Reviewerly/exHarmony huggingface dataset:
Identifying Transitional High Cost Users from Unstructured Patient Profiles Written by Primary Care Physicians

Identification and subsequent intervention of patients at risk of becoming High Cost Users (HCUs) presents the opportunity to improve outcomes while also providing significant savings for the healthcare system. In this paper, the 2016 HCU status of patients was predicted using free-form text data from the 2015 cumulative patient profiles within the electronic medical records of family care practices in Ontario. These unstructured notes make substantial use of domain-specific spellings and abbreviations; we show that word embeddings derived from the same context provide more informative features than pre-trained ones based on Wikipedia, MIMIC, and Pubmed. We further demonstrate that a model using features derived from aggregated word embeddings (EmbEncode) provides a significant performance improvement over the bag-of-words representation (82.48±0.35% versus 81.85±0.36% held-out AUROC, p = 3.2 × 10-4), using far fewer input features (5,492 versus 214,750) and fewer non-zero coefficients (1,177 versus 4,284). The future HCUs of greatest interest are the transitional ones who are not already HCUs, because they provide the greatest scope for interventions. Predicting these new HCU is challenging because most HCUs recur. We show that removing recurrent HCUs from the training set improves the ability of EmbEncode to predict new HCUs, while only slightly decreasing its ability to predict recurrent ones.
How contact centres can leave businesses exposed to cybercrime

More than 30% of organisations have experienced cyber attacks on their IT infrastructure, which can have severe consequences for companies that have not built cyber security into their operational budgets. 1 The average cost of a data breach in 2018 was $3.86m and it typically took approximately 266 days to detect and contain a smaller breach. 2 The costs associated with this kind of incident are crippling for average businesses, representing a significant fraction of revenue, given that the average annual receipts for a firm approaching 100 employees is $7.12m. 3
Web Search Personalization Using Semantic Similarity Measure

Web search personalization is the process of providing personalized results to the user for his query. In this paper, we present a relevance model to personalize search results which is based on query personalization. The user query is directly matched to the keywords of the user profile, and the original query is altered according to the keywords which is more likely similar or related according to the similarity measure. By finding the similarity between the user original query and user profile, a linear combination of preference space is generated at run-time to determine more accurately which pages are truly the most important with respect to the modified query. A heuristic algorithm is used to maintain the user profile based on the ongoing behavior. Our experiments prove that retrieving the search results based on query modification is effective in providing the personalized results to the user.
Eva: A Virtual pet in Augmented Reality

Augmented Reality applications are present in our daily lives and the smartphones are nowadays the common tool to make this possible. Several applications are being built for different areas, like medicine, engineering, education and entertainment. The main purpose of this paper is to present an Open Source application called Eva, which is a virtual pet that lives in the Augmented Reality world. This application is being built for Android devices and its main features are described in this paper as well.
Parsing adverbial complex sentences in ASD-STE100 within ARTEMIS

One of the main objectives of Natural Language Processing is the simulation of natural language understanding. Within the applications designed for this purpose today, ARTEMIS follows the paradigm of unification grammars (Sag, I., Wasow, T. &amp; Bender, E. 2003), and unlike other trending computational resources, it is theoretically grounded in linguistic models like RRG (Van Valin &amp; LaPolla 1997 and Van Valin 2005), whose linking algorithm lies at the basis of the parsing process of our interlingua-based system.&#x0D; A fundamental component of ARTEMIS is the Grammar Development Environment (GDE), where feature-based production rules (syntactic, constructional and lexical) are stored and ready to allow the generation of the enhanced layered structure of the clause of natural language expressions (Periñán-Pascual 2013: 222). Syntactic rules for phrasal constituents and simple sentences have already been described (Cortés-Rodríguez 2016; Cortés-Rodríguez &amp; Mairal-Usón 2016; Díaz-Galán &amp; Fumero-Pérez 2015; Fumero-Pérez &amp; Díaz-Galán 2017; Martín-Díaz 2017 and Martín-Díaz 2018), but it now turns to focus on complex sentences, and, to be more precise, on adverbial subordination.&#x0D; Bearing in mind the validation of these syntactic rules and the common problems that may arise in such parsing applications, our research will concentrate on the analysis of these structures as found in a Controlled Natural Language: ASD-STE100.
Is the Use of Reference Managers Driven by Necessity or Policy Pressure?

Abstract The use of a reference manager (RefWorks, Mendeley, EndNote, Zotero) has become very important in the writing of scientific papers for students and lecturers. The Faculty of Economics of Semarang State University (FE UNNES) has called on students to use a reference manager when composing a thesis. This study aims to analyze factors that influence the utilization of reference managers by FE UNNES students. The theories used are the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Theory of Planned Behavior (TPB). The research population was students working on their theses at FE UNNES. The data collection method used is a questionnaire distributed online to respondents through the Google platform. The data analysis method used is path analysis. The results show that performance expectancy and social influence have positive and significant effects on a student’s intention to use a reference manager. Meanwhile, effort expectancy is not proven to have a significant effect on a student’s intention to use a reference manager. Effort expectancy, performance expectancy, and social influence have a significant effect on the use of reference managers. Other results show that behavioral intention, lecturer guidance, and policy pressure do not significantly influence the use of reference managers. The results of this study demonstrate that the use of reference managers by students is more influenced by perceptions of their usefulness. That is, students assume that the use of a reference manager is a necessity.
On the expressiveness and semantics of information flow types

Information Flow Control (IFC) is a form of dependence analysis that tracks and prohibits dependence of public outputs on secret inputs. Such a dependence analysis is often carried out using a type system. IFC type systems can track dependence (via confidentiality labels) at varying levels of granu larity. On one extreme, there are fine-grained type systems that track dependence at the level of individual values. They label individual values. On the other extreme, there are coarse-grained type systems that track dependence at the level of entire computations. These type systems do not label individual values but instead label entire sub-computations. An important foundational question is one of the relative expressiveness of these two classes of IFC type systems. In this paper we show that, despite the glaring differences in how they track dependence, the two classes of type systems are actually equally expressive. We do this by showing translations from FG, a fine-grained IFC type system derived from SLAM (In Proceedings of the ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL) (1998)), to SLIO∗, a coarse-grained IFC type system derived from HLIO (In Proceedings of the ACM SIGPLAN International Conference on Functional Programming (ICFP) (2015)), and vice-versa. The translation from SLIO∗ to FG is straightforward since FG tracks dependence at a granularity finer than SLIO∗ does. However, the translation from FG to SLIO∗ is quite involved and relies extensively on label quantification. We further examine the reason for this complexity using a slight variant of SLIO∗, called CG, to which FG can be translated more easily. As a separate, more foundational contribution we show how to extend logical relation models of information flow to languages with higher-order state. Specifically, we build world-indexed (Kripke) logical relations for FG, SLIO∗ and CG, which we use to prove these type systems sound and also to prove the translations between them correct.
Subjective and Viewport-based Objective Quality Assessment of 360-degree videos

Visual distortions in processed 360-degree visual content and consumed through head-mounted displays (HMDs) are perceived very differently when compared to traditional 2D content. To better understand how compression-related artifacts affect the overall perceived quality of 360-degree videos, this paper presents a subjective quality assessment study and analyzes the performance of objective metrics to correlate with the gathered subjective scores. In contrast to previous related work, the proposed study focuses on the equiangular cubemap projection and includes specific visual distortions (blur, blockiness, H.264 compression, and cubemap seams) on both monoscopic and stereoscopic sequences. The objective metrics performance analysis is based on metrics computed in both the projection domain and the viewports, which is closer to what the user sees. The results show that overall objective metrics computed on viewports are more correlated with the subjective scores in our dataset than the same metrics computed in the projection domain. Moreover, the proposed dataset and objective metrics analysis serve as a benchmark for the development of new perception-optimized quality assessment algorithms for 360-degree videos, which is still a largely open research problem.
Research on the Optimizing Method of Question Answering System in Natural Language Processing

Natural language processing technology can not only enrich the functions of computers, but also fundamentally promote the development of artificial intelligence technology. Based on natural language processing technology, many useful systems for people's survival and life have been produced, such as the question-and-answer system described in this thesis. This system mainly uses natural language processing technology and information retrieval technology. Although it is based on text retrieval, it is quite different from traditional search engine. Traditional search engines point out that users need to input a series of keyword combinations, and users can only get a variety of related websites, but also rely on their own discrimination ability to select useful information. However, the question answering system can allow users to input a question in the form of natural language. Finally, according to the search and judgment, the system can get a short and accurate answer to the user, which greatly improves the convenience of people's production and life. This thesis mainly elaborates the content of question answering system of natural language processing and analyses how to optimize it.
Identification of Geochemical Anomalies Using Fractal and LOLIMOT Neuro-Fuzzy modeling in Mial Area, Central Iran

The Urumieh-Dokhtar Magmatic Arc (UDMA) is recognized as an important porphyry, disseminated, vein-type and polymetallic mineralization arc. The aim of this study is to identify and subsequently determine geochemical anomalies for exploration of Pb, Zn and Cu mineralization in Mial district situated in UDMA. Factor analysis, Concentration-Number (C-N) fractal model and Local Linear Model Tree (LOLIMOT) algorithm used for this purpose. Factor analysis utilized in recognition of the correlation between elements and their classification. This classified data used for training the LOLIMOT algorithm based on relevant elements. The results of the LOLIMOT algorithm represent anomalies in areas with no lithogeochemical samples. Although, the C-N log-log plot for target elements were generated based on stream sediment and lithogeochemical samples which could be delineated mineral potential maps of the target elements. Results obtained by the LOLIMOT and fractal modeling show that the SW and the Eastern parts of the area are proper for further exploration of Cu, Pb, and Zn.
Optimized Resource Allocation Model in Cloud Computing System

The resource allocation in the cloud system is the major role to determine the performances, resource utilization and total overall power consumption of the cloud data center. The optimal allocation of the virtual machines is one of the resource optimization problems in cloud due to the resource contention and starvation. This paper proposes the important parameters consideration proved with linear programming to be applied for limitation of resource allocation conditions in cloud and to identify the resource minimized allocation model at first. Then, the optimal resource allocation model (ORAM) based on modification of cost matrix in Hungarian algorithm is proposed as a solution and experimental evaluation is carried out for the proposed model. According to the performance evaluation, the proposed model outperforms both the conventional binding policy and existing Hungarian based allocation model HABBP in term of the optimal execution time.
Large-Scale Estimation of Distribution Algorithms with Adaptive Heavy Tailed Random Projection Ensembles

We present new variants of Estimation of Distribution Algorithms (EDA) for large-scale continuous optimisation that extend and enhance a recently proposed random projection (RP) ensemble based approach. The main novelty here is to depart from the theory of RPs that require (sub-)Gaussian random matrices for norm-preservation, and instead for the purposes of high-dimensional search we propose to employ random matrices with independent and identically distributed entries drawn from a t-distribution. We analytically show that the implicitly resulting high-dimensional covariance of the search distribution is enlarged as a result. Moreover, the extent of this enlargement is controlled by a single parameter, the degree of freedom. For this reason, in the context of optimisation, such heavy tailed random matrices turn out to be preferable over the previously employed (sub-)Gaussians. Based on this observation, we then propose novel covariance adaptation schemes that are able to adapt the degree of freedom parameter during the search, and give rise to a flexible approach to balance exploration versus exploitation. We perform a thorough experimental study on high-dimensional benchmark functions, and provide statistical analyses that demonstrate the state-of-the-art performance of our approach when compared with existing alternatives in problems with 1 000 search variables.
An Analysis of Oral English Teaching in College Based on Virtual Reality Technology

The main feature of language teaching is that it requires a high learning environment and builds a specific language environment for students, so as to enable them to improve the oral English teaching effect scientifically and efficiently in a good language environment. However, not every student can really integrate into the English environment. At present, with the continuous development of science and technology, it has also begun to be applied to oral English teaching, which fundamentally changes the rapid development of science and technology. At the same time, it has gradually been applied to the field of oral English teaching in an all-round way, making oral English teaching easier. In this paper, the concept of it is analyzed in detail. After that, this paper points out that the current it has been combined with the teaching mode, and has broken through the traditional teaching mode. Finally, this paper also analyses and discusses the significance of it in college oral English teaching, and points out that it has brought great significance to college oral English teaching.
JSP-Based Pet Adoption System

The system is mainly divided into four modules: user handling module, pet handling module, pet adoption module and pet statistics module. Through Eclipse, MySql, etc., using SSM framework, bootstrap framework, various plug-ins and related JSP technology. Among them, the modal box in the bootstrap framework to reduce the number of JSP pages. When browsing pets, I chose a plug-in written in pure CSS to complete the waterfall stream layout for users to browse. In the adoption of pet statistics module, the plug-in provided by Echarts is used to complete the production of the chart, making the data look more vivid and more intuitive. It also provides the function of downloading the image to the local for administrators to download and use. When the user logs in, the user name of the user is determined according to the user name that is logged in, and the identity of the user is an ordinary member or an administrator, to identify the right and jump to the page corresponding to the permission. When you log in, you have the ability to remember your password; you can log in two functions to make the design more user-friendly.
NATIONAL TECHNOLOGY INITIATIVE: ON THE RISKS OF EDUCATION QUALITY

The article discusses the pedagogical aspects of forecasting the future in the context of preparing schoolchildren in the framework of the National Technology Initiative (NTI). It is planned to train highly qualified specialists of the “new formation” using cross-cutting educational technologies. The risks arising from the training of schoolchildren in the technology of NTI are analyzed. The technology of risk identification is presented, which includes the following stages. The first stage is the construction of a universal model of an NTI specialist. The second stage is the differentiation of the model based on the specifics of the NTI markets. The third stage is the definition of an action plan to achieve the desired result in the process of training students in NTI technologies. The fourth stage is the definition of areas of responsibility in the end-to-end technology. The fifth stage is the identification of risks in the assimilation of learning technologies of NTI. The sixth stage is the identification of ways to minimize and eliminate risks. The seventh stage is the determination of the risks of integrating general, vocational and additional education. The eighth stage is the determination of ways to minimize and eliminate risks. The article identifies the main actions within each stage.
Nearest Excellent Potential Location Using Distance Algorithm

Abstract This study aims to find the proper distance calculation method that will be applied to the Sidoarjo on Hands (SoH) application. This study was conducted by comparing three distance algorithms namely Euclidean Distance, Manhattan Distance, and Haversine Formula. The results showed that the Euclidean Distance method was the proper method because this method had has the smallest Mean Absolute Deviation (MAD) with 1.71 in amount.
The teaching staff of secondary specialized educational institutions of the Komi ASSR in the 1950s–1980s: characteristics, dynamics of the number

Introduction. Since the early 1950s, for developing spheres of the country’s national economy there has been an active training of personnel, including those with secondary specialized education. One of the key aspects of the development of the system of vocational education is an increase in the number of teachers. The main indicators of the quality of teaching in educational institutions, and accordingly the quality of training, are the educational level and teachers’ work experience. Materials and Methods. The article shows the dynamics of the numbers and characteristics of the teaching staff of vocational educational institutions of Komi ASSR in the 1950s – 1980s, based on archival statistical data. The paper is based on the principles of scientific objectivity, historicism and authenticity. When working with sources the author used methods of statistical analysis and comparison. Results and Discussion. The data obtained as a result of the study are presented in tables form for better transparency. Conclusion. Analysis of the available data showed a positive trend both in the increase in the number of teaching staff in vocational educational institutions, and in the change in their main characteristics.
Motion Control for Autonomous Tugger Vehicles in Dynamic Factory Floors Shared with Human Operators

We present a motion controller that generates collision free trajectories for autonomous Tugger vehicles operating in dynamic factory environments, where human operators may coexist. The controller is formalized as a dynamic system of path velocity and heading direction, whose vector fields change as sensory information varies. By design the parameters are tuned so that the control variables are close to an attractor of the resultant dynamics most of the time. This contributes to the overall asymptotically stability of the system and makes it robust against perturbations. We present several experiments, in a real factory environment, that highlight different innovative features of the navigation system - flexible and safe solutions for human-aware autonomous navigation in dynamic and cluttered environments. This means, besides generating online collision free trajectories between via points, the system detects the presence of humans, interact with them showing awareness of their presence, and generate adequate motor behavior.
Sentiment Analysis of Text Classification Algorithms Using Confusion Matrix

Sentiment analysis on text mining has a vital role in the process of review classification. Text classification needs some techniques like natural language processing, text mining, and machine learning to get meaningful knowledge. This paper focuses on performance analysis of text classification algorithms commonly named Support vector machine, random forest and extreme Gradient Boosting by creating confusion matrices for training and testing applying features on a product review dataset. We did comparison research on the performance of the three algorithms by computing the confusion matrix for accuracy, positive and negative prediction values. We used unigram, bigram and trigrams for the future extraction on the three classifiers using different number of features with and without stop words to determine which algorithms works better in case of text mining for sentiment analysis.
Rethinking the financial Kuznets curve in the framework of income inequality: Empirical evidence on advanced and developing economies

Contrary to the empirical findings that there is a negative link between financial sector development and income inequality, we introduce a different result: in the earlier stages of the financial and economic development, the level of income inequality decreases, but with an ongoing developmental process, the later stages show that the above-mentioned link between finance and inequality turns into positive within the framework of financial Kuznets curve. In terms of finance-inequality nexus, we find that neither markets nor institutions play a significant role for the decrease in income inequality. When the results are measured within this context, the study concludes that the U-shaped financial Kuznets curve hypothesis is valid in the sample countries.