The COVID-19 pandemic required clear communication of risk and community engagement. A gap is noted in scholarly studies portraying strong community engagement for risk handling, particularly in resource-constrained regions in the HCI community. This study covers community engagement and its use of technology during COVID-19 through a qualitative study of Bangladesh. The study looks at marginalized communities who have struggled through the pandemic yet handled the difficult time through their effective problem solving, working together as a community when there was not enough support from authorities. It is a qualitative study during the pandemic consisting of nine communities, including 58 participants ( N = 58, Female = 33, Male = 23, Transgender = 2) across four divisions of Bangladesh covering urban, semi-urban, and rural regions. The study uncovers the challenges and close community structures. It also shows the enhanced and increased positive role of technology during the pandemic while also pointing out that a few communities were digitally disconnected and could benefit from digital connectivity in the future through increased awareness and support.

A key question in e-participation is what roles public officials can play to harness the benefits of emerging technologies and practices, mitigate potential harms, and, ultimately, ensure more inclusive and effective public involvement in decision making. This article presents results from a desktop analysis of e-participation projects from the African continent to highlight the diversity of public official roles and associated skills and perspectives that would be relevant to e-participation implementation. The identified roles and activities range from legal specialists developing guidelines to comply with personal data protection legislation and stakeholder managers designing models of collaboration with commons-based platforms to communications officials learning how to moderate social media conversations and technology developers exploring new ways of verifying online identity.

Human–vehicle interaction is an important factor for safe driving. The driver needs to interact with the in-vehicle steering wheel and infotainment system properly during driving. Specifically, driving guidelines require the driver to hold the steering wheel at the 3 o’clock and 9 o’clock positions. Moreover, the in-vehicle infotainment system should be more adaptive for the driver and front-seat passenger during driving (i.e., the in-vehicle infotainment system should be part and even fully disabled for the driver, whereas the front-seat passenger should be able to enjoy the full in-vehicle infotainment system). However, affordable vehicles are usually designed to achieve basic driving functions without considering safe human–vehicle interactions, which require an add-on, affordable, and ready-to-use human–vehicle interaction monitoring system. In this article, we present RFDrive, a system that can simultaneously locate the driver’s hand positions on the steering wheel and automate in-vehicle infotainment system touch discrimination for safe driving using commodity passive RFID tags. Since these commodity passive RFID tags are low cost (i.e., around 5 cents per tag), battery free, and are small, like a sticker, our design will enable not only safe driving but is also low cost, which can lead to sustainable solutions. To do so, we attach RFID tags on the steering wheel for the driver’s hand position location and attach RFID tags on the roof of the vehicle’s interior for in-vehicle infotainment system touch discrimination (i.e., differentiating the driver’s infotainment system touch and front-seat passenger’s infotainment system touch). However, the wheel steering will distort the wireless channel-based driver’s hand position location on the steering wheel. Thus, we propose a novel tag ID-based algorithm to locate the driver’s hand position on the steering wheel by harnessing the human body as part of the RFID tag’s antenna. Since the in-vehicle infotainment system touch from the driver or front-seat passenger will affect different RFID tags attached to the roof of the vehicle’s interior, we propose to use the differential amplitude of backscattered signals from all the tags to discriminate in-vehicle infotainment system touch sources. Our experiments show that RFDrive can achieve the average accuracy of 0.98 and 0.98 for in-vehicle touch source discrimination and driver’s hand position location, respectively.

Driving stress significantly impacts driving behavior primarily from roadside factors, where driving is more challenging in developing countries (i.e., Bangladesh) for unique cultural and infrastructural setups. We conduct an exploratory study (Qualitative n = 26, and Subjective Feedback n = 80) and a correlational analysis involving professional and private car drivers in urban Bangladesh. The study reveals drivers' demography and driving stress factors on the road. These findings motivate us to identify driving stress from physiological factors by developing a low-cost wearable, Stress Wear . This can detect stress from varying Heart Rates, validated by expensive commercial wearables. Between subject experiments on drivers (total n = 14 in two phases) with wearables, we also found that road factors are responsible for driving stress. Therefore, the developed system is helpful for these drivers to self-sense their stress.

The negative impact humans have on the environment is partly caused by thoughtless consumption leading to unnecessary waste. A likely contributing factor is the relative invisibility of waste: waste produced by individuals is either out of their sight or quickly taken away. Nevertheless, waste disposal systems sometimes break down, creating natural information displays of waste production that can have educational value. We take inspiration from such natural displays and introduce a class of situated visualizations we call augmented reality waste accumulation visualizations , or ARwav s, which are literal representations of waste data embedded in users’ familiar environment. We implemented examples of ARwav s and demonstrated them in feedback sessions with experts in pro-environmental behavior, and during a large tech exhibition event. We discuss general design considerations for ARwav s. Finally, we conducted a study with 20 participants suggesting that ARwav s yield stronger emotional responses than nonimmersive waste accumulation visualizations and plain numbers.

Social media offers increasingly diverse mechanisms for the distribution of motivated information, with multiple propaganda communities exhibiting overlaps with respect to user, content, and network characteristics. This has particularly been an issue in the Global South, where recent work has shown various forms of strife related to polarizing speech online. It has also emerged that propagandist information, including fringe positions on issues, can find its way into the mainstream when sufficiently reinforced in tone and frequency, some of which often requires sophisticated organizing and information manipulation. In this study, we analyze the overlap between three events with varying degrees of propagandist messaging by analyzing the content and network characteristics of users leading to overlap between their users and discourse. We find that a significant fraction of users leading to overlap between the three event communities are influential in information spread across the three event networks, and political leaning is one of the factors that helps explain what brings the communities together. Our work sheds light on the importance of network characteristics of users, which can prove to be instrumental in establishing the role of political leaning on overlap between multiple propaganda communities.

Developing countries need to better manage fast increasing traffic flows, owing to rapid urbanization. Else, increasing traffic congestion would increase fatalities due to reckless driving, as well as keep vehicular emissions and air pollution critically high in cities like New Delhi. State-of-the-art traffic signal control methods in developed countries, however, use expensive sensing, computation, and communication resources. How far can control algorithms go, under resource constraints, is explored through the design and evaluation of FrugalLight (FL) in this article. We also captured and processed a real traffic dataset at a busy intersection in New Delhi, India, using efficient techniques on low cost embedded devices. This dataset ( https://delhi-trafficdensity-dataset.github.io ) contains traffic density information at fine time granularity of one measurement every second, from all approaches of the intersection for 40 days. FrugalLight ( https://github.com/sachin-iitd/FrugalLight ) is evaluated on the collected traffic dataset from New Delhi and another open source traffic dataset from New York. FrugalLight matches the performance of state-of-the-art Convolutional Neural Network (CNN) based sensing and Deep Reinforcement Learning (DRL) based control algorithms, while utilizing resources less by an order of magnitude. We further explore improvements using a careful combination of knowledge distillation and domain knowledge based DRL model compression, with employing Model-Agnostic Meta-Learning to quickly adapt to traffic at new intersections. The collected real dataset and FrugalLight therefore opens up opportunities for resource efficient RL based intersection control design for the ML research community, where the controller should have limited carbon footprint. Such intelligent, green, intersection controllers can help reduce traffic congestion and associated vehicular emissions, even if compute and communication infrastructure is limited in low resource regions. This is a critical step toward achieving two of the United Nations Sustainable Development Goals (SDG), namely sustainable cities and communities and climate action.

In this article, we explore the complex phenomena behind why people “hold on” to disused connected devices, focusing especially on differences between “traditional” smartphones and computers, and newer categories of smart home devices, wearables, and single-function Internet of Things (IoT) devices. We investigate why and in what contexts different categories of connected devices become disused by their owners; what owners value about their disused devices; and what they perceive to be the barriers to adopting circular practices, for example, by fixing, recycling, or reusing them. Our contribution is to provide a descriptive account of how functional, sentimental, and other values associated with devices shape owners’ perceptions and attitudes toward their “end of life,” for an expanded range of connected products. By highlighting how perceptions of concepts including convenience, ownership, and wastefulness mediate how owners approach the “end of life” of a device, we map the barriers for device owners to engage in more circular practices and highlight opportunities to address them through design. Our study replicates previous findings in the domain, as well as extending them, contributing to how the design of modern IoT devices leads to new barriers, opportunities, and considerations for more circular design.

Understanding the energy usage of a community is crucial for policymaking, energy planning, and achieving sustainable development. The advent of the smart grid has made it feasible to gather fine-grain energy usage data at large-scales, providing us with new opportunities to understand demand patterns at different spatial and temporal scales. In this paper, we conduct a large-scale empirical study of energy usage of 14,849 residential and commercial energy consumers from a small city in the United States. We conduct a wide ranging analysis of energy usage at multiple granularities—citywide, transformer-level, and individual home levels. In doing so, we demonstrate how city-wide smart meter datasets can answer a variety of questions on energy consumption, such as the impact of weather on energy usage. For example, we show that extreme weather events significantly increase energy usage, e.g., by 36% and 11.5% on hot summer and cold winter days, respectively. As another example, we show 19.2% of transformers in the grid get overloaded during peak load periods. Finally, we evaluate the impact of incorporating varying amounts of energy storage within the distribution grid and the impact such deployments will have on the peak demand patterns seen by the grid as well as the ability to reduce overloads seen by distribution transformers during peak periods.

Over 30 million people globally consume illicit opiates. In recent decades, Afghanistan has accounted for 70–90% of the world’s illicit supply of opium. This production provides livelihoods to millions of Afghans, while also funneling hundreds of millions of dollars to insurgent groups every year, exacerbating corruption and insecurity, and impeding development. Remote sensing and field surveys are currently used in official estimates of total poppy cultivation area. These aggregate estimates are not suited to study the local socioeconomic conditions surrounding cultivation. Few avenues exist to generate comprehensive, fine-grained data under poor security conditions, without the use of costly surveys or data collection efforts. Here, we develop and test a new unsupervised approach to mapping cultivation using only freely available satellite imagery. For districts accounting for over 90% of total cultivation, our aggregate estimates track official statistics closely (correlation coefficient of 0.76 to 0.81). We combine these predictions with other grid-level data sources, finding that areas with poppy cultivation have poorer outcomes such as infant mortality and education, compared to areas with exclusively other agriculture. Surprisingly, poppy-growing areas have better healthcare accessibility. We discuss these findings, the limitations of mapping opium poppy cultivation, and associated ethical concerns.