Python Code of Solving Quaternionic Quadratic Equations

In this project, we introduce Python code for solving Quaternionic Quadratic Equations(QQE). Liping Huang and Wasin So [2] derive explicit formulas for computing the roots of quaternionic quadratic equations. We study and motivated by their mathematical work and able to give convenient Python code to get solutions for any QQE. In section one, we give a brief introduction about quaternions, its history, algebra, and geometry[4]. Later we explain Huang and Wasin [2] work of how to derive an explicit formula for solving QQE and include the Python code to solve any QQE of the form $x^2+bx+c=0$ where $a,b\in\mathbb{H}$ in general. All necessary details about how to install and code using Python can be found from Python official website [5] and a wide range of practical examples can be learned from the book, Computational Physics with Python by Mark Newman[3]

Health and wellbeing promotion strategies for ‘hard to reach’ older people in England: a mapping exercise.

The version of the article that has been accepted for publication. This version may include revisions resulting from peer review but may be subject to further editorial input by Cambridge University Press.Background: Older people from deprived areas, the oldest old and those from ethnic minorities engage less in health promotion interventions and related research, potentially generating inequities. Aim: To explore and map the extent to which such ‘hard to reach’ groups of older people, are the focus of local health and wellbeing strategies in England. Methods: Document analysis of current health and wellbeing promotion strategies in a purposive sample of 10 localities in England with high proportions of some or all of the three hard-to-reach groups. Documents were analysed using an interpretive approach. Findings: A total of 254 documents were retrieved and reviewed. Much of the content of the documents was descriptive and reported the implications for resources/services of population ageing rather than actual initiatives. All localities had an Older People’s Strategy. Strategies to counter deprivation included redistribution of winter fuel payments, income maximisation, debt reduction and social inclusion initiatives, a focus on older owner occupiers and recruitment of village ‘agents’ to counter rural deprivation. The needs of the oldest old were served by integrated services for older people, a community alarm service with total coverage of the 85+ population, and dietary advice. The needs of Black and Ethnic Minority (BME) older people were discussed in all localities and responses included community work with BME groups, attention to housing needs and monitoring of service use by BME older people. Three other themes that emerged were: use of telecare technologies; a challenge to the idea of ‘hard to reach’ groups; and outreach services to those at most risk. Conclusions: Document analysis revealed a range of policy statements that may indicate tailoring of policy and practice to local conditions, the salience of national priorities, some innovative local responses to policy challenges and even dissenting views that seek to redefine the policy problem.Peer reviewe

TrustPass Blockchain based Trusted Digital Identity Platform towards Digital Transformation

According to the United States Census Bureau, by June 2019 world population on earth was 7.5 billion, which exceeds the world population of 7.2 billion as of 2015. Each of these citizens needs to prove their identity in order to fulfill their day-to-day routine. In this current digital revolution whole world is transforming to digitalization. Therefore, proving someones identity in the digital space is a must, because being able to track a person digitally can result in elimination of the identity theft and most incidents related to online harassments, while focusing on data privacy and security of citizens, we have proposed Trust Pass: Cyber Security Intelligence based trusted digital identity platform capable of registering and verifying service providers based on document validation neural network model (95.4% accuracy) and allowing citizens to authenticate themselves to service providers with three factor biometrics authentication with liveness detection neural network model (99.8% accuracy)

Explainable and Accurate Natural Language Understanding for Voice Assistants and Beyond

Joint intent detection and slot filling, which is also termed as joint NLU (Natural Language Understanding) is invaluable for smart voice assistants. Recent advancements in this area have been heavily focusing on improving accuracy using various techniques. Explainability is undoubtedly an important aspect for deep learning-based models including joint NLU models. Without explainability, their decisions are opaque to the outside world and hence, have tendency to lack user trust. Therefore to bridge this gap, we transform the full joint NLU model to be `inherently' explainable at granular levels without compromising on accuracy. Further, as we enable the full joint NLU model explainable, we show that our extension can be successfully used in other general classification tasks. We demonstrate this using sentiment analysis and named entity recognition.Comment: Accepted at CIKM 202

Engaging ‘hard to reach’ groups in health promotion: the views of older people and professionals from a qualitative study in England

Background Older people living in deprived areas, from black and minority ethnic groups (BME) or aged over 85 years (oldest old) are recognised as ‘hard to reach’. Engaging these groups in health promotion is of particular importance when seeking to target those who may benefit the most and to reduce health inequalities. This study aimed to explore what influences them practicing health promotion and elicit the views of cross-sector professionals with experiences of working with ‘hard to reach’ older people, to help inform best practice on engagement. Methods ‘Hard to reach’ older people were recruited through primary care by approaching those not attending for preventative healthcare, and via day centres. Nineteen participated in an interview (n = 15) or focus group (n = 4); including some overlaps: 17 were from a deprived area, 12 from BME groups, and five were oldest old. Cross-sector health promotion professionals across England with experience of health promotion with older people were identified through online searches and snowball sampling. A total of 31 of these 44 professionals completed an online survey including open questions on barriers and facilitators to uptake in these groups. Thematic analysis was used to develop a framework of higher and lower level themes. Interpretations were discussed and agreed within the team. Results Older people’s motivation to stay healthy and independent reflected their everyday behaviour including practicing activities to feel or stay well, level of social engagement, and enthusiasm for and belief in health promotion. All of the oldest old reported trying to live healthily, often facilitated by others, yet sometimes being restricted due to poor health. Most older people from BME groups reported a strong wish to remain independent which was often positively influenced by their social network. Older people living in deprived areas reported reluctance to undertake health promotion activities, conveyed apathy and reported little social interaction. Cross-sector health professionals consistently reported similar themes as the older people, reinforcing the views of the older people through examples. Conclusions The study shows some shared themes across the three ‘hard-to-reach’ groups but also some distinct differences, suggesting that a carefully outlined strategy should be considered to reach successfully the group targeted.Peer reviewedFinal Published versio

ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs

Conversational Information Seeking (CIS) is a relatively new research area within conversational AI that attempts to seek information from end-users in order to understand and satisfy users’ needs. If realized, such a system has far-reaching benefits in the real world; for example, a CIS system can assist clinicians in pre-screening or triaging patients in healthcare. A key open sub-problem in CIS that remains unaddressed in the literature is generating Information Seeking Questions (ISQs) based on a short initial query from the end user. To address this open problem, we propose Information SEEking Question generator (ISEEQ), a novel approach for generating ISQs from just a short user query, given a large text corpus relevant to the user query. Firstly, ISEEQ uses a knowledge graph to enrich the user query. Secondly, ISEEQ uses the knowledge-enriched query to retrieve relevant context passages to ask coherent ISQs adhering to a conceptual flow. Thirdly, ISEEQ introduces a new deep generative adversarial reinforcement learning-based approach for generating ISQs. We show that ISEEQ can generate high-quality ISQs to promote the development of CIS agents. ISEEQ significantly outperforms comparable baselines on five ISQ evaluation metrics across four datasets having user queries from diverse domains. Further, we argue that ISEEQ is transferable across domains for generating ISQs, as it shows the acceptable performance when trained and tested on different pairs of domains. The qualitative human evaluation confirms ISEEQ-generated ISQs are comparable in quality to human-generated questions and outperform the best comparable baseline