Designing Privacy Policies with Users: A Human-Centered Approach

Users’ privacy concerns over their electronic data and how it is used across different digital platforms have grown in recent years. New regulations and policies (e.g., General Data Protection Regulation; GDRP) have been developed to grant users their rights to data transparency and intervenability. To that end, ex-post transparency tools have been offered to provide users with insights into how their data is used by business entities. Nonetheless, these tools do not consider individuals’ privacy concerns ex-ante technology design. While ex-post transparency tools attempt to address users’ privacy concerns, they remain limited in terms of users’ agency and autonomy, and thereby do not consider users’ voices. In contrast, ex-ante human-centered design processes would achieve that. Therefore, this research proposes a human-centered approach for designing data privacy policies with users rather than for users. To develop this approach, we primarily draw upon the human-centered design framework, commonly used in the field of Human-Computer Interaction (HCI). We compile and then use the design principles in the extant literature. The overarching objective of this approach is to understand users’ “privacy” needs and thus facilitate a mutual understanding of users’ priorities, values, and constraints. As such, co-designing data policies with users would give them agency and autonomy to actively participate in the design process. We hope that our proposed approach will allow for designing more effective privacy policies

Vehicle Pair Activity Classification Using QTC and Long Short Term Memory Neural Network

The automated recognition of vehicle interaction is crucial for self-driving, collision avoidance and security surveillance applications. In this paper, we present a novel Long-Short Term Memory Neural Network (LSTM) based method for vehicle trajectory classification. We use Qualitative Trajectory Calculus (QTC) to represent the relative motion between a pair of vehicles. The spatio-temporal features of the interacting vehicles are captured as a sequence of QTC states and then encoded using one hot vector representation. Then, we develop an LSTM network to classify QTC trajectories that represent vehicle pairwise activities. Most of the high performing LSTM models are manually designed and require expertise in hyperparameter configuration. We adapt Bayesian Optimisation method to find an optimal LSTM architecture for classifying QTC trajectories of vehicle interaction. We evaluated our method on three different datasets comprising 7257 trajectories of 9 unique vehicle activities in different traffic scenarios. We demonstrate that our proposed method outperforms the state-of-the-art techniques. Further, we evaluated our approach with a combined dataset of the three datasets and achieved an error rate of no more than 1.79%. Though, our work mainly focuses on vehicle trajectories, the proposed method is generic and can be used on pairwise analysis of other interacting objects

Vehicle Activity Recognition using Mapped QTC Trajectories

The automated analysis of interacting objects or vehicles has many uses, including autonomous driving and security surveillance. In this paper we present a novel method for vehicle activity recognition using Deep Convolutional Neural Network (DCNN). We use Qualitative Trajectory Calculus (QTC) to represent the relative motion between pair of vehicles, and encode their interactions as a trajectory of QTC states. We then use one-hot vectors to map the trajectory into 2D matrix which conserves the essential position information of each QTC state in the sequence. Specifically, we project QTC sequences into a two dimensional image texture, and subsequently our method adapt layers trained on the ImageNet dataset and transfer this knowledge to the activity recognition task. We have evaluated our method using two different datasets, and shown that it out-performs state-of-the-art methods, achieving an error rate of no more than 1.16%. Our motivation originates from an interest in automated analysis of vehicle movement for the collision avoidance application, and we present a dataset of vehicle-obstacle interaction, collected from simulator-based experiments

Towards Scene Understanding Implementing the Stixel World

In this paper, we present our work towards scene understanding based on modeling the scene prior to understanding its content. We describe the environment representation model used, the Stixel World, and its benefits for compact scene representation. We show our preliminary results of its application in a diverse environment and the limitations reached in our experiments using imaging systems. We argue that this method has been developed in an ideal scenario and does not generalise well to uncommon changes in the environment. We also found that this method is sensitive to the quality of the stereo rectification and the calibration of the optics, among other parameters, which makes it time-consuming and delicate to prepare in real-time applications. We think that pixel-wise semantic segmentation techniques can address some of the shortcomings of the concept presented in a theoretical discussion

Bank size and capital: A trade-off between risk-taking incentives and diversification

This paper analyzes the importance of size and capital for risk-taking incentives of Jordanian banks using panel data of 13 commercial banks for the period 2007–2017. The results reveal that size and capital add to stability, consistent with the economies of scale and scope hypothesis. In developing countries, banks are more conservative and less involved in market-based activities; however, they are interconnected just as in developed countries. The results of the first model and second model reveal that as size increases by 1 percent, risk decreases by 0.11 percent and 0.03 percent, respectively, implying that too-big-to-fail is not present and that moral hazard is not a serious issue. In both models, large size is driven by diversification not by risk-taking incentives. In terms of capital, the results of the first model and second model reveal that as capital increases by 1 percent, risk decreases by 0.48 and 0.12 percent, respectively. The fact that Jordanian banks are overcapitalized indicates that the central bank regulation is not binding. Banks increase their capital adequacy ratios to reduce risk. It is clear that there is economic benefit from increased size. However, the failures of large banks are systemic due to their interconnectedness. Therefore, regulators need to pay special attention to them in accordance with Basel III Accord

Analysing Fish Behaviours Using Three-Dimensional Qualitative Trajectory Calculus 2016

Fish swim freely in water and their 3D spatial interactions may carry important information for biological study. The two-dimensional Qualitative Trajectory Calculus (QTC2D), a spatiotemporal calculus, is a method for representing and reasoning about movements of objects in a qualitative framework. This emerging technique encodes the spatial geometric relationships between distinct objects, such as machines, humans or animals. This paper presents a method for generalising QTC2D into 3D space, called 3DQTC, as a work-in-progress. The 3DQTC method is based on a geometrical analysis method of estimating the 3D orientation of �fish. Initial results indicate the potential of representing and analysing the �fish behaviours in 3D space. 3DQTC is also demonstrated as a means of extracting useful information (e.g. follow and non-follow behaviours)which cannot be achieved by QTC2D. We conclude by discussing further work and development

Classification of breast lesions in ultrasound images using deep convolutional neural networks: transfer learning versus automatic architecture design

Deep convolutional neural networks (DCNNs) have demonstrated promising performance in classifying breast lesions in 2D ultrasound (US) images. Exiting approaches typically use pre-trained models based on architectures designed for natural images with transfer learning. Fewer attempts have been made to design customized architectures specifically for this purpose. This paper presents a comprehensive evaluation on transfer learning based solutions and automatically designed networks, analyzing the accuracy and robustness of different recognition models in three folds. First, we develop six different DCNN models (BNet, GNet, SqNet, DsNet, RsNet, IncReNet) based on transfer learning. Second, we adapt the Bayesian optimization method to optimize a CNN network (BONet) for classifying breast lesions. A retrospective dataset of 3034 US images collected from various hospitals is then used for evaluation. Extensive tests show that the BONet outperforms other models, exhibiting higher accuracy (83.33%), lower generalization gap (1.85%), shorter training time (66 min), and less model complexity (approximately 0.5 million weight parameters). We also compare the diagnostic performance of all models against that by three experienced radiologists. Finally, we explore the use of saliency maps to explain the classification decisions made by different models. Our investigation shows that saliency maps can assist in comprehending the classification decisions

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic