977 resultados para Probabilistic Networks
Resumo:
Image annotation is a significant step towards semantic based image retrieval. Ontology is a popular approach for semantic representation and has been intensively studied for multimedia analysis. However, relations among concepts are seldom used to extract higher-level semantics. Moreover, the ontology inference is often crisp. This paper aims to enable sophisticated semantic querying of images, and thus contributes to 1) an ontology framework to contain both visual and contextual knowledge, and 2) a probabilistic inference approach to reason the high-level concepts based on different sources of information. The experiment on a natural scene database from LabelMe database shows encouraging results.
Resumo:
To date, automatic recognition of semantic information such as salient objects and mid-level concepts from images is a challenging task. Since real-world objects tend to exist in a context within their environment, the computer vision researchers have increasingly incorporated contextual information for improving object recognition. In this paper, we present a method to build a visual contextual ontology from salient objects descriptions for image annotation. The ontologies include not only partOf/kindOf relations, but also spatial and co-occurrence relations. A two-step image annotation algorithm is also proposed based on ontology relations and probabilistic inference. Different from most of the existing work, we specially exploit how to combine representation of ontology, contextual knowledge and probabilistic inference. The experiments show that image annotation results are improved in the LabelMe dataset.
Resumo:
From the perspective of network, a project team’s social capital consists of conduits network, and resource exchange network. Prior research intensively studies the effect of the structure of conduits network on the team’s performance, assuming knowledge transfer is the causal mechanism linking conduits network to performance. This paper attempts to explore the interrelations between conduits network and knowledge network, and further distinguish the different influence between various conduit networks, and hypothesizes that a project team’s knowledge network mediates the effect of various conduit networks on the team’s performance. This research can enrich our knowledge of disparate influence of the various conduit networks on knowledge transfer, and imply some management practices to enhance the organization’s social capital, and hence improve the organization’s performance.
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This paper introduces friendwork as a new term in social networks studies. A friendwork is a network of friends. It is a specific case of an interpersonal social network. Naming this seemingly well known and familiar group of people as a friendwork facilitates its differentiation from the overall social network, while highlighting this subgroup's specific attributes and dynamics. The focus on one segment within social networks stimulates a wider discussion regarding the different subgroups within social networks. Other subgroups also discussed in this paper are: family dependent, work related, location based and virtual acquaintances networks. This discussion informs a larger study of social media, specifically addressing interactive communication modes that are in use within friendworks: direct (face-to-face) and mediated (mainly fixed telephone, internet and mobile phone). It explores the role of social media within friendworks while providing a communication perspective on social networks.
Resumo:
The research on project learning has recognised the significance of knowledge transfer in project based organisations (PBOs). Effective knowledge transfer across projects avoids reinventions, enhances knowledge creation and saves lots of time that is crucial in project environment. In order to facilitate knowledge transfer, many PBOs have invested lots of financial and human resources to implement IT-based knowledge repository. However, some empirical studies found that employees would rather turn for knowledge to colleagues despite their ready access to IT-based knowledge repository. Therefore, it is apparent that social networks play a pivotal role in the knowledge transfer across projects. Some scholars attempt to explore the effect of network structure on knowledge transfer and performance, however, focused only on egocentric networks and the groups’ internal social networks. It has been found that the project’s external social network is also critical, in that the team members can not handle critical situations and accomplish the projects on time without the assistance and knowledge from external sources. To date, the influence of the structure of a project team’s internal and external social networks on project performance, and the interrelation between both networks are barely known. In order to obtain such knowledge, this paper explores the interrelation between the structure of a project team’s internal and external social networks, and their effect on the project team’s performance. Data is gathered through survey questionnaire distributed online to respondents. Collected data is analysed applying social network analysis (SNA) tools and SPSS. The theoretical contribution of this paper is the knowledge of the interrelation between the structure of a project team’s internal and external social networks and their influence on the project team’s performance. The practical contribution lies in the guideline to be proposed for constructing the structure of project team’s internal and external social networks.
Resumo:
This paper presents a novel algorithm for the gateway placement problem in Backbone Wireless Mesh Networks (BWMNs). Different from existing algorithms, the new algorithm incrementally identifies gateways and assigns mesh routers to identified gateways. The new algorithm can guarantee to find a feasible gateway placement satisfying Quality-of-Service (QoS) constraints, including delay constraint, relay load constraint and gateway capacity constraint. Experimental results show that its performance is as good as that of the best of existing algorithms for the gateway placement problem. But, the new algorithm can be used for BWMNs that do not form one connected component, and it is easy to implement and use.
Networks in the shadow of markets and hierarchies : calling the shots in the visual effects industry
Resumo:
The nature and organisation of creative industries and the creative economy has received increased attention in recent academic and policy literatures (Florida 2002; Grabher 2002; Scott 2006a). Constituted as one variant on new economy narratives, creativity, alongside knowledge, has been presented as a key competitive asset, Such industries – ranging from advertising, to film and new media – are seen as not merely expanding their scale and scope, but as leading edge proponents of a more general trend towards new forms of organization and economic coordination (Davis and Scase 2000). The idea of network forms (and the consequent displacement of markets and hierarchies) has been at the heart of attempts to differentiate the field economically and spatially. Across both the discussion of production models and work/employment relations is the assertion of the enhanced importance of trust and non-market relations in coordinating structures and practices. This reflects an influential view in sociological, management, geography and other literatures that social life is ‘intrinsically networked’ (Sunley 2008: 12) and that we can confidently use the term ‘network society’ to describe contemporary structures and practices (Castells 1996). Our paper is sceptical of the conceptual and empirical foundations of such arguments. We draw on a number of theoretical resources, including institutional theory, global value chain analysis and labour process theory (see Smith and McKinlay 2009) to explore how a more realistic and grounded analysis of the nature of and limits to networks can be articulated. Given space constraints, we cannot address all the dimensions of network arguments or evidence. Our focus is on inter and intra-firm relations and draws on research into a particular creative industry – visual effects – that is a relatively new though increasingly important global production network. Through this examination a different model of the creative industries and creative work emerges – one in which market rules and patterns of hierarchical interaction structure the behaviour of economic actors and remain a central focus of analysis. The next section outlines and unpacks in more detail arguments concerning the role and significance of networks, markets and hierarchies in production models and work organisation in creative industries and the ‘creative economy’.
Resumo:
Successful project delivery of construction projects depends on many factors. With regard to the construction of a facility, selecting a competent contractor for the job is paramount. As such, various approaches have been advanced to facilitate tender award decisions. Essentially, this type of decision involves the prediction of a bidderÕs performance based on information available at the tender stage. A neural network based prediction model was developed and presented in this paper. Project data for the study were obtained from the Hong Kong Housing Department. Information from the tender reports was used as input variables and performance records of the successful bidder during construction were used as output variables. It was found that the networks for the prediction of performance scores for Works gave the highest hit rate. In addition, the two most sensitive input variables toward such prediction are ‘‘Difference between Estimate’’ and ‘‘Difference between the next closest bid’’. Both input variables are price related, thus suggesting the importance of tender sufficiency for the assurance of quality production.
Resumo:
This research investigates wireless intrusion detection techniques for detecting attacks on IEEE 802.11i Robust Secure Networks (RSNs). Despite using a variety of comprehensive preventative security measures, the RSNs remain vulnerable to a number of attacks. Failure of preventative measures to address all RSN vulnerabilities dictates the need for a comprehensive monitoring capability to detect all attacks on RSNs and also to proactively address potential security vulnerabilities by detecting security policy violations in the WLAN. This research proposes novel wireless intrusion detection techniques to address these monitoring requirements and also studies correlation of the generated alarms across wireless intrusion detection system (WIDS) sensors and the detection techniques themselves for greater reliability and robustness. The specific outcomes of this research are: A comprehensive review of the outstanding vulnerabilities and attacks in IEEE 802.11i RSNs. A comprehensive review of the wireless intrusion detection techniques currently available for detecting attacks on RSNs. Identification of the drawbacks and limitations of the currently available wireless intrusion detection techniques in detecting attacks on RSNs. Development of three novel wireless intrusion detection techniques for detecting RSN attacks and security policy violations in RSNs. Development of algorithms for each novel intrusion detection technique to correlate alarms across distributed sensors of a WIDS. Development of an algorithm for automatic attack scenario detection using cross detection technique correlation. Development of an algorithm to automatically assign priority to the detected attack scenario using cross detection technique correlation.
Resumo:
Efficient and effective urban management systems for Ubiquitous Eco Cities require having intelligent and integrated management mechanisms. This integration includes bringing together economic, socio-cultural and urban development with a well orchestrated, transparent and open decision making mechanism and necessary infrastructure and technologies. In Ubiquitous Eco Cities telecommunication technologies play an important role in monitoring and managing activities over wired, wireless or fibre-optic networks. Particularly technology convergence creates new ways in which the information and telecommunication technologies are used and formed the back bone or urban management systems. The 21st Century is an era where information has converged, in which people are able to access a variety of services, including internet and location based services, through multi-functional devices such as mobile phones and provides opportunities in the management of Ubiquitous Eco Cities. This research paper discusses the recent developments in telecommunication networks and trends in convergence technologies and their implications on the management of Ubiquitous Eco Cities and how this technological shift is likely to be beneficial in improving the quality of life and place of residents, workers and visitors. The research paper reports and introduces recent approaches on urban management systems, such as intelligent urban management systems, that are suitable for Ubiquitous Eco Cities.
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A successful urban management system for a Ubiquitous Eco City requires an integrated approach. This integration includes bringing together economic, socio-cultural and urban development with a well orchestrated, transparent and open decision making mechanism and necessary infrastructure and technologies. Rapidly developing information and telecommunication technologies and their platforms in the late 20th Century improves urban management and enhances the quality of life and place. Telecommunication technologies provide an important base for monitoring and managing activities over wired, wireless or fibre-optic networks. Particularly technology convergence creates new ways in which the information and telecommunication technologies are used. The 21st Century is an era where information has converged, in which people are able to access a variety of services, including internet and location based services, through multi-functional devices such as mobile phones and provides opportunities in the management of Ubiquitous Eco Cities. This paper discusses the recent developments in telecommunication networks and trends in convergence technologies and their implications on the management of Ubiquitous Eco Cities and how this technological shift is likely to be beneficial in improving the quality of life and place. The paper also introduces recent approaches on urban management systems, such as intelligent urban management systems, that are suitable for Ubiquitous Eco Cities.
Resumo:
The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.
Resumo:
Network-based Intrusion Detection Systems (NIDSs) analyse network traffic to detect instances of malicious activity. Typically, this is only possible when the network traffic is accessible for analysis. With the growing use of Virtual Private Networks (VPNs) that encrypt network traffic, the NIDS can no longer access this crucial audit data. In this paper, we present an implementation and evaluation of our approach proposed in Goh et al. (2009). It is based on Shamir's secret-sharing scheme and allows a NIDS to function normally in a VPN without any modifications and without compromising the confidentiality afforded by the VPN.
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This position paper examines the development of a dedicated service aggregator role in business networks. We predict that these intermediaries will soon emerge in service ecosystems and add value through the application of dedicated domain knowledge in the process of creating new, innovative services or service bundles based on the aggregation, composition, integration or orchestration of existing services procured from different service providers in the service ecosystem. We discuss general foundations of service aggregators and present Fourth-Party Logistics Providers as a real-world example of emerging business service aggregators. We also point out a demand for future research, e.g. into governance models, risk management tools, service portfolio management approaches and service bundling techniques, to be able to better understand core determinants of competitiveness and success of service aggregators.
Resumo:
There is wide agreement that in order to manage the increasingly complex and uncertain tasks of business, government and community, organizations can no longer operate in supreme isolation, but must develop a more networked approach. Networks are not ‘business as usual’. Of particular note is what has been referred to as collaborative networks. Collaborative networks now constitute a significant part of our institutional infrastructure. A key driver for the proliferation of these multiorganizational arrangements is their ability to facilitate the learning and knowledge necessary to survive or to respond to increasingly complex social issues In this regard the emphasis is on the importance of learning in networks. Learning applies to networks in two different ways. These refer to the kinds of learning that occur as part of the interactive processes of networks. This paper looks at the importance of these two kinds of learning in collaborative networks. The first kind of learning relates to networks as learning networks or communities of practice. In learning networks people exchange ideas with each other and bring back this new knowledge for use in their own organizations. The second type of learning is referred to as network learning. Network learning refers to how people in collaborative networks learn new ways of communicating and behaving with each other. Network learning has been described as transformational in terms of leading to major systems changes and innovation. In order to be effective, all networks need to be involved as learning networks; however, collaborative networks must also be involved in network learning to be effective. In addition to these two kinds of learning in collaborative networks this paper also focuses on the importance of how we learn about collaborative networks. Maximizing the benefits of working through collaborative networks is dependent on understanding their unique characteristics and how this impacts on their operation. This requires a new look at how we specifically teach about collaborative networks and how this is similar to and/or different from how we currently teach about interorgnizational relations.