Abstract
Interest in artificial intelligence (AI)-driven crowd work has increased during the last few years as a line of inquiry that expands upon prior research on microtasking to represent a means of scaling up complex tasks through AI mediation. Despite the increasing attention to the macrotask phenomenon in crowdsourcing, there is a need to understand the processes, elements, and constraints underlying the infrastructural and behavioral aspects in such form of crowd work when involving collaboration. To this end, this paper provides a first attempt to characterize some of the research conducted in this direction to identify important paths for an agenda comprising key drivers, challenges, and prospects for integrating human-centered AI in collaborative crowdsourcing environments.

Abstract
Community-based electricity markets, which are defined as groups of members that share common interests in renewable distributed generation, allow prosumers to embrace more active roles by opening up several opportunities for trading electricity. At the same time, such markets may favor conventional consumers by allowing them to choose cheaper electricity providers. Due to trends in power sector modernization, community-based electricity markets are of great research interest, and there are already some associated models. However, there is a research gap in searching for integrated and holistic approaches that go beyond economic aspects, consider social and environmental aspects, and assume the balanced co-existence of community-based and conventional markets. This work fills this critical research gap by adapting/applying the optimized tariff model, Bass diffusion model, life cycle assessment, and multi-objective optimization to the context of community-based markets. Results indicate that favoring conventional markets in the short term and community-based markets in the medium term is beneficial. Moreover, regulated tariffs should increase slightly in the short/medium-term to accommodate DG growth. Additionally, community-based markets can decrease electricity expenses by around 13.6 % considering the market participants. Thus, such markets can be significantly beneficial in mitigating energy poverty.

Abstract
Pruning consists on an agricultural trimming procedure that is crucial in some species of plants to promote healthy growth and increased yield. Generally, this task is done through manual labour, which is costly, physically demanding, and potentially dangerous for the worker. Robotic pruning is an automated alternative approach to manual labour on this task. This approach focuses on selective pruning and requires the existence of an end-effector capable of detecting and cutting the correct point on the branch to achieve efficient pruning. This paper reviews and analyses different end-effectors used in robotic pruning, which helped to understand the advantages and limitations of the different techniques used and, subsequently, clarified the work required to enable autonomous pruning.

Abstract
Executing Artificial Intelligence (AI) at the edge is challenging due to tight energy and computational constraints. Heterogeneous platforms, particularly those incorporating Coarse-Grained Reconfigurable Arrays (CGRAs), offer a compelling trade-off between hardware specialization and programmability, supporting spatially distributed and energy-efficient computation. Despite their potential, the deployment of applications on CGRA accelerators remains limited by the lack of practical toolchains and methodologies. In this work, we propose a compilation flow based on MLIR to enable the seamless integration of both C/C++ kernels and ONNX-based AI models into a RISC-V system augmented with a CGRA accelerator. Our approach extracts the underlying Data Flow Graph (DFG) from the high-level representation. It maps it onto the CGRA using an Integer Linear Programming (ILP) mapper that accounts for the accelerator's architectural constraints. A custom backend completes the toolchain by generating the necessary binaries for coordinated execution across the RISC-V processor and the CGRA. This framework enables the practical deployment of heterogeneous edge workloads, combining the flexibility of software execution with the efficiency of hardware acceleration.

Abstract
The cryptocurrency market presents immense opportunities and significant risks due to its high volatility. Accurate price forecasting is crucial for informed investment decisions, enabling investors to optimize portfolio allocation, mitigate risk, and potentially maximize returns. Existing forecasting methods often struggle with the inherent non-stationarity and complexity of cryptocurrency price dynamics. This study addresses this challenge by developing a system for high-frequency forecasting of the closing prices of ten leading cryptocurrencies. We compare various machine learning models, including recurrent neural networks (RNNs), time series analysis (ARIMA), and conventional regression algorithms, using minute-step Bitcoin price data over a 30-day period to predict prices 60 min ahead. Our findings demonstrate that the GRU neural network exhibits superior predictive accuracy (MAPE = 0.09%, MSE = 5954.89, RMSE = 77.17, MAE = 60.20), outperforming other models considered. This improved forecasting accuracy contributes to the existing literature by providing empirical evidence for GRU's effectiveness in the volatile cryptocurrency market and offers practical insights for investment strategies. A web application integrating the best-performing model further facilitates real-time price prediction for multiple cryptocurrencies.

Abstract
The increasing needs for high-performance computing capabilities in real-time systems is pushing the need for the use of complex parallel and heterogeneous platforms. This complicates and even invalidates the use of traditional mechanisms to derive and provide real-time guarantees. Task-to-thread mapping is a key process, not only to provide an optimal performance by enhancing load balance and reducing the overheads of the parallel runtime system, but also to enable predictable execution. To address these somehow contradictory requirements, this work leverages the OpenMP programming model and its implementation in the LLVM compilation framework by virtue of its programmability, performance, portability, and the recently demonstrated time-predictability. In this context, the paper provides an evaluation of different approaches to perform task-to-thread mapping, particularly comparing dynamic mapping, with mapping decisions made during execution, with a static mapping approach, where a fixed mapping is recorded in a preliminary execution, and then repeated in all executions. Static mapping is usually the preferred approach in real-time systems, due to being perceived as more predictable, whilst dynamic mapping is generally perceived as providing higher average performance. The obtained results allow to understand that although dynamic mapping indeed provides in general higher performance, it also may provide less variability, thus partially addressing the predictability concern, and opening opportunities for dynamic mapping in real-time highperformance systems.