As an important extension of symbolic execution (SE), probabilistic symbolic execution (PSE) computes execution probabilities of program paths. Using this information, PSE can prioritize path exploration strategies. To calculate the probability of a path PSE relies on solution counting approaches for the path constraint. The correctness of a solution counting approach depends on the methodology used to count solutions and whether a path constraint maintains a one-to-one relation with program input values. This work focuses on the latter aspect of the solution counting correctness for string constraints. In general, maintaining a one-to-one relation is not always possible, especially in the presence of non-linear constraints. To deal with this issue, researchers that work on PSE for numerical domains either analyze programs with linear constraints, or develop novel techniques to handle solution counting of non-linear constraints. For the string domain, however, previous work on PSE mainly focuses on efficient and accurate solution counting for automata-based string models and has not investigated whether a one-to-one relationship between the strings encoded by automata and input string values is preserved. In this work we demonstrate that traditional automata-based string models fail to maintain one-to-one relations and propose to use the weighted automata model, which preserves the one-to-one relation between the path constraint it encodes and the input string values. We use this model to implement a string constraint solver and show its correctness on a set of non-trivial synthetic benchmarks. We also present an empirical evaluation of traditional and proposed automata solvers on real-world string constraints. The evaluations show that while being less efficient than traditional automata models, the weighted automata model maintains correct solution counts.

While conducting research focused on individuals with impairments is vitally important, such experiments often have high costs (time and money), and researchers may be limited in the instructions they can give, or participant feedback they can gather (due to the impairment). We present how an impairment emulation system (ACES) can be used by researchers in the behavioral sciences. By repurposing this new technology within the context of a "traditional" psychology experiment, we were able to analyze impaired linguistic and communication in a manner that was not possible without a system such as ACES. Our experiment on 96 participants provided strong support for a theory in the aphasia psychology community, and uncovered new understandings of how people communicate when one interlocutor's speech is distorted with aphasia. These findings illustrate a new direction of HCI research that directly helps researchers in Psychology, Communication, and Speech and Hearing Science.

The ability of children to combine syllables represents an important developmental milestone. This ability is often delayed or impaired in a variety of clinical groups, including children with autism spectrum disorders (ASD) and speech delays (SPD). Prior work has demonstrated successful use of computer-based voice visualizations to facilitate speech production and vocalization in children with and without ASD/SPD. While prior work has focused on increasing frequency of speech-like vocalizations or accuracy of speech sound production, we believe that there is a potential new direction of research: exploration of real-time visualizations to shape multisyllabic speech. Over two years we developed VocSyl, a real-time voice visualization system. Rather than building visualizations based on what adult clinicians and software designers may think is needed, we designed VocSyl using the Task Centered User Interface Design (TCUID) methodology throughout the design process. Children with ASD and SPD, targeted users of the software, were directly involved in the development process, allowing us to focus on what these children demonstrate they require. This paper presents the results of our TCUID design cycle of VocSyl, as well as design guidelines for future work with children with ASD and SPD.

Individuals with aphasia, an acquired communication disorder, constantly struggle against a world that does not understand them. This lack of empathy and understanding negatively impacts their quality of life. While aphasic individuals may appear to have lost cognitive functioning, their impairment relates to receptive and expressive language, not to thinking processes. We introduce a novel system and model, Aphasia Characteristics Emulation Software (ACES), enabling users (e.g., caregivers, speech therapists and family) to experience, firsthand, the communication-distorting effects of aphasia. By allowing neurologically typical individuals to "walk in another's shoes," we aim to increase patience, awareness and understanding. ACES was grounded in the communication science and psychological literature, and informed by an initial pilot study. Results from an evaluation of 64 participants indicate that ACES provides a rich experience that increases understanding and empathy for aphasia.

The Defense Advanced Research Projects Agency Revolutionizing Prosthetics 2009 program has the goal of developing a neurally-integrated upper limb prosthesis to provide amputee soldiers with pre-injury levels of function. A team of over 30 collaborating institutions from industry and academia set out to design and construct a prosthetic limb that achieves natural human limb performance by combining the state of the art in neuroscience, robotics, sensors, power systems, actuation, and complex embedded software. The Modular Prosthetic Limb (MPL) is the result of Phase II of the program which successfully demonstrated robotic limb operation in December 2009. MPL design and development addressed several areas of technical complexity such as space, power, and mass constraints, safety-critical operation, and modularity for patient customizations based on the location of disarticulation. This paper provides an overview of the MPL system architecture, the robotic arm control strategy, and detailed information on the embedded system software that integrates the MPL components to enable patient-based control.

We describe a method to analyze materials on a conveyor belt using natural gamma spectra collected with a BGO (Bismuth Germanate) gamma ray detector, which collects emissions from Potassium (K), Uranium (U), and Thorium (Th) in the materials. A statistical model is proposed based on a Poisson process and an approximate maximum likelihood (ML) technique via the expectation-maximization (EM) algorithm is then used to estimate the amount of each of the three elements in the material. A refinement of the statistical model is used to estimate linear drift in the detector.