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Ip, Lai Cheng. "Mining on social network community for marketing." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950661.
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Leung, Kwan Wai. "Commentary-based social media clustering with concept and social network discovery." HKBU Institutional Repository, 2011. https://repository.hkbu.edu.hk/etd_ra/1303.
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Abdulrahman, Ruqayya. "Multi agent system for web database processing, on data extraction from online social networks." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5502.
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In recent years, there has been a flood of continuously changing information from a variety of web resources such as web databases, web sites, web services and programs. Online Social Networks (OSNs) represent such a field where huge amounts of information are being posted online over time. Due to the nature of OSNs, which offer a productive source for qualitative and quantitative personal information, researchers from various disciplines contribute to developing methods for extracting data from OSNs. However, there is limited research which addresses extracting data automatically. To the best of the author's knowledge, there is no research which focuses on tracking the real time changes of information retrieved from OSN profiles over time and this motivated the present work. This thesis presents different approaches for automated Data Extraction (DE) from OSN: crawler, parser, Multi Agent System (MAS) and Application Programming Interface (API). Initially, a parser was implemented as a centralized system to traverse the OSN graph and extract the profile's attributes and list of friends from Myspace, the top OSN at that time, by parsing the Myspace profiles and extracting the relevant tokens from the parsed HTML source files. A Breadth First Search (BFS) algorithm was used to travel across the generated OSN friendship graph in order to select the next profile for parsing. The approach was implemented and tested on two types of friends: top friends and all friends. In case of top friends, 500 seed profiles have been visited; 298 public profiles were parsed to get 2197 top friends' profiles and 2747 friendship edges, while in case of all friends, 250 public profiles have been parsed to extract 10,196 friends' profiles and 17,223 friendship edges. This approach has two main limitations. The system is designed as a centralized system that controlled and retrieved information of each user's profile just once. This means that the extraction process will stop if the system fails to process one of the profiles; either the seed profile (first profile to be crawled) or its friends. To overcome this problem, an Online Social Network Retrieval System (OSNRS) is proposed to decentralize the DE process from OSN through using MAS. The novelty of OSNRS is its ability to monitor profiles continuously over time. The second challenge is that the parser had to be modified to cope with changes in the profiles' structure. To overcome this problem, the proposed OSNRS is improved through use of an API tool to enable OSNRS agents to obtain the required fields of an OSN profile despite modifications in the representation of the profile's source web pages. The experimental work shows that using API and MAS simplifies and speeds up the process of tracking a profile's history. It also helps security personnel, parents, guardians, social workers and marketers in understanding the dynamic behaviour of OSN users. This thesis proposes solutions for web database processing on data extraction from OSNs by the use of parser and MAS and discusses the limitations and improvements.
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Yelasani, kailash kumar yadav. "ECONOMIZED SENSOR DATA PROCESSING WITH VEHICLE PLATOONING." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/theses/2305.
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We present platooning as a special case of crowd-sensing framework. After offering a policy that governs platooning, we review common scenarios and components surrounding platooning. We present a prototype that illustrates efficiency of road usage and vehicle travel time derived from platooning. We have argued that beyond the commonly reported benefits of platooning, there are substantial savings in acquisition and processing of sensory data sharing the road. Our results show that data transmission can be reduced to low of 3% compared to normal data transmission using a platoon formation with sensor sharing.
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Rezayidemne, Seyedsaed. "Characterizing Online Social Media: Topic Inference and Information Propagation." Thesis, University of Oregon, 2018. http://hdl.handle.net/1794/23904.
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Word-of-mouth (WOM) communication is a well studied phenomenon in the literature and content propagation in Online Social Networks (OSNs) is one of the forms of WOM mechanism that have been prevalent in recent years specially with the widespread surge of online communities and online social networks. The basic piece of information in most OSNs is a post (e.g., a tweet in Twitter or a post in Facebook). A post can contain different types of content such as text, photo, video, etc, or a mixture of two or more them. There are also various ways to enrich the text by mentioning other users, using hashtags, and adding URLs to external contents. The goal of this study is to investigate what factors contribute into the propagation of messages in Google+. To answer to this question a multidimensional study will be conducted. On one hand this question could be viewed as a natural language processing problem where topic or sentiment of posts cause message dissemination. On the other hand the propagation can be effect of graph properties i.e., popularity of message originators (node degree) or activities of communities. Other aspects of this problem are time, external contents, and external events. All of these factors are studied carefully to find the most highly correlated attribute(s) in the propagation of posts.
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Li, Yafei. "Efficient group queries in location-based social networks." HKBU Institutional Repository, 2015. https://repository.hkbu.edu.hk/etd_oa/203.
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Nowadays, with the rapid development of GPS-equipped mobile devices, location-based social networks have been emerging to bridge the gap between the physical world and online social networking services. Various types of data, such as personal locations, check-ins, microblogs and social relations, have been available in location-based social networks. Efficiently managing and analyzing such data to meet users' daily query requirements become a challenging task. Among all the existing works in location-based social networks, group query is one of the most important research topics. In this thesis, we investigate query techniques for location-based services in social networking applications. Specifically, considering a location-based social network, we study spatial-aware interest group queries, geo-social {dollar}k{dollar}-cover group queries, and social-aware ridesharing group queries. Firstly, we study the spatial-aware interest group queries in location-based social networks. Recently, most of the location-based social networks release check-in services that allow users to share their visiting locations with their friends. These locations, considered as spatial objects, are usually associated with a few tags that describe the features of those locations. Utilizing such information, we propose a new type of \emph{Spatial-aware Interest Group} (SIG) query that retrieves a user group of size {dollar}k{dollar} where each user is interested in the query keywords and the users are close to each other in the Euclidean space. We prove this query problem is NP-complete, and develop two efficient algorithms IOAIR and DOAIR based on the IR-tree for the processing of SIG queries. We also validate the performance efficiency of the proposed query processing algorithms by empirical evaluation. Secondly, we study the problem of geo-social {dollar}k{dollar}-cover group queries for collaborative spatial computing. In this problem, we propose a novel type of geo-social queries, called \emph{Geo-Social K-Cover Group} (GSKCG) query, which is based on spatial containment and a new modeling of social relationships. Intuitively, given a set of spatial query points and an underlying social network, a GSKCG query finds a minimum user group in which the members satisfy certain social relationship and their associated regions can jointly cover all the query points. Albeit its practical usefulness, the GSKCG query problem is NP-complete. We consequently explore a set of effective pruning strategies to derive an efficient algorithm for finding the optimal solution. Moreover, we design a novel index structure tailored to our problem to further accelerate query processing. Extensive experiments demonstrate that our algorithm achieves desirable performance on real-life datasets. Thirdly, we study the problem of social-aware ridesharing group queries. With the deep penetration of smartphones and geo-locating devices, ridesharing is envisioned as a promising solution to transportation-related problems such as congestion and air pollution for metropolitan cities. Despite the potential to provide significant societal and environmental benefits, ridesharing has not so far been as popular as expected. Notable barriers include the social discomfort and safety concerns when traveling with strangers. To overcome these barriers, in this thesis, we propose a new type of \emph{Social-aware Ridesharing Group} (SaRG) query which retrieves a group of riders by taking into account their social connections besides traditional spatial proximities. Because the SaRG query problem is NP-hard, we design an efficient algorithm with a set of powerful pruning techniques to tackle this problem. We also present several incremental strategies to accelerate the search speed by reducing the repeated computations. Moreover, we propose a novel index tailored to the proposed problem to further speed up the query processing. Experimental results on real datasets show that our proposed algorithms achieve desirable performance. The works of this thesis show that the group query processing techniques are effective, which would facilitate the wider deployment of such query services in real applications
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Wang, Hao, and 王皓. "Advanced rank-aware queries and recommendation with novel types of data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206672.
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Nowadays we are living in an era of rich data, not only in the sense of the amount of data, but also in the sense of various sources and content of data. Efficient search, management, and exploitation of data have, over decades, been a major direction of database research. In this thesis, three challenging problems are proposed and studied, targeting (i) time series data, (ii) user preference data, and (iii) location-based social network data, respectively, providing efficient solutions to corresponding real-life applications.
First, durability queries are studied in historical time series databases, which identify objects that have durable quality over time. For example, a sociologist may be interested in the top 10 web search terms during the period of some historical events; the police may seek for vehicles that move close to a suspect 70% of the time during a certain time, etc. Such durable top-k (DTop-k) and durable k-nearest neighbor (DkNN) queries can be viewed as natural extensions of the standard snapshot top-k and NN queries to timestamped sequences of values or locations. Although their snapshot counterparts have been studied extensively, there is little prior work that addresses this new class of durability queries. Efficient and scalable algorithms are proposed based on novel indexing techniques.
Next, an efficient solution to k-nearest neighbor search over top-m lists is investigated. A top-m list is a ranking of m items, typically representing some user’s preference over these items. For example, a user may have a list of her 10 most favourite books; the result from a search engine is typically a list of webpages ranked according to their relevance to some keywords. The search problem aims at extracting k top-m lists from the database that are the “closest” to some query list where the closeness is evaluated using commonly used measures such as the Fagin’s intersection metric, Spearman’s footrule, Kendall’s tau, etc. Despite of the importance of such queries, there’s little prior work suggesting any efficient solution. In this thesis, a unified framework is proposed to answer such queries efficiently.
Finally, the problem of top-N venue recommendation in location-based social networks (LBSNs) is studied, which recommends new venues to users. As an increasingly larger number of users partake in LBSNs, the recommendation problem in this setting has attracted significant attention in research and in practical applications. The detailed information about past user behavior that is traced by the LBSN differentiates the problem significantly from its traditional settings. The spatial nature in the past user behavior and also the information about the user social interaction with other users, provide a richer background to build a more accurate and expressive recommendation model. Although there have been extensive studies on recommender systems working with user-item ratings, GPS trajectories, and other types of data, there are very few approaches that exploit the unique properties of the LBSN user check-in data. In this thesis, effective and efficient algorithms that create recommendations are proposed based on such properties.published_or_final_version
Computer Science
Doctoral
Doctor of Philosophy
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Zhao, Xiaoyun. "Road network and GPS tracking with data processing and quality assessment." Licentiate thesis, Högskolan Dalarna, Mikrodataanalys, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:du-17354.
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GPS technology has been embedded into portable, low-cost electronic devices nowadays to track the movements of mobile objects. This implication has greatly impacted the transportation field by creating a novel and rich source of traffic data on the road network. Although the promise offered by GPS devices to overcome problems like underreporting, respondent fatigue, inaccuracies and other human errors in data collection is significant; the technology is still relatively new that it raises many issues for potential users. These issues tend to revolve around the following areas: reliability, data processing and the related application. This thesis aims to study the GPS tracking form the methodological, technical and practical aspects. It first evaluates the reliability of GPS based traffic data based on data from an experiment containing three different traffic modes (car, bike and bus) traveling along the road network. It then outline the general procedure for processing GPS tracking data and discuss related issues that are uncovered by using real-world GPS tracking data of 316 cars. Thirdly, it investigates the influence of road network density in finding optimal location for enhancing travel efficiency and decreasing travel cost. The results show that the geographical positioning is reliable. Velocity is slightly underestimated, whereas altitude measurements are unreliable.Post processing techniques with auxiliary information is found necessary and important when solving the inaccuracy of GPS data. The densities of the road network influence the finding of optimal locations. The influence will stabilize at a certain level and do not deteriorate when the node density is higher.
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Schlenkrich, Lara. "An investigation of social computing." Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1006194.
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Social network sites have recently become extremely popular online destinations as they offer users easy ways to build and maintain their relationships with each other. Consequently, students, lecturers, teachers, parents and businesses are using these tools to communicate with each other in a fast and cost-effective manner. However, literature suggests that the full potential of social network sites has not yet been revealed since users are still battling to overcome the various negative characteristics surrounding these sites. A framework for appropriate use of these sites is needed so that users are able to overcome these negative aspects, allowing them to be more effective and use the sites successfully. The goal of this research is to construct a framework for perceived successful use of social computing tools in educational institutions. This framework will include critical success factors that need to be adopted by users in order to develop the positive aspects of social computing, while at the same time overcoming the disadvantages experienced by users. Factors for successful use were derived from the literature and consolidated into a theoretical framework in order to understand the factors that drive successful use of social network sites. Measures used to test successful use of social network sites were also derived from these sources and were included in the same theoretical framework; these measures allow users to evaluate the extent of perceived successful use of social network sites. This framework was tested empirically by means of a pilot study and online survey, and revised according to the results of the survey. The factors were identified using Cronbach alpha coefficients (in the pilot study) and exploratory factor analysis to confirm the reliability of the scales developed. Pearson product-moment correlation coefficient analysis, t-tests and Pearson Chi-Square tests were used to measure the relationships amongst the variables in the framework proposed in this research. The factors influencing perceived successful use of social network sites were identified by the empirical study as: • Privacy and Security Settings need to be enabled. These are split into: - Settings: content that users allow others to see - Viewers: people who are allowed onto a user's profile • It is necessary for users to practise Legal and Acceptable Activities when using social network sites • Suspect Information needs to be checked before sharing it with others • Personal and Professional Time needs to be separated to ensure that work is completed before social activities occur • Users need to practise Professional and Ethical Behaviour • Users need to have a Positive Attitude when using social network sites • Usability of sites affects their success. This includes: - technical capacity (broadband) - ease of use - functionality (range of features and functions) • Current and Controversial Issues need to be discussed on social network sites. The extent to which social network sites are being used successfully can be evaluated by the presence of the following measures: • Range of Content must be available to users. This includes: - Content displayed on profiles - Viewers able to visit profiles • Visitors Behaviour is monitored and no unwanted visitors are present users' profiles • Social Contracts found on sites are followed by users • Critical Thinking Skills and Accurate Information are displayed by users • Work is completed before social activities occur on sites • A Variety of Users is present on sites • Collaboration between people as well as variety of opinions exist on sites • Social Capital (well-being) is present after users have been on sites • Learning and Advising Skills are enhanced on sites. The framework developed provides users with a useful instrument to overcome the negative characteristics associated with social network sites. If used successfully, social network sites can offer lecturers and students a unique method to develop their relationship, creating a positive learning experience.
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Igboayaka, Jane-Vivian Chinelo Ezinne. "Using Social Media Networks for Measuring Consumer Confidence: Problems, Issues and Prospects." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32341.
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This research examines the confluence of consumers’ use of social media to share information with the ever-present need for innovative research that yields insight into consumers’ economic decisions.
Social media networks have become ubiquitous in the new millennium. These networks, including, among others: Facebook, Twitter, Blog, and Reddit, are brimming with conversations on an expansive array of topics between people, private and public organizations, governments and global institutions. Preliminary findings from initial research confirms the existence of online conversations and posts related to matters of personal finance and consumers’ economic outlook.
Meanwhile, the Consumer Confidence Index (CCI) continues to make headline news. The issue of consumer confidence (or sentiment) in anticipating future economic activity generates significant interest from major players in the news media industry, who scrutinize its every detail and report its implications for key players in the economy. Though the CCI originated in the United States in 1946, variants of the survey are now used to track and measure consumer confidence in nations worldwide.
In light of the fact that the CCI is a quantified representation of consumer sentiments, it is possible that the level of confidence consumers have in the economy could be deduced by tracking the sentiments or opinions they express in social media posts. Systematic study of these posts could then be transformed into insights that could improve the accuracy of an index like the CCI. Herein lies the focus of the current research—to analyze the attributes of data from social media posts, in order to assess their capacity to generate insights that are novel and/or complementary to traditional CCI methods.
The link between data gained from social media and the survey-based CCI is perhaps not an obvious one. But our research will use a data extraction tool called NetBase Insight Workbench to mine data from the social media networks and then apply natural language processing to analyze the social media content. Also, KH Coder software will be used to perform a set of statistical analyses on samples of social media posts to examine the co-occurrence and clustering of words. The findings will be used to expose the strengths and weaknesses of the data and to assess the validity and cohesion of the NetBase data extraction tool and its suitability for future research.
In conclusion, our research findings support the analysis of opinions expressed in social media posts as a complement to traditional survey-based CCI approaches. Our findings also identified a key weakness with regards to the degree of ‘noisiness’ of the data. Although this could be attributed to the ‘modeling’ error of the data mining tool, there is room for improvement in the area of association—of discerning the context and intention of posts in online conversations.
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Chiarella, Andrew Francesco 1971. "Enabling the collective to assist the individual : a self-organising systems approach to social software and the creation of collaborative text signals." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=115618.
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Authors augment their texts using devices such as bold and italic typeface to signal important information to the reader. These typographical text signals are an example of a signal designed to have some affect on others. However, some signals emerge through the unplanned, indirect, and collective efforts of a group of individuals. Paths emerge in parks without having been designed by anyone. Objects accumulate wear patterns that signal how others have interacted with the object. Books open to important, well studied pages because the spine has worn, for example (Hill, Hollan, Wroblewski, & McCandless, 1992). Digital text and the large-scale collaboration made possible through the internet provide an opportunity to examine how unplanned, collaborative text signals could emerge. A software application was designed, called CoREAD, that enables readers to highlight sections of the text they deem important. In addition, CoREAD adds text signals to the text using font colour, based on the group's collective history and an aggregation function based on self-organising systems. The readers are potentially influenced by the text signals presented by CoREAD but also help to modify these same signals. Importantly, readers only interact with each other indirectly through the text. The design of CoREAD was greatly inspired by the previous work on history-enriched digital objects (Hill & Hollan, 1993) and at a more general level it can be viewed as an example of distributed cognition (Hollan, Hutchins, & Kirsh, 2000).Forty undergraduate students read two texts on topics from psychology using CoREAD. Students were asked to read each text in order to write a summary of it. After each new student read the text, the text signals were changed to reflect the current group of students. As such, each student read the text with different text signals presented.
The data were analysed for each text to determine if the text signals that emerged were stable and valid representations of the semantic content of the text. As well, the students' summaries were analysed to determine if students who read the text after the text signals had stabilised produced better summaries. Three methods demonstrated that CoREAD was capable of generating stable typographical text signals. The high importance text signals also appeared to capture the semantic content of the texts. For both texts, a summary made of the high signals performed as well as a benchmark summary. The results did not suggest that the stable text signals assisted readers to produce better summaries, however. Readers may not respond to these collaborative text signals as they would to authorial text signals, which previous research has shown to be beneficial (Lorch, 1989). The CoREAD project has demonstrated that readers can produce stable and valid text signals through an unplanned, self-organising process.
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Zhou, Ke. "Extending low-rank matrix factorizations for emerging applications." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50230.
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Low-rank matrix factorizations have become increasingly popular to project high dimensional data into latent spaces with small dimensions in order to obtain better understandings of the data and thus more accurate predictions. In particular, they have been widely applied to important applications such as collaborative filtering and social network analysis. In this thesis, I investigate the applications and extensions of the ideas of the low-rank matrix factorization to solve several practically important problems arise from collaborative filtering and social network analysis.
A key challenge in recommendation system research is how to effectively profile new users, a problem generally known as \emph{cold-start} recommendation.
In the first part of this work, we extend the low-rank matrix factorization by allowing the latent factors to have more complex structures --- decision trees to solve the problem of cold-start recommendations. In particular, we present \emph{functional matrix
factorization} (fMF), a novel cold-start recommendation method that
solves the problem of adaptive interview construction based on low-rank matrix factorizations.
The second part of this work considers the efficiency problem of making recommendations in the context of large user and item spaces.
Specifically, we address the problem through learning binary codes for collaborative filtering, which can be viewed as restricting the latent factors in low-rank matrix factorizations to be binary vectors that represent the binary codes for both users and items.
In the third part of this work, we investigate the applications of low-rank matrix factorizations in the context of social network analysis. Specifically, we propose a convex optimization approach to discover the hidden network of social influence with low-rank and sparse structure by modeling the recurrent events at different individuals as multi-dimensional Hawkes processes, emphasizing the mutual-excitation nature of the dynamics of event occurrences. The proposed framework combines the estimation of mutually exciting process and the low-rank matrix factorization in a principled manner.
In the fourth part of this work, we estimate the triggering kernels for the Hawkes process. In particular, we focus on estimating the triggering kernels from an infinite dimensional functional space with the Euler Lagrange equation, which can be viewed as applying the idea of low-rank factorizations in the functional space.
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Tam, Weng Tong. "WeChat in work environment in Macao, a use and gratification study." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3952599.
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Dimond, Jill Patrice. "Feminist HCI for real: designing technology in support of a social movement." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45778.
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How are technologies are designed and used tactically by activists? As the HCI community starts to contend with social inequalities, there has been debate about how HCI researchers should address approach this type of research. However, there is little research examining practitioners such as social justice activists who confront social problems, and are using technology, such as mobile phones, blogging, and social media to do so. In this dissertation, I build on this knowledge within the context of a social movement organization working to stop street harassment (harassment towards women and minorities in public) called Hollaback (ihollaback.org). I position myself as an action researcher doing research and building technologies such as mobile apps and a blogging platform to collect stories of harassment and to support activists. The organization has collected over 3000 stories and represents 50 different locales in 17 countries. Through a series of studies, I examined how technology impacts the organization, activists, and those who contribute stories of harassment. I found evidence that the storytelling platform helps participants fundamentally shift their cognitive and emotional orientation towards their experience and informs what activists do on the ground. My results suggest that doing activism using technology can help remove some barriers to participation but can also lower expectations for the amount of work required. I also looked at how different social media tactics can increase the number of followers and how traditional media plays a role in these tactics. My work contributes theoretically to the HCI community by building on social movement theory, feminist HCI, and action research methodology. My investigation also sheds light empirically on how technology plays a role in a social movement organization, and how it impacts those who participate.
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Riley, Will. "We the undersigned." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28102.
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Thesis (M. S.)--Literature, Communication, and Culture, Georgia Institute of Technology, 2009.Committee Chair: DiSalvo, Carl; Committee Member: Bogost, Ian; Committee Member: Klein, Hans; Committee Member: Murray, Janet; Committee Member: Pearce, Celia
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Siracusa, Pedro Correia de. "New perspectives on analyzing data from biological collections based on social network analytics." Laboratório Nacional de Computação Científica, 2018. https://tede.lncc.br/handle/tede/279.
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Coleções biológicas são consideradas fundamentais fontes de informação científica sobre biodiversidade, tendo historicamente suportado uma ampla gama de iniciativas para conservação de recursos naturais. Por serem tipicamente compostas de registros pontuais de espécies (muitos dos quais derivam de amostragem não aleatória e oportunística), dados de coleções biológicas são comumente associados a uma variedade de vieses, que precisam ser caracterizados e mitigados antes que dados possam ser consumidos. Nesta dissertação temos como principal motivação os vieses taxonômico e de coletor, que podem ser compreendidos como o efeito de preferências pessoais de coletores-chave na composição taxonômica das coleções com as quais eles contribuem. Neste contexto, propomos dois modelos de redes como um primeiro passo para um modelo conceitual, com o objetivo de compreender a formação de coleções biológicas como resultado da composição dos interesses e atividades de seus coletores. Os modelos estendem o campo bem estabelecido da análise de redes sociais, beneficiando-se de uma variedade de métricas e algoritmos para a caracterização de aspectos topológicos. Redes Espécie-Coletor (SCNs) modelam os interesses dos coletores em espécies, e se estruturam por meio de enlaces entre coletores e espécies que eles registram. De forma complementar, SCNs permitem tanto a investigação de coletores compartilhando interesses comuns em conjuntos de espécies; quanto de espécies normalmente coletadas por conjuntos similares de coletores. Redes Colaborativas de Coletores (CWNs) são um tipo especial de redes de colaboração, estruturadas a partir de enlaces colaborativos que se formam entre coletores que registram espécies em conjunto em campo. Tais relações de colaboração são criadas entre pares de coletores caso ambos tenham sido incluídos como coletores responsáveis pelo mesmo registro. Com base nos modelos definidos, nós também apresentamos um estudo de caso em que exploramos a comunidade de coletores e a composição taxonômica dos herbário da Universidade de Brasília. Descrevemos aspectos topológicos gerais das redes e indicamos alguns dos coletores mais relevantes na coleção, bem como grupos taxonômicos de seus respectivos interesses. Nós também investigamos o comportamento colaborativo de coletores durante a coleta de espécimes. Ao final, discutimos perspectivas futuras para a incorporação das dimensões temporal e geográfica nos modelos. Também indicamos algumas possíveis direções de investigação que poderiam se beneficiar de nossa abordagem para a modelagem e análise de coleções biológicas.
Biological collections have been historically regarded as fundamental sources of scientific information on biodiversity, supporting a wide range of scientific and management initiatives in the scope of natural resources conservation. As they are typically composed of discrete records of specimens (most of which derived from non-random and opportunistic sampling), biological collection datasets are commonly associated with a variety of biases, which must be characterized and mitigated before data can be consumed. In this dissertation, we are particularly motivated by taxonomic and collector biases, which can be understood as the effect of particular recording preferences of key collectors on shaping the overall taxonomic composition of biological collections they contribute to. In this context, we propose two network models as the first steps towards a network-based conceptual framework for understanding the formation of biological collections as a result of the composition of collectors’ interests and activities. Both models extend the well-established framework of social network analytics, benefiting from a whole set of metrics and algorithms for characterizing network topological features. Species-Collector Networks (SCNs) model the interests of collectors towards particular species, and are structured by linking collectors to each species they have recorded in biological collection datasets. From complementary perspectives, SCNs allow one to investigate which collectors share common interest for sets of species; and conversely, which species are usually recorded by similar sets of collectors. Collector CoWorking Networks (CWNs) are a special type of collaboration networks, structured from collaboration ties that are formed between collectors who record specimens together in field. Such collaborative ties are created between pairs of collectors whenever they are both included as collectors in the same record. Building upon the defined network models, we also present a case study in which we use our models to explore the community of collectors and the taxonomic composition of the University of Brasília herbarium. We describe general topological features of the networks and point out some of the most relevant collectors in the biological collection as well as their taxonomic groups of interest. We also investigate the collaborative behavior of collectors while recording specimens. Finally, we discuss future perspectives for incorporating temporal and geographical dimensions to the models. Moreover, we indicate some possible investigation directions that could possibly benefit from our approach based on social network analytics to model and analyze biological collections.
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Methawut, Elena. "The effect of computer mediated communication to communication patterns." CSUSB ScholarWorks, 2004. https://scholarworks.lib.csusb.edu/etd-project/2644.
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Computer mediated communication (CMC) fundamentally influences the function of communication. It influences the organization's management and administration, but it most affects the dynamics of middle and lower level employees. The most simplistic model is that of an electronic office in which its employees need to know and understand the role of CMC. The purpose of this study is to investigate the performance and satisfaction of co-workers who use CMC to communicate within their organization, and to check employees' performance when using CMC.
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Banoobhai-Anwar, Ilhaam. "The role of e-commerce in five-star hotels in the Cape Town Metropole." Thesis, Cape Peninsula University of Technology, 2016. http://hdl.handle.net/20.500.11838/2489.
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Thesis (MTech (Tourism and Hospitality Management))--Cape Peninsula University of Technology, 2016.Electronic commerce, popularly referred to as e-commerce, is the latest catchphrase surrounding the Internet and its many functions. In the past, the Internet was solely used for information seeking, but the 21st century has brought a new global economy to the fore – one that is conducted online. Tourism is seen as a driver of economic growth, contributing both to the Gross Domestic Product (GDP) of South Africa (SA) and creating jobs in the hospitality sector. E-commerce does not only provide a platform for business to be conducted online but also an opportunity for consumers to interact directly with industry. Many studies have been done previously about the perceived benefits of e-commerce in the retail sector, yet none in Cape Town and at five-star hotels in particular. It is well known that holidaymakers conduct an online search prior to booking accommodation; some of those potential guests in turn choose to finalise their bookings online. The purpose of the study was to investigate the role of e-commerce in five-star hotels in the Cape Town Metropole. The main objective of this study was to define what e-commerce is for five-star hotels in the Cape Town Metropole and determine if the hotels are using e-commerce as a booking method. Online questionnaires were used to gather primary data and a literature review was presented as secondary data. The findings of this study showed that all the hotels surveyed use online bookings and half of the respondents believe Online Travel Agents (OTAs) generate more revenue than traditional methods of room reservation. When asked to rank methods of room’s reservations, there was a slight difference between telephone bookings and e-commerce as number one. This could mean that while electronic methods are popular, they have not completely replaced traditional methods. The researcher recommends that five-star hotel managers train the rooms’ division staff to respond to negative reviews on social media, as this was one of the findings. The hotels should also learn how to effectively use their social media presence to increase room sales.
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Xu, Yan. "Exploring social play in a shared hybrid space enabled by handheld augmented reality." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45940.
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Reality-based interfaces bring new design opportunities to social games. These novel game interfaces, exemplified by Wii, Kinect, and Smart phones, leverage players' existing physics, bodily, environmental, and social skills. Moreover, they enable a shared hybrid physical-digital space in which the players' co-presence can be enhanced by their physical and digital co-location. However, many digital social games occupy players' attention with the digital display and content, reducing their attention spent on one another and limiting the synchronization of actions and emotions among players. How do we design technologies that do not interfere with social play but enhance and innovate it?
In this thesis work, I focus on one particular kind of reality-based interfaces, Handheld Augmented Reality (HAR), to extend players' interaction from the small mobile devices to the shared hybrid space around a computationally trackable surface. This thesis explores how to encourage social play with HAR interfaces, which brings in challenges of designing with the affordances and constraints of the HAR interface, understanding the complicated phenomenon of social play, and integrating these understandings in multiplayer HAR game design. Adopting Research-through Design as the overarching research method, I collaborate with multiple teams, design and study three multiplayer HAR game prototypes. I present four main contributions. First, this work yields design artifacts and examples of social games with HAR interfaces. I communicate to the game design and Augmented Reality communities through these prototypes, including BragFish, ARt of Defense, and NerdHerder. Second, I provide empirical findings on social play in a shared hybrid space. Through lab-based user studies, observation, video analysis, interviews, and surveys, I collect and analyze interpersonal play behaviors and emotions in the shared hybrid space enabled by the HAR interface. Third, I adopt and adapt sociological theories to the domain of social games. I generate a list of theory-based design guidelines for co-located social games, especially HAR games. I prove its usefulness through the user study on an HAR game that embodies a subset of these design guidelines. Fourth, this thesis work is a case study that bridges Human Computer Interaction knowledge and methods with game design practices. It shows the outcome and benefits of multidisciplinary research, calling for more effort in integrating reality-based interfaces, as playful and experimental design materials, into different phases of game design process.
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Newlon, Christine Mae. "The effect of shared dynamic understanding on willingness to contribute information| Design and analysis of a mega-collaborative interface." Thesis, Indiana University - Purdue University Indianapolis, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10159859.
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Collaborative helping via social networking conversation threads can pose serious challenges in emergency situations. Interfaces that support complex group interaction and sense-making can help. This research applies human-computer interaction (HCI), computer-supported cooperative work (CSCW), and collaboration engineering in developing an interactive design, the Mega-Collaboration Tool (MCT). The goal is to reduce the cognitive load of a group’s growing mental model, thus increasing the general public’s ability to organize spontaneous collaborative helping.
The specific aims of this research include understanding the dynamics of mental model negotiation and determining whether MCT can assist the group’s sense-making ability without increasing net cognitive load.
The proposed HCI theory is that interfaces supporting collaborative cognition motivate contribution and reduce information bias, thus increasing the information shared. These research questions are addressed: 1. Does MCT support better collaborative cognition? 2. Does increasing the size of the shared data repository increase the amount of information shared? 3. Does this happen because group members experience 1) a greater sense of strategic commitment to the knowledge structure, 2) increased intrinsic motivation to contribute, and 3) reduced resistance to sharing information?
These questions were affirmed to varying degrees, giving insight into the collaborative process. Greater content did not motive group members directly; instead, half of their motivation came from awareness of their contribution’s relevance. Greater content and organization improved this awareness, and also encouraged sharing through increased enthusiasm and reduced bias. Increased commitment was a result of this process, rather than a cause. Also, MCT increased collaborative cognition but was significantly hampered by Internet performance. This challenge indicates MCT’s system components should be redesigned to allow asynchronous interaction. These results should contribute to the development of MCT, other collaboration engineering applications, and HCI and information science theory.
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Skepetzis, Vasilios, and Pontus Hedman. "The Effect of Beautification Filters on Image Recognition : "Are filtered social media images viable Open Source Intelligence?"." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44799.
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In light of the emergence of social media, and its abundance of facial imagery, facial recognition finds itself useful from an Open Source Intelligence standpoint. Images uploaded on social media are likely to be filtered, which can destroy or modify biometric features. This study looks at the recognition effort of identifying individuals based on their facial image after filters have been applied to the image. The social media image filters studied occlude parts of the nose and eyes, with a particular interest in filters occluding the eye region. Our proposed method uses a Residual Neural Network Model to extract features from images, with recognition of individuals based on distance measures, based on the extracted features. Classification of individuals is also further done by the use of a Linear Support Vector Machine and XGBoost classifier. In attempts to increase the recognition performance for images completely occluded in the eye region, we present a method to reconstruct this information by using a variation of a U-Net, and from the classification perspective, we also train the classifier on filtered images to increase the performance of recognition. Our experimental results showed good recognition of individuals when filters were not occluding important landmarks, especially around the eye region. Our proposed solution shows an ability to mitigate the occlusion done by filters through either reconstruction or training on manipulated images, in some cases, with an increase in the classifier’s accuracy of approximately 17% points with only reconstruction, 16% points when the classifier trained on filtered data, and 24% points when both were used at the same time. When training on filtered images, we observe an average increase in performance, across all datasets, of 9.7% points.
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Yalamanchili, Hari Krishna. "Computational approaches for protein functions and gene association networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206477.
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Entire molecular biology revolves primarily around proteins and genes (DNA and RNA). They collaborate with each other facilitating various biomolecular systems. Thus, to comprehend any biological phenomenon from very basic cell division to most complex cancer, it is fundamental to decode the functional dynamics of proteins and genes. Recently, computational approaches are being widely used to supplement traditional experimental approaches. However, each automated approach has its own advantages and limitations. In this thesis, major shortcomings of existing computational approaches are identified and alternative fast yet precise methods are proposed.
First, a strong need for reliable automated protein function prediction is identified. Almost half of protein functional interpretations are enigmatic. Lack of universal functional vocabulary further elevates the problem. NRProF, a novel neural response based method is proposed for protein functional annotation. Neural response algorithm simulates human brain in classifying images; the same is applied here for classifying proteins. Considering Gene Ontology (GO) hierarchical structure as background, NRProF classifies a protein of interest to a specific GO category and thus assigns the corresponding function.
Having established reliable protein functional annotations, protein and gene collaborations are studied next. Interactions amongst transcription factors (TFs) and transcription factor binding sites (TFBSs) are fundamental for gene regulation and are highly specific, even in evolution background. To explain this binding specificity a Co-Evo (co-evolutionary) relationship is hypothesized. Pearson correlation and Mutual Information (MI) metrics are used to validate the hypothesis. Residue level MI is used to infer specific binding residues of TFs and corresponding TFBSs, assisting a thorough understanding of gene regulatory mechanism and aid targeted gene therapies.
After comprehending TF and TFBS associations, interplay between genes is abstracted as Gene Regulatory Networks. Several methods using expression correlations are proposed to infer gene networks. However, most of them ignore the embedded dynamic delay induced by complex molecular interactions and other riotous cellular mechanisms, involved in gene regulation. The delay is rather obvious in high frequency time series expression data. DDGni, a novel network inference strategy is proposed by adopting gapped smith-waterman algorithm. Gaps attune expression delays and local alignment unveils short regulatory windows, which traditional methods overlook.
In addition to gene level expression data, recent studies demonstrated the merits of exon-level RNA-Seq data in profiling splice variants and constructing gene networks. However, the large number of exons versus small sample size limits their practical application. SpliceNet, a novel method based on Large Dimensional Trace is proposed to infer isoform specific co-expression networks from exon-level RNA-Seq data. It provides a more comprehensive picture to our understanding of complex diseases by inferring network rewiring between normal and diseased samples at isoform resolution. It can be applied to any exon level RNA-Seq data and exon array data.
In summary, this thesis first identifies major shortcomings of existing computational approaches to functional association of proteins and genes, and develops several tools viz. NRProF, Co-Evo, DDGni and SpliceNet. Collectively, they offer a comprehensive picture of the biomolecular system under study.published_or_final_version
Biochemistry
Doctoral
Doctor of Philosophy
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Rahman, Mahmudur. "Data Verifications for Online Social Networks." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2299.
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Social networks are popular platforms that simplify user interaction and encourage collaboration. They collect large amounts of media from their users, often reported from mobile devices. The value and impact of social media makes it however an attractive attack target. In this thesis, we focus on the following social media vulnerabilities. First, review centered social networks such as Yelp and Google Play have been shown to be the targets of significant search rank and malware proliferation attacks. Detecting fraudulent behaviors is thus paramount to prevent not only public opinion bias, but also to curb the distribution of malware. Second, the increasing use of mobile visual data in news networks, authentication and banking applications, raises questions of its integrity and credibility. Third, through proof-of- concept implementations, we show that data reported from wearable personal trackers is vulnerable to a wide range of security and privacy attacks, while off-the-shelves security solutions do not port gracefully to the constraints introduced by trackers. In this thesis we propose novel solutions to address these problems. First, we introduce Marco, a system that leverages the wealth of spatial, temporal and network information gleaned from Yelp, to detect venues whose ratings are impacted by fraudulent reviews. Second, we propose FairPlay, a system that correlates review activities, linguistic and behavioral signals gleaned from longitudinal app data, to identify not only search rank fraud but also malware in Google Play, the most popular Android app market. Third, we describe Movee, a motion sensor based video liveness verification system, that analyzes the consistency between the motion inferred from the simultaneously and independently captured camera and inertial sensor streams. Finally, we devise SensCrypt, an efficient and secure data storage and communication protocol for affordable and lightweight personal trackers. We provide the correctness and efficacy of our solutions through a detailed theoretic and experimental analysis.
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Subramaniam, Suresh. "All-optical networks with sparse wavelength conversion /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/6032.
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Kirkin, S., and K. V. Melnyk. "Intelligent Data Processing in Creating Targeted Advertising." Thesis, National Technical University "Kharkiv Polytechnic Institute", 2017. http://repository.kpi.kharkov.ua/handle/KhPI-Press/44710.
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Murshed, Md Golam. "Energy efficient data gathering in wireless sensor networks." Thesis, University of Aberdeen, 2013. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=210783.
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Along with the rapid growth of Wireless Sensor Networks, a wide range of challenges have come to existence to make the network more robust and versatile. Gaining energy efficiency and maximizing network lifetime are the most important of all that can affect the performance of the network directly. In this thesis, a number of research aspects related to energy efficient data gathering have been investigated and some promising proposals are presented. In large, hierarchical multi-hop Wireless Sensor Networks, power consumption characteristics of the static sensor nodes and data traffic distribution across the network are largely determined by the node position and the adopted routing protocol. In this thesis, these phenomena of the network are addressed analytically and we proposed some methods to divide the monitoring field into partitions that act as the basis for even load distribution in the network. We proposed an algorithm to calculate the area of the partitions that exploits the energy efficient features of optimal transmission range. The partition works as the bedrock of the other proposals in this thesis. Considering the influential factors of the proximity and the recent state of the network, we also developed a routing protocol that minimises over all energy consumption of the network and is able to dynamically select a route to the sink. Further, we proposed a rotational order for data gathering scheme that works along with the routing protocol to ensure load balancing and to alleviate data congestion around the sink. Clustered organization of the nodes in sensor networks can further save energy consumption and facilitates scope for better network management. In this thesis, we address the fact that equal sized clusters can cause unbalanced data traffic around the sink. So, we propose a method to calculate suitable cluster radii in different regions of the monitoring field in order to form clusters of different sizes. To ensure unequal clusters in the field, a cluster construction procedure is also proposed targeting minimal data generation, minimal energy consumption and providing capacity for reliability preservation. Furthermore, the notion of redundant nodes and the outlines of a possible solution to identify and deactivate redundant nodes are explained in this thesis. Since the clusterheads play an important role as coordinators in the clusters, it is vital that there is a clusterhead in every cluster all the time. In this thesis, a message optimal and distributed leader election algorithm is proposed to select a new clusterhead in case of unexpected and unnoticed failure of a clusterhead node. Detailed analysis and simulation of the proposed methods clarify the effectiveness of the research. In comparison with other methods of similar kind, our methods confirm better balanced energy dissipation, energy efficient route selection, message optimal clusterhead selection and prolonged lifetime of the network.
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Kallumadi, Surya Teja. "Data aggregation in sensor networks." Thesis, Manhattan, Kan. : Kansas State University, 2010. http://hdl.handle.net/2097/2387.
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Sastry, Nishanth Ramakrishna. "Social network support for data delivery infrastructures." Thesis, University of Cambridge, 2011. https://www.repository.cam.ac.uk/handle/1810/240631.
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Network infrastructures often need to stage content so that it is accessible to consumers. The standard solution, deploying the content on a centralised server, can be inadequate in several situations. Our thesis is that information encoded in social networks can be used to tailor content staging decisions to the user base and thereby build better data delivery infrastructures. This claim is supported by two case studies, which apply social information in challenging situations where traditional content staging is infeasible. Our approach works by examining empirical traces to identify relevant social properties, and then exploits them. The first study looks at cost-effectively serving the ``Long Tail'' of rich-media user-generated content, which need to be staged close to viewers to control latency and jitter. Our traces show that a preference for the unpopular tail items often spreads virally and is localised to some part of the social network. Exploiting this, we propose Buzztraq, which decreases replication costs by selectively copying items to locations favoured by viral spread. We also design SpinThrift, which separates popular and unpopular content based on the relative proportion of viral accesses, and opportunistically spins down disks containing unpopular content, thereby saving energy. The second study examines whether human face-to-face contacts can efficiently create paths over time between arbitrary users. Here, content is staged by spreading it through intermediate users until the destination is reached. Flooding every node minimises delivery times but is not scalable. We show that the human contact network is resilient to individual path failures, and for unicast paths, can efficiently approximate flooding in delivery time distribution simply by randomly sampling a handful of paths found by it. Multicast by contained flooding within a community is also efficient. However, connectivity relies on rare contacts and frequent contacts are often not useful for data delivery. Also, periods of similar duration could achieve different levels of connectivity; we devise a test to identify good periods. We finish by discussing how these properties influence routing algorithms.
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Gardner, Lee S., and Charles Jones. "A ROADMAP TO TELEMETRY NETWORKS." International Foundation for Telemetering, 1999. http://hdl.handle.net/10150/607310.
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International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, NevadaVisions of future airborne data acquisition systems include the “network in the sky” concept where a test or training participant logs on to the range network, just like today's computer users on the ground log onto the local area network (LAN). Through two-way telemetry links, the test or training participant seamlessly becomes a node in the range network. Thus, easily sharing data with event controllers and other airborne, ship-, ground-, and space-based network nodes. Such a network would allow the conduct of highly integrated test/training scenarios involving virtual and real participants without requiring physical proximity. This technology has a high payoff for the warfighter, making it a desirable objective of present and future DoD-funded development of data acquisition systems. This much-anticipated, future state-of-the-art in data acquisition will require extensive changes from today's test/training instrumentation architecture. Based on technology that is currently emerging for computer networks, this paper describes an evolutionary path for data acquisition systems to follow in order to achieve the anticipated bandwidth required for future bandwidth-intensive applications like the network in the sky. New networking paradigms, like Sun's Jini™ project, point the way to impressive usability with dramatically lower costs and network administration in the near term for ground-based networks, but they require support for just-in-time delivery of software “drivers” and other applications. These need gigabit network speeds to be viable. If this and other new networking technology is to be transferred to the test/training domain, bit rates will require two orders of magnitude improvement from today's 10-megabit range. This paper explores the technological and political telemetry issues that must be addressed before there can be a network in the sky (or anywhere else).
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Woon, Thean Hung Wilson, and 云天恒. "Effective distributed broadcasting protocols for wireless networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45880190.
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San, Martín Ramas Mauro Adolfo. "A model for social networks data management." Tesis, Universidad de Chile, 2012. http://www.repositorio.uchile.cl/handle/2250/111467.
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Doctor en Ciencias, Mención ComputaciónEn el contexto de la administración de datos para redes sociales, esta tesis aborda sus necesidades de manipulación de datos proponiendo un modelo de datos basado en un conjunto exhaustivo de casos de uso tomados del dominio de las redes sociales (SN, del inglés social networks ), y en el trabajo teórico existente sobre modelos de datos, bases de datos, y lenguajes de consulta. Un modelo para la administración de datos de redes sociales debe permitir compartir los datos de redes sociales, así como su reutilización e integración, con apoyo para esquemas flexibles y metadatos apropiados para datos con estructura de grafos. El lenguaje de consulta deseado debe proveer la expresividad adecuada bajo límites factibles de complejidad, siendo además accesible y atractivo para los usuarios. Un requisito encontrado frecuentemente en los casos de uso de SN es la necesidad de reestructurar una red, por ejemplo creando nuevos nodos a partir de grupos existentes, o a partir de valores de atributos. Los lenguajes de consulta tradicionales que son capaces de crear valores u objetos suelen tener la capacidad de expresar todas las consultas computables, por lo tanto la evaluación de las consultas se vuelve computacionalmente costosa. Para abordar estos requisitos se introduce un modelo de datos (SNDM), y un lenguaje de consulta (SNQL). La estructura de de datos utilizada es semiestructurada y está basada en un modelo de triples. SNQL se ha diseñado siguiendo las líneas de lenguajes de consulta ampliamente conocidos, usando como punto de partida una versión de Datalog con una extensión que facilita el cómputo de nuevos valores e identificadores de acuerdo a los requisitos de la manipulación de SN. Dicha extensión se basa en las "second-order tuple-generating dependencies", originalmente propuestas en el contexto de intercambio de datos para capturar la composición de asignaciones entre esquemas. El lenguaje así definido resuelve, con una complejidad computacional eficiente, los requisitos de los casos de uso típicos del análisis de redes sociales. En efecto, su poder expresivo abarca todas las operaciones de SN relevantes, y su evaluación permanece en NLOGSPACE. Se muestra que las características de este lenguaje satisfacen estas metas demostrando sus propiedades formales y con implementaciones prototípicas del modelo, así como con traducciones desde y hacia a otros modelos.
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Singh, Priyanka. "Using linked data in purposive social networks." Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/409708/.
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The Web has provided a platform for people to collaborate by using collective intelligence. Messaging boards, Q&A forums are some examples where people broadcast their issues and other people provide solutions. Such communities are defined as a Purposive Social Network (PSN) in this thesis. PSN is a community where people with similar interest and varied expertise come together, use collective intelligence to solve common problems in the community and build tools for common purpose. Usually, Q&A forums are closed or semi-open. The data are controlled by the websites. Diculties in the search and discovery of information is an issue. People searching for answers or experts in a website can only see results from its own network, while losing a whole communityof experts in other websites. Another issue in Q&A forums is not getting any response from the community. There is a long tail of questions that get no answer. The thesis introduces the Suman system that utilises Semantic Web (SW) and Linked Data technologies to solve above challenges. SW technologies are used to structure the community data so it can be decentralized and used across platforms. Linked Data helps to find related information about linked resources. The Suman system uses available tools to solve name entity disambiguation problem and add semantics to the PSN data. It uses a novel combination of semantic keyword search with traditional text search techniquesto nd similar questions with answers for unanswered questions to expand thequery term with added semantics and uses corwdsourced data to rank the results. Furthermore, the Suman system also recommends experts who can answer those questions. This helps to narrow down the long tail of unanswered questions in such communities. The Suman system is designed using the Design Science methodology and evaluated by users in two experiments. The results were statistically analysed to show that the keywords generated by the Suman system were rated higher than the original keywords from the websites. It also showed that the participants agreed with the algorithm rating for answers provided by the Suman system. StackOverflow and Reddit are used as an example of PSN and to build an application as a proof of concept of the Suman system.
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Hong, Dan. "Sharing private data in online social networks /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20HONG.
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Wang, Chao. "Exploiting non-redundant local patterns and probabilistic models for analyzing structured and semi-structured data." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1199284713.
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Hoya, Tetsuya. "Graph theoretic methods for data partitioning." Thesis, Imperial College London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286542.
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Danna, Nigatu Mitiku, and Esayas Getachew Mekonnen. "Data Processing Algorithms in Wireless Sensor Networks får Structural Health Monitoring." Thesis, KTH, Bro- och stålbyggnad, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-72241.
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The gradual deterioration and failure of old buildings, bridges and other civil engineering structures invoked the need for Structural Health Monitoring (SHM) systems to develop a means to monitor the health of structures. Dozens of sensing, processing and monitoring mechanisms have been implemented and widely deployed with wired sensors. Wireless sensor networks (WSNs), on the other hand, are networks of large numbers of low cost wireless sensor nodes that communicate through a wireless media. The complexity nature and high cost demand of the highly used wired traditional SHM systems have posed the need for replacement with WSNs. However, the major fact that wireless sensor nodes have memory and power supply limitations has been an issue and many efficient options have been proposed to solve this problem and preserve the long life of the network. This is the reason why data processing algorithms in WSNs focus mainly on the accomplishment of efficient utilization of these scarce resources. In this thesis, we design a low-power and memory efficient data processing algorithm using in-place radix-2 integer Fast Fourier Transform (FFT). This algorithm requires inputs with integer values; hence, increases the memory efficiency by more than 40% and highly saves processor power consumption over the traditional floating-point implementation. A standard-deviation-based peak picking algorithm is next applied to measure the natural frequency of the structure. The algorithms together with Contiki, a lightweight open source operating system for networked embedded systems, are loaded on Z1 Zolertia sensor node. Analogue Device’s ADXL345 digital accelerometer on board is used to collect vibration data. The bridge model used to test the target algorithm is a simply supported beam in the lab.
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Jardak, Christine [Verfasser]. "The storage and data processing in wireless sensor networks / Christine Jardak." Aachen : Hochschulbibliothek der Rheinisch-Westfälischen Technischen Hochschule Aachen, 2012. http://d-nb.info/1024800121/34.
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Cai, Hua. "Scalable visual contents delivery over heterogeneous networks /." View abstract or full-text, 2003. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202003%20CAI.
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Leung, Shuen-yi, and 梁舜頤. "Predicting metabolic pathways from metabolic networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42664317.
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蘇金照 and Kam-chiu Ivan So. "Social workers' and NGOs' attitudes towards using computers in social welfare services." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1993. http://hub.hku.hk/bib/B31977467.
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Kulatunga, Chamil. "Enforcing receiver-driven multicast congestion control using ECN-Nonce." Thesis, Available from the University of Aberdeen Library and Historic Collections Digital Resources, 2009. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?application=DIGITOOL-3&owner=resourcediscovery&custom_att_2=simple_viewer&pid=33532.
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Guven, Ahmet. "Speeding up a path-based policy language compiler." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Mar%5FGuven.pdf.
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Mokhtar, Ahmed. "Routing, switching, and multiaccess in all-optical networks /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/5942.
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Alsaleh, Slah. "Recommending people in social networks using data mining." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/61736/1/Slah_Alsaleh_Thesis.pdf.
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This thesis improves the process of recommending people to people in social networks using new clustering algorithms and ranking methods. The proposed system and methods are evaluated on the data collected from a real life social network. The empirical analysis of this research confirms that the proposed system and methods achieved improvements in the accuracy and efficiency of matching and recommending people, and overcome some of the problems that social matching systems usually suffer.
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Nikolaidis, Ioanis. "Cell loss equalization and time-parallel simulation for multimedia networks." Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/8230.
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Zhong, Cheng. "Generating Contour Maps for Dynamic Fields Monitored by Sensor Networks." Fogler Library, University of Maine, 2008. http://www.library.umaine.edu/theses/pdf/ZhongC2008.pdf.
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Hu, Wei Shu. "Community detection and credibility analysis on social networks." Thesis, University of Macau, 2015. http://umaclib3.umac.mo/record=b3335428.
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Hague, Darren S. "Neural networks for image data compression : improving image quality for auto-associative feed-forward image compression networks." Thesis, Brunel University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262478.
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Deri, Joya A. "Graph Signal Processing: Structure and Scalability to Massive Data Sets." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/725.
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Large-scale networks are becoming more prevalent, with applications in healthcare systems, financial networks, social networks, and traffic systems. The detection of normal and abnormal behaviors (signals) in these systems presents a challenging problem. State-of-the-art approaches such as principal component analysis and graph signal processing address this problem using signal projections onto a space determined by an eigendecomposition or singular value decomposition. When a graph is directed, however, applying methods based on the graph Laplacian or singular value decomposition causes information from unidirectional edges to be lost. Here we present a novel formulation and graph signal processing framework that addresses this issue and that is well suited for application to extremely large, directed, sparse networks. In this thesis, we develop and demonstrate a graph Fourier transform for which the spectral components are the Jordan subspaces of the adjacency matrix. In addition to admitting a generalized Parseval’s identity, this transform yields graph equivalence classes that can simplify the computation of the graph Fourier transform over certain networks. Exploration of these equivalence classes provides the intuition for an inexact graph Fourier transform method that dramatically reduces computation time over real-world networks with nontrivial Jordan subspaces. We apply our inexact method to four years of New York City taxi trajectories (61 GB after preprocessing) over the NYC road network (6,400 nodes, 14,000 directed edges). We discuss optimization strategies that reduce the computation time of taxi trajectories from raw data by orders of magnitude: from 3,000 days to less than one day. Our method yields a fine-grained analysis that pinpoints the same locations as the original method while reducing computation time and decreasing energy dispersal among spectral components. This capability to rapidly reduce raw traffic data to meaningful features has important ramifications for city planning and emergency vehicle routing.
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Riley, George F. "Techniques for large scale distributed simulations of computer networks." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/10010.
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