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Inaug.-Diss.--Basel. / Curriculum vitae.

Inaug.-Diss.--Basel. / Curriculum vitae.

Thesis (M.A.)--Brigham Young University. Dept. of English, 2007. / Includes bibliographical references (p. 72-75).

Dairy farmers in the Northeastern Unites States are paid based on the amount of fat and protein in their cows' milk, and improving fat and protein production is linked with improved financial sustainability for dairy farms. However, not all farmers are motivated to make changes to increase milk fat and protein production. Previous research has identified a positive correlation between a group of fatty acids, known as the de novo fatty acids, and the fat and protein content of bulk tank milk from commercial dairy farms. Therefore, the first objective of this research was to explore the relationship of farm management, the cow's diet, and lactation performance with de novo fatty acid content on Northeastern US dairy farms. Results from the first objective were communicated with dairy farmers; therefore, the second objective was to understand how to communicate with farmers to influence their behavior. We hypothesized that farms with high de novo fatty acids in bulk tank milk would manage and feed their cows to optimize rumen fermentation conditions. The first (Chapter 2) and second (Chapter 3) studies were methodologically very similar. Farms were categorized as either high de novo (HDN) or low de novo (LDN) based on the concentration of de novo fatty acids in their bulk tank milk for the 6 months prior to the farm visit. Farms were then visited once in March or April, 2014 (Chapter 2) or between February and April, 2015 (Chapter 3) to assess management practices and collect samples of the cows' diet. There were no differences in days in milk in Chapter 2 or Chapter 3. Yield of milk, fat, and true protein per cow were higher for HDN versus LDN farms in Chapter 2. In both chapters, HDN farms had higher milk fat and true protein content and higher de novo fatty acid yield per day. The HDN farms had lower freestall stocking density in Chapter 2 and provided more feedbunk space per cow in Chapter 3. Additionally, tiestall feeding frequency was higher for HDN than LDN farms. No differences were detected for dietary chemical composition, except ether extract was lower for HDN than LDN farms in both chapters. Chapter 4 explored how to communicate the results of Chapter 2 and Chapter 3 through eleven qualitative, semi-structured interviews and insight from the 83 farm visits. Farmers identified the cooperative, expert consultants (nutritionist, veterinarian, and agronomists), financial advisers, print publications, and other farmers as principal sources of information. However, barriers to the transfer of information included family dynamics, lack of access to high speed internet, and difficulties evaluating divergent recommendations from experts. Several farmers expressed an incorrect perception of their farms' fat and protein production compared with cooperative averages which reduced their motivation to incorporate management changes. Recommendations to overcome these barriers include integrating management team meetings and facilitating informal discussion groups between farmers. This research is correlational in nature, and future research is needed to verify a causal relationship between de novo fatty acids and milk fat and protein content. However, the results of this research can be used to help farmers increase their cows' milk fat and protein content, improve the transfer of knowledge to dairy farmers, and ultimately support the financial sustainability of dairy farms in the Northeastern US.

Submitted by Valdinei Souza (neisouza@hotmail.com) on 2015-12-22T16:37:25Z No. of bitstreams: 1 dissertaçao maira souza 2015.pdf: 4878608 bytes, checksum: 33c298d58fa9e0e85694877960a722a3 (MD5) / Approved for entry into archive by Urania Araujo (urania@ufba.br) on 2016-01-12T20:05:28Z (GMT) No. of bitstreams: 1 dissertaçao maira souza 2015.pdf: 4878608 bytes, checksum: 33c298d58fa9e0e85694877960a722a3 (MD5) / Made available in DSpace on 2016-01-12T20:05:28Z (GMT). No. of bitstreams: 1 dissertaçao maira souza 2015.pdf: 4878608 bytes, checksum: 33c298d58fa9e0e85694877960a722a3 (MD5) / Pretendeu-se identificar a mediação da informação nas redes de arquivos históricos na internet, tendo em vista que as redes são criadas a partir de um ambiente humanizado e que sugere relações entre indivíduos e documento arquivístico. Ressalta-se a influência das tecnologias da informação e comunicação na redefinição dos arquivos permanentes tradicionais e virtuais, especialmente em possibilitar a recuperação, garantindo a integridade e segurança da informação para a construção do conhecimento e da memória social. Justifica-se o tema através do diálogo e do compartilhamento de informações e experiências que intensificam as relações pessoais. Sobretudo nos canais informacionais mediados por computadores, em que a comunicação e a produção de sentido contribuem para a construção das redes sociais e informacionais que promovem o acesso à informação. Em virtude do fenômeno informacional, os procedimentos metodológicos revelam uma pesquisa descritiva e documental, com abordagem qualitativa e método de investigação de múltiplos casos. Para o levantamento de dados, a pesquisa elegeu a técnica de observação direta para a análise das redes e de documentos arquivísticos. As redes de arquivos históricos exercem mediação e permitem o acesso remoto as descrições arquivísticas, instrumentos de pesquisa e documentos digitalizados. Os documentos respondem ao princípio da incerteza na busca pela informação, essenciais para a reinterpretação da memória. Para uma efetiva mediação, as ações devem reforçar o reconhecimento do arquivo permanente como espaço social, dotado de significação coletiva e individual. / ABSTRACT - It was intended to identify the mediation of information in networks of historical archives, considering that networks are created from a humane enviroment and suggesting relationships between individuals and document archival. Emphasizes the influence of information and communication technologies in the redefinition of traditional and virtual archives, especially by enabling information retrieval, ensuring the integrity and information security for the construction of knowledge and the social memory. Justified the issue through dialogue and the compartment of information and experiences intensify personal relationships, especially in informational media mediated by computers, wherein comunication and the production of meaning contribute to the building of social and informational networks, to promote access to information. In view of the informational phenomenon, methodological procedures revel a descriptive and documentary research, with qualitative approach emethod multiple case research. For data collection, research has chosen direct observation technique for the analysis of networks and documents. The networks of historical archives exert mediation and allow remote access to archival descriptions, research instruments and digital documents. The documents respond to the uncertainty principle in the search for information, essential for the reinterpretation of memory. For effective mediation, actions should reinforce the recognition of the permanent archives as social space, endowed with individual and collective significance.

Exploring privacy risks in information networks is analysing the dangers and hazards that are related to personal information about users of a network. It is about investigating the dynamics and complexities of a setting where humans are served by technology in order to exploit the network for their own good. In the information network, malicious activities are motivated by commercial factors in that the attacks to privacy are happening, not in the name of national security, but in the name of the free market together with technological advancements. Based on the assumption of Machiavellian Intelligence, we have modelled our analyses by way of concepts such as Arms Race, Tragedy of the Commons, and the Red Queen effect. In a number of experiments on spam, adware, and spyware, we have found that they match the characteristics of privacy-invasive software, i.e., software that ignores users’ right to decide what, how and when information about themselves is disseminated by others. Spam messages and adware programs suggest a hazard in that they exploit the lives of millions and millions of users with unsolicited commercial and/or political content. Although, in reality spam and adware are rather benign forms of a privacy risks, since they, e.g., do not collect and/or transmit user data to third parties. Spyware programs are more serious forms of privacy risks. These programs are usually bundled with, e.g., file-sharing tools that allow a spyware to secretly infiltrate computers in order to collect and distribute, e.g., personal information and data about the computer to profit-driven third parties on the Internet. In return, adware and spam displaying customised advertisements and offers may be distributed to vast amounts of users. Spyware programs also have the capability of retrieving malicious code, which can make the spyware act like a virus when the file-sharing tools are distributed in-between the users of a network. In conclusion, spam, spyware and virulent programs invade user privacy. However, our experiments also indicate that privacy-invasive software inflicts the security, stability and capacity of computerised systems and networks. Furthermore, we propose a description of the risk environment in information networks, where network contaminants (such as spam, spyware and virulent programs) are put in a context (information ecosystem) and dynamically modelled by their characteristics both individually and as a group. We show that network contamination may be a serious threat to the future prosperity of an information ecosystem. It is therefore strongly recommended to network owners and designers to respect the privacy rights of individuals. Privacy risks have the potential to overthrow the positive aspects of belonging to an information network. In a sound information network the flow of personal information is balanced with the advantages of belonging to the network. With an understanding of the privacy risk environment, there is a good starting-point for recognising and preventing intrusions into matters of a personal nature. In reflect, mitigating privacy risks contributes to a secure and efficient use of information networks.

Although different authors mean different thing by the term "digital libraries," one common thread is that they include or are built around collections of digital objects. Digital libraries also provide services to large communities, one of which is almost always search. Digital library collections, however, have several characteristic features that make search difficult. They are typically very large. They typically involve many different kinds of objects, including but not limited to books, e-published documents, images, and hypertexts, and often including items as esoteric as subtitled videos, simulations, and entire scientific databases. Even within a category, these objects may have widely different formats and internal structure. Furthermore, they are typically in complex relationships with each other and with such non-library objects as persons, institutions, and events. Relationships are a common feature of traditional libraries in the form of "See / See also" pointers, hierarchical relationships among categories, and relations between bibliographic and non-bibliographic objects such as having an author or being on a subject. Binary relations (typically in the form of directed links) are a common representational tool in computer science for structures from trees and graphs to semantic networks. And in recent years the World-Wide Web has made the construct of linked information objects commonplace for millions. Despite this, relationships have rarely been given "first-class" treatment in digital library collections or software. MARIAN is a digital library system designed and built to store, search over, and retrieve large numbers of diverse objects in a network of relationships. It is designed to run efficiently over large collections of digital library objects. It addresses the problem of object diversity through a system of classes unified by common abilities including searching and presentation. Divergent internal structure is exposed and interpreted using a simple and powerful graphical representation, and varied format through a unified system of presentation. Most importantly, MARIAN collections are designed to specifically include relations in the form of an extensible collection of different sorts of links. This thesis presents MARIAN and argues that it is both effective and efficient. MARIAN is effective in that it provides new and useful functionality to digital library end-users, and in that it makes constructing, modifying, and combining collections easy for library builders and maintainers. MARIAN is efficient since it works from an abstract presentation of search over networked collections to define on the one hand common operations required to implement a broad class of search engines, and on the other performance standards for those operations. Although some operations involve a high minimum cost under the most general assumptions, lower costs can be achieved when additional constraints are present. In particular, it is argued that the statistics of digital library collections can be exploited to obtain significant savings. MARIAN is designed to do exactly that, and in evidence from early versions appears to succeed. In conclusion, MARIAN presents a powerful and flexible platform for retrieval on large, diverse collections of networked information, significantly extending the representation and search capabilities of digital libraries. / Ph. D.

The amount of published news articles is steadily increasing, and readers are shifting toward online platforms because of the convenience and affordable technology costs (Shearer, 2021). Users have become more engaged with online news articles. This engagement creates a rich corpus, which makes it a powerful means to understand public opinion, emerging events, and their evolvement. Therefore, many organizations invest in mining this large-scale user-generated content to improve their products, services, and, more importantly, their decision-making process. Studying users’ reactions to online news is essential for social scientists, policymakers, and journalists. This type of engagement is an area of study introduced previously. In the statistical and machine learning community, many survey-based studies tried to understand the users’ behavior by characterizing and categorizing comments in online news. Some studies focus on mining user opinions from social media and online news comments. Other works look into bias in the news and its influence on user-generated content. At the same time, the social network community addresses the problem of mining large-scale online news from different angles. Some work focuses on constructing knowledge graphs from the text. Others focus on building high-level graphs, where nodes are users and posts or documents, and links represent the relationship between nodes. Another line of work looked into the word level of the text. They extracted entities and topics by combining Natural Language Processing and graph techniques. From a Machine Learning perspective, there are three main challenges in all these studies 1) jointly mining massive user-generated data, 2) from multiple sources and platforms, and 3) the unpredictable quality of user-generated content. To address these issues, we tackle the problem of jointly learning and mining valuable information from online news articles and user-generated content. We start by studying and understating the relationship between users’ comments and articles in online news. Where the focus is to understand the level of relevancy between articles and their comments, we labeled a few article-comment pairs in this work. We proposed BERTAC (Alshehri et al.,2021), a BERT-based model that jointly learns article-comment embeddings and infers the relevance class of comment. However, we found that the disagreement among annotators as a part of a human (expert) labeling process produces noisy labels, which affect the performance of supervised learning algorithms. On the other hand, working only with high agreement annotations introduces another challenge: the data imbalance problem (Alshehri et al., 2022). As in many machine learning problems, labeling a sufficient number of examples is costly and time-consuming. Therefore, we propose a framework for aligning comments and news articles under a constrained budget(Alshehri et al., 2023a). The proposed model considers the data imbalanced, where we have only a few examples from one class, in addition, it considers the degrees of annotator disagreement. Within the framework, we consider two solutions, 1) semi-automatic labeling based on human-AI collaboration and 2) synthetic data augmentation. Another critical aspect of mining news articles and user-generated content is understanding emerging events and their associated entities. However, this is challenging, especially with the massive growth of online articles and user-generated content across different platforms. Therefore, we proposed MultiLayerET (Alshehri et al., 2023b), a unified representation of online news articles and comments. This work highlights the relationship between entities and topics in news articles and user-generated content. It projects entities and topics as a multi-layer graph, which gives a high-level understanding of the story behind the large pile of the corpus. We showed that such graphs enrich the textual representation and enhance the model learning performance in many downstream applications, such as media bias classification and fake news detection. / Computer and Information Science

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