In the context of mental wellness support, trust and intimacy between a counselor and a patient are necessary to converge healing processes positively. However, convincing students to trust a virtual human for topics regarding mental wellness is a complex problem that requires understanding students' experiences. Based on research that discusses mental health as a concerning topic regarding Computer Science (CS) students, this paper investigates how undergraduate computing-related students perceive virtual humans' expertise on mental wellness support based on demographic resemblance on spoken accent and gender. Four virtual human counselors were developed to conduct the study, as 58 undergraduate computing-related students from two North American universities were recruited and assessed. Our findings suggest that students were less inclined to interact with a male virtual human than a female one. Also, that spoken accents can impact students' perceptions of expertise under students' multilingualism.

Vehicular ad hoc networks (VANETs) are a particular subclass of mobile ad hoc networks that raise a number of security challenges, notably from the way users authenticate the network. Authentication technologies based on existing security policies and access control rules in such networks assume full trust on roadside unit (RSU) and authentication servers. The disclosure of authentication parameters enables user's traceability over the network. VANETs' trusted entities (e.g., RSU) can utilize such information to track a user traveling behavior, violating user privacy and anonymity. In this paper, we proposed a novel, light-weight, adaptive group-based zero knowledge proof-authentication protocol (AGZKP-AP) for VANETs. The proposed authentication protocol is capable of offering various levels of users' privacy settings based on the type of services available on such networks. Our scheme is based on the zero-knowledge-proof crypto approach with the support of tradeoff options. Users have the option to make critical decisions on the level of privacy and the amount of resources usage they prefer such as short system response time versus the number of private information disclosures. Furthermore, AGZKP-AP is incorporated with a distributed privilege control and revoking mechanism that render user's private information to law enforcement in case of a traffic violation.

The simulation of tactile sensation using haptic devices is increasingly investigated in conjunction with simulation and training. This work explores the most popular haptic frameworks and APIs. We provide a comprehensive review and comparison of their features and capabilities, from the perspective of a software developer. In order to compare the studied frameworks and APIs, we identified and applied a set of eleven criteria and we obtained a classification of platforms, from the perspective of these criteria. According to this classification, we decide on the best platform to develop a visuo-haptic prototype for liver diagnostics.

Augmented reality (AR) systems add visual information to the world by using advanced display techniques. The advances in miniaturization and reduced hardware costs make some of these systems feasible for applications in a wide set of fields. We present a potential component of the cyber infrastructure for the operating room of the future: a distributed AR-based software-hardware system that allows real-time visualization of three-dimensional (3-D) lung dynamics superimposed directly on the patient's body. Several emergency events (e.g., closed and tension pneumothorax) and surgical procedures related to lung (e.g., lung transplantation, lung volume reduction surgery, surgical treatment of lung infections, lung cancer surgery) could benefit from the proposed prototype.

Medical simulations of lung dynamics promise to be effective tools for teaching and training clinical and surgical procedures related to lungs. Their effectiveness may be greatly enhanced when visualized in an augmented reality (AR) environment. However, the computational requirements of AR environments limit the availability of the central processing unit (CPU) for the lung dynamics simulation for different breathing conditions. In this paper, we present a method for computing lung deformations in real time by taking advantage of the programmable graphics processing unit (GPU). This will save the CPU time for other AR-associated tasks such as tracking, communication, and interaction management. An approach for the simulations of the three-dimensional (3-D) lung dynamics using Green's formulation in the case of upright position is taken into consideration. We extend this approach to other orientations as well as the subsequent changes in breathing. Specifically, the proposed extension presents a computational optimization and its implementation in a GPU. Results show that the computational requirements for simulating the deformation of a 3-D lung model are significantly reduced for point-based rendering.

Many tracking systems utilize collections of fiducial markers arranged in rigid configurations, called tracking probes, to determine the pose of objects within an environment. In this paper, we present a technique for designing tracking probes called the viewpoints algorithm. The algorithm is generally applicable to tracking systems that use at least three fiduciary marks to determine the pose of an object. The algorithm is used to create an integrated, head-mounted display tracking probe. The predicted accuracy of this probe was 0.032 /spl plusmn/ 0.02 degrees in orientation and 0.09 /spl plusmn/ 0.07 mm in position. The measured accuracy of the probe was 0.028 /spl plusmn/ 0.01 degrees in orientation and 0.11 /spl plusmn/ 0.01 mm in position. These results translate to a predicted, static positional overlay error of a virtual object presented at 1m of less than 0.5 mm. The algorithm is part of a larger framework for designing tracking probes based upon performance goals and environmental constraints.