AI-driven educational technologies are expanding rapidly, yet their design rarely reflects the linguistic and infrastructural realities of Deaf learners in low-resource contexts. This qualitative study investigates how teachers, parents, and Deaf students in Bangladesh navigate fragile visual access, inconsistent Bangla Sign Language, and unreliable technology in everyday learning. Through interviews and focus groups with 13 teachers, eight parents, and five Deaf students, we show how small disruptions in sightlines, pacing, or sign clarity can quickly collapse comprehension, making accessibility a condition that must be constantly protected. We identify opportunities for AI to act as access support by stabilizing visual information, ensuring teacher-validated Bangla Sign Language, enabling offline use, and protecting emotional safety when seeking help. We contribute a model of learning continuity that explains how visual, linguistic, and affective stability interact in Deaf education and offer concrete design directions for AI-driven learning tools in the Global South.

Indigenous languages face significant cultural oppression from official state languages, particularly in the Global South. We investigate the Bangladeshi Chakma language revitalization movement, a community grappling with language liquidity and amalgamation into the dominant Bengali language. Our six-month-long qualitative study involving interviews and focus group discussions with Chakma language learning stakeholders uncovered existing community socio-economic challenges and resilience strategies. We noted the need for culturally grounded digital tools and resources. We propose an ICT-mediated community-centric framework for Indigenous language revitalization in the Global South, emphasizing the integration of historical identity elements, stakeholder-defined requirements, and effective digital engagement strategies to empower communities in preserving their linguistic and cultural heritage.

The increasing use of social media platforms for seeking religious information raises critical questions about the interplay of technology, algorithms, and faith. Prior HCI literature has explored users’ algorithmic experience in various domains such as news, music, mental health, etc. However, religion, a highly sensitive and significant domain, remains underexplored within the HCI scholarship. Therefore, we investigate how social media algorithms influence users’ religious content engagement through semi-structured interviews and focus groups with 24 Muslim participants from Bangladesh. Our findings reveal that the benefits of algorithms in the Islamic context are often overshadowed by the significant harm of inappropriate recommendations. As a result, participants envisioned algorithms that actively support their spiritual growth and faith practices. Building on these insights, we outline a potential design space for religion-aware algorithms and propose a conceptual framework of users’ hierarchical religious needs for Islamic content to inform future faith-based technology design.

Bangladesh’s expansion of online public services has improved access on paper, yet literacy barriers, fragile infrastructure, and inconsistent design continue to exclude many citizens. Through a convergent mixed-methods study, 293 survey responses, 28 administrator and mediator interviews, and heuristic evaluation, we show how digital literacy strongly predicts task success and autonomy, while qualitative accounts reveal how irreversibility, payment failures, and mistrust drive users toward informal mediators. These dynamics produce a Digital Democracy Paradox: systems justified as efficient and publicly framed as accessible can, in practice, deepen exclusion. We contribute: (1) the Literacy-Adaptive Usability Framework (LAUF), which treats literacy as a dynamic design parameter and integrates adaptation, mediation, and infrastructural resilience; (2) Civix UI, a lightweight illustrative prototype demonstrating bilingual, low-bandwidth, literacy-sensitive components; and (3) a mediator-inclusive design model that formalizes assisted use through accountable, gender-inclusive support. Together, these offer a pathway toward more equitable and trustworthy digital public services.

Indigenous children in Bangladesh’s Chittagong Hill Tracts (CHT) face persistent barriers to primary education, including language mismatch, economic constraints, and limited infrastructure. Prior work often frames these challenges as problems of access; however, we argue that educational inequality in this context is fundamentally mediated through social and institutional structures.

Online video platforms have become an important resource for learning technical skills, yet their role is often understood as supplementary to formal education. This paper examines Bangla-language developer vlogs in Bangladesh and argues that, in resource-constrained contexts, such content functions as a primary pathway into software development rather than an optional aid. Drawing on a mixed-methods study combining learner survey data, creator interviews, and vlog content analysis, we show how Bangla-language developer vlogs support technical learning where formal guidance, English-language resources, and local mentoring are limited. We further demonstrate that language operates as a structural condition of access, enabling learners to engage with technical concepts that would otherwise remain inaccessible. Yet this informal learning infrastructure depends on creator labor that remains financially, technically, and socially vulnerable. By reframing developer vlogs as infrastructure rather than content, this work contributes to ongoing discussions in HCI and ICTD about access, participation, and the design of equitable learning systems in the Global South.

The rapid evolution of artificial intelligence and large language models have created a new dynamic of student engagement beyond academic contexts. While prior research focused on AI’s role in providing academic and emotional support, less is known about the motivations to personalize AI, its diverse usage, and the intricate relationship between AI and the student population, especially in the context of the Global South. Our study fills this gap with findings drawn from 21 semi-structured interviews with undergraduate students from diverse backgrounds in Bangladesh, revealing usage as companionship, intimate partnership, worldbuilding, reminiscence of dead family members, and prioritization of AI over human relationships. These findings shed light accounting the local cultural norms on shaping AI adoption in Bangladesh, highlighting both opportunities and risks, and provide essential insights to inform necessary guidelines. We further connect our findings to the broader issues in Human-AI interaction and offer future research directions.

Chatbots are being widely used to support emotional well-being, yet many are designed from secular perspectives that overlook spiritual and religious values. This paper examines how Muslim users perceive spiritually grounded emotional support through AI. Through qualitative interviews with 17 participants, we find that users seek systems that are not only empathetic but also theologically grounded, emotionally responsive, and sensitive to privacy and credibility. Chatbots are envisioned as spiritually aligned companions embedded in everyday practices. We frame this role as a mediated religious agent, whose credibility comes from the religious authority it represents. These findings contribute to design considerations for faith-aligned mental health technologies that move beyond generic support toward contextually meaningful care.

The NBD (Network Block Device) protocol enhances the Android ecosystem's storage management and power efficiency, particularly on ARM devices. The study addresses storage limitations on ARM devices and explores how NBD can provide a solution. By leveraging NBD, Android devices can connect to remote storage resources and expand storage capacity without physical upgrades. This enables Android devices to be used as controllers for IoT networks and smart home appliances. The study includes statistical comparisons of NBD with alternative protocols like sFTP, iSCSI, NFS, SMB, and HTTP. Through read-and-write tests, NBD exhibits superior performance in bandwidth speed and operation duration, with speeds more than twice as fast as other protocols. Furthermore, the evaluation compares NBD's cloud storage capabilities with popular services such as Google Drive, Dropbox, OneDrive, and GCP. NBD outperforms these services regarding read and write speeds, completing operations much faster. The study also examines NBD's power consumption, demonstrating its energy efficiency compared to other protocols and cloud storage services. NBD's lower power consumption makes it ideal for energy-sensitive Android applications. In conclusion, the integration of NBD into the Android ecosystem enhances efficiency, storage flexibility, and adaptability. It empowers ARM-based Android devices with increased capabilities and versatility.

In the rapidly advancing field of online learning, interface design plays a pivotal role in shaping student engagement and motivation, especially in countries like Bangladesh, which feature diverse technological infrastructures, cultural dynamics, and educational landscapes.This study delves into the complex relationship between online learning platforms and student experiences.Despite the increasing adoption of online education, the influence of interface design on fostering engagement across diverse student backgrounds remains underexplored.To address this gap, we employed a mixed-methods approach, conducting surveys with 115 students and semi-structured interviews with 60 students from both urban and rural regions of Bangladesh.Our findings underscore the critical requirements for online learning platforms in Bangladesh and identify key interface design elements that significantly impact student participation.These results highlight the importance of context-sensitive interface design in promoting inclusivity and effectiveness.We discuss the broader implications of our findings for creating more inclusive and adaptable online learning platforms, particularly for Bangladesh and other regions facing similar challenges and diverse educational needs.

Docker is a vital tool in modern development, enabling the creation, deployment, and execution of applications using containers, thereby ensuring consistency across various environments. However, developers often face challenges, particularly with filesystem complexities and performance bottlenecks when working directly within Docker containers. This is where Mutagen comes into play, significantly enhancing the Docker experience by offering efficient network file synchronization, reducing latency in file operations, and improving overall data transfer rates in containerized environments. By exploring Docker’s architecture, examining Mutagen’s role, and evaluating their combined performance impacts, particularly in terms of file operation speeds and development workflow efficiencies, this research provides a deep understanding of these technologies and their potential to streamline development processes in networked and distributed environments.

With the rapid advancement of cloud technologies, cloud services have enormously contributed to the cloud community for application development life-cycle. In this context, Kubernetes has played a pivotal role as a cloud computing tool, enabling developers to adopt efficient and automated deployment strategies. Using Kubernetes as an orchestration tool and a cloud computing system as a manager of the infrastructures, developers can boost the development and deployment process. With cloud providers such as GCP, AWS, Azure, and Oracle offering Kubernetes services, the availability of both x86 and ARM platforms has become evident. However, while x86 currently dominates the market, ARM-based solutions have seen limited adoption, with only a few individuals actively working on ARM deployments. This study explores the efficiency and cost-effectiveness of implementing Kubernetes on different CPU platforms. By comparing the performance of x86 and ARM platforms, this research seeks to ascertain whether transitioning to ARM presents a more advantageous option for Kubernetes deployments. Through a comprehensive evaluation of scalability, cost, and overall performance, this study aims to shed light on the viability of leveraging ARM on different CPUs by providing valuable insights. • ARM architecture enables heterogeneous CPU design for enhanced performance. • Tests on x86 vs. ARM across clouds revealed varied performance outcomes. • ARM outperformed x86 in most performance comparisons with significant gains.

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.

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 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.

By analyzing data gathered through Online Learning (OL) systems, data mining can be used to unearth hidden relationships between topics and trends in student performance. Here, in this paper, we show how data mining techniques such as clustering and association rule algorithms can be used on historical data to develop a unique recommendation system module. In our implementation, we utilize historical data to generate association rules specifically for student test marks below a threshold of 60%. By focusing on marks below this threshold, we aim to identify and establish associations based on the patterns of weakness observed in the past data. Additionally, we leverage K-means clustering to provide instructors with visual representations of the generated associations. This strategy aids instructors in better comprehending the information and associations produced by the algorithms. K-means clustering helps visualize and organize the data in a way that makes it easier for instructors to analyze and gain insights, enabling them to support the verification of the relationship between topics. This can be a useful tool to deliver better feedback to students as well as provide better insights to instructors when developing their pedagogy. This paper further shows a prototype implementation of the above-mentioned concepts to gain opinions and insights about the usability and viability of the proposed system.

With rapid urbanization and increasing traffic congestion in major cities, alternative modes of transportation have gained significant attention. The ride-sharing app revolution also has sparked a significant transformation in the transportation industry, along with “Khep”, an unusual ride-sharing approach where individuals negotiate fares directly with drivers personally, which has become a popular means of transportation. In this research, we investigate various factors that influence the preference for such unconventional contractual rides over ride-sharing apps, such as cultural norms, trust issues, affordability, and accessibility. Moreover, we explore the role of technology literacy, marketing strategies, and regulatory frameworks in shaping the adoption landscape. After conducting a survey and interviews, we conduct a thorough analysis to determine the expected findings and uncover meaningful insights regarding the utilization, preferences, and challenges within the ride-sharing industry in Bangladesh. The findings of this study reveal that cultural factors, such as the preference for bargaining and personal connections, strongly influence the popularity of contractual rides. Additionally, concerns related to safety, data privacy, and trust act as barriers to ride-sharing app adoption. Being associated with UN SDG Goals 11 and 9, the implications of this research extend beyond Bangladesh and can provide insights for policymakers, transportation companies, and technology developers seeking to understand the factors shaping the adoption of ride-sharing apps in similar contexts.

Since massive numbers of images are now being communicated from, and stored in different cloud systems, faster retrieval has become extremely important. This is more relevant, especially after COVID-19 in bandwidth-constrained environments. However, to the best of our knowledge, a coherent solution to overcome this problem is yet to be investigated in the literature. In this article, by customizing the Progressive JPEG method, we propose a new Scan Script to ensure Faster Image Retrieval. Furthermore, we also propose a new lossy PJPEG architecture to reduce the file size as a solution to overcome our Scan Script's drawback. In order to achieve an orchestration between them, we improve the scanning of Progressive JPEG's picture payloads to ensure Faster Image Retrieval using the change in bit pixels of distinct Luma and Chroma components ($Y$,$C_{b}$, and$C_{r}$). The orchestration improves user experience even in bandwidth-constrained cases. We evaluate our proposed orchestration in a real-world setting across two continents encompassing a private cloud. Compared to existing alternatives, our proposed orchestration can improve user waiting time by up to 54% and decrease image size by up to 27%. Our proposed work is tested in cutting-edge cloud apps, ensuring up to 69% quicker loading time.

Designing and implementing visual question answering tasks using Bengali datasets and native VQA based smart systems are important, as a huge number of people speak in Bengali who are relatively less advanced to technology adoption due to the language barrier. The important designing and implementing tasks are little explored in the literature. Therefore, we attempt to investigate the tasks in depth in this study. To do so, we follow a step-by-step procedure for overcoming different barriers encountered while adopting datasets as well as creating our own Bengali CLEVR and Bengali VQA. We perform different sets of experiments to demonstrate the efficacy of our proposed approach. Various VQA-based smart systems for Bengali speakers covering virtual doctors, navigation systems, smart glasses for the visually-impaired people, and so on can be benefited from this study through making the applications usable and understandable to those who are not fluent in a foreign language such as English.

Agriculture is the fundamental source of revenue and Gross Domestic Product (GDP) in many countries where economically developing countries; especially the Global South are no exception. Various types of plant-based diseases are strongly intertwined with the everyday lives of those who are connected with agriculture. Among the diseases, most of them can be diagnosed by leaves. However, due to the variety of illnesses, identifying and classifying any plant leaf disease is difficult and time-consuming. Besides, late identifications of diseases cause losses for the farmers on a large scale, which in turn affects their financial state. Therefore, to overcome this problem, we present a lightweight approach (called PLeaD-Net) to accurately recognize and categorize plant leaf diseases in this paper. Here, leveraging a limited-resource deep convolutional network (Deep CNN) model, we extract information from sick sections of a leaf to accurately identify locations of disease. In comparison to existing deep learning methods and other prior research, our proposed approach achieves a much higher performance using fewer parameters as per our experimental results. In our study and experimentation, we develop and implement an architecture based on Deep CNN. We test our architecture on a publicly available dataset that contains different types of plant leaves images and backgrounds.

Increased processing power of MapReduce clusters generally enhances performance and availability at the cost of substantial energy consumption that often incurs higher operational costs (e.g., electricity bills) and negative environmental impacts (e.g., carbon dioxide emissions). There exist a few greening methods for computing clusters in the literature that focus mainly on computational energy consumption leaving cooling energy, which occupies a significant portion of the total energy consumed by the clusters. To this extent, in this article, we propose a machine learning-based approach that reduces the total energy consumption of a MapReduce cluster considering both computational energy and cooling energy. Our approach predicts the number of machines that results in minimum total energy consumption. We perform the prediction through applying different machine learning techniques over year-long data collected from a real setup. We evaluate performance of our approach through both real test-bed experimentation and simulation. Our evaluation reveals that our approach achieves substantial reduction in total energy consumption compared to other state-of-the-art alternatives while experiencing marginal performance degradation in a few cases.

Nowadays, Internet-based technologies possess immense processing power, capacity, flexibility, and are gradually moving towards a service- oriented functionality in order to build new distributed storage systems in the cloud. Several distributed systems are currently running in different geographically located data centers for successful deployment of modern web and social services such as Facebook, Twitter, ringID, etc. Both cache and backend servers in such distributed systems must be functional and reliable for incoming workloads by means of efficient allocation of capacity along with proper configuration and tuning of multiple system resources. To address these challenges, in this paper, we propose a test-driven automated architecture for load testing, named as 'svLoad' to compare the performance of cache and backend servers. Here, we designed test cases considering diversified real scenarios such as different protocol types, same or different URLs, with or without load, cache hit or miss, etc. using tools namely JMeter, Ansible, and some custom utility bash scripts. To validate the efficacy of our proposed methodology, we conduct a set of experiments by running these test cases over a real private cloud development setup using two open source projects - Varnish as the cache server and OpenStack Swift as the backend server. Our focus is also to find out bottlenecks of Varnish and Swift by executing load requests, and then tune the system based on our load test analysis. After successfully tuning the Swift, Varnish, and network system, based on our test analysis, we were able to improve the response time by up to 80%.

A majority of traffic in Internet today is media sharing, which is increasing at a rapid pace. Consequently, media industries face challenges due to the associated steep rise in bandwidth, computing, and storage requirements. To face the challenges, in this paper, we sketch a new methodology, named `Secure Processing-aware Media Storage' (SPMS) through expanding cloud file sharing capabilities from only storing media files to also processing them along with encryption. Our proposed approach is to package application requirements and move data after processing them based on the package of requirements. Here, first, we propose to perform resizing of images and transcoding of videos according to several resolutions for covering diversified remote devices. Afterwards, we perform necessary media encryption-decryption. These tasks are performed using several middlewares. To validate efficacy of our proposed methodology, we perform a set of experiments with implementation of the middlewares over a real private cloud enabling file sharing. Experimental results confirm substantial performance improvement using our proposed methodology compared to that obtained using conventional one.