Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Infer Entities, Properties and their Relations from Clinical Conversations

Recently we proposed the Span Attribute Tagging (SAT) Model to infer clinical entities (e.g., symptoms) and their properties (e.g., duration). It tackles the challenge of large label space and limited training data using a hierarchical two-stage approach that identifies the span of interest in a tagging step and assigns labels to the span in a classification step. We extend the SAT model to jointly infer not only entities and their properties but also relations between them. Most relation extraction models restrict inferring relations between tokens within a few neighboring sentences, mainly to avoid high computational complexity. In contrast, our proposed Relation-SAT (R-SAT) model is computationally efficient and can infer relations over the entire conversation, spanning an average duration of 10 minutes. We evaluate our model on a corpus of clinical conversations. When the entities are given, the R-SAT outperforms baselines in identifying relations between symptoms and their properties by about 32% (0.82 vs 0.62 F-score) and by about 50% (0.60 vs 0.41 F-score) on medications and their properties. On the more difficult task of jointly inferring entities and relations, the R-SAT model achieves a performance of 0.34 and 0.45 for symptoms and medications respectively, which is significantly better than 0.18 and 0.35 for the baseline model. The contributions of different components of the model are quantified using ablation analysis.
Mobile Cloud Computing in Healthcare Using Dynamic Cloudlets for Energy-Aware Consumption

Mobile cloud computing (MCC) has increasingly been adopted in healthcare industry by healthcare professionals (HCPs) which has resulted in the growth of medical software applications for these platforms. There are different applications which help HCPs with many important tasks. Mobile cloud computing has helped HCPs in better decision making and improved patient care. MCC enables users to acquire the benefit of cloud computing services to meet the healthcare demands. However, the restrictions posed by network bandwidth and mobile device capacity has brought challenges with respect to energy consumption and latency delays. In this paper we propose dynamic energy consumption mobile cloud computing model (DEMCCM) which addresses the energy consumption issue by healthcare mobile devices using dynamic cloudlets.
Denotation/connotation and verbal/nonverbal communication

In the literature on nonverbal communication, one of the classic themes is the range of functions that nonverbal processes can fulfill in interaction, either independently of the verbal processes or in conjunction with them. One function that is constantly cited derives from the argument that there is a task division between verbal (natural language) and nonverbal communication — in other words, 'that NVC and Verbal Communication normally play two contrasted roles' (Argyle 1972: 253). Natural language, it is argued, is more suited to conveying explicit cognitive information or meaning. The nonverbal is better suited to conveying implicitly one's inner reality (feelings, affects, attitudes, evaluations, etcetera), one's personality, or the way in which the interactants evaluate each other ('interpersonal attitudes' — cf., for example, Argyle 1972 and 1975). This notion of task division is often accompanied by the argument that nonverbal communication in this respect plays the same role in human and animal behavior. This point of view is explicit in what I will call the Ruesch-BatesonWatzlawick tradition — that is, the system-theory approach to communication and communication therapy. However, the notion of a task division is also found in Goffman (1959:2, 1963: 13-14), among others. The intention of the present paper is to demonstrate that the denotation/connotation opposition as it is used in structural semiotics can be fruitful in this regard. My central idea is that there is indeed a 'task division', but that this task division is between denotative and connotative processes, rather than between natural language and nonverbal codes. The 'explicit cognitive information' is here denotative information (or, in semiotic terms, denotative meaning), while the 'implicit information' (or meaning) is the result of a connotative process. Both verbal and nonverbal processes can connote. I would hold that denotation is reserved to natural language and to systems and languages analogous to natural language were it not for the class of gestures — in the sense that Kendon
Innovative design and gearshift control for direct-drive electromagnetic gearshift system equipped with servo synchronizer

Automated mechanical transmission has many advantages such as simple structure, high mechanical efficiency, and low cost. But the poor gearshift performance restricts the massive application of the automated mechanical transmission, and it can be improved through innovation of structure and control. To reduce the requirement of shift force and improve the shift performance, a new direct-drive electromagnetic gearshift system which consists of servo synchronizer and 2-degree-of-freedom electromagnetic actuator is adopted. The specific structure and working principle of the gearshift system including servo synchronizer are described, and the equation of force-amplifying ratio is deduced. Due to the complexity of the gearshift system and uncertainties of the gearshift process, active disturbance rejection control method is designed. The active disturbance rejection controller can eliminate the nonlinearity of the 2-degree-of-freedom actuator. The extended state observer can estimate and compensate the uncertainties, parameter variations, and external disturbances. Simulations are carried out, and the result comparison with proportional–integral–derivative controller indicates the superiority of the active disturbance rejection control method. Test bench and control system are developed to verify the performance of the newly designed system and control method. The experimental results show that, when the gearshift system is equipped with servo synchronizer, the driving force and the maximum volatility of driving force can be reduced by 35% and 5%, respectively, and the impact generated by active disturbance rejection control method is reduced by 36% compared with proportional–integral–derivative method. The new gearshift system achieves a better gearshift performance. Combined with the newly designed control strategy, the direct-drive electromagnetic gearshift system provides a new solution for automated mechanical transmission applications.
Smart Device Authentication Based on Online Handwritten Script Identification and Word Recognition in Indic Scripts Using Zone-Wise Features

Secure﻿authentication﻿is﻿a﻿vital﻿component﻿for﻿device﻿security. ﻿The﻿most﻿basic﻿form﻿of﻿authentication﻿ is﻿ by﻿ using﻿ passwords. ﻿ With﻿ the﻿ evolution﻿ of﻿ smart﻿ devices,﻿ selecting﻿ stronger﻿ and﻿ unbreakable﻿ passwords﻿have﻿become﻿a﻿challenging﻿task. ﻿Such﻿passwords﻿if﻿written﻿in﻿native﻿languages﻿tend﻿to﻿offer﻿ improved﻿security﻿since﻿attackers﻿having﻿no﻿knowledge﻿of﻿such﻿scripts﻿finding﻿it﻿hard﻿to﻿crack. ﻿This﻿ article﻿proposes﻿two﻿zone-wise﻿feature﻿extraction﻿approaches﻿-﻿zone-wise﻿structural﻿and﻿directional﻿ (ZSD)﻿and﻿zone-wise﻿slopes﻿of﻿dominant﻿points﻿(ZSDP),﻿to﻿recognize﻿online﻿handwritten﻿script﻿and﻿ word﻿in﻿four﻿major﻿Indic﻿scripts﻿-﻿Devanagari,﻿Bengali,﻿Telugu﻿and﻿Tamil. ﻿These﻿features﻿have﻿been﻿ used﻿separately﻿and﻿in﻿combination﻿in﻿HMM-based﻿platform﻿for﻿recognition﻿purpose. ﻿The﻿dimension﻿ reduction﻿of﻿the﻿ZSD-ZSDP﻿combination﻿with﻿factor﻿analysis﻿has﻿shown﻿the﻿best﻿performance﻿in﻿all﻿ the﻿four﻿scripts. ﻿This﻿work﻿can﻿be﻿utilized﻿for﻿setting﻿up﻿the﻿authentication﻿schemes﻿with﻿the﻿Indic﻿ scripts’﻿passwords﻿thus﻿rendering﻿it﻿difficult﻿to﻿crack﻿by﻿hackers﻿having﻿no﻿knowledge﻿of﻿such﻿scripts. KeyWORDS Authentication, HMM, Online Handwriting, Script Identification, Smart Device, Word Recognition, Zone-Wise Features
Sometimes I’ll start a sentence in Spanish Y TERMINO EN ESPAÑOL: toward a typology of code-switching1

The occurrence of code-switching, or the seemingly random alternation of two languages both between and within sentences, has been shown (Gumperz, 1976; Pfaff, 1975; Wentz, 1977) to be governed not only by extra-linguistic but also linguistic factors. For the balanced bilingual, code-switching appears to be subject to an 'equivalence constraint' (Poplack, 1978): i.e. it tends to occur at points in discourse where juxtaposition of L^ and L 2 elements does not violate a surface syntactic rule of either language. If correct, the equivalence constraint on code-switching may be used to measure degree of bilingual ability. It was hypothesized that equivalence would either be violated by non-fluent bilinguals, or that switch points which are 'risky' in terms of syntactic well-formedness (i.e. those which occur within a sentence) would tend to be avoided altogether. To test this hypothesis, I analysed the speech of20 Puerto Rican residents of a stable bilingual community, exhibiting varying degrees of bilingual ability. Quantitative analysis of their switches revealed that both fluent and non-fluent bilinguals were able to code-switch frequently and still maintain grammaticality in both LI and L 2 . While fluent bilinguals tended to switch at various syntactic boundaries within the sentence, non-fluent bilinguals favoured switching between sentences, allowing them to participate in the code-switching mode, without fear of violating a grammatical rule of either of the languages involved. These results suggest that the code-switching mode proceeds from that area of the bilingual's grammar where the surface structures of L± and L 2 overlap, and that code-switching, rather than representing debasement of linguistic skill, is actually a sensitive indicator of bilingual ability.
Design Patterns: Current Challenges, Trends, and Research Directions

: Design patterns solve recurring design problems. We can classify the literature on design patterns into two main categories: 1) Pattern Application, i.e., applying design patterns on a design model which is a forward engineering approach, and 2) Pattern Detection and Identiﬁcation, i.e, extracting applied design patterns from a given source code which is a reverse engineering approach. With the above classiﬁcation in mind, this report intends to explore the current challenges behind the design patterns and related tools.
Sex‐specific changes in the aphid DNA methylation landscape

Aphids present an ideal system to study epigenetics as they can produce diverse, but genetically identical, morphs in response to environmental stimuli. Here, using whole genome bisulphite sequencing and transcriptome sequencing of the green peach aphid (Myzus persicae), we present the first detailed analysis of cytosine methylation in an aphid and investigate differences in the methylation and transcriptional landscapes of male and asexual female morphs. We find that methylation primarily occurs in a CG dinucleotide (CpG) context and that exons are highly enriched for methylated CpGs, particularly at the 3’ end of genes. Methylation is positively associated with gene expression, and methylated genes are more stably expressed than un-methylated genes. Male and asexual female morphs have distinct methylation profiles. Strikingly, these profiles are divergent between the sex chromosome and the autosomes; autosomal genes are hypo-methylated in males compared to asexual females, whereas genes belonging to the sex chromosome, which is haploid in males, are hyper-methylated. Overall, we find correlated changes in methylation and gene expression between males and asexual females, and this correlation is particularly strong for genes located on the sex chromosome. Our results suggest that differential methylation of sex-biased genes plays a role in M. persicae sexual differentiation.
Motor stroke recovery after tDCS: a systematic review

Abstract The purpose of the present study was to investigate the effects of transcranial direct current stimulation (tDCS) on motor recovery in adult patients with stroke, taking into account the parameters that could influence the motor recovery responses. The second aim was to identify the best tDCS parameters and recommendations available based on the enhanced motor recovery demonstrated by the analyzed studies. Our systematic review was performed by searching full-text articles published before February 18, 2019 in the PubMed database. Different methods of applying tDCS in association with several complementary therapies were identified. Studies investigating the motor recovery effects of tDCS in adult patients with stroke were considered. Studies investigating different neurologic conditions and psychiatric disorders or those not meeting our methodologic criteria were excluded. The main parameters and outcomes of tDCS treatments are reported. There is not a robust concordance among the study outcomes with regard to the enhancement of motor recovery associated with the clinical application of tDCS. This is mainly due to the heterogeneity of clinical data, tDCS approaches, combined interventions, and outcome measurements. tDCS could be an effective approach to promote adaptive plasticity in the stroke population with significant positive premotor and postmotor rehabilitation effects. Future studies with larger sample sizes and high-quality studies with a better standardization of stimulation protocols are needed to improve the study quality, further corroborate our results, and identify the optimal tDCS protocols.
Isolated soft-switched boost DC–DC converter with low-voltage stress and high step-up ratio

This study presents a novel soft-switched isolated boost converter which can provide a high-step-up ratio and high efficiency with low-voltage stress for portable application as well as a photovoltaic-assisted converter system. A lossless active clamp snubber circuit helps in achieving zero-voltage-switching turn-off and zero-current-switching (ZCS) turn-on of the main switch and ZCS turn-off of diodes that reduce switching losses. An auxiliary switch with an energy recovery circuit helps to increase power conversion efficiency and reduces switching stress across the main switch. The proposed idea is simulation through computer-assisted software PSIM 9.1. 1 and validated experimentally through a designed prototype 250 W direct current (DC)–DC converter.
Distilling Deep Reinforcement Learning Policies in Soft Decision Trees

An important step in Reinforcement Learning (RL) research is to create mechanisms that give higher level insights into the black-box policy models used nowadays and provide explanations for these learned behaviors or motivate the choices behind certain decision steps. In this paper, we illustrate how Soft Decision Tree (SDT) distillation can be used to make policies that are learned through RL more interpretable. Soft Decision Trees create binary trees of predetermined depth, where each branching node represents a hierarchical ﬁlter that inﬂuences the classiﬁcation of input data. We distill SDTs from a deep neural network RL policy for the Mario AI benchmark and inspect the learned hierarchy of ﬁlters, showing which input features lead to speciﬁc action distributions in the episode. We realize preliminary steps towards interpreting the learned behavior of the policy and discuss future improvements.
Fill the GAP: Exploiting BERT for Pronoun Resolution

In this paper, we describe our entry in the gendered pronoun resolution competition which achieved fourth place without data augmentation. Our method is an ensemble system of BERTs which resolves co-reference in an interaction space. We report four insights from our work: BERT’s representations involve significant redundancy; modeling interaction effects similar to natural language inference models is useful for this task; there is an optimal BERT layer to extract representations for pronoun resolution; and the difference between the attention weights from the pronoun to the candidate entities was highly correlated with the correct label, with interesting implications for future work.
SAS 3016-2019 Introducing the MBC Procedure for Model-Based Clustering

Clustering has long been used to learn the structure of data and to classify individuals into distinct groups. For example, cluster analysis can be used for marketing segmentation that distinguishes potential buyers from nonbuyers. Classical clustering methods use heuristics or simple Euclidean distance to form clusters, but in some cases, the clustering solution is more parsimonious and more useful if it is based on the formal likelihood of a model. The model and its associated likelihood allow the clustering to accommodate more complex covariance structure that is exhibited in the data. The MBC procedure, available in SAS® Visual Statistics 8.3 in SAS® Viya®, enables you to fit mixtures of multivariate Gaussian (normal) distributions to your data to learn a cluster structure in an unsupervised manner. In addition to clustering the observations in a data set, you can use the fitted model to classify new observations. PROC MBC provides a weight of association for each new observation, enabling you to decide whether a new classification is a strong match for one cluster or needs closer expert examination to determine its cluster membership. This paper describes the concepts behind model-based clustering and presents the basic mode of operation of PROC MBC. Several examples illustrate different use cases, including automated model selection through information criteria, the modeling of outliers, saving models, and applying saved models to new input data.
Automatic classification of journalistic documents on the Internet 1 Classificação automática de documentos jornalísticos na Internet

Online journalism is increasing every day. There are many news agencies, newspapers, and magazines using digital publication in the global network. Documents published online are available to users, who use search engines to find them. In order to deliver documents that are relevant to the search, they must be indexed and classified. Due to the vast number of documents published online every day, a lot of research has been carried out to find ways to facilitate automatic document classification. The objective of the present study is to describe an experimental approach for the automatic classification of journalistic documents published on the Internet using the Vector Space Model for document representation. The model was tested based on a real journalism database, using algorithms that have been widely reported in the literature. This article also describes the metrics used to assess the performance of these algorithms and their required configurations. The results obtained show the efficiency of the method used and justify further research to find ways to facilitate the automatic classification of documents.
Thin section microscopy and DVS study to determine the influence of graphene materials on the microstructure of cement based composite

INTRODUCTION Graphene materials have prospects to modify the cement-based composite performance at nano-micro scale. This paper presents the results of microscopic image analysis and dynamic vapor sorption (DVS) analysis to determine the influence of graphene materials on the graphene-cement composites. The graphene-cement composites were prepared with three types of graphene materials, pure graphene (G), graphene oxide (GO), and reduced graphene oxide (rGO). The concentration of graphene materials was 0.04 and 0.16 wt% of cement. The evaluation of the microstructure was performed through back-scattered electron (BSE) imaging using scanning electron microscopy (SEM) and optical fluorescence microscopy on thin sections. The thin sections were impregnated with epoxy containing a fluorescent dye and were examined using ordinary light, crossed polarized light and UV light. Fluorescent image intensity evaluation and BSE evaluation were used to compare density and capillary porosity within the hardened cement paste. Dynamic vapor sorption (DVS) analysis was used to study the gel porosity. The results showed that graphene materials increased C-S-H gel pores and densified the microstructure. GO had the best dispersion and compatibility with cement hydration products followed by rGO and G.
Communicating Brands in Television Advertising

Many studies have quantified the effects of TV ad spending or gross rating points on brand sales. Yet this effect is likely moderated by the different types of brand-related messages or cues (e.g., logo, brand attributes) embedded in the ads and by the ways (e.g., explicitly or implicitly) these cues are conveyed to TV audiences. The authors thus measure 17 cues often used within ads to build brand awareness (or salience) and brand image and investigate their influence on ad effectiveness. Technically, the study builds a dynamic model to quantify the effects of advertising on sales; builds a robust and interpretable (i.e., nonparametric and sparse) factor model that integrates correlated, left-censored branding cues; and then models the effects of advertising as a function of the factors identified by these cues. An analysis of 177 campaigns aired by 62 brands finds that salience cues (e.g., logo) and benefit and attribute messages moderate ad effectiveness. It also finds that explicit cues are more effective than implicit ones; nonetheless, the primary drivers of ad effectiveness are visual salience cues: the duration and frequency with which the logo and the duration with which the product are displayed. The study can thus suggest ways brand and ad agency managers can improve the effects of creative ad content on sales.
Synthesis of Silver Nanoparticles in Bean ( Phaseolus vulgaris L . )

The present work shows a brief review of some natural sources used to produce metallic nanoparticles and leaves the issue open for further discussion and new investigations. The use of nanoparticles has gained an increased attention due to its potential application as a drug delivery medium. The nanoparticle drug delivery characteristics can be engineered to obtain a certain rate or localization, increase the drug load per particle, among others. Some plants have biomolcules for specific functions as reduction and stabilization of the particles formed inside. These biomolecules are for example polyphenolic compounds, hydroxyflavons, oxalic acid, terpenoids and many more. Even the exact nature of the bioreduction of metal ions is not completely understood, the production and investigation of metallic nanoparticles formed in plants have been increased on last 10 years. A wide variety of sizes, 5-150 nm, and shapes, spherical, triangular, rods, hexagonal, have been obtained in plants and fruit extracts. The plant Phaseolus vulgaris (beans) was used to form silver nanoparticles through bioreduction of Ag (I) to Ag (0) in the living plant. Two groups of plants were used. One group of plants grew at garden soil and the other in cotton. To determine the nanoparticles formed in plants, they have been analyzed by using X-ray absorption spectroscopy (XAS). In both cases, a solution of AgNO3 was added initially in a concentration of 0.01M then the concentration was changed to 0.1 mM. ). In stem and leaves silver were found as Ag (0). The XAS spectra were adjusted for more accurate results. Plants may reduce the valence of silver and form nanoparticles. The TEM images show that the average particle size is 18 nm, showing in various forms and a greater number of them in the leaves of plants grown in soil. Results also indicate that nanoparticles obtained from the stem and leaves have different forms and they can affect the soil pH.
The attempt of holistic approach

K E Y W O R D S sustainability, HRM, approaches to SHRM DOI: 10.12846/j.em. 2015.02. 01 pages: 7-12 Corresponding author: Barbara Mazur Bialystok University of Technology Faculty of Management e-mail: b.mazur@pb.edu. pl received: 11 February 2015 accepted: 20 June 2015