From big smartphone data to worldwide research: The Mobile Data Challenge

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.

Towards End-User Driven Power Saving Control in Android devices

Abstract. During the last decade mobile communications increasingly became part of people's daily routine. Such usage raises new challenges regarding devices' battery lifetime management when using most popular wireless access technologies, such as IEEE 802.11. This paper investigates the energy/delay trade-off of using an end-user driven power saving approach, when compared with the standard IEEE 802.11 power saving algorithms. The assessment was conducted in a real testbed using an Android mobile phone and high-precision energy measurement hardware. The results show clear energy benefits of employing user-driven power saving techniques, when compared with other standard approaches.

Verloren im Patentdickicht oder Ein Smartphone ist kein Lastkahn

Unglaublich, aber wahr: Wir versuchen heutiges Hightech mit Patentgesetzen in den Griff zu bekommen, die aus dem 15. Jahrhundert stammen. Das kann nicht gutgehen. Ein kleiner historischer Abriss.

Crowdsourcing language change with smartphone applications

Crowdsourcing linguistic phenomena with smartphone applications is relatively new. In linguistics, apps have predominantly been developed to create pronunciation dictionaries, to train acoustic models, and to archive endangered languages. This paper presents the first account of how apps can be used to collect data suitable for documenting language change: we created an app, Dialäkt Äpp (DÄ), which predicts users’ dialects. For 16 linguistic variables, users select a dialectal variant from a drop-down menu. DÄ then geographically locates the user’s dialect by suggesting a list of communes where dialect variants most similar to their choices are used. Underlying this prediction are 16 maps from the historical Linguistic Atlas of German-speaking Switzerland, which documents the linguistic situation around 1950. Where users disagree with the prediction, they can indicate what they consider to be their dialect’s location. With this information, the 16 variables can be assessed for language change. Thanks to the playfulness of its functionality, DÄ has reached many users; our linguistic analyses are based on data from nearly 60,000 speakers. Results reveal a relative stability for phonetic variables, while lexical and morphological variables seem more prone to change. Crowdsourcing large amounts of dialect data with smartphone apps has the potential to complement existing data collection techniques and to provide evidence that traditional methods cannot, with normal resources, hope to gather. Nonetheless, it is important to emphasize a range of methodological caveats, including sparse knowledge of users’ linguistic backgrounds (users only indicate age, sex) and users’ self-declaration of their dialect. These are discussed and evaluated in detail here. Findings remain intriguing nevertheless: as a means of quality control, we report that traditional dialectological methods have revealed trends similar to those found by the app. This underlines the validity of the crowdsourcing method. We are presently extending DÄ architecture to other languages.

Documenting sound change with smartphone apps

Crowdsourcing linguistic phenomena with smartphone applications is relatively new. Apps have been used to train acoustic models for automatic speech recognition (de Vries et al. 2014) and to archive endangered languages (Iwaidja Inyaman Team 2012). Leemann and Kolly (2013) developed a free app for iOS—Dialäkt Äpp (DÄ) (>78k downloads)—to document language change in Swiss German. Here, we present results of sound change based on DÄ data. DÄ predicts the users’ dialects: for 16 variables, users select their dialectal variant. DÄ then tells users which dialect they speak. Underlying this prediction are maps from the Linguistic Atlas of German-speaking Switzerland (SDS, 1962-2003), which documents the linguistic situation around 1950. If predicted wrongly, users indicate their actual dialect. With this information, the 16 variables can be assessed for language change. Results revealed robustness of phonetic variables; lexical and morphological variables were more prone to change. Phonetic variables like to lift (variants: /lupfə, lʏpfə, lipfə/) revealed SDS agreement scores of nearly 85%, i.e., little sound change. Not all phonetic variables are equally robust: ladle (variants: /xælə, xællə, xæuə, xæɫə, xæɫɫə/) exhibited significant sound change. We will illustrate the results using maps that show details of the sound changes at hand.

GoCARB: Smartphone-App zur Kohlenhydratberechnung bei Typ-1-Diabetes

Smartphone-App zur Kohlenhydratberechnung Neue Technologien wie Blutzuckersensoren und moderne Insulinpumpen prägten die Therapie des Typ-1-Diabetes (T1D) in den letzten Jahren in wesentlichem Ausmaß. Smartphones sind aufgrund ihrer rasanten technischen Entwicklung eine weitere Plattform für Applikationen zur Therapieunterstützung bei T1D. GoCARB Hierbei handelt es sich um ein zur Kohlenhydratberechnung entwickeltes System für Personen mit T1D. Die Basis für Endanwender stellt ein Smartphone mit Kamera dar. Zur Berechnung werden 2 mit dem Smartphone aus verschiedenen Winkeln aufgenommene Fotografien einer auf einem Teller angerichteten Mahlzeit benötigt. Zusätzlich ist eine neben dem Teller platzierte Referenzkarte erforderlich. Die Grundlage für die Kohlenhydratberechnung ist ein Computer-Vision-gestütztes Programm, das die Mahlzeiten aufgrund ihrer Farbe und Textur erkennt. Das Volumen der Mahlzeit wird mit Hilfe eines dreidimensional errechneten Modells bestimmt. Durch das Erkennen der Art der Mahlzeiten sowie deren Volumen kann GoCARB den Kohlenhydratanteil unter Einbeziehung von Nährwerttabellen berechnen. Für die Entwicklung des Systems wurde eine Bilddatenbank von mehr als 5000 Mahlzeiten erstellt und genutzt. Resümee Das GoCARB-System befindet sich aktuell in klinischer Evaluierung und ist noch nicht für Patienten verfügbar.

How Preliminary Are Preliminary Decisions?

The present research focuses on the question of whether even a preliminary decision causes a confirmation bias in order to maintain the status quo and examines individual differences in consistency between the preliminary and final decision and biased information processing. Dispositional Need for Closure (NFC, Webster & Kruglanski, 1994) was expected to predict revision or maintenance of the preliminary decision (decision consistency) after additional information on the issue was searched for and evaluated. Participants higher on dispositional NFC were less likely to change their preliminary decision than participants lower on dispositional NFC. Furthermore, the effect of NFC on decision consistency was fully mediated by biased information evaluation following the preliminary decision.

Agent-based Content Retrieval for Opportunistic Content-Centric Networks

In this paper, we describe agent-based content retrieval for opportunistic networks, where requesters can delegate content retrieval to agents, which retrieve the content on their behalf. The approach has been implemented in CCNx, the open source CCN framework, and evaluated on Android smart phones. Evaluations have shown that the overhead of agent delegation is only noticeable for very small content. For content larger than 4MB, agent-based content retrieval can even result in a throughput increase of 20% compared to standard CCN download applications. The requester asks every probe interval for agents that have retrieved the desired content. Evaluations have shown that a probe interval of 30s delivers the best overall performance in our scenario because the number of transmitted notification messages can be decreased by up to 80% without significantly increasing the download time.

A web-based non-intrusive ambient system to measure and classify activities of daily living.

BACKGROUND The number of older adults in the global population is increasing. This demographic shift leads to an increasing prevalence of age-associated disorders, such as Alzheimer's disease and other types of dementia. With the progression of the disease, the risk for institutional care increases, which contrasts with the desire of most patients to stay in their home environment. Despite doctors' and caregivers' awareness of the patient's cognitive status, they are often uncertain about its consequences on activities of daily living (ADL). To provide effective care, they need to know how patients cope with ADL, in particular, the estimation of risks associated with the cognitive decline. The occurrence, performance, and duration of different ADL are important indicators of functional ability. The patient's ability to cope with these activities is traditionally assessed with questionnaires, which has disadvantages (eg, lack of reliability and sensitivity). Several groups have proposed sensor-based systems to recognize and quantify these activities in the patient's home. Combined with Web technology, these systems can inform caregivers about their patients in real-time (e.g., via smartphone). OBJECTIVE We hypothesize that a non-intrusive system, which does not use body-mounted sensors, video-based imaging, and microphone recordings would be better suited for use in dementia patients. Since it does not require patient's attention and compliance, such a system might be well accepted by patients. We present a passive, Web-based, non-intrusive, assistive technology system that recognizes and classifies ADL. METHODS The components of this novel assistive technology system were wireless sensors distributed in every room of the participant's home and a central computer unit (CCU). The environmental data were acquired for 20 days (per participant) and then stored and processed on the CCU. In consultation with medical experts, eight ADL were classified. RESULTS In this study, 10 healthy participants (6 women, 4 men; mean age 48.8 years; SD 20.0 years; age range 28-79 years) were included. For explorative purposes, one female Alzheimer patient (Montreal Cognitive Assessment score=23, Timed Up and Go=19.8 seconds, Trail Making Test A=84.3 seconds, Trail Making Test B=146 seconds) was measured in parallel with the healthy subjects. In total, 1317 ADL were performed by the participants, 1211 ADL were classified correctly, and 106 ADL were missed. This led to an overall sensitivity of 91.27% and a specificity of 92.52%. Each subject performed an average of 134.8 ADL (SD 75). CONCLUSIONS The non-intrusive wireless sensor system can acquire environmental data essential for the classification of activities of daily living. By analyzing retrieved data, it is possible to distinguish and assign data patterns to subjects' specific activities and to identify eight different activities in daily living. The Web-based technology allows the system to improve care and provides valuable information about the patient in real-time.

How need for cognition affects the processing of achievement-related information

The present article analyzed, how need for cognition (NFC) influences the formation of performance expectancies. When processing information, individuals with lower NFC often rely on salient information and shortcuts compared to individuals higher in NFC. We assume that these preferences of processing will also make individuals low in NFC more responsive to salient achievement-related cues because the processing of salient cues is cognitively less demanding than the processing of non-salient cues. Therefore, individuals lower in NFC should tend to draw wider ranging inferences from salient achievement-related information. In a sample of N = 197 secondary school students, achievement-related feedback (grade on an English examination) affected changes in expectancies in non-corresponding academic subjects (e.g., expectation of final grade in mathematics or history) when NFC was lower, whereas for students with higher NFC, changes in expectancies in non-corresponding academic subjects were not affected.

High-school students' need for cognition, self-control capacity, and school achievement: testing a mediation hypothesis

In the present article, we examine the hypothesis that high-school students' motivation to engage in cognitive endeavors (i.e., their need for cognition; NFC) is positively related to their dispositional self-control capacity. Furthermore, we test the prediction that the relation between NFC and school achievement is mediated by self-control capacity. A questionnaire study with grade ten high-school students (N = 604) revealed the expected relations between NFC, self-control capacity, and school achievement. Sobel tests showed that self-control capacity mediated the relation between NFC and school grades as well as grade retention.

University and school students' motivation for effortful thinking: Factor structure, reliability, and validity of the German Need for Cognition Scale

Need for cognition (NFC) reflects a relatively stable trait regarding the degree to which one enjoys and engages in cognitive endeavors. We examined whether the previously demonstrated one-dimensional structure of the German NFC Scale could be replicated in three samples of undergraduates and secondary school students. Moreover, we investigated the test-retest reliability of the German NFC Scale, which has not yet been tested. Further, we investigated whether the scale would be valid in a sample of secondary school students. Multigroup confirmatory factor analyses established the one-dimensional factor structure of the long form as well as the short form of the German NFC Scale for undergraduates (N = 559), students of academic track secondary schools (German Gymnasium; N = 555), and students of vocational track secondary schools (German Realschule; N = 486). The scale proved to have a high test-retest reliability in a university student sample (N = 43). For secondary school students, we again found a high test-retest reliability (N = 157), and also found the scale to be valid (N = 181).

Passionate thinkers feel better: Self-control capacity as mediator of the relationship between need for cognition and affective adjustment

The present study tested a possible explanation for the positive relationship between the motivation to engage in cognitive endeavors (need for cognition, NFC) and indicators of affective adjustment (e.g., higher self-esteem, lower depression) that has been demonstrated in previous studies. We suggest that dispositional self-control capacity mediates this relationship, since NFC has been found to be related to self-control capacity, and self-control capacity is crucial for adjustment. NFC, dispositional self-control capacity, self-esteem, habitual depressive mood, and tendency to respond in a socially desirable manner were measured among 150 university students via self-report. Regression analyses and Sobel tests revealed that self-control capacity was a potential mediator of the positive relationship between NFC and affective adjustment. The findings were robust in terms of social desirability.