Computer Science

The recent advances of Augmented Reality (AR) in healthcare have shown that technology is a significant part of the current healthcare system. In recent days, augmented reality has proposed numerous smart applications in healthcare domain including wearable access, telemedicine, remote surgery, diagnosis of medical reports, emergency medicine, etc. The aim of the developed augmented healthcare application is to improve patient care, increase efficiency, and decrease costs. This article puts on an effort to review the advances in AR-based healthcare technologies and goes to peek into the strategies that are being taken to further this branch of technology. This article explores the important services of augmented-based healthcare solutions and throws light on recently invented ones as well as their respective platforms. It also addresses concurrent concerns and their relevant future challenges. In addition, this paper analyzes distinct AR security and privacy including security requirements and attack terminologies. Furthermore, this paper proposes a security model to minimize security risks. Augmented reality advantages in healthcare, especially for operating surgery, emergency diagnosis, and medical training is being demonstrated here thorough proper analysis. To say the least, the article illustrates a complete overview of augmented reality technology in the modern healthcare sector by demonstrating its impacts, advancements, current vulnerabilities; future challenges, and concludes with recommendations to a new direction for further research.

By design, distributed ledger technologies persist low-level data which makes conducting complex business analysis of the recorded operations challenging. Existing blockchain visualization and analytics tools such as block explorers tend to rely on this low-level data and complex interfacing to provide enriched level of analytics. The ability to derive richer analytics could be improved through the availability of a higher level abstraction of the data. This article proposes an abstraction layer architecture that enables the design of high-level analytics of distributed ledger systems and the decentralized applications that run on top. Based on the analysis of existing initiatives and identification of the relevant user requirements, this work aims to establish key insights and specifications to improve the auditability and intuitiveness of distributed ledger systems by leveraging the development of future user interfaces. To illustrate the benefits offered by the proposed abstraction layer architecture, a regulated sector use case is explored.

The targeted direct-to-customer marketing of unapproved stem cell treatments by a questionable online industry is directed at vulnerable users who search the Internet in the hope of a cure. This behavior especially poses a threat to individuals who find themselves in hopeless and desperate phases in their lives. They might show low reluctance to try therapies that solely promise a cure but are not scientifically proven to do so. In the worst case, they suffer serious side-effects. Therefore, this thesis examines the display of advertisements of unapproved stem cell treatments for Parkinson's Disease, Multiple Sclerosis, Diabetes on Google's results page. The company announced a policy change in September 2019 that was meant to prohibit and ban the practices in question. However, there was evidence that those ads were still being delivered. A browser extension for Firefox and Chrome was developed and distributed to conduct a crowdsourced Black Box analysis. It was delivered to volunteers and virtual machines in Australia, Canada, the USA and the UK. Data on search results, advertisements and top stories was collected and analyzed. The results showed that there still is questionable advertising even though Google announced to purge it from its platform.

Health insurance companies cover half of the United States population through commercial employer-sponsored health plans and pay 1.2 trillion US dollars every year to cover medical expenses for their members. The actuary and underwriter roles at a health insurance company serve to assess which risks to take on and how to price those risks to ensure profitability of the organization. While Bayesian hierarchical models are the current standard in the industry to estimate risk, interest in machine learning as a way to improve upon these existing methods is increasing. Lumiata, a healthcare analytics company, ran a study with a large health insurance company in the United States. We evaluated the ability of machine learning models to predict the per member per month cost of employer groups in their next renewal period, especially those groups who will cost less than 95\% of what an actuarial model predicts (groups with "concession opportunities"). We developed a sequence of two models, an individual patient-level and an employer-group-level model, to predict the annual per member per month allowed amount for employer groups, based on a population of 14 million patients. Our models performed 20\% better than the insurance carrier's existing pricing model, and identified 84\% of the concession opportunities. This study demonstrates the application of a machine learning system to compute an accurate and fair price for health insurance products and analyzes how explainable machine learning models can exceed actuarial models' predictive accuracy while maintaining interpretability.

In the development of governmental policy for artificial intelligence (AI) that is informed by ethics, one avenue currently pursued is that of drawing on AI Ethics Principles. However, these AI Ethics Principles often fail to be actioned in governmental policy. This paper proposes a novel framework for the development of Actionable Principles for AI. The approach acknowledges the relevance of AI Ethics Principles and homes in on methodological elements to increase their practical implementability in policy processes. As a case study, elements are extracted from the development process of the Ethics Guidelines for Trustworthy AI of the European Commissions High Level Expert Group on AI. Subsequently, these elements are expanded on and evaluated in light of their ability to contribute to a prototype framework for the development of Actionable Principles for AI. The paper proposes the following three propositions for the formation of such a prototype framework: (1) preliminary landscape assessments; (2) multi-stakeholder participation and cross-sectoral feedback; and, (3) mechanisms to support implementation and operationalizability.

While past information technology (IT) advances have transformed society, future advances hold even greater promise. For example, we have only just begun to reap the changes from artificial intelligence (AI), especially machine learning (ML). Underlying IT's impact are the dramatic improvements in computer hardware, which deliver performance that unlock new capabilities. For example, recent successes in AI/ML required the synergy of improved algorithms and hardware architectures (e.g., general-purpose graphics processing units). However, unlike in the 20th Century and early 2000s, tomorrow's performance aspirations must be achieved without continued semiconductor scaling formerly provided by Moore's Law and Dennard Scaling. How will one deliver the next 100x improvement in capability at similar or less cost to enable great value? Can we make the next AI leap without 100x better hardware? This whitepaper argues for a multipronged effort to develop new computing approaches beyond Moore's Law to advance the foundation that computing provides to US industry, education, medicine, science, and government. This impact extends far beyond the IT industry itself, as IT is now central for providing value across society, for example in semi-autonomous vehicles, tele-education, health wearables, viral analysis, and efficient administration. Herein we draw upon considerable visioning work by CRA's Computing Community Consortium (CCC) and the IEEE Rebooting Computing Initiative (IEEE RCI), enabled by thought leader input from industry, academia, and the US government.

The use of the Internet of Things (IoT) in agricultural knowledge management systems is one of the most promising approaches to increasing the efficiency of agriculture. However, the existing physical sensors in agriculture are limited for monitoring various changes in the characteristics of crops and may be expensive for the average farmer. We propose a combination of physical and human sensors (the five human senses). By using their own eyes, ears, noses, tongues, and fingers, farmers could check the various changes in the characteristics and conditions (colors of leaves, diseases, pests, faulty or malfunctioning equipment) of their crops and equipment, verbally describe their observations, and capture the descriptions with audio recording devices, such as smartphones. The voice recordings could be transcribed into text by web servers. The data captured by the physical and human sensors (voice messages) are analyzed by data and text mining to create and improve agricultural knowledge. An agricultural knowledge management system using physical and human sensors encourages to share and transfer knowledge among farmers for the purpose of improving the efficiency and productivity of agriculture. We applied one such agricultural knowledge management system (smart voice messaging system) to a greenhouse vegetable farm in Hokkaido. A qualitative analysis of accumulated voice messages and an interview with the farmer demonstrated the effectiveness of this system. The contributions of this study include a new and practical approach to an "agricultural Internet of Everything (IoE)" and evidence of its effectiveness as a result of our trial experiment at a real vegetable farm.

We show how to assess a language model's knowledge of basic concepts of morality. We introduce the ETHICS dataset, a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. Models predict widespread moral judgments about diverse text scenarios. This requires connecting physical and social world knowledge to value judgements, a capability that may enable us to steer chatbot outputs or eventually regularize open-ended reinforcement learning agents. With the ETHICS dataset, we find that current language models have a promising but incomplete ability to predict basic human ethical judgements. Our work shows that progress can be made on machine ethics today, and it provides a steppingstone toward AI that is aligned with human values.

Measles is considered as a highly contagious disease that leads to serious complications around the world. Thus, the paper determined the trend and the five-year forecasted data of the Measles in the Philippines. This study utilized the time series data for trend analysis and data forecasting using the ARIMA model to visualize the measles cases. Figures for the time-series and forecasted results are individually presented with the use of GRETL software. Results showed that there was an increasing pattern of the disease from 2016 to 2019. However, there was a decreasing pattern of its occurrence in the next five years based on the five-year forecast. Nevertheless, with the results of the study, there is still a need to improve the different intervention plans of the authority in alleviating the occurrence of the disease though it yielded a decreasing pattern in the future since it is evident that the figure of the forecasted data is still approximately 15,000 and above.

Opportunities such as higher education can promote intergenerational mobility, leading individuals to achieve levels of socioeconomic status above that of their parents. We develop a dynamic model for allocating such opportunities in a society that exhibits bottlenecks in mobility; the problem of optimal allocation reflects a trade-off between the benefits conferred by the opportunities in the current generation and the potential to elevate the socioeconomic status of recipients, shaping the composition of future generations in ways that can benefit further from the opportunities. We show how optimal allocations in our model arise as solutions to continuous optimization problems over multiple generations, and we find in general that these optimal solutions can favor recipients of low socioeconomic status over slightly higher-performing individuals of high socioeconomic status -- a form of socioeconomic affirmative action that the society in our model discovers in the pursuit of purely payoff-maximizing goals. We characterize how the structure of the model can lead to either temporary or persistent affirmative action, and we consider extensions of the model with more complex processes modulating the movement between different levels of socioeconomic status.