Are the popular users always important for information dissemination in online social networks?

This article verifies the importance of popular users in OSNs. The results are counter-intuitive. First, for dissemination speed, a large amount of users can swiftly distribute information to the masses, but they are not highly-connected users. Second, for dissemination scale, many powerful forwarders in OSNs cannot be identified by the degree measure. Furthermore, to control dissemination, popular users cannot capture most bridges of social communities.

Complex network theoretical analysis on information dissemination over vehicular networks

How to enhance the communication efficiency and quality on vehicular networks is one critical important issue. While with the larger and larger scale of vehicular networks in dense cities, the real-world datasets show that the vehicular networks essentially belong to the complex network model. Meanwhile, the extensive research on complex networks has shown that the complex network theory can both provide an accurate network illustration model and further make great contributions to the network design, optimization and management. In this paper, we start with analyzing characteristics of a taxi GPS dataset and then establishing the vehicular-to-infrastructure, vehicle-to-vehicle and the hybrid communication model, respectively. Moreover, we propose a clustering algorithm for station selection, a traffic allocation optimization model and an information source selection model based on the communication performances and complex network theory.

Optimal multicast tree routing for cluster computing in hypercube interconnection networks

Cluster computation has been used in the applications that demand performance, reliability, and availability, such as cluster server groups, large-scale scientific computations, distributed databases, distributed media-on-demand servers and search engines etc. In those applications, multicast can play the vital roles for the information dissemination among groups of servers and users. This paper proposes a set of novel efficient fault-tolerant multicast routing algorithms on hypercube interconnection of cluster computers using multicast shared tree approach. We present some new algorithms for selecting an optimal core (root) and constructing the shared tree so as to minimize the average delay for multicast messages. Simulation results indicate that our algorithms are efficient in the senses of short end-to-end average delay, load balance and less resource utilizations over hypercube cluster interconnection networks.

Integrated supply chain construction ecosystem management

Agenda 21 may be considered the most significant programme of action influencing environmental policy for the Australian development and construction industry. The industry has remained one of the most rapidly expanding sectors; yet, we have seen the gradual process of exhausting natural resources and irreversible environmental degradation. Even with the introduction of numerous new environmental policies, it remains questionable as to whether real improvements have occurred across the industry. Legislative mechanisms to direct on-site environmental management appear deficient; information flows between participants along the supply chain appear to impact upon environmental management performance; and industry fragmentation remains compounded by ill-defined external, non-contractual supply chain influences that directly impact on contractual systems. Limited research has considered construction supply chain theory and environmental management particularly in reference to policy. The literature highlighted a need to develop a supply chain model which seeks to integrate chain actors and government regulators through holistic information management. The model assumes that fundamental to industry change is statutory control to mandate construction environmental management plans. However, industry change and subsequent environmental management rely upon effective information dissemination. The next stage involves model refinement, investigating barriers and enablers to widespread diffusion of such an innovative integrated environmental management system.

Confronting the pedagogical challenge of cyber safety

Cyber violence and the antidote of cyber safety are fast becoming a global concern for governments, educational authorities, teachers, parents and children alike. Despite substantial funding for information dissemination on preventative strategies and the development of electronic responses to hinder perpetrators, the phenomenon of cyber violence has received little attention in the educational research literature. This review paper outlines the status on existing research into cyber violence. Documenting and summarizing the facts on the nature and extend of the issue will inform future debate. It also highlights the need for pre-service and in-service teacher education programs to prepare educators to manage this phenomenon.

GOM : new genetic optimizing model for broadcasting tree in MANET

Data broadcasting in a mobile ad-hoc network (MANET) is the main method of information dissemination in many applications, in particular for sending critical information to all hosts. Finding an optimal broadcast tree in such networks is a challenging task due to the broadcast storm problem. The aim of this work is to propose a new genetic model using a fitness function with the primary goal of finding an optimal broadcast tree. Our new method, called Genetic Optimisation Model (GOM) alleviates the broadcast storm problem to a great extent as the experimental simulations result in efficient broadcast tree with minimal flood and minimal hops. The result of this model also shows that it has the ability to give different optimal solutions according to the nature of the network.

Framing ICT policies to facilitate participatory e-government

This paper examines the role of information and communication technology (ICT) policies in shaping the participatory nature of local e-government. It suggests that civic involvement through e-government practices requires a combination of direct and indirect ICT policies (Cohen, van Geenhuizen and Nijkamp, 2005). Direct policies focus on ICT infrastructure development and enhance civic adoption and use of ICTs. ICTs also support policies indirectly through data organisation, information dissemination and the provision of spaces for discourse, deliberation and contributions to decision-making processes. Drawing from policy examples from Australia and the United Kingdom (UK), this paper suggests the need to combine federal guidance with local knowledge, while using policies to support ICTs and using ICTs to support policies. Such a cohesive and integrated policy relationship between federal and local government bodies is needed if local e-government is to advance to facilitate civic engagement.

Farmers groups within extension networks in Northern Uganda : inclusive or exclusive?

Group extension methods are widely recognized as the most effective extension method in agricultural development internationally. Research in this area tends to look at group function, and factors that inhibit or promote successful group activity. Most development projects start with an analysis of whether groups exist or may need to be formed, and then focus on group function. However, very little research to date has considered the farmer group from a whole community context, when assessing knowledge and information dissemination in rural areas. This paper presents and discusses research finding from a case study with three communities in Gulu district of Northern Uganda, where household surveys were used to map networks within andbetween community members and external organizations identified as promoting agricultural development in the region. The potential impact of inclusion or exclusion in such a group within small communities emerged as a significant issue, as well as the strong disconnect between community and external organizational perceptions of group existence, function and impact.

An ensemble learning approach for addressing the class imbalance problem in twitter spam detection

Being an important source for real-time information dissemination in recent years, Twitter is inevitably a prime target of spammers. It has been showed that the damage caused by Twitter spam can reach far beyond the social media platform itself. To mitigate the threat, a lot of recent studies use machine learning techniques to classify Twitter spam and report very satisfactory results. However, most of the studies overlook a fundamental issue that is widely seen in real-world Twitter data, i.e., the class imbalance problem. In this paper, we show that the unequal distribution between spam and non-spam classes in the data has a great impact on spam detection rate. To address the problem, we propose an ensemble learning approach, which involves three steps. In the first step, we adjust the class distribution in the imbalanced data set using various strategies, including random oversampling, random undersampling and fuzzy-based oversampling. In the next step, a classification model is built upon each of the redistributed data sets. In the final step, a majority voting scheme is introduced to combine all the classification models. Experimental results obtained using real-world Twitter data indicate that the proposed approach can significantly improve the spam detection rate in data sets with imbalanced class distribution.

Effectively finding relevant web pages from linkage information

This paper presents two hyperlink analysis-based algorithms to find relevant pages for a given Web page (URL). The first algorithm comes from the extended cocitation analysis of the Web pages. It is intuitive and easy to implement. The second one takes advantage of linear algebra theories to reveal deeper relationships among the Web pages and to identify relevant pages more precisely and effectively. The experimental results show the feasibility and effectiveness of the algorithms. These algorithms could be used for various Web applications, such as enhancing Web search. The ideas and techniques in this work would be helpful to other Web-related researches.

Accessible information for people with complex communication needs

Information can be empowering if it is accessible. While a number of known information access barriers have been reported for the broader group of people with disabilities, specific information issues for people with complex communication needs have not been previously reported. In this consumer-focused study, the accessibility of information design and dissemination practices were discussed by 17 people with complex communication needs; by eight parents, advocates, therapists, and agency representatives in focus groups; and by seven individuals in individual interviews. Participants explored issues and made recommendations for content, including language, visual and audio supports; print accessibility; physical access; and human support for information access. Consumer-generated accessibility guidelines were an outcome of this study.

Evolutionary structure learning algorithm for Bayesian network and penalized mutual information metric

This paper formulates the problem of learning Bayesian network structures from data as determining the structure that best approximates the probability distribution indicated by the data. A new metric, Penalized Mutual Information metric, is proposed, and a evolutionary algorithm is designed to search for the best structure among alternatives. The experimental results show that this approach is reliable and promising.

How practice size impacts on the implementation and dissemination of post occupancy evaluation

There has been much research on the subject of environmentally sustainable design (ESD), with emerging techniques and technologies improving rapidly and informing sustainability higher education teaching to architects and prospective architects. By examining the success of sustainable designs using post occupancy evaluations, architectural practices might also increase their knowledge of sustainable building practice. Post occupancy evaluations could be useful for improving the designs of future buildings and the design processes that generated them. This paper aims to evaluate these claims by asking: "Do sustainable design practices use the feedback gained from post occupancy evaluations?," "How does the feedback refine the design process?," "How is the information gained in these evaluations absorbed within the firm's design practices?," and, "Does the size of a practice impact on its implementation and
dissemination of POE?" This paper investigates the questions posed above through the questioning of architectural practices that have gained a reputation for environmentally sustainable design by having a strong sustainable design philosophy and/or by being recognised for this by winning a sustainability design award. The interviewed practices will have provided some form of post occupancy evaluation as a service or employed them to add to their own knowledge.

The crossroads approach to information discovery in wireless sensor networks

Wireless sensor networks (WSN) are attractive for information gathering in large-scale data rich environments. In order to fully exploit the data gathering and dissemination capabilities of these networks, energy-efficient and scalable solutions for data storage and information discovery are essential. In this paper, we formulate the information discovery problem as a load-balancing problem, with the combined aim being to maximize network lifetime and minimize query processing delay resulting in QoS improvements. We propose a novel information storage and distribution mechanism that takes into account the residual energy levels in individual sensors. Further, we propose a hybrid push-pull strategy that enables fast response to information discovery queries.

Simulations results prove the proposed method(s) of information discovery offer significant QoS benefits for global as well as individual queries in comparison to previous approaches.

Exploiting affinity propagation for energy-efficient information discovery in sensor networks

Wireless sensor networks (WSN) are attractive for information gathering in large-scale data rich environments. Emerging WSN applications require dissemination of information to interested clients within the network requiring support for differing traffic patterns. Further, in-network query processing capabilities are required for autonomic information discovery. In this paper, we formulate the information discovery problem as a load-balancing problem, with the combined aim being to maximize network lifetime and minimize query processing delay. We propose novel methods for data dissemination, information discovery and data aggregation that are designed to provide significant QoS benefits. We make use of affinity propagation to group "similar" sensors and have developed efficient mechanisms that can resolve both ALL-type and ANY-type queries in-network with improved energy-efficiency and query resolution time. Simulation results prove the proposed method(s) of information discovery offer significant QoS benefits for ALL-type and ANY-type queries in comparison to previous approaches.