FutureWare: Designing a Middleware for Anticipatory Mobile Computing

Ubiquitous computing is moving from context-awareness to context-prediction. In order to build truly anticipatory systems developers have to deal with many challenges, from multimodal sensing to modeling context from sensed data, and, when necessary, coordinating multiple predictive models across devices. Novel expressive programming interfaces and paradigms are needed for this new class of mobile and ubiquitous applications. In this paper we present FutureWare, a middleware for seamless development of mobile applications that rely on context prediction. FutureWare exposes an expressive API to lift the burden of mobile sensing, individual and group behavior modeling, and future context querying, from an application developer. We implement FutureWare as an Android library, and through a scenario-based testing and a demo app we show that it represents an efficient way of supporting anticipatory applications, reducing the necessary coding effort by two orders of magnitude

Properties of latent interface-trap buildup in irradiated metal-oxide-semiconductor transistors determined by switched bias isothermal annealing experiments

Isothermal annealing experiments with switched gate bias have been performed to determine the properties of the latent interface-trap buildup during postirradiation annealing of metal-oxide-semiconductor transistors. It has been found that a bias-independent process occurs until the start of the latent interface-trap buildup. During the buildup itself, oxide-trap charge is not permanently neutralized, but is temporarily compensated. (C) 2000 American Institute of Physics. (DOI: 10.1063/1.1336159

Progmosis:evaluating risky individual behavior during epidemics using mobile network data

The possibility to analyze, quantify and forecast epidemic outbreaks is fundamental when devising effective disease containment strategies. Policy makers are faced with the intricate task of drafting realistically implementable policies that strike a balance between risk management and cost. Two major techniques policy makers have at their disposal are: epidemic modeling and contact tracing. Models are used to forecast the evolution of the epidemic both globally and regionally, while contact tracing is used to reconstruct the chain of people who have been potentially infected, so that they can be tested, isolated and treated immediately. However, both techniques might provide limited information, especially during an already advanced crisis when the need for action is urgent. In this paper we propose an alternative approach that goes beyond epidemic modeling and contact tracing, and leverages behavioral data generated by mobile carrier networks to evaluate contagion risk on a per-user basis. The individual risk represents the loss incurred by not isolating or treating a specific person, both in terms of how likely it is for this person to spread the disease as well as how many secondary infections it will cause. To this aim, we develop a model, named Progmosis, which quantifies this risk based on movement and regional aggregated statistics about infection rates. We develop and release an open-source tool that calculates this risk based on cellular network events. We simulate a realistic epidemic scenarios, based on an Ebola virus outbreak; we find that gradually restricting the mobility of a subset of individuals reduces the number of infected people after 30 days by 24%

My Phone and Me: Understanding People's Receptivity to Mobile Notifications

Notifications are extremely beneficial to users, but they often demand their attention at inappropriate moments. In this paper we present an in-situ study of mobile interruptibility focusing on the effect of cognitive and physical factors on the response time and the disruption perceived from a notification. Through a mixed method of automated smartphone logging and experience sampling we collected 10372 in-the-wild notifications and 474 questionnaire responses on notification perception from 20 users. We found that the response time and the perceived disruption from a notification can be influenced by its presentation, alert type, sender-recipient relationship as well as the type, completion level and complexity of the task in which the user is engaged. We found that even a notification that contains important or useful content can cause disruption. Finally, we observe the substantial role of the psychological traits of the individuals on the response time and the disruption perceived from a notification

Disease Containment Strategies based on Mobility and Information Dissemination

Human mobility and social structure are at the basis of disease spreading. Disease containment strategies are usually devised from coarse-grained assumptions about human mobility. Cellular networks data, however, provides finer-grained information, not only about how people move, but also about how they communicate. In this paper we analyze the behavior of a large number of individuals in Ivory Coast using cellular network data. We model mobility and communication between individuals by means of an interconnected multiplex structure where each node represents the population in a geographic area (i.e., a sous-pr\ue9fecture, a third-level administrative region). We present a model that describes how diseases circulate around the country as people move between regions. We extend the model with a concurrent process of relevant information spreading. This process corresponds to people disseminating disease prevention information, e.g., hygiene practices, vaccination campaign notices and other, within their social network. Thus, this process interferes with the epidemic. We then evaluate how restricting the mobility or using preventive information spreading process affects the epidemic. We find that restricting mobility does not delay the occurrence of an endemic state and that an information campaign might be an effective countermeasure

Sensitivity of Radfet for Gamma and X-Ray Doses Used in Medicine

In this paper, the results of radiation sensitive field effect transistors (Al-gate p-channel metal-oxide-semiconductor field effect transistors) sensitivity to gamma and X-ray irradiation are presented. Radiation fields were created using Co-60 source for three dose ranges (0-1 Gy, 0-5 Gy, and 0-50 Gy), as well as X-ray unit of 280 kVp spectrum for a single dose range from 0 to 5 Gy. The sensitivity was characterized by the threshold voltage shift, determined from reader circuit measurements, as a function of absorbed radiation dose. It was shown that for the three dose ranges of gamma radiation, as well as for the X-ray range from 0 Gy to 5 Gy there is approximately a linear dependence between threshold voltage shift Delta V-T and radiation dose D. The application of positive bias of +5 Vat the RADFET gate during irradiation, for these ranges of gamma radiation, also for X-ray dose range, leads to the increase in Delta V-T and also, approximately a linear dependence between Delta V-T and D, is established. Moreover, it was shown that the sensitivity of RADFET is much higher in the case of X-ray irradiation then in the case of gamma-ray irradiation for the same dose range

Changes in hospital treatment of children and adolescents with mental health problems

This is the peer-reviewed version of the article: Pejovic-Milovancevic, M.; Grujicic, R.; Stojkovic, A.; Radosavljev-Kircanski, J. Changes in Hospital Treatment of Children and Adolescents with Mental Health Problems. Gen. Hosp. Psychiatry 2020, 64, 108–109. [https://doi.org/10.1016/j.genhosppsych.2019.05.003

Natural language processing for aviation safety: Extracting knowledge from publicly-available loss of separation reports

Background: The air traffic management (ATM) system has historically coped with a global increase in traffic demand ultimately leading to increased operational complexity. When dealing with the impact of this increasing complexity on system safety it is crucial to automatically analyse the losses of separation (LoSs) using tools able to extract meaningful and actionable information from safety reports. Current research in this field mainly exploits natural language processing (NLP) to categorise the reports,with the limitations that the considered categories need to be manually annotated by experts and that general taxonomies are seldom exploited. Methods: To address the current gaps,authors propose to perform exploratory data analysis on safety reports combining state-of-the-art techniques like topic modelling and clustering and then to develop an algorithm able to extract the Toolkit for ATM Occurrence Investigation (TOKAI) taxonomy factors from the free-text safety reports based on syntactic analysis. TOKAI is a tool for investigation developed by EUROCONTROL and its taxonomy is intended to become a standard and harmonised approach to future investigations. Results: Leveraging on the LoS events reported in the public databases of the Comisión de Estudio y Análisis de Notificaciones de Incidentes de Tránsito Aéreo and the United Kingdom Airprox Board,authors show how their proposal is able to automatically extract meaningful and actionable information from safety reports,other than to classify their content according to the TOKAI taxonomy. The quality of the approach is also indirectly validated by checking the connection between the identified factors and the main contributor of the incidents. Conclusions: Authors' results are a promising first step toward the full automation of a general analysis of LoS reports supported by results on real-world data coming from two different sources. In the future,authors' proposal could be extended to other taxonomies or tailored to identify factors to be included in the safety taxonomies