Abstract
Habitat mapping is an important task to manage ecosystems. This task becomes most challenging when it comes to marine habitats as it is hard to get good images in underwater conditions and to precisely locate them. In this paper we present a novel technique for performing habitat mapping automating all phases, from data collection to classification, lowering costs and increasing efficiency throughout the process. For mapping habitats in a vast coastal region, we use visible light cameras mounted on autonomous underwater vehicles, capable of collecting and geo-locating all acquired data. The optic images are enhanced using Computer Vision techniques, to help specialists identify the habitats they contain (during training phase). In a later stage, we employ convolutional neural networks to automatically identify habitats in all imagery. Habitats are classified according to the European Nature Information System, an European classification standard for habitats.

Abstract
Can we forecast future connections in a social network? Can we predict who will start using a given hashtag in Twitter, leveraging contextual information such as who follows or retweets whom to improve our predictions? In this paper we present an abridged report of TENSORCAST, a method for forecasting time-evolving networks, that uses coupled tensors to incorporate multiple information sources. TENSORCAST is scalable (linearithmic on the number of connections), effective (more precise than competing methods) and general (applicable to any data source representable by a tensor). We also showcase our method when applied to forecast two large scale heterogeneous real world temporal networks, namely Twitter and DBLP.

Abstract
Cascading Style Sheets (CSS) is a W3C specification for a style sheet language used for describing the presentation of a document written in a markup language, more precisely, for styling Web documents. However, in the last few years, the landscape for CSS development has changed dramatically with the appearance of several languages and tools aiming to help developers build clean, modular and performance-aware CSS. These new approaches give developers mechanisms to preprocess CSS rules through the use of programming constructs, defined as CSS preprocessors, with the ultimate goal to bring those missing constructs to the CSS realm and to foster stylesheets structured programming. At the same time, a new set of tools appeared, defined as postprocessors, for extension and automation purposes covering a broad set of features ranging from identifying unused and duplicate code to applying vendor prefixes. With all these tools and techniques in hands, developers need to provide a consistent workflow to foster CSS modular coding. This paper aims to present an introductory survey on the CSS processors. The survey gathers information on a specific set of processors, categorizes them and compares their features regarding a set of predefined criteria such as: maturity, coverage and performance. Finally, we propose a basic set of best practices in order to setup a simple and pragmatic styling code workflow.

Abstract
Technology is constantly evolving, as a result, users have become more demanding and the applications more complex. In the realm of Web development, JavaScript is growing in a surprising way, already leaving the boundaries of the browser, mainly due to the advent of Node.js. In fact, JavaScript is constantly being reinvented and, from the ES2015 version, began to include the OO concepts typically found in other programming languages. With Web access being mostly made by mobile devices, developers face now performance challenges and need to perform a plethora of tasks that weren’t necessary a decade ago, such as managing dependencies, bundling files, minifying code, optimizing images and others. Many of these tasks can be achieved by using the right tools for the job. However, developers not only have to know those tools, but they also must know how to access and operate them. This process can be tedious, confusing, time-consuming and error-prone. In this paper, we present Kaang, an automatic generator of RESTFul Web applications. The ultimate goal of Kaang is to minimize the impact of creating a RESTFul service by automating all its workflow (e.g., files structuring, boilerplate code generation, dependencies management, and task building). This kind of generators will benefit two types of users: will help novice developers to decrease their learning curve while facing the new frameworks and libraries commonly found in the modern Web and speed up the work of expert developers avoiding all the repetitive and bureaucratic work. At the same time, Kaang promotes the good development principles by adding automatic testing and documentation generation. For this accomplishment, Kaang generates the main API content based on the user’s input and a set of templates which will help developers to manage and test routes, define resources, store data models and others. In order to provide an addition level of confidence to the generator’s end-users, the generator will be integrated on Travis CI and published on both the npmjs and Yeoman registries. © Ricardo Queirós.

Abstract
The JavaScript ecosystem is evolving dramatically. Nowadays, the language is no longer confined to the boundaries of the browser and is now running in both sides of the Web stack. At the same time, JavaScript it’s starting to play also an important role in desktop and mobile applications development. These facts are leading companies to massively adopt JavaScript in their Web/mobile projects and schools to augment the language spectrum among their courses curricula. Several platforms appeared in recent years aiming to foster the learning of the JavaScript language. Those platforms are mainly characterized with sophisticated UI which allow users to learn JavaScript in a playful and interactive way. Despite its apparent success, these environments are not suitable to be integrated in existent educational platforms. Beyond these interoperability issues, most of these platforms are rigid not allowing teachers to contribute with new exercises, organize the existent exercises in more suitable and modular activities to be deployed in their courses, neither keep track of student’s progress. This paper presents LearnJS as a simple and flexible platform to teach and learn JavaScript. In this platform, instructors can contribute with new exercises and combine them with expositive resources (e.g videos) to define specific course activities. These activities can be gamified with the injection of dynamic attributes to reward the most successful attempts. Finally, instructors can deploy activities in their educational platforms. On the other hand, learners can solve exercises and receive immediate feedback on their solutions through static and dynamic analyzers. Since we are in the early stages of implementation, the paper focus on the presentation of the LearnJS architecture, their main components and their data and integration models. Nevertheless, a prototype of the platform is available in a GitHub repository. © Ricardo Queirós

Abstract