poorly designed layout leads to the introduction of noise that can hugely impact data quality, therefore rendering expensive, high performance equipment inadequate.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;To facilitate quality installations and encourage better practices in the global seismic monitoring community, G&amp;amp;#252;ralp has developed a system that will allow for observatory-grade data.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The system is a classic seismic station composed of an ultra-low noise broadband seismometer: a G&amp;amp;#252;ralp 3T (120s or 360s), a high-performance digitizer-datalogger: the G&amp;amp;#252;ralp Affinity, a sensor cable and an atmospheric pressure enclosure.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The custom-built pressure enclosure enhances performance in vault installations, protecting the sensor from minuscule fluctuations in temperature and pressure, hence considerably reducing noise levels.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;G&amp;amp;#252;ralp also provide best practice guidelines to assist researchers in designing their station, from site selection, installation and training to data retrieval and analysis.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Since 2001, G&amp;amp;#252;ralp have been managing the Eskdalemuir seismic array (EKA), the United Kingdom&amp;amp;#8217;s auxiliary station for the International Monitoring System. These years of experience with CTBT related monitoring have taught us that good results do not come from the instrument alone. This is why G&amp;amp;#252;ralp endeavors to accompany operators through every step of the process with a team of specialist engineers, applying 35 years of expertise from project conception to data retrieval.&amp;lt;/p&amp;gt;