Strengthening children's privacy literacy through contextual integrity

Researchers and policymakers advocate teaching children about digital privacy, but privacy literacy has not been theorized for children. Drawing on interviews with 30 families, including 40 children, we analyze children’s perspectives on password management in three contexts -family life, friendship, and education- and develop a new approach to privacy literacy grounded in Nissenbaum’s contextual integrity framework. Contextual integrity equates privacy with appropriate flows of information, and we show how children’s perceptions of the appropriateness of disclosing a password varied across contexts. We explain why privacy literacy should focus on norms rather than rules and discuss how adults can use learning moments to strengthen children’s privacy literacy. We argue that equipping children to make privacy-related decisions serves them better than instructing them to follow privacy-related rules

A low-cost mechanism to reconfigure the operating frequency band of a Vivaldi antenna for cognitive radio and spectrum monitoring applications

A frequency agile Vivaldi antenna whose operating frequency band can be switched between two selected bands is proposed in this paper for spectrum monitoring and cognitive radio applications. A RF switch is introduced into the back-slot of a Vivaldi antenna to allow switching of the operational band. The realised gains of the antenna are 10.5 dBi in the low band around 3.1 GHz, and 12 dBi in high band around 4.1 GHz. The radiation pattern is stable and its direction is consistent across the two bands. This design can be applied to multiple reconfigurable bands by using more RF switches to tune the desired operating frequency. A set of reliable design equations has been provided as well. This reconfigurable antenna offers improved gain and isolation over multiple, wideband and multiband antennas without increasing the cost and size when compared to those designs reported

Incident HIV during pregnancy and early postpartum period: a population-based cohort study in a rural area in KwaZulu-Natal, South Africa

BACKGROUND: The evidence on the effect of pregnancy on acquiring HIV is conflicting, with studies reporting both higher and lower HIV acquisition risk during pregnancy when prolonged antiretroviral therapy was accessible. The aim of this study was to assess the pregnancy effect on HIV acquisition where antiretroviral therapy was widely available in a high HIV prevalence setting. METHODS: This is a retrospective cohort study nested within a population-based surveillance to determine HIV incidence in HIV-uninfected women from 15 to 49 years from 2010 through 2015 in rural KwaZulu-Natal. HIV incidence per 100 person-years according to pregnancy status (not pregnant, pregnant, to eight weeks postpartum) were measured in 5260 HIV-uninfected women. Hazard ratios (HR) were estimated by Cox proportional hazards regression with pregnancy included as a time varying variable. RESULTS: Overall, pregnancy HIV incidence was 4.5 per 100 person-years (95% CI 3.4-5.8), higher than non-pregnancy (4.0; 95% CI 3.7-4.3) and postpartum incidences (4.2 per 100 person-years; 95% CI 2.3-7.6). However, adjusting for age, and demographic factors, pregnant women had a lower risk of acquiring HIV (HR 0.4; 95% CI 0.2-0.9, P = 0.032) than non-pregnant women; there were no differences between postpartum and non-pregnant women (HR 1.2; 95% CI 0.4-3.2; P = 0.744). In models adjusting for the interaction of age and gravidity, pregnant women under 25 years with two or more pregnancies had a 2.3 times greater risk of acquiring HIV than their older counterparts (95% CI 1.3-4.3; P = 0.008). CONCLUSIONS: Pregnancy had a protective effect on HIV acquisition. Elevated HIV incidence in younger women appeared to be driven by those with higher gravidity. The sexual and biological factors in younger women should be explored further in order to design appropriate HIV prevention interventions

Comparison between a novel liquid switch and a GaAs MMIC switch for reconfiguring the operating frequency of a Vivaldi antenna

This article proposes a novel liquid switch to reconfigure the operating frequency of a frequency-independent antenna. A Vivaldi antenna using a low-cost GaAs MMIC RF switch is used as a landmark to compare the measured results. Two prototypes are measured in an anechoic chamber and the results have been compared. The antennas operate in two modes: low-band mode at 3 GHz with 11 dBi of gain and high-band mode operating at 4.5 GHz with a measured gain of 10.8 dBi. The reconfigurable Vivaldi antenna proposed here presents high isolation between operating bands, a minimum of 12 dB, while maintaining high gain and stable radiation pattern which is suitable for cognitive radio applications

Causal Factors of Breeding Success and Frequency in Threatened Grassland Birds on the Ingula Nature Reserve, South Africa

The high-altitude grasslands covering the eastern escarpment of South Africa is one of the country’s most valuable habitats for biodiversity, livestock and water production. The habitat hosts several threatened bird species including endangered species such as the Rudd\u27s Lark (Heteromirafra ruddi) and Grey Crowned Crane (Balearica regulorum), and vulnerable species such as the Blue Crane (Grus paradisea), Wattled Crane (Bugeranus carunculatus), Southern Bald Ibis (Geronticus calvus), and Yellow-breasted Pipit (Anthus chloris). Avian research and monitoring have been ongoing within the recently declared Ingula Nature Reserve for more than 15 years as part of the activities of the Ingula Partnership - a partnership between BirdLife South Africa, Eskom Holdings SOC Ltd and the Middelpunt Wetland Trust - with the objective of effectively conserving birds and their habitat surrounding the Ingula Pumped Storage Scheme development. Avian monitoring on Ingula refocused in 2014 to confirm the presence of threatened species on site, followed by the determination of the breeding status of these species. An initiative was then launched to assess the breeding frequency and success of each identified species. Breeding monitoring for 13 out of the 24 occurring threatened species commenced in 2014 and was conducted for five consecutive seasons. Breeding success per season was measured in relation to the grassland management regime of that season (including both fire and grazing), as well as weather data, adjusting for dry and wet seasons. Results confirm that various grassland management regimes directly influenced the initiation of breeding activities and density of several of the species studied, while others’ breeding success and frequency were more dependent on macro-weather patterns (including climate change) and fire frequency and timing. These results have direct implications for the management of highland grasslands and associated species in the given region

Passive WiFi Radar for Human Sensing Using A Stand-Alone Access Point

Human sensing using WiFi signal transmissions is attracting significant attention for future applications in ehealthcare, security and the Internet of Things (IoT). The majority of WiFi sensing systems are based around processing of Channel State Information (CSI) data which originates from commodity WiFi Access Points (AP) that have been primed to transmit high data-rate signals with high repetition frequencies. However, in reality, WiFi APs do not transmit in such a continuous uninterrupted fashion, especially when there are no users on the communication network. To this end, we have developed a passive WiFi radar system for human sensing which exploits WiFi signals irrespective of whether the WiFi AP is transmitting continuous high data-rate OFDM signals, or periodic WiFi beacon signals whilst in an idle status (no users on the WiFi network). In a data transmission phase, we employ the standard cross ambiguity function (CAF) processing to extract Doppler information relating to the target, whilst a modified version is used for lower data-rate signals. In addition, we investigate the utility of an external device that has been developed to stimulate idle WiFi APs to transmit usable signals without requiring any type of user authentication on the WiFi network. In the paper we present experimental data which verifies our proposed methods for using any type of signal transmission from a stand-alone WiFi device, and demonstrate the capability for human activity sensing

Feature Selection of Post-Graduation Income of College Students in the United States

This study investigated the most important attributes of the 6-year post-graduation income of college graduates who used financial aid during their time at college in the United States. The latest data released by the United States Department of Education was used. Specifically, 1,429 cohorts of graduates from three years (2001, 2003, and 2005) were included in the data analysis. Three attribute selection methods, including filter methods, forward selection, and Genetic Algorithm, were applied to the attribute selection from 30 relevant attributes. Five groups of machine learning algorithms were applied to the dataset for classification using the best selected attribute subsets. Based on our findings, we discuss the role of neighborhood professional degree attainment, parental income, SAT scores, and family college education in post-graduation incomes and the implications for social stratification.Comment: 14 pages, 6 tables, 3 figure

Persuading consumers to reduce their consumption of electricity in the home

Previous work has identified that providing real time feedback or interventions to consumers can persuade consumers to change behaviour and reduce domestic electricity consumption. However, little work has investigated what exactly those feedback mechanisms should be. Most past work is based on an in-home display unit, possibly complemented by lower tariffs and delayed use of non-essential home appliances such as washing machines. In this paper we focus on four methods for real time feedback on domestic energy use, developed to gauge the impact on energy consumption in homes. Their feasibility had been tested using an experimental setup of 24 households collecting minute-by-minute electricity consumption data readings over a period of 18 months. Initial results are mixed, and point to the difficulties of sustaining a reduction in energy consumption, i.e. persuading consumers to change their behaviour. Some of the methods we used exploit small group social dynamics whereby people want to conform to social norms within groups they identify with. It may be that a variety of feedback mechanisms and interventions are needed in order to sustain user interest

SimHumalator: An Open Source End-to-End Radar Simulator For Human Activity Recognition

Radio-frequency based non-cooperative monitor ing of humans has numerous applications ranging from law enforcement to ubiquitous sensing applications such as ambient assisted living and bio-medical applications for non-intrusively monitoring patients. Large training datasets, almost unlimited memory capacity, and ever- increasing processing speeds of computers could drive forward the data- driven deep-learning focused research in the above applications. However, generating and labeling large volumes of high-quality, diverse radar datasets is an onerous task. Furthermore, unlike the fields of vision and image processing, the radar community has limited access to databases that contain large volumes of experimental data. Therefore, in this article, we present an open-source motion capture data-driven simulation tool, SimHumalator, that can generate large volumes of human micro-Doppler radar data in passive WiFi scenarios. The simulator integrates IEEE 802.11 WiFi standard(IEEE 802.11g, n, and ad) compliant transmissions with the human animation data to generate the micro-Doppler features that incorporate the diversity of human motion characteristics and the sensor parameters. The simulated signatures have been validated with experimental data gathered using an in-house-built hardware prototype. This article describes simulation methodology in detail and provides case studies on the feasibility of using simulated micro-Doppler spectrograms for data augmentation tasks

Augmenting Experimental Data with Simulations to Improve Activity Classification in Healthcare Monitoring

Human micro-Doppler signatures in most passive WiFi radar (PWR) scenarios are captured through real-world measurements using various hardware platforms. However, gathering large volumes of high quality and diverse real radar datasets has always been an expensive and laborious task. This work presents an open-source motion capture data-driven simulation tool SimHumalator that is able to generate human microDoppler radar data in PWR scenarios. We qualitatively compare the micro-Doppler signatures generated through SimHumalator with the measured real signatures. Here, we present the use of SimHumalator to simulate a set of human actions. We demonstrate that augmenting a measurement database with simulated data, using SimHumalator, results in an 8% improvement in classification accuracy. Our results suggest that simulation data can be used to augment experimental datasets of limited volume to address the cold-start problem typically encountered in radar research