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AbstractUnderlying information about failure, including observations made in free text, can be a good source for understanding, analyzing, and extracting meaningful information for determining causation. The unstructured nature of natural language expression demands advanced methodology to identify its underlying features. There is no available solution to utilize unstructured data for risk assessment purposes. Due to the scarcity of relevant data, textual data can be a vital learning source for developing a risk assessment methodology. This work addresses the knowledge gap in extracting relevant features from textual data to develop cause–effect scenarios with minimal manual interpretation. This study applies natural language processing and text‐mining techniques to extract features from past accident reports. The extracted features are transformed into parametric form with the help of fuzzy set theory and utilized in Bayesian networks as prior probabilities for risk assessment. An application of the proposed methodology is shown in microbiologically influenced corrosion‐related incident reports available from the Pipeline and Hazardous Material Safety Administration database. In addition, the trained named entity recognition (NER) model is verified on eight incidents, showing a promising preliminary result for identifying all relevant features from textual data and demonstrating the robustness and applicability of the NER method. The proposed methodology can be used in domain‐specific risk assessment to analyze, predict, and prevent future mishaps, ameliorating overall process safety.

There is a growing body of literature that focuses on the similarities and differences between how people behave in the offline world vs. how they behave in these virtual environments. Data mining has aided in discovering interesting insights with respect to how people behave in these virtual environments. The book addresses prediction, mining and analysis of offline characteristics and behaviors from online data and vice versa. Each chapter will focus on a different aspect of virtual worlds to real world prediction e.g., demographics, personality, location, etc.

This book covers an approach to conversational informatics which encompasses science and technology for understanding and augmenting conversation in the network age. A major challenge in engineering is to develop a technology for conveying not just messages but also underlying wisdom. Relevant theories and practices in cognitive linguistics and communication science, as well as techniques developed in computational linguistics and artificial intelligence, are discussed.

The use of technology for workplace and occupational testing blossomed in the early years of this century. This book offers a demonstration that the first generation of these technologies have now been implemented long enough to observe the patterns and issues that emerge when these approaches evolve through technical advancement and successive application. A new set of issues and opportunities has emerged and the next generation of these applications is now coming of age. This book reflects on the last few decades of this evolutionary process from a vantage point of global experience across a wide range of workplace applications, including employment selection, development, and occupational certification. The themes and issues that arise as this broad treatment unfolds provide an essential foundation for students, researchers, and professionals who are involved with the assessment of human capability and potential in organizational and workplace contexts

Data-driven criminal justice operations have led to the transformation of criminal records into millions of data points. These records are publicly disclosed on the internet, commodified into valuable big data, and leveraged against people. In Digitial Punishment, Sarah Lageson demonstrates the consequences this system has for people, society, and public policy.

This article discusses how computer technology was implemented within the US federal government in the 1960s and 1970s. Drawing on sources from the Bureau of the Budget (BOB), later renamed the Office of Management and Budget (OMB), the article demonstrates that the Johnson administration centralised the management of Automatic Data Processing (ADP) and also set technical standards. To characterise this process, the article proposes to use the expression "early phase of digitalisation". At the time, a debate arose about how the processing of personal data would affect individual rights. Several studies analysed the functioning of technology with regard to computer privacy and suggested general guidelines. In addition, Congress passed data privacy legislation. Regardless of the legal debate, computerisation continued under the Nixon administration. The article argues that digitalisation shaped the privacy debate at the time.

"This book presents trends and techniques for successful intelligent decision-making and transfer of products through digital signal processing, including research in supply chain management with examples and case studies useful for those involved with various levels of management"--Provided by publisher

"The military produces, senses, and consumes a tremendous amount of data. Developing ways to effectively leverage these troves of data to enhance operations is a persistent goal. This book explores data analytics techniques that military scientists and practitioners around the world are doing to achieve this. The book is organized around data analytics applications in the primary military domains of Personnel, Intelligence, Operations, and Logistics."--Provided by publisher.