1,721,253 research outputs found
PRUCC-RM: Permission-Role-Usage Cardinality Constrained Role Mining
No full textRole Based Access Control (RBAC) models have been adopted in many organizations as the standard way to implement security policies and assign access to restricted resources to roles and roles to users. To capture the business relationships within the organization and efficiently migrate towards RBAC, several role mining techniques have been defined. Constraints on the resulting roles and assignments to users can be imposed to filter out inconsistent situations produced by the automatic algorithm and to better capture the status of the organization. In this paper we are interested in constraints on the number of permissions that can be included in a role and on the number of persons a role can be assigned to. We analyze the problem and propose a couple of heuristics. The heuristics have been applied to standard datasets to validate their performance
MOBILE BASED SYMPTOM MANAGEMENT FOR PALLIATIVE CARE
Get PDFThe goal of palliative care is to improve the quality of life of terminally ill patients through the management of pain and other symptoms. Though the term `palliative care\u27 is well known in the developed world, it is relatively a new term in the developing world. According to WHO, each year 4.8 million people suffering from severe pain caused by cancer, fail to receive treatment due to lack of resources and other barriers.
In this thesis we have elaborated on the challenges faced by the rural breast cancer (BC) patients of Bangladesh and a solution for their palliative care treatment. Although breast cancer is commonly thought of as a disease of the developed world, the WHO statistics show that 69% of all BC deaths occur in developing countries. Unlike western countries where 89% of the women have a survival rate of more than 5 years, most BC patients in Bangladesh die because the majority of cases are diagnosed in late stages. These patients need palliative care which is almost absent in rural Bangladesh. These issues show the desperate need of a low cost palliative care system solution for the terminally ill patients of the developing world.
Based on detailed field studies, we have developed and deployed a mobile based remote symptom monitoring and management system named e-ESAS. Design of e-ESAS has evolved through continuous feedback from both the patients and doctors. e-ESAS is being used by 10 breast cancer patients to submit symptom values from their home for the last 10 months (Nov\u2711- Sep \u2712). Our results show how e-ESAS with motivational videos not only helped the patients to have a `dignified\u27 life but also helped the doctors to achieve the goals of palliative care. Also the analyzed results are shown in 4 categories to appropriately measure the contribution of e-ESAS in improving the QoL.
This thesis also focuses on developing a mobile based pain intensity detection tool which is a first step in replacing the manual paper based scale for measuring pain. The tool also might play a big role in assessing the pain level of verbally impaired patients
Global Challenges in Accessing Mental Health Services and Addressing the Impact of Alzheimer’s Disease and Depression
Get PDFThis research project focuses on developing a quantum sensing system that can detect biomarkers associated with health disorders, like Alzheimer’s and depression. Our goal is to create a sensitive and highly selective quantum sensing device using a diamond nitrogen vacancy (NV) center. To train and test our quantum machine learning algorithms we will preprocess data from the available Human Connectome Project dataset. This dataset forms the basis of our quantum-based methods. The core of our project revolves around developing quantum machine learning algorithms that utilize techniques such as Support Vector Machines and neural networks to diagnose health disorders using data from quantum sensors. The integrated quantum computing resources in our system will efficiently handle the volumes of generated data. We will tailor the quantum algorithms and software for platforms like IBM Qiskit ensuring they are well trained, optimized, precise and efficient in diagnosing these disorders. To evaluate their performance, we will compare them against AI and ML techniques using the Human Connectome Project dataset. In collaboration with health professionals and stakeholders we aim to explore applications while addressing implementation challenges and strategies, for translating our research into clinical practice. Our research project serves as a connection, between quantum technology, machine learning and mental health with the goal of enhancing precision and transforming the way we treat Alzheimer’s disease and depression. This interdisciplinary approach holds promise in improving the level of care and overall results, for individuals grappling with these health conditions
Quantitative Multidimensional Stress Assessment from Facial Videos
Get PDFStress has a significant impact on the physical and mental health of an individual and is a growing concern for society, especially during the COVID-19 pandemic. Facial video-based stress evaluation from non-invasive cameras has proven to be a significantly more efficient method to evaluate stress in comparison to approaches that use questionnaires or wearable sensors. Plenty of classification models have been built for stress detection. However, most do not consider individual differences. Also, the results for such models are limited by a uni-dimensional definition of stress levels lacking a comprehensive quantitative definition of stress. The dissertation focuses on building a framework that utilizes the multilevel video frame representations from deep learning and the remote photoplethysmography signals extracted from the facial videos for stress assessment. The fusion model takes the inputs of a baseline video and a target video of the subject. The physiological features such as heart rate and heart rate variability are used with the initial stress scores generated from deep learning are used to predict the stress scores in cognitive anxiety, somatic anxiety, and self-confidence. To generate stress scores with better accuracy, the signal extraction method is improved by introducing the CWT-SNR method that uses the signal-to-noise ratio to assist the adaptive bandpass filtering in the post-processing of the signals. A study on phase space reconstruction features is performed and the results show the potential for additional accuracy improvement for the heart rate variability detection. To select the best deep learning architecture, multiple deep learning architectures are tested to build the deep learning model. Support Vector Regression is used to generate the output stress score results. Testing with the data from the UBFC-Phys dataset, the fusion model shows a strong correlation between ground truth and the predicted results
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
The pedagogic prosthetic: Augmented learning as content-in-motion in hybrid educational spheres
No full textThis article draws on the design and implementation of three mobile learning projects introduced by Flanagan in 2011, 2012 and 2014 engaging a total of 206 participants. The latest of these projects is highlighted in this article. Two other projects provide additional examples of innovative strategies to engage mobile and cloud systems describing how electronic and mobile technology can help facilitate teaching and learning, assessment for learning and assessment as learning, and support communities of practice. The second section explains the theoretical premise supporting the implementation of technology and promulgates a hermeneutic phenomenological approach. The third section discusses mobility, both in terms of the exploration of wearable technology in the prototypes developed as a result of the projects, and the affordances of mobility within pedagogy. Finally the quantitative and qualitative methods in place to evaluate m-learning are explained
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
A Comprehensive Context-Aware Interruption Management System
Get PDFThe interruption system is an application that prevents the user from noticing phone calls when he/she is busy, by turning off the ringtone. In a previous project, the user can enter his/her class and work schedule on Google Calendar. The intelligent interruption system can detect if the current time matches the range of one of the events in the user\u27s Google Calendar. Other contexts considered were: driving, relationship of the callers, and proximity of Bluetooth devices. This project is a continuation of the interruption system. We consider additional context, social media such as Twitter. Research is done on when is the best time to turn off the ringer when the user is using Twitter. If the user is using social media, the user isn\u27t as busy compared to, if the user is in class or at work. We further granularize social media activity such as reading messages, writing messages, and use these to help predict interruptions. We use the feedback provided by the user and employ machine learning approach which takes as input the different contexts and predicts if the user should be interrupted. We implemented a prototype application on Android operating system
mPeer: A Mobile Health Approach to Monitoring PTSD in Veterans
Get PDFMore than 2.2 million US service members have seen deployment to Iraq and Afghanistan over the past decade. As the number of veterans returning home has increased, the need for new and innovative approaches to the variety and severity of mental health issues experienced after deployment remains a national priority. Affecting between 15-20\% of the veteran population and largely treatment resistant, Post Traumatic Stress Disorder (PTSD) poses a challenging problem for the mental health community. Recent veteran related studies have suggested a paradigm shift in conceptualizing PTSD in terms of specific high-risk behaviors rather than traditional symptoms. Young and technology savvy, many veteran populations are uniquely poised to embrace mobile health (mHealth) approaches to monitoring and addressing health related issues. In this thesis, we document the design and implementation of a smartphone-based system that coordinates the collection of data potentially relevant for monitoring high-risk behavior in veterans. We describe the details of an unobtrusive smartphone application for the Android platform that collects data from a variety of smartphone sensors and administers daily self-report questionnaires. Finally, we confirm system performance with data from student volunteers
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