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The number and variety of cyber-attacks is rapidly increasing, and the rate of new software vulnerabilities is also rising dramatically. The cybersecurity community typically reacts to attacks after they occur. Being reactive is costly and can be fatal, where attacks threaten lives, important data, or mission success. Taking a proactive approach, we are: (I) identifying potential attacks before they come to fruition, and based on this identification; (II) developing preventive counter-measures. We describe a Proactive Cybersecurity System (PCS), a layered, modular service platform that applies big data collection and processing tools a wide variety of unstructured data sources to identify potential attacks and develop countermeasures. The PCS provides security analysts a holistic, proactive, and systematic approach to cybersecurity. Here we describe our research vision and progress towards that vision.

Research on machine learning in design has concentrated on the use and development of techniques that can solve simple well-defined problems. Invariably, this effort, while important at the early stages of the development of the field, cannot scale up to address real design problems since all existing techniques are based on simplifying assumptions that do not hold for real design. In particular, they do not address the dependence on context and multiple, often conflicting, interests that are constitutive of design. This paper analyzes the present situation and criticises a number of prevailing views. Subsequently, the paper offers an alternative approach whose goal is to advance the use of machine learning in design practice. The approach is partially integrated into a modeling system called n-dim. The use of machine learning in n-dim is presented and open research issues are outlined.

The authors characterize the problems inherent to mapping across or combining terms from different vocabularies, focusing especially on MeSH and SNOMED. They also describe the exploration of the development of a frame-based representation of SNOMED.

The authors describe CLARIT processing as an approach to automatic indexing. They also explore two elements of the indexing process, identifying concepts in text and selecting concepts to reflect the perspective of a domain.

Individual users of medical language manifest great variation in the expression of concepts and have difficulty in selecting appropriate terminology when confronted with systems that rely on standardized language, such as MeSH, SNOMED, or ICD, and the special terms sets of systems such as HELP, INTERNIST-I/QMR, and DXplain. Indeed, the need to map natural language into appropriate special terms—as well as the need to map one system’s specialized terminology into another’s—is one of the problems being addressed by the National Library of Medicine’s UMLS System, with its associated information sources maps. The problem is extremely difficult, in part, because such mappings depend on semantic equivalences among terms, not merely the superficial matching of words or phrases.

This study applies various tools, techniques, and methods that the Software Engineering Institute is evaluating for analyzing information being produced at a very rapid rate in the discipline--both in practice and in research. The focus here is on mapping the evolution of the research literature as a means to characterize software engineering and distinguish it from other disciplines. Software engineering is a term often used to describe programming-in-the-large activities. Yet, any precise empirical characterization of its conceptual contours and their evolution is lacking. In this study, a large number of publications from 1982-1994 are analyzed to determine themes and trends in software engineering. The method used to analyze the publications was co-word analysis. This methodology identifies associations among publication descriptors (indexing terms) from the Computing Classification System and produces networks of terms that reveal patterns of associations. The results suggest that certain research themes in software engineering remain constant, but with changing thrusts. Other themes mature and then diminish as major research topics, while still others seem transient or immature. Certain themes are emerging as predominate for the most recent time period covered (1991-1994): object-oriented methods and user interfaces are identifiable as central themes.