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The popularity of the Internet of Things (IoT) has driven its usage in our homes and industries over the past 10-12 years. However, there have been some major issues related to identity management and ownership transfer involving IoT devices, particularly for consumer IoT devices, e. g. smart appliances such as smart TVs, smart refrigerators, and so on. There have been a few attempts to address this issue; however, user-centric and effective ownership and identity management of IoT devices have not been very successful so far. Recently, blockchain technology has been used to address these issues with limited success. This article presents a Self-sovereign Identity (SSI) based system that facilitates a secure and user-centric ownership management and transfer of consumer IoT devices. The system leverages a number of emerging technologies, such as blockchain and decentralized identifiers (DID), verifiable credentials (VC), under the umbrella of SSI. We present the architecture of the system based on a threat model and requirement analysis, discuss the implementation of a Proof-of-Concept based on the proposed system and illustrate a number of use-cases with their detailed protocol flows. Furthermore, we analyse its security using ProVerif, a state-of-the art protocol verification tool and examine its performance.

Sharing cooking recipes is a great way to exchange culinary ideas and provide instructions for food preparation. However, categorizing raw recipes found online into appropriate food genres can be challenging due to a lack of adequate labeled data. In this study, we present a dataset named the ``Assorted, Archetypal, and Annotated Two Million Extended (3A2M+) Cooking Recipe Dataset" that contains two million culinary recipes labeled in respective categories with extended named entities extracted from recipe descriptions. This collection of data includes various features such as title, NER, directions, and extended NER, as well as nine different labels representing genres including bakery, drinks, non-veg, vegetables, fast food, cereals, meals, sides, and fusions. The proposed pipeline named 3A2M+ extends the size of the Named Entity Recognition (NER) list to address missing named entities like heat, time or process from the recipe directions using two NER extraction tools. 3A2M+ dataset provides a comprehensive solution to the various challenging recipe-related tasks, including classification, named entity recognition, and recipe generation. Furthermore, we have demonstrated traditional machine learning, deep learning and pre-trained language models to classify the recipes into their corresponding genre and achieved an overall accuracy of 98.6\%. Our investigation indicates that the title feature played a more significant role in classifying the genre.

Cardiovascular diseases (CVD) are the leading cause of death globally, and early detection can significantly improve outcomes for patients. Machine learning (ML) models can help diagnose CVDs early, but their performance is limited by the data available for model training. Privacy concerns in healthcare make it harder to acquire data to train accurate ML models. Federated learning (FL) is an emerging approach to machine learning that allows models to be trained on data from multiple sources without compromising the privacy of the individual data owners. This survey paper provides an overview of the current state-of-the-art in FL for CVD detection. We review the different FL models proposed in various papers and discuss their advantages and challenges. We also compare FL with traditional centralized learning approaches and highlight the differences in terms of model accuracy, privacy, and data distribution handling capacity. Finally, we provide a critical analysis of FL's current challenges and limitations for CVD detection and discuss potential avenues for future research. Overall, this survey paper aims to provide a comprehensive overview of the current state-of-the-art in FL for CVD detection and to highlight its potential for improving the accuracy and privacy of CVD detection models.

The higher education management systems first identified and realized the trap of pitting innovation against privacy while first addressing COVID-19 social isolation challenges in 2020. In the age of data sprawl, we observe the situation has been exacerbating since then. Integrating blockchain technology has the potential to address the recent and emerging challenges in the higher education management system. This paper unravels the Good (scopes and benefits), Bad (limitations), and Ugly (challenges and trade-offs) of blockchain technology integration in the higher education management paradigm in the existing landscape. Our study adopts both qualitative and quantitative approaches to explore the experiences of educators, researchers, students, and other stakeholders and fully understand the blockchain's potential and contextual challenges. Our findings will envision an efficient, secure, and transparent higher education management system and help shape the debate (and trade-offs) pertaining to the recent shift in relevant business and management climate and regulatory sentiment.

Graphical passwords are implemented as an alternative scheme to replace alphanumeric passwords to help users to memorize their password. However, most of the graphical password systems are vulnerable to shoulder-surfing attack due to the usage of the visual interface. In this research, a method that uses shifting condition with digraph substitution rules is proposed to address shoulder-surfing attack problem. The proposed algorithm uses both password images and decoy images throughout the user authentication procedure to confuse adversaries from obtaining the password images via direct observation or watching from a recorded session. The pass-images generated by this suggested algorithm are random and can only be generated if the algorithm is fully understood. As a result, adversaries will have no clue to obtain the right password images to log in. A user study was undertaken to assess the proposed method's effectiveness to avoid shoulder-surfing attacks. The results of the user study indicate that the proposed approach can withstand shoulder-surfing attacks (both direct observation and video recording method).The proposed method was tested and the results showed that it is able to resist shoulder-surfing and frequency of occurrence analysis attacks. Moreover, the experience gained in this research can be pervaded the gap on the realm of knowledge of the graphical password.

Cooking recipes allow individuals to exchange culinary ideas and provide food preparation instructions. Due to a lack of adequate labeled data, categorizing raw recipes found online to the appropriate food genres is a challenging task in this domain. Utilizing the knowledge of domain experts to categorize recipes could be a solution. In this study, we present a novel dataset of two million culinary recipes labeled in respective categories leveraging the knowledge of food experts and an active learning technique. To construct the dataset, we collect the recipes from the RecipeNLG dataset. Then, we employ three human experts whose trustworthiness score is higher than 86.667% to categorize 300K recipe by their Named Entity Recognition (NER) and assign it to one of the nine categories: bakery, drinks, non-veg, vegetables, fast food, cereals, meals, sides and fusion. Finally, we categorize the remaining 1900K recipes using Active Learning method with a blend of Query-by-Committee and Human In The Loop (HITL) approaches. There are more than two million recipes in our dataset, each of which is categorized and has a confidence score linked with it. For the 9 genres, the Fleiss Kappa score of this massive dataset is roughly 0.56026. We believe that the research community can use this dataset to perform various machine learning tasks such as recipe genre classification, recipe generation of a specific genre, new recipe creation, etc. The dataset can also be used to train and evaluate the performance of various NLP tasks such as named entity recognition, part-of-speech tagging, semantic role labeling, and so on. The dataset will be available upon publication: https://tinyurl.com/3zu4778y.

In teleradiology, medical images are transmitted to offsite radiologists for interpretation and the dictation report is sent back to the original site to aid timely diagnosis and proper patient care. Although teleradiology offers great benefits including time and cost efficiency, after-hour coverages, and staffing shortage management, there are some technical and operational limitations to overcome in reaching its full potential. We analyzed the current teleradiology workflow to identify inefficiencies. Image unavailability and delayed critical result communication stemmed from lack of system integration between teleradiology practice and healthcare institutions are among the most substantial factors causing prolonged turnaround time. In this paper, we propose a blockchain-based medical image sharing and automated critical-results notification platform to address the current limitation. We believe the proposed platform will enhance efficiency in workflow by eliminating the need for intermediaries and will benefit patients by eliminating the need for storing medical images in hard copies. While considerable progress was achieved, further research on governance and HIPAA compliance is required to optimize the adoption of the new application. Towards an idea to a working paradigm, we will implement the prototype during the next phase of our study.

The promise of quantum computing is not speeding up conventional computing rather delivering an exponential advantage for certain classes of problems, with profound implications for cybersecurity for instance. With the advent and development of quantum computers, cyberspace security can surely become the most critical problem for the Internet in near future. On contrary, prosaic quantum technology can be promising to transform cybersecurity. This research aims to synthesize basic and fundamental studies concerning quantum cybersecurity that can be emerged both as a threat and solution to critical cybersecurity issues based on a systematic study. We provide a comprehensive, illustrative description of the current state-of-the-art quantum computing and cybersecurity and present the proposed approaches to date. Findings in quantum computing cybersecurity suggest that quantum computing can be adopted for the betterment of cybersecurity threats while it poses the most unexpected threats to cybersecurity. The focus and depth of this systematic survey not only provide quantum and cybersecurity practitioners and researchers with a consolidated body of knowledge about current trends in this area but also underpins a starting point for further research in this field.

Nursing homes (NHs) are critical healthcare infrastructures for caring frail older adults with 24/7 formal care and personal assistance. Adequate NH resource planning is of great importance to ensure desired quality of care and resident outcomes yet challenging. The challenge lies in the heterogeneous service demand of NH residents, due to the varied individual characteristics, the diverse dwelling duration with multiple competing discharge dispositions, and the diverse service need. Existing healthcare staffing literature often assumed a homogeneous population of NH residents and neglected the complexity of service demand heterogeneity. This work proposes an analytics-based modeling framework with a user-friendly decision support platform for NH resource planning. The proposed framework characterizes the heterogeneous service demand of NH residents via novel integration of advanced statistical modeling, computer simulation and optimization techniques. We further provide a case study using real data from our industrial collaborator to demonstrate the effectiveness and superior performance of the proposed work. The impacts of service utilization heterogeneity and service need heterogeneity on resource planning decisions are investigated as well., Comment: 19 pages

Nursing homes are critical facilities for caring frail older adults with round-the-clock formal care and personal assistance. To ensure quality care for nursing home residents, adequate staffing level is of great importance. Current nursing home staffing practice is mainly based on experience and regulation. The objective of this paper is to investigate the viability of experience-based and regulation-based strategies, as well as alternative staffing strategies to minimize labor costs subject to heterogeneous service demand of nursing home residents under various scenarios of census. We propose a data-driven analysis framework to model heterogeneous service demand of nursing home residents and further identify appropriate staffing strategies by combing survival model and computer simulation techniques as well as domain knowledge. Specifically, in the analysis, we develop an agent-based simulation tool consisting of four main modules, namely individual length of stay predictor, individual daily staff time generator, facility level staffing strategy evaluator, and graphical user interface. We use real nursing home data to validate the proposed model, and demonstrate that the identified staffing strategy significantly reduces the total labor cost of certified nursing assistants compared to the benchmark strategies. Additionally, the proposed length of stay predictive model that considers multiple discharge dispositions exhibits superior accuracy and offers better staffing decisions than those without the consideration. Further, we construct different census scenarios of nursing home residents to demonstrate the capability of the proposed framework in helping adjust staffing decisions of nursing home administrators in various realistic settings., Comment: 19 pages

Epidemics and pandemics have ravaged human life since time. To combat these, novel ideas have always been created and deployed by humanity, with varying degrees of success. At this very moment, the COVID-19 pandemic is the singular global health crisis. Now, perhaps for the first time in human history, almost the whole of humanity is experiencing some form of hardship as a result of one invisible pathogen. This once again entails novel ideas for quick eradication, healing and recovery, whether it is healthcare, banking, travel, education or any other. For efficient policy-making, clear trends of past, present and future are vital for policy-makers. With the global impacts of COVID-19 so severe, equally important is the analysis of correlations between disease spread and various socio-economic and environmental factors. Furthermore, all of these need to be presented in an integrated manner in real-time to facilitate efficient policy making. To address these issues, in this study, we report results on our development and deployment of a web-based integrated real-time operational dashboard as an important decision support system for COVID-19. In our study, we conducted data-driven analysis based on available data from diverse authenticated sources to predict upcoming consequences of the pandemic through rigorous modeling and statistical analyses. We also explored correlations between pandemic spread and important socio-economic and environmental factors. Furthermore, we also present how outcomes of our work can facilitate efficient policy making in this critical hour., Comment: 36 pages, 19 figures, CSCW 2021/UbiComp 2021

We propose a method to tackle the problem of mapless collision-avoidance navigation where humans are present using 2D laser scans. Our proposed method uses ego-safety to measure collision from the robot's perspective while social-safety to measure the impact of our robot's actions on surrounding pedestrians. Specifically, the social-safety part predicts the intrusion impact of our robot's action into the interaction area with surrounding humans. We train the policy using reinforcement learning on a simple simulator and directly evaluate the learned policy in Gazebo and real robot tests. Experiments show the learned policy can be smoothly transferred without any fine tuning. We observe that our method demonstrates time-efficient path planning behavior with high success rate in mapless navigation tasks. Furthermore, we test our method in a navigation among dynamic crowds task considering both low and high volume traffic. Our learned policy demonstrates cooperative behavior that actively drives our robot into traffic flows while showing respect to nearby pedestrians. Evaluation videos are at https://sites.google.com/view/ssw-batman, Comment: Accepted in ICRA 2020

Over the last decade, smartphones have changed radically to support us with mHealth technology, cloud computing, and machine learning algorithm. Having its multifaceted facilities, we present a novel smartphone-based noninvasive hemoglobin (Hb) level prediction model by analyzing hue, saturation and value (HSV) of a fingertip video. Here, we collect 60 videos of 60 subjects from two different locations: Blood Center of Wisconsin, USA and AmaderGram, Bangladesh. We extract red, green, and blue (RGB) pixel intensities of selected images of those videos captured by the smartphone camera with flash on. Then we convert RGB values of selected video frames of a fingertip video into HSV color space and we generate histogram values of these HSV pixel intensities. We average these histogram values of a fingertip video and consider as an observation against the gold standard Hb concentration. We generate two input feature matrices based on observation of two different data sets. Partial Least Squares (PLS) algorithm is applied on the input feature matrix. We observe R2=0.95 in both data sets through our research. We analyze our data using Python OpenCV, Matlab, and R statistics tool.

We introduce transversely structured all-dielectric waveguides which exploit the vectorial nature of light to achieve extreme sub-wavelength confinement in high index dielectrics, enabling characteristic mode dimensions below $\lambda_0^2$/1,000 without metals or plasmonics. We also derive the metric of optical concentration and show its convenient usage in characterizing enhanced linear and non-linear interactions at the nanoscale. This work expands the toolbox of nanophotonics and opens the door to new types of ultra-efficient and record performing linear and nonlinear devices with broad applications spanning classical and quantum optics.

We combined optical and atomic force microscopy to observe morphology and kinetics of microstructures that formed at free surfaces of unmodified pavement-grade 64-22 asphalt binders upon cooling from 150 $^{\circ}$C to room temperature (RT) at 5 $^{\circ}$C/min, and changes in these microstructures when the surface was terminated with a transparent solid (glass) or liquid (glycerol) over-layer. The main findings are: (1) At free binder surfaces, wrinkled microstructures started to form near the wax crystallization temperature ($\sim$45 $^{\circ}$C), then grew to $\sim$5 $\mu$m diameter, $\sim$25 nm wrinkle amplitude and 10-30$\%$ surface area coverage upon cooling to RT, where they persisted indefinitely without observable change in shape or density. (2) Glycerol coverage of the binder surface during cooling reduced wrinkled area and wrinkle amplitude three-fold compared to free binder surfaces upon initial cooling to RT; continued glycerol coverage at RT eliminated most surface microstructures within $\sim$4 hours. (3) No surface microstructures were observed to form at binder surfaces covered with glass. (4) Sub-micron bulk microstructures were observed by near-infrared microscopy beneath the surfaces of all binder samples, with size, shape and density independent of surface coverage. No tendency of such structures to float to the top or sink to the bottom of mm-thick samples was observed. (5) We attribute the dependence of surface wrinkling on surface coverage to variation in interface tension, based on a thin-film continuum mechanics model.

In this paper, we use reinforcement learning for safety driving in adversary settings. In our work, the knowledge in state-of-art planning methods is reused by single-step options whose action suggestions are compared in parallel with primitive actions. We show two advantages by doing so. First, training this reinforcement learning agent is easier and faster than training the primitive-action agent. Second, our new agent outperforms the primitive-action reinforcement learning agent, human testers as well as the state-of-art planning methods that our agent queries as skill options.