Abstract
Network security encompasses distinct technologies and protocols, being behaviour based network Intrusion Detection Systems (IDS) a promising application to detect and identify zero-day attacks and vulnerabilities exploits. In order to overcome the weaknesses of signature-based IDS, behaviour-based IDS applies a wide set of machine learning technologies to learn the normal behaviour of the network, making it possible to detect malicious and not yet seen activities. The machine learning techniques that can be applied to IDS are vast, as are the methods to generate the datasets used for testing. This paper aims to evaluate CSE-CIC-IDS2018 dataset and benchmark a set of supervised bioinspired machine learning algorithms, namely CLONALG Artificial Immune System, Learning Vector Quantization (LVQ) and Back-Propagation Multi-Layer Perceptron (MLP). The results obtained were also compared with an ensemble strategy based on a majority voting algorithm. The results obtained show the appropriateness of using the dataset to test behaviour based network intrusion detection algorithms and the efficiency of MLP algorithm to detect zero-day attacks, when comparing with CLONALG and LVQ. © 2021, Springer Nature Singapore Pte Ltd.

Abstract
With the appearance of Shared Autonomous Vehicles there will no longer be a driver responsible for maintaining the car interior and well-being of passengers. To counter this, it is imperative to have a system that is able to detect any abnormal behaviors, more specifically, violence between passengers. Traditional action recognition algorithms build models around known interactions but activities can be so diverse, that having a dataset that incorporates most use cases is unattainable. While action recognition models are normally trained on all the defined activities and directly output a score that classifies the likelihood of violence, video anomaly detection algorithms present themselves as an alternative approach to build a good discriminative model since usually only non-violent examples are needed. This work focuses on anomaly detection and action recognition algorithms trained, validated and tested on a subset of human behavior video sequences from Bosch's internal datasets. The anomaly detection network architecture defines how to properly reconstruct normal frame sequences so that during testing, each sequence can be classified as normal or abnormal based on its reconstruction error. With these errors, regularity scores are inferred showing the predicted regularity of each frame. The resulting framework is a viable addition to traditional action recognition algorithms since it can work as a tool for detecting unknown actions, strange/violent behaviors and aid in understanding the meaning of such human interactions.

Abstract
<p>Atmospheric new particle formation (NPF) generates secondary aerosol particles into the lower atmosphere via gas-to-particle phase transition. Secondary aerosol particles dominate the total particle number concentration and are an important source for cloud condensation nuclei <sup>[1]</sup>. NPF typically begins with clustering among gaseous molecules. Once the newly formed clusters attain a size larger than the critical cluster size (~1.5 nm), their growth to larger sizes is energetically favoured and eventually they become nanoparticles <sup>[2]</sup>. NPF is often observed with the participation of air ions <sup>[3]</sup> and sometimes is induced by ions <sup>[4]</sup>. Air ions are a constituent of atmospheric electricity. The presence of the Earth-atmosphere electric field poses an electrical force on air ions. The earth-atmosphere electric field exhibits variability at different time scales under fair-weather conditions <sup>[5]</sup>. It is therefore interesting to understand whether the Earth-atmosphere electric field influences atmospheric new particle formation.</p> <p>We analysed the Earth-atmosphere electric field together with the number size distribution data of air ions and aerosol particles under fair-weather conditions measured at Hyytiälä SMEAR II station in Southern Finland <sup>[6]</sup>. The electric field were measured by two Campbell CS 110 field mills in parallel. Air ion data were obtained with a Balance Scanning Mobility Analyser (BSMA) and a Neutral and Air Ion Spectrometer (NAIS), and aerosol particle data with a Differential Mobility Particle Sizer (DMPS). We used condensation Sinks (CS) derived from the DMPS measurement, air temperature, relative humidity, wind speed, global radiation as well as brightness derived from the global radiation measurement to assist the analysis. The measured earth-atmosphere electric field on NPF days was higher than on non-NPF days. We found that under low CS conditions, the electric field can enhance the formation of 1.7-3 nm air ions, but the concentration of 1.7-3 nm ions decreased with an increasing electric field under high CS conditions.</p> <p>References:</p> <p>[1]       Kerminen V.-M. et al., Environ. Res. Lett. <strong>2018</strong>, 13, 103003.</p> <p>[2]       Kulmala M. et al., Science <strong>2013</strong>, 339, 943-946.</p> <p>[3]       Manninen H. E. et al., Atmos. Chem. Phys. <strong>2010</strong>, 10, 7907-7927.</p> <p>[4]       Jokinen T. et al., Science Advances <strong>2018</strong>, 4, eaat9744.</p> <p>[5]       Bennett A. J., Harrison R. G., Journal of Physics: Conference Series <strong>2008</strong>, 142, 012046.</p> <p>[6]       Hari P., Kulmala M., Boreal Environ. Res. <strong>2005</strong>, 10, 315-322.</p>

Abstract
Language designers usually need to implement parsers and printers. Despite being two closely related programs, in practice they are often designed separately, and then need to be revised and kept consistent as the language evolves. It will be more convenient if the parser and printer can be unified and developed in a single program, with their consistency guaranteed automatically. Furthermore, in certain scenarios (like showing compiler optimisation results to the programmer), it is desirable to have a more powerful reflective printer that, when an abstract syntax tree corresponding to a piece of program text is modified, can propagate the modification to the program text while preserving layouts, comments, and syntactic sugar. To address these needs, we propose a domain-specific language BiYacc, whose programs denote both a parser and a reflective printer for a fully disambiguated context-free grammar. BiYacc is based on the theory of bidirectional transformations, which helps to guarantee by construction that the generated pairs of parsers and reflective printers are consistent. Handling grammatical ambiguity is particularly challenging: we propose an approach based on generalised parsing and disambiguation filters, which produce all the parse results and (try to) select the only correct one in the parsing direction; the filters are carefully bidirectionalised so that they also work in the printing direction and do not break the consistency between the parsers and reflective printers. We show that BiYacc is capable of facilitating many tasks such as Pombrio and Krishnamurthi's 'resugaring', simple refactoring, and language evolution.

Abstract

Abstract
Salterns are extreme environments, where the high salt concentration is the main limitation to microbial growth, along with solar radiation, temperature and pH. These selective pressures might lead to the acquisition of unique genetic adaptations that can manifest in the production of interesting natural products. The present study aimed at obtaining the culturable microbial diversity from two Portuguese salterns located in different geographic regions. A total of 190 isolates were retrieved and identified as belonging to 30 genera distributed among 4 phyla-Firmicutes, Proteobacteria, Actinobacteria and Bacteroidetes. Specifically, members of the genus Bacillus were the most frequently isolated from both salterns and all actinobacterial isolates belong to the rare members of this group. The molecular screening of NRPS and PKS-I genes allowed the detection of 38 isolates presenting PKS-I, 25 isolates presenting NRPS and 23 isolates presenting both types of biosynthetic genes. Sequencing of randomly selected amplicons revealed similarity with known PKS-I and NRPS genes or non-annotated hypothetical proteins. This study is the first contribution on the culturable bacterial diversity of Portuguese salterns and on their bioactive potential. Ultimately, these findings provide a novel contribution to improve the understanding on the microbial diversity of salterns.