Unsupervised Learning for Human Mobility Behaviors

Learning human mobility behaviors from location-sensing data are crucial to mobility data mining because of its potential to address a range of analytical purposes in mobile context reasoning, including exploration, inference, and prediction. However, existing approaches suffer from two practical problems: temporal and spatial sparsity. To address these shortcomings, we present two unsupervised learning methods to model the mobility behaviors of multiple users (i.e., a population), considering efficiency and accuracy. These methods intelligently overcome the sparsity in individual data by seeking temporal commonality among users’ heterogeneous location behaviors. The advantages of our models are highlighted through experiments on several real-world mobility data sets, which also show how our methods can realize the three analytical purposes in a unified manner.

A Revised Framework to Evaluate the Consistency Assumption Globally in a Network of Interventions

Background The unrelated mean effects (UME) model has been proposed for evaluating the consistency assumption globally in the network of interventions. However, the UME model does not accommodate multiarm trials properly and omits comparisons between nonbaseline interventions in the multiarm trials not investigated in 2-arm trials. Methods We proposed a refinement of the UME model that tackles the limitations mentioned above. We also accompanied the scatterplots on the posterior mean deviance contributions of the trial arms under the network meta-analysis (NMA) and UME models with Bland-Altman plots to detect outlying trials contributing to poor model fit. We applied the refined and original UME models to 2 networks with multiarm trials. Results The original UME model omitted more than 20% of the observed comparisons in both networks. The thorough inspection of the individual data points’ deviance contribution using complementary plots in conjunction with the measures of model fit and the estimated between-trial variance indicated that the refined and original UME models revealed possible inconsistency in both examples. Conclusions The refined UME model allows proper accommodation of the multiarm trials and visualization of all observed evidence in complex networks of interventions. Furthermore, considering several complementary plots to investigate deviance helps draw informed conclusions on the possibility of global inconsistency in the network. Highlights We have refined the unrelated mean effects (UME) model to incorporate multiarm trials properly and to estimate all observed comparisons in complex networks of interventions. Forest plots with posterior summaries of all observed comparisons under the network meta-analysis and refined UME model can uncover the consequences of potential inconsistency in the network. Using complementary plots to investigate the individual data points’ deviance contribution in conjunction with model fit measures and estimated heterogeneity aid in detecting possible inconsistency.

Crucial File Selection Strategy (CFSS) for Enhanced Download Response Time in Cloud Replication Environments

Cloud Computing is a mass platform to serve high volume data from multi-devices and numerous technologies. Cloud tenants have a high demand to access their data faster without any disruptions. Therefore, cloud providers are struggling to ensure every individual data is secured and always accessible. Hence, an appropriate replication strategy capable of selecting essential data is required in cloud replication environments as the solution. This paper proposed a Crucial File Selection Strategy (CFSS) to address poor response time in a cloud replication environment. A cloud simulator called CloudSim is used to conduct the necessary experiments, and results are presented to evidence the enhancement on replication performance. The obtained analytical graphs are discussed thoroughly, and apparently, the proposed CFSS algorithm outperformed another existing algorithm with a 10.47% improvement in average response time for multiple jobs per round.

Yields variability as a determinant of production risk in agriculture

Agricultural activity is exposed to a number of adverse external factors known as production risks, which affect the quantity and value of production. One of the measures of production risk assessment in crops production is yields variability. The aim of the study is to determine the scope of the variability of the yields of selected agricultural crops on the basis of various sources of data covering the years 2015–2019. The study uses aggregated official statistics data compiled by Statistics Poland, published in the Statistical Yearbook of Agriculture, and individual data from the Polish Farm Accountancy Data Network (FADN), which takes into account commercial farms. The analysis of the differences in crops variability determined on the basis of different sources of information was carried out using basic statistical parameters: the arithmetic mean, standard deviation and the coefficient of variation. The research shows that the variability of yields in Poland depends not only on the type of crops, but also on the location of the production. Moreover, it indicates a greater stability of crops production among commercial farms when compared with farms in general. The comparison of the values of the coefficient of variation calculated from individual data with the values calculated on the basis of the average yields indicates fundamental differences which should be taken into account when applying the variation coefficient to assess production risk and, consequently, to optimise insurance tools.

Examining Sex Differences in Conditioned Place Preference or Aversion to Methamphetamine in Adolescent and Adult Mice

Adolescence marks a particularly vulnerable period to developing substance use disorders. Human and rodent studies suggest that hypersensitivity to reward may contribute towards such vulnerability when adolescents are exposed to casual drug use. Methamphetamine is a popular illicit substance used by male and female youths. However, age- and sex-specific research in methamphetamine is scarce. The present study therefore aimed to examine potential sex differences in methamphetamine-conditioned place preference in adolescent and adult mice. Mice (n = 16–24/group) were conditioned to methamphetamine (0.1 mg/kg). We observed that regardless of age, females were more hyperactive compared to males. Individually normalized score against baseline preference indicated that on average, adolescents formed stronger preference compared to adults in both sexes. This suggests that adolescents are more sensitive to the rewarding effects of methamphetamine compared to adults. Surprisingly, individual data showed that some mice formed a conditioned place aversion instead of preference, with females less likely to form an aversion compared to males. These results suggest that adolescents may be hypersensitive to methamphetamine’s rewarding effects. In addition, female resistance to the aversive effects of methamphetamine may relate to the sex-specific findings in humans, including quicker transition to regular methamphetamine use observed in females compared to males.

ABA, ABC, and AAB renewal in fear conditioning using social stimuli and its quantitative description

A relapse in clinical anxiety following exposure therapy for social anxiety disorder is prevalent and causes serious problems. According to the fear conditioning theory of social anxiety disorder, a part of this relapse can be caused by the renewal effect. This study aimed to investigate whether three renewal effects occur in a fear conditioning procedure that uses social stimuli as both unconditioned and conditioned stimuli, which is an analog preparation of acquisition of social anxiety and reduction by exposure therapy. Sixty-four participants were randomly allocated to four groups (AAA, ABA, ABC, and AAB). They received 9 pairings with a natural face and a negative comment during the acquisition phase and then received 18 pairings with the same face and a neutral comment from the person in the extinction phase. Following extinction, the testing phase was conducted. Context, defined as background colors, used in each phase was different between groups. We conducted two analyses, the ANOVA and Bayesian modeling, to investigate whether three types of renewal effects occur, whether the individual data can be described by an associative model and whether individual differences in learning are related to social anxiety. The ANOVA showed the occurrence of three renewals in the procedure, although the size of their effects was the same. The Bayesian modeling indicated that individual data were generally consistent with the model, and there were some relationships among the estimated parameters and between their parameters and social anxiety. These findings suggest that the relapse following exposure therapy is related to renewal effects, the effect of each exposure session can be represented by mathematical associative models, and some features of learning in the procedure are related to other features and social anxiety.

Systematic factors and individual variation affecting litter size of Boer and Jawarandu goat cross

We studied the systematic factors and individual variation affecting litter size in the crossbreds between Boer and Jawarandu goat. The data were obtained from the records of litter size of Boerja goats from 2012 to 2015. The systematic factors consisted of season and year of birth, doe breeds and the kid’s sex; along with individual data including pedigree, date of birth, and parental breeds. The data consisted of 107 Boer does, 687 Jawarandu does, and 495 Boerja does with a total of 3804 kids. A linear model was developed to account the effect of systematic factors on litter size of Boerja goats. Later, a mixed model was solved with Restricted Maximum Likelihood (REML) method to estimate the individual variations on litter size. The results showed that litter size trait in goat was influenced by doe breed (P<0.05). Individual variation of this trait was also high (46%). Based on this research, it can be concluded that litter size of Boer goats and their crosses were affected by the doe’s breed with high individual variation. Doe’s selection is potential to improve liter size in goat crossbred population in the future.