144 resultados para information flow properties


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Although popular media narratives about the role of social media in driving the events of the 2011 “Arab Spring” are likely to overstate the impact of Facebook and Twitter on these uprisings, it is nonetheless true that protests and unrest in countries from Tunisia to Syria generated a substantial amount of social media activity. On Twitter alone, several millions of tweets containing the hashtags #libya or #egypt were generated during 2011, both by directly affected citizens of these countries and by onlookers from further afield. What remains unclear, though, is the extent to which there was any direct interaction between these two groups (especially considering potential language barriers between them). Building on hashtag data sets gathered between January and November 2011, this article compares patterns of Twitter usage during the popular revolution in Egypt and the civil war in Libya. Using custom-made tools for processing “big data,” we examine the volume of tweets sent by English-, Arabic-, and mixed-language Twitter users over time and examine the networks of interaction (variously through @replying, retweeting, or both) between these groups as they developed and shifted over the course of these uprisings. Examining @reply and retweet traffic, we identify general patterns of information flow between the English- and Arabic-speaking sides of the Twittersphere and highlight the roles played by users bridging both language spheres.

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Lyngbya majuscula is a cyanobacterium (blue-green algae) occurring naturally in tropical and subtropical coastal areas worldwide. Deception Bay, in Northern Moreton Bay, Queensland, has a history of Lyngbya blooms, and forms a case study for this investigation. The South East Queensland (SEQ) Healthy Waterways Partnership, collaboration between government, industry, research and the community, was formed to address issues affecting the health of the river catchments and waterways of South East Queensland. The Partnership coordinated the Lyngbya Research and Management Program (2005-2007) which culminated in a Coastal Algal Blooms (CAB) Action Plan for harmful and nuisance algal blooms, such as Lyngbya majuscula. This first phase of the project was predominantly of a scientific nature and also facilitated the collection of additional data to better understand Lyngbya blooms. The second phase of this project, SEQ Healthy Waterways Strategy 2007-2012, is now underway to implement the CAB Action Plan and as such is more management focussed. As part of the first phase of the project, a Science model for the initiation of a Lyngbya bloom was built using Bayesian Networks (BN). The structure of the Science Bayesian Network was built by the Lyngbya Science Working Group (LSWG) which was drawn from diverse disciplines. The BN was then quantified with annual data and expert knowledge. Scenario testing confirmed the expected temporal nature of bloom initiation and it was recommended that the next version of the BN be extended to take this into account. Elicitation for this BN thus occurred at three levels: design, quantification and verification. The first level involved construction of the conceptual model itself, definition of the nodes within the model and identification of sources of information to quantify the nodes. The second level included elicitation of expert opinion and representation of this information in a form suitable for inclusion in the BN. The third and final level concerned the specification of scenarios used to verify the model. The second phase of the project provides the opportunity to update the network with the newly collected detailed data obtained during the previous phase of the project. Specifically the temporal nature of Lyngbya blooms is of interest. Management efforts need to be directed to the most vulnerable periods to bloom initiation in the Bay. To model the temporal aspects of Lyngbya we are using Object Oriented Bayesian networks (OOBN) to create ‘time slices’ for each of the periods of interest during the summer. OOBNs provide a framework to simplify knowledge representation and facilitate reuse of nodes and network fragments. An OOBN is more hierarchical than a traditional BN with any sub-network able to contain other sub-networks. Connectivity between OOBNs is an important feature and allows information flow between the time slices. This study demonstrates more sophisticated use of expert information within Bayesian networks, which combine expert knowledge with data (categorized using expert-defined thresholds) within an expert-defined model structure. Based on the results from the verification process the experts are able to target areas requiring greater precision and those exhibiting temporal behaviour. The time slices incorporate the data for that time period for each of the temporal nodes (instead of using the annual data from the previous static Science BN) and include lag effects to allow the effect from one time slice to flow to the next time slice. We demonstrate a concurrent steady increase in the probability of initiation of a Lyngbya bloom and conclude that the inclusion of temporal aspects in the BN model is consistent with the perceptions of Lyngbya behaviour held by the stakeholders. This extended model provides a more accurate representation of the increased risk of algal blooms in the summer months and show that the opinions elicited to inform a static BN can be readily extended to a dynamic OOBN, providing more comprehensive information for decision makers.

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This paper presents a new framework for distributed intrusion detection based on taint marking. Our system tracks information flows between applications of multiple hosts gathered in groups (i.e., sets of hosts sharing the same distributed information flow policy) by attaching taint labels to system objects such as files, sockets, Inter Process Communication (IPC) abstractions, and memory mappings. Labels are carried over the network by tainting network packets. A distributed information flow policy is defined for each group at the host level by labeling information and defining how users and applications can legally access, alter or transfer information towards other trusted or untrusted hosts. As opposed to existing approaches, where information is most often represented by two security levels (low/high, public/private, etc.), our model identifies each piece of information within a distributed system, and defines their legal interaction in a fine-grained manner. Hosts store and exchange security labels in a peer to peer fashion, and there is no central monitor. Our IDS is implemented in the Linux kernel as a Linux Security Module (LSM) and runs standard software on commodity hardware with no required modification. The only trusted code is our modified operating system kernel. We finally present a scenario of intrusion in a web service running on multiple hosts, and show how our distributed IDS is able to report security violations at each host level.

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The Chinese government should be commended for its open, concerted, and rapid response to the recent H7N9 influenza outbreak. However, the first known case was not reported until 48 days after disease onset.1 Although the difficulties in detecting the virus and the lack of suitable diagnostic methods have been the focus of discussion,2 systematic limitations that may have contributed to this delay have hardly been discussed. The detection speed of surveillance systems is limited by the highly structured nature of information flow and hierarchical organisation of these systems. Flu surveillance usually relies on notification to a central authority of laboratory confirmed cases or presentations to sentinel practices for flu-like illness. Each step in this pathway presents a bottleneck at which information and time can be lost; this limitation must be dealt with...

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Although there are many approaches for developing secure programs, they are not necessarily helpful for evaluating the security of a pre-existing program. Software metrics promise an easy way of comparing the relative security of two programs or assessing the security impact of modifications to an existing one. Most studies in this area focus on high level source code but this approach fails to take compiler-specific code generation into account. In this work we describe a set of object-oriented Java bytecode security metrics which are capable of assessing the security of a compiled program from the point of view of potential information flow. These metrics can be used to compare the security of programs or assess the effect of program modifications on security using a tool which we have developed to automatically measure the security of a given Java bytecode program in terms of the accessibility of distinguished ‘classified’ attributes.

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This paper presents a novel framework to further advance the recent trend of using query decomposition and high-order term relationships in query language modeling, which takes into account terms implicitly associated with different subsets of query terms. Existing approaches, most remarkably the language model based on the Information Flow method are however unable to capture multiple levels of associations and also suffer from a high computational overhead. In this paper, we propose to compute association rules from pseudo feedback documents that are segmented into variable length chunks via multiple sliding windows of different sizes. Extensive experiments have been conducted on various TREC collections and our approach significantly outperforms a baseline Query Likelihood language model, the Relevance Model and the Information Flow model.

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2011 ‘Arab Spring’ are likely to overstate the impact of Facebook and Twitter on these uprisings, it is nonetheless true that protests and unrest in countries from Tunisia to Syria generated a substantial amount of social media activity. On Twitter alone, several millions of tweets containing the hashtags #libya or #egypt were generated during 2011, both by directly affected citizens of these countries, and by onlookers from further afield. What remains unclear, though, is the extent to which there was any direct interaction between these two groups (especially considering potential language barriers between them). Building on hashtag datasets gathered between January and November 2011, this paper compares patterns of Twitter usage during the popular revolution in Egypt and the civil war in Libya. Using custom-made tools for processing ‘big data’, we examine the volume of tweets sent by English-, Arabic-, and mixed-language Twitter users over time, and examine the networks of interaction (variously through @replying, retweeting, or both) between these groups as they developed and shifted over the course of these uprisings. Examining @reply and retweet traffic, we identify general patterns of information flow between the English- and Arabic-speaking sides of the Twittersphere, and highlight the roles played by users bridging both language spheres.

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This research applies a multidimensional model of publicness to the analysis of organisational change and in so doing enriches understanding of the public nature of organisations and how public characteristics facilitate change. Much of the prior literature describes public organisations as bureaucratic, with characteristics that are resistant to change, hierarchical structures that impede information flow, goals that are imposed and scrutinised by political authority and red tape that constrains decision-making. This dissertation instead reports a more complex picture and explains how public characteristics can also work in ways that enable organisational change.

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This study investigates how markets for different levels of copper purity are interrelated by testing the long-run price linkage and causalities among the copper futures, primary, copper scrap, and brass scrap markets. It is expected that copper markets that deal with high purity levels, such as the futures, primary, and copper scrap markets, have a long-run relationship. However, brass scrap markets where copper with a lower purity is traded may not have a price linkage with other copper markets. The results reveal that a long-run relationship holds between the futures, primary, and copper scrap markets but the brass scrap market does not have a long-run relationship with the other markets. From the short-run and long-run causality tests, we determine that the futures market plays an important role in transmitting price information to other copper markets while such information flow is not found for the brass scrap market.

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Building on hashtag datasets gathered since January 2011, this paper will compare patterns of Twitter usage during the popular revolution in Egypt and the civil war in Libya. Using custom-made tools for processing ‘big data’ (boyd & Crawford, 2011), we will examine the volume of tweets sent by English-, Arabic-, and mixed-language Twitter users over time, and examine the networks of interaction (variously through @replying, retweeting, or both) between these groups as they developed and shifted over the course of these uprisings. Examining @reply and retweet traffic, we will identify general patterns of information flow between the English- and Arabic-speaking sides of the Twittersphere, and highlight the roles played by key boundary riders connecting both language spheres. Further, we will examine the URLs shared in these hashtags by Twitter participants, to identify the most prominent overall information sources, examine differences in the information diet experienced by English- and Arabic-language users, and investigate whether there are any online sources whose URLs are transcending language boundaries more frequently than others.

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With the introduction of the PCEHR (Personally Controlled Electronic Health Record), the Australian public is being asked to accept greater responsibility for the management of their health information. However, the implementation of the PCEHR has occasioned poor adoption rates underscored by criticism from stakeholders with concerns about transparency, accountability, privacy, confidentiality, governance, and limited capabilities. This study adopts an ethnographic lens to observe how information is created and used during the patient journey and the social factors impacting on the adoption of the PCEHR at the micro-level in order to develop a conceptual model that will encourage the sharing of patient information within the cycle of care. Objective: This study aims to firstly, establish a basic understanding of healthcare professional attitudes toward a national platform for sharing patient summary information in the form of a PCEHR. Secondly, the studies aims to map the flow of patient related information as it traverses a patient’s personal cycle of care. Thus, an ethnographic approach was used to bring a “real world” lens to information flow in a series of case studies in the Australian healthcare system to discover themes and issues that are important from the patient’s perspective. Design: Qualitative study utilising ethnographic case studies. Setting: Case studies were conducted at primary and allied healthcare professionals located in Brisbane Queensland between October 2013 and July 2014. Results: In the first dimension, it was identified that healthcare professionals’ concerns about trust and medico-legal issues related to patient control and information quality, and the lack of clinical value available with the PCEHR emerged as significant barriers to use. The second dimension of the study which attempted to map patient information flow identified information quality issues, clinical workflow inefficiencies and interoperability misconceptions resulting in duplication of effort, unnecessary manual processes, data quality and integrity issues and an over reliance on the understanding and communication skills of the patient. Conclusion: Opportunities for process efficiencies, improved data quality and increased patient safety emerge with the adoption of an appropriate information sharing platform. More importantly, large scale eHealth initiatives must be aligned with the value proposition of individual stakeholders in order to achieve widespread adoption. Leveraging an Australian national eHealth infrastructure and the PCEHR we offer a practical example of a service driven digital ecosystem suitable for co-creating value in healthcare.

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This study aims to explore the use of Twitter for professional purposes. The researcher discovered that Twitter is widely perceived as an information ground in online spaces. Information grounds are social settings where information, people, and places come together to create an information flow within a physical environment. Twitter provides a sense of place as well as a sense of belonging that enables IT professionals to use Twitter for professional development. The data for this study were collected using online observations and interviews. The online observations helped the researcher to distinguish the ‘information behaviours’ (the objective and observable actions) of the participants. The interviews were used to understand the way IT professionals use Twitter for professional purposes through their own individual perspectives. The data were analysed using a constructive grounded theory. The findings show that building professional networking is extremely important to IT professionals; rather than the information-seeking and information-sharing aspects of Twitter. Building professional networking in microblogging has a significant influence on an individual’s professional development. The results also demonstrate that IT professionals are more likely to exploit their weak-ties rather than their strong-ties on Twitter. In short, these users experience Twitter as a real place or ‘information grounds’ where they meet and socialise with experts.

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Optical flow (OF) is a powerful motion cue that captures the fusion of two important properties for the task of obstacle avoidance − 3D self-motion and 3D environmental surroundings. The problem of extracting such information for obstacle avoidance is commonly addressed through quantitative techniques such as time-to-contact and divergence, which are highly sensitive to noise in the OF image. This paper presents a new strategy towards obstacle avoidance in an indoor setting, using the combination of quantitative and structural properties of the OF field, coupled with the flexibility and efficiency of a machine learning system.The resulting system is able to effectively control the robot in real-time, avoiding obstacles in familiar and unfamiliar indoor environments, under given motion constraints. Furthermore, through the examination of the networks internal weights, we show how OF properties are being used toward the detection of these indoor obstacles.

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The existence of Macroscopic Fundamental Diagram (MFD), which relates space-mean density and flow, has been shown in urban networks under homogeneous traffic conditions. Since MFD represents the area-wide network traffic performances, studies on perimeter control strategies and an area traffic state estimation utilizing the MFD concept has been reported. One of the key requirements for well-defined MFD is the homogeneity of the area-wide traffic condition with links of similar properties, which is not universally expected in real world. For the practical application of the MFD concept, several researchers have identified the influencing factors for network homogeneity. However, they did not explicitly take the impact of drivers’ behaviour and information provision into account, which has a significant impact on simulation outputs. This research aims to demonstrate the effect of dynamic information provision on network performance by employing the MFD as a measurement. A microscopic simulation, AIMSUN, is chosen as an experiment platform. By changing the ratio of en-route informed drivers and pre-trip informed drivers different scenarios are simulated in order to investigate how drivers’ adaptation to the traffic congestion influences the network performance with respect to the MFD shape as well as other indicators, such as total travel time. This study confirmed the impact of information provision on the MFD shape, and addressed the usefulness of the MFD for measuring the dynamic information provision benefit.

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Numerical simulation of a geothermal reservoir, modelled as a bottom-heated square box, filled with water-CO2 mixture is presented in this work. Furthermore, results for two limiting cases of a reservoir filled with either pure water or CO2 are presented. Effects of different parameters including CO2 concentration as well as reservoir pressure and temperature on the overall performance of the system are investigated. It has been noted that, with a fixed reservoir pressure and temperature, any increase in CO2concentration leads to better performance, i.e. stronger convection and higher heat transfer rates. With a fixed CO2 concentration, however, the reservoir pressure and temperature can significantly affect the overall heat transfer and flow rate from the reservoir. Details of such variations are documented and discussed in the present paper.