Media Framing of Financial Mechanisms for Resolving Human–Predator Conflict in Namibia

The decline in carnivore populations is largely exacerbated by lethal methods used to reduce livestock depredation. Financial mechanisms are designed to limit lethal control by reducing the cost of depredation. The media can affect how the general public feel about issues like financial mechanisms but no study has been undertaken to understand the framing of this topic. This article filled this gap by using content analysis of newspapers to analyze economic incentives designed to mitigate human–carnivore conflict in Namibia. Forty-six percent of the articles were framed positively toward incentives, 24% ambivalently, 19% negatively, and 11% neutrally. Compensation was commonly framed positively whereas community-based conservation, trophy hunting, and tourism were framed ambivalently. Incentives were framed more negatively where perceived costs outweighed benefits. These results can help conservationists plan more effective communication interventions and anticipate issues that can affect the success of mitigation strategies

What Google Teaches Us About The Child Rights Movement

Technology both helps and hinders what we know about human rights. Use of Google is of central importance to both the Sociology of Knowledge and the creation of internet literacy. In this study, different search engines are compared regarding content of “child rights” in the fifty United States. Findings include: importance of algorithmic loading of sites; number of hits may not reflect the importance or accuracy of a topic; different search engines produce different findings; and personalized searches result in different results. Personalization of searches in accordance to one’s previous search history may result in people being given information that reinforces their views, not challenge them. This means that people opposed to child rights may not be afforded the same information as people who have a search history supporting them. Because searches do not necessarily yield the same information about human rights, scholars and the public must be attentive to adequately assess the accurate or skewed nature of a keyword search

On automatic testing of web search engines

Web search engines are very important because they are the means by which people retrieve information from the World Wide Web. However, testing these web search engines is difficult because there are no test oracles, so this research proposes seven new metrics based on the idea of metamorphic relations to alleviate the oracle problem in search engine testing. Using these metrics, our method can test search engines automatically in the absence of an ideal oracle. Using this method, we further conduct large-scale empirical studies to investigate and compare the qualities of four major search engines, namely, Google (www.google.com), Baidu (www.baidu.com), Bing (www.bing.com), and Chinese Bing (www.bing.com.cn). Our empirical studies involve more than 50 million queries sent to the search engines across 9 months, and about 300 GB data collected from the search engine responses. It is found that different search engines have significantly different performance and that the nature of the query terms can have a significant impact on the performance of the search engines. These empirical study results demonstrate that our method can effectively alleviate the oracle problem in search engine testing, and can help both developers and users to obtain a better understanding of the search engine behaviour under different operational profiles

Modern Data Mining for Software Engineer, A Machine Learning PaaS Review

Using data mining methods to produce information from the data has been proven to be valuable for individuals and society. Evolution of technology has made it possible to use complicated data mining methods in different applications and systems to achieve these valuable results. However, there are challenges in data-driven projects which can affect people either directly or indirectly. The vast amount of data is collected and processed frequently to enable the functionality of many modern applications. Cloud-based platforms have been developed to aid in the development and maintenance of data-driven projects. The field of Information Technology (IT) and data-driven projects have become complex, and they require additional attention compared to standard software development. On this thesis, a literature review is conducted to study the existing industry methods and practices, to define the used terms, and describe the relevant data mining process models. We analyze the industry to find out the factors impacting the evolution of tools and platforms, and the roles of project members. Furthermore, a hands-on review is done on typical machine learning Platforms-as-a-Service (PaaS) with an example case, and heuristics are created to aid in choosing a machine learning platform. The results of this thesis provide knowledge and understanding for the software developers and project managers who are part of these data-driven projects without the in-depth knowledge of data science. In this study, we found out that it is necessary to have a valid process model or methodology, precise roles, and versatile tools or platforms when developing data-driven applications. Each of these elements affects other elements in some way. We noticed that traditional data mining process models are insufficient in the modern agile software development. Nevertheless, they can provide valuable insights and understanding about how to handle the data in the correct way. The cloud-based platforms aid in these data-driven projects to enable the development of complicated machine learning projects without the expertise of either a data scientist or a software developer. The platforms are versatile and easy to use. However, developing functionalities and predictive models which the developer does not understand can be seen as bad practice, and cause harm in the future

Study of result presentation and interaction for aggregated search

The World Wide Web has always attracted researchers and commercial search engine companies due to the enormous amount of information available on it. "Searching" on web has become an integral part of today's world, and many people rely on it when looking for information. The amount and the diversity of information available on the Web has also increased dramatically. Due to which, the researchers and the search engine companies are making constant efforts in order to make this information accessible to the people effectively. Not only there is an increase in the amount and diversity of information available online, users are now often seeking information on broader topics. Users seeking information on broad topics, gather information from various information sources (e.g, image, video, news, blog, etc). For such information requests, not only web results but results from different document genre and multimedia contents are also becoming relevant. For instance, users' looking for information on "Glasgow" might be interested in web results about Glasgow, Map of Glasgow, Images of Glasgow, News of Glasgow, and so on. Aggregated search aims to provide access to this diverse information in a unified manner by aggregating results from different information sources on a single result page. Hence making information gathering process easier for broad topics. This thesis aims to explore the aggregated search from the users' perspective. The thesis first and foremost focuses on understanding and describing the phenomena related to the users' search process in the context of the aggregated search. The goal is to participate in building theories and in understanding constraints, as well as providing insights into the interface design space. In building this understanding, the thesis focuses on the click-behavior, information need, source relevance, dynamics of search intents. The understanding comes partly from conducting users studies and, from analyzing search engine log data. While the thematic (or topical) relevance of documents is important, this thesis argues that the "source type" (source-orientation) may also be an important dimension in the relevance space for investigating in aggregated search. Therefore, relevance is multi-dimensional (topical and source-orientated) within the context of aggregated search. Results from the study suggest that the effect of the source-orientation was a significant factor in an aggregated search scenario. Hence adds another dimension to the relevance space within the aggregated search scenario. The thesis further presents an effective method which combines rule base and machine learning techniques to identify source-orientation behind a user query. Furthermore, after analyzing log-data from a search engine company and conducting user study experiments, several design issues that may arise with respect to the aggregated search interface are identified. In order to address these issues, suitable design guidelines that can be beneficial from the interface perspective are also suggested. To conclude, aim of this thesis is to explore the emerging aggregated search from users' perspective, since it is a very important for front-end technologies. An additional goal is to provide empirical evidence for influence of aggregated search on users searching behavior, and identify some of the key challenges of aggregated search. During this work several aspects of aggregated search will be uncovered. Furthermore, this thesis will provide a foundations for future research in aggregated search and will highlight the potential research directions

Mustang Daily, March 2, 2011

Student newspaper of California Polytechnic State University, San Luis Obispo, CA.https://digitalcommons.calpoly.edu/studentnewspaper/8159/thumbnail.jp

A ranking framework and evaluation for diversity-based retrieval

There has been growing momentum in building information retrieval (IR) systems that consider both relevance and diversity of retrieved information, which together improve the usefulness of search results as perceived by users. Some users may genuinely require a set of multiple results to satisfy their information need as there is no single result that completely fulfils the need. Others may be uncertain about their information need and they may submit ambiguous or broad (faceted) queries, either intentionally or unintentionally. A sensible approach to tackle these problems is to diversify search results to address all possible senses underlying those queries or all possible answers satisfying the information need. In this thesis, we explore three aspects of diversity-based document retrieval: 1) recommender systems, 2) retrieval algorithms, and 3) evaluation measures. This first goal of this thesis is to provide an understanding of the need for diversity in search results from the users’ perspective. We develop an interactive recommender system for the purpose of a user study. Designed to facilitate users engaged in exploratory search, the system is featured with content-based browsing, aspectual interfaces, and diverse recommendations. While the diverse recommendations allow users to discover more and different aspects of a search topic, the aspectual interfaces allow users to manage and structure their own search process and results regarding aspects found during browsing. The recommendation feature mines implicit relevance feedback information extracted from a user’s browsing trails and diversifies recommended results with respect to document contents. The result of our user-centred experiment shows that result diversity is needed in realistic retrieval scenarios. Next, we propose a new ranking framework for promoting diversity in a ranked list. We combine two distinct result diversification patterns; this leads to a general framework that enables the development of a variety of ranking algorithms for diversifying documents. To validate our proposal and to gain more insights into approaches for diversifying documents, we empirically compare our integration framework against a common ranking approach (i.e. the probability ranking principle) as well as several diversity-based ranking strategies. These include maximal marginal relevance, modern portfolio theory, and sub-topic-aware diversification based on sub-topic modelling techniques, e.g. clustering, latent Dirichlet allocation, and probabilistic latent semantic analysis. Our findings show that the two diversification patterns can be employed together to improve the effectiveness of ranking diversification. Furthermore, we find that the effectiveness of our framework mainly depends on the effectiveness of the underlying sub-topic modelling techniques. Finally, we examine evaluation measures for diversity retrieval. We analytically identify an issue affecting the de-facto standard measure, novelty-biased discounted cumulative gain (α-nDCG). This issue prevents the measure from behaving as desired, i.e. assessing the effectiveness of systems that provide complete coverage of sub-topics by avoiding excessive redundancy. We show that this issue is of importance as it highly affects the evaluation of retrieval systems, specifically by overrating top-ranked systems that repeatedly retrieve redundant information. To overcome this issue, we derive a theoretically sound solution by defining a safe threshold on a query-basis. We examine the impact of arbitrary settings of the α-nDCG parameter. We evaluate the intuitiveness and reliability of α-nDCG when using our proposed setting on both real and synthetic rankings. We demonstrate that the diversity of document rankings can be intuitively measured by employing the safe threshold. Moreover, our proposal does not harm, but instead increases the reliability of the measure in terms of discriminative power, stability, and sensitivity