Reviewerly/exHarmony huggingface dataset:
Geohistorical FAIR data: data integration and Interoperability using the OntoME platform

The poster focus on the basic workflow of model alignment and data integration that can be built using the Ontology management environment OntoME application in connection with the virtual research environment Geovistory (http://geovistory.com/). The integration of data from different sources is based on an explicit documentation of each original model, then on the alignment of these models with classes and properties designed within the conceptual framework of CIDOC CRM (ISO 21127:2014). For this purpose, new classes and properties close to the research domain can be added in OntoME within namespaces dedicated to the different projects. They will then be aligned with the more abstract classes of CIDOC CRM in order to achieve data interoperability.The poster present the essential steps and first results in the process of integrating data from different sources carried out in the Huygens ING/CLARIAH Geovistory pilot project (http://forum.dataforhistory.org/node/150). The aim of this project is to import sets of data into the Geovistory online application, currently developed by Kleio Lab GmbH, in order to allow historians to reuse the data, analyze it and visualize it. At the end of the process, the wrangled data can be made available to other researchers and the public on a human readable webpage or a SPARQL endpoint.
Heap Allocator: Uma Política de Alocação de Máquinas Virtuais em Ambiente de Computação em Nuvens baseada em Heap com Prioridade

Cloud Computing represents a paradigm that provides computing resources on virtual machines that are grouped and allocated according to customer requests. The increasing seeking for this type of service caused an increasing demand for electric energy. Several types of research aim to meet the demand for resources in Cloud Computing, and at the same time, reduce energy consumption in the data center. Within this context, this paper presents a virtual machine allocation policy called Heap Allocator, which mitigates thepower consumption of the data center. A comparative analysis of the proposed solution with other existing mechanisms showed the proposed solution of the data center power consumption by approximately 1: 5%.
Роль психодиагностики в управленческом общении при подготовке специалистов в области права

In the article the author reveals the priority directions of personality-oriented approach in management psychology in the field of communication and conflictology. Attention is focused on the psychodiagnostics of students of legal specialties in order to determine the optimal strategy of communication with the younger generation.
Research in theoretical computer science

research-article Share on Research in theoretical computer science Authors: Meena Mahajan Institute of Mathematical Sciences, Chennai, India Institute of Mathematical Sciences, Chennai, IndiaView Profile , Madhavan Mukund Chennai Mathematical Institute, Chennai, India Chennai Mathematical Institute, Chennai, IndiaView Profile , Nitin Saxena Indian Institute of Technology Kanpur, Kanpur, India Indian Institute of Technology Kanpur, Kanpur, IndiaView Profile Authors Info & Claims Communications of the ACMVolume 62Issue 11November 2019 pp 92–95https://doi.org/10.1145/3343449Published:24 October 2019Publication History 0citation4,505DownloadsMetricsTotal Citations0Total Downloads4,505Last 12 Months158Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Cross-Media Sentiment Analysis in Brazilian Blogs

The use of social media is becoming highly present in our lives. It is through images, texts, and videos that human beings try to communicate in social networks and expose their opinions in the face of everyday events. Due to the increased volume of data transmitted over the Internet, it becomes difficult to do a human analysis of this content. For this reason, it is necessary to automate the task of classifying feelings’ polarity. Although the area of classification of feelings in images and texts is well developed and applied in social network context, the classification of feelings from images together with texts is still under development. A challenge is to build algorithms and methods that can infer feelings just like humans perceive it. Firstly, we present Cross-media Brazilian Blog corpus, the dataset we built based on BlogSet-BR, which goal is to have a data ground truth (based on subjects opinions) concerning feeling perceived in texts and images, when analyzed separately as well as when presented together. Therefore, we tested some available technologies to detect sentiment polarities in texts and images and compare with the ground truth. In addition, we pursue a research specifically on the contradiction posts, i.e. when image is positive and text is negative from the same blog. Results indicate that subjectivity affects emotional judgments because there are variances between cultures.
Dealing with “Hate Speech”

This chapter discusses the controversies over “hate speech” that have dogged President Trump’s early years in office. It provides a brief primer on the First Amendment doctrine relating to “hate speech,” under which derogatory expression is mostly considered protected speech. The chapter examines both the harms associated with “hate speech” and the principal justifications for protecting it. It criticizes the Trump administration’s handling of issues relating to “hate speech.” It also addresses the special problems associated with governmental “hate speech.” The chapter concludes with a consideration of the extent to which the government can use its own expressive powers to educate Americans about the need to preserve both freedom of speech and the security and equal dignity that “hate speech” undermines.
Could Seismic Networks Reveal Hard-to-Detect Nuclear Tests?

In the age of monitoring nuclear weapons testing, existing regional seismic networks may be a key to discovering small, undetected explosions around the world.
1623. Implementation of Electronic Readmission Alert for Discharged Patients Reduces Risk of Secondary Measles Exposure Events

Abstract Background Measles is a highly infectious illness that is causing increased numbers of outbreaks in the United States. Patients involved in a healthcare measles exposure and who have been discharged before identification of exposure are at risk of becoming infectious in the community, and may seek healthcare within their infectious window, creating a secondary exposure risk for healthcare systems. A measles exposure in an integrated healthcare system occurred, resulting in patient exposures in multiple locations at three campuses, including two community-based emergency departments and three inpatient units. There were 159 patients who were included in the exposure group; 123 were exposed in an ED, and 36 were exposed in an inpatient setting. Ninety-four percent (149/159) of the patients had been discharged at the time of measles case identification and were in the pre-infectious phase of illness. Of those, 36 percent (54/149) presented back to the healthcare system within the potentially infectious window; these 54 patients had 97 individual healthcare contacts in the potentially infectious period following the exposure event. Sixty-one of the 97 return visits (63%) were within the window in which the exposed patients were potentially infectious. Return locations included the three exposure facilities and inpatient and outpatient locations at 10 other system campuses. Methods An alert system was developed within the electronic medical record that identified patients that were involved in the exposure, and guided clinicians to mask and place in airborne isolation until measles immunity was verified. Results The alert activated 13 days after the exposure was identified, and identified 100% of returns to healthcare at all sites within the system, representing 48% of all potential secondary exposure events (29 /61). No secondary exposures or transmission occurred. Conclusion Measles exposures are an enormous burden on healthcare organizations and public health systems. When exposures occur, healthcare organizations need systems to rapidly identify discharged patients who may return within the potentially infectious window. Rapid development of electronic readmission alerts can help standardize identification and reduce the risk of subsequent exposure. Disclosures All authors: No reported disclosures.
Data Center Fast Congestion Management

A good congestion control for data centers (DC) should provide lowlatency, fast convergence and high link utilization. Since multipleapplications with different requirements may run on the DC network itis important to provide fairness between different applications thatmay use different congestion algorithms. An important issue from theuser perspective is to achieve short Flow Completion Time (FCT). Thisdocument proposes data center congestion control direction aiming toachieve high performance while proving fairness.
Matrix-Variate Restricted Boltzmann Machine Classification Model

Recently, Restricted Boltzmann Machine (RBM) has demonstrated excellent capacity of modelling vector variable. A variant of RBM, Matrix-variate Restricted Boltzmann Machine (MVRBM), extends the ability of RBM and is able to model matrix-variate data directly without vectorized process. However, MVRBM is still an unsupervised generative model, and is usually used to feature extraction or initialization of deep neural network. When MVRBM is used to classify, additional classifiers are necessary. This paper proposes a Matrix-variate Restricted Boltzmann Machine Classification Model (ClassMVRBM) to classify 2D data directly. In the novel ClassMVRBM, classification constraint is introduced to MVRBM. On one hand, the features extracted by MVRBM are more discriminative, on the other hand, the proposed model can be directly used to classify. Experiments on some publicly available databases demonstrate that the classification performance of ClassMVRBM has been largely improved, resulting in higher image classification accuracy than conventional unsupervised RBM, its variants and Restricted Boltzmann Machine Classification Model (ClassRBM).
Broad learning system for human activity recognition using sensor data

In a multi-sensor environment, it is efficient to record and reflect people's information of activities, using the large amount of data. However, the data cannot directly display the form of activity itself so that it is necessary to do the further job of exploration and processing. Deep Learning (DL) methods have attracted more attention and have shown some superior performance, while they have the problem of structural complexity. Therefore, this paper creatively uses Broad Learning System (BLS) method for human activity recognition. We use sliding window to get the data segmented. The weights involved are fine-tuned by pseudo-inverse and ridge regression algorithms, and we achieve an accurate classification of activities. The method is verified by using OPPORTUNITY data set. The results show that this method can greatly shorten the learning time and improve the accuracy, as well as the performance in comparison with traditional methods.
Effect of Exercise Intervention Combined with Counterbalance Exercise on Joint Function and Quality of Life of Orthopedic Postoperative Patients

In order to explore the effect of exercise intervention combined with counterbalance exercise on joint functionand quality of life of orthopedic postoperative patients, this paper took total hip arthroplasty (THA) as anexample, and chose 160 THA patients who underwent artificial hip replacement at designated hospitals by thisstudy from June 2013 to June 2018, and divided them into 4 groups according to the random number tablemethod: routine postoperative nursing (RPN), routine postoperative nursing + exercise intervention (RPN+EI),routine postoperative nursing + counterbalance exercise (RPN+CE) and routine postoperative nursing + exerciseintervention + counterbalance exercise (RPN+EI+CE) with 40 cases in each group. The RPN group was controlgroup, and only conducted the routine orthopedic postoperative nursing; the RPN+EI group was supplementedwith exercise intervention training on the basis of routine rehabilitation nursing; the RPN+EI group wassupplemented with counterbalance function exercise on the basis of routine rehabilitation nursing; and theRPN+EI+CE group was supplemented with exercise intervention combined with counterbalance exercise on thebasis of routine rehabilitation nursing. The observation indicators of Harris hip score and Barthel index scores,total effectiveness, postoperative exercise compliance rate (PECR), satisfaction, visual analogue scale (VAS)score and SF-36 scale score in the 4 groups were observed, evaluated and recorded before and 3 and 6 monthsafter intervention, respectively. The results shows that 3 months after operation, the Harris hip scores, Barthelindex scores, total effectiveness, PECRs, satisfactions, SF-36 scale scores (6 dimensions) of RPN+EI group,RPN+CE group and RPN+EI+CE group were higher than that of the RPN group, and the difference wasstatistically significant (P<0.05), while the VAS scores were lower than that of RPN group, and the differencewas statistically significant (P<0.05). 6 months after operation, the Harris hip scores, Barthel index scores, totaleffectiveness, PECRs, satisfactions, and SF-36 scale scores (8 dimensions) in RPN+EI+CE group weresignificantly higher than that of RPN group, RPN+EI group and RPN+CE group, and the difference wasstatistically significant (P<0.05), while the VAS scores were significantly lower than that of RPN group,RPN+EI group and RPN+CE group and the difference was statistically significant (P<0.05). Therefore, theexercise intervention combined with counterbalance exercise can effectively reduce the body pain and othercomplications in orthopedic postoperative patient, improve their postoperative exercise compliance, patient jointfunction, and quality of life.
Implementasi Algoritma DES (Data Encryption Standard) Pada Enkripsi Dan Deskripsi SMS Berbasis Android

The development of technology makes the birth of a tool that is the forerunner to the formation of Short Messaging Service (SMS), namely pagers. However, with the community's need to send messages quickly, accurately and safely make technology experts create a device that is a cellphone. Cellphone or mobile was first created already equipped with SMS feature. When a message or data becomes important and is privacy, it is necessary to protect the data. Therefore experts created an algorithm method to secure data. This method is called the encryption and decryption method to secure highly confidential messages or information from irresponsible and unauthorized people. There are various kinds of encryption algorithms with each characteristic that can be used, one of which is the DES (Data Enryption Standard) algorithm. DES is a symmetric encryption algorithm, where the process of encrypting and decrypting messages uses the same key. So even though a cryptografe understands the algorithm used to encode the message, but if you don't know the key used, it won't be able to decrypt the message so that the SMS message is really safe. Keywords: SMS, Cryptography, Enkripsi, Dekripsi, DES Algorithm
IEEE Intelligent Transportation Systems Society?s Column [Technical Activities]

Reports on the work of the IEEE ITS society Technical Activities Committee.
Multi-Lingual Text Localization via Language-Specific Convolutional Neural Networks

Scene text localization and recognition is a topic in computer vision that aims to delimit candidate regions in an input image containing incidental scene text elements. The challenge of this research consists in devising detectors capable of dealing with a wide range of variability, such as font size, font style, color, complex background, text in different languages, among others. This work presents a comparison between two strategies of building classification models, based on a Convolution Neural Network method, to detect textual elements in multiple languages in images: (i) classification model built on a multi-lingual training scenario; and (ii) classification model built on a language-specific training scenario. The experiments designed in this work indicate that language-specific model outperforms the classification model trained over a multi-lingual scenario, with an improvement of 14.79%, 8.94%, and 11.43%, in terms of precision, recall, and F-measure values, respectively.
N.E.A.T. - Novel Emergency Analysis Tool

We present N.E.A.T. - a Visual Analytics approach to the collaborative management of large-scale emergencies. N.E.A.T. unifies the analysis and annotation of heterogeneous, uncertainty-afflicted data sources in a single, adjustable screen. Stakeholders can create individual or shared workspaces providing configurable views tailored to the needs of different emergency responders. Within each workspace, annotated findings are automatically shared in real-time for effective collaboration. We illustrate the functionality of the tool and showcase exemplary findings on the St. Himark incident.
Color processing and management in Ghostscript

Ghostscript has a long history in the open source community and was developed at the same time that page description languages were evolving to the complex specification of PDF today. Color is a key component in this specification and its description and proper implementation is as complex as any other part of the specification. In this document, the color processing and management that takes place in Ghostscript is reviewed with a focus on how its design achieves computational efficiency while providing flexibility for the developer and user.
Sistem Informasi Penggajian Pegawai SMA Muhammadiyah Al-Kautsar Program Khusus Kartasura

Information system are a very effective and efficient thing managing transaction data, especially in payroll data transaction. Payroll is one of the most important aspect of the company because it’s a form of appreciation from a company services that have been provided by its employees. The problem face by Muhammadiyah High School Al-Kautsar Kartasura special program (PK) at this time is, there are often mistakes in calculating monthly salaries, the salary calculation process that take a long time, and data cannot be accessed dynamically. To overcome this problem this study aims to develop a web-based payroll information system that produces reports and salary slips that can be downloaded to be the archive of the agency. The method used is System Development Live Cycle (SDLC) which includes the needs analysis phase, system design, implementation, system testing and maintenance. Based on the result of the blackbox test, the results show that all features and system functionality are running well and the usability test results state that 81.64% of prospective users strongly agree that the system makes it easier and improved in the payroll data transaction process.
Data center visualization based on uDC-Builder

Taking visualization for a research object, first this paper analyses the development of data center and the advantages of applying visualization technique to data center. Then a construction plan of data center visualization platform is proposed, and the novelties is expounded. This paper is for the purpose of promoting the development and application of data center visualization.
Face recognition using traditional machine learning algorithms and deep neural networks with application to face verification

Biometrics authentication has been very useful and necessary nowadays due to the great developments in technology and the transaction of huge amounts of sensitive data on a daily basis. Traditionally, access to some data or service is achieved by means of some documents or a password. However, these methods are not very convenient. Alternatively, typical biometric systems can be employed that use fingerprint, iris, voice, face recognition or a combination of them. This project focuses on the task of face recognition from still images and investigates how different algorithms for face verification perform under various adverse conditions modelled by blur, salt-and-pepper noise and changes in illumination. Conventional pattern recognition algorithms are first presented. Pixel intensities, Gabor features, Local Binary Patterns (LBP) and 2D-DCT coefficients are considered as features while for classification the nearest neighbor (NNC), nearest mean (NMC), SVM classifiers, and Likelihood Ratio Tests (LRT) with Gaussian Mixture Models (GMM) are examined. Out of all these methods, Gabor features combined with the linear SVM classifier are shown to produce best results across all degradations giving an average Equal Error Rate (EER) of 0:97% using the ORL face dataset. Then, emphasis is placed on deep learning and Convolutional Neural Networks (CNN). Specically, VGG-Face with triplet loss training for face verification is suggested. VGG-Face achieves an average EER of 2:63% when both test images of a query image pair are drawn from the same degradation conditions and an average EER of 3:80% when only one image in the given pair is degraded and the other one is derived from the clean ORL dataset. We also experimented with the extracted VGG-Face features and NNC, linear SVM and Gaussian SVM and it is seen that a linear SVM gives an average EER of 1:10% by macro-averaging the Detection Error Tradeoff (DET) curves.
Immersive Imagination in Urban Oases of Mindfulness: The VR-SenseCity Toolbox for Sensible,Emotional and Measurable Experiences in Future Smart Cities

Quality of life, health and well-being, as well as the creative potential originating from positive experiencesand interaction with our environment and interaction are pivotal human factors underlying a rich andproductive social context. Respect and careful treatment of users, i.e., the citizen who will populate, cocreate and co-operate in the scenarios, mandate overarching topics for developing user centered designschematas. Virtual Reality (VR) technologies have recently offered tools to anticipate the affordances offuture scenarios. In the serious game VR-SenseCity (Paletta et al., 2017) we created tools for the imaginationof affective and sensory experiences, in particular, referring to visual affordances that enable induction ofimaginated sensory experiences in real urban environments. We investigated the potential for the motivationof elderly people, in particular, persons with dementia, to encounter their daily environment in positiveattitude, with pleasant and aware sensory experiences, the purpose to improve their cognitive reserve (Stern,2009). The functionalities were developed in cooperation with a Co-creation group of four elderly personsproviding substantial critical and constructive feedback for the development of the toolbox.