Most programmers rely on visual tools (block-based editors, auto-indentation, bracket matching, syntax highlighting, etc.), which are inaccessible to visually-impaired programmers. While prior language-specific, downloadable tools have demonstrated benefits for the visually-impaired, we lack language-independent, cloud-based tools, both of which are critically needed. We present a new toolkit for building fully-accessible, browser-based programming environments for multiple languages. Given a parser that meets certain specifications, this toolkit will generate a block editor familiar to sighted users that also communicates the structure of a program using spoken descriptions, and allows for navigation using standard (accessible) keyboard shortcuts.

Social media is an increasingly important part of modern life. We investigate the use of and usability of Twitter by blind users, via a combination of surveys of blind Twitter users, large-scale analysis of tweets from and Twitter profiles of blind and sighted users, and analysis of tweets containing embedded imagery. While Twitter has traditionally been thought of as the most accessible social media platform for blind users, Twitter's increasing integration of image content and users' diverse uses for images have presented emergent accessibility challenges. Our findings illuminate the importance of the ability to use social media for people who are blind, while also highlighting the many challenges such media currently present this user base, including difficulty in creating profiles, in awareness of available features and settings, in controlling revelations of one's disability status, and in dealing with the increasing pervasiveness of image-based content. We propose changes that Twitter and other social platforms should make to promote fuller access to users with visual impairments.

CAVIAR is designed to aid people with vision impairment in locating, identifying, and acquiring objects in their peripersonal space. A mobile phone, worn on the chest, captures video in front of the user; the computer vision component locates the user's hand and objects in the video stream. The auditory component informs the user about the presence of objects. On user confirmation, the reaching component sends signals to vibrotactile actuators on the user's wristband, guiding the hand to a specific object. This paper describes an end-to-end prototype of CAVIAR and its formative evaluation.

This paper describes a glove with which users enter input by tapping fingertips with the thumb or by rubbing the thumb over the palmar surfaces of the middle and index fingers. The glove has been informally tested as the controller for two semi-autonomous robots in a a 3D simulation environment. A preliminary evaluation of the glove's performance is presented.

Targeting the operating system (OS) kernel, kernel rootkits pose a formidable threat to computer systems and their users. Recent efforts have made significant progress in blocking them from injecting malicious code into the OS kernel for execution. Unfortunately, they cannot block the emerging so-called return-oriented rootkits (RORs). Without the need of injecting their own malicious code, these rootkits can discover and chain together "return-oriented gadgets" (that consist of only legitimate kernel code) for rootkit computation.

Virtual machine (VM) introspection is a powerful technique for determining the specific aspects of guest VM execution from outside the VM. Unfortunately, existing introspection solutions share a common questionable assumption. This assumption is embodied in the expectation that original kernel data structures are respected by the untrusted guest and thus can be directly used to bridge the well-known semantic gap. In this paper, we assume the perspective of the attacker, and exploit this questionable assumption to subvert VM introspection. In particular, we present an attack called DKSM (Direct Kernel Structure Manipulation), and show that it can effectively foil existing VM introspection solutions into providing false information. By assuming this perspective, we hope to better understand the challenges and opportunities for the development of future reliable VM introspection solutions that are not vulnerable to the proposed attack.

ProofChecker is a graphical program based on the notion of formal correctness proofs that allows students, both sighted and visually impaired, to draw a deterministic finite automaton (DFA) and determine whether or not it correctly recognizes a given language. Sighted students use the mouse and graphical controls to draw and manipulate the DFA. Keyboard shortcuts, together with the use of a screen reader to voice the accessible descriptions provided by the program, allow visually impaired students to do the same.