Abstract
Protocols for password authenticated key exchange (PAKE) allow two parties who share only a weak password to agree on a crypto-graphic key. We revisit the notion of PAKE in the universal composability (UC) framework, and propose a relaxation of the PAKE functionality of Canetti et al. that we call lazy-extraction PAKE (lePAKE). Our relaxation allows the ideal-world adversary to postpone its password guess until after a session is complete. We argue that this relaxed notion still provides meaningful security in the password-only setting. As our main result, we show that several PAKE protocols that were previously only proven secure with respect to a "game-based" definition of security can be shown to UC-realize the lePAKE functionality in the random-oracle model. These include SPEKE, SPAKE2, and TBPEKE, the most efficient PAKE schemes currently known.

Abstract
We present the complete genome sequences of two Xanthomonas euroxanthea strains isolated from buds of a walnut tree. The whole-genome sequences of strains CPBF 367 and CPBF 426 consist of two circular chromosomes of 4,923,218 bp and 4,883,254 bp and two putative plasmids of 45,241 bp and 17,394 bp, respectively. These data may contribute to the understanding of Xanthomonas species-specific adaptations to walnut.

Abstract
Prediction of labor is of extreme importance in obstetric care to allow for preventive measures, assuring that both baby and mother have the best possible care. In this work, the authors studied how important nonlinear parameters (entropy and compression) can be as labor predictors. Linear features retrieved from the SisPorto system for cardiotocogram analysis and nonlinear measures were used to predict labor in a dataset of 1072 antepartum tracings, at between 30 and 35 weeks of gestation. Two groups were defined: Group A—fetuses whose traces date was less than one or two weeks before labor, and Group B—fetuses whose traces date was at least one or two weeks before labor. Results suggest that, compared with linear features such as decelerations and variability indices, compression improves labor prediction both within one (C-Statistics of 0.728) and two weeks (C-Statistics of 0.704). Moreover, the correlation between compression and long-term variability was significantly different in groups A and B, denoting that compression and heart rate variability look at different information associated with whether the fetus is closer to or further from labor onset. Nonlinear measures, compression in particular, may be useful in improving labor prediction as a complement to other fetal heart rate features.

Abstract
Industrial Cyber-Physical Systems (CPS) are promoting the development of smart machines and products, leading to the next generation of intelligent production systems. In this context, Artificial Intelligence (AI) is posed as a key enabler for the realization of CPS requirements, supporting the data analysis and the system dynamic adaptation. However, the centralized Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitivity. Edge Computing can address the new challenges, enabling the decentralization of data analysis along the cyber-physical components. In this context, distributed AI approaches such as those based onMulti-agent Systems (MAS) are essential to handle the distribution and interaction of the components. Based on that, this work uses a MAS approach to design cyber-physical agents that can embed different data analysis capabilities, supporting the decentralization of intelligence. These concepts were applied to an industrial automobile multi-stage production system, where different kinds of data analysis were performed in autonomous and cooperative agents disposed along Edge, Fog and Cloud computing layers.

Abstract
We live in an age of constant technological evolution where we witness an increasing need for adaptation, in view of market challenges. These transformations create interactions between human beings and machines. This article is a case study based on qualitative and quantitative research, which approaches the implementation of a robotized system in a retail convenience shop, at a petrol station. The main objective was to focus on understanding the applicability of this type of system in a shop, as well as to ascertain its future in the short, medium and long term. Field research performed involved a personal interview with an executive at the Portuguese firm PRIO Energy – the Director of Research, Development and Innovation. The essence of the interview was to enquire about the robot experiment and to understand how innovation occurs, and where the ideas come from. Two other firm employees were also contacted for their testimonials on the project – the convenience store manager and the project innovation manager. Light was shed on which phases the innovation project went through. Finally, the authors has access to the results of a consumer survey involving 210 customers, who interacted with the robot station during its test phase. While not all feedback was good, namely some consumers are concerned that robots will be replacing humans in the workplace, leading to unemployment; the vast majority of the 210 survey respondents saw the experience as positive and one which they would repeat in the future. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

Abstract
Proliferative diabetic retinopathy (PDR) is an advanced diabetic retinopathy stage, characterized by neovascularization, which leads to ocular complications and severe vision loss. However, the available DR-labeled retinal image datasets have a small representation of images of the severest DR grades, and thus there is lack of PDR cases for training DR grading models. Additionally, the criteria for labelling these images in the publicly available datasets is not always clear, with some images which do not show typical PDR lesions being labeled as PDR due to the presence of photo-coagulation treatment and laser marks. This problem, together with the datasets' high class imbalance, leads to a limited variability of the samples, which the typical data augmentation and class balancing cannot fully mitigate. We propose a heuristic-based data augmentation scheme based on the synthesis of neovessel (NV)-like structures that compensates for the lack of PDR cases in DR-labeled datasets. The proposed neovessel generation algorithm relies on the general knowledge of common location and shape of these structures. NVs are generated and introduced in pre-existent retinal images which can then be used for enlarging deep neural networks' training sets. The data augmentation scheme was tested on multiple datasets, and allows to improve the model's capacity to detect NVs.