Faceless identification based on temporal strips

This paper first presents a novel approach for modelling facial features, Local Directional Texture (LDT), which exploits the unique directional information in image textures for the problem of face recognition. A variant of LDT with privacy-preserving temporal strips (TS) is then considered to achieve faceless recognition with a higher degree of privacy while maintaining high accuracy. The TS uses two strips of pixel blocks from the temporal planes, XT and YT, for face recognition. By removing the reliance on spatial context (i.e., XY plane) for this task, the proposed method withholds facial appearance information from public view, where only one-dimensional temporal information that varies across time are extracted for recognition. Thus, privacy is assured, yet without impeding the facial recognition task which is vital for many security applications such as street surveillance and perimeter access control. To validate the reliability of the proposed method, experiments were carried out using the Honda/UCSD, CK+, CAS(ME)² and CASME II databases. The proposed method achieved a recognition rate of 98.26% in the standard video-based face recognition database, Honda/UCSD. It also offers a 81.92% reduction in the dimension length required for storing the extracted features, in contrast to the conventional LBP-TOP.

     

Avatar mobility in user-created networked virtual worlds: measurements, analysis, and implications

We collected mobility traces of avatars spanning multiple regions in Second Life, a popular user-created virtual world. We analyzed the traces to characterize the dynamics of the avatars’ mobility and behavior, both temporally and spatially. We discuss the implications of our findings on the design of peer-to-peer architecture, interest management, mobility modeling of avatars, server load balancing and zone partitioning, caching, and prefetching for user-created virtual worlds.     

Development of an epileptic seizure prediction algorithm using R–R intervals with self-attentive autoencoder

Epilepsy is a neurological disorder that may affect the autonomic nervous system (ANS) from 15 to 20 min before seizure onset, and disturbances of ANS affect R–R intervals (RRI) on an electrocardiogram (ECG). This study aims to develop a machine learning algorithm for predicting focal epileptic seizures by monitoring R–R interval (RRI) data in real time. The developed algorithm adopts a self-attentive autoencoder (SA-AE), which is a neural network for time-series data. The results of applying the developed seizure prediction algorithm to clinical data demonstrated that it functioned well in most patients; however, false positives (FPs) occurred in specific participants. In a future work, we will investigate the causes of FPs and optimize the developing seizure prediction algorithm to further improve performance using newly added clinical data.

     

An effective 3-in-1 keyword search method over heterogeneous data sources

Conventional keyword search engines are restricted to a given data model and cannot easily adapt to unstructured, semi-structured or structured data. In this paper, we propose an efficient and adaptive keyword search method, called EASE, for indexing and querying large collections of heterogeneous data. To achieve high efficiency in processing keyword queries, we first model unstructured, semi-structured and structured data as graphs, and then summarize the graphs and construct graph indices instead of using traditional inverted indices. We propose an extended inverted index to facilitate keyword-based search, and present a novel ranking mechanism for enhancing search effectiveness. We have conducted an extensive experimental study using real datasets, and the results show that EASE achieves both high search efficiency and high accuracy, and outperforms the existing approaches significantly.     

Exploring factors influencing consumers’ behavioral intention to adopt broadband in Malaysia

The purpose of this paper is to examine various normative, attitudinal and control factors influencing consumers’ intention to adopt broadband internet in a developing country such as Malaysia. This study is based on empirical data collected using a self-administered questionnaire relating to the normative, attitudinal and control variables. Structural equation modeling analysis is conducted to test the role of numerous variables on consumers’ behavioral intentions to adopt broadband internet in Malaysia. Results revealed that Primary Influences (PI), Relative Advantage (RA), Hedonic Outcomes (HO), Facilitating Conditions Resources (FCR), and Self-Efficacy (SE) are positively associated with consumers’ Behavioral Intention (BI) to adopt broadband in Malaysia. The significance of this research study is twofold. Practically, this research study provide some useful guidelines to industry players, be it the internet service providers (ISPs) or the policy makers, to understand which are the factors (primary influences, self-efficacy, relative advantage, hedonic outcomes and facilitating conditions resources) that can have an influence on consumers’ intention to adopt broadband technology. It was found that primary influences, self-efficacy and relative advantage are the key determinants that influences consumers’ broadband adoption. From here, practitioners could take into consideration our findings when revising and restructuring their marketing strategy. Theoretically, the research framework used in this study is an extension from the past research models used (i.e. TPB, DOI and MATH). With the newly integrated framework, a greater level of comprehension can be attained with regards to the broadband acceptance among the Malaysian consumers.     

Dynamic behaviour of a magnetically actuated floating liquid marble

This paper reports the dynamic behaviour of a magnetically actuated floating liquid marble by analysing the oscillation of the marble. A liquid marble is a liquid droplet coated with hydrophobic powder. Magnetite particles inside the marble make it magnetic. The marble floats on a carrier liquid that contains aqueous glycerol of various concentrations. A permanent magnet located under the carrier liquid drives the floating marble with the initial velocity. Stopping the magnet abruptly causes the marble to oscillate around its final position for a few seconds. The oscillation was recorded and analysed using customised image processing and evaluation software. The damped harmonic motion model was then applied to the data and tested. Subsequently, critical parameters of the system such as the initial displacement, friction correction factor, the apparent frequency and the spring constant were determined and discussed. The simple experimental set-up and convenient theoretical approach allow us to characterise the marble motion under the influence of a magnet with good accuracy.     

Exploiting spatial indexes for semijoin-based join processing indistributed spatial databases

In a distributed spatial database system, a user may issue a query that relates two spatial relations not stored at the same site. Because of the sheer volume and complexity of spatial data, spatial joins between two spatial relations at different sites are expensive in terms of computational and transmission costs. In this paper, we address the problems of processing spatial joins in a distributed environment. We propose a semijoin-like operator, called the spatial semijoin, to prune away objects that do not contribute to the join result. This operator also reduces both the transmission and local processing costs for a later join operation. However, the cost of the elimination process must be taken into account, and we consider approaches to minimize these overheads. We also study and compare two families of distributed join algorithms that are based on the spatial semijoin operator. The first is based on multi-dimensional approximations obtained from an index such as the R-tree, and the second is based on single-dimensional approximations obtained from object mapping. We have conducted experiments on real data sets and report the results in this paper     

An empirical analysis of the determinants of 3G adoption in China

The main aim of this study is to examine the factors that affect Chinese consumers’ intention to adopt 3G. This study has extended the Technology Acceptance Model (TAM) by incorporating the determinants of perceived usefulness, cost, trust, and demographic profiles of Chinese consumers. Data is collected from Chinese consumers via self-administered questionnaire. Structural equation modeling was applied to examine the research model proposed. Our result showed that social influence, service quality and perceived ease of use have a direct and significant relationship with perceived usefulness of 3G, and this in turn affects the consumers’ decision to adopt 3G. Contrary to existing TAM research, perceived ease of use was not found to have a direct and significant influence with Chinese consumers’ intention to adopt 3G. Our study also revealed that those with higher educational level are more likely to adopt 3G. Based on the findings, this research is able to propose several practical recommendations to 3G providers in China, such as enhancing the variety of services provided through collaborations with mobile software and content developers. Furthermore, 3G providers can focus on promoting 3G through services such as “Friends and Family” packages as Chinese consumers’ 3G adoption decisions is influenced by their social network. In terms of theoretical contributions, this study has answered many calls from past researchers to investigate the determinants of perceived usefulness. This research was also conducted in China, which is one of the largest telecommunication markets in the world.