Miracle-dz/UniFAR-FTUs huggingface dataset:
Socioeconomic and Geographic Disparities in Emergency Department Visits due to Alcohol in Ontario: A Retrospective Population-level Study from 2003 to 2017

Objective: While the overall health system burden of alcohol is large and increasing in Canada, little is known about how this burden differs by sociodemographic factors. The objectives of this study were to assess sociodemographic patterns and temporal trends in emergency department (ED) visits due to alcohol to identify emerging and at-risk subgroups. Methods: We conducted a retrospective population-level cohort study of all individuals aged 10 to 105 living in Ontario, Canada. We identified ED visits due to alcohol between 2003 and 2017 using defined International Classification of Diseases, 10th edition, codes from a pre-existing indicator. We calculated annual age- and sex-standardized, and age- and sex-specific rates of ED visits and compared overall patterns and changes over time between urban and rural settings and income quintiles. Results: There were 829,662 ED visits due to alcohol over 15 years. Rates of ED visits due to alcohol were greater for individual living in the lowest- compared to the highest-income quintile neighbourhoods, and disparities (rate ratio lowest to highest quintile) increased with age from 1.22 (95% CI, 1.19 to 1.25) in 15- to 18-year-olds to 4.17 (95% CI, 4.07 to 4.28) in 55- to 59-year-olds. Rates of ED visits due to alcohol were significantly greater in rural settings (56.0 per 10,000 individuals, 95% CI, 55.7 to 56.4) compared to urban settings (44.8 per 10,000 individuals, 95% CI, 44.7 to 44.9), particularly for young adults. Increases in rates of visits between 2003 and 2017 were greater in rural versus urban settings (82 vs. 68% increase in age- and sex-standardized rates) and varied across sociodemographic subgroups with the largest annual increases in rates of visits in young (15 to 29) low-income women (6.9%, 95%CI, 6.7 to 7.3) and the smallest increase in older (45 to 59) high-income men (2.7, 95%CI, 2.4 to 3.0). Conclusion: Alcohol harms display unique patterns with the highest burden in rural and lower-income populations. Rural–urban and income-based disparities differ by age and sex and have increased over time, which offers an imperative and opportunity for further interventions by clinicians and policy makers.
The application of the SVSF into maneuvering system using FPGA

In this paper, the Smooth Variable Structure Filter is used to extract the position and speed of a vehicle on a complex road. The filter is realized using FPGA (field programmer gate array). The FPGA’s resources and speed are examined at an optimal configuration. FPGA (Field Programmer Gate Array) Z-board from Xilinx is used in this work. The performance is examined and presented.
Improving weighted Hausdorff distance loss for small object centroid detection in infra-red and RGB imagery

Detection of moving objects at very long distances using infrared sensors is a challenging problem due to the small object size and heavy background clutter. To mitigate these problems, we propose to employ a convolutional neural network (CNN) with mean squared error (MSE) loss and show that this network detects the small objects with fewer false alarm rate than frame differencing methods. Furthermore, we modify a U-net architecture (introduced in1 ) and use both a weighted Hausdorff distance (WHD) loss and MSE loss which jointly achieve higher recall and lower false alarm rate. We compare our proposed method with state-of-the-art methods on a publicly available dataset of infrared images from Night Vision and Electronic Sensors directorate (NVESD) for the detection of small moving targets. We also show the effectiveness of our loss function on Mall dataset reported in.1 Our method achieves 5% and 2% more recall on the NVESD and Mall datasets, respectively. Furthermore, our method also achieves 0.3 per frame and 1 per frame fewer false alarm rate on these datasets, respectively.
Coincidence technique to study ion-induced electron emission from atomically thin materials

We introduce our multi-coincidence setup used to study the interaction of highly charged ions with atomically thin materials. Ions are transmitted through freestanding two-dimensional materials and detected charge separated on a position sensitive microchannel plate detector with delay line anode. Electrons emitted upon the interaction of the projectile with the target can be detected either using a silicon surface barrier detector or a hemispherical analyser in order to gain information on the number of emitted electrons and their energy distribution, respectively. All data is stored in a listmode file allowing to set filters (e.g., time of flight, charge states) post measurement to identify the origin of correlated ion-electron pairs, i.e. we can determine whether detected ions were transmitted solely through the two-dimensional material layer or through its support structure. Using this coincidence technique we can correlate specific charge exchange and ion stopping channels (i.e. energy deposition) with particular electron emission scenarios (yield and energy distribution).
Energy Consumption and Economic Growth Linkage: Global Evidence from Symmetric and Asymmetric Simulations

Abstract The literature reveals that linear models do not accurately represent the asymmetric relationship between economic growth and energy consumption (EC). To fill this gap, we examined the asymmetric relationship between EC and economic growth in a non-linear panel autoregressive distributed lag (ARDL) framework of 85 countries as a whole sample and of Brazil, Russia, India, China, and South Africa (BRICS), the Next Eleven, Big Four in Western Europe, Asia-Pacific region, Group of Seven, South Asian Association for Regional Cooperation (SAARC), Economic Cooperation Organization (ECO) and the Arab League as a sub-sample analysis from 1977 to 2014. A second generational unit root test has been applied to check the problem of cross-sectional dependency. Asymmetric co-integration was employed to analyse the co-integration between the variables of interest. Long-run and short-run estimates have been calculated using the non-linear panel ARDL method. Results indicate that positive shocks to energy use tend to have a growth-enhancing effect in ECO and the Next Eleven while in the rest of the regions, the effect is growth contraction. Moreover, economic recovery from a positive shock to energy use is the case in the Arab League, Asia-Pacific region, Group of Seven and in the whole sample. However, a negative shock to EC is observed in the Group of Seven, Asia Pacific region, Big Four in Western Europe and ECO, and the whole sample worsens the economic contraction. We can deduct from this study’s results that information on the asymmetric relationship between the subject variables is needed to design sound economic policy decisions across different economic regions.
Pre-schoolers’ Stewardship – Embracing Higgledy-piggledy Behaviours through Participatory Plaything

Child-centred design acknowledges the significance of a child's input during collaborative design processes, allowing the potential residing in their imagination and experiences to come forth. However, when younger pre-schoolers —3–6-year-olds—are involved in participatory research, it is difficult for design researchers to connect diverse children towards collaborating on fruitful design outcomes while accepting pre-schoolers’ own-initiated play expressions. This paper argues that by incorporating enjoyment of construction play, pre-schoolers can provide distinctive insights concerning key co-design themes: participation, design activities and material exploration. This study proposes a plaything with intent methodology that scaffolds serious exploratory design process embracing the pre-schoolers’ unpredictable higgledy-piggledy behaviour. Results demonstrate that this methodology enables the empowerment of pre-schoolers as self-reliant stewards while (a) capturing their voice of self-expression effectively, and (b) achieving a self-determined specific design concept. As further outcome, a dialogue tool that enables reflexivity concerning participant experiences within participatory inquiries was produced.
Fork-based user migration in Blockchain Online Social Media

Nowadays, Online Social Media (OSM) are among the most popular web services. Traditional OSM are known to be affected by serious issues including misinformation, fake news, censorship, and privacy violations, to the point that a pressing demand for new paradigms is raised by users all over the world. Among such paradigms, the concepts around the Web 3.0 are fueling a new revolution of online sociality, pushing towards the adoption of innovative and groundbreaking technologies. In particular, the decentralization of social services through the blockchain technology is representing the most valid alternative to current OSM, enabling the development of rewarding strategies for value redistribution, and fake news detection. However, the so-called Blockchain Online Social Media (BOSMs) are far from being mature, with different platforms that continually try to redefine their services in order to attract larger audiences, thus causing blockchain forks and massive user migrations, with the latter dominating the dynamics of the current OSM landscape, too. In this paper, we deal with the evolution of BOSMs from the perspective of user migration across platforms as a consequence of a fork event. We propose a general user migration model applicable to BOSMs to represent the evolution patterns of fork-based migrations, the multi-interaction structural complexity of BOSMs, and their growth characteristics. Within this framework, we also cope with the task of predicting how users will behave in the case of a fork, i.e. they will remain on the original blockchain or they will migrate to the new one. We apply our framework to the case study of the Steem-Hive fork event, and show the importance of considering both social and economic information, regardless of the learning algorithm considered. To the best of our knowledge, this is the first study on blockchain fork and its related user migration.
Note: Using Causality to Mine Sjögren’s Syndrome related Factors from Medical Literature

Research articles published in medical journals often present findings from causal experiments. In this paper, we use this intuition to build a model that leverages causal relations expressed in text to unearth factors related to Sjögren’s syndrome. Sjögren’s syndrome is an auto-immune disease affecting up to 3.1 million Americans. The uncommon nature of the disease, coupled with common symptoms with other autoimmune conditions make the timely diagnosis of this disease very hard. A centralized information system with easy access to common and uncommon factors related to Sjögren’s syndrome may alleviate the problem. We use automatically extracted causal relationships from text related to Sjögren’s syndrome collected from the medical literature to identify a set of factors, such as “signs and symptoms” and “associated conditions”, related to this disease. We show that our approach is capable of retrieving such factors with a high precision and recall values. Comparative experiments show that this approach leads to 25% improvement in retrieval F1-score compared to several state-of-the-art biomedical models, including BioBERT and Gram-CNN.
Exploring Failures in Child-Computer Interaction.: Shifting the paradigm from “failures need to be hidden” to “failure is a learning opportunity”.

Failures are not reported enough in the literature as the pressure of publishing successful stories has moved us away from considering that even unsuccessful stories can be equally relevant to increase knowledge in research. Analyzing failures can be a great learning opportunity for the community of researchers. In this paper, we present the outcomes of a research that investigated the challenges experienced when designing and running studies with children. The study began with a literature review which led to an inquiry focused on exploring failures within the Child-Computer Interaction community by interviewing 14 researchers. Interviews were analyzed in the vein of the thematic analysis approach. We deliver three themes (unpredictability, technology targeted at adults, children willing to achieve the goal), three action points (the context, the technology, and the activity) and a paradigm shift from “failures need to be hidden” to “failure is a learning opportunity”.
Reliable Energy Consumption Modeling for an Electric Vehicle Fleet

Accurately predicting the energy consumption of an electric vehicle (EV) under real-world circumstances (such as varying road, traffic, weather conditions, etc.) is critical for a number of decisions like range estimation and route planning. A major concern for electric vehicle owners is the uncertain nature of the battery consumption. This results in the “range anxiety” and reluctance from users for mass adoption of EVs, since they are concerned about untimely drainage of battery. Even at the organizational level, a company running a fleet of electric vehicles must understand the battery consumption profiles accurately for tasks such as route and driver planning, battery sizing, maintenance planning, etc. In this paper, firstly, we highlight the challenges in modelling energy consumption and demonstrate the nature of data which is required to understand the energy consumption of electric vehicles under real-world conditions. Then, through a large and diverse dataset collected over 23,500 hours spanning ≈ 460,000 km with 27 vehicles, we demonstrate our two-stage approach to predict the energy consumption of an EV before the start of the trip. In our energy consumption modelling approach, apart from the primary features recorded directly before the trip, we also construct and predict secondary features through an extensive feature engineering process, both of which are then used to predict the energy consumption. We show that our approach outperforms Deep Learning based modelling for EV energy consumption prediction, and also provides explainable and interpretable models for domain experts. This novel method results in energy consumption modelling with of Mean Absolute Percentage Error (MAPE) on our dataset and significantly outperforms state-of-the-art results in EV energy consumption modeling.
KOALA Hero: Inform Children of Privacy Risks of Mobile Apps

Children’s online activities are routinely tracked, aggregated, and exploited by online services, to manipulate children’s online behaviour or monetise. This contributes to the so-called datafied childhood. Unfortunately, such datafication remains largely invisible behind the services and is practically impossible to avoid. Existing approaches largely focus on direct online harms, and provide limited support to raise children’s awareness or understanding of how their data may be processed, transmitted across platforms, and used to affect their best interests. Through co-design workshops, we identified key barriers for children and families to cope with this type of data privacy risk. Our contribution is that instead of regarding children as passive users and needing protection, we draw on critical digital literacy theories and design a KOALA Hero app, which is aimed to enhance children’s cognitive, situated and critical thinking of datafication and online data privacy risks. KOALA Hero represents our first step towards facilitating children’s understanding of the invisible data privacy risks. We hope future empirical evaluations will further inform us regarding how our design approaches may affect the thinking process and behaviours of children and families.
Uncovering Children's Situated Design Capital – A Nexus Analytic Inquiry

Nurturing children's competences needed for their digital futures and inviting them to adopt a protagonist role within design process have recently been emphasized in child-computer interaction research. For children to be able to act as design protagonists, they need design capital. We carried out a project with 13-14-year-olds and inquired under what circumstances the situated design capital of children emerged, enabling them to act as design protagonists without us deliberately steering them towards that. By employing the theoretical lens of nexus analysis, several factors were discovered that mediated children to utilize their situated design capital, including distributed agency, positive peer pressure, peer learning, and identity positioning. The findings imply that children's situated capital emanates from interactional phenomena, within which historical trajectories of the place, children, discourses, ideas, and objects intermingle.
Application of Hybrid PSO and SQP Algorithm in Optimization of the Retardance of Citrate Coated Ferrofluids

The citrate (citric acid, CA) coated ferrofluids with great magneto-optical retardance can meet the high magnetic responsive demand, especially in widely potential biomedical applications such as hyperthermia and magnetic resonance imaging. In this study, the measured retardances are based on the Taguchi method with nine tests for four parameters, including pH of suspension, molar ratio of CA to Fe3O4, CA volume, and coating temperature. The retardance obtained from the double centrifugation test is also included. Three optimization algorithms including the particle swarm optimization (PSO), the sequential quadratic programming (SQP), and a hybrid PSO-SQP algorithm are executed to obtain high retardance. The comparisons are made among the retardance results obtained from these algorithms. Seven start points chosen from the orthogonal test are input into the SQP, the PSO is applied to the stepwise regression equation, and while executing the hybrid PSO-SQP algorithm, the parametric combination obtained by the PSO is adopted as the start point in the SQP simulation. The global optimum retardance and the corresponding parameter values are effectively assured by the global search ability of the PSO and the local search ability of the SQP.
Social Norms on Reddit: A Demographic Analysis

Social norms, the shared informal rules of acceptable behavior, drive and reflect the evolution of societies. As an increasingly large part of social interactions happens online, social media data offers an unprecedented opportunity to assess the perception of social norm boundaries in-the-wild. In this regard, Reddit’s r/AITA represents an invaluable source of codified social norms. This subreddit is an online forum where individuals describe how they acted in a specific situation in the past, and ask for the feedback of the community about whether their behavior was deviant or socially acceptable. Other users in the community share their views and express a judgment codified by a tag. This study focuses on assessing which factors are associated with judgements expressed by the community. Specifically, we investigate two main factors: the demographics of the author of the submission and the topic of the submission. Our analysis shows a clear gender imbalance in the judgements, with submissions by male authors receiving negative judgements with a 62% higher likelihood. Older authors (≥ 28) also have a higher chance of receiving negative judgements (+21%). Regarding topics, submissions about romantic relationships and work tend to be judged more positively (+69% and +70%, respectively), thus hinting towards a role of the community as a support group, especially for female participants. We then focus on controversial submissions which garner heterogeneous judgements. We find that these submissions are clearly separable from those ones that are unanimously judged, and that male and older (≥ 28) authors are more likely to describe controversial situations that split the community (+26% and +22%, respectively). Finally, we focus on the characteristics of the evaluators. We find that their judgements are associated with the other communities they belong to (signifying other interests and experiences), with an effect size comparable to the demographic group of the author. By combining all these variables—demographics of the author and communities of the evaluator—we are able to build a classifier that predicts a deviance judgement on a submission with AUC = 0.85.
Socio-Economic Diversity in Human Annotations

Human annotations can help indexing digital resources as well as improving search and recommendation systems. Human annotators may carry their bias and stereotypes in the labels they create when annotating digital content. These are then reflected in machine learning models trained with such data. The result is a reinforcement loop where end-users are pushed stereotypical content by the search and recommendation systems they use on a daily basis. In order to break the loop, the impact on models of using diverse data that can better represent a diverse population has been looked at. In this work, we look at how human annotators in the US annotate digital content different from content which is popular on the Web and social media. We present the results of a controlled user study in which participants are asked to annotate videos of common tasks performed by people from various socio-economic backgrounds around the world. We test for the presence of social stereotypes and investigate the diversity of the provided annotations, especially since some abstract labels may reveal information about annotators’ emotional state and judgment. We observe different types of annotations for content from different socio-economic levels. Furthermore, we find regional and income level biases in annotation sentiment.
Signing-on-the-Fly: Technology Preferences to Reduce Communication Gap between Hearing Parents and Deaf Children

Over 90 percent of Deaf and Hard of Hearing (DHH) children in the United States are born to hearing parents, who have little to no command of American Sign Language (ASL). This leaves the majority of DHH children at risk of language deprivation in early childhood. This study investigates the design space of Augmented Reality (AR) and wearable technologies in supporting hearing parents to offer sign language environments for young DHH children. We conducted an online survey with 65 participants (hearing/DHH parents and teachers of DHH children aged 6 months to 5 years) to gather preferences and interests of technologies that support hearing parents to deliver ASL on-the-fly, and stay attentive to the DHH child’s visual attention during joint toy play. We found that Near-Object Projection is most preferred for real-time ASL delivery, and haptic feedback is most preferred for raising the parent’s awareness of a child’s attention. Results also show a strong interest in using the proposed technologies in interacting with and maintaining joint attention with DHH children on a daily basis. We discuss key design recommendations that inform the design of future technologies that support just-in-time and contextual-aware communication in ASL, with minimal obtrusion to face-to-face interaction.
The effects of greenfield foreign direct investment and cross‐border mergers and acquisitions on energy intensity in upper-middle income countries and low- and lower-middle income countries

ABSTRACT This study examines the effects of greenfield foreign direct investment (FDI) and cross‐border mergers and acquisitions (M&As) on energy intensity for a sample of 35 upper-middle-income countries (UMICs) and a sample of 49 low- and lower-middle-income countries (LLMICs). It is the first to examine the energy intensity effects of greenfield FDI and cross‐border M&As for subgroups of developing countries with different levels of income. It is also the first study of its kind to use stationary and non-stationary panel methods. The main findings are: first, both greenfield FDI and cross-border M&As have an insignificant impact on energy intensity in LLMICs, and second, while the effect of cross-border M&As on energy intensity is insignificant in UMICs, greenfield FDI exerts a negative and significant impact on energy intensity in UMICs.