Linking repeated subjective judgments and ConvNets for multimodal assessment of the immediate living environment

The integration of alternative data extraction approaches for multimodal data, can significantly reduce modeling difficulties for the automatic location assessment. We develop a method for assessing the quality of the immediate living environment by incorporating human judgments as ground truth into a neural network for generating new synthetic data and testing the effects in surrogate hedonic models. We expect that the quality of the data will be less biased if the annotation is performed by multiple independent persons applying repeated trials which should reduce the overall error variance and lead to more robust results. Experimental results show that linking repeated subjective judgements and Deep Learning can reliably determine the quality scores and thus expand the range of information for the quality assessment. The presented method is not computationally intensive, can be performed repetitively and can also be easily adapted to machine learning approaches in a broader sense or be transferred to other use cases. Following aspects are essential for the implementation of the method:•Sufficient amount of representative data for human assessment.•Repeated assessment trials by individuals.•Confident derivation of the effect of human judgments on property price as an approbation for further generation of synthetic data.

Coup in the coop: Rank changes in chicken dominance hierarchies over maturation

Chicken dominance hierarchies or pecking orders are established before maturation and maintained by consistent submissive responses of subordinate individuals, leading to stable ranks within unchanged groups. We observed interactions of 418 laying hens (Gallus gallus domesticus) distributed across three small (20) and three large (~120) groups. The observations were performed before sexual maturation (young period) and additionally after onset of maturation (mature period) to confirm stability of ranks. Dominance ranks were estimated via the Elo rating system across both observation periods. Diagnostics of the ranks revealed unexpected uncertainty and rank instability for the full dataset, although sampling appeared to be adequate. Subsequent evaluations of ranks based on the mature period only, showed more reliable ranks than across both observation periods. Furthermore, winning success during the young period did not directly predict high rank during the mature period. These results indicated rank changes between observation periods. The current study design could not discern whether ranks were stable in all pens before maturation. However, our data rather suggested active rank mobility after hierarchy establishment to be the cause for our findings. Once thought to be stable, chicken hierarchies may provide an excellent system to study causes and implications of active rank mobility.

Toward Personalized Web-Based Cognitive Rehabilitation for Patients With Ischemic Stroke: Elo Rating Approach

Background

Stroke is a worldwide cause of disability; 40% of stroke survivors sustain cognitive impairments, most of them following inpatient rehabilitation at specialized clinical centers. Web-based cognitive rehabilitation tasks are extensively used in clinical settings. The impact of task execution depends on the ratio between the skills of the treated patient and the challenges imposed by the task itself. Thus, treatment personalization requires a trade-off between patients’ skills and task difficulties, which is still an open issue. In this study, we propose Elo ratings to support clinicians in tasks assignations and representing patients’ skills to optimize rehabilitation outcomes.

Objective

This study aims to stratify patients with ischemic stroke at an early stage of rehabilitation into three levels according to their Elo rating; to show the relationships between the Elo rating levels, task difficulty levels, and rehabilitation outcomes; and to determine if the Elo rating obtained at early stages of rehabilitation is a significant predictor of rehabilitation outcomes.

Methods

The PlayerRatings R library was used to obtain the Elo rating for each patient. Working memory was assessed using the DIGITS subtest of the Barcelona test, and the Rey Auditory Verbal Memory Test (RAVLT) was used to assess verbal memory. Three subtests of RAVLT were used: RAVLT learning (RAVLT075), free-recall memory (RAVLT015), and recognition (RAVLT015R). Memory predictors were identified using forward stepwise selection to add covariates to the models, which were evaluated by assessing discrimination using the area under the receiver operating characteristic curve (AUC) for logistic regressions and adjusted R² for linear regressions.

Results

Three Elo levels (low, middle, and high) with the same number of patients (n=96) in each Elo group were obtained using the 50 initial task executions (from a total of 38,177) for N=288 adult patients consecutively admitted for inpatient rehabilitation in a clinical setting. The mid-Elo level showed the highest proportions of patients that improved in all four memory items: 56% (54/96) of them improved in DIGITS, 67% (64/96) in RAVLT075, 58% (56/96) in RAVLT015, and 53% (51/96) in RAVLT015R (P<.001). The proportions of patients from the mid-Elo level that performed tasks at difficulty levels 1, 2, and 3 were 32.1% (3997/12,449), 31.% (3997/12,449), and 36.9% (4595/12,449), respectively (P<.001), showing the highest match between skills (represented by Elo level) and task difficulties, considering the set of 38,177 task executions. Elo ratings were significant predictors in three of the four models and quasi-significant in the fourth. When predicting RAVLT075 and DIGITS at discharge, we obtained R²=0.54 and 0.43, respectively; meanwhile, we obtained AUC=0.73 (95% CI 0.64-0.82) and AUC=0.81 (95% CI 0.72-0.89) in RAVLT075 and DIGITS improvement predictions, respectively.

Conclusions

Elo ratings can support clinicians in early rehabilitation stages in identifying cognitive profiles to be used for assigning task difficulty levels.

Discovering associations between players' performance indicators and matches' results in the European Soccer Leagues

The application of data mining techniques and statistical analysis to the sports field has received increasing attention in the last decade. One of the most famous sports in the world is soccer, and the present work deals with it, using data from the 2009/2010 season to the 2015/2016 season from nine European leagues extracted from the Kaggle European Soccer database. Overall performance indicators of the four roles in a soccer team (forward, midfielder, defender and goalkeeper) for home and away teams are used to investigate the relationships between them and the results of matches, and to predict the wins of the home team. The model used to answer both these demands is the Bayesian Network. This study shows that this model can be very useful for mining the relations between players' performance indicators and for improving knowledge of the game strategies applied by coaches in different leagues. Moreover, it is shown that the ability to predict match results of the proposed Bayesian Network is roughly the same as that of the Naive Bayes model.

Documenting Aggression, Dominance and the Impacts of Visitor Interaction on Galápagos Tortoises (Chelonoidis nigra) in a Zoo Setting

Ensuring high levels of welfare is imperative for modern zoos, but such organisations must also engage visitors in order to successfully spread awareness and raise conservation funds. It is therefore important to understand the responses of animals to visitor interaction to optimise welfare. Often, the opportunity to interact with humans may be enriching for animals, but in other contexts, this interaction may have negative welfare effects. We observed captive female Galápagos giant tortoises (Chelonoidis nigra) to describe aggressive interactions, characterize hierarchy using Elo ratings and assess the impact of visitor interactions. Elo ratings indicated that one individual was dominant over two equally ranked subordinates; aggressive interactions are discussed in this context. We detected significant effects of the presence of visitors and visitor type (keepers, vets or public) within the enclosure on aggression and activity. We suggest that previous miscategorisation of a natural behaviour (the finch response) as an operantly conditioned behaviour, rather than a fixed action pattern, may have triggered aggression. We then document changes made to the management of the animals to mitigate the impacts discovered. This work highlights the importance of empirical evidence in determining optimal management strategies for zoo animals with regards to public interactions and animal welfare.

Modeling Human Perception of Image Quality

Humans can determine image quality instantly and intuitively, but the mechanism of human perception of image quality is unknown. The purpose of this work was to identify the most important quantitative metrics responsible for the human perception of digital image quality. Digital images from two different datasets-CT tomography (MedSet) and scenic photographs of trees (TreeSet)-were presented in random pairs to unbiased human viewers. The observers were then asked to select the best-quality image from each image pair. The resulting human-perceived image quality (HPIQ) ranks were obtained from these pairwise comparisons with two different ranking approaches. Using various digital image quality metrics reported in the literature, we built two models to predict the observed HPIQ rankings, and to identify the most important HPIQ predictors. Evaluating the quality of our HPIQ models as the fraction of falsely predicted pairwise comparisons (inverted image pairs), we obtained 70-71% of correct HPIQ predictions for the first, and 73-76%for the second approach. Taking into account that 10-14% of inverted pairs were already present in the original rankings, limitations of the models, and only a few principal HPIQ predictors used, we find this result very satisfactory. We obtained a small set of most significant quantitative image metrics associated with the human perception of image quality. This can be used for automatic image quality ranking, machine learning, and quality-improvement algorithms.