Individuals\u27 Concern about Information Privacy in AR Mobile Games

Augmented Reality (AR) proves to be an attractive technology in mobile games. While AR techniques energize mobile games, the privacy issue is raised to be discussed. Employing social media analytics (SMA) techniques, this research makes efforts to examines Twitter postings of “PokemonGo” case and explores individuals’ attitudes toward privacy in AR games. In this research, we examine what are the privacy concerns of individuals in AR games and what are the individuals’ sentiments toward privacy. In the interesting case of PokemonGo, this paper suggests that individuals’ concerns about privacy are emphasized on six dimensions - collection, improper access, unauthorized secondary use, errors, post event reimbursement and proactive announcement. The findings could benefit AR game industry to identify privacy problem in discussion and to manage post privacy-event intervention. Keywords: Information Privacy, Individuals’ Concern, AR Games, Social Media Analytic

Observation of vacancy-induced suppression of electronic cooling in defected graphene

Previous studies of electron-phonon interaction in impure graphene have found that static disorder can give rise to an enhancement of electronic cooling. We investigate the effect of dynamic disorder and observe over an order of magnitude suppression of electronic cooling compared with clean graphene. The effect is stronger in graphene with more vacancies, confirming its vacancy-induced nature. The dependence of the coupling constant on the phonon temperature implies its link to the dynamics of disorder. Our study highlights the effect of disorder on electron-phonon interaction in graphene. In addition, the suppression of electronic cooling holds great promise for improving the performance of graphene-based bolometer and photo-detector devices.Comment: 13 pages, 4 figure

P and S wave tomography of Japan subduction zone from joint inversions of local and teleseismic travel times and surface-wave data

AbstractWe determined P and S wave velocity tomography of the Japan subduction zone down to a depth of 700km by conducting joint inversions of a large number of high-quality arrival-time data of local earthquakes and teleseismic events which are newly collected for this study. We also determined 2-D phase-velocity images of fundamental mode Rayleigh waves at periods of 20–150s beneath Japan and the surrounding oceanic regions using amplitude and phase data of teleseismic Rayleigh waves. A detailed 3-D S-wave tomography of the study region is obtained by jointly inverting S-wave arrival times of local and teleseismic events and the Rayleigh-wave phase-velocity data. Our inversion results reveal the subducting Pacific and Philippine Sea slabs clearly as dipping high-velocity zones from a 1-D starting velocity model. Prominent low-velocity (low-V) anomalies are revealed in the mantle wedge above the slabs and in the mantle below the Pacific slab. The distinct velocity contrasts between the subducting slabs and the surrounding mantle reflect significant lateral variations in temperature as well as water content and/or the degree of partial melting. The low-V anomalies in the mantle wedge are attributed to slab dehydration and corner flows in the mantle wedge. A sheet-like low-V zone is revealed under the Pacific slab beneath NE Japan, which may reflect hot upwelling from the deeper mantle and subduction of a plume-fed asthenosphere as well. Our present results indicate that joint inversions of different seismic data are very effective and important for obtaining robust tomographic images of the crust and mantle

Attentive Tensor Product Learning

This paper proposes a new architecture - Attentive Tensor Product Learning (ATPL) - to represent grammatical structures in deep learning models. ATPL is a new architecture to bridge this gap by exploiting Tensor Product Representations (TPR), a structured neural-symbolic model developed in cognitive science, aiming to integrate deep learning with explicit language structures and rules. The key ideas of ATPL are: 1) unsupervised learning of role-unbinding vectors of words via TPR-based deep neural network; 2) employing attention modules to compute TPR; and 3) integration of TPR with typical deep learning architectures including Long Short-Term Memory (LSTM) and Feedforward Neural Network (FFNN). The novelty of our approach lies in its ability to extract the grammatical structure of a sentence by using role-unbinding vectors, which are obtained in an unsupervised manner. This ATPL approach is applied to 1) image captioning, 2) part of speech (POS) tagging, and 3) constituency parsing of a sentence. Experimental results demonstrate the effectiveness of the proposed approach

Technology humanness, trust and e-government adoption

With regards to technology adoption, users may be influenced by trust in two forms – human-like trust (e.g., benevolence, integrity, and ability) and system-like trust (e.g., helpfulness, reliability, and functionality). While the literature interestingly differentiates the use of these two types of trust, insufficient efforts have been devoted to examine and explain which type of trust should be used in the context of e-government. Additionally, when government agencies increasingly experience security breaches, insufficient literature examines how human-like trust and system-like trust may be influenced by such important antecedents as security threats and citizens’ security concerns in e-government settings. We propose a conceptual model to address this gap in the literature