This paper addresses the High Multiplicity Resource-Constrained Project Scheduling Problem (HM-RCPSP), in which multiple projects are performed iteratively while sharing limited resources. We extend this problem by integrating the learning effect, which makes the duration of some activities decrease when they are repeated. Learning effect can be represented by any decreasing function, allowing us to get flexibility in modeling various scenarios. Additionally, we take composition of activities into consideration for reasoning about precedence and resources in a more abstract way. A Constraint Programming model is proposed for this richer problem, including a symmetry-breaking technique applied to some activities. We also present a heuristic-based search strategy. The effectiveness of these solving approaches is evaluated through an experimentation conducted on data concerning real-world satellite assembly lines, as well as on some adapted literature benchmarks. Obtained results demonstrate that our methods serve as robust baselines for addressing this novel problem (denoted by HM-RCPSP/L-C).

Background. Stack Overflow is a prominent Q&A forum, supporting developers in seeking suitable resources on programming-related matters. Having high-quality question titles is an effective means to attract developers’ attention. Research has been conducted, predominantly leveraging pre-trained models to generate titles from code snippets and problem descriptions. Yet, getting high-quality titles is still a challenging task, attributed to both the quality of the input data (e.g., containing noise and ambiguity) and inherent constraints in sequence generation models. Aims. In this paper, we present FILLER as a solution to generating Stack Overflow post titles using a fine-tuned language model with self-improvement and post ranking. Method. Our study focuses on enhancing pre-trained language models for generating titles for posts, employing a training and subsequent fine-tuning paradigm for these models. To this end, we integrate the model’s predictions into the training process, enabling it to learn from its errors, thereby lessening the effects of exposure bias. Moreover, we apply a post-ranking method to produce a variety of sample candidates, subsequently selecting the most suitable one. Results. The empirical findings indicate that FILLER provides high-quality recommendations. Moreover, it significantly outperforms all the baselines, including Code2Que, SOTitle, CCBERT, M3NSCT5, and GPT3.5-turbo. A user study also shows that FILLER provides more relevant titles, with respect to SOTitle and GPT3.5-turbo. Conclusion. We conclude that FILLER has the potential to be used in practice to support developers in generating suitable post titles.

In the last decades, scene text recognition has gained worldwide attention from both the academic community and actual users due to its importance in a wide range of applications. Despite achievements in optical character recognition, scene text recognition remains challenging due to inherent problems such as distortions or irregular layout. Most of the existing approaches mainly leverage recurrence or convolution-based neural networks. However, while recurrent neural networks (RNNs) usually suffer from slow training speed due to sequential computation and encounter problems as vanishing gradient or bottleneck, CNN endures a trade-off between complexity and performance. In this paper, we introduce SAFL, a self-attention-based neural network model with the focal loss for scene text recognition, to overcome the limitation of the existing approaches. The use of focal loss instead of negative log-likelihood helps the model focus more on low-frequency samples training. Moreover, to deal with the distortions and irregular texts, we exploit Spatial TransformerNetwork (STN) to rectify text before passing to the recognition network. We perform experiments to compare the performance of the proposed model with seven benchmarks. The numerical results show that our model achieves the best performance.