Conference Proceedings

In resource-constrained environments, the adoption of digital services faces formidable barriers. To address these challenges, we introduce BlendNet, an innovative platform designed to foster intermediated adoption of digital services in remote and rural areas. BlendNet harnesses satellite and intelligent edge technologies and creates a reliable and cost-effective channel for intermediaries to distribute digital content and services in remote areas. The key technology component of the system is the ‘Blendnet Hub’, a small satellite connected hardware device placed in the local kirana stores, facilitating seamless access to users without incurring data expenses, thereby addressing access, awareness, and affordability issues. We conducted a pilot study in Bihar, signing up 258 retailers who reached over 68,000 users in three months, demonstrating the crucial role of intermediaries in digital service adoption. This paper offers detailed insights into BlendNet’s design and the pilot study’s outcomes, emphasizing user and retailer engagement and adoption of services. It explores the platform’s potential as a scalable and financially viable solution.

Traditional localization systems often rely on a network of external sensors, making the setups cumbersome, expensive, and requiring significant calibration effort. The advent of Bluetooth 5.1 and later versions brought enhancements that enable precise localization using constant tone extension (CTE) in the signal through Angle of Arrival (AoA) and Angle of Departure (AoD) techniques. This work examines the capacity of a single Bluetooth Low-Energy (BLE) locator with an antenna array based on AoA in terms of performance, efficiency, and latency in real-time indoor positioning. While traditional neural networks train measured entities to match calculated distances, we utilize the azimuth and elevation angle components in the AoA measured and train neural networks to match their theoretical counterparts. We conducted extensive experiments in a real-world lab environment, providing ablation studies in the design. The results demonstrate the system’s capability in real-time with many potential interference variables. Under lab conditions, our results show the capacity of a single locator wanes past 4m with the best average accuracy of 0.09m error in positioning within a 5m radius to as much as ∼ 1m of error beyond 6m up to the maximum possible measuring distance in the lab.

In Low- and Middle-Income Countries (LMICs), poor health outcomes come from a high burden of disease, a shortage of healthcare professionals, and inefficient health information exchange leading to substantial economic losses. In this paper, we highlight critical gaps in healthcare delivery in Pakistan and propose solutions to improve patient outcomes in resource-constrained environments. We have built Darcheeni, an AI-driven healthcare framework that leverages artificial intelligence to assist and supplement physicians, streamline healthcare processes, and prioritize patient-centered care. Darcheeni analyzes doctor-patient interactions in real-time, integrates lab and imaging data, generates and distributes care plans customized to the patient’s needs, and sends them directly to patients’ smartphones. We also discuss the challenges and limitations associated with sustainable AI integration by centering our learnings from the pilot deployment of Darcheeni. By focusing on Pakistan as a case study, this work offers practical insights and strategies for deploying AI-driven technologies sustainably in similar resource-constrained environments and contributes to the broader discourse on the role of AI in global health improvement.

Estimating and locating occupants indoors is crucial for automating operations within buildings. However, current privacy-preserving occupancy estimation systems using thermal cameras have not been thoroughly evaluated in dense settings, such as classrooms or movie theaters, where occupants sit close to one another. Estimating occupancy in such settings presents challenges, as nearby occupants often lead to clusters of thermal signatures, thereby affecting the accuracy of the estimation. In response to these challenges, our work proposes Machine Learning (ML) and Deep Learning (DL) based methods for occupancy estimation and localization, both in dense and sparse settings. The ML method complements existing occupancy estimation techniques by incorporating new manual features, while the DL method performs automatic feature extraction, accurate occupancy estimation and localization. Remarkably, the results demonstrate a significant enhancement in occupancy estimation accuracy of up to 10%, achieving an impressive overall accuracy rate of 97%. Moreover, our evaluation on an edge device confirms the practicality and relevance of the proposed methods in real-world applications.

African American Vernacular English (AAVE) is a dialect of the English language spoken in the United States by members of the Black community. The stark differences between AAVE and Standard American English (SAE), as well as a historically negative stigma towards its use, have contributed to an academic performance gap between Black students and their non-Black counterparts. This research works to generate educational resources similar to what is available in English Second Language (ESL) classrooms. Exposure to these resources has been shown to both improve the negative stigma towards the use of AAVE as well as facilitate code-switching between AAVE and SAE. The resources to be generated in this research are a parallel corpora for AAVE and SAE using both professionally translated text and AI-generated text, and a Neural Machine Translation (NMT) model to translate SAE into AAVE using novel network architectures used language to language translation including LSTM, Bi-LSTM, Attention, and Transformer network components. The parallel corpora will be quantitatively reviewed and validated before using tested dialect translation model methods. Methodology will additionally be focused on low-resource machine translation due to the lack of large corpora containing AAVE. Both professional translators and large language model, ChatGPT, will be used to create parallel corpora containing AAVE and SAE. This short paper details the preliminary results of the assessment of these generated corpora as well as the accuracy of dialect machine translation models trained on them.

Targeting the poor is an integral part of social program design in low-income countries. Geographical targeting gives priority to areas with high concentrations of poverty. However, traditional data sources, such as household surveys often lack the spatial resolution to estimate poverty at a highly disaggregated level and are costly to collect on a regular basis. We leverage the proliferation of big data obtained from mobile devices and satellites to generate poverty measures at a highly disaggregated spatial level in a new country context. Previous applications rely on computationally intensive methods to extract information from raw cellphone transaction data. We show how similar levels of prediction accuracy can be achieved by using key performance indicators (KPIs) that Mobile Network Operators produce regularly as part of their operations, lowering the financial and data access barriers to estimate and update prediction models. This can help to facilitate the use of these data for policy in low- and middle-income contexts.

Childhood vaccinations are vital for protecting children from preventable disease and improving overall public health. However, generating reliable estimates of routine immunisation uptake, essential for appropriate policy planning and resource allocation is complicated by various data challenges. A specific challenge in estimating coverage with household surveys such as the National Family Health Survey is that the presence of vaccination is obtained via maternal recall if a health-based record is absent. This study examines the extent to which estimates of childhood immunisation coverage derived using depend on maternal recall: a mother’s ability to correctly identify which vaccines a child has received. In this study, we leverage spatial Bayesian models to estimate routine childhood immunisation rates at sub-national resolutions in 2015 and 2020 using various assumptions about the accuracy of maternal recall. This modelling approach and explicit consideration of maternal recall allows us to identify local regions whose previous estimates of vaccine coverage rates may be overstated due to low rates of the presence of health-based records. We create detailed vaccination coverage maps to analyze the models with and without maternal recall data. By highlighting vaccination “coldspots” and their change over time, this study reveals the potential benefits or limitations of using maternal recall in generating vaccine coverage estimates and provides a basis for more informed decision-making for immunization interventions in India and similar contexts.

Precision gas analyzers are widely used in ecological research for manual measurement of soil carbon flux, a key metric used in the study of climate change. We present a generational update to the first low-cost, autonomous, closed-chamber style soil CO2 flux sensors (Fluxbots). Fluxbot 2.0 is the first such low-cost autonomous flux chamber capable of real-time wireless data transmission, which enables ecologists conducting in situ soil carbon flux surveys to set up their own wireless sensor arrays, reporting carbon flux data in real time at a very high level of temporal resolution. The system’s low cost (less than 500 USD per unit) and long-range cellular data transmission capabilities also allow for greatly improved spatial resolution. Additionally, the updated system consumes significantly less power, resulting in the ability to be deployed for longer than 10 × the battery lifetime of the original version on a single charge.

We present a study to assess the feasibility and implications of replacing internal combustion engine vehicles with battery-powered electric vehicles (EVs) in a car-sharing fleet. For the analysis, we used operational data from a local car-sharing company, which encompasses various aspects such as trip distance, start and duration, vehicle type, and pickup and return locations. To evaluate the impact of transitioning the entire fleet to EVs, we used EV and charger models to simulate the battery-powered trips and also the necessary post-trip recharging. Both could affect the service quality of car sharing services, as the requested trip distance might not be covered by an electric vehicle due to range or charging time limitations. Specifically, in our simulation-based analysis, we identified chains of consecutive bookings as a critical factor for car-sharing electrification. Furthermore, to assess the potential impact of electrification on the energy grid, we used data about the local grid load and its composition to relate it to the predicted vehicle charging times.

In today’s digital age, YouTube stands as a vital hub for information across various domains, from entertainment to education. However, the persistent challenge of misinformation threatens its integrity. This study explores the complex dynamics of trust and legitimacy within YouTube’s content ecosystem, uncovering factors shaping user perceptions in Bangladesh. We highlight the crucial roles of creator reputation, content presentation, and social validation in building trust while emphasizing the need to address misleading content effectively. Employing a mixed-methods approach, we conducted surveys (n=71) and interviews (n-19) to gain insights into user experiences comprehensively. By addressing gaps in current research, our study contributes to the HCI and ICT4D communities, illuminating critical issues in the digital realm. As YouTube continues to influence our online landscape, grasping trust and misinformation becomes essential for fostering an informed and trustworthy online community.

The evolving landscape of gig labor, particularly within food delivery platforms, has been influenced by a shift towards algorithmic management, emphasizing performance metrics as a daily burden to be carried through delivery work. Through a study of Indian food delivery agents, our paper suggests an emerging gamification culture between delivery platforms and workers. Agents gradually yet consistently adapt to stringent algorithmic management to overcome everyday work’s precarity. Despite the challenges of performance pressure on food delivery labor, we capture the daily rhythms and routines of delivery work punctuated by flashes of agenting, revealing small capacities to make choices and influence work outcomes. By employing a qualitative research framework, we uncover the mechanisms through which delivery platforms manipulate labor, simultaneously exploring delivery agents’ tactics to extract agency and exploit the platforms’ rules. We develop the notion of ‘agency’ to disentangle the idea of ‘gamification by agents’ as a socio-technical derivative governing food delivery agents in urban India. The findings highlight the potential of platform work empowering, albeit with limits, delivery agents with agency and decision-making authority.

In countries like India which have historically been reliant on rainfed agriculture, the increasing need of water for irrigation to support greater cropping intensity and shifts towards horticulture, has largely been supported through groundwater based irrigation. Cheap electricity has enabled a rapid increase in borewells almost across the country, which to some extent has enabled more equitable access to water than other irrigation approaches like canals, but has also led to groundwater stress in many regions. One way to indirectly estimate groundwater abstraction is to estimate evapotranspiration from cropping areas as a proxy for crop water consumption. Remote sensing based methods have been used to estimate evapotranspiration but existing open data products largely have a low spatial resolution which is not adequate to support local decision making for water use. In this study, we build machine learning methods to develop downscaled data outputs of evapotranspiration at fine spatial scales. Our approach uses satellite data, meteorological variables, and land surface characteristics as input features to obtain field-scale fortnightly time-series of evapotranspiration. We validate the results results across multiple geographic locations and also study its correlation with in situ evapotranspiration measurements. We find that our method is not able to accurately match in situ data but is able to successfully provide relative differences in evapotranspiration. We make our trained models available on the Google Earth Engine platform for use by other researchers and practitioners to obtain evapotranspiration outputs for their areas of interest. Our research contributes a scalable and adaptable solution to address the growing demand for fine-resolution hydro-climatic information.

Rash driving detection is vital to prevent accidents and improve public safety. Existing rash driving solutions using hand-crafted features have several limitations. We propose a simple yet efficient two-step process to overcome the limitations of the existing works by leveraging smartphone sensor (accelerometer and gyroscope) data. The first step filters out normal driving data and retains only the abnormal driving data with the proposed Adaptive Time Window (ATW) algorithm. This not only enhances the accuracy of detection but also reduces computation time, making our solution more efficient. Importantly, the proposed ATW algorithm completely eliminates window overlap redundancy and edge effects in the system. The second step classifies abnormal driving patterns with the proposed 1D Convolutional Neural Network (CNN) model. Our results demonstrate that the proposed solution is highly accurate and has a weighted accuracy of 97.14%. Additionally, as part of this research, we have curated and released a labeled Indian dataset comprising five distinct rash driving patterns: Lane Weaving, Lane Swerving, Hard Braking, Hard Cornering, and Quick U-turn. This dataset can be valuable for further studies and aid in developing multimodal rash driving detection systems.

While audio-based interfaces make information accessible to people with visual impairments, some information, such as diagrams, graphs, and charts, can be better interpreted tactilely. We introduce a new Passive Refreshable Tactile (PRET) Printer concept. Using off-the-shelf components of a laser engraver and the nascent Tacilia technology, the prototype enables the creation of refreshable tactile graphics. By leveraging Pixel Art as a rendering process, we enhance the diversity of image production on this medium. We contribute technical specifications, open-source files to make the PRET printer and a qualitative evaluation of the concept by tactile learners. The prototype facilitates rapid and cost-effective development of refreshable tactile media, crucial for improving comprehension. The work builds upon existing research, furthering the groundwork to address the needs of tactile learners worldwide and establishing a foundation for further innovation and development in this domain.

Complementary feeding is crucial to promote healthy nutrition in infant and young children (IYC) and prevent malnutrition. Mothers, families, and healthcare professionals (HCPs) are crucial in helping IYC develop healthy eating habits. However, limited access to adequate nutritional information and health services impacts children’s nutrition, especially in low-resource settings. Technology opens up opportunities to address these challenges and potentially improve IYC feeding practices. Taking a co-design approach, we conducted low-fidelity prototyping workshops with caregivers and HCPs to explore the potential of tangible interfaces to facilitate play and promote healthy nutrition for IYC in two low-resource healthcare settings in Peru. Participants envisioned diverse tangible objects and interactions that could augment the waiting spaces of the healthcare centres, encouraging play and enhancing children’s and caregivers’ experiences, while promoting healthy nutrition and dietary diversity. We outline design opportunities to facilitate tangible play, shared playful experiences, and promote healthy nutrition in low-resource healthcare settings.

Although Bangladesh is a country that values matchmaking and matchmakers greatly, online dating is a relatively new phenomenon that became popular only recently mostly among the young population. While anecdotal reports suggest a considerable number of individuals utilize online dating platforms in Bangladesh, a comprehensive investigation of their experiences and the potential impact on their well-being has yet to be conducted. Given the growing prevalence of online dating apps among young adults in Bangladesh, the present study aims to conduct in-depth interviews with a sample of 33 participants between the ages of 18 and 25 who actively use these platforms. The sample comprises 14 male and 19 female participants, each of whom has experience using online dating apps. By gaining insights into the experiences of young adult users of dating apps in Bangladesh and examining the effects of such platforms on their physical and mental well-being, the findings of this study highlight three areas: a) youth's intention to fight existing social norms, b) gender experiences relating to abuse and harassment in online dating platforms, c) alternative usage of online dating application space, particularly during the Covid-19 pandemic period. This study also seeks to raise awareness of online usage patterns among young adults in Bangladesh and identify potential interventions that may address any negative outcomes associated with excessive online activity.

Delirium is a syndrome characterized by acute and fluctuating change in attention, awareness, and cognition. Common in older adults, especially those with underlying medical conditions, delirium is associated with higher mortality rates, longer hospital stays, and increased healthcare costs. Early identification and management of delirium therefore becomes essential in order to prevent adverse outcomes, improve patient health, and reduce healthcare costs. As multiple predisposing (e.g., neurological disorders) and precipitating factors (e.g., medications) can be involved in the aetiology of delirium, detecting the syndrome early on can be challenging. In this work, we present an interpretable multimodal checklist that can aid clinicians in delirium detection. Specifically, we leverage causal decision trees to extract most relevant features for delirium detection which are then used in learning the predictive checklist. Experiments demonstrate the efficacy of the approach over existing methods in terms of both detection accuracy and interpretability.

The COVID-19 pandemic has threatened disproportionately rural older adults’ health and well-being as they suffer from unique social exclusion due to a lack of services, such as transportation, communication infrastructure, healthcare, and social services. Although older adults can uniquely cope with pandemic adversity compared to younger adults, less attention has been directed to investigating the coping and resilience of rural older adults. To understand how diverse coping strategies impact the resilience of rural older adults, we conducted interviews with 26 rural and small-town older adults. Older adult participants adopted different coping strategies, such as following protective measures, keeping themselves busy, providing and receiving social support, and having a positive mindset. They experienced positive changes, such as increased interpersonal connectivity. Older adults’ individual-level coping processes are influenced by their social and physical environments. We explore design opportunities to support older adults’ resilient practices and harness their skills to facilitate community resilience.

Effective traffic intersection control is crucial for urban sustainability. State of the art research seeking Artificial Intelligence (AI), for example Deep Reinforcement Learning (DRL) based traffic control requires environment states through various Computer Vision methods, where the collective state of multiple cameras across an intersection constitute the single state for AI. This brings in serious robustness or fault-tolerance concerns on the deployed system. Camera systems are highly susceptible to faults due to multiple possible points of failure. A single fault collapses the AI state and hence the capacity of AI controller to manage the traffic is gone. Also, infrastructure deployment and maintenance is a slow bureaucratic process in the developing countries, which makes camera faults a regular event. In the given paper, we build WebLight  (https://github.com/sachin-iitd/WebLight), a web based, independent and alternative, traffic state processing method which can replace the camera dependency completely, or support as a backup mechanism until the camera system is back online, making the AI intersection control robust to camera failures.

Marine debris poses a significant ecological threat to birds, fish, and other animal life. Traditional methods for assessing debris accumulation involve labor-intensive and costly manual surveys. This study introduces a framework that utilizes aerial imagery captured by drones to conduct remote trash surveys. Leveraging computer vision techniques, our approach detects, classifies, and maps marine debris distributions. The framework uses Grounding DINO, a transformer-based zero-shot object detector, and CLIP, a vision-language model for zero-shot object classification, enabling the detection and classification of debris objects based on material type without the need for training labels. To mitigate over-counting due to different views of the same object, Scale-Invariant Feature Transform (SIFT) is employed for duplicate matching using local object features. Additionally, we have developed a user-friendly web application that facilitates end-to-end analysis of drone images, including object detection, classification, and visualization on a map to support cleanup efforts. Our method achieves competitive performance in detection (0.69 mean IoU) and classification (0.74 F1 score) across seven debris object classes without labeled data, comparable to state-of-the-art supervised methods. This framework has the potential to streamline automated trash sampling surveys, fostering efficient and sustainable community-led cleanup initiatives.

The garment industry is one of the world’s largest carbon and waste polluters, expected to produce 150 billion garments per year in the next decade, while currently recycling about 1%. The lack of reliable fabric identification limits scalable recycling. Without traceability, governments cannot enforce circular economy legislation. We propose solutions to both issues that leverage low cost hardware and deep learning. Using microscope fabric images and Convolutional Neural Networks, we demonstrate classification accuracy of over 90% for 14 fabric classes. By marking fabrics with a UV-visible code readable via YOLOv8 object detection, we demonstrate an mAP of over 0.98, retaining up to 0.93 after wash cycles while remaining resilient to fabric-unique challenges such as creasing. These methods can be implemented worldwide at low cost to enable fabric identification and traceability for a textile circular economy. We demonstrate a prototype reader application and discuss pathways to impact. We also provide three new datasets for future research.

Recent studies have highlighted the challenges low-literacy users face with complex user interfaces, often preventing them from utilizing essential smartphone applications for an improved quality of life. This paper introduces the EvolveUI design approach that diverges from conventional interface design by evolving in complexity alongside a user’s growing proficiency. Initially presenting a single navigation point to simplify interaction, EvolveUI systematically expands, introducing more features and navigation points as users become more adept at interacting with the interface. We build upon previous adaptive user interface research by uniquely focusing on expanding functionality based on user proficiency, offering a tailored experience that aligns with individual learning curves. By conceptualizing the interface as a dynamically expanding hierarchy, starting as a minimalist “tree” and unfolding into a more complex structure, EvolveUI facilitates a more accessible and engaging user experience. Our study compares this evolving interface approach with conventional designs through usability tests on a mobile health application, demonstrating EvolveUI’s potential to enhance technology accessibility for low-literacy users and suggesting new directions for inclusive design practices.

Analyzing agricultural imagery for farm level insights has been an active area of research in the recent times. For providing the necessary information to stakeholders - be it farmers, financial institutions or governments, various computer vision tasks have to come together. For example, to provide information to a farmer about crop stress in their farm, accurate localization of the farm, identification of the crop type and a monitoring of the field’s micro-climate must be done together. In this work, we set performance benchmarks for three computer vision tasks - farm boundary detection, crop classification and sub-field stress estimation with different modalities of images - Sentinel2, PlanetScope and Drone Imagery. We use public dataset benchmarks for farm boundaries and crop classification and do a controlled field study on a large sugarcane farm in Uttar Pradesh, India for the stress estimation.

Our work benchmarks farm boundary detection for small farms with state of the art deep learning algorithms achieving a dice score of 67%, improves the sugarcane classification accuracy by 10% coming to 98% and demonstrates an accuracy of 72% for water and nitrogen stress estimation.

Research on mobile money smishing is hindered, especially in the African context, as there is a lack of data. The absence of datasets and the fact that Mobile Network Operators don’t maintain such data pose a significant challenge. Additionally, the absence of a data collection infrastructure further complicates the data acquisition process. In response to this challenge, we developed a scalable and cost-effective honeynet infrastructure tailored for the efficient collection of organic Short Message Service (SMS) messages. The innovative approach involves harnessing the capabilities of Raspberry Pi units, USB multipliers, SIM cards from MNOs and GSM modems to create a scalable and adaptable solution. This aims to enhance smishing data collection, facilitating more efficient research into mobile money smishing.

South Africa is experiencing extreme weather conditions, often resulting in failed crops. The country has many subsistence farmers who rely on small-scale farming for food security and livelihoods. This paper presents [project name omitted to ensure anonymity], an Artificial Intelligence (AI), and Internet of Things (IoT) hydroponic grow tent implementing the Nutrient Film Technique (NFT) approach for leafy green plant production. The system uses a Random Forest Classifier (RFC) and various sensors for the real-time, accurate management of nutrient levels, pH, light, temperature, and humidity to provide an optimal environment for crop growth, in most cases, regardless of the weather patterns outside of the tent. The system further includes a mobile application that allows farmers to interject and manage all the elements in the tent. This paper reports on the design, development, and first deployment of [project name omitted to ensure anonymity] with rural subsistence farmers in South Africa. We found that South African farmers are keen to explore new agricultural technologies and were able to co-farm using [project name omitted to ensure anonymity] to grow crops that are no longer successfully cultivated due to the erratic weather conditions in South Africa.

In this paper, we investigate the potential of Large Language Models (LLMs) to improve English speaking skills. This is particularly relevant in countries like India, where English is crucial for academic, professional, and personal communication but remains a non-native language for many. Traditional methods for enhancing speaking skills often rely on human experts, which can be limited in terms of scalability, accessibility, and affordability. Recent advancements in Artificial Intelligence (AI) offer promising solutions to overcome these limitations.

We propose Comuniqa, a novel LLM-based system designed to enhance English speaking skills. We adopt a human-centric evaluation approach, comparing Comuniqa with the feedback and instructions provided by human experts. In our evaluation, we divide the participants in three groups: those who use LLM-based system for improving speaking skills, those guided by human experts for the same task and those who utilize both the LLM-based system as well as the human experts. Using surveys, interviews, and actual study sessions, we provide a detailed perspective on the effectiveness of different learning modalities. Our preliminary findings suggest that while LLM-based systems have commendable accuracy, they lack human-level cognitive capabilities, both in terms of accuracy and empathy. Nevertheless, Comuniqa represents a significant step towards achieving Sustainable Development Goal 4: Quality Education by providing a valuable learning tool for individuals who may not have access to human experts for improving their speaking skills.

In regions where traditional gender norms and limited access to resources have historically posed significant challenges, mobile financial services (MFS) have emerged as a potent instrument for enhancing the economic and social empowerment of women. This study explores the impact of sustainable development on rural Bangladeshi women, with a particular focus on their use of MFS. Through qualitative interviews with 39 participants, including 35 rural women and 4 MFS agents, our research highlights the broader societal implications of women’s increased involvement in economic activities. It also underscores advancements in women’s rights and their growing influence. Along with that, the study acknowledges the presence of persistent obstacles such as unequal pay, limited access to education, and cultural biases that continue to hinder women’s full economic participation, especially in rural settings. Ultimately, our study emphasizes the importance of financial stability and savings for both families and society as a whole.

The efficacy of a reconfigurable intelligent surface (RIS)-aided network for enhanced connectivity in skip zones is demonstrated through real-time video streaming. The demonstration is carried out with the help of National Instruments universal software radio peripheral (NI-USRP) devices integrated with an RIS prototype. The RIS prototype is designed and fabricated using a 16 × 10 metasurface operating at 5.3 GHz carrier frequency. Utilizing LabVIEW’s long-term evolution (LTE) application framework module, good connectivity between the transmitter and receiver in an otherwise skip zone is exhibited. Various modulation and coding schemes have been applied to the streamed data to observe the throughput, SINR, and constellation diagrams with respect to different positions of the receiver in the non-line of sight (NLOS) skip zone. The demonstration ratifies the potential of RIS system in practical applications related to future wireless communications.

Government welfare schemes such as the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) in India fund the creation of assets for natural resource management in rural villages to support farmers for their agricultural and livelihoods-based needs. With most agriculture in India being rain-fed, structures such as farm ponds, checkdams, trenches and bunds play a crucial role in supporting groundwater recharge and providing critical lifesaving irrigation in times of dry spells and droughts. In this study, we investigate the impact of farm ponds built under the MGNREGA scheme in Northern India as a source of protective irrigation for cropping areas in their immediate neighbourhood. We assess the impact of farm ponds on the following aspects: (i) we study their impact on agricultural productivity for up to five years since their construction, (ii) we separately study their impact in drought years during this period, (iii) we study the extent to which they are able to to reduce the sensitivity to droughts of sites having farm ponds. A causal analysis framework was designed by identifying control sites that did not have farm ponds, and the treatment effect of having farm ponds was computed using the difference-in-differences approach. Remote sensing data was processed to compute changes in vegetation indices around the treated and control locations before and after the construction of farm ponds. Our results indicate that farm ponds were instrumental in improving the agricultural productivity during the monsoon season in general. The impact during the monsoon season in drought years is also positive and significant. Furthermore, farm ponds also facilitated in reducing drought sensitivity during the monsoon season. The impact during the post-monsoon season was found to be lower, and the impact during the summer agricultural season was found to be the least.

This research study proposes a social framework for birth declaration in rural Bangladesh, utilizing cloud storage technology and ad hoc models, based on interviews with Community Healthcare Workers (n=13). The traditional process of birth registration in rural areas of Bangladesh is paper-based and often subject to errors, leading to incomplete or inaccurate data. The proposed framework addresses this issue by providing a secure and efficient system with minimal hardware requirements for recording and storing birth information in resource-constrained settings. In this study, we integrate HCI principles into a cloud-based solution for rural communities, addressing infrastructure and usability concerns, in line with HCI’s focus on technology in societal contexts. Furthermore, we tackle the digital literacy gap in rural areas, designing our solution with HCI to effectively bridge this divide. We leverage existing infrastructure to introduce three progressive execution models, each constituting modular components within the overarching solution framework. The initial step in mitigating child marriage rates is securing accurate and easily retrievable birth data since paper trails are prone to manipulation, destruction, and fraud. Therefore, our study advocates for the implementation of the primary and pivotal measures to combat the vulnerable exploitation of children.

In this work we present SensiTrain - a data collection system designed with the goal of curating a dataset for understanding how differences in cultural backgrounds, sexual orientation, gender, race and national origin affect the way one perceives statements made on social media as being either benign or hurtful and sometimes even hateful and aggressive. SensiTrain displays social media posts to users and asks them to categorize the posts as being either benign or hurtful while providing a reason for such classification. We plan to use the data thus collected to build a public dataset repository and use the same to train a baseline system for identifying potentially hurtful statements before they are posted on social media platforms. The resulting system would be able to explain why a given statement could be perceived as hurtful by one or more groups thus making the inference more trustworthy to the users of the system. We hope that through this endeavor we will be able to mitigate unintentional harm caused due to lack of understanding of different cultures and backgrounds, thus making the world a kinder place.

Although coral reefs are special and vital marine ecosystems, massive coral degradation began to occur due to the increase in global temperatures and the intensification of human industrial activities. Coral reef protection requires accurate coral recognition because it is the foundation for learning the distribution, disease, and growth of coral reefs, hereby informing the proper ways for further action. Recently, CNNs have been applied in automated coral image classification. These classifier models, however, are difficult to be generalized from the trained coral images in a marine region (source domain) to the coral images in a different marine region (target domain) since the corals have significant within-species morphological variability among the different geographic location domains. In this paper, a novel coral recognition algorithm is introduced via knowledge transfer across domains and its advantages lie in the following aspects. (1) It simultaneously transfers corals’ texture and structure features across domains thus providing useful knowledge to assist the coral recognition tasks in the target marine domain. (2) To overcome the difficulty that the confusing coral images (e.g., bleached corals) are prone to be misclassified and transfer useless or even negative information, our algorithm is equipped with the reject option for the confusing corals while adapting. These corals can be sent to an expert or a more expensive but accurate system, resulting in strengthened transferability and reliability. Furthermore, we develop a new cross-domain coral image dataset to enhance coral research. Without the label information from the target marine region, our method significantly reduces the distribution gap and domain shift among the different marine regions. In addition, CR-Cross goes a step further in tackling the challenges of missing coral data, maximizing the utilization of available coral datasets, and enhancing the reusability of both coral data and coral recognition models. A series of empirical studies show that our method remarkably outperforms a broad range of baselines.

Hydroponic farming is a sustainable alternative to traditional agriculture, allowing farmers to grow crops more efficiently. This type of farming involves growing plants in water without needing soil. There is increasing agreement that this agricultural approach should be utilized to mitigate food insecurity, particularly considering the fluctuating and unpredictability of weather conditions. However, this approach requires technical expertise in addition to consistent monitoring. Existing research has focused on applying The Internet of Things (IoT) and Artificial Intelligence (AI) technologies to the automation and monitoring of hydroponic farming systems. However, limited work tested the viability of these systems in the field with farmers. This study focused on including rural subsistence farmers in farming alongside a seven-month and ongoing deployment of an AI and IoT-enabled hydroponics system following a qualitative approach that entailed focus groups with the farmers and observations of the hydroponic systems. This paper discusses the lessons learned regarding unpredictable and uncontrollable extreme weather conditions, equipment failure, choices in nutrient solutions, and pest infestations, which often all have unplanned and unintended consequences for these systems and farmers. We argue that more deployments are needed to better understand how these systems function in real-world settings.

Soil microbial fuel cells (SMFCs) are an emerging technology which offer clean and renewable energy in environments where more traditional power sources, such as chemical batteries or solar, are not suitable. With further development, SMFCs show great promise for use in robust and affordable outdoor sensor networks, particularly for farmers. One of the greatest challenges in the development of this technology is understanding and predicting the fluctuations of SMFC energy generation, as the electro-generative process is not yet fully understood. Very little work currently exists attempting to model and predict the relationship between soil conditions and SMFC energy generation, and we are the first to use machine learning to do so. In this paper, we train Long Short Term Memory (LSTM) models to predict the future energy generation of SMFCs across timescales ranging from 3 minutes to 1 hour, with results ranging from 2.33% to 5.71% MAPE for median voltage prediction. For each timescale, we use quantile regression to obtain point estimates and to establish bounds on the uncertainty of these estimates. When comparing the median predicted vs. actual values for the total energy generated during the testing period, the magnitude of prediction errors ranged from 2.29% to 16.05%. To demonstrate the real-world utility of this research, we also simulate how the models could be used in an automated environment where SMFC-powered devices shut down and activate intermittently to preserve charge, with promising initial results. Our deep learning-based prediction and simulation framework would allow a fully automated SMFC-powered device to achieve a median 100+% increase in successful operations, compared to a naive model that schedules operations based on the average voltage generated in the past.