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Everything .NET developers need to take advantage of Silverlight 4 Silverlight 4 is a major new release of Microsoft's flagship product for building rich, interactive applications that combine animation, graphics, audio, and video. This book, by seasoned Wrox authors and Silverlight experts, gives professional Web developers all the tools necessary to build RIAs using the new Silverlight capabilities. You will gain a complete, thorough understanding of both core and advanced platform concepts, with examples in C#. Professional Silverlight 4 prepares Web developers to take full advantage of the newest release of the most popular platform for developing rich interactive applications. Coverage Includes: Introduction to Silverlight Building Applications with Visual Studio Building Applications with Expression Blend 4 Working with the Navigation Framework Controlling Layout with Panels Working with Visual Controls Accessing Data WCF RIA Services Out-of-Browser Experiences Networking Applications Building Line of Business Applications Application Architecture DOM Interaction Securing Your Applications Accessing Audio and Video Devices Working with File I/O Using Graphics and Visuals Working with Animations in Silverlight Working with Text Making It Richer with Media Styling and Themes.

SFML Game Development is a fast-paced, step-by-step guide, providing you with all the knowledge and tools you need to create your first game using SFML 2.0.SFML Game Development addresses ambitious C programmers who want to develop their own game. If you have plenty of ideas for an awesome and unique game, but don’t know how to start implementing them, then this book is for you. The book assumes no knowledge about SFML or game development, but a solid understanding of C is required.

Coding skills are becoming more and more important in today’s world, especially within the context of the fourth industrial revolution. They also help practice other 21 century skills such as computational thinking, problem solving and teamwork. Unfortunately, learning how to program is tough and can be also frustrating for beginner students. In this work we introduce RoboTIC, a serious game based on gamification and Augmented Reality that facilitates the learning of programming to students in lower levels of the education system by using a novel set of visual metaphors derived from a notation of roads and traffic signs. The architecture that supports RoboTIC has been designed to allow the integration of multimedia components when new programming concepts and techniques must be addressed and to add game levels that enable students to learn incrementally. Experiments have been conducted in a youth center with children who do not have coding skills at all to demonstrate the feasibility of the proposal. The results show promising conclusions in terms of children’s motivation and interest in programming.

In the last few years, multimedia technologies in general, and Real-Time multimedia Communications (RTC) in particular, are becoming mainstream among WWW and smartphone developers, who have an increasing interest in richer media capabilities for creating their applications. The engineering literature proposing novel algorithms, protocols and architectures for managing and processing multimedia information is currently overwhelming. However, most of these results do not arrive to applications due to the lack of simple and usable APIs. Interestingly, in this context in which APIs are the critical ingredient for reaching wide developer audiences, the scientific literature about multimedia APIs and their usability is scarce. In this paper we try to contribute to fill this gap by proposing the RTC Media API: a novel type of API designed with the aim of making simple for developers the use of latest trends in RTC multimedia including WebRTC, Video Content Analysis or Augmented Reality. We provide a specification of such API and discuss how it satisfies a set of design requirements including programming-language agnosticism, adaptation to cloud environments, support to multisensory multimedia, etc. After that, we describe an implementation of such an API that has been created in the context of the Kurento open source software project, and present a study evaluating the API usability performed in a group of more than 40 professional developers distributed worldwide. In the light of the obtained results, we conclude that the usability of the API is adequate across the main development activities (i.e. API learning, code creation and code maintenance), with an average usability score of 3.39 over 5 in a Likert scale, and that this result is robust with respect to developers’ profiles, cultures, professional experiences and preferred programming languages.

Overfitting and long training time are two fundamental challenges in multilayered neural network learning and deep learning in particular. Dropout and batch normalization are two well-recognized approaches to tackle these challenges. While both approaches share overlapping design principles, numerous research results have shown that they have unique strengths to improve deep learning. Many tools simplify these two approaches as a simple function call, allowing flexible stacking to form deep learning architectures. Although their usage guidelines are available, unfortunately no well-defined set of rules or comprehensive studies to investigate them concerning data input, network configurations, learning efficiency, and accuracy. It is not clear when users should consider using dropout and/or batch normalization, and how they should be combined (or used alternatively) to achieve optimized deep learning outcomes. In this paper we conduct an empirical study to investigate the effect of dropout and batch normalization on training deep learning models. We use multilayered dense neural networks and convolutional neural networks (CNN) as the deep learning models, and mix dropout and batch normalization to design different architectures and subsequently observe their performance in terms of training and test CPU time, number of parameters in the model (as a proxy for model size), and classification accuracy. The interplay between network structures, dropout, and batch normalization, allow us to conclude when and how dropout and batch normalization should be considered in deep learning. The empirical study quantified the increase in training time when dropout and batch normalization are used, as well as the increase in prediction time (important for constrained environments, such as smartphones and low-powered IoT devices). It showed that a non-adaptive optimizer (e.g. SGD) can outperform adaptive optimizers, but only at the cost of a significant amount of training times to perform hyperparameter tuning, while an adaptive optimizer (e.g. RMSProp) performs well without much tuning. Finally, it showed that dropout and batch normalization should be used in CNNs only with caution and experimentation (when in doubt and short on time to experiment, use only batch normalization).

Many two-sided platforms, such as eBay, iOS, Android, and Twitter, invest in developer integration tools, such as modular interfaces, interactive development environments, application programming interfaces, and help desks, in order to reduce the cost and improve the functionality of third-party content developed for the platform. Although these integration tools are crucial to platform success, they are costly to create, and therefore, managers need to understand where and when to deploy them. In particular, when the necessary integration investment is high, the advice to subsidize one side of a two-sided market while charging the other may not hold. This means that integration investment should be carefully coordinated with market pricing decisions. In general, higher levels of investment by hardware/software platforms into integration become desirable when the platform (1) has access to a large pool of content providers and consumers, (2) is able to develop integration tools that are highly effective in reducing third-party development costs, and (3) operates in high-consumer value markets. However, there are nuances. For example, business to business platforms can make investments in integration to facilitate participation by both sides of the market. We find that such investments are complements, not—as one might expect—substitutes. Many two-sided platforms (for example, eBay, Google, iOS, Android, Twitter, and Amazon) provide integration tools, such as modular interfaces, interactive development environments, application programming interfaces, and help desks, to reduce the costs and improve the functionality of third-party content developed for the platform. The need for such investment is increasing with the rise of major new markets as the result of technologies, such as the “Internet of Things.” Although crucial to platform success, platform integration tools are costly to create. We develop an analytic model to explore the key tradeoffs behind investment in integration tools and how that investment interacts with pricing decisions in a two-sided market. We model these decisions for hardware/software platforms as well as hybrid retail platforms and analyze them under various scenarios, including monopoly and competition. Our results suggest that considering integration investment can create market regimes in which the standard pricing results from the extant platform literature no longer hold. For example, the tendency to reduce prices to one side of a market in response to increasing the benefit of the network to the other side may be suboptimal in the presence of integration investment. Therefore, integration investments must be well coordinated with pricing decisions made for both sides of the market. In general, higher levels of investment by hardware/software platforms into integration become desirable when the platform (1) has access to a large pool of content providers and consumers, (2) is able to develop integration tools that are highly effective in reducing third-party development costs, and (3) operates in a market in which content providers earn a high-enough profit margin creating content that is highly valued by the consumer market. Hybrid retail platforms often show similar behavior. However, there are some nuances. For example, business to business platforms can make investments in integration to facilitate participation by both sides of the market. We find that these investments are complements, not—as one might expect—substitutes. We conclude by discussing this work’s implications for theory and practice.

One of the goals of smart environments is to improve the quality of human life in terms of comfort and efficiency. The Internet of Things (IoT) paradigm has recently evolved into a technology for building smart environments. Security and privacy are considered key issues in any real-world smart environment based on the IoT model. The security vulnerabilities in IoT-based systems create security threats that affect smart environment applications. Thus, there is a crucial need for intrusion detection systems (IDSs) designed for IoT environments to mitigate IoT-related security attacks that exploit some of these security vulnerabilities. Due to the limited computing and storage capabilities of IoT devices and the specific protocols used, conventional IDSs may not be an option for IoT environments. This article presents a comprehensive survey of the latest IDSs designed for the IoT model, with a focus on the corresponding methods, features, and mechanisms. This article also provides deep insight into the IoT architecture, emerging security vulnerabilities, and their relation to the layers of the IoT architecture. This work demonstrates that despite previous studies regarding the design and implementation of IDSs for the IoT paradigm, developing efficient, reliable and robust IDSs for IoT-based smart environments is still a crucial task. Key considerations for the development of such IDSs are introduced as a future outlook at the end of this survey.

The multimedia-based e-Learning methodology provides virtual classrooms to students. The teacher uploads learning materials, programming assignments and quizzes on university’ Learning Management System (LMS). The students learn lessons from uploaded videos and then solve the given programming tasks and quizzes. The source code plagiarism is a serious threat to academia. However, identifying similar source code fragments between different programming languages is a challenging task. To solve the problem, this paper proposed a new plagiarism detection technique between C++ and Java source codes based on semantics in multimedia-based e-Learning and smart assessment methodology. First, it transforms source codes into tokens to calculate semantic similarity in token by token comparison. After that, it finds semantic similarity in scalar value for the complete source codes written in C++ and Java. To analyse the experiment, we have taken the dataset consists of four (4) case studies of Factorial, Bubble Sort, Binary Search and Stack data structure in both C++ and Java. The entire experiment is done in R Studio with R version 3.4.2. The experimental results show better semantic similarity results for plagiarism detection based on comparison.