Abstract

Abstract
Machine Learning (ML) progressed significantly in the last decade, evolving the computer-based learning/prediction paradigm to a much more effective class of models known as Deep learning (DL). Since then, hyperspectral data processing relying on DL approaches is getting more popular, competing with the traditional classification techniques. In this paper, a valid ML/DL-based works applied to hyperspectral data processing is reviewed in order to get an insight regarding the approaches available for the effective meaning extraction from this type of data. Next, a general DL-based methodology focusing on hyperspectral data processing to provide farmers and winemakers effective tools for earlier threat detection is proposed.

Abstract
A Community of Practice (CoP) allows practitioners of a clearly defined domain to share knowledge, experience, and best practices. It provides a social context for practitioners, often distributed across multiple organizations, and emerged over the last few decades as a fundamental mechanism for knowledge sharing, management, and generation within organizations. Best practices, innovations, and solutions to shared problems first emerge within CoPs. These are, and must be perceived as, an investment in organizations' future and competitiveness. Establishing a CoP is a straightforward process, the most challenging factor being the recruitment of members to attain critical mass. The challenge is to maintain the CoP active, with members contributing with high quality, innovative content. Increasing a CoP's medium / long-term survival probabilities requires careful planning to avoid incurring in some well-known pitfalls. This paper proposes and discusses a set of nine guidelines for establishing and maintaining a community of practice within the context of Electronic Governance (EGOV) and Government Chief Information Officers (GCIO). This research was motivated by the initiative of the government of a developing country. Results are based on a review of the relevant literature, together with the detailed analysis of interviews to members or coordinators of large communities of practice. This analysis was further validated against the opinions of public servants directly involved on EGOV-GCIO-related functions during two focus groups meetings. © 2018 Association for Computing Machinery.

Abstract
Cloud software continues to expand globally, highly motivated by how widespread the Internet is and the possibilities it unlocks with cloud computing. Still, cloud development has some intrinsic properties to it, making it complex to unexperienced developers. This research is capturing those intricacies in the form of a pattern language that gathers ten patterns for engineering software for the cloud. This paper elaborates on that research by contributing with two new patterns: Automated Recovery, which checks if a container is working properly, automatically recovering it in case of failure and Scheduler, which periodically executes actions within the infrastructure. The described patterns are useful for anyone designing software for the cloud, either to bootstrap or to validate their design decisions with the end goal of enabling them to create better software for the cloud.

Abstract
Cloud software continues to expand globally, highly motivated by how widespread the Internet is and the possibilities it unlocks with cloud computing. Still, cloud development has some intrinsic properties to it, making it complex to unexperienced developers. This research is capturing those intricacies in the form of a pattern language, gathering ten patterns for engineering software for the cloud. This paper elaborates on that research by contributing with two new patterns: External Monitoring, which continuously monitors the system as a black box, validating its status and Failure Injection, which continuously verifies system reliability by injecting failures into the cloud environment and confirming that the system recovers from it. The described patterns are useful for anyone designing software for the cloud, either to bootstrap or to validate their design decisions with the end goal of enabling them to create better software for the cloud.

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.