Abstract
Application execution requests in cloud architecture and fog paradigm are generally heterogeneous in terms of contexts at the device and application level. The scheduling of requests in these architectures is an optimization problem with multiple constraints. Despite numerous efforts, task scheduling in these architectures and paradigms still presents some enticing challenges that make us question how tasks are routed between different physical devices, fog, and cloud nodes. The fog is defined as an extension of the cloud, which provides processing, storage, and network services near the edge network, and due to the density and heterogeneity of devices, the scheduling is very complex, and, in the literature, we still find few studies. Trying to bring innovative contributions in these areas, in this paper, we propose a solution to the context-aware task-scheduling problem for fog paradigm. In our proposal, different context parameters are normalized through Min-Max normalization, requisition priorities are defined through the application of the Multiple Linear Regression (MLR) technique and scheduling is performed using Multi-Objective Non-Linear Programming Optimization (MONLIP) technique. The results obtained from simulations in the iFogSim toolkit, show that our proposal performs better compared to the non-context-aware proposals.

Abstract
This package provides all published resources used and produced in the context of the research study leading to the article "Designing Microservice Systems Using Patterns: An Empirical Study on Quality Trade-Offs", presented in ICSA 2022's technical track. It includes materials used to conduct the study as well as aggregated and anonymized data produced in its context. Making this package available intends to foster transparency and to support researchers attempting to replicate the study. The package complies with the Research Object Reviewed (ROR) and Open Research Object (ORO) badges, awarded by the Artifact Evaluation Track at ICSA 2022, and is available under Creative Commons Attribution 4.0 International. The package is openly available in Zenodo [1] and the article is available in ICSA 2022's proceedings [2] and as a pre-print [3]. © 2022 IEEE.

Abstract
This paper presents a data market aimed at trading energy forecasts data. The system architecture is built using blockchain as a service, allowing access to data streams and establishing a distributed settlement between stakeholders. Energy Forecasts data is presented as the commodity traded in the market, whose settlement is provided through the blockchain on the basis of the extracted value provided by market stakeholders. Our proposal allows market stakeholders to acquire energy forecasts and pay according to the data accuracy, solving the confidentiality problem of freely sharing data. A data quality reward is introduced, steering the compensation sent to market participants. The data market design is presented and an evaluation campaign is performed, showing that the data market produced functionally valid results in comparison with the results achieved with a central simulated approach. Moreover, results show that the data market architecture is able to scale.

Abstract
Visualizations for Streaming Big Data need to handle high volumes of information in real-time, making it challenging to convey significant data changes without confusing users. A simple first approach would be switching from the current visual idiom to another, highlighting a significant change. Unfortunately, there are no guidelines to design effective transitions between two visual idioms in Streaming Big Data. Therefore, we created a tree of animation concepts to serve as a starting point for designing such animated transitions. The concepts represent several ways in which a visual idiom can be transformed into another. We chose three visual idioms to test our idea and arranged several concepts to apply at each possible pairing (six possibilities). For each pairing, we tested the accuracy of people's perceptions. Finally, we conducted a user study with 100 participants, where each participant answered various questions about transitions between two visual idioms shown in several videos. We concluded that to conceive appropriate animated transitions for Streaming Big Data (which also applies just for Data Streaming) that allow users to understand the changes in incoming data, varying how the proposed concepts are applied is not enough, highlighting the need for future research to address this challenge.

Abstract
Developing and optimizing software applications for high performance and energy efficiency is a very challenging task, even when considering a single target machine. For instance, optimizing for multicore-based computing systems requires in-depth knowledge about programming languages, application programming interfaces (APIs), compilers, performance tuning tools, and computer architecture and organization. Many of the tasks of performance engineering methodologies require manual efforts and the use of different tools not always part of an integrated toolchain. This paper presents Pegasus, a performance engineering approach supported by a framework that consists of a source-to-source compiler, controlled and guided by strategies programmed in a Domain-Specific Language, and an autotuner. Pegasus is a holistic and versatile approach spanning various decision layers composing the software stack, and exploiting the system capabilities and workloads effectively through the use of runtime autotuning. The Pegasus approach helps developers by automating tasks regarding the efficient implementation of software applications in multicore computing systems. These tasks focus on application analysis, profiling, code transformations, and the integration of runtime autotuning. Pegasus allows developers to program their strategies or to automatically apply existing strategies to software applications in order to ensure the compliance of non-functional requirements, such as performance and energy efficiency. We show how to apply Pegasus and demonstrate its applicability and effectiveness in a complex case study, which includes tasks from a smart navigation system.

Abstract
Typical VR interactions can be tiring, including standing up, walking, and mid-air gestures. Such interactions result in decreased comfort and session duration compared with traditional non-VR interfaces, which may, in turn, reduce productivity. Nevertheless, current approaches often neglect this aspect, making the VR experience not as promising as it can be. As we see it, desk VR experiences provide the convenience and comfort of a desktop experience and the benefits of VR immersion, being a good compromise between the overall experience and ergonomics. In this work, we explore navigation techniques targeted at desk VR users, using both controllers and a large multi-touch surface. We address travel and orientation techniques independently, considering only continuous approaches for travel as these are better suited for exploration and both continuous and discrete approaches for orientation. Results revealed advantages for a continuous controller-based travel method and a trend for a dragging-based orientation technique. Also, we identified possible trends towards task focus affecting overall cybersickness symptomatology.