Predicting cybersickness based on user’s gaze behaviors in HMD-based virtual reality

Cybersickness refers to a group of uncomfortable symptoms experienced in virtual reality (VR). Among several theories of cybersickness, the subjective vertical mismatch (SVM) theory focuses on an individual’s internal model, which is created and updated through past experiences. Although previous studies have attempted to provide experimental evidence for the theory, most approaches are limited to subjective measures or body sway. In this study, we aimed to demonstrate the SVM theory on the basis of the participant’s eye movements and investigate whether the subjective level of cybersickness can be predicted using eye-related measures. 26 participants experienced roller coaster VR while wearing a head-mounted display with eye tracking. We designed four experimental conditions by changing the orientation of the VR scene (upright vs. inverted) or the controllability of the participant’s body (unrestrained vs. restrained body). The results indicated that participants reported more severe cybersickness when experiencing the upright VR content without controllability. Moreover, distinctive eye movements (e.g. fixation duration and distance between the eye gaze and the object position sequence) were observed according to the experimental conditions. On the basis of these results, we developed a regression model using eye-movement features and found that our model can explain 34.8% of the total variance of cybersickness, indicating a substantial improvement compared to the previous work (4.2%). This study provides empirical data for the SVM theory using both subjective and eye-related measures. In particular, the results suggest that participants’ eye movements can serve as a significant index for predicting cybersickness when considering natural gaze behaviors during a VR experience.

A Markov Chain Position Prediction Model Based on Multidimensional Correction

User location prediction in location-based social networks can predict the density of people flow well in terms of intelligent transportation, which can make corresponding adjustments in time to make traffic smooth, reduce fuel consumption, reduce greenhouse gas emissions, and help build a green cycle low-carbon transportation green system. This paper proposes a Markov chain position prediction model based on multidimensional correction (MDC-MCM). Firstly, extract corresponding information from the user’s historical check-in position sequence as a position-position conversion map. Secondly, the influence of check-in period, space distance, and other factors on the position prediction is linearly weighted and merged with the position prediction of the n-order Markov chain to construct MDC-MCM. Finally, we conduct a comprehensive performance evaluation of MDC-MCM using the dataset collected from Brightkite. Experimental results show that compared with other advanced location prediction technologies, MDC-MCM achieves better location prediction results.

Researches on Crack Propagation of the Two Filled Noncoalescent Coplanar Flaws under the High Strain Rate Loading by means of AUTODYN-Based Simulation

To study the influence of fillings on rock failure. By turning to the Drucker-Prager strength model and cumulative damage criteria, investigations are made, with the nonlinear AUTODYN software, into crack propagation behaviors in crack-filled and unfilled specimens under uniaxial dynamic loading. Under investigations are crack initiation position, sequence and angle, and coalescence mode. According to the mode of propagation and coalescence, cracks are divided into three types, i.e., the tensile wing, the antiwing, and the horsetail. The simulation results show that under uniaxial dynamic loading, differences are found in initiation position, angle, and coalescence mode for specimens with cracks filled or otherwise. However, filling does not affect crack initiation sequence. Under the same loading, the damage to filled specimen is less severe than that to the unfilled specimen.

A novel image encryption algorithm based on the chaotic system and DNA computing

A novel image encryption algorithm using the chaotic system and deoxyribonucleic acid (DNA) computing is presented. Different from the traditional encryption methods, the permutation and diffusion of our method are manipulated on the 3D DNA matrix. Firstly, a 3D DNA matrix is obtained through bit plane splitting, bit plane recombination, DNA encoding of the plain image. Secondly, 3D DNA level permutation based on position sequence group (3DDNALPBPSG) is introduced, and chaotic sequences generated from the chaotic system are employed to permutate the positions of the elements of the 3D DNA matrix. Thirdly, 3D DNA level diffusion (3DDNALD) is given, the confused 3D DNA matrix is split into sub-blocks, and XOR operation by block is manipulated to the sub-DNA matrix and the key DNA matrix from the chaotic system. At last, by decoding the diffused DNA matrix, we get the cipher image. SHA 256 hash of the plain image is employed to calculate the initial values of the chaotic system to avoid chosen plaintext attack. Experimental results and security analyses show that our scheme is secure against several known attacks, and it can effectively protect the security of the images.

A Study on the Convergence of Family Particle Swarm Optimization

The sociological concept of family has been introduced in the particle swarm optimization (PSO) and the family PSO (FPSO) has been proposed, in which the particle swarm consisted of different families, each family consisted of different members, and there were different constraint relationships between family members. To further study the sensitivity of FPSO to the control parameters, this paper proposed a special model of FPSO and analyzed the convergence of FPSO theoretically. This model offered a new view to research the particle trajectory and divided the position sequence of particle into the even and odd subsequences. By mathematical analysis, the condition of two subsequences convergence was obtained and the related convergent theories and corollaries were proved. Simulations for benchmark functions showed that the convergence behavior of model and experimental results provided a valuable guideline for selecting control parameters.

What kind of priming is most effective in the processing of relative clauses in context?

This paper examines the influence of different kinds of preceding contexts on the processing of Chinese relative clauses (RCs). We systematically compared the processing of RCs in a canonical, non-canonical, and “null” context. This paper is the first to systematically examine three accounts of priming (the thematic pattern priming account proposed by Lin (2014), in addition to both the verb phrase constituent priming account and the syntactic position sequence priming account proposed by Fedorenko, Piantadosi, and Gibson (2012)) in RC processing. Results showed discrepancies between predictions from each priming account and the actual results. None of the three priming accounts could sufficiently explain the results in Chinese. Alternative possible explanations were suggested, including: (1) having a context makes RC reading more natural and frequency effects less obvious; (2) the NPs inside the RCs are primed by the original thematic roles or grammatical functions of same NPs in the critical context sentence; (3) an interplay of all three different kinds of priming in the processing of RCs in context may occur.