Abstract
Temporal expression identification is crucial for understanding texts written in natural language. Although highly effective systems such as HeidelTime exist, their limited runtime performance hampers adoption in large-scale applications and production environments. In this paper, we introduce the TEI2GO models, matching HeidelTime's effectiveness but with significantly improved runtime, supporting six languages, and achieving state-of-the-art results in four of them. To train the TEI2GO models, we used a combination of manually annotated reference corpus and developed Professor HeidelTime, a comprehensive weakly labeled corpus of news texts annotated with HeidelTime. This corpus comprises a total of 138, 069 documents (over six languages) with 1, 050, 921 temporal expressions, the largest open-source annotated dataset for temporal expression identification to date. By describing how the models were produced, we aim to encourage the research community to further explore, refine, and extend the set of models to additional languages and domains. Code, annotations, and models are openly available for community exploration and use. The models are conveniently on HuggingFace for seamless integration and application.

Abstract

Abstract
In Europe, the Common Agricultural Policy (CAP) encouraged the specialisation of agriculture and forestry systems by supporting schemes that promoted productivity, despite the socio-ecological changes' detrimental effects on ecosystem services and biodiversity. In the case of mountain viticulture of southern Europe, the adoption of intensive management techniques triggered noticeable changes in farming systems, namely the removal of traditional stonewalls and semi-natural vegetation, partially compensated by eco schemes and agri-environment-climate measures. By combining fieldwork information with spatio-temporal modelling techniques, a novel hybrid framework is explained and implemented to predict the population trends of a critically en-dangered bird species in Portugal, the Black Wheatear (Oenanthe leucura), to the individual and/or combined effects of the removal of traditional stonewall terraced vineyards and the implementation of cover crops. The results obtained demonstrate the relevance of stonewall terraced vineyards (and the negative effects of their removal) for the conservation of Black Wheatear, namely during the breeding season when holes and crevices are used for nesting. Conversely, and in accordance with our simulations, the increase in the area occupied by vineyards with cover crops seems particularly detrimental for the species, by decreasing the quality of the feeding grounds. As cover crops, and possibly other eco schemes and agri-environment-climate measures, might not be the panacea for halting biodiversity loss in mountain viticulture, adaptation of measures to species' ecological requirements is urgent for a successful EU biodiversity strategy for 2030.

Abstract
[No abstract available]

Abstract
In the last few years we have been seeing a drastic change in the way software is developed. Large-scale software projects are being assembled by a flexible composition of many (small) components possibly written in different programming languages and deployed anywhere in the cloud - the so-called microservices-based applications. The dramatic growth in popularity of microservices-based applications has pushed several companies to apply major refactorings to their software systems. However, this is a challenging task that may take several months or even years. We propose a methodology to automatically evolve monolithic web applications that use object-relational mapping into microservices-based ones. Our methodology receives the source code and a microservices proposal and refactors the original code to create each microservice. Our methodology creates an API for each method call to classes that are in other services. The database entities are also refactored to be included in the corresponding service. The evaluation performed in 120 applications shows that our tool can successfully refactor about 72% of them. The execution of the unit tests in both versions of the applications yield exactly the same results.

Abstract
In a partitioned Bloom Filter (PBF) the bit vector is split into disjoint parts, one per hash function. Contrary to hardware designs, where they prevail, software implementations mostly ignore PBFs, considering them worse than standard Bloom filters (SBF), due to the slightly larger false positive rate (FPR). In this paper, by performing an in-depth analysis, first we show that the FPR advantage of SBFs is smaller than thought; more importantly, by deriving the per-element FPR, we show that SBFs have weak spots in the domain: elements that test as false positives much more frequently than expected. This is relevant in scenarios where an element is tested against many filters. Moreover, SBFs are prone to exhibit extremely weak spots if naive double hashing is used, something occurring in mainstream libraries. PBFs exhibit a uniform distribution of the FPR over the domain, with no weak spots, even using naive double hashing. Finally, we survey scenarios beyond set membership testing, identifying many advantages of having disjoint parts, in designs using SIMD techniques, for filter size reduction, test of set disjointness, and duplicate detection in streams. PBFs are better, and should replace SBFs, in general purpose libraries and as the base for novel designs.