On-line Context Aware Physical Activity Recognition from the Accelerometer and Audio Sensors of Smartphones

International audienceActivity Recognition (AR) from smartphone sensors has be-come a hot topic in the mobile computing domain since it can provide ser-vices directly to the user (health monitoring, fitness, context-awareness) as well as for third party applications and social network (performance sharing, profiling). Most of the research effort has been focused on direct recognition from accelerometer sensors and few studies have integrated the audio channel in their model despite the fact that it is a sensor that is always available on all kinds of smartphones. In this study, we show that audio features bring an important performance improvement over an accelerometer based approach. Moreover, the study demonstrates the interest of considering the smartphone location for on-line context-aware AR and the prediction power of audio features for this task. Finally, an-other contribution of the study is the collected corpus that is made avail-able to the community for AR recognition from audio and accelerometer sensors

Scalable real-time classification of data streams with concept drift

Inducing adaptive predictive models in real-time from high throughput data streams is one of the most challenging areas of Big Data Analytics. The fact that data streams may contain concept drifts (changes of the pattern encoded in the stream over time) and are unbounded, imposes unique challenges in comparison with predictive data mining from batch data. Several real-time predictive data stream algorithms exist, however, most approaches are not naturally parallel and thus limited in their scalability. This paper highlights the Micro-Cluster Nearest Neighbour (MC-NN) data stream classifier. MC-NN is based on statistical summaries of the data stream and a nearest neighbour approach, which makes MC-NN naturally parallel. In its serial version MC-NN is able to handle data streams, the data does not need to reside in memory and is processed incrementally. MC-NN is also able to adapt to concept drifts. This paper provides an empirical study on the serial algorithm’s speed, adaptivity and accuracy. Furthermore, this paper discusses the new parallel implementation of MC-NN, its parallel properties and provides an empirical scalability study

The Origin of a New Sex Chromosome by Introgression between Two Stickleback Fishes.

Introgression is increasingly recognized as a source of genetic diversity that fuels adaptation. Its role in the evolution of sex chromosomes, however, is not well known. Here, we confirm the hypothesis that the Y chromosome in the ninespine stickleback, Pungitius pungitius, was established by introgression from the Amur stickleback, P. sinensis. Using whole genome resequencing, we identified a large region of Chr 12 in P. pungitius that is diverged between males and females. Within but not outside of this region, several lines of evidence show that the Y chromosome of P. pungitius shares a most recent common ancestor not with the X chromosome, but with the homologous chromosome in P. sinensis. Accumulation of repetitive elements and gene expression changes on the new Y are consistent with a young sex chromosome in early stages of degeneration, but other hallmarks of Y chromosomes have not yet appeared. Our findings indicate that porous species boundaries can trigger rapid sex chromosome evolution

Family composition and age at menarche: findings from the international Health Behaviour in School-Aged Children Study

This research was funded by The University of St Andrews and NHS Health Scotland.Background Early menarche has been associated with father absence, stepfather presence and adverse health consequences in later life. This article assesses the association of different family compositions with the age at menarche. Pathways are explored which may explain any association between family characteristics and pubertal timing. Methods Cross-sectional, international data on the age at menarche, family structure and covariates (age, psychosomatic complaints, media consumption, physical activity) were collected from the 2009–2010 Health Behaviour in School-aged Children (HBSC) survey. The sample focuses on 15-year old girls comprising 36,175 individuals across 40 countries in Europe and North America (N = 21,075 for age at menarche). The study examined the association of different family characteristics with age at menarche. Regression and path analyses were applied incorporating multilevel techniques to adjust for the nested nature of data within countries. Results Living with mother (Cohen’s d = .12), father (d = .08), brothers (d = .04) and sisters (d = .06) are independently associated with later age at menarche. Living in a foster home (d = −.16), with ‘someone else’ (d = −.11), stepmother (d = −.10) or stepfather (d = −.06) was associated with earlier menarche. Path models show that up to 89% of these effects can be explained through lifestyle and psychological variables. Conclusions Earlier menarche is reported amongst those with living conditions other than a family consisting of two biological parents. This can partly be explained by girls’ higher Body Mass Index in these families which is a biological determinant of early menarche. Lower physical activity and elevated psychosomatic complaints were also more often found in girls in these family environments.Publisher PDFPeer reviewe

Foodways in transition: food plants, diet and local perceptions of change in a Costa Rican Ngäbe community

Background Indigenous populations are undergoing rapid ethnobiological, nutritional and socioeconomic transitions while being increasingly integrated into modernizing societies. To better understand the dynamics of these transitions, this article aims to characterize the cultural domain of food plants and analyze its relation with current day diets, and the local perceptions of changes given amongst the Ngäbe people of Southern Conte-Burica, Costa Rica, as production of food plants by its residents is hypothesized to be drastically in recession with an decreased local production in the area and new conservation and development paradigms being implemented. Methods Extensive freelisting, interviews and workshops were used to collect the data from 72 participants on their knowledge of food plants, their current dietary practices and their perceptions of change in local foodways, while cultural domain analysis, descriptive statistical analyses and development of fundamental explanatory themes were employed to analyze the data. Results Results show a food plants domain composed of 140 species, of which 85 % grow in the area, with a medium level of cultural consensus, and some age-based variation. Although many plants still grow in the area, in many key species a decrease on local production–even abandonment–was found, with much reduced cultivation areas. Yet, the domain appears to be largely theoretical, with little evidence of use; and the diet today is predominantly dependent on foods bought from the store (more than 50 % of basic ingredients), many of which were not salient or not even recognized as ‘food plants’ in freelists exercises. While changes in the importance of food plants were largely deemed a result of changes in cultural preferences for store bought processed food stuffs and changing values associated with farming and being food self-sufficient, Ngäbe were also aware of how changing household livelihood activities, and the subsequent loss of knowledge and use of food plants, were in fact being driven by changes in social and political policies, despite increases in forest cover and biodiversity. Conclusions Ngäbe foodways are changing in different and somewhat disconnected ways: knowledge of food plants is varied, reflecting most relevant changes in dietary practices such as lower cultivation areas and greater dependence on food from stores by all families. We attribute dietary shifts to socioeconomic and political changes in recent decades, in particular to a reduction of local production of food, new economic structures and agents related to the State and globalization

QUOTIENT: Two-Party Secure Neural Network Training and Prediction

Recently, there has been a wealth of effort devoted to the design of secure protocols for machine learning tasks. Much of this is aimed at enabling secure prediction from highly-accurate Deep Neural Networks (DNNs). However, as DNNs are trained on data, a key question is how such models can be also trained securely. The few prior works on secure DNN training have focused either on designing custom protocols for existing training algorithms, or on developing tailored training algorithms and then applying generic secure protocols. In this work, we investigate the advantages of designing training algorithms alongside a novel secure protocol, incorporating optimizations on both fronts. We present QUOTIENT, a new method for discretized training of DNNs, along with a customized secure two-party protocol for it. QUOTIENT incorporates key components of state-of-the-art DNN training such as layer normalization and adaptive gradient methods, and improves upon the state-of-the-art in DNN training in two-party computation. Compared to prior work, we obtain an improvement of 50X in WAN time and 6% in absolute accuracy

Predicting sex as a soft-biometrics from device interaction swipe gestures

Touch and multi-touch gestures are becoming the most common way to interact with technology such as smart phones, tablets and other mobile devices. The latest touch-screen input capacities have tremendously increased the quantity and quality of available gesture data, which has led to the exploration of its use in multiple disciplines from psychology to biometrics. Following research studies undertaken in similar modalities such as keystroke and mouse usage biometrics, the present work proposes the use of swipe gesture data for the prediction of soft-biometrics, specifically the user's sex. This paper details the software and protocol used for the data collection, the feature set extracted and subsequent machine learning analysis. Within this analysis, the BestFirst feature selection technique and classification algorithms (naïve Bayes, logistic regression, support vector machine and decision tree) have been tested. The results of this exploratory analysis have confirmed the possibility of sex prediction from the swipe gesture data, obtaining an encouraging 78% accuracy rate using swipe gesture data from two different directions. These results will hopefully encourage further research in this area, where the prediction of soft-biometrics traits from swipe gesture data can play an important role in enhancing the authentication processes based on touch-screen devices