User Privacy in Mobile Advertising

With the pervasiveness of mobile devices in our daily life continuously increasing, mobile advertising is emerging as an important marketing strategy. However, due to its intrusive nature in practice, there has been a growing concern over users’ privacy in mobile advertising, especially push-based mode, which can affect consumers’ acceptance and effectiveness of mobile advertising. Aiming to gain a deeper understanding of not only users’ concerns of privacy intrusion in mobile advertising, but also the potential solutions to addressing those concerns, we conducted a survey in this study. The findings of this study provide a few useful insights for researchers, advertisers, and businesses on both the importance and methods of privacy protection in mobile advertising from a user perspective

Available Bandwidth Estimation Tools Metrics, Approaches and Performance

The estimation of the available bandwidth (av bw) between two end nodes through the Internet, is an area that has motivated researchers around the world in the last twenty years, to have faster and more accurate tools; Due to the utility it has in various network applications; Such as routing management, intrusion detection systems and the performance of transport protocols. Different tools use different estimation techniques but generally only analyze the three most used metrics as av bw, relative error and estimation time. This work expands the information regarding the evaluation literature of the current Available Bandwidth Estimation Tools (ABET’s), where they analyze the estimation techniques, metrics, different generation tools of cross-traf?c and evaluation testbed; Concentrating on the techniques and estimation methodologies used, as well as the challenges faced by open-source tools in high-performance networks of 10Gbps or higher

Available Bandwidth Estimation Tools Metrics, Approaches and Performance

The estimation of the available bandwidth (av_bw) between two end nodes through the Internet, is an area that has motivated researchers around the world in the last twenty years, to have faster and more accurate tools; Due to the utility it has in various network applications; Such as routing management, intrusion detection systems and the performance of transport protocols. Different tools use different estimation techniques but generally only analyze the three most used metrics as av_bw, relative error and estimation time. This work expands the information regarding the evaluation literature of the current Available Bandwidth Estimation Tools (ABET's), where they analyze the estimation techniques, metrics, different generation tools of cross-traffic and evaluation testbed; Concentrating on the techniques and estimation methodologies used, as well as the challenges faced by open-source tools in high-performance networks of 10 Gbps or higher

Entropy-Based Dynamic Ad Placement Algorithms for In-Video Advertising

With the evolution of the Internet and the increasing number of users over last years, online advertising has become one of the pillars models that sustains many of the Internet businesses. In this dissertation, we review the history of online advertising, will be made, as well as the state-of-the-art of the major scientific contributions in online advertising,in particularly in respect to in-video advertising. In in-video advertising, one of the major issues is to identify the best places for insertion of ads. In the literature, this problem is addressed in different ways. Some methods are designed for a specific genres of video, e.g., football or tennis, while others are independent of genre, trying to identify the meaningful video scenes (a set of continuous and related frames) where ads will be displayed. However, the vast majority of online videos in the Internet are not long enough to identify large scenes. So, in this dissertation we will address a new solution for advertisement insertion in online videos, a solution that can be utilized independently of the duration and genre of the video in question. When developing a solution for in-video advertising, a major challenge rests on the intrusiveness that the ad inserted will take upon the viewer. The intrusiveness is related to the place and timing used by the advertising to be inserted. For these reasons, the algorithm has to take in consideration the "where", "when" and "how" the advertisement should be inserted in the video, so that it is possible to reduce the intrusiveness of the ads to the viewer. In short, in addition to besides being independent of duration and genre, the proposed method for ad placement in video was developed taking in consideration the ad intrusiveness to the user.Com a evolução da Internet e o número crescente de utilizadores ao longo destes últimos anos, a publicidade on-line tornou-se um dos modelos base que tem sustentado muitos negócios na Internet. Da mesma forma, vídeos on-line constituem uma parte significativa do tráfego na Internet. É por isso possível entender desta forma, o potencial que ferramentas que possão explorar eficientemente ambas estas áreas possuem no mercado. Nesta dissertação será feita uma revisão da história da publicidade online, mas também será apresentado ao leitor uma revisão sobre o estado da arte das principais contribuições científicas para a publicidade on-line, em especial para a publicidade em video. Na publicidade em vídeo, uma das principais preocupações é identificar os melhores locais para a inserir os anúncios. Na literatura, este problema é abordado de diferentes maneiras, alguns criaram métodos para gêneros específicos de vídeo, por exemplo, futebol ou ténis, outros métodos são independentes do gênero, mas tentam identificar as cenas de vídeo (um conjunto contínuo de frames relacionadas) e apenas exibir anúncios neles. No entanto, a grande maioria dos vídeos on-line na Internet não são suficiente longos para serem identificadas cenas suficientemente longas para inserir os anúncios. Assim, nesta dissertação iremos abordar uma nova solução para a inserção de anúnicios em vídeos, uma solução que pode ser utilizada de forma independente da duração e gênero do vídeo em questão. Ao desenvolver uma solução para inserir anúncos em vídeos a grande preocupação recai sobre a intromissão que o anúncio inserido poderá ter sobre o utilizador. A intrusão está relacionada com o local e tempo utilizado pela publicidade quando é inserida. Por estas razões, o algoritmo tem que levar em consideração "onde", "quando" e "como" o anúncio deve ser inserido no vídeo, de modo que seja possível reduzir a intromissão dos anúncios para o utilizador. Em suma, para além de ser independente da duração e gênero do vídeo, o método proposto será também desenvolvido tendo em consideração a intromissáo do anúncio para o utilizador. Por fim, o método proposto será testado e comparado com outros métodos, de modo a que seja possivel perceber as suas capacidades

Data fusion by using machine learning and computational intelligence techniques for medical image analysis and classification

Data fusion is the process of integrating information from multiple sources to produce specific, comprehensive, unified data about an entity. Data fusion is categorized as low level, feature level and decision level. This research is focused on both investigating and developing feature- and decision-level data fusion for automated image analysis and classification. The common procedure for solving these problems can be described as: 1) process image for region of interest\u27 detection, 2) extract features from the region of interest and 3) create learning model based on the feature data. Image processing techniques were performed using edge detection, a histogram threshold and a color drop algorithm to determine the region of interest. The extracted features were low-level features, including textual, color and symmetrical features. For image analysis and classification, feature- and decision-level data fusion techniques are investigated for model learning using and integrating computational intelligence and machine learning techniques. These techniques include artificial neural networks, evolutionary algorithms, particle swarm optimization, decision tree, clustering algorithms, fuzzy logic inference, and voting algorithms. This work presents both the investigation and development of data fusion techniques for the application areas of dermoscopy skin lesion discrimination, content-based image retrieval, and graphic image type classification --Abstract, page v

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Banking theory based distributed resource management and scheduling for hybrid cloud computing

Cloud computing is a computing model in which the network offers a dynamically scalable service based on virtualized resources. The resources in the cloud environment are heterogeneous and geographically distributed. The user does not need to know how to manage those who support the cloud computing infrastructure. From the view of cloud computing, all hardware, software and networks are resources. All of the resources are dynamically scalable on demand. It can offer a complete service for the user even when these service resources are geographically distributed. The user pays for only what they use (pay-per-use). Meanwhile, the transaction environment will decide how to manage resource usage and cost, because all of the transactions have to follow the rule of the market. How to manage and schedule resources effectively becomes a very important part of cloud computing, and how to setup a new framework to offer a reliable, safe and executable service are very important issues. The approach herein is a new contribution to cloud computing. It not only proposes a hybrid cloud computing model based on banking theory to manage transactions among all participants in the hybrid cloud computing environment, but also proposes a "Cloud Bank" framework to support all the related issues. There are some of technology and theory been used to offer contributions as below: 1. This thesis presents an Optimal Deposit-loan Ratio Theory to adjust the pricing between the resource provider and resource consumer to realize both benefit maximization and cloud service optimization for all participants. 2. It also offers a new pricing schema using Centralized Synchronous Algorithm and Distributed Price Adjustment Algorithm to control all lifecycles and dynamically price all resources. 3. Normally, commercial banks apply four factors mitigation and to predict the risk: Probability of Default, Loss Given Default, Exposure at Default and Maturity. This thesis applies Probability of Default model of credit risk to forecast the safety supply of the resource. The Logistic Regression Model been used to control some factors in resource allocation. At the same time, the thesis uses Multivariate Statistical analysis to predict risk. 4. The Cloud Bank model applies an improved Pareto Optimality Algorithm to build its own scheduling system. 5. In order to archive the above purpose, this thesis proposes a new QoS-based SLA-CBSAL to describe all the physical resource and the processing of thread. In order to support all the related algorithms and theories, the thesis uses the CloudSim simulation tools give a test result to support some of the Cloud Bank management strategies and algorithms. The experiment shows us that the Cloud Bank Model is a new possible solution for hybrid cloud computing. For future research direction, the author will focus on building real hybrid cloud computing and simulate actual user behaviour in a real environment, and continue to modify and improve the feasibility and effectiveness of the project. For the risk mitigation and prediction, the risks can be divided into the four categories: credit risk, liquidity risk, operational risk, and other risks. Although this thesis uses credit risk and liquidity risk research, in a real trading environment operational risks and other risks exist. Only through improvements to the designation of all risk types of analysis and strategy can our Cloud Bank be considered relatively complete