scholarly journals Panacea: Visual exploration system for analyzing trends in annual recruitment using time-varying graphs

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247587
Author(s):  
Toshiyuki T. Yokoyama ◽  
Masashi Okada ◽  
Tadahiro Taniguchi
Keyword(s):  
Early Stage ◽  
Visual Exploration ◽  
Recruitment Strategy ◽  
Time Varying ◽  
Graph Visualization ◽  
Dynamic Graph ◽  
Visualization System ◽  
New Graduates ◽  
Real World Datasets ◽  
The Relationship

Annual recruitment data of new graduates are manually analyzed by human resources (HR) specialists in industries, which signifies the need to evaluate the recruitment strategy of HR specialists. Different job seekers send applications to companies every year. The relationships between applicants’ attributes (e.g., English skill or academic credentials) can be used to analyze the changes in recruitment trends across multiple years. However, most attributes are unnormalized and thus require thorough preprocessing. Such unnormalized data hinder effective comparison of the relationship between applicants in the early stage of data analysis. Thus, a visual exploration system is highly needed to gain insight from the overview of the relationship among applicant qualifications across multiple years. In this study, we propose the Polarizing Attributes for Network Analysis of Correlation on Entities Association (Panacea) visualization system. The proposed system integrates a time-varying graph model and dynamic graph visualization for heterogeneous tabular data. Using this system, HR specialists can interactively inspect the relationships between two attributes of prospective employees across multiple years. Further, we demonstrate the usability of Panacea with representative examples for finding hidden trends in real-world datasets, and we discuss feedback from HR specialists obtained throughout Panacea’s development. The proposed Panacea system enables HR specialists to visually explore the annual recruitment of new graduates.

scholarly journals COVID-19 uncertainty and sleep: the roles of perceived stress and intolerance of uncertainty during the early stage of the COVID-19 outbreak

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dan Wu ◽  
Tingzhong Yang ◽  
Daniel L. Hall ◽  
Guihua Jiao ◽  
Lixin Huang ◽  
...  
Keyword(s):  
Path Analysis ◽  
Perceived Stress ◽  
Mediation Analysis ◽  
Early Stage ◽  
Intolerance Of Uncertainty ◽  
Policy Implications ◽  
Self Report ◽  
Poor Sleep ◽  
The Relationship

Abstract Background The COVID-19 pandemic brings unprecedented uncertainty and stress. This study aimed to characterize general sleep status among Chinese residents during the early stage of the outbreak and to explore the network relationship among COVID-19 uncertainty, intolerance of uncertainty, perceived stress, and sleep status. Methods A cross-sectional correlational survey was conducted online. A total of 2534 Chinese residents were surveyed from 30 provinces, municipalities, autonomous regions of China and regions abroad during the period from February 7 to 14, 2020, the third week of lockdown. Final valid data from 2215 participants were analyzed. Self-report measures assessed uncertainty about COVID-19, intolerance of uncertainty, perceived stress, and general sleep status. Serial mediation analysis using the bootstrapping method and path analysis were applied to test the mediation role of intolerance of uncertainty and perceived stress in the relationship between uncertainty about COVID-19 and sleep status. Results The total score of sleep status was 4.82 (SD = 2.72). Age, place of residence, ethnicity, marital status, infection, and quarantine status were all significantly associated with general sleep status. Approximately half of participants (47.1%) reported going to bed after 12:00 am, 23.0% took 30 min or longer to fall asleep, and 30.3% slept a total of 7 h or less. Higher uncertainty about COVID-19 was significantly positively correlated with higher intolerance of uncertainty (r = 0.506, p < 0.001). The mediation analysis found a mediating role of perceived stress in the relationship between COVID-19 uncertainty and general sleep status (β = 0.015, 95%C.I. = 0.009–0.021). However, IU was not a significant mediator of the relationship between COVID-19 uncertainty and sleep (β = 0.009, 95%C.I. = − 0.002–0.020). Moreover, results from the path analysis further showed uncertainty about COVID-19 had a weak direct effect on poor sleep (β = 0.043, p < 0.05); however, there was a robust indirect effect on poor sleep through intolerance of uncertainty and perceived stress. Conclusions These findings suggest that intolerance of uncertainty and perceived stress are critical factors in the relationship between COVID-19 uncertainty and sleep outcomes. Results are discussed in the context of the COVID-19 pandemic, and practical policy implications are also provided.

scholarly journals Linearity extensions of the market model: a case of the top 10 cryptocurrency prices during the pre-COVID-19 and COVID-19 periods

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Serdar Neslihanoglu
Keyword(s):  
State Space Model ◽  
Additive Model ◽  
Market Model ◽  
Time Varying ◽  
Daily Data ◽  
Forecasting Performance ◽  
Modeling And Forecasting ◽  
Crypto Currency ◽  
Beta Risk ◽  
The Relationship

AbstractThis research investigates the appropriateness of the linear specification of the market model for modeling and forecasting the cryptocurrency prices during the pre-COVID-19 and COVID-19 periods. Two extensions are offered to compare the performance of the linear specification of the market model (LMM), which allows for the measurement of the cryptocurrency price beta risk. The first is the generalized additive model, which permits flexibility in the rigid shape of the linearity of the LMM. The second is the time-varying linearity specification of the LMM (Tv-LMM), which is based on the state space model form via the Kalman filter, allowing for the measurement of the time-varying beta risk of the cryptocurrency price. The analysis is performed using daily data from both time periods on the top 10 cryptocurrencies by adjusted market capitalization, using the Crypto Currency Index 30 (CCI30) as a market proxy and 1-day and 7-day forward predictions. Such a comparison of cryptocurrency prices has yet to be undertaken in the literature. The empirical findings favor the Tv-LMM, which outperforms the others in terms of modeling and forecasting performance. This result suggests that the relationship between each cryptocurrency price and the CCI30 index should be locally instead of globally linear, especially during the COVID-19 period.

Scholar2vec: Vector Representation of Scholars for Lifetime Collaborator Prediction

2021 ◽  
Vol 15 (3) ◽  
pp. 1-19
Author(s):  
Wei Wang ◽  
Feng Xia ◽  
Jian Wu ◽  
Zhiguo Gong ◽  
Hanghang Tong ◽  
...  
Keyword(s):  
Scientific Collaboration ◽  
Early Stage ◽  
Collaboration Network ◽  
Vector Representation ◽  
Network Embedding ◽  
Machine Learning Methods ◽  
Academic Networks ◽  
Special Relationships ◽  
Real World Datasets ◽  
Vector Representations

While scientific collaboration is critical for a scholar, some collaborators can be more significant than others, e.g., lifetime collaborators. It has been shown that lifetime collaborators are more influential on a scholar’s academic performance. However, little research has been done on investigating predicting such special relationships in academic networks. To this end, we propose Scholar2vec, a novel neural network embedding for representing scholar profiles. First, our approach creates scholars’ research interest vector from textual information, such as demographics, research, and influence. After bridging research interests with a collaboration network, vector representations of scholars can be gained with graph learning. Meanwhile, since scholars are occupied with various attributes, we propose to incorporate four types of scholar attributes for learning scholar vectors. Finally, the early-stage similarity sequence based on Scholar2vec is used to predict lifetime collaborators with machine learning methods. Extensive experiments on two real-world datasets show that Scholar2vec outperforms state-of-the-art methods in lifetime collaborator prediction. Our work presents a new way to measure the similarity between two scholars by vector representation, which tackles the knowledge between network embedding and academic relationship mining.

scholarly journals Effect of number of medications and use of potentially inappropriate medications on frailty among early-stage older outpatients

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yuya Uragami ◽  
Kazuhiro Takikawa ◽  
Hajime Kareki ◽  
Koji Kimura ◽  
Kazuyuki Yamamoto ◽  
...  
Keyword(s):  
Early Stage ◽  
Linear Trend ◽  
Cross Sectional Study ◽  
Potentially Inappropriate Medications ◽  
Sectional Study ◽  
Antithrombotic Drugs ◽  
Cross Sectional ◽  
The Relationship ◽  
Inappropriate Medications ◽  
Frailty Score

Abstract Background Frailty is an urgent concern among an aging population worldwide. However, the relationship between frailty and number and types of medications has not been studied in detail among early-stage older patients, and it is unclear what prescriptions may have a role in preventing frailty. This study aimed to clarify the effects of number of medications and use of potentially inappropriate medications (PIMs) on frailty among early-stage older outpatients in Japan. Methods A cross-sectional study was undertaken. Frailty scores and medications of outpatients aged 65–74 years who regularly visited community pharmacies were investigated. Frailty scores were classified as 0 (non-frailty), 1–2 (pre-frailty), and ≥ 3 (frailty). The association between frailty and number of medications was analyzed by age and compared between PIM use and non-use groups. The proportion of patients who used PIMs was also analyzed by frailty score. Results Of 923 older outpatients, 49 (5.3%) and 318 (34.5%) patients had frailty and pre-frailty scores, respectively. The numbers of medications among patients with pre-frailty and frailty were significantly higher than among those with non-frailty (p <  0.001 for both). A similar increase was shown for PIM use groups aged 69–71 and 72–74 years, but not for the PIM use group aged 65–68 years and all groups without PIM use. An increasing linear trend was observed for the relationship between the proportion of patients who used any PIM, as well as some subcategories of PIMs (such as NSAIDs, benzodiazepines, loop diuretics and antithrombotic drugs) and frailty score. Conclusions Unnecessary medication use among early-stage older outpatients, especially patients aged ≥69 years who use PIMs and many medications, seems to be associated with frailty, but further research is needed to confirm these findings.

Factor-Bounded Nonnegative Matrix Factorization

2021 ◽  
Vol 15 (6) ◽  
pp. 1-18
Author(s):  
Kai Liu ◽  
Xiangyu Li ◽  
Zhihui Zhu ◽  
Lodewijk Brand ◽  
Hua Wang
Keyword(s):  
Matrix Factorization ◽  
Clustering Algorithm ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Optimization Methods ◽  
Auxiliary Function ◽  
Image Clustering ◽  
Real World Datasets ◽  
The Relationship ◽  
Matrix Factors

Nonnegative Matrix Factorization (NMF) is broadly used to determine class membership in a variety of clustering applications. From movie recommendations and image clustering to visual feature extractions, NMF has applications to solve a large number of knowledge discovery and data mining problems. Traditional optimization methods, such as the Multiplicative Updating Algorithm (MUA), solves the NMF problem by utilizing an auxiliary function to ensure that the objective monotonically decreases. Although the objective in MUA converges, there exists no proof to show that the learned matrix factors converge as well. Without this rigorous analysis, the clustering performance and stability of the NMF algorithms cannot be guaranteed. To address this knowledge gap, in this article, we study the factor-bounded NMF problem and provide a solution algorithm with proven convergence by rigorous mathematical analysis, which ensures that both the objective and matrix factors converge. In addition, we show the relationship between MUA and our solution followed by an analysis of the convergence of MUA. Experiments on both toy data and real-world datasets validate the correctness of our proposed method and its utility as an effective clustering algorithm.

TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data

2019 ◽  
Vol 19 (1) ◽  
pp. 3-23
Author(s):  
Aurea Soriano-Vargas ◽  
Bernd Hamann ◽  
Maria Cristina F de Oliveira
Keyword(s):  
Visual Analytics ◽  
Visual Analysis ◽  
Multivariate Data ◽  
Visual Exploration ◽  
Data Sets ◽  
Time Varying ◽  
Domain Experts ◽  
Data Mining Algorithms ◽  
Temporal Relationships ◽  
Visualization Techniques

We present an integrated interactive framework for the visual analysis of time-varying multivariate data sets. As part of our research, we performed in-depth studies concerning the applicability of visualization techniques to obtain valuable insights. We consolidated the considered analysis and visualization methods in one framework, called TV-MV Analytics. TV-MV Analytics effectively combines visualization and data mining algorithms providing the following capabilities: (1) visual exploration of multivariate data at different temporal scales, and (2) a hierarchical small multiples visualization combined with interactive clustering and multidimensional projection to detect temporal relationships in the data. We demonstrate the value of our framework for specific scenarios, by studying three use cases that were validated and discussed with domain experts.

scholarly journals Anosognosia in People with Cognitive Impairment: Association with Cognitive Deficits and Behavioral Disturbances

2015 ◽  
Vol 5 (1) ◽  
pp. 42-50 ◽  
Author(s):  
Antonella De Carolis ◽  
Virginia Cipollini ◽  
Valentina Corigliano ◽  
Anna Comparelli ◽  
Micaela Sepe-Monti ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Cognitive Deficits ◽  
Rating Scale ◽  
Motor Behavior ◽  
Psychiatric Symptoms ◽  
Early Stage ◽  
Functional Anatomy ◽  
Night Time ◽  
Behavioral Disturbances ◽  
The Relationship

Aims: To investigate, in a group of subjects at an early stage of cognitive impairment, the relationship between anosognosia and both cognitive and behavioral symptoms by exploring the various domains of insight. Methods: One hundred and eight subjects affected by cognitive impairment were consecutively enrolled. The level of awareness was evaluated by means of the Clinical Insight Rating Scale (CIRS). Psychiatric symptoms were evaluated using the Italian version of the Neuropsychiatric Inventory (NPI), whereas memory (memory index, MI) and executive (executive index, EI) functions were explored using a battery of neuropsychological tests and qualified by means of a single composite cognitive index score for each function. Results: A significant positive correlation between the total NPI score and global anosognosia score was found. Furthermore, both the MI and EI scores were lower in subjects with anosognosia than in those without anosognosia (p < 0.001 and p < 0.007, respectively). When the single domains of the CIRS were considered, anosognosia of reason of visit correlated with the EI score (r = -0.327, p = 0.01) and night-time behavioral disturbances (r = 0.225; p = 0.021); anosognosia of cognitive deficit correlated with depression (r = -0.193; p = 0.049) and the MI score (r = -0.201; p = 0.040); anosognosia of functional deficit correlated with the MI score (r = -0.257; p = 0.008), delusions (r = 0.232; p = 0.015) and aberrant motor behavior (r = 0.289; p = 0.003); anosognosia of disease progression correlated with the MI score (r = -0.236; p = 0.015), agitation (r = 0.247; p = 0.011), aberrant motor behavior (r = 0.351; p = 0.001) and night-time behavioral disturbances (r = 0.216; p = 0.027). Conclusions: Our study suggests that, in the early stage of cognitive impairment, anosognosia is associated with both cognitive deficits and behavioral disorders according to the specific functional anatomy of the symptoms.

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