Characterizing the Effects of Local Latency on Aim Performance in First Person Shooters

Real-time games such as first-person shooters (FPS) are sensitive to even small amounts of lag. The effects of network latency have been studied, but less is known about local latency -- that is, the lag caused by local sources such as input devices, displays, and the application. While local latency is important to gamers, we do not know how it affects aiming performance and whether we can reduce its negative effects. To explore these issues, we tested local latency in a variety of real-world gaming systems and carried out a controlled study focusing on targeting and tracking activities in an FPS game with varying degrees of local latency. In addition, we tested the ability of a lag compensation technique (based on aim assistance) to mitigate the negative effects. To motivate the need for these studies, we also examined how aim in FPS differs from pointing in standard 2D tasks, showing significant differences in performance metrics. Our studies found local latencies in the real-world range from 23 to 243~ms that cause significant and substantial degradation in performance (even for latencies as low as 41~ms). The studies also showed that our compensation technique worked well, reducing the problems caused by lag in the case of targeting, and removing the problem altogether in the case of tracking. Our work shows that local latency is a real and substantial problem -- but game developers can mitigate the problem with appropriate compensation methods

Evaluation of the Tobii EyeX Eye tracking controller and Matlab toolkit for research

The Tobii Eyex Controller is a new low-cost binocular eye tracker marketed for integration in gaming and consumer applications. The manufacturers claim that the system was conceived for natural eye gaze interaction, does not require continuous recalibration, and allows moderate head movements. The Controller is provided with a SDK to foster the development of new eye tracking applications. We review the characteristics of the device for its possible use in scientific research. We develop and evaluate an open source Matlab Toolkit that can be employed to interface with the EyeX device for gaze recording in behavioral experiments. The Toolkit provides calibration procedures tailored to both binocular and monocular experiments, as well as procedures to evaluate other eye tracking devices. The observed performance of the EyeX (i.e. accuracy < 0.6°, precision < 0.25°, latency < 50 ms and sampling frequency ≈55 Hz), is sufficient for some classes of research application. The device can be successfully employed to measure fixation parameters, saccadic, smooth pursuit and vergence eye movements. However, the relatively low sampling rate and moderate precision limit the suitability of the EyeX for monitoring micro-saccadic eye movements or for real-time gaze-contingent stimulus control. For these applications, research grade, high-cost eye tracking technology may still be necessary. Therefore, despite its limitations with respect to high-end devices, the EyeX has the potential to further the dissemination of eye tracking technology to a broad audience, and could be a valuable asset in consumer and gaming applications as well as a subset of basic and clinical research settings

Modeling Three-Dimensional Interaction Tasks for Desktop Virtual Reality

A virtual environment is an interactive, head-referenced computer display that gives a user the illusion of presence in real or imaginary worlds. Two most significant differences between a virtual environment and a more traditional interactive 3D computer graphics system are the extent of the user's sense of presence and the level of user participation that can be obtained in the virtual environment. Over the years, advances in computer display hardware and software have substantially progressed the realism of computer-generated images, which dramatically enhanced user’s sense of presence in virtual environments. Unfortunately, such progress of user’s interaction with a virtual environment has not been observed. The scope of the thesis lies in the study of human-computer interaction that occurs in a desktop virtual environment. The objective is to develop/verify 3D interaction models that can be used to quantitatively describe users’ performance for 3D pointing, steering and object pursuit tasks and through the analysis of the interaction models and experimental results to gain a better understanding of users’ movements in the virtual environment. The approach applied throughout the thesis is a modeling methodology that is composed of three procedures, including identifying the variables involved for modeling a 3D interaction task, formulating and verifying the interaction model through user studies and statistical analysis, and applying the model to the evaluation of interaction techniques and input devices and gaining an insight into users’ movements in the virtual environment. In the study of 3D pointing tasks, a two-component model is used to break the tasks into a ballistic phase and a correction phase, and comparison is made between the real-world and virtual-world tasks in each phase. The results indicate that temporal differences arise in both phases, but the difference is significantly greater in the correction phase. This finding inspires us to design a methodology with two-component model and Fitts’ law, which decomposes a pointing task into the ballistic and correction phase and decreases the index of the difficulty of the task during the correction phase. The methodology allows for the development and evaluation of interaction techniques for 3D pointing tasks. For 3D steering tasks, the steering law, which was proposed to model 2D steering tasks, is adapted to 3D tasks by introducing three additional variables, i.e., path curvature, orientation and haptic feedback. The new model suggests that a 3D ball-and-tunnel steering movement consists of a series of small and jerky sub-movements that are similar to the ballistic/correction movements observed in the pointing movements. An interaction model is originally proposed and empirically verified for 3D object pursuit tasks, making use of Stevens’ power law. The results indicate that the power law can be used to model all three common interaction tasks, which may serve as a general law for modeling interaction tasks, and also provides a way to quantitatively compare the tasks

Adaptive Gesture Recognition with Variation Estimation for Interactive Systems

This paper presents a gesture recognition/adaptation system for Human Computer Interaction applications that goes beyond activity classification and that, complementary to gesture labeling, characterizes the movement execution. We describe a template-based recognition method that simultaneously aligns the input gesture to the templates using a Sequential Montecarlo inference technique. Contrary to standard template- based methods based on dynamic programming, such as Dynamic Time Warping, the algorithm has an adaptation process that tracks gesture variation in real-time. The method continuously updates, during execution of the gesture, the estimated parameters and recognition results which offers key advantages for continuous human-machine interaction. The technique is evaluated in several different ways: recognition and early recognition are evaluated on a 2D onscreen pen gestures; adaptation is assessed on synthetic data; and both early recognition and adaptation is evaluation in a user study involving 3D free space gestures. The method is not only robust to noise and successfully adapts to parameter variation but also performs recognition as well or better than non-adapting offline template-based methods

Deep Learning Based Malware Classification Using Deep Residual Network

The traditional malware detection approaches rely heavily on feature extraction procedure, in this paper we proposed a deep learning-based malware classification model by using a 18-layers deep residual network. Our model uses the raw bytecodes data of malware samples, converting the bytecodes to 3-channel RGB images and then applying the deep learning techniques to classify the malwares. Our experiment results show that the deep residual network model achieved an average accuracy of 86.54% by 5-fold cross validation. Comparing to the traditional methods for malware classification, our deep residual network model greatly simplify the malware detection and classification procedures, it achieved a very good classification accuracy as well. The dataset we used in this paper for training and testing is Malimg dataset, one of the biggest malware datasets released by vision research lab of UCSB

Proceedings, MSVSCC 2019

Old Dominion University Department of Modeling, Simulation & Visualization Engineering (MSVE) and the Virginia Modeling, Analysis and Simulation Center (VMASC) held the 13th annual Modeling, Simulation & Visualization (MSV) Student Capstone Conference on April 18, 2019. The Conference featured student research and student projects that are central to MSV. Also participating in the conference were faculty members who volunteered their time to impart direct support to their students’ research, facilitated the various conference tracks, served as judges for each of the tracks, and provided overall assistance to the conference. Appreciating the purpose of the conference and working in a cohesive, collaborative effort, resulted in a successful symposium for everyone involved. These proceedings feature the works that were presented at the conference. Capstone Conference Chair: Dr. Yuzhong Shen Capstone Conference Student Chair: Daniel Pere

A Low Complexity 6DoF Magnetic Tracking System For Biomedical Applications

L'abstract è presente nell'allegato / the abstract is in the attachmen