Code reuse attacks have been the subject of a substantial amount of research during the past decade. This research largely resulted from early work on Return-Oriented Programming (ROP), which showed that the then newly proposed Non-Executable Memory (NX) defense could be bypassed. More recently, the research community has been simultaneously investigating new defenses that are believed to thwart code reuse attacks, such as Control Flow Integrity (CFI), and defense-aware techniques for attacking these defenses, such as Data-Oriented Programming (DOP). Unfortunately, the feasibility of defense-aware attacks are very dependent on the behaviors of the attacked program, which makes it difficult for defenders to understand how much protection a defense such as CFI may provide. To better understand this, researchers have introduced automated defense-aware code reuse attack systems. Unfortunately, the handful of existing systems implement a single fixed, defense-specific strategy that is complex and cannot be used to consider other defenses.

Abstract Information graphics (bar charts, line graphs, grouped bar charts, etc) often appear in popular media such as newspapers and magazines. In most cases, the information graphic is intended to convey a high‐level message. This message plays a role in facilitating the discourse purpose of the document but is seldom repeated in the document's text, headlines, or captions. We present a methodology and an implemented system for recognizing the intended message of a grouped bar chart. The recognition system relies on the following components: (1) a linguistic classifier that processes text in the graphic and predicts the most linguistically salient entity from those that are mentioned in text, (2) a cognitive model that estimates the relative perceptual effort required for an individual to recognize some high‐level message in a graph, and (3) a Bayesian network that captures the probabilistic relationship between the high‐level intended message of a graphic and its communicative signals. This research contributes to three applications: accessibility of information graphics for sight‐impaired individuals, retrieval of information graphics from a digital library, and summarization of multimodal documents.

Although intelligent interactive systems have been the focus of many research efforts, very few have addressed systems for individuals with disabilities. This article presents our methodology for an intelligent interactive system that provides individuals with sight impairments with access to the content of information graphics (such as bar charts and line graphs) in popular media. The article describes the methodology underlying the system's intelligent behavior, its interface for interacting with users, examples processed by the implemented system, and evaluation studies both of the methodology and the effectiveness of the overall system. This research advances universal access to electronic documents.

Interactive SIGHT is a system that is intended to provide people with visual impairments access to the kind of information graphics found in popular media (i.e., electronic newspapers or magazines). The majority of such graphics are intended to convey a message; the graphic designer chose the graphic and its design in order to make a point. Interactive SIGHT, which is implemented as a browser extension that works on simple bar charts, provides a brief high-level summary of the graphic through natural language text that is conveyed to the user as speech. The user may request further information about the graphic through a follow-up question facility which allows many follow-up responses to be generated. The demo will illustrate the system's methodology on several bar charts that have been preloaded along with some accompanying text.

Information graphics are non-pictorial graphics such as bar charts and line graphs that depict attributes of entities and relations among entities. Most information graphics appearing in popular media have a communicative goal or intended message; consequently, information graphics constitute a form of language. This paper argues that information graphics are a valuable knowledge resource that should be retrievable from a digital library and that such graphics should be taken into account when summarizing a multimodal document for subsequent indexing and retrieval. But to accomplish this, the information graphic must be understood and its message recognized. The paper presents our Bayesian system for recognizing the primary message of one kind of information graphic (simple bar charts) and discusses the potential role of an information graphic's message in indexing graphics and summarizing multimodal documents.

This paper presents a corpus study that explores the extent to which captions contribute to recognizing the intended message of an information graphic. It then presents an implemented graphic interpretation system that takes into account a variety of communicative signals, and an evaluation study showing that evidence obtained from shallow processing of the graphic's caption has a significant impact on the system's success. This work is part of a larger project whose goal is to provide sight-impaired users with effective access to information graphics.

A natural language collaborative consultation system must take user preferences into account. A model of user preferences allows a system to appropriately evaluate alternatives using criteria of importance to the user. Additionally, decision research suggests both that an accurate model of user preferences could enable the system to improve a user's decision‐making by ensuring that all important alternatives are considered, and that such a model of user preferences must be built dynamically by observing the user's actions during the decision‐making process. This paper presents two strategies: one for dynamically recognizing user preferences during the course of a collaborative planning dialogue and the other for exploiting the model of user preferences to detect suboptimal solutions and suggest better alternatives. Our recognition strategy utilizes not only the utterances themselves but also characteristics of the dialogue in developing a model of user preferences. Our generation strategy takes into account both the strength of a preference and the closeness of a potential match in evaluating actions in the user's plan and suggesting better alternatives. By modeling and utilizing user preferences, our system is able to fulfill its role as a collaborative agent.