Knowledge-based assets play a very important role in the Information Age. How to organize existing knowledge and present to the user properly are important research issue for decision support. Previous literature has indicated that multiple documents can be organized in different ways and different modes of knowledge presentation may result in different learning effects. Typical presentation modes include textual summarization and graphical presentation. The purpose of this thesis is to evaluate whether textual and graphical presentations of a news event may result in different effects for the user. In particular, this study is focused on comparing the textual summary and ontology-base graphical presentation and use the Bloom Theory of Educational Objectives to measure the learning effect of the user An experiment was conducted to assess the knowledge and cognitive process dimension in the Bloom¡¦s theory. We also measured the learning time, system quality, content quality, and overall satisfaction. The result shows that the textual system performed better in learning factual knowledge, and the ontology-base system performed better in learning conceptual and procedural knowledge.

Knowledge-based assets play a very important role in the Information Age, and its increasingly influence on organizational competition makes Knowledge Management a hot issue in business research.Content analysis of documents is a core function of knowledge management. In previous research, many techniques have been developed to generate textual summary and/or generating ontology-based episodic knowledge from multipl documents. However, not much research has been done to compare different ways of organizing and presenting knowledge. Since different knowledge presentations may result in different effects on the user, the purpose of this thesis is to develop a method for investigating different document summary and presentation systems. In this research, we have developed an effect measurement method based on the extended Bloom¡¦s Taxonomy of Educational Objectives.More specifically, we proposes evaluation criteria based on memory and cognition of the user. A field experiment was conducted to compare graphical and textual systems. Results indicate that the ontology-based system has significantly superior performance in concept memorizing and procedural memorizing. On the other hand, the textual summary-based system performed better in remembering facts.

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abstract: Social media platforms such as Twitter, Facebook, and blogs have emerged as valuable - in fact, the de facto - virtual town halls for people to discover, report, share and communicate with others about various types of events. These events range from widely-known events such as the U.S Presidential debate to smaller scale, local events such as a local Halloween block party. During these events, we often witness a large amount of commentary contributed by crowds on social media. This burst of social media responses surges with the "second-screen" behavior and greatly enriches the user experience when interacting with the event and people's awareness of an event. Monitoring and analyzing this rich and continuous flow of user-generated content can yield unprecedentedly valuable information about the event, since these responses usually offer far more rich and powerful views about the event that mainstream news simply could not achieve. Despite these benefits, social media also tends to be noisy, chaotic, and overwhelming, posing challenges to users in seeking and distilling high quality content from that noise. In this dissertation, I explore ways to leverage social media as a source of information and analyze events based on their social media responses collectively. I develop, implement and evaluate EventRadar, an event analysis toolbox which is able to identify, enrich, and characterize events using the massive amounts of social media responses. EventRadar contains three automated, scalable tools to handle three core event analysis tasks: Event Characterization, Event Recognition, and Event Enrichment. More specifically, I develop ET-LDA, a Bayesian model and SocSent, a matrix factorization framework for handling the Event Characterization task, i.e., modeling characterizing an event in terms of its topics and its audience's response behavior (via ET-LDA), and the sentiments regarding its topics (via SocSent). I also develop DeMa, an unsupervised event detection algorithm for handling the Event Recognition task, i.e., detecting trending events from a stream of noisy social media posts. Last, I develop CrowdX, a spatial crowdsourcing system for handling the Event Enrichment task, i.e., gathering additional first hand information (e.g., photos) from the field to enrich the given event's context. Enabled by EventRadar, it is more feasible to uncover patterns that have not been explored previously and re-validating existing social theories with new evidence. As a result, I am able to gain deep insights into how people respond to the event that they are engaged in. The results reveal several key insights into people's various responding behavior over the event's timeline such the topical context of people's tweets does not always correlate with the timeline of the event. In addition, I also explore the factors that affect a person's engagement with real-world events on Twitter and find that people engage in an event because they are interested in the topics pertaining to that event; and while engaging, their engagement is largely affected by their friends' behavior. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2014

The anadromous Atlantic salmon (Salmo salar) is dependent on river connectivity to reach spawning and rearing habitats in rivers. Most rivers are today fragmented by artificial barriers such as hydroelectric powerplants (HEP) which impede this migratory movement. As an effort to mitigate river fragmentation, upstream passages have been built at some impediments, but passage facilitating downstream migration has been commonly disregarded until recently. The increased mortality associated with downstream movement through hydroelectric turbines have caused population declines and extirpation in some areas. A common first measure to improve downstream passage is to increase spill discharge during critical time periods. In this study we aim to evaluate the efficiency of increased spill as a management tool and investigate the effect on environmental and physiological factors on fish passage in River Mörrumsån, Sweden. This was done by tracking salmon smolt using high resolution acoustic telemetry and time-to-event models. The results were then further explored in relation to 2D-hydraulic models of different spill scenarios. The results show that increasing the spill at Upper Hemsjö HEP resulted in a relatively high impediment passage efficiency. Spill gate passage was best explained by the proportion of spill in relation to intake channel discharge and diel period. Increased spill and nights without ambient lights where positively associated with spill gate passage rate. Increased levels of spill seamed to increase the attraction to the spill gate passage zone which could be explained by the increased area of flow directed toward the spill gate observed in the hydraulic model, showing the usefulness of hydraulic models as an additional tool in evaluation and planning of remedial measures. The result in this study shows that increased spill can be an effective way to ameliorate river fragmentation, however this should be put in relation to the goal of the mitigation measure as increased spill only creates a temporary solution for a permanent problem. / Den anadroma Atlantlaxen (Salmo salar) är beroende av fria vandringsvägar mellan hav och älv, då de nyttjar olika habitat under olika delar av livscykeln. Laxen vandra mellan dess tillväxt område i havet och deras parnings samt uppväxt område i älvar. Det flesta vattendrag är i dag fragmenterade av artificiella barriärer som tillexempel vattenkraftverk. För att minska de negativa effekterna av dessa har uppströmspassager konstruerats, men passage för nedströmsvandring har ofta åsidosatts. Den ökade dödligheten associerad med nedströmspassage genom vattenkraftturbiner har haft kraftig påverkan på många populationer. En vanlig första åtgärd för att förbättra nedströmspassagen är att öka mängden vatten som spills under kritiska perioder, som laxsmoltens nedströmsvandring under våren. Målet med denna studie är att utvärdera ökat spill som en förvaltningsåtgärd och analyserar hur olika miljöfaktorer och fysiologiska faktorer påverkar passageeffektiviteten för nedströmsmigrerande smolt. Detta undersöktes genom att använda högupplöst hydroakustisk telemetri för att spåra fisken och genom att analysera rörelsen med hjälp av time-to-event modeller. Tvådimensionella hydrauliska modeller skapades för att ytterligare utforska resultaten i förhållande till olika spill regimer. Den ökade spill regimen resulterade i relativt hög passage effektivitet förbi hindret. Passage genom spill porten kunde bäst förklaras av period på dygnet samt proportionen av spill i förhållande till flödet genom intagskanalen. Ökad passage effektivitet kunde associeras med passage under natten när belysningen på dammen var avstängd samt ökad andel spill i förhållande till flödet genom intagaskanalen. Ökat spill verkade resultera i ökad attraktion till området framför spill porten, vilket skulle kunna förklaras av den ökade ytan av vatten som rör sig mot spill porten som observerades i den hydrauliska modellen. Dessa resultat visar hur hydrauliskmodellering kan användas som ytterligare ett förvaltningsverktyg för att utvärdera eller planera förbättringsåtgärder. Resultaten Vidare indikerar studien att ökat spill är en metod som kan förbättrar laxsmoltens möjligheter till nedströms passage, detta bör dock sättas i relation till vad som är målet med åtgärden, ökat spill gynnar ett fåtal arter under en begränsad tid på året. Således kvarstår problemet med begränsad konnektivitet i vattendraget under resterande delar av året.

Time-to-event data is very common in medical research. Thus, clinicians and patients need analysis of this data to be accurate, as it is often used to interpret disease screening results, inform treatment decisions, and identify at-risk patient groups (ie, sex, race, gene expressions, etc.). This thesis tackles three statistical issues pertaining to time-to-event data. The first issue was incurred from an Institute for Clinical and Evaluative Sciences lung cancer registry data set, which was de-identified by censoring patients at an earlier date. This resulted in an underestimate of the observed times of censored patients. Five methods were proposed to account for the underestimation incurred by de-identification. A subsequent simulation study was conducted to compare the effectiveness of each method in reducing bias, and mean squared error as well as improving coverage probabilities of four different KM estimates. The simulation results demonstrated that situations with relatively large numbers of censored patients required methodology with larger perturbation. In these scenarios, the fourth proposed method (which perturbed censored times such that they were censored in the final year of study) yielded estimates with the smallest bias, mean squared error, and largest coverage probability. Alternatively, when there were smaller numbers of censored patients, any manipulation to the altered data set worsened the accuracy of the estimates. The second issue arises when investigating model validation via the concordance (c) statistic. Specifically, the c-statistic is intended for measuring the accuracy of statistical models which assess the risk associated with a binary outcome. The c-statistic estimates the proportion of patient pairs where the patient with a higher predicted risk had experienced the event. The definition of a c-statistic cannot be uniquely extended to time-to-event outcomes, thus many proposals have been made. The second project developed a parametric c-statistic which assumes to the true survival times are exponentially distributed to invoke the memoryless property. A simulation study was conducted which included a comparative analysis of two other time-to-event c-statistics. Three different definitions of concordance in the time-to-event setting were compared, as were three different c-statistics. The c-statistic developed by the authors yielded the smallest bias when censoring is present in data, even when the exponentially distributed parametric assumptions do not hold. The c-statistic developed by the authors appears to be the most robust to censored data. Thus, it is recommended to use this c-statistic to validate prediction models applied to censored data. The third project in this thesis developed and assessed the appropriateness of an empirical time-to-event c-statistic that is derived by estimating the survival times of censored patients via the EM algorithm. A simulation study was conducted for various sample sizes, censoring levels and correlation rates. A non-parametric bootstrap was employed and the mean and standard error of the bias of 4 different time-to-event c-statistics were compared, including the empirical EM c-statistic developed by the authors. The newly developed c-statistic yielded the smallest mean bias and standard error in all simulated scenarios. The c-statistic developed by the authors appears to be the most appropriate when estimating concordance of a time-to-event model. Thus, it is recommended to use this c-statistic to validate prediction models applied to censored data. / Thesis / Doctor of Philosophy (PhD)

Motion is an important cue in videos that captures the dynamics of moving objects. It helps in effective analysis of various event related tasks such as human action recognition, anomaly detection, tracking, crowd behavior analysis, traffic monitoring, etc. Generally, accurate motion information is computed using various optical flow estimation techniques. On the other hand, coarse motion information is readily available in the form of motion vectors in compressed videos. Utilizing these encoded motion vectors reduces the computational burden involved in flow estimation and enables rapid analysis of video streams. In this work, the focus is on analyzing motion patterns, retrieved from either motion vectors or optical flow, in order to do various event analysis tasks such as video classification, anomaly detection and crowd flow segmentation. In the first section, we utilize the motion vectors from H.264 compressed videos, a compression standard widely used due to its high compression ratio, to address the following problems. i) Video classification: This work proposes an approach to classify videos based on human action by capturing spatio-temporal motion pattern of the actions using Histogram of Oriented Motion Vector (HOMV) ii) Crowd flow segmentation: In this work, we have addressed the problem of flow segmentation of the dominant motion patterns of the crowds. The proposed approach combines multi-scale super-pixel segmentation of the motion vectors to obtain the final flow segmentation. iii) Anomaly detection: This problem is addressed by local modeling of usual behavior by capturing features such as magnitude and orientation of each moving object. In all the above approaches, the focus was to reduce computations while retaining comparable accuracy to pixel domain processing. In second section, we propose two approaches for anomaly detection using optical flow. The first approach uses spatio-temporal low level motion features and detects anomalies based on the reconstruction error of the sparse representation of the candidate feature over a dictionary of usual behavior features. The main contribution is in enhancing each local dictionary by applying an appropriate transformation on dictionaries of the neighboring regions. The other algorithm aims to improve the accuracy of anomaly localization through short local trajectories of super pixels belonging to moving objects. These trajectories capture both spatial as well as temporal information effectively. In contrast to compressed domain analysis, these pixel level approaches focus on improving the accuracy of detection with reasonable detection speed.

<p>The co-operative as an economic and social institution has long been recognized for its contribution to economic development as well as its positive effect on local communities. However, over the last decade or so substantial structural changes in the agricultural sector have undermined some of the most prominent North American co-operatives. In some cases, co-ops asked for bankruptcy protection, others ceased operations while some were transformed to for-profit firms. The present study offers three essays that explore the challenges that co-operatives are facing in terms of their relationship with their members in local markets, the decision-making process of their leaders and the co-ops' role in the modern economy.</p> <p>These first two essays are linked by the fact that they both develop models that are about cognitions. Examining cognition offers some new insights to understanding the process behind the decline of agricultural co-ops. In the first essay the model examines consumers' cognitions, while the model in the second essay examines management's cognitions. The essays differ on the agent's ability to change the perceptions that result from those cognitions. Essay One assumes that consumers' perceptions are partially flexible and thus can change over time with some cost; on the other hand, essay Two assumes that beliefs are inflexible due to the high cost of changing them.</p> <p>Essay One examines the relationship between a co-operative and its membership in a local market using an economic psychological approach. More specifically, the essay presents a modified rational-choice model to investigate how cognitive dissonance can influence members' loyalty. The effect of cognitive dissonance is analyzed in a case where a local co-operative operates alongside with an investor-owned firm (IOF) in a market. The model illustrates how cognitive dissonance can give rise to switching costs for those consumers who wish to switch to the IOF. Analytical results demonstrate the effect of these switching costs on equilibrium market shares and discuss how a drop in the dissonance cost because of managerial decisions by the co-op can result in dramatic drops in its market share.</p> <p>Essay Two illustrates how management's mental frame can be incorporated into an economic model and develops a theoretical underpinning for the link between a strong mental frame and the financial difficulties that a firm might experience. The case of the Saskatchewan Wheat Pool with its Project Horizon plan is proposed as an example of a situation where the established mental frame gave rise to a belief regarding future member support that had a significant influence on the decision-making process of the co-op's CEO. The analysis includes a game theoretic model of a duopoly between a co-operative and an IOF, where mental framing is explicitly incorporated into the primitives of the model. Analytical results illustrate how the CEO's belief regarding member commitment can influence decision-making and therefore affect the market share and profits of the firm.</p> <p>Essay Three uses non-parametric econometric techniques to examine the stock price effect of a co-op's acquisition by a publicly-traded IOF. The potential for this study emerged as a result of the takeover of Dairyworld, a dairy co-op, by Saputo, a publicly-traded private corporation. The study uses the prediction-error approach to estimate Saputo's returns after the acquisition as a deviation from its expected returns. A non-parametric bootstrap technique simulates Saputo's stock returns and examines its behavior around the acquisition date. The empirical results are consistent with a number of hypotheses, including the pro-competitive role that co-operatives are believed to have in the economy. The essay also includes a comprehensive discussion regarding the greater financial value that co-ops have for IOFs.</p>

<p>The co-operative as an economic and social institution has long been recognized for its contribution to economic development as well as its positive effect on local communities. However, over the last decade or so substantial structural changes in the agricultural sector have undermined some of the most prominent North American co-operatives. In some cases, co-ops asked for bankruptcy protection, others ceased operations while some were transformed to for-profit firms. The present study offers three essays that explore the challenges that co-operatives are facing in terms of their relationship with their members in local markets, the decision-making process of their leaders and the co-ops' role in the modern economy.</p> <p>These first two essays are linked by the fact that they both develop models that are about cognitions. Examining cognition offers some new insights to understanding the process behind the decline of agricultural co-ops. In the first essay the model examines consumers' cognitions, while the model in the second essay examines management's cognitions. The essays differ on the agent's ability to change the perceptions that result from those cognitions. Essay One assumes that consumers' perceptions are partially flexible and thus can change over time with some cost; on the other hand, essay Two assumes that beliefs are inflexible due to the high cost of changing them.</p> <p>Essay One examines the relationship between a co-operative and its membership in a local market using an economic psychological approach. More specifically, the essay presents a modified rational-choice model to investigate how cognitive dissonance can influence members' loyalty. The effect of cognitive dissonance is analyzed in a case where a local co-operative operates alongside with an investor-owned firm (IOF) in a market. The model illustrates how cognitive dissonance can give rise to switching costs for those consumers who wish to switch to the IOF. Analytical results demonstrate the effect of these switching costs on equilibrium market shares and discuss how a drop in the dissonance cost because of managerial decisions by the co-op can result in dramatic drops in its market share.</p> <p>Essay Two illustrates how management's mental frame can be incorporated into an economic model and develops a theoretical underpinning for the link between a strong mental frame and the financial difficulties that a firm might experience. The case of the Saskatchewan Wheat Pool with its Project Horizon plan is proposed as an example of a situation where the established mental frame gave rise to a belief regarding future member support that had a significant influence on the decision-making process of the co-op's CEO. The analysis includes a game theoretic model of a duopoly between a co-operative and an IOF, where mental framing is explicitly incorporated into the primitives of the model. Analytical results illustrate how the CEO's belief regarding member commitment can influence decision-making and therefore affect the market share and profits of the firm.</p> <p>Essay Three uses non-parametric econometric techniques to examine the stock price effect of a co-op's acquisition by a publicly-traded IOF. The potential for this study emerged as a result of the takeover of Dairyworld, a dairy co-op, by Saputo, a publicly-traded private corporation. The study uses the prediction-error approach to estimate Saputo's returns after the acquisition as a deviation from its expected returns. A non-parametric bootstrap technique simulates Saputo's stock returns and examines its behavior around the acquisition date. The empirical results are consistent with a number of hypotheses, including the pro-competitive role that co-operatives are believed to have in the economy. The essay also includes a comprehensive discussion regarding the greater financial value that co-ops have for IOFs.</p>

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