Geographically intelligent disclosure control for flexible aggregation of census data

This paper describes a geographically intelligent approach to disclosure control for protecting flexibly aggregated census data. Increased analytical power has stimulated user demand for more detailed information for smaller geographical areas and customized boundaries. Consequently it is vital that improved methods of statistical disclosure control are developed to protect against the increased disclosure risk. Traditionally methods of statistical disclosure control have been aspatial in nature. Here we present a geographically intelligent approach that takes into account the spatial distribution of risk. We describe empirical work illustrating how the flexibility of this new method, called local density swapping, is an improved alternative to random record swapping in terms of risk-utility

Ontology Reuse: the Real Test of Ontological Design

Reusing ontologies in practice is still very challenging, especially when multiple ontologies are (jointly) involved. Moreover, despite recent advances, the realization of systematic ontology quality assurance remains a difficult problem. In this work, the quality of thirty biomedical ontologies, and the Computer Science Ontology are investigated, from the perspective of a practical use case. Special scrutiny is given to cross-ontology references, which are vital for combining ontologies. Diverse methods to detect potential issues are proposed, including natural language processing and network analysis. Moreover, several suggestions for improving ontologies and their quality assurance processes are presented. It is argued that while the advancing automatic tools for ontology quality assurance are crucial for ontology improvement, they will not solve the problem entirely. It is ontology reuse that is the ultimate method for continuously verifying and improving ontology quality, as well as for guiding its future development. Specifically, multiple issues can be found and fixed primarily through practical and diverse ontology reuse scenarios.Comment: Accepted into SOMET 2022 conferenc

Flexure based mounts for sensitive payloads : a management and engineering study

Thesis (S.M. and S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2001.Includes bibliographical references (p. 51-52).With the cooperation of the Los Alamos National Laboratory and the Massachusetts Institute of Technology, an investigative and design study was performed to examine the history of the W80- 0 Area Aft Mount, understand its performance, and explore potential new designs. Simultaneously, professional and technical enhancement of the author was achieved. The historical organization of LANL influences the design space for this project, and understanding those relationships provides insight into concept generation and selection. In addition, the current organizational structure within the laboratory as well as with its customers provides additional constraints that must be managed technically. The new design concepts attempt to simulate the nonlinear load vs. displacement characteristics of the previously employed B3223 cellular silicone Pad Mount. New concepts separate the spring and damping characteristics of the cellular silicone into separate component parts. This uncoupled method should allow the new designs increased variability and control with respect to matching original Aft Area Mount performance in shock mitigation and deflection limiting.by Daniel K. Moon.S.M.and S.B

Institutional plan. Fiscal Year 1997--2002

Sandia National Laboratories is operated for the United States Department of Energy by a subsidiary of Lockheed Martin Corporation. National security programs and defense-related environmental programs for DOE comprise 65 percent of Sandia`s work. We are the DOE Defense Programs laboratory responsible for engineering development of all US nuclear weapons and for systems integration of the nuclear weapons with their delivery vehicles. Our responsibilities include design, certification, and assessment of the nonnuclear subsystems of nuclear weapons; safety, security, reliability, and use control; issues associated with production and dismantlement of nuclear weapons; surveillance and support of weapons in the stockpile; and work in nuclear intelligence, nonproliferation, and treaty verification technologies. Sandia is a multiprogram laboratory. Ten percent of our work supports DOE missions in energy science, research, and development. When appropriate, we also perform work for other government agencies, particularly the Department of Defense, in programs where unique competencies built our mission responsibilities can add value. This report discusses the activities of these responsibilities at the Sandia National Laboratories

Cross-Inlining Binary Function Similarity Detection

Binary function similarity detection plays an important role in a wide range of security applications. Existing works usually assume that the query function and target function share equal semantics and compare their full semantics to obtain the similarity. However, we find that the function mapping is more complex, especially when function inlining happens. In this paper, we will systematically investigate cross-inlining binary function similarity detection. We first construct a cross-inlining dataset by compiling 51 projects using 9 compilers, with 4 optimizations, to 6 architectures, with 2 inlining flags, which results in two datasets both with 216 combinations. Then we construct the cross-inlining function mappings by linking the common source functions in these two datasets. Through analysis of this dataset, we find that three cross-inlining patterns widely exist while existing work suffers when detecting cross-inlining binary function similarity. Next, we propose a pattern-based model named CI-Detector for cross-inlining matching. CI-Detector uses the attributed CFG to represent the semantics of binary functions and GNN to embed binary functions into vectors. CI-Detector respectively trains a model for these three cross-inlining patterns. Finally, the testing pairs are input to these three models and all the produced similarities are aggregated to produce the final similarity. We conduct several experiments to evaluate CI-Detector. Results show that CI-Detector can detect cross-inlining pairs with a precision of 81% and a recall of 97%, which exceeds all state-of-the-art works.Comment: Accepted at ICSE 2024 (Second Cycle). Camera-ready versio