Abstract
Multi-view has the potential to offer immersive viewing experiences to users, as an alternative to 360 degrees and Virtual Reality (VR) applications. In multi-view, a limited number of camera views are sent to the client and missing views are synthesised locally. Given the substantial complexity associated to view synthesis, considerable attention has been given to optimise the trade-off between bandwidth gains and computing resources, targeting smooth navigation and viewing quality. A still relatively unexplored field is the optimisation of the way navigation interactivity is achieved, i.e. how the user indicates to the system the selection of new viewpoints. In this article, we introduce SmoothMV, a multi-view system that uses a non-intrusive head tracking approach to enhance navigation and Quality of Experience (QoE) of the viewer. It relies on a novel Hot&Cold matrix concept to translate head positioning data into viewing angle selections. Streaming of selected views is done using MPEG-DASH, where a proposed extension to the standard descriptors enables to achieve consistent and flexible view identification.

Abstract
Due to convenience and usability, many deep learning (DL) jobs resort to the available shared parallel file system (PFS) for storing and accessing training data when running in HPC environments. Under such a scenario, however, where multiple I/O-intensive applications operate concurrently, the PFS can quickly get saturated with simultaneous storage requests and become a critical performance bottleneck, leading to throughput variability and performance loss. We present MONARCH, a framework-agnostic middleware for hierarchical storage management. This solution leverages the existing storage tiers present at modern supercomputers (e.g., compute node's local storage, PFS) to improve DL training performance and alleviate the current I/O pressure of the shared PFS. We validate the applicability of our approach by developing and integrating an early prototype with the TensorFlow DL framework. Results show that MONARCH can reduce I/O operations submitted to the shared PFS by up to 45%, decreasing training time by 24% and 12%, for I/O-intensive models, namely LeNet and AlexNet.

Abstract
We present a model for measuring the impact of offloading soft real-time jobs over multi-tier cloud infrastructures. The jobs originate in mobile devices and offloading strategies may choose to execute them locally, in neighbouring devices, in cloudlets or in infrastructure cloud servers. Within this specification, we put forward several such offloading strategies characterised by their differential use of the cloud tiers with the goal of optimizing execution time and/or energy consumption. We implement an instance of the model using Jay, a software framework for adaptive computation offloading in hybrid edge clouds. The framework is modular and allows the model and the offloading strategies to be seamlessly implemented while providing the tools to make informed runtime offloading decisions based on system feedback, namely through a built-in system profiler that gathers runtime information such as workload, energy consumption and available bandwidth for every participating device or server. The results show that offloading strategies sensitive to runtime conditions can effectively and dynamically adjust their offloading decisions to produce significant gains in terms of their target optimization functions, namely, execution time, energy consumption and fulfilment of job deadlines.

Abstract
Deductive verification has gained paramount attention from both academia and industry. Although intensive research in this direction covers almost all mainstream languages, the research community has paid little attention to the verification of database applications. This paper proposes a comprehensive set of Verification Conditions (VCs) generation techniques from database programs, adapting Symbolic Execution, Conditional Normal Form, and Weakest Precondition. The validity checking of the generated VCs for a database program determines its correctness w.r.t. the annotated database properties. The developed prototype DBverify based on our theoretical foundation allows us to instantiate VC generation from PL/SQL codes, yielding to detailed performance analysis of the three approaches under different circumstances. With respect to the literature, the proposed approach shows its competence to support crucial SQL features (aggregate functions, nested queries, NULL values, and set operations) and the embedding of SQL codes within a host imperative language. For the chosen set of benchmark PL/SQL codes annotated with relevant properties of interest, our experiment shows that only 38% of procedures are correct, while 62% violate either all or part of the annotated properties. The primary cause for the latter case is mostly due to the acceptance of runtime inputs in SQL statements without proper checking.

Abstract
Modern communications have moved away from point-to-point models to increasingly heterogeneous network models. In this article, we propose a novel controller-based architecture to deploy adaptive causal network coding in heterogeneous and highly meshed communication networks. Specifically, we consider using the software-defined network as the main controller. We first present an architecture for highly meshed heterogeneous multi-source multi-destination networks that represent the practical communication networks encountered in the fifth generation of wireless networks and beyond. Next, we present a promising solution to deploy network coding over the new architecture. We also present a new controller-based setting with which network coding modules communicate to attain the required information. Finally, we briefly discuss how the proposed architecture and network coding solution provide a good opportunity for future technologies.

Abstract
Learning computer programming is a complex activity and requires a lot of practice. The viral pandemic that we are facing has intensified these difficulties. In this context, programming learning platforms play a crucial role. Most of them are characterized by providing a wide range of exercises with progressive complexity, multi-language support, sophisticated interfaces and automatic evaluation and gamification services. Nevertheless, despite the various features provided, others features, which influence user experience, are not emphasized, such as performance and usability. This article presents an user experience evaluation of the LearnJS playground, a JavaScript learning platform which aims to foster the practice of coding. The evaluation highlights two facets of the code playground: performance and a usability. In the former, lab and field data were collected based on Google Lighthouse and PageSpeed Insights reports. In the later, an inquiry was distributed among students from a Web Technologies course with a set of questions based on flexibility, usability and consistency heuristics. Both evaluation studies have a twofold goal: to improve the learning environment in order to be officially used in the next school year and to foster the awareness of user experience in all phases of the software development life-cycle as a key facet in Web applications engagement and loyalty. © Ricardo Queirós, Mário Pinto, and Teresa Terroso; licensed under Creative Commons License CC-BY 4.0 Second International Computer Programming Education Conference (ICPEC 2021).