Binary-level pointer analysis can be of use in symbolic execution, testing, verification, and decompilation of software binaries. In various such contexts, it is crucial that the result is trustworthy, i.e., it can be formally established that the pointer designations are overapproximative. This paper presents an approach to formally proven correct binary-level pointer analysis. A salient property of our approach is that it first generically considers what proof obligations a generic abstract domain for pointer analysis must satisfy. This allows easy instantiation of different domains, varying in precision, while preserving the correctness of the analysis. In the trade-off between scalability and precision, such customization allows “meaningful” precision (sufficiently precise to ensure basic sanity properties, such as that relevant parts of the stack frame are not overwritten during function execution) while also allowing coarse analysis when pointer computations have become too obfuscated during compilation for sound and accurate bounds analysis. We experiment with three different abstract domains with high, medium, and low precision. Evaluation shows that our approach is able to derive designations for memory writes soundly in COTS binaries, in a context-sensitive interprocedural fashion.

An option is a financial agreement between two parties to trade two assets. One party is given the right, but not the obligation, to complete the swap before a specified termination time. In today's financial markets, an option is considered an asset which can itself be transferred: while an option is active, one party can sell its rights (or obligations) to another.

Automated market makers (AMMs) are automata that trade electronic assets at rates set by mathematical formulas. AMMs are usually implemented by smart contracts on blockchains. In practice, AMMs are often composed: trades can be split across AMMs, and outputs from one AMM can be directed to another. This paper proposes a mathematical model for AMM composition. We define sequential and parallel composition operators for AMMs in a way that ensures that AMMs are closed under composition, in a way that works for "higher-dimensional" AMMs that manage more than two asset classes, and so the composition of AMMs in "stable" states remains stable.

The increasing amount of data generated by Location Based Social Networks (LBSN) such as Twitter, Flickr, or Foursquare, is currently drawing the attention of urban planners, as it is a new source of data that contains valuable information about the behavior of the inhabitants of a city. Making this data accessible to the urban planning domain can add value to the decision making processes. However, the analysis of the spatial and temporal characteristics of this data in the context of urban planning is an ongoing research problem. This paper describes ongoing work in the design and development of a visual exploration tool to facilitate this task. The proposed design provides an approach towards the integration of a visual exploration tool and the capabilities of a visual query system from a multilevel perspective (e.g., multiple spatial scales and temporal resolutions implicit in LBSN data). A preliminary discussion about the design and the potential insights that can be gained from the exploration and analysis of this data with the proposed tool is presented, along with the conclusions and future work for the continuation of this work.

Performance attributes such as energy use or natural ventilation are becoming rapidly more important in the design of modern buildings. As a basis for the improvement of existing visualization techniques in this application domain, we provide a detailed tasks and requirements analysis using feedback from an architect. State-of-the-art visualization strategies used for building performance simulation results are then evaluated by comparing them to the quality aspects derived before. This assessment specifically reveals shortcomings with respect to the applied techniques for visualizing spatiotemporal data. Therefore, we discuss the potential of utilizing other visualization techniques to meet the identified prerequisites and reveal future directions based on these findings.

Abstract Analysis of chemical constituents from mass spectrometry of aerosols involves non‐negative matrix factorization, an approximation of high‐dimensional data in lower‐dimensional space. The associated optimization problem is non‐convex, resulting in crude approximation errors that are not accessible to scientists. To address this shortcoming, we introduce a new methodology for user‐guided error‐aware data factorization that entails an assessment of the amount of information contributed by each dimension of the approximation, an effective combination of visualization techniques to highlight, filter, and analyze error features, as well as a novel means to interactively refine factorizations. A case study and the domain‐expert feedback provided by the collaborating atmospheric scientists illustrate that our method effectively communicates errors of such numerical optimization results and facilitates the computation of high‐quality data factorizations in a simple and intuitive manner.

The study of aerosol composition for air quality research involves the analysis of high-dimensional single particle mass spectrometry data. We describe, apply, and evaluate a novel interactive visual framework for dimensionality reduction of such data. Our framework is based on non-negative matrix factorization with specifically defined regularization terms that aid in resolving mass spectrum ambiguity. Thereby, visualization assumes a key role in providing insight into and allowing to actively control a heretofore elusive data processing step, and thus enabling rapid analysis meaningful to domain scientists. In extending existing black box schemes, we explore design choices for visualizing, interacting with, and steering the factorization process to produce physically meaningful results. A domain-expert evaluation of our system performed by the air quality research experts involved in this effort has shown that our method and prototype admits the finding of unambiguous and physically correct lower-dimensional basis transformations of mass spectrometry data at significantly increased speed and a higher degree of ease.

Abstract Researchers and analysts in modern industrial and academic environments are faced with a daunting amount of multi‐dimensional data. While there has been significant development in the areas of data mining and knowledge discovery, there is still the need for improved visualizations and generic solutions. The state‐of‐the‐art in visual analytics and exploratory data visualization is to incorporate more profound analysis methods while focusing on fast interactive abilities. The common trend in these scenarios is to either visualize an abstraction of the data set or to better utilize screen‐space. This paper presents a novel technique that combines clustering, dimension reduction and multi‐dimensional data representation to form a multivariate data visualization that incorporates both detail and overview. This amalgamation counters the individual drawbacks of common projection and multi‐dimensional data visualization techniques, namely ambiguity and clutter. A specific clustering criterion is used to decompose a multi‐dimensional data set into a hierarchical tree structure. This decomposition is embedded in a novel Dimensional Anchor visualization through the use of a weighted linear dimension reduction technique. The resulting Structural Decomposition Tree (SDT) provides not only an insight of the data set's inherent structure, but also conveys detailed coordinate value information. Further, fast and intuitive interaction techniques are explored in order to guide the user in highlighting, brushing, and filtering of the data.