Undergraduate honors paper--Mount Holyoke College, 2007. Dept. of Anthropology. / Includes bibliographical references (leaves 121-124).

Thesis (M.A.)--Ohio State University, 2006. / Includes bibliographical references (leaves 110-116). Available online via OhioLINK's ETD Center

South Africa may expand their biotechnology industry through increased foreign direct investment. However, the main challenges facing South Africa are human capital development and social networking The objective of this report was to gain a better understanding of the value that human capital development and social networks have on the biotechnology sector in South Africa. Used correctly, this understanding could enhance the success rate of foreign direct investment and provide a platform to increase South Africa’s contribution as a serious global contender. The researcher’s objectives were to answer research questions on skills and social networks. Twenty eight respondents were interviewed via e-mail and face-to-face surveys, using a structured questionnaire for the skills survey. For the social networking survey, the same approach was adopted but only 8 responses were received. Although the research only uncovered specific answers related to the research questions, delving into the various sources on information improved the current understanding of the role of skills and social networks in the biotechnology sector. These additional findings relate to the importance of clusters, female participation in the industry, collaboration efforts over geographically dispersed areas as well as which skills are important now and which will become important in the future. Copyright / Dissertation (MBA)--University of Pretoria, 2010. / Gordon Institute of Business Science (GIBS) / unrestricted

This study examines the use of Twitter by decision-makers, the media, and the public during the pre-crisis stage of the 2013 Fargo-Moorhead flood. Three research questions guide this study in order to gain understanding of the content and assumed motives that drive users to utilize Twitter prior to a crisis. Data analysis revealed that decision-makers and the media active in tweeting were consistent with what would have been expected in a crisis situation. Additionally, the public were driven by the assumed motive of sharing and seeking information during the pre-crisis stage, consistent with previous research regarding the crisis stage.

As individuals use social media to create and maintain relationships and connections, they must also decide how to manage the private information that they disclose to their connections. If private information is handled improperly online, it may evoke varying responses that affect previously held privacy boundaries. Using communication privacy management theory (Petronio, 2002) as a framework, this study seeks to understand how the severity of a privacy violation impacts the Facebook users respond to online privacy turbulence. It also investigates how personality characteristics influence these responses. Results reveal that more severe privacy violations are met with more discussion of the privacy violation and thicker privacy boundaries both between the owner and the violator and between the owner and their social media network. Findings also imply that some of the Big Five personality traits impact the relationship between severity and the outcome variables.

Popular Online Social Networks (OSNs), such as Facebook or Twitter, are logically centralized systems. The massive information aggregation of sensitive personal data at the central providers of these services is an inherent threat to the privacy of the users. Leakages of these data collections happen regularly – both intentionally, for example by selling of user data to third parties and unintentionally, for example when outsiders successfully attack a provider. Motivated by this insight, the concept of Decentralized Online Social Networks (DOSNs) has emerged. In these proposed systems, no single, central provider keeps a data collection of all users. Instead, the data is spread out across multiple servers or is distributed completely among user devices that form a peer-to-peer (P2P) network. Encryption is used to enforce access rights of shared content and communication partners ideally connect directly to each other. DOSNs solve one of the biggest privacy concerns of centralized OSNs in a quite forthright way – by getting rid of the central provider. Furthermore, these decentralized systems can be designed to be more immune to censorship than centralized services. But when decentralizing OSNs, two main challenges have to be met: to provide user privacy under a significantly different threat model, and to implement equal usability and functionality without centralized components. In this work we analyze the general privacy-problems in DOSNs, especially those arising from the more exposed metadata in these systems. Furthermore, we suggest three privacy-preserving implementations of standard OSN features, i.e. user authentication via password-login, user search via a knowledge threshold and an event invitation system with fine-grained privacy-settings. These implementations do not rely on a trusted, central provider and are therefore applicable in a DOSN scenario but can be applied in other P2P or low-trust environments as well. Finally, we analyze a concrete attack on a specific decentralized system, the Tor anonymization network, and suggest improvements for mitigating the identified threats. / Populära sociala nätverkstjänster som Facebook och Instagram bygger på en logiskt centraliserad systemarkitektur. Tjänsteleverantörerna har därför tillgång till omfattande ansamlingar av känsliga personuppgifter,vilket innebär en oundviklig risk för integritetskränkningar. Med jämna mellanrum läcks dessa informationsansamlingar till tredje part – antingen när tjänsteleverantören själv säljer eller ger dem tillexterna aktörer, eller när obehöriga får åtkomst till tjänsteleverantörens datasystem. Decentraliserade sociala nätverkstjänster (eng. Decentralized Online Social Networks, DOSNs) är en lovande utveckling för att minska denna risk och för att skydda användarnas personliga information såväl från tjänsteleverantören som från tredje part. Ett vanligt sätt att implementera ett DOSN är genom en icke-hierarkisk nätverksarkitektur (eng. peer-to-peer network) för att undvika att känsliga personuppgifter samlas på ett ställe som är under tjänsteleverantörens kontroll. Kryptering används för att skydda kommunikationen och för att realisera åtkomstkontrollen av information som ska delas med andra användare. Att inte längre ha en tjänsteleverantör som har tillgång till all data innebär att den största riskfaktorn for integritetskränkningar tas bort. Men genom att ersätta den centrala tjänsteleverantören med ett decentraliserat system tar vi även bort ett visst integritetsskydd. Integritetsskyddet var en konsekvens av att förmedlingen av all användarkommunikation skedde genom tjänsteleverantörens servrar. När ansvaret för lagring av innehållet, hantering av behörigheterna, åtkomst och andra administrativa uppgifter övergår till användarna själva, blir det en utmaning att skydda metadata för objekt och informationsflöden, även om innehållet är krypterat. I ett centraliserat system är dessa metadata faktiskt skyddade av tjänsteleverantören – avsiktligt eller som en sidoeffekt. För att implementera de olika funktioner som ska finnas i ett integritetsskyddande DOSN, är det nödvändigt både att lösa dessa generella utmaningar och att hantera frånvaron av en betrodd tjänsteleverantör som har full tillgång till all data. Användarautentiseringen borde till exempel ha samma användbarhet som i centraliserade system. Det vill säga att det är lätt att ändra lösenordet, upphäva rättigheterna för en stulen klientenhet eller återställa ett glömt lösenord med hjälp av e-post eller säkerhetsfrågor – allt utan att förlita sig på en betrodd tredje part. Ett annat exempel är funktionen att kunna söka efter andra användare. Utmaningen där är att skydda användarinformationen samtidigt som det måste vara möjligt att hitta användare baserad på just denna informationen. En implementation av en sådan funktion i ett DOSN måste klara sig utan en betrodd tjänsteleverantör som med tillgång till alla användardata kan upprätthålla ett globalt sökindex. I den här avhandlingen analyserar vi de generella risker för integritetskränkningar som finns i DOSN, särskilt de som orsakas av metadata. Därutöver föreslår vi tre integritetsskyddande implementationer av vanliga funktioner i en social nätverkstjänst: lösenordsbaserad användarautentisering, en användarsökfunktion med en kunskapströskel och en inbjudningsfunktion för evenemang med detaljerade sekretessinställningar. Alla tre implementationerna är lämpliga för DOSN-scenarier eftersom de klarar sig helt utan en betrodd, central tjänsteleverantör, och kan därför även användas i andra sammanhang såsom icke-hierarkiska nätverk eller andra system som måste klara sig utan en betrodd tredje part. Slutligen analyserar vi en attack på ett specifikt decentraliserat system, anonymitetstjänsten Tor, och diskuterar hur systemet kan skyddas mot de analyserade sårbarheterna. / <p>QC 20161115</p>

Thesis (Honors)--College of William and Mary, 2009. / Includes bibliographical references (p. 60-62). Also available via the World Wide Web.

Zhu, Yi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 136-148). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.vi / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Motivations of Detecting Commercial Intention --- p.4 / Chapter 1.3 --- Problem Definition for Commercial Intention Detection --- p.6 / Chapter 1.4 --- Contributions --- p.8 / Chapter 1.5 --- Thesis Organization --- p.9 / Chapter 2 --- Literature Review --- p.12 / Chapter 2.1 --- Twitter and Tweets Analysis --- p.13 / Chapter 2.2 --- Intention Detection --- p.17 / Chapter 2.2.1 --- User Intention Mining --- p.17 / Chapter 2.2.2 --- Commercial Intention Mining --- p.18 / Chapter 2.3 --- Similar Task: Opinion Mining --- p.18 / Chapter 2.4 --- NLP Techniques for Commercial Intention Detection --- p.20 / Chapter 2.4.1 --- Words Semantic Similarity --- p.21 / Chapter 2.4.2 --- Short Text Similarity --- p.25 / Chapter 2.5 --- Hierarchical Classification --- p.26 / Chapter 2.5.1 --- Hierarchical Classifiers Overview --- p.26 / Chapter 2.5.2 --- Construction of Hierarchy --- p.27 / Chapter 2.5.3 --- Taxonomy of Hierarchical Classification --- p.28 / Chapter 3 --- System Overview --- p.31 / Chapter 3.1 --- Feasibility of Commercial Intention Detection --- p.31 / Chapter 3.2 --- System Design and Architecture --- p.33 / Chapter 3.3 --- Components of READ-MIND --- p.35 / Chapter 3.3.1 --- Preprocessing --- p.35 / Chapter 3.3.2 --- Centroid Word Locator --- p.37 / Chapter 3.3.3 --- Commercial Intention Detector --- p.38 / Chapter 3.3.4 --- Tweet Classifier --- p.40 / Chapter 3.3.5 --- Advertisement Mapping --- p.41 / Chapter 3.4 --- System Work Flow --- p.42 / Chapter 3.4.1 --- System Dataflow and Controlflow --- p.42 / Chapter 3.4.2 --- User Interface --- p.42 / Chapter 3.5 --- System Speed Up --- p.43 / Chapter 3.6 --- Summary --- p.45 / Chapter 4 --- Natural Language Processing on Tweets --- p.46 / Chapter 4.1 --- NLP Techniques in READ-MIND --- p.46 / Chapter 4.2 --- Centroid Word Locator --- p.47 / Chapter 4.2.1 --- Centroid Word --- p.47 / Chapter 4.2.2 --- Locating Centroid Word --- p.48 / Chapter 4.2.3 --- Centroid Word Pair --- p.50 / Chapter 4.2.4 --- Locating Centroid Word Pair --- p.54 / Chapter 4.3 --- Semantic Relatedness Between Tweets --- p.59 / Chapter 4.3.1 --- Relatedness with a Words Set --- p.60 / Chapter 4.3.2 --- Relatedness between Tweets --- p.62 / Chapter 4.3.3 --- Words Similarity --- p.63 / Chapter 4.4 --- Summary --- p.65 / Chapter 5 --- Tweets Classification --- p.66 / Chapter 5.1 --- Two Stages of Tweets Classification --- p.66 / Chapter 5.2 --- Commercial Intention Detector --- p.68 / Chapter 5.2.1 --- Intuitive Method --- p.68 / Chapter 5.2.2 --- Binary Classification --- p.70 / Chapter 5.3 --- Tweet Categorization --- p.72 / Chapter 5.3.1 --- Build Hierarchical Classifier --- p.73 / Chapter 5.3.2 --- Hierarchical Classification --- p.81 / Chapter 5.4 --- Summary --- p.83 / Chapter 6 --- Empirical Study --- p.84 / Chapter 6.1 --- Objective of Empirical Study --- p.84 / Chapter 6.2 --- Experiment Setup and Evaluation Methodology --- p.85 / Chapter 6.2.1 --- Simulation Environment --- p.85 / Chapter 6.2.2 --- Tweets Data Set --- p.86 / Chapter 6.2.3 --- Labeling Process --- p.87 / Chapter 6.2.4 --- Evaluation Methodology --- p.88 / Chapter 6.3 --- Compare Algorithms in Components --- p.90 / Chapter 6.3.1 --- Centroid Word VS. Centroid Word Pair --- p.91 / Chapter 6.3.2 --- Semantic Similarity Comparison --- p.92 / Chapter 6.3.3 --- Methods in Commercial Intention Detector --- p.93 / Chapter 6.3.4 --- Structure of Hierarchy --- p.94 / Chapter 6.3.5 --- Training Source of Tweets Classifier --- p.95 / Chapter 6.3.6 --- Summary --- p.96 / Chapter 6.4 --- Parameter Settings Comparison --- p.97 / Chapter 6.4.1 --- Impact of Varying Parameters --- p.97 / Chapter 6.4.2 --- Discussion on Parameter Setting --- p.98 / Chapter 6.5 --- Comparison of READ-MIND and Baseline Method --- p.100 / Chapter 6.6 --- Time Cost Analysis --- p.101 / Chapter 6.6.1 --- Time Cost to Process Tweets --- p.101 / Chapter 6.6.2 --- Comparison with Baseline --- p.102 / Chapter 6.6.3 --- Analysis on Real-Time Property --- p.103 / Chapter 6.7 --- TCI Categories Comparison --- p.106 / Chapter 6.7.1 --- Results for Different TCIs --- p.106 / Chapter 6.7.2 --- Comparison of Different TCIs --- p.107 / Chapter 6.8 --- Summary --- p.108 / Chapter 7 --- Conclusion --- p.109 / Chapter 7.1 --- Conclusion --- p.109 / Chapter 7.2 --- Future Work --- p.111 / Chapter A --- List of Abbreviations --- p.112 / Chapter B --- List of Symbols --- p.114 / Chapter C --- Proof --- p.117 / Chapter D --- System Work Flow --- p.120 / Chapter E --- Algorithms --- p.123 / Chapter F --- Detailed Experimental Results --- p.129 / Bibliography --- p.136

Thesis (M. Phil.)--University of Hong Kong, 2010. / Includes bibliographical references (p. 109-126). Also available in print.

Includes bibliographical references (p. 105-117).