behavioral data
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2022 ◽  
pp. 146144482110699
Author(s):  
Grace H Wolff ◽  
Cuihua Shen

User participation has long been recognized as a cornerstone of thriving online communities. Social live-streaming service (SLSS) communities are built on a subscription-based model and rely on viewers’ participation and financial support. Using the collective effort model and heuristics of social influence, this study examines the influence of streamer and viewer behaviors on viewers’ participation and financial commitment on the SLSS, Twitch.tv. Findings from behavioral data collected over 7 weeks show larger audiences diminish individual participation and financial commitment while moderation may encourage more. Female streamers benefit from increased moderation, earning two to three times more in financial commitment compared to men, who streamed more frequently and for longer durations but attracted much smaller audiences. Viewers’ participation and financial commitment did not differ across streams with more content diversity. Our results demonstrate how group factors influence individual participation and financial commitment in newer subscription-based media.


2022 ◽  
Vol 42 ◽  
pp. 1-22
Author(s):  
Ivanklin S. Campos-Filho ◽  
Jéssica S. Gallo ◽  
Jonas E. Gallão ◽  
Dayana F. Torres ◽  
Lília Horta ◽  
...  

Two new troglobitic species of Xangoniscus are described from two caves of Serra do Ramalho karst area, Bambuí geomorphological group, state of Bahia. Xangoniscus lapaensissp. nov. is described from Gruna Boca da Lapa cave, and X. loboisp. nov. from Gruna da Pingueira II cave. Both species are blind and depigmented and show amphibious habits, as observed for all species of Xangoniscus described until now. Xangoniscus lapaensissp. nov. occurs in travertine pools fed by water of the upper aquifer, and X. loboisp. nov. occurs in a small stream, an upper vadose tributary. Both species occur in fragile microhabitats. Ecological and behavioral data, conservation remarks, and IUCN conservation assessments are included to provide background data for conservation efforts in this unique karst area.


Author(s):  
Bogyeong Lee ◽  
Hyunsoo Kim

Walking is the most basic means of transportation. Therefore, continuous management of the walking environment is very important. In particular, the identification of environmental barriers that can impede walkability is the first step in improving the pedestrian experience. Current practices for identifying environmental barriers (e.g., expert investigation and survey) are time-consuming and require additional human resources. Hence, we have developed a method to identify environmental barriers based on information entropy considering that every individual behaves differently in the presence of external stimuli. The behavioral data of the gait process were recorded for 64 participants using a wearable sensor. Additionally, the data were classified into seven gait types using two-step k-means clustering. It was observed that the classified gaits create a probability distribution for each location to calculate information entropy. The values of calculated information entropy showed a high correlation in the presence or absence of environmental barriers. The results obtained facilitated the continuous monitoring of environmental barriers generated in a walking environment.


Author(s):  
Elena M. Auer ◽  
Gabriel Mersy ◽  
Sebastian Marin ◽  
Jason Blaik ◽  
Richard N. Landers

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lei Li

The growth and popularity of streaming music have changed the way people consume music, and users can listen to online music anytime and anywhere. By integrating various recommendation algorithms/strategies (user profiling, collaborative filtering, content filtering, etc.), we capture users’ interests and preferences and recommend the content of interest to them. To address the sparsity of behavioral data in digital music marketing, which leads to inadequate mining of user music preference features, a metric ranking learning recommendation algorithm with fused content representation is proposed. Relative partial order relations are constructed using observed and unobserved behavioral data to enable the model to be fully trained, while audio feature extraction submodels related to the recommendation task are constructed to further alleviate the data sparsity problem, and finally, the preference relationships between users and songs are mined through metric learning. Convolutional neural networks are used to extract the high-level semantic features of songs, and then the high-level semantic features of songs extracted from the previous layer are reformed into a session time sequence list according to the time sequence of user listening in order to build a bidirectional recurrent neural network model based on the attention mechanism so that it can reduce the influence of noisy data and learn the strong dependencies between songs.


2021 ◽  
Vol 11 (11) ◽  
pp. 1531
Author(s):  
Anastasia Papaioannou ◽  
Eva Kalantzi ◽  
Christos C. Papageorgiou ◽  
Kalliopi Korombili ◽  
Anastasia Bokou ◽  
...  

We aim to investigate whether EEG dynamics differ in adults with ASD (Autism Spectrum Disorders) and ADHD (attention-deficit/hyperactivity disorder) compared with healthy subjects during the performance of an innovative cognitive task, Aristotle’s valid and invalid syllogisms, and how these differences correlate with brain regions and behavioral data for each subject. We recorded EEGs from 14 scalp electrodes (channels) in 21 adults with ADHD, 21 with ASD, and 21 healthy, normal subjects. The subjects were exposed in a set of innovative cognitive tasks (inducing varying cognitive loads), Aristotle’s two types of syllogism mentioned above. A set of 39 questions were given to participants related to valid–invalid syllogisms as well as a separate set of questionnaires, in order to collect a number of demographic and behavioral data, with the aim of detecting shared information with values of a feature extracted from EEG, the multiscale entropy (MSE), in the 14 channels (‘brain regions’). MSE, a nonlinear information-theoretic measure of complexity, was computed to extract a feature that quantifies the complexity of the EEG. Behavior-Partial Least Squares Correlation, PLSC, is the method to detect the correlation between two sets of data, brain, and behavioral measures. -PLSC, a variant of PLSC, was applied to build a functional connectivity of the brain regions involved in the reasoning tasks. Graph-theoretic measures were used to quantify the complexity of the functional networks. Based on the results of the analysis described in this work, a mixed 14 × 2 × 3 ANOVA showed significant main effects of group factor and brain region* syllogism factor, as well as a significant brain region* group interaction. There are significant differences between the means of MSE (complexity) values at the 14 channels of the members of the ‘pathological’ groups of participants, i.e., between ASD and ADHD, while the difference in means of MSE between both ASD and ADHD and that of the control group is not significant. In conclusion, the valid–invalid type of syllogism generates significantly different complexity values, MSE, between ASD and ADHD. The complexity of activated brain regions of ASD participants increased significantly when switching from a valid to an invalid syllogism, indicating the need for more resources to ‘face’ the task escalating difficulty in ASD subjects. This increase is not so evident in both ADHD and control. Statistically significant differences were found also in the behavioral response of ASD and ADHD, compared with those of control subjects, based on the principal brain and behavior saliences extracted by PLSC. Specifically, two behavioral measures, the emotional state and the degree of confidence of participants in answering questions in Aristotle’s valid–invalid syllogisms, and one demographic variable, age, statistically and significantly discriminate the three groups’ ASD. The seed-PLC generated functional connectivity networks for ASD, ADHD, and control, were ‘projected’ on the regions of the Default Mode Network (DMN), the ‘reference’ connectivity, of which the structural changes were found significant in distinguishing the three groups. The contribution of this work lies in the examination of the relationship between brain activity and behavioral responses of healthy and ‘pathological’ participants in the case of cognitive reasoning of the type of Aristotle’s valid and invalid syllogisms, using PLSC, a machine learning approach combined with MSE, a nonlinear method of extracting a feature based on EEGs that captures a broad spectrum of EEGs linear and nonlinear characteristics. The results seem promising in adopting this type of reasoning, in the future, after further enhancements and experimental tests, as a supplementary instrument towards examining the differences in brain activity and behavioral responses of ASD and ADHD patients. The application of the combination of these two methods, after further elaboration and testing as new and complementary to the existing ones, may be considered as a tool of analysis in helping detecting more effectively such types of disorders.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Masud Rabbani ◽  
Munirul M. Haque ◽  
Dipranjan Das Dipal ◽  
Md Ishrak Islam Zarif ◽  
Anik Iqbal ◽  
...  

AbstractCommunity-wide lockdowns in response to COVID-19 influenced many families, but the developmental cascade for children with autism spectrum disorder (ASD) may be especially detrimental. Our objective was to evaluate behavioral patterns of risk and resilience for children with ASD across parent-report assessments before (from November 2019 to February 2020), during (March 2020 to May 2020), and after (June 2020 to November 2020) an extended COVID-19 lockdown. In 2020, our study Mobile-based care for children with ASD using remote experience sampling method (mCARE) was inactive data collection before COVID-19 emerged as a health crisis in Bangladesh. Here we deployed “Cohort Studies”, where we had in total 300 children with ASD (150 test group and 150 control group) to collect behavioral data. Our data collection continued through an extended COVID-19 lockdown and captured parent reports of 30 different behavioral parameters (e.g., self-injurious behaviors, aggression, sleep problems, daily living skills, and communication) across 150 children with ASD (test group). Based on the children’s condition, 4–6 behavioral parameters were assessed through the study. A total of 56,290 behavioral data points was collected (an average of 152.19 per week) from parent cell phones using the mCARE platform. Children and their families were exposed to an extended COVID-19 lockdown. The main outcomes used for this study were generated from parent reports child behaviors within the mCARE platform. Behaviors included of child social skills, communication use, problematic behaviors, sensory sensitivities, daily living, and play. COVID-19 lockdowns for children with autism and their families are not universally negative but supports in the areas of “Problematic Behavior” could serve to mitigate future risk.


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