998 resultados para multimedia computing


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The final shape of the "Internet of Things" ubiquitous computing promises relies on a cybernetic system of inputs (in the form of sensory information), computation or decision making (based on the prefiguration of rules, contexts, and user-generated or defined metadata), and outputs (associated action from ubiquitous computing devices). My interest in this paper lies in the computational intelligences that suture these positions together, and how positioning these intelligences as autonomous agents extends the dialogue between human-users and ubiquitous computing technology. Drawing specifically on the scenarios surrounding the employment of ubiquitous computing within aged care, I argue that agency is something that cannot be traded without serious consideration of the associated ethics.

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Knowledge based urban development (KBUD) is a new paradigm in urban planning tailoring to the era of knowledge economy. It aims mainly to assist a contemporary city to promote a more sustainable socio-spatial order. The paper reports on the investigation of KBUD initiative in Malaysia which is manifested through the establishment of a project called Multimedia Super Corridor (MSC). MSC Malaysia aims to attract knowledge workers and industries to invest and operate within the area by creating a world class urban corridor with state-of-the-art multimedia infrastructure, efficient transportation system and an attractive living environment. Based on documents analysis and interviews, this paper analyses the strategies, implementations, and achievements of KBUD initiative in Cyberjaya, being the leading intelligent city of the unique Malaysia’s KBUD project-MSC Malaysia. A critical evaluation is made to assess the achievements of MSC, by looking at the physical changes after about ten years since its official launching. The findings recommend some valuable lessons for other cities that strive to develop KBUD strategies, strengthen their sustainable socio-spatial policies, and seek a global recognition.

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This paper presents a comprehensive study to find the most efficient bitrate requirement to deliver mobile video that optimizes bandwidth, while at the same time maintains good user viewing experience. In the study, forty participants were asked to choose the lowest quality video that would still provide for a comfortable and long-term viewing experience, knowing that higher video quality is more expensive and bandwidth intensive. This paper proposes the lowest pleasing bitrates and corresponding encoding parameters for five different content types: cartoon, movie, music, news and sports. It also explores how the lowest pleasing quality is influenced by content type, image resolution, bitrate, and user gender, prior viewing experience, and preference. In addition, it analyzes the trajectory of users’ progression while selecting the lowest pleasing quality. The findings reveal that the lowest bitrate requirement for a pleasing viewing experience is much higher than that of the lowest acceptable quality. Users’ criteria for the lowest pleasing video quality are related to the video’s content features, as well as its usage purpose and the user’s personal preferences. These findings can provide video providers guidance on what quality they should offer to please mobile users.

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On obstacle-cluttered construction sites where heavy equipment is in use, safety issues are of major concern. The main objective of this paper is to develop a framework with algorithms for obstacle avoidance and path planning based on real-time three-dimensional job site models to improve safety during equipment operation. These algorithms have the potential to prevent collisions between heavy equipment vehicles and other on-site objects. In this study, algorithms were developed for image data acquisition, real-time 3D spatial modeling, obstacle avoidance, and shortest path finding and were all integrated to construct a comprehensive collision-free path. Preliminary research results show that the proposed approach is feasible and has the potential to be used as an active safety feature for heavy equipment.

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This paper reports results from a study exploring the multimedia search functionality of Chinese language search engines. Web searching in Chinese (Mandarin) is a growing research area and a technical challenge for popular commercial Web search engines. Few studies have been conducted on Chinese language search engines. We investigate two research questions: which Chinese language search engines provide multimedia searching, and what multimedia search functionalities are available in Chinese language Web search engines. Specifically, we examine each Web search engine's (1) features permitting Chinese language multimedia searches, (2) extent of search personalization and user control of multimedia search variables, and (3) the relationships between Web search engines and their features in the Chinese context. Key findings show that Chinese language Web search engines offer limited multimedia search functionality, and general search engines provide a wider range of features than specialized multimedia search engines. Study results have implications for Chinese Web users, Website designers and Web search engine developers. © 2009 Elsevier Ltd. All rights reserved.

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This special issue of the Journal of Urban Technology brings together five articles that are based on presentations given at the Street Computing workshop held on 24 November 2009 in Melbourne in conjunction with the Australian Computer-Human Interaction conference (OZCHI 2009). Our own article introduces the Street Computing vision and explores the potential, challenges and foundations of this research vision. In order to do so, we first look at the currently available sources of information and discuss their link to existing research efforts. Section 2 then introduces the notion of Street Computing and our research approach in more detail. Section 3 looks beyond the core concept itself and summarises related work in this field of interest.

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This is the final report of an Australian Learning and Teaching Council Teaching Fellowship which addressed the needs of two separate groups of learners: (1) final year law students studying ethics and (2) law academics and other interested educators in higher education wishing to use information and communication technologies (ICT) to create engaging learning environments for their students but lacking the capacity to do so. The Fellowship resulted in final year law students being infused with an improved appreciation of ethical practice than they receive from traditional lecture/tutorial means by the development of an integrated program of blended learning including an online program entitled "Entry into Valhalla". This "ethics capstone‟ utilises multimedia produced using cost effective resources (including the "Second Life" virtual environment) to create engaging, contextualised learning experiences. The Fellowship also constructed the knowledge of producing cost-effective multimedia projects in other law academics and other educators in higher education by staff development activities comprising workshops, conference presentations and an interactive website using the "Entry into Valhalla" program as a case study exemplar.

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From location-aware computing to mining the social web, representations of context have promised to make better software applications. The opportunities and challenges of context-aware computing from representational, situated and interactional perspectives have been well documented, but arguments from the perspective of design are somewhat disparate. This paper draws on both theoretical perspectives and a design framing, using the problem of designing a social mobile agile ridesharing system, in order to reflect upon and call for broader design approaches for context-aware computing and human-computer Interaction research in general.

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Projects funded by the Australian National Data Service(ANDS). The specific projects that were funded included: a) Greenhouse Gas Emissions Project (N2O) with Prof. Peter Grace from QUT’s Institute of Sustainable Resources. b) Q150 Project for the management of multimedia data collected at Festival events with Prof. Phil Graham from QUT’s Institute of Creative Industries. c) Bio-diversity environmental sensing with Prof. Paul Roe from the QUT Microsoft eResearch Centre. For the purposes of these projects the Eclipse Rich Client Platform (Eclipse RCP) was chosen as an appropriate software development framework within which to develop the respective software. This poster will present a brief overview of the requirements of the projects, an overview of the experiences of the project team in using Eclipse RCP, report on the advantages and disadvantages of using Eclipse and it’s perspective on Eclipse as an integrated tool for supporting future data management requirements.

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Software as a Service (SaaS) is gaining more and more attention from software users and providers recently. This has raised many new challenges to SaaS providers in providing better SaaSes that suit everyone needs at minimum costs. One of the emerging approaches in tackling this challenge is by delivering the SaaS as a composite SaaS. Delivering it in such an approach has a number of benefits, including flexible offering of the SaaS functions and decreased cost of subscription for users. However, this approach also introduces new problems for SaaS resource management in a Cloud data centre. We present the problem of composite SaaS resource management in Cloud data centre, specifically on its initial placement and resource optimization problems aiming at improving the SaaS performance based on its execution time as well as minimizing the resource usage. Our approach differs from existing literature because it addresses the problems resulting from composite SaaS characteristics, where we focus on the SaaS requirements, constraints and interdependencies. The problems are tackled using evolutionary algorithms. Experimental results demonstrate the efficiency and the scalability of the proposed algorithms.

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Recently, Software as a Service (SaaS) in Cloud computing, has become more and more significant among software users and providers. To offer a SaaS with flexible functions at a low cost, SaaS providers have focused on the decomposition of the SaaS functionalities, or known as composite SaaS. This approach has introduced new challenges in SaaS resource management in data centres. One of the challenges is managing the resources allocated to the composite SaaS. Due to the dynamic environment of a Cloud data centre, resources that have been initially allocated to SaaS components may be overloaded or wasted. As such, reconfiguration for the components’ placement is triggered to maintain the performance of the composite SaaS. However, existing approaches often ignore the communication or dependencies between SaaS components in their implementation. In a composite SaaS, it is important to include these elements, as they will directly affect the performance of the SaaS. This paper will propose a Grouping Genetic Algorithm (GGA) for multiple composite SaaS application component clustering in Cloud computing that will address this gap. To the best of our knowledge, this is the first attempt to handle multiple composite SaaS reconfiguration placement in a dynamic Cloud environment. The experimental results demonstrate the feasibility and the scalability of the GGA.

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This chapter reviews common barriers to community engagement for Latino youth and suggests ways to move beyond those barriers by empowering them to communicate their experiences, address the challenges they face, and develop recommendations for making their community more youth-friendly. As a case study, this chapter describes a program called Youth FACE IT (Youth Fostering Active Community Engagement for Integration and Transformation)in Boulder County, Colorado. The program enables Latino youth to engage in critical dialogue and participate in a community-based initiative. The chapter concludes by explaining specific strategies that planners can use to support active community engagement and develop a future generation of planners and engaged community members that reflects emerging demographics.

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For facial expression recognition systems to be applicable in the real world, they need to be able to detect and track a previously unseen person's face and its facial movements accurately in realistic environments. A highly plausible solution involves performing a "dense" form of alignment, where 60-70 fiducial facial points are tracked with high accuracy. The problem is that, in practice, this type of dense alignment had so far been impossible to achieve in a generic sense, mainly due to poor reliability and robustness. Instead, many expression detection methods have opted for a "coarse" form of face alignment, followed by an application of a biologically inspired appearance descriptor such as the histogram of oriented gradients or Gabor magnitudes. Encouragingly, recent advances to a number of dense alignment algorithms have demonstrated both high reliability and accuracy for unseen subjects [e.g., constrained local models (CLMs)]. This begs the question: Aside from countering against illumination variation, what do these appearance descriptors do that standard pixel representations do not? In this paper, we show that, when close to perfect alignment is obtained, there is no real benefit in employing these different appearance-based representations (under consistent illumination conditions). In fact, when misalignment does occur, we show that these appearance descriptors do work well by encoding robustness to alignment error. For this work, we compared two popular methods for dense alignment-subject-dependent active appearance models versus subject-independent CLMs-on the task of action-unit detection. These comparisons were conducted through a battery of experiments across various publicly available data sets (i.e., CK+, Pain, M3, and GEMEP-FERA). We also report our performance in the recent 2011 Facial Expression Recognition and Analysis Challenge for the subject-independent task.

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A composite SaaS (Software as a Service) is a software that is comprised of several software components and data components. The composite SaaS placement problem is to determine where each of the components should be deployed in a cloud computing environment such that the performance of the composite SaaS is optimal. From the computational point of view, the composite SaaS placement problem is a large-scale combinatorial optimization problem. Thus, an Iterative Cooperative Co-evolutionary Genetic Algorithm (ICCGA) was proposed. The ICCGA can find reasonable quality of solutions. However, its computation time is noticeably slow. Aiming at improving the computation time, we propose an unsynchronized Parallel Cooperative Co-evolutionary Genetic Algorithm (PCCGA) in this paper. Experimental results have shown that the PCCGA not only has quicker computation time, but also generates better quality of solutions than the ICCGA.