Embodied Conversational Agents (ECAs) can influence users through verbal and nonverbal social cues. Focusing on dominance, we examine how verbal and nonverbal dominance cues influence users’ decision-making and perceptions of the agent in VR. We conducted a user study using a 2 (verbal: dominant vs. submissive) × 2 (nonverbal: dominant vs. submissive) full factorial design, operationalized through a route-selection task at a virtual crossroads. Results indicated that verbal dominance cues shaped participants’ dominance perception but did not influence decision-making, while nonverbal dominance cues affected route-selection behavior without altering perceived dominance. Both verbal and nonverbal cues also affected broader social evaluations of the agent (e.g., intelligence, competence, warmth, and trustworthiness), with nonverbal cues uniquely affecting likability and social presence. These findings highlight the complementary roles of verbal and nonverbal dominance cues in human–agent interaction in VR and inform the design of context-sensitive, dominance-calibrated ECAs for training, education, and decision support.

Room-scale locomotion in VR can promote physical movement to combat the negative effects of a sedentary lifestyle. Therefore, it is useful to investigate factors which may motivate users to walk instead of teleport. In a within-subjects study with 24 participants, we investigated the effects of avatar appearance and target distance on users' locomotion behavior, when given the option to either walk or teleport. In particular, we manipulated three aspects of avatar appearance-body completeness (full-body vs upper-body only), body weight (normal vs obese), and clothing (casual vs superhero armor), and we provided three target distances (1.5 meters, 3.0 meters, and 4.5 meters). We found that avatar obesity decreased willingness to walk, but only for full-body avatars. We also found that close target distances encouraged more walking than teleporting, up to a certain threshold distance. Additionally, we found that users' perceived similarity to their avatar correlated with their willingness to walk. Our findings can be applied to motivate users to walk more in VR, such as in VR exergames.

Vection can elicit a compelling illusion of self-motion, even when the user remains physically stationary. When combined with user actions, this illusion can enhance the sense of presence in virtual environments. However, vection may unintentionally influence physical locomotion, causing unintended positional drift (UPD). UPD is especially problematic for Walk-In-Place (WIP) navigation in virtual reality (VR), where users simulate locomotion by mimicking walking motions without actual displacement. In this study, we investigated how vection affects UPD during WIP navigation in immersive VR. We specifically manipulated the location of peripheral optic flow (left, right, bilateral) and speed (low, high) using dynamic textures on the lateral walls of the virtual environment to induce various vection conditions. Twenty-seven participants performed WIP under six vection conditions (three locations × two speeds) and one baseline condition without optic flow. Our results revealed that bilateral vection caused the greatest UPD, particularly along the lateral axis, while unilateral vection tended to suppress lateral drift. However, peripheral optic flow speed did not significantly affect UPD. We discuss these findings and their implications for designing immersive virtual environments that mitigate UPD while maintaining the sense of presence.

Managing negative emotions through emotion regulation (ER) is key to mental well-being. While virtual reality (VR) shows promise for supporting ER, prior work has primarily focused on selfregulation. This paper introduces a virtual agent that helps users manage emotions through conversation-based ER strategies. We compared three conditions: no agent, an agent with non-supportive responses, and an agent with ER-supportive responses. Results showed that the ER-supportive agent significantly improved users' emotional states and overall experience. Building on this, we conducted a second experiment to examine how the agent's appearance (realistic vs. cartoon) and voice tone (emotional vs. neutral) affect ER. Results indicated that an emotional voice tone improved users' ability to regulate emotions. Although a realistic appearance did not directly improve ER, it increased users' trust and sense of social presence. This paper contributes to VR and human-agent interaction by demonstrating the potential of virtual agents to support ER and offering design implications for future ER-supportive agents.

According to Hall’s theory, the space individuals maintain between one another depends on relational closeness and situational context. Prior research suggests that interpersonal distance (IPD) varies not only between virtual and real humans, but also among virtual humans depending on their perceived agency. However, little is known about how people spatially negotiate entry into mixed groups comprising different types of agents in extended reality (XR) settings. In this study, we examine participants’ entry behavior as they join a circular F-formation composed of three distinct entities: an agent, an avatar, and a real human. Specifically, we investigate how participants position themselves relative to each entity, analyzing their preferences and behaviors in terms of IPD and entry dynamics. Our findings reveal that participants maintained the greatest IPD from the real human, followed by the avatar and the agent, suggesting nuanced social distinctions among these three entities. Furthermore, when the real human was absent, participants tended to maintain a greater distance from the avatar compared to the agent. These results offer valuable insights for the design of XR collaboration environments and for understanding social dynamics in multi-agent interactions.

Virtual Reality (VR) can create experiences that conflict with a user’s prior knowledge; however, how such conflicts influence subsequent real-world behavior remains unclear. This study explores how a virtual experience that contradicts real-world expectations affects later perception and motor actions, using the size-weight illusion—where people expect larger objects to be heavier than smaller ones. We conducted a 2 (internal model robustness: reinforced vs. weakened) by 2 (presence: high vs. low) mixed-design experiment. Participants first received real-world training to either strengthen or weaken their size-weight expectations, then experienced a reversed size-weight mapping in VR under varying levels of presence. We assessed how this virtual experience influenced real-world weight estimation and object lifting behavior. Results showed that participants with weakened prior knowledge exhibited lower confidence in their weight judgments and greater motor instability when lifting objects. However, the level of presence in VR did not significantly affect transfer outcomes. These findings suggest that the strength of prior knowledge modulates how conflicting virtual experiences influence real-world behavior, underscoring the need for careful VR design, particularly for younger users with less stable internal models.

Embodied conversational agents (ECAs) capable of non-verbal behaviors have been developed to address the limitations of voice-only assistants, with research exploring their use in mixed and augmented reality (AR), suggesting they may soon interact with us more naturally in physical spaces. Traditionally, AI voice assistants are activated through wake-up keywords, and since they are invisible, their method of appearance has not been a concern. However, for ECAs in AR, the question of how they should enter the user's space when summoned remains underexplored. In this paper, we focused on the plausibility of ECAs' entering action into the user's field of view in AR. We analyzed its impact on user experience, concentrating on perceived social presence and co-presence of the agent. Three entrance styles were chosen for comparison: an obviously impossible one, a possible one, and an intermediate one, alongside a voice-only condition. We designed and conducted a within-subjects study with 38 participants. Our results indicated that while the plausibility of the action had less impact on functionality compared to the embodiment itself, it significantly affected social/co-presence. These findings highlight the importance of entrance design for future AR agent experiences.

In this experience paper, we share our experience on enhancing automatic unit test generation to more effectively find Java null pointer exceptions (NPEs). NPEs are among the most common and critical errors in Java applications. However, as we demonstrate in this paper, existing unit test generation tools such as Randoop and EvoSuite are not sufficiently effective at catching NPEs. Specifically, their primary strategy of achieving high code coverage does not necessarily result in triggering diverse NPEs in practice. In this paper, we detail our observation on the limitations of current state-of-the-art unit testing tools in terms of NPE detection and introduce a new strategy to improve their effectiveness. Our strategy utilizes both static and dynamic analyses to guide the test case generator to focus specifically on scenarios that are likely to trigger NPEs. We implemented this strategy on top of EvoSuite, and evaluated our tool, NpeTest, on 108 NPE benchmarks collected from 96 real-world projects. The results show that our NPE-guidance strategy can increase EvoSuite's reproduction rate of the NPEs from 56.9% to 78.9%, a 38.7% improvement. Furthermore, NpeTest successfully detected 89 previously unknown NPEs from an industry project.

In this paper, we present a technique for learning seed-adaptive mutation strategies for fuzzers. The performance of mutation-based fuzzers highly depends on the mutation strategy that specifies the probability distribution of selecting mutation methods. As a result, developing an effective mutation strategy has received much attention recently, and program-adaptive techniques, which observe the behavior of the target program to learn the optimized mutation strategy per program, have become a trending approach to achieve better performance. They, however, still have a major limitation; they disregard the impacts of different characteristics of seed inputs which can lead to explore deeper program locations. To address this limitation, we present SEAMFUZZ, a novel fuzzing technique that automatically captures the characteristics of individual seed inputs and applies different mutation strategies for different seed inputs. By capturing the syntactic and semantic similarities between seed inputs, SEAMFUZZ clusters them into proper groups and learns effective mutation strategies tailored for each seed cluster by using the customized Thompson sampling algorithm. Experimental results show that SEAMFUZZ improves both the path-discovering and bug-finding abilities of state-of-the-art fuzzers on real-world programs.

We present SymTuner, a novel technique to automatically tune external parameters of symbolic execution. Practical symbolic execution tools have important external parameters (e.g., symbolic arguments, seed input) that critically affect their performance. Due to the huge parameter space, however, manually customizing those parameters is notoriously difficult even for experts. As a consequence, symbolic execution tools have typically been used in a suboptimal manner that, for example, simply relies on the default parameter settings of the tools and loses the opportunity for better performance. In this paper, we aim to change this situation by automatically configuring symbolic execution parameters. With SymTuner that takes parameter spaces to be tuned, symbolic executors are run without manual parameter configurations; instead, appropriate parameter values are learned and adjusted during symbolic execution. To achieve this, we present a learning algorithm that observes the behavior of symbolic execution and accordingly updates the sampling probability of each parameter space. We evaluated SymTuner with KLEE on 12 open-source C programs. The results show that SymTuner increases branch coverage of KLEE by 56% on average and finds 8 more bugs than KLEE with its default parameters over the latest releases of the programs.

The demand to utilize digital humans for conversation systems in multiple areas has increased. Time delays in a conversation with digital humans can be occurred because of network and natural language processing, which can deteriorate the users’ satisfaction and the availability of the digital humans. In this study, we designed a gestural filler that can be exhibited when delay occurs during a conversation with a digital human, and compared the effects of the gestural filler with conversational filler.

This paper investigated whether increased presence from experiencing haptic feedback on one virtual object can transfer to another virtual object. Two similar studies were run in different environments: an immersive virtual environment and a mixed environment. Results showed that participants reported a high presence of untouched virtual object after touching a virtual object in a virtual reality environment. On the other hand, it was difficult to confirm that such presence transfers occurred in an augmented reality environment.

Walk-In-Place (WIP) is a technique in which users perform walking or jogging-like movements in a stationary place to move around in virtual environments (VEs). However, unintended positional drift (UPD) while performing WIP often occurs, thus weakening its benefits of keeping users in a fixed position in a physical space. In this paper, we present our preliminary study exploring whether users’ gaze while WIP affects the direction of the UPD. Participants of the study jogged in a VE five times. Each time, we manipulated their gaze direction by displaying visual information in 5 different locations in their view. Although a correlation between the gaze and UPD direction was not found, we report the results from this study, including the amount of observed drift and preferred location of visual information, and discuss future research directions.

In this article, we investigate the effects of the physical influence of a virtual human (VH) in the context of face-to-face interaction in a mixed reality environment. In Experiment 1, participants played a tabletop game with a VH, in which each player takes a turn and moves their own token along the designated spots on the shared table. We compared two conditions as follows: the VH in the virtual condition moves a virtual token that can only be seen through augmented reality (AR) glasses, while the VH in the physical condition moves a physical token as the participants do; therefore the VH's token can be seen even in the periphery of the AR glasses. For the physical condition, we designed an actuator system underneath the table. The actuator moves a magnet under the table which then moves the VH's physical token over the surface of the table. Our results indicate that participants felt higher co-presence with the VH in the physical condition, and participants assessed the VH as a more physical entity compared to the VH in the virtual condition. We further observed transference effects when participants attributed the VH's ability to move physical objects to other elements in the real world. Also, the VH's physical influence improved participants' overall experience with the VH. In Experiment 2, we further looked into the question how the physical-virtual latency in movements affected the perceived plausibility of the VH's interaction with the real world. Our results indicate that a slight temporal difference between the physical token reacting to the virtual hand's movement increased the perceived realism and causality of the mixed reality interaction. We discuss potential explanations for the findings and implications for future shared mixed reality tabletop setups.

We present VERISMART, a highly precise verifier for ensuring arithmetic safety of Ethereum smart contracts. Writing safe smart contracts without unintended behavior is critically important because smart contracts are immutable and even a single flaw can cause huge financial damage. In particular, ensuring that arithmetic operations are safe is one of the most important and common security concerns of Ethereum smart contracts nowadays. In response, several safety analyzers have been proposed over the past few years, but state-of-the-art is still unsatisfactory; no existing tools achieve high precision and recall at the same time, inherently limited to producing annoying false alarms or missing critical bugs. By contrast, VERISMART aims for an uncompromising analyzer that performs exhaustive verification without compromising precision or scalability, thereby greatly reducing the burden of manually checking undiscovered or incorrectly-reported issues. To achieve this goal, we present a new domain-specific algorithm for verifying smart contracts, which is able to automatically discover and leverage transaction invariants that are essential for precisely analyzing smart contracts. Evaluation with real-world smart contracts shows that VERISMART can detect all arithmetic bugs with a negligible number of false alarms, far outperforming existing analyzers.

In this paper, we applied the concept of diminished reality to remove content-irrelevant pedestrian (i.e., real object) in the context of handheld augmented reality (AR). We prepared three view conditions: in Transparent (TP) condition, we removed the pedestrian entirely; in Semi-transparent (STP) condition, the pedestrian became semi-transparent; lastly, in Default (DF) condition, the pedestrian appeared as is. We conducted a user study to compare the effects of the three conditions on users’ engagement and perception of a virtual pet in the AR content. Our findings revealed that users felt less distracted to the AR content in TP and STP conditions, compared to the DF condition. Furthermore, users felt the virtual pet as more life-like, its behavior more plausible, and felt a higher spatial presence in the real environment, in the TP condition.

We investigate the effects of different ways of visualizing the virtual gait of the avatar in the context of Walk-in-Place (WIP) based navigation in a virtual environment (VE). In Study 1, participants navigated through a VE using the WIP method while inhabiting an avatar. We varied the visualization of the avatar’s leg motion while performing the WIP gesture: (1) Fixed Body: the legs stood still; (2) Pre-recorded Animation: the legs moved in a fixed predetermined pace (plausible but not in accordance to that of the user in general); (3) Synchronized Motion the legs moved according (synchronized) to those of the user. Our results indicated that the sense of presence and body ownership improved significantly when the leg motion was rendered synchronized to that of the user (Synchronized Motion). In addition, we developed a deep neural network (DNN) that predicted the users’ leg postures only with the head position tracking, eliminating the need for any external sensors. We carried out Study 2, to assess the effects of different gait visualizations, under two new factors: (1) virtual gait seen directly by the user looking down, or already visible by one’s shadow (i.e., no need to look down); and (2) playing a pre-recorded animation, or pre-recorded animation whose playback speed was adjusted to match with pace of the users’ actual leg motions as predicted by the DNN. The results of Study 2 showed that the virtual gait temporally synchronized with that of the user greatly improved the sense of body ownership, whether it was witnessed directly or indirectly with the shadow. However, the effect of virtual gait on presence was less marked when indirectly observed. We discuss our findings and the implications for representing the avatar locomotion in immersive virtual environments.

Abstract Interactions with embodied conversational agents can be enhanced using human‐like co‐speech gestures. Traditionally, rule‐based co‐speech gesture mapping has been utilized for this purpose. However, the creation of this mapping is laborious and often requires human experts. Moreover, human‐created mapping tends to be limited, therefore prone to generate repeated gestures. In this article, we present an approach to automate the generation of rule‐based co‐speech gesture mapping from publicly available large video data set without the intervention of human experts. At run‐time, word embedding is utilized for rule searching to get the semantic‐aware, meaningful, and accurate rule. The evaluation indicated that our method achieved comparable performance with the manual map generated by human experts, with a more variety of gestures activated. Moreover, synergy effects were observed in users' perception of generated co‐speech gestures when combined with the manual map.

We present Graphick, a new technique for automatically learning graph-based heuristics for pointer analysis. Striking a balance between precision and scalability of pointer analysis requires designing good analysis heuristics. For example, because applying context sensitivity to all methods in a real-world program is impractical, pointer analysis typically uses a heuristic to employ context sensitivity only when it is necessary. Past research has shown that exploiting the program's graph structure is a promising way of developing cost-effective analysis heuristics, promoting the recent trend of ``graph-based heuristics'' that work on the graph representations of programs obtained from a pre-analysis. Although promising, manually developing such heuristics remains challenging, requiring a great deal of expertise and laborious effort. In this paper, we aim to reduce this burden by learning graph-based heuristics automatically, in particular without hand-crafted application-specific features. To do so, we present a feature language to describe graph structures and an algorithm for learning analysis heuristics within the language. We implemented Graphick on top of Doop and used it to learn graph-based heuristics for object sensitivity and heap abstraction. The evaluation results show that our approach is general and can generate high-quality heuristics. For both instances, the learned heuristics are as competitive as the existing state-of-the-art heuristics designed manually by analysis experts.

This paper presents the optimal implementations of single- and double-precision general matrix-matrix multiplication (GEMM) routines for the Intel Xeon Phi Processor code-named Knights Landing (KNL) and the Intel Xeon Scalable Processors based on an auto-tuning approach with the Intel AVX-512 intrinsic functions. Our auto-tuning approach precisely determines the parameters reflecting the target architectural features. Our approach significantly reduces the search space and derives optimal parameter sets including the size of submatrices, prefetch distances, loop unrolling depth, and parallelization scheme. Without a single line of assembly code, our GEMM kernels show the comparable performance results to the Intel MKL and outperform other open-source BLAS libraries.

Domestic animals have a long history of enriching human lives physically and mentally by filling a variety of different roles, such as service animals, emotional support animals, companions, and pets. Despite this, technological realizations of such animals in augmented reality (AR) are largely underexplored in terms of their behavior and interactions as well as effects they might have on human users' perception or behavior. In this paper, we describe a simulated virtual companion animal, in the form of a dog, in a shared AR space. We investigated its effects on participants' perception and behavior, including locomotion related to proxemics, with respect to their AR dog and other real people in the environment. We conducted a 2 by 2 mixed factorial human-subject study, in which we varied (i) the AR dog's awareness and behavior with respect to other people in the physical environment and (ii) the awareness and behavior of those people with respect to the AR dog. Our results show that having an AR companion dog changes participants' locomotion behavior, proxemics, and social interaction with other people who can or can not see the AR dog. We also show that the AR dog's simulated awareness and behaviors have an impact on participants' perception, including co-presence, animalism, perceived physicality, and dog's perceived awareness of the participant and environment. We discuss our findings and present insights and implications for the realization of effective AR animal companions.

We present a new technique for automatically detecting logical errors in functional programming assignments. Compared to syntax or type errors, detecting logical errors remains largely a manual process that requires hand-made test cases. However, designing proper test cases is nontrivial and involves a lot of human effort. Furthermore, manual test cases are unlikely to catch diverse errors because instructors cannot predict all corner cases of diverse student submissions. We aim to reduce this burden by automatically generating test cases for functional programs. Given a reference program and a student's submission, our technique generates a counter-example that captures the semantic difference of the two programs without any manual effort. The key novelty behind our approach is the counter-example generation algorithm that combines enumerative search and symbolic verification techniques in a synergistic way. The experimental results show that our technique is able to detect 88 more errors not found by mature test cases that have been improved over the past few years, and performs better than the existing property-based testing techniques. We also demonstrate the usefulness of our technique in the context of automated program repair, where it effectively helps to eliminate test-suite-overfitted patches.

The field of intelligent virtual agents (IVAs) has evolved immensely over the past 15 years, introducing new application opportunities in areas such as training, health care, and virtual assistants. In this survey paper, we provide a systematic review of the most influential user studies published in the IVA conference from 2001 to 2015 focusing on IVA development, human perception, and interactions. A total of 247 papers with 276 user studies have been classified and reviewed based on their contributions and impact. We identify the different areas of research and provide a summary of the papers with the highest impact. With the trends of past user studies and the current state of technology, we provide insights into future trends and research challenges.

A physical embodiment of a virtual human has shown benefits in applications that involve social interaction with virtual humans. However, it often incorporates cumbersome haptic devices or robotic bodies. In this position paper, we first discuss our motivation for utilizing a surrounding environment in human-virtual human interaction and present our preliminary studies and results. Considering the previous studies and related literature, we define the concept of Mediated Physicality for virtual humans, which utilizes environmental objects to increase perceived physicality of the virtual humans, and discuss fundamental aspects of the Mediated Physicality as well as future research plans.

In this paper, we investigate the effects of the physical influence of a virtual human (VH) in the context of face-to-face interaction in augmented reality (AR). In our study, participants played a tabletop game with a VH, in which each player takes a turn and moves their own token along the designated spots on the shared table. We compared two conditions as follows: the VH in the virtual condition moves a virtual token that can only be seen through AR glasses, while the VH in the physical condition moves a physical token as the participants do; therefore the VH's token can be seen even in the periphery of the AR glasses. For the physical condition, we designed an actuator system underneath the table. The actuator moves a magnet under the table which then moves the VH's physical token over the surface of the table. Our results indicate that participants felt higher co-presence with the VH in the physical condition, and participants assessed the VH as a more physical entity compared to the VH in the virtual condition. We further observed transference effects when participants attributed the VH's ability to move physical objects to other elements in the real world. Also, the VH's physical influence improved participants' overall experience with the VH. We discuss potential explanations for the findings and implications for future shared AR tabletop setups.

In this paper, we investigate factors and issues related to human locomotion behavior and proxemics in the presence of a real or virtual human in augmented reality (AR). First, we discuss a unique issue with current-state optical see-through head-mounted displays, namely the mismatch between a small augmented visual field and a large unaugmented periphery, and its potential impact on locomotion behavior in close proximity of virtual content. We discuss a potential simple solution based on restricting the field of view to the central region, and we present the results of a controlled human-subject study. The study results show objective benefits for this approach in producing behaviors that more closely match those that occur when seeing a real human, but also some drawbacks in overall acceptance of the restricted field of view. Second, we discuss the limited multimodal feedback provided by virtual humans in AR, present a potential improvement based on vibrotactile feedback induced via the floor to compensate for the limited augmented visual field, and report results showing that benefits of such vibrations are less visible in objective locomotion behavior than in subjective estimates of co-presence. Third, we investigate and document significant differences in the effects that real and virtual humans have on locomotion behavior in AR with respect to clearance distances, walking speed, and head motions. We discuss potential explanations for these effects related to social expectations, and analyze effects of different types of behaviors including idle standing, jumping, and walking that such real or virtual humans may exhibit in the presence of an observer.

We investigate the effect of vibrotactile feedback delivered to one's feet in an immersive virtual environment (IVE). In our study, participants observed a virtual environment where a virtual human (VH) walked toward the participants and paced back and forth within their social space. We compared three conditions as follows: participants in the “Sound” condition heard the footsteps of the VH; participants in the “Vibration” condition experienced the vibration of the footsteps along with the sounds; while participants in the “Mute” condition were not exposed to sound nor vibrotactile feedback. We found that the participants in the “Vibration” condition felt a higher social presence with the VH compared to those who did not feel the vibration. The participants in the “Vibration” condition also exhibited greater avoidance behavior while facing the VH and when the VH invaded their personal space.

We explore whether a peripheral observation of apparent mutual social presence between a real human (RH) and a virtual human (VH) can in turn increase a subject's sense of social presence with the VH. In other words, we explore whether social presence can “transfer” from one RH-VH interaction to another. Specifically, we carried out an experiment where human subjects were asked to play a game with a VH. As they entered the game room, approximately half of the subjects were exposed to a brief but apparently engaging conversation between an RH and the VH. The subjects who were exposed to the brief RH-VH interaction had significantly higher measures of both emotional connection and the attentional allocation dimension of social presence for the VH, compared to those who were not. We describe the motivation, the experiment, and the results.

While performing everyday interactions, we often incidentally touch and move objects in subtle ways. These objects are not necessarily directly related to the task at hand, and the movement of an object might even be entirely unintentional. If another person is touching the object at the same time, the movement can transfer through the object and be experienced — however subtly — by the other person. For example, when one person hands a drink to another, at some point both individuals will be touching the glass, and consequently exerting small (often unnoticed) forces on the other person. Despite the frequency of such subtle incidental movements of shared objects in everyday interactions, few have examined how these movements affect human-virtual human (VH) interaction. We ran an experiment to assess how presence and social presence are affected when a person experiences subtle, incidental movement through a shared real-virtual object. We constructed a real-virtual room with a table that spanned the boundary between the real and virtual environments. The participant was seated on the real side of the table, which visually extended into the virtual world via a projection screen, and the VH was seated on the virtual side of the table. The two interacted by playing a game of “Twenty Questions,” where one player asked the other a series of 20 yes/no questions to deduce what object the other player was thinking about. During the game, the “wobbly” group of subjects experienced subtle incidental movements of the real-virtual table: the entire real-virtual table tilted slightly away/toward the subject when the virtual/real human leaned on it. The control group also played the same game, except the table did not wobble. Results indicate that the wobbly group had higher presence and social presence with the virtual human in general, with statistically significant increases in presence, co-presence, and attentional allocation. We present the experiment and results, and discuss some potential implications for virtual human systems and some potential future studies.

Lattice Boltzmann Method (LBM) is a powerful numerical simulation method of the fluid flow. With its data parallel nature and the simple kernel structure, it is a promising candidate for a parallel implementation on a GPU. The LBM, however, is heavily data-intensive and memory bound. In particular, moving the data to the adjacent cells in the streaming computation phase of the LBM incurs a lot of uncoalesced accesses on the GPU which affects the overall performance. In this paper, we parallelize the LBM on a GPU by incorporating memory-efficient techniques such as the tiling optimization with the data layout changes and the data update scheme so called a pull scheme. Furthermore, we developed optimization techniques such as removing branch divergences, reducing the register uses, and reducing the number of double precision floating-point instructions. Experimental results on Nvidia Tesla K20 GPU show that our approach delivers up to 1105 MLUPS (Million Lattice Updates Per Second) and 156-times speedup compared with a serial implementation.

In this article, we present a design and an evaluation of a hand-held, trackball-based, touch-haptic interface, named ‘Touchball’, for conveying directional cues. By the nature of the trackball mechanism, the device provides flexibility in terms of directional degrees of freedom compared with conventional vibro-tactile devices. It also has the advantage of a direct transfer of force feedback through frictional touch (leveraging on human's high skin sensitivity), thus requiring only a relatively small amount of inertia by the actuator. This leads to a compact hand-held design appropriate for mobile and 3D interactive applications. We first conducted psychophysical experiments to discover the detection thresholds and measure the perceived amount of directional force feedback. In addition, we conducted a performance and usability test, comparing the Touchball to a vibro-tactile (another touch-based) and aural (another modality) feedback device for an actual application, namely a navigational guide. Experimental results have shown that while both the Touchball and vibro-tactile devices produced comparable task completion time to that with aural feedback, their accuracies were significantly lower. The usability, in most categories, was higher in the order of the sound feedback, Touchball, then vibro-tactile feedback with statistically significant differences. To convey more than just on–off information through tactility such as directions, the vibro-tactile device requires relatively many vibrators and contact points, whereas the Touchball is a more compact and equally effective but with higher usability (e.g. mental load, ease of use, naturalness, etc.). Thus, Touchball offers a viable or even better alternative to a single vibrator or grid-based tactile feedback device especially as a supplementary modality device (to the visual or aural centric interface).