Large-Scale Analysis of Auditory Segregation Behavior Crowdsourced via a Smartphone App

The human auditory system is adept at detecting sound sources of interest from a complex mixture of several other simultaneous sounds. The ability to selectively attend to the speech of one speaker whilst ignoring other speakers and background noise is of vital biological significance—the capacity to make sense of complex ‘auditory scenes’ is significantly impaired in aging populations as well as those with hearing loss. We investigated this problem by designing a synthetic signal, termed the ‘stochastic figure-ground’ stimulus that captures essential aspects of complex sounds in the natural environment. Previously, we showed that under controlled laboratory conditions, young listeners sampled from the university subject pool (n = 10) performed very well in detecting targets embedded in the stochastic figure-ground signal. Here, we presented a modified version of this cocktail party paradigm as a ‘game’ featured in a smartphone app (The Great Brain Experiment) and obtained data from a large population with diverse demographical patterns (n = 5148). Despite differences in paradigms and experimental settings, the observed target-detection performance by users of the app was robust and consistent with our previous results from the psychophysical study. Our results highlight the potential use of smartphone apps in capturing robust large-scale auditory behavioral data from normal healthy volunteers, which can also be extended to study auditory deficits in clinical populations with hearing impairments and central auditory disorders.     

The Impacts of Using Smartphone Dating Applications on Sexual Risk Behaviours in College Students in Hong Kong

Dating applications (apps) on smartphones have become increasingly popular. The aim of this study was to explore the association between the use of dating apps and risky sexual behaviours. Data were collected in four university campuses in Hong Kong. Subjects completed a structured questionnaire asking about the use of dating apps, sexual behaviours, and sociodemographics. Multiple linear and logistics regressions were used to explore factors associated with sexual risk behaviours. Six hundred sixty-six subjects were included in the data analysis. Factors associated with having unprotected sexual intercourse with more lifetime sexual partners included use of dating apps (β = 0.93, p<0.01), having one’s first sexual intercourse before 16 years of age (β = 1.74, p<0.01), being older (β = 0.4, p<0.01), currently being in a relationship (= 0.69, p<0.05), having a monthly income at least HKD$5,000 (β = 1.34, p<0.01), being a current smoker (β = 1.52, p<0.01), and being a current drinker (β = 0.7, p<0.01). The results of a multiple logistic regression analysis found that users of dating apps (adjust odds ratio: 0.52, p<0.05) and current drinkers (adjust odds ratio: 0.40, p<0.01) were less likely to have consistent condom use. Users of dating apps (adjust odds ratio: 1.93, p<0.05), bisexual/homosexual subjects (adjust odds ratio: 2.57, p<0.01) and female subjects (adjust odds ratio: 2.00, p<0.05) were more likely not to have used condoms the last time they had sexual intercourse. The present study found a robust association between using dating apps and sexual risk behaviours, suggesting that app users had greater sexual risks. Interventions that can target app users so that they can stay safe when seeking sexual partners through dating apps should be developed.     

Social Networking Smartphone Applications and Sexual Health Outcomes among Men Who Have Sex with Men

Several smartphone applications (apps) designed to help men who have sex with men (MSM) find casual sexual partners have appeared on the market recently. Apps of this nature have the potential to impact sexual health and behavior by providing constant access to a large supply of available partners. In this study, the sexual health history, behavior, and personality of MSM who use these apps was compared to MSM who meet partners in other ways. A sample of 110 adult MSM was recruited online to complete a cross-sectional survey. All participants were either single or involved in a non-exclusive romantic relationship. There were no statistically significant differences between app users and non-users in frequency of insertive or receptive anal sex without a condom. However, app users reported significantly more sexual partners and had a higher prevalence of ever being diagnosed with an STI than did non-users. App users did not differ from non-users on any demographic or personality variables (including erotophilia, sensation seeking, and self-control); however, when adjusting lifetime total sex partners for those met specifically through apps, app users still had significantly more partners. This pattern of results suggests that app users may be more sexually active in general. More work is needed to fully understand the association between this emerging technology and potential sexual health risks.     

Social media user’s knowledge on soil plant relationship in Saudi Arabia

The study aimed to investigate the soil-plant relationship by looking at knowledge levels of social media users. The study examined the relationship between the users’ characteristics and their knowledge on soil-plant relationship. Online survey was designed and distributed to gather the data. The number of response was received from 383 respondents where 375 participants provided completed information and the remaining eight responses were eliminated due to quality standards. The results show most of the participants (68.8%) are moderately depend on social media to acquire knowledge on soil-plant relationship and indicated “Twitter” as the highly utilized platform followed by the “Youtube”. Meanwhile, 48.8% of the participants indicated that social media have a high impact on their knowledge-based information on soil-plant relationship and agricultural contents. Inferential analysis shows there was a significant positive relationship (p < 0.01) between independent variables; Twitter, Youtube, Instagram, and Snapchat and the degree of trust on these platforms, and the level’s knowledge on soil-plant relationship (dependent variable). Only 13% of the variance accounted for the impact of social media on participants’ agricultural knowledge-base can be predicted from the combination of participants’ reliance on a set of social media platforms (Twitter, Instagram, Youtube, and Snapchat). The study revealed the trust and confidence of the users using social media on agricultural information/soil plant relationship had a medium effect in social and educational research.     

TransNewGuinea.org: An Online Database of New Guinea Languages

The island of New Guinea has the world’s highest linguistic diversity, with more than 900 languages divided into at least 23 distinct language families. This diversity includes the world’s third largest language family: Trans-New Guinea. However, the region is one of the world’s least well studied, and primary data is scattered across a wide range of publications and more often then not hidden in unpublished “gray” literature. The lack of primary research data on the New Guinea languages has been a major impediment to our understanding of these languages, and the history of the peoples in New Guinea. TransNewGuinea.org aims to collect data about these languages and place them online in a consistent format. This database will enable future research into the New Guinea languages with both traditional comparative linguistic methods and novel cutting-edge computational techniques. The long-term aim is to shed light into the prehistory of the peoples of New Guinea, and to understand why there is such major diversity in their languages.     

How smartphones are changing the face of mobile and participatory healthcare: an overview,with example from eCAALYX

The latest generation of smartphones are increasingly viewed as handheld computers rather than as phones, due to their powerful on-board computing capability, capacious memories, large screens and open operating systems that encourage application development. This paper provides a brief state-of-the-art overview of health and healthcare smartphone apps (applications) on the market today, including emerging trends and market uptake. Platforms available today include Android, Apple iOS, RIM BlackBerry, Symbian, and Windows (Windows Mobile 6.x and the emerging Windows Phone 7 platform). The paper covers apps targeting both laypersons/patients and healthcare professionals in various scenarios, e.g., health, fitness and lifestyle education and management apps; ambient assisted living apps; continuing professional education tools; and apps for public health surveillance. Among the surveyed apps are those assisting in chronic disease management, whether as standalone apps or part of a BAN (Body Area Network) and remote server configuration. We describe in detail the development of a smartphone app within eCAALYX (Enhanced Complete Ambient Assisted Living Experiment, 2009-2012), an EU-funded project for older people with multiple chronic conditions. The eCAALYX Android smartphone app receives input from a BAN (a patient-wearable smart garment with wireless health sensors) and the GPS (Global Positioning System) location sensor in the smartphone, and communicates over the Internet with a remote server accessible by healthcare professionals who are in charge of the remote monitoring and management of the older patient with multiple chronic conditions. Finally, we briefly discuss barriers to adoption of health and healthcare smartphone apps (e.g., cost, network bandwidth and battery power efficiency, usability, privacy issues, etc.), as well as some workarounds to mitigate those barriers.     

A Smartphone Ecological Momentary Assessment/Intervention “App” for Collecting Real-Time Data and Promoting Self-Awareness

We have designed a flexible ecological momentary assessment/intervention smartphone (EMA/EMI) “app”. We examine the utility of this app for collecting real-time data, and assessing intra-subject variability, by using it to assess how freshman undergraduates spend their time. We also explore whether its use can promote greater self-awareness. Participants were randomly divided into an experimental group, who used the app, and a control group, who did not. We used the app to collect both randomized in-the-moment data as well as end-of-day data to assess time use. Using a posttest survey we asked participants questions about how they spent time throughout the school semester. We also asked the experimental group about their experience with the app. Among other findings, 80.49% participants indicated that they became more aware of how they spent their time using the app. Corroborating this report, among the experimental group, end-of-semester self-assessment of time spent wasted, and time spent using electronics recreationally, predicted semester GPA at a strength comparable to high school GPA and ACT score (two of the best single predictors for first semester college GPA), but had no correlation among controls. We discuss the advantages and limitations of using apps, such as ours, for EMA and/or EMI.     

ISBP: Understanding the Security Rule of Users' Information-Sharing Behaviors in Partnership

The rapid growth of social network data has given rise to high security awareness among users, especially when they exchange and share their personal information. However, because users have different feelings about sharing their information, they are often puzzled about who their partners for exchanging information can be and what information they can share. Is it possible to assist users in forming a partnership network in which they can exchange and share information with little worry? We propose a modified information sharing behavior prediction (ISBP) model that can help in understanding the underlying rules by which users share their information with partners in light of three common aspects: what types of items users are likely to share, what characteristics of users make them likely to share information, and what features of users’ sharing behavior are easy to predict. This model is applied with machine learning techniques in WEKA to predict users’ decisions pertaining to information sharing behavior and form them into trustable partnership networks by learning their features. In the experiment section, by using two real-life datasets consisting of citizens’ sharing behavior, we identify the effect of highly sensitive requests on sharing behavior adjacent to individual variables: the younger participants’ partners are more difficult to predict than those of the older participants, whereas the partners of people who are not computer majors are easier to predict than those of people who are computer majors. Based on these findings, we believe that it is necessary and feasible to offer users personalized suggestions on information sharing decisions, and this is pioneering work that could benefit college researchers focusing on user-centric strategies and website owners who want to collect more user information without raising their privacy awareness or losing their trustworthiness.     

Evidence for the Identification of Carabayo,the Language of an Uncontacted People of the Colombian Amazon,as Belonging to the Tikuna-Yurí Linguistic Family

This paper provides evidence for the identification of the language of the uncontacted indigenous group called Carabayo, who live in voluntary isolation in the Colombian Amazon region. The only linguistic data available from this group is a set of about 50 words, most of them without reliable translations, that were collected in 1969 during a brief encounter with one Carabayo family. We compare this material with various languages (once) spoken in the region, showing that four attested Carabayo forms (a first person singular prefix and words for ‘warm’, ‘father’, and ‘boy’) display striking similarities with Yurí and at least 13 Carabayo forms display clear correspondences with contemporary Tikuna. Tikuna and Yurí are the only two known members of the Tikuna-Yurí linguistic family. Yurí was documented in the 19th century but has been thought to have become extinct since. We conclude that the Carabayo – directly or indirectly – descend from the Yurí people whose language and customs were described by explorers in the 19th century, before they took up voluntary isolation, escaping atrocities during the rubber boom in the early 20th century.     

Association between Electronic Cigarette Use and Asthma among High School Students in South Korea

Objectives

Little is known about health outcomes related to electronic cigarette (EC) use, despite its growing popularity. The aim of this study is to investigate the association between EC use and asthma.

Methods

The study design is a cross-sectional study. A total of 35,904 high school students were included as the final study population. The presence of asthma was based on a student’s self-reported doctor diagnosis of asthma in the past 12 months.

Results

Prevalence rates of asthmatics in ‘current EC users’ (n = 2,513), ‘former EC users’ (n = 2,078), and ‘never EC users’ (n = 31,313), were 3.9% (n = 98), 2.2% (n = 46) and 1.7% (n = 530), respectively. Comparing ‘current EC’ users with ‘never EC’ users, the unadjusted OR for asthma was 2.36 (95% CI: 1.89–2.94). In order to control for the effect of conventional cigarette (CC) smoking, after stratifying the subjects by the three CC smoking categories (never CC, former CC, and current CC), within the ‘never CC’ category, the unadjusted OR for asthma for ‘current EC’ users was 3.41 (95% CI: 1.79–6.49), and the adjusted OR was 2.74 (95% CI: 1.30–5.78). Severe asthma was reflected by the number of days absent from school due to asthma symptoms; current EC users had the highest adjusted OR for severe asthma compared to ‘never EC’ users.

Conclusions

When compared to a reference population of high school students in South Korea, EC users have an increased association with asthma and are more likely to have had days absent from school due to severe asthma symptoms. In conclusion, the results indicate that EC use may be a risk factor for asthma. The results may be useful in developing a scientific basis for the evaluation of a potential health hazard by EC.     

Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics

The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users’ behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012–2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news signal where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a “wisdom-of-the-crowd” effect that allows to exploit users’ activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment.     

Words as alleles: connecting language evolution with Bayesian learners to models of genetic drift

Scientists studying how languages change over time often make an analogy between biological and cultural evolution, with words or grammars behaving like traits subject to natural selection. Recent work has exploited this analogy by using models of biological evolution to explain the properties of languages and other cultural artefacts. However, the mechanisms of biological and cultural evolution are very different: biological traits are passed between generations by genes, while languages and concepts are transmitted through learning. Here we show that these different mechanisms can have the same results, demonstrating that the transmission of frequency distributions over variants of linguistic forms by Bayesian learners is equivalent to the Wright–Fisher model of genetic drift. This simple learning mechanism thus provides a justification for the use of models of genetic drift in studying language evolution. In addition to providing an explicit connection between biological and cultural evolution, this allows us to define a ‘neutral’ model that indicates how languages can change in the absence of selection at the level of linguistic variants. We demonstrate that this neutral model can account for three phenomena: the s-shaped curve of language change, the distribution of word frequencies, and the relationship between word frequencies and extinction rates.     

Apps can help bridge restoration science and restoration practice

Scientists need to find innovative ways to communicate their findings with restoration practitioners in an era of global change. Apps are a promising bridge between restoration science and practice because they apply broad scientific concepts to specific situations. For example, habitat connectivity promotes ecological function, but practitioners lack ways to incorporate connectivity into decision‐making. We created an app where users calculate how habitat restoration or loss affects connectivity. By providing our app as an example and discussing the benefits and challenges in creating apps for practitioners, we encourage other restoration ecologists to similarly create apps that bridge science with practice.     

The efficacy of smartphone‐based mental health interventions for depressive symptoms: a meta‐analysis of randomized controlled trials

The rapid advances and adoption of smartphone technology presents a novel opportunity for delivering mental health interventions on a population scale. Despite multi‐sector investment along with wide‐scale advertising and availability to the general population, the evidence supporting the use of smartphone apps in the treatment of depression has not been empirically evaluated. Thus, we conducted the first meta‐analysis of smartphone apps for depressive symptoms. An electronic database search in May 2017 identified 18 eligible randomized controlled trials of 22 smartphone apps, with outcome data from 3,414 participants. Depressive symptoms were reduced significantly more from smartphone apps than control conditions (g=0.38, 95% CI: 0.24‐0.52, p<0.001), with no evidence of publication bias. Smartphone interventions had a moderate positive effect in comparison to inactive controls (g=0.56, 95% CI: 0.38‐0.74), but only a small effect in comparison to active control conditions (g=0.22, 95% CI: 0.10‐0.33). Effects from smartphone‐only interventions were greater than from interventions which incorporated other human/computerized aspects along the smartphone component, although the difference was not statistically significant. The studies of cognitive training apps had a significantly smaller effect size on depression outcomes (p=0.004) than those of apps focusing on mental health. The use of mood monitoring softwares, or interventions based on cognitive behavioral therapy, or apps incorporating aspects of mindfulness training, did not affect significantly study effect sizes. Overall, these results indicate that smartphone devices are a promising self‐management tool for depression. Future research should aim to distil which aspects of these technologies produce beneficial effects, and for which populations.     

Speech rhythm: a metaphor?

Is speech rhythmic? In the absence of evidence for a traditional view that languages strive to coordinate either syllables or stress-feet with regular time intervals, we consider the alternative that languages exhibit contrastive rhythm subsisting merely in the alternation of stronger and weaker elements. This is initially plausible, particularly for languages with a steep ‘prominence gradient’, i.e. a large disparity between stronger and weaker elements; but we point out that alternation is poorly achieved even by a ‘stress-timed’ language such as English, and, historically, languages have conspicuously failed to adopt simple phonological remedies that would ensure alternation. Languages seem more concerned to allow ‘syntagmatic contrast’ between successive units and to use durational effects to support linguistic functions than to facilitate rhythm. Furthermore, some languages (e.g. Tamil, Korean) lack the lexical prominence which would most straightforwardly underpin prominence of alternation. We conclude that speech is not incontestibly rhythmic, and may even be antirhythmic. However, its linguistic structure and patterning allow the metaphorical extension of rhythm in varying degrees and in different ways depending on the language, and it is this analogical process which allows speech to be matched to external rhythms.     

The efficacy of app‐supported smartphone interventions for mental health problems: a meta‐analysis of randomized controlled trials

Although impressive progress has been made toward developing empirically‐supported psychological treatments, the reality remains that a significant proportion of people with mental health problems do not receive these treatments. Finding ways to reduce this treatment gap is crucial. Since app‐supported smartphone interventions are touted as a possible solution, access to up‐to‐date guidance around the evidence base and clinical utility of these interventions is needed. We conducted a meta‐analysis of 66 randomized controlled trials of app‐supported smartphone interventions for mental health problems. Smartphone interventions significantly outperformed control conditions in improving depressive (g=0.28, n=54) and generalized anxiety (g=0.30, n=39) symptoms, stress levels (g=0.35, n=27), quality of life (g=0.35, n=43), general psychiatric distress (g=0.40, n=12), social anxiety symptoms (g=0.58, n=6), and positive affect (g=0.44, n=6), with most effects being robust even after adjusting for various possible biasing factors (type of control condition, risk of bias rating). Smartphone interventions conferred no significant benefit over control conditions on panic symptoms (g=–0.05, n=3), post‐traumatic stress symptoms (g=0.18, n=4), and negative affect (g=–0.08, n=5). Studies that delivered a cognitive behavior therapy (CBT)‐based app and offered professional guidance and reminders to engage produced larger effects on multiple outcomes. Smartphone interventions did not differ significantly from active interventions (face‐to‐face, computerized treatment), although the number of studies was low (n≤13). The efficacy of app‐supported smartphone interventions for common mental health problems was thus confirmed. Although mental health apps are not intended to replace professional clinical services, the present findings highlight the potential of apps to serve as a cost‐effective, easily accessible, and low intensity intervention for those who cannot receive standard psychological treatment.     

Automatic Fall Detection System Based on the Combined Use of a Smartphone and a Smartwatch

Due to their widespread popularity, decreasing costs, built-in sensors, computing power and communication capabilities, Android-based personal devices are being seen as an appealing technology for the deployment of wearable fall detection systems. In contrast with previous solutions in the existing literature, which are based on the performance of a single element (a smartphone), this paper proposes and evaluates a fall detection system that benefits from the detection performed by two popular personal devices: a smartphone and a smartwatch (both provided with an embedded accelerometer and a gyroscope). In the proposed architecture, a specific application in each component permanently tracks and analyses the patient’s movements. Diverse fall detection algorithms (commonly employed in the literature) were implemented in the developed Android apps to discriminate falls from the conventional activities of daily living of the patient. As a novelty, a fall is only assumed to have occurred if it is simultaneously and independently detected by the two Android devices (which can interact via Bluetooth communication). The system was systematically evaluated in an experimental testbed with actual test subjects simulating a set of falls and conventional movements associated with activities of daily living. The tests were repeated by varying the detection algorithm as well as the pre-defined mobility patterns executed by the subjects (i.e., the typology of the falls and non-fall movements). The proposed system was compared with the cases where only one device (the smartphone or the smartwatch) is considered to recognize and discriminate the falls. The obtained results show that the joint use of the two detection devices clearly increases the system’s capability to avoid false alarms or ‘false positives’ (those conventional movements misidentified as falls) while maintaining the effectiveness of the detection decisions (that is to say, without increasing the ratio of ‘false negatives’ or actual falls that remain undetected).     

Open Data in Global Environmental Research: The Belmont Forum’s Open Data Survey

This paper presents the findings of the Belmont Forum’s survey on Open Data which targeted the global environmental research and data infrastructure community. It highlights users’ perceptions of the term “open data”, expectations of infrastructure functionalities, and barriers and enablers for the sharing of data. A wide range of good practice examples was pointed out by the respondents which demonstrates a substantial uptake of data sharing through e-infrastructures and a further need for enhancement and consolidation. Among all policy responses, funder policies seem to be the most important motivator. This supports the conclusion that stronger mandates will strengthen the case for data sharing.