National Institute of Standards and Technology における超伝導研究及び生活

The Architecture Of Cognition

Manuale per la consultazione di specifici punti, per ulteriori informazioni vedere alla URL: http://unstats.un.org/unsd/nationalaccount/sna2008.asp

System of National Accounts 2008

Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML extol its ability to deal with large, complex and disparate data, often found within medicine and feel that ML is the future for biomedical research, personalized medicine, computer-aided diagnosis to significantly advance global health care. However, the concepts of ML are unfamiliar to many medical professionals and there is untapped potential in the use of ML as a research tool. In this article, we provide an overview of the theory behind ML, explore the common ML algorithms used in medicine including their pitfalls and discuss the potential future of ML in medicine.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/joim.12822

eDoctor: machine learning and the future of medicine

Provenance, i.e., the origin or source of something, is becoming an important concern, since it offers the means to verify data products, to infer their quality, to analyse the processes that led to them, and to decide whether they can be trusted. For instance, provenance enables the reproducibility of scientific results; provenance is necessary to track attribution and credit in curated databases; and, it is essential for reasoners to make trust judgements about the information they use over the Semantic Web.

As the Web allows information sharing, discovery, aggregation, filtering and flow in an unprecedented manner, it also becomes very difficult to identify, reliably, the original source that produced an information item on the Web. Since the emerging use of provenance in niche applications is undoubtedly demonstrating the benefits of provenance, this monograph contends that provenance can and should reliably be tracked and exploited on the Web, and investigates the necessary foundations to achieve such a vision.

Multiple data sources have been used to compile the largest bibliographical database on provenance so far. This large corpus permits the analysis of emerging trends in the research community. Specifically, the CiteSpace tool identifies clusters of papers that constitute research fronts, from which characteristics are extracted to structure a foundational framework for provenance on the Web. Such an endeavour requires a multi-disciplinary approach, since it requires contributions from many computer science sub-disciplines, but also other non-technical fields given the human challenge that is anticipated.

To develop such a vision, it is necessary to provide a definition of provenance that applies to the Web context. A conceptual definition of provenance is expressed in terms of processes, and is shown to generalise various definitions of provenance commonly encountered. Furthermore, by bringing realistic distributed systems assumptions, this definition is refined as a query over assertions made by applications.

Given that the majority of work on provenance has been undertaken by the database, workflow and e-science communities, some of their work is reviewed, contrasting approaches, and focusing on important topics believed to be crucial for bringing provenance to the Web, such as abstraction, collections, storage, queries, workflow evolution, semantics and activities involving human interactions.

However, provenance approaches developed in the context of databases and workflows essentially deal with closed systems. By that, it is meant that workflow or database management systems are in full control of the data they manage, and track their provenance within their own scope, but not beyond. In the context of the Web, a broader approach is required by which chunks of provenance representation can be brought together to describe the provenance of information flowing across multiple systems. For this purpose, this monograph puts forward the Open Provenance Vision, which is an approach that consists of controlled vocabulary, serialisation formats and interfaces to allow the provenance of individual systems to be expressed, connected in a coherent fashion, and queried seamlessly. In this context, the Open Provenance Model is an emerging community-driven representation of provenance, which has been actively used by some 20 teams to exchange provenance information, in line with the Open Provenance Vision.

After identifying an open approach and a model for provenance, techniques to expose provenance over the Web are investigated. In particular, Semantic Web technologies are discussed since they have been successfully exploited to express, query and reason over provenance. Symmetrically, Semantic Web technologies such as RDF, underpinning the Linked Data effort, are analysed since they offer their own difficulties with respect to provenance.

A powerful argument for provenance is that it can help make systems transparent, so that it becomes possible to determine whether a particular use of information is appropriate under a set of rules. Such capability helps make systems and information accountable. To offer accountability, provenance itself must be authentic, and rely on security approaches, which are described in the monograph. This is then followed by systems where provenance is the basis of an auditing mechanism to check past processes against rules or regulations. In practice, not all users want to check and audit provenance, instead, they may rely on measures of quality or trust; hence, emerging provenance-based approaches to compute trust and quality of data are reviewed.

/pdf/the-foundations-for-provenance-on-the-web-sktzvgz5mp.pdf

The Foundations for Provenance on the Web

The quality of Wikipedia articles is debatable. On the one hand, existing research indicates that not only are people willing to contribute articles but the quality of these articles is close to that found in conventional encyclopedias. On the other hand, the public has never stopped criticizing the quality of Wikipedia articles, and critics never have trouble finding low-quality Wikipedia articles. Why do Wikipedia articles vary widely in qualityq We investigate the relationship between collaboration and Wikipedia article quality. We show that the quality of Wikipedia articles is not only dependent on the different types of contributors but also on how they collaborate. Based on an empirical study, we classify contributors based on their roles in editing individual Wikipedia articles. We identify various patterns of collaboration based on the provenance or, more specifically, who does what to Wikipedia articles. Our research helps identify collaboration patterns that are preferable or detrimental for article quality, thus providing insights for designing tools and mechanisms to improve the quality of Wikipedia articles.

Who does what: Collaboration patterns in the wikipedia and their impact on article quality

Data Provenance refers to the lineage of data including its origin, key events that occur over the course of its lifecycle, and other details associated with data creation, processing, and archiving. We believe that tracking provenance enables users to share, discover, and reuse the data, thus streamlining collaborative activities, reducing the possibility of repeating dead ends, and facilitating learning. It also provides a mechanism to transition from static to active conceptual modeling. The primary goal of our research is to investigate the semantics or meaning of data provenance. We describe the W7 model that represents different components of provenance and their relationships to each other. We conceptualize provenance as a combination of seven interconnected elements including "what", "when", "where", "how", "who", "which" and "why". Each of these components may be used to track events that affect data during its lifetime. A homeland security example illustrates how current conceptual models can be extended to embed provenance.

Understanding the semantics of data provenance to support active conceptual modeling

Data Provenance refers to the "origin", "lineage", and "source" of data. In this work, we examine provenance from a semantics perspective and present the W7 model, an ontological model of data provenance. In the W7 model, provenance is conceptualized as a combination of seven interconnected elements including "what", "when", "where", "how", "who", "which" and "why". Each of these components may be used to track events that affect data during its lifetime. The W7 model is general and extensible enough to capture provenance semantics for data in different domains. Using the example of the Wikipedia, we illustrate how the W7 model can capture domain or application specific provenance.

/pdf/a-new-perspective-on-semantics-of-data-provenance-42xsbmkcgm.pdf

A new perspective on semantics of data provenance

Data quality in the Wikipedia is debatable. On the one hand, existing research indicates that not only are people willing to contribute articles but the quality of those articles is close to that found in conventional encyclopedias. On the other hand, the public has never stopped criticizing the quality of Wikipedia articles, and critics never have trouble finding low quality Wikipedia articles. Why do Wikipedia articles vary widely in quality? We investigate the relationship between collaboration and data quality. We show that the quality of Wikipedia articles is not only dependent on the different types of contributors but also on how they collaborate. Based on an empirical study, we classify contributors based on their roles in editing individual Wikipedia articles. We identify various patterns of collaboration based on the provenance or, more specifically, who does what to Wikipedia articles. Our research helps identify collaboration patterns that are preferable or detrimental for data quality, thus providing insights for improving data quality in Wikipedia.

Who does what: Collaboration patterns in the wikipedia and their impact on data quality

The recent outbreak of the coronavirus (COVID-19) brought with its public concerns and fears about a global epidemic With the increase in the popularity, usage, and reach of social media, this research examined the early public outlook on COVID-19 using SM-Platform, Twitter com The current study employed a mixed-method approach in collecting and analyzing public tweets by combining quantitative sentiment analysis with a qualitative thematic analysis Our results revealed positive sentiment prior to the spread of the disease The sentiment then turned negative as the disease spread, accompanied by a large amount of fear as rumors In a thematic analysis we also uncovered nine key topics on the disease including, but not limited to, prevention, symptoms and spread of disease Our study will provide an understanding of social media and public health outbreak surveillance The findings of the research revealed the usefulness of twitter mining to illuminate public health education

Jun Liu

Papers

Who does what: Collaboration patterns in the wikipedia and their impact on article quality

Understanding the semantics of data provenance to support active conceptual modeling

A new perspective on semantics of data provenance

Who does what: Collaboration patterns in the wikipedia and their impact on data quality

Early Public Outlook on the Coronavirus Disease (COVID-19): A Social Media Study