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.

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.

Peer Reviewed

Even though the traffic systems in developed countries have been analyzed rigorously and operated efficiently, the same does not generally hold for developing countries due to inadequate planning, design, and operations of their transportation systems. Because of inherent differences between internal infrastructures, the strategies deployed in developed countries may not be amenable to developing ones. Besides, developing countries’ traffic systems are not well-studied in the literature to the best of our knowledge. For example, it is yet to explore how a developed country’s lane-based traffic flow would perform in the context of a developing country, which generally experiences non-lane-based traffic. As such, by using our newly developed traffic simulator ‘RoadBird,’ we investigate outcomes of both lane-based and non-lane-based traffic from the contexts of both developing and developed countries. To do so, we run simulations over real road topologies (extracted from the GIS maps of major cities such as Dhaka, Miami, and Riyadh), considering different scenarios such as lane-based or non-lane-based flows, homogeneous or heterogeneous traffic, with or without pedestrians, etc. We also incorporate various car-following and lane-changing models to mimic traffic behaviors and investigate their performances. While the lane changing dilemma remains an open research question, our experimental evidence indicates: 1) lane-based approaches will not necessarily perform better in the case of currently-adopted non-lane-based scenarios, and 2) non-lane-based strategies may benefit system performance in lane-based scenarios while having heavy mixed traffic. Nonetheless, we reveal several new insights for on-road experiences both in developing and developed countries.

One of the critical limitations quantum computers are yet to overcome is quantum errors. Efforts have been made in quantum error correction fields to overcome them. However, since we are bound by the technology of this noisy intermediate-scale quantum era, it is important that we focus on the efficiency and optimization of our error correction circuits. In this paper, we present a way of extending quantum error correction circuits using quantum adders by utilizing the notion of majority voting over all the qubits. We use adder circuits and a comparator to achieve majority voting. We demonstrate how our proposed methodology exhibits a linear trend in the required number of quantum gates. Further, we confirm the accuracy and efficacy of our proposed methodology for optimizing the number of quantum gates through extensive simulation in IBM's Qiskit and compare them with existing literature.

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.

Epidemics and pandemics have been affecting human lives since time, and have sometimes altered the course of history. At this very moment, Coronavirus (COVID-19) pandemic has been the defining global health crisis. Now, perhaps for the first time in history, humanity as a whole has undergone major disruptions to life and some form of lockdown. New policies need to be forged by policy-makers for various sectors such as trading, banking, education, etc., to lessen losses and to heal quickly. For efficient policy-making, in turn, some prerequisites needed are historical trend analysis on the pandemic spread, future forecasting, the correlation between the spread of the disease and various socio-economic and environmental factors, etc. Besides, all of these need to be presented in an integrated manner in real-time to facilitate efficient policy-making. Therefore, in this work, we developed a web-based integrated real-time operational dashboard as a one-stop decision support system for COVID-19. In our study, we conducted a detailed data-driven analysis based on available data from multiple authenticated sources to predict the upcoming consequences of the pandemic through rigorous modeling and statistical analyses. We also explored the correlations between disease spread and diverse socio-economic as well as environmental factors. Furthermore, we presented how the outcomes of our work can facilitate both contemporary and future policy-making.

The recent Covid-19 pandemic elucidates the need for a better disease outbreak analysis and surveillance system, which can harness state-of-the-art data mining and machine learning techniques to produce better forecasting. In this regard, understanding the correlation between disease outbreaks and socioeconomic factors should pave the way for such systems by providing useful indicators, which are yet to be explored in the literature to the best of our knowledge. Therefore, in this study, we accumulated data on 72 infectious diseases and their outbreaks all over the globe over a period of 23 years as well as corresponding different socioeconomic data. We, then, performed point-biserial and spearman correlation analysis over the collected data. Our analysis of the obtained correlations demonstrates that various disease outbreak attributes are positively and negatively correlated with different socioeconomic indicators. For example, indicators such as lifetime risk of maternal death, adolescent fertility rate, etc., are positively correlated, while indicators such as life expectancy at birth, measles immunization, etc., are negatively correlated, with disease outbreaks that affect the digestive organ system. In this paper, we find and summarize the correlations between 126 outbreak attributes derived from the characteristics of the 72 diseases in consideration and 192 socioeconomic factors which is a novel contribution to the field of disease outbreak analysis and prediction.

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.

Secure management of healthcare records in dynamic contexts requires an understanding of the overall infrastructure of record flows and poses more challenges for vulnerable environments such as amongst the Rohingya refugees in Bangladesh. Understanding the overall infrastructure of how health clinics are providing medical treatments and how they are collecting and storing patient records is crucial as any changes or mismanagement in these records enables misuse or deliberate misinterpretations of medical data on various levels amongst individuals and Rohingya communities. Through an extensive field study in the Rohingya refugee camps in Bangladesh, we explored the management of healthcare records in different organizations. Over the course of our fieldwork, we interviewed 22 medical service providers from nine healthcare organizations connected to the Rohingya camps. Based on our findings, we design an abstract record management model and analyze it using a data provenance approach to identify the limitations of the existing record management. Our study shows vulnerabilities in ID management and security practices in healthcare record management. We further illustrate potential exploitation of these vulnerabilities through political, financial, and social lenses. To the best of our knowledge, this study is the first to discuss vulnerabilities in Rohingya refugees’ medical record management from political, social and economic views.

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.

Derailments present a frequent phenomenon in several developing countries, which result in massive loss of property along with death tolls. For preventing derailments, a real-time automated system is needed to detect uprooted or faulty rail blocks. One of the solutions in this context is to sense the vibration of the rail track having an incoming train and transmit the information to the train notifying it about the condition of the rail track ahead. However, existing studies in this regard are yet to present a pragmatic solution that enables much-demanded long-distance networking to transmit the sensed data. The demand for long-distance network communication between the sensor nodes and the incoming train is unavoidable, as stopping the train after sensing an uprooted or faulty rail block ahead needs a considerable response time and distance. Therefore, in this paper, we develop a low-cost, long-range, and highly reliable mobile multi-hop networking scheme to successfully transmit data sensed from rail tracks to an approaching train at a distance of around 2000m. By considering the effect of Fresnel’s Region in our study, we determine the suitable placement of the networking module on the rail track, which leads us to achieve a delivery ratio of more than 99%. We confirm this finding through rigorous experiments over a real testbed scenario enabling mobile multi-hop networking.

Microscopic car-following models can be applied in Autonomous Vehicles (AVs) to control the real-time longitudinal interactions among individual vehicles. Moreover, they can have a vital role in Advanced Vehicle Control and Safety Systems (AVCSSs) such as collision warning, adaptive cruise control, lane guidance driver assistance, brake assist as well as in modeling simulation of safety studies and capacity analysis in transportation science. In reality, sensor measurements are generally inaccurate. Surprisingly no comparative assessment of the car-following models in presence of sensor (measurement) errors for AVs exist till now to the best of our knowledge. Therefore, in this paper we evaluate the prominent car-following models for Avs in presence of sensor errors in terms of safety, trip times, flow and fuel efficiency through simulations. We show that sensor errors significantly impact safety and flow in all models, while they do increase trip times and fuel consumption of some of the models. None of the models is completely fault-tolerant and suitable for AVs as some models produce collisions and/or negative velocity while all models violate traffic light. Nonetheless, the k-leader Fuel-efficient Traffic Model (kFTM) is the most fault-tolerant negative velocity and collision free model having reasonable trip times and energy consumption among the investigated models.

Inefficient traffic signal control system is one of the most important causes of traffic congestion in the cities of developing countries such as Bangladesh, India, Kenya, etc. This can be mitigated by adopting a decentralized traffic-responsive signal system, where vehicle detection is performed on the road through different image-based deep learning architectures amenable to limited-resource embedded platforms as available in developing countries. Deep learning architectures currently available in this regard demand high computational resources to achieve higher inference speed and better accuracy. Besides, the few existing limited-resource deep learning architectural alternatives neither attain higher inference speed nor substantial accuracy due to not overcoming the inherent limitations. To this extent, in this study, we propose a novel limited-resource deep learning architecture, namely DhakaNet, for real-time vehicle detection in on-road (street-view) traffic images. Our proposed architecture leverages enhancing Cross-Stage Partial Network and Path Aggregation Network to build the backbone and head networks, respectively. Besides, we develop a novel multi-scale attention module to extract multi-scale meaningful features from the images, where the developed multi-scale attention module boosts the detection accuracy at the cost of small overhead. Rigorous experimental evaluation of our proposed DhakaNet over three benchmark street-view traffic datasets such as DhakaAI, IITM-HeTra-A, and IITM-HeTra-B shows up to 51% faster inference speed at a similar accuracy, or up to 13% higher accuracy at a similar inference speed compared to other state-of-the-art limited-resource deep learning architectural alternatives.

Bangladesh (a low-income country) has a significant number of people dependent on alms for daily survival. These people, who we address as extremely impoverished people (EIP) are deprived of even basic healthcare. Their extreme levels of poverty, coupled with low literacy skills, and complete lack of access to technology means that they are unaware of existing low-cost/free healthcare services (as arranged by local hospitals) available for EIPs. In this paper, we address this gap by means of a carefully-crafted solution, Dakter Bari (a term in Bengali that translates to "Doctor's Home'' in English), that is contextually tailored to enable healthcare access to impoverished people. Extracting critical insights from our field study with (N=70) EIPs, we create a pathway for availing lower-cost healthcare solutions using intermediaries for information dissemination. These intermediaries are small businesses that impoverished people visit often. We also conduct field studies with (N=71) intermediary partners and (N=10) hospitals to identify challenges and realities of such intermediary-based solutions. Based on our findings, we design, iteratively develop, deploy, and user-test our system in real cases and collect feedback from related stakeholders. Preliminary analysis on usage of our system (deployed at intermediaries) revealed 255 healthcare requests made by EIPs via our system in six months. We connect our finding to the broader interests of CSCW around contextualized intermediation, inclusive healthcare, and sustainability of deployed systems.

A recent development emanating from RFID technology is Near Field Communication (NFC). Basically, NFC is a popular short range (<10 cm) wireless communication technology with applications in areas sensitive to security and privacy concerns such as contactless payment. Since NFC communications require very close proximity between two communicating devices (e.g., a smartcard and a terminal), it is generally believed that Man-in-the-Middle (MITM) attacks are practically infeasible here. Contrasting this belief, in this paper, we successfully establish MITM attack in NFC communications between a passive tag and an active terminal. We carefully present physical fundamentals of the attack, our engineering design, and results of successful attack implementation. Subsequently, we present the practical applicability of our MITM attack that exploits a potential vulnerability in EMV based contactless payment protocol, which arises due to separation between card authentication and transaction authorization phases. We demonstrate how an attacker can compromise the integrity of a contactless payment using a malicious MITM card, and also present multiple attack/victim scenarios to analyze different types of impacts of our attack. Further, we conduct rigorous experimental studies to analyze both hardware and practical ramifications of our attack. Finally, we propose a mechanism to detect the MITM attack based on experimental analysis that demands no additional hardware.

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.

Securing information transmission is critical today. However, with rapidly developing powerful quantum technologies, conventional cryptography techniques are becoming more prone to attacks each day. New techniques in the realm of quantum cryptography to preserve security against powerful attacks are slowly emerging. What is important though now is the fidelity of the cryptography, because security with massive processing power is not worth much if it is not correct. Focusing on this issue, we propose a method to enhance the fidelity of quantum cryptography using maximally entangled qubit pairs. For doing so, we created a graph state along a path consisting of all the qubits of ibmqx4 and ibmq_16_melbourne respectively and we measure the strength of the entanglement using negativity measurement of the qubit pairs. Then, using the qubits with maximal entanglement, we send the modified encryption key to the receiver. The key is modified by permutation and superdense coding before transmission. The receiver reverts the process and gets the actual key. We carried out the complete experiment in the IBM Quantum Experience project. Our result shows a 15% to 20% higher fidelity of encryption and decryption than a random selection of qubits.

Social media platforms are widely used by people to report, access, and share information during outbreaks and epidemics. Although government agencies and healthcare institutions in developed regions are increasingly relying on social media to develop epidemic forecasts and outbreak response, there is a limited understanding of how people in developing regions interact on social media during outbreaks and what useful insights this dataset could offer during public health crises. In this work, we examined 28,688 tweets to identify public health issues during dengue epidemic in Bangladesh and found several insights, such as irregularities in dengue diagnosis and treatment, shortage of blood supply for Rh negative blood groups, and high local transmission of dengue during Eid-ul-Adha, that impact disease preparedness and outbreak response. We discuss the opportunities and challenges in analyzing tweets and outline how government agencies and healthcare institutions can use social media health data to inform policy making during public health crises.

Demand for fast data sharing among smart devices is rapidly increasing. This trend creates challenges towards ensuring essential security for online shared data while maintaining the resource usage at a reasonable level. Existing research studies attempt to leverage compression based encryption for enabling such secure and fast data transmission replacing the traditional resource-heavy encryption schemes. Current compression-based encryption methods mainly focus on error insensitive digital data formats and prone to be vulnerable to different attacks. Therefore, in this paper, we propose and implement a new Huffman compression based Encryption scheme using lightweight dynamic Order Statistic tree (HEliOS) for digital data transmission. The core idea of HEliOS involves around finding a secure encoding method based on a novel notion of Huffman coding, which compresses the given digital data using a small sized "secret" (called as secret_intelligence in our study). HEliOS does this in such a way that, without the possession of the secret intelligence, an attacker will not be able to decode the encoded compressed data. Hence, by encrypting only the small-sized intelligence, we can secure the whole compressed data. Moreover, our rigorous real experimental evaluation for downloading and uploading digital data to and from a personal cloud storage Dropbox server validates efficacy and lightweight nature of HEliOS.

Lying is a (practically) unavoidable component of our day to day interactions with other people, and it includes both oral and textual communications (e.g. text entered via smartphones). Detecting when a person is lying has important applications, especially with the ubiquity of messaging via smart-phones, coupled with rampant increases in (intentional) spread of mis-information today. In this paper, we design a technique to detect whether or not a person's textual inputs when typed via a smartphone indicate lying. To do so, first, we judiciously develop a smartphone based survey that guarantees any participant to provide a mix of true and false responses. While the participant is texting out responses to each question, the smartphone measures readings from its inbuilt inertial sensors, and then computes features like shaking, acceleration, tilt angle, typing speed etc. experienced by it. Subsequently, for each participant (47 in total), we glean the true and false responses using our own experiences with them, and also via informal discussions with each participant. By comparing the responses of each participant, along with the corresponding motion features computed by the smartphone, we implement several machine learning algorithms to detect when a participant is lying, and our accuracy is around 70% in the most stringent leave-one-out evaluation strategy. Later, utilizing findings of our analysis, we develop an architecture for real-time lie detection using smartphones. Yet another user evaluation of our lie detection system yields 84%-90% accuracy in detecting false responses.

Abuse in cyber space is a problem requiring immediate attention. Unfortunately, despite advances in Natural Language Processing techniques, there are clear limitations in detecting instances of cyber abuse today. Challenges arising due to different languages that teens communicate with today, and usage of codes along with code mixing and code switching make the design of a comprehensive approach very hard. Existing NLP based approaches for detecting cyber abuse thus suffer from a high degree of false negatives and positives. In this paper, we investigate a new approach to detect instances of cyber abuse. Our approach is motivated by the premise that abusers tend to have unique facial expressions while engaging in an actual abuse episode, and if we are successful, such an approach will be language-agnostic. Here, using only four carefully identified facial features without any language processing, and realistic experiments with 15 users, our system proposed in this paper achieves 98% accuracy for same-user evaluation and up to 74% accuracy for cross-user evaluation in detecting instances of cyber abuse.

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%.

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 paper, we propose a machine learning based approach named as Green MapReduce Cluster (GMC) that reduces the total energy consumption of a MapReduce cluster considering both computational energy and cooling energy. GMC 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 GMC over a real testbed. Our evaluation reveals that GMC reduces total energy consumption by up to 47% compared to other alternatives while experiencing marginal throughput degradation in a few cases.

Detecting count of human beings accurately in a closed indoor environment is crucial in diverse application areas including search and rescue, surveillance, customer analytics, abnormal event detection, human gait characterization, congestion analysis and many more. Moreover, it has significant importance in preventing any intrusion in a secured indoor space such as a bank vault. Sensors-based technologies (for example camera, PR, etc.) are becoming more popular day by day as the regular methodologies are not good enough to ensure enhanced security in a closed indoor environment. As sensors used in these technologies have to be deployed in visible places, there exist possibilities of damaging the sensors by the intruder. Therefore, this paper proposes a novel methodology to detect human count in such closed indoor setting, which can be deployed in any hidden place. Here, human count is done based on four environmental gaseous parameters (Carbon Dioxide, Liquefied Petroleum Gas or LPG, Nitrogen Dioxide, and Sulfur Dioxide) and two weather parameters (temperature and humidity). Real experiments are done under closed controlled settings and counting is done using machine learning algorithms such as Bagging, Random-Forest, IBK, and J48. We achieve more than 99% accuracy for some of the classifiers in detecting the number of humans present.

In the context of developed countries, a considerable amount of research studies have been done to make Human-Computer Interaction (HCI) as natural and intuitive as possible for the public computing devices. Given the proliferation of public computing devices in the developing regions, a context-aware HCI design, focusing the low-literate and novice users from these regions, has the utmost importance. Taking the hygiene issue with the usage of touch-based public computing devices into account, in-air hand gestures are considered as one of the most natural forms of non-verbal interaction in this regard. In this paper, we present Duity -- a low-cost finger-count based hand gesture recognition system -- which reliably tackles the hurdle faced by novice and low-literacy populations. Exploiting the reflective property of visible light spectrum, we develop a mechanism that can reliably count fingers and thus identify different gestures. The system is fine-tuned to such an extent that varying lighting conditions and environmental setups can not perturb the accurate identification of the gestures. Our claims are supported by a user evaluation, which takes multiple scenarios into accounts, confirming the robustness, usability, and acceptability of Duity.

The ability of smart devices to recognize their owners or valid users gains attention with the advent of widespread highly sensitive usage of these devices such as storing secret and personal information. Unlike the existing techniques, in this paper, we propose a very lightweight single-time user identification technique that can ensure a unique authentication by presenting a system near-to-impossible to breach for intruders. Here, we have conducted a thorough study over single-time usage data collected from 33 users. The study reveals several new findings, which in turn, leads us to a novel solution exploiting a new machine learning technique. Our evaluation confirms that the proposed solution operates with only 5% False Acceptance Rate (FAR) and only 6% False Rejection Rate (FRR) over the data collected from 33 users. We further evaluate the performance through comparing its performance with some existing machine learning techniques. Finally, we perform a real implementation of our proposed solution as a mobile application to conduct a rigorous user evaluation over 27 participants using three different devices in order to show how the solution works in practical situations. Outcomes of the user evaluation demonstrate as low as 0% FAR after letting intruders to mimic the actual user, which ensures extremely low probability of being breached. Moreover, we let 2 users to continuously use our application over 25 days in different states during their operation. Outcomes of this evaluation demonstrate as low as 1% FRR confirming the usability of our technique in long-term usage.

Reading printed documents and writing on a paper pose a great challenge for visually-impaired people. Existing studies that attempt to solve these challenges are expensive and not feasible in low-income context. Moreover, these studies solve reading and writing problems separately. On the contrary, in this study, we propose FLight, a low-cost reading and writing system for economically less-privileged people. FLight uses ink-based Braille characters as the medium of textual representation. This helps in keeping a compact spatial representation of texts, yet achieving a low-cost status. Additionally, FLight utilizes a low-cost wearable device to enhance ease of reading by visually-impaired people. We conduct a participatory design and iterative evaluation involving five visually-impaired children in Bangladesh for more than 18 months. Our user evaluation reveals that FLight is easy-to-use, and exhibits a potential low-cost solution for economically less-privileged visually-impaired people.

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.

One of the crucial aspects in designing Network-on-Chip (NoC) is to ensure reliability, which is little explored for 3D NoCs in the literature till now. Therefore, in this paper, we investigate a novel fault-tolerant design for 3D NoCs that can go beyond existing fault-tolerant designs that are mostly applicable for 2D NoCs. In our design, we propose to divide a 3D NoC into 2×2×2 sub-networks or blocks. Then, we place four spare routers at the centers of four different planes. Here, we propose sparing of routers considering the fact that routers incur much lower cost compared to that of main processing elements such as processor. Later, we formulate separate mathematical models pertinent to our proposed design along with two benchmark designs. Subsequently, based on the models, we simulate performances of our proposed design along with the benchmark ones in terms of reliability and mean time to failure (MTTF). Simulation results demonstrate that our proposed design can enhance fault tolerance of 3D NoCs by significant margins compared to that of existing benchmark designs.

In a heterogeneous computing cluster, cluster objectives are conflicting to each other. Selecting a right combination of machines is necessary to enhance cluster performance, and to optimize all the cluster objectives. In this paper, we perform empirical performance analyses of a real cluster with our year-long collected data, formulate a new many-objective optimization problem for clusters, and integrate a greedy approach with the existing NSGA-III algorithm to solve this problem. From our experimental results, we find our approach performs better than existing optimization approaches.

A recent development emanating from the widely used RFID technology is Near Field Communication (NFC). Basically, NFC is a popular short range (<;10cm) wireless communication technology with applications in areas sensitive to security and privacy concerns including contact-less payment. Since NFC communications require very close proximity between two communicating devices (for example a smartcard and a reader), it is generally believed that Man-in-the-Middle (MITM) attacks are practically infeasible here. On the contrary to this general belief, in this paper, we successfully establish MITM attacks in NFC communications between a passive tag and an active reader. We present physical fundamentals of the attack, our engineering design, and results of our successful implementation. We also present practical impacts of the attack from the perspective of how a malicious user can leverage our MITM attack to compromise integrity of contact-less payment transactions. Finally, we present insights to combat the MITM attack in NFC communications towards the end of the paper.

Communication overWireless Nanosensor Networks (WNSNs) is envisioned to experience different challenges due to having unique features such as specialized nano-antenna behavior, short transmission range, limited operating capabilities, etc. Among all these features, nano-antenna behavior plays a noteworthy role on overall network-level performance. To the best of our knowledge, the role is yet to be investigated in the literature from the perspective of directional nano-antenna. Therefore, in this paper, we present a model of directional nano-antenna and analyze behavior of the nano-antenna through simulation conducted by CST microwave studio. Furthermore, using directional nano-antennas, we propose a new propagation loss aware routing protocol for WNSNs with a view to ensuring minimum path loss along with a delicate balance among different network-level performance metrics such as throughput, end-toend delay, and energy consumption. In addition, to evaluate the performance of our proposed protocol and compare that against the performances of existing alternatives, we incorporate the nano-antenna model and our proposed protocol in ns-2 simulator after performing necessary customizations in the simulator. We perform rigorous simulation in ns-2 and confirm efficacy of our proposed protocol through the simulation results.

In this paper, we study the cooperation model to design and develop a system that improves the reception rate of delay sensitive packets in realtime applications. While making a cooperation decision, a user with real-time traffic must consider the queue size, packet deadline, the traffic rate, channel condition. Existing cooperation models do not take these factor into consideration and therefore, are not applicable to the improvement of reliability of real-time traffic. We formulate the cooperation model using a Markov decision process (MDP) which is NP-Hard. The MDP-based optimization problem of a single pair of primary and secondary user is analyzed to find the optimum transmission decision in different states of the network. Based on the findings of a single pair case, we develop a distributed cooperation algorithm where a user (primary or secondary) quantifies the immediate and future impact of cooperation and takes its decision accordingly. Extensive simulation is performed to evaluate the performance of the proposed cooperation algorithm which reveals its efficacy compared to the non-cooperative scheme.

Dynamic spectrum access through Cognitive Radio Networks (CRNs) and exploiting multiple radios on a single node are two different well accepted techniques for enhancing network performance. However, simultaneous usage of both the techniques, i.e., augmenting dynamic spectrum access with multiple radios, is yet to be investigated in the literature. Therefore, in this paper, we investigate simultaneous usage of both the techniques. In our investigation, we perform rigorous ns-2 simulation. Simulation results reveal a key finding - augmenting spectrum harvesting with multiple radios makes throughput worse, however, can improve delay. Besides, as the simulation results do not reveal micro-level aspects of the improved delay performance, we perform mathematical modeling of the delay to do so. Further, we present numerical results based on the models to demonstrate the micro-level aspects.

Ubiquitous heterogeneous wireless networks demand a general-purpose multi-objective vertical hand-off mechanism taking into account network dynamics as one of its decision attributes. However, to the best of our knowledge, such a mechanism is yet to be devised. Therefore, in this paper, we propose a new multi-objective vertical hand-off (MOVH) mechanism that takes into account network dynamics as one of its decision attributes. We customize Multi-Objective Genetic Algorithm (MOGA) in this regard. The results obtained from our numerical simulation demonstrate that the proposed mechanism yields better scalability and stability. We also evaluate the performance of our MOVH mechanism against two most popular alternatives: GRA and TOPSIS. This evaluation comprises of both test-bed experiments and ns-2 simulation. The results from both test-bed experiments and ns-2 simulation demonstrate that the proposed MOVH mechanism has significant performance improvement over both GRA and TOPSIS.

Congestion control in multi-hop infrastructure wireless mesh networks is both an important and a unique problem. It is unique because it has two prominent causes of failed transmissions which are difficult to tease apart - lossy nature of wireless medium and high extent of congestion around gateways in the network. The concurrent presence of these two causes limits applicability of already available congestion control mechanisms, proposed for wireless networks. Prior mechanisms mainly focus on the former cause, ignoring the latter one. Therefore, we address this issue to design an end-to-end congestion control mechanism for infrastructure wireless mesh networks in this paper. We formulate the congestion control problem and map that to the restless multi-armed bandit problem, a well-known decision problem in the literature. Then, we propose three myopic policies to achieve a near-optimal solution for the mapped problem since no optimal solution is known to this problem. We perform comparative evaluation through ns-2 simulation and a real testbed experiment with a wireline TCP variant and a wireless TCP protocol. The evaluation reveals that our proposed mechanism can achieve up to 52% increased network throughput and 34% decreased average energy consumption per transmitted bit in comparison to the other end-to-end congestion control variants.

Specialized physical and medium access control layer operations of WiMAX introduce a challenging problem to formulate a precise cross-layer analytical model for its capacity estimation. Although the cross-layer formulation is yet to be attempted in the literature, it can effectively serve as a fast and cost-effective tool to facilitate future deployment planning, efficient network maintenance, etc., and thus can expedite to cope up with the recent blast in WiMAX deployment. Therefore, we propose a novel formulation of cross-layer analytical model for WiMAX capacity estimation in this paper. Our formulation addresses intricate interrelationships between different layers in a protocol stack. Besides, the formulation separately addresses both reliable and unreliable transport layer transmissions. We verify our formulated model using ns-2 simulation, which reveals that the model can estimate the capacity of a WiMAX network with as low as 1% average error having a standard deviation of only 1%. Consequently, we find that the proposed model can achieve up to a 97% decrease in the average error compared to other available analytical models in the literature. Finally, we present different applications of the proposed model by utilizing it in network planning and protocol overhead analysis.

The early success of wireless sensor networks has led to a new generation of increasingly sophisticated sensor network applications, such as HP's CeNSE. These applications demand high network throughput that easily exceeds the capability of low-power 802.15.4 radios that are most commonly used in today's sensor nodes. To address this issue, this paper investigates an energy-efficient approach to supplementing an 802.15.4 based sensor network with high bandwidth, high power, longer range radios such as 802.11. Exploiting a key observation that the high bandwidth radio achieves low energy consumption per transmitted bit of data due to its inherent transmission efficiency, we propose a hybrid network architecture that utilizes an optimal density of dual-radio (802.15.4 and 802.11) nodes to augment a sensor network having only 802.15.4 radios. We present a cross-layer mathematical model to calculate this optimal density, which strikes a balance between the low energy per bit of the high-bandwidth radio and the low sleep power of 802.15.4 radio. Experimental results obtained using a wireless testbed reveal that our architecture improves the average energy per bit, the time elapsed before half of the nodes drain their battery, and the end-to-end delay by 62%, 106%, and 73% respectively, compared to a network that uses only 802.15.4 radios.

Maintaining the performance of reliable transport protocols, such as TCP, over wireless mesh networks is a challenging problem due to the unique characteristics of wireless mesh networks such as the lossy nature of the communication medium, absence of a base station, similarity in traffic pattern experienced by neighboring mesh nodes, etc. One of the reasons for the poor performance of conventional TCP variants over wireless mesh networks is that the congestion control mechanisms in conventional TCP variants do not explicitly account for these unique characteristics. To address this problem, this paper proposes a novel neural network based congestion control technique for reliable data transfer over wireless mesh networks. We analyze the proposed congestion control technique in detail and incorporate it into TCP to create a variant that we name intelligent TCP or iTCP. We evaluate the performance of iTCP using ns-2 simulations. Our results demonstrate that our proposed congestion control technique exhibits a significant improvement in total network throughput and average energy consumption per bit compared to congestion control techniques used in other variants of TCP.