In recent years, students and educators alike have utilized new technologies such as tablet computers as a means of enhancing the learning process. While prior research suggests that these implementations within the classroom provide a new and beneficial method of relaying and learning information, scientists have begun to explore the possible side effects that these technologies have on the learning process. Although much of the current literature suggests that learning from an electronic screen does not affect efficacy compared to learning from printed text (Bayliss et al., 2012; Dundar & Akcayir, 2012), researchers continue to explore the possible consequences that using said technologies may have in academia. The current study aims to address how tablet computers affect the process of learning differently across levels of education. It is proposed that older generations, such as college students, who did not grow up with tablets in the classroom may suffer from the effects of proactive interference when compared to younger students who have been exposed to technologies much more profoundly in their education (e.g. elementary students). If this is so, the current study also proposes a possible intervention that would help students at any educational level overcome this interference in order to integrate tablets into their studies effectively.

Digital Forensics (DF) is defined as the ensemble of methods, tools and techniques used to collect, preserve and analyse digital data originating from any type of digital media involved in an incident with the purpose of extracting valid evidence for a court of law. DF investigations are usually performed as a response to a digital crime and, as such, they are termed Reactive Digital Forensic (RDF). An RDF investigation takes the traditional (or post-mortem) approach of investigating digital crimes after incidents have occurred. This involves identifying, preserving, collecting, analyzing, and generating the final report. Although RDF investigations are effective, they are faced with many challenges, especially when dealing with anti-forensic incidents, volatile data and event reconstruction. To tackle these challenges, Proactive Digital Forensic (PDF) is required. By being proactive, DF is prepared for incidents. In fact, the PDF investigation has the ability to proactively collect data, preserve it, detect suspicious events, analyze evidence and report an incident as it occurs. This dissertation focuses on the detection and analysis phase of the proactive investigation system, as it is the most expensive phase of the system. In addition, theories behind such systems will be discussed. Finally, implementation of the whole proactive system will be tested on a botnet use case (Zeus). / Graduate / 0984 / 0537 / soltanalharbi@hotmail.com

Component obsolescence in the "high-tech" electronics industry has become a problem that cannot be ignored. Although recent attention has been given to component obsolescence, in general this issue is still dealt with reactively. This often results in sustainment of a long-life system such as ships, airplanes, power plant, and space based programs to be extremely costly. In addition, delayed schedules, extended downtimes, and technology lags are common occurrences in approaches that deal with obsolescence as it occurs. In wake of the rapid pace of technology innovation, turbulent markets and growing globalization, developing proactive approaches for dealing with obsolescence is a necessity for companies to remain competitive in the marketplace. Thus this dissertation focuses on three fundamental objectives that highlight the importance, provide new insight, and offer solutions to the problem of component obsolescence. The first objective concentrates on the importance of prediction models in determining the life cycle of a component. Obsolescence prediction is key in identifying the items most vulnerable and allows the company to effectively hedge against future uncertainty long before the problem arises. The second objective concentrates on proactive management approaches. This is accomplished through a case study with an industry partner. The purpose of an obsolescence management strategy is to ensure that, issues of obsolescence are anticipated, identified, analyzed, mitigated, reported, and dealt with in a cost effective and timely manner. In addition, it provides life cycle "support and guidance" to the management team. Dealing intelligently with flexibility and uncertainty is characteristic of the Real Options Pricing approach. Thus, the third objective concentrates on options pricing as a decision making tool for mitigating the effects of obsolescence. Making strategic decisions about when to invest, what technology to invest in, waiting until a future point in time when a new technology may be available, are all complex questions to answer. Real options pricing offers a novel approach to addressing issues of obsolescence in sustainment based technologies. Thus this dissertation demonstrates that obsolescence prediction, proactive management and mitigation and the use of real options is key in determining optimal decisions and staying competitive in the "high-tech" electronics industry.

Ce travail de thèse s'inscrit dans un contexte multidisciplinaire à la frontière entre les domaines de l'intelligence ambiante, de l'interaction homme-machine et des agents conversationnels. Nous y étudions la problématique de l'assistance proactive contextuelle dans le cadre de l'informatique ambiante. A cet égard, notre challenge consiste à fournir à l'utilisateur souhaitant disposer d'un assistant virtuel, des services proactifs capables de communiquer selon le contexte de l'utilisateur. Mais l'intelligence ambiante proactive, ou encore l'assistance proactive contextuelle, n'a pas suivi un développement aussi important que l'intelligence ambiante réactive où l'utilisateur doit exprimer explicitement ses besoins. C'est pourquoi l'objectif de cette thèse CIFRE consiste à étudier et évaluer la notion d'assistance proactive contextuelle via la mise en place d'un agent conversationnel en ligne nommé X-CAMPUS. Cet acronyme signifie eXtensible Conversational Agent for Multichannel Proactive Ubiquitous Services. Ce système a pour vocation d'assister l'utilisateur dans ses tâches quotidiennes grâce à sa capacité de percevoir l'état de l'environnement et d'interagir pertinemment selon les besoins de l'utilisateur (météo, programmes TV, etc.). Nous avons mené une étude expérimentale pour étudier l'intérêt de X-CAMPUS et évaluer la satisfaction qu'il procure aux utilisateurs. Nous décrivons ensuite les résultats quantitatifs et qualitatifs de cette étude obtenus via un ensemble de scénarios et utilisant différents paramètres d'interaction (multimodalité, multicanalité, comportement réactif/proactif de l'agent, type de capteurs, contexte mono/multi-utilisateurs...). / This thesis is part of a multidisciplinary context on the border between the domains of the ambient intelligence, the human-machine interaction and the conversational agents. We study the problem of contextual proactive assistance in the course of the ambient computing. In this regard, our challenge is to provide to the user who wish have a virtual assistant, a proactive services which can communicate according to the user’s context. However proactive ambient intelligence, or contextual proactive assistance, did not follow an important development as reactive ambient intelligence where the user must explicitly express their needs. This is why the objective of this CIFRE thesis is to study and evaluate the concept of contextual proactive assistance through the implementation of an online conversational agent named X-CAMPUS. This acronym stands for eXtensible Proactive Multichannel Conversational Agent for Ubiquitous Services. This system aims to assist the user in their daily tasks thanks to its ability to perceive the state of the environment and interact effectively according to user’s needs (weather, TV programs, etc.). We conducted an experimental study to investigate the interest of X-CAMPUS and evaluate the satisfaction that it provides to users. We describe quantitative and qualitative results of this study obtained through a set of scenarios using different parameters of interaction (multimodal, multi-channels, behavior reactive/proactive agent, type of sensors, context mono/multi-user…).

京都大学 / 0048 / 新制・課程博士 / 博士(経済学) / 甲第22228号 / 経博第616号 / 新制||経||293(附属図書館) / 京都大学大学院経済学研究科経済学専攻 / (主査)教授 関口 倫紀, 教授 若林 直樹, 教授 椙山 泰生 / 学位規則第4条第1項該当 / Doctor of Economics / Kyoto University / DGAM

Cloud computing has been widely used for some time now, and its area of use is growing larger and larger year by year. It is very convenient for companies to use cloud computing when creating certain products, however it comes with a great price. In this thesis it will be evaluated if one could optimize the expenses for a product regardless of what platform that is used. And would it be possible to anticipate how much resources a product will need, and allocate those machines in a dynamic fashion?       In this thesis the work of predicting the need of rendering machines based on response times from user requests, and dynamically allocate rendering machines to a product based on this need will be evaluated. The solution used will be based on machine learning, where different types of regression models will try to predict the response times of the future, and evaluate whether or not they are acceptable. During the thesis both a simulation and a replica of the real architecture will be implemented. The replica of the real architecture will be implemented by using AWS cloud services.       The resulting regression model that turned out to be best, was the simplest possible. A linear regression model with response times as the independent variable, and the queue size per rendering machine was used as the dependent variable. The model performed very good in the region of realistic response times, but not necessarily that good at very high response times or at very low response times. That is not considered as a problem though, since response times in those regions should not be of concern for the purpose of the regression model. The effects of the usage of the regression model seems to be better than in the case of using a completely reactive scaling method. Although the effects are not really clear, since there is no user data available. In order for the effects to be evaluated in a fair way, there is a need of user patterns in terms of daily usage of the product. Because the requests in the used simulation are based on pure randomness, there is no correlation in what happened 10 minutes back in the simulation and what will happen 10 minutes in the future. The effect of that is that it is really hard to estimate how the dependent variable will change over time. And if that can not be estimated in a proper way, the results with the inclusion of the regression model can not be tested in a realistic scenario either.

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Exclusionary discipline practices are often considered to be generally ineffective and inequitable, however, they are one of the more common disciplinary practices used in schools today. Although there are many positive alternatives to school exclusion, there are limited disciplinary practices specifically intended to target discipline with racial equity in mind. The GREET-STOP-PROMPT (GSP) intervention, developed by Cook and colleagues (2018), is one intervention found in the literature explicitly created to reduce racial discipline disparities. Although there is currently limited research evaluating the GSP intervention, the results indicate it to be a potentially promising method of equitably addressing student behavior. The purpose of the current study was to extend the literature to examine the effects of the GSP intervention on racial discipline disparities, as well as students’ academic engagement and disruptive behavior in the classroom. The current study evaluated the GSP intervention across three elementary classrooms in a rural school district located in the southeastern United States. Results indicated the GSP intervention did not have meaningful effects on student exclusion, racial disparities, academic engagement, and disruptive behavior, which ultimately failed to support the current researcher’s hypotheses. However, there was limited discipline data available to draw conclusions specifically related to the intervention’s effect on student exclusion and discipline disparities. Further exploration is necessary to evaluate the effectiveness of the GSP intervention in addressing student behavior in the classroom and reducing racial discipline disparities.

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