RL-IoT: Reinforcement Learning to Interact with IoT Devices

Our life is getting filled by Internet of Things (IoT) devices. These devices often rely on closed or poorly documented protocols, with unknown formats and semantics. Learning how to interact with such devices in an autonomous manner is the key for interoperability and automatic verification of their capabilities. In this paper, we propose RL-IoT, a system that explores how to automatically interact with possibly unknown IoT devices. We leverage reinforcement learning (RL) to recover the semantics of protocol messages and to take control of the device to reach a given goal, while minimizing the number of interactions. We assume to know only a database of possible IoT protocol messages, whose semantics are however unknown. RL-IoT exchanges messages with the target IoT device, learning those commands that are useful to reach the given goal. Our results show that RL-IoT is able to solve both simple and complex tasks. With properly tuned parameters, RL-IoT learns how to perform actions with the target device, a Yeelight smart bulb in our case study, completing non-trivial patterns with as few as 400 interactions. RL-IoT paves the road for automatic interactions with poorly documented IoT protocols, thus enabling interoperable systems

A Quick QGIS-Based Procedure to Preliminarily Define Time-Independent Rockfall Risk: The Case Study of Sorba Valley, Italy

Rockfalls are widespread, rapid, and high-energy landslide phenomena that could poten- tially affect large portions of populated lands. The preliminary identification of the most rockfall- prone zones is a challenging task, especially in times of extreme and unpredictable climate change. Even slight environmental modifications can produce significant consequences in terms of exposure, hazard, and risk. Therefore, a timely risk assessment is paramount for territorial administrators to plan and prioritize adequate countermeasures. Risk assessment is crucial to guaranteeing the safety of human lives, the integrity of structures and infrastructures, the preservation of historic and environmental heritage, and the safeguard of economic activities. Hence, new and rapid evaluation methods for rockfall hazard, vulnerability, and risk are needed to identify the most critical areas where more indepth analyses aimed at the design of protective works should be carried out. This study proposes a quick, innovative, and completely GIS-based procedure to preliminarily assess rockfall time-independent hazard and risk in large areas. Propagation analysis is performed by integrating powerful QGIS plugin QPROTO, which can estimate rockfall energy within the invasion area in a simplified way, with the slope units polygons of the Italian territory for the definition of the input parameters. The quantification of risk was obtained by the application of the multidisciplinary IMIRILAND methodology, again within a free and open QGIS environment. Lastly, to test the capabilities of the method, the procedure was applied to a case study of the Sorba Valley (Piemonte, Italy), a tourist region in the northwestern Italian Alps. The findings offer an important contribution to the field of land-planning activities and risk-management strategies

Digital Twinning for 20th Century Concrete Heritage: HBIM Cognitive Model for Torino Esposizioni Halls

In the wide scenario of heritage documentation and conservation, the multi-scale nature of digital models is able to twin the real object, as well as to store information and record investigation results, in order to detect and analyse deformation and materials deterioration, especially from a structural point of view. The contribution proposes an integrated approach for the generation of an n-D enriched model, also called a digital twin, able to support the interdisciplinary investigation process conducted on the site and following the processing of the collected data. Particularly for 20th Century concrete heritage, an integrated approach is required in order to adapt the more consolidated approaches to a new conception of the spaces, where structure and architecture are often coincident. The research plans to present the documentation process for the halls of Torino Esposizioni (Turin, Italy), built in the mid-twentieth century and designed by Pier Luigi Nervi. The HBIM paradigm is explored and expanded in order to fulfil the multi-source data requirements and adapt the consolidated reverse modelling processes based on scan-to-BIM solutions. The most relevant contributions of the research reside in the study of the chances of using and adapting the characteristics of the IFC (Industry Foundation Classes) standard to the archiving needs of the diagnostic investigations results so that the digital twin model can meet the requirements of replicability in the context of the architectural heritage and interoperability with respect to the subsequent intervention phases envisaged by the conservation plan. Another crucial innovation is a proposal of a scan-to-BIM process improved by an automated approach performed by VPL (Visual Programming Languages) contribution. Finally, an online visualisation tool enables the HBIM cognitive system to be accessible and shareable by stakeholders involved in the general conservation process

LsbB Bacteriocin Interacts with the Third Transmembrane Domain of the YvjB Receptor

The Zn-dependent membrane-located protease YvjB has previously been shown to serve as a target receptor for LsbB, a class II leaderless lactococcal bacteriocin. Although yvjB is highly conserved in the genus Lactococcus, the bacteriocin appears to be active only against the subspecies L. lactis subsp. lactis. Comparative analysis of the YvjB proteins of a sensitive strain (YvjB(MN)) and a resistant strain (YvjB(MG)) showed that they differ from each other in 31 positions. In this study, we applied site-directed mutagenesis and performed directed binding studies to provide biochemical evidence that LsbB interacts with the third transmembrane helix of YvjB in susceptible cells. The site-directed mutagenesis of LsbB and YvjB proteins showed that certain amino acids and the length of LsbB are responsible for the bacteriocin activity, most probably through adequate interaction of these two proteins; the essential amino acids in LsbB responsible for the activity are tryptophan (Trp(25)) and terminal alanine (Ala(30)). It was also shown that the distance between Trp(25) and terminal alanine is crucial for LsbB activity. The crucial region in YvjB for the interaction with LsbB is the beginning of the third transmembrane helix, particularly amino acids tyrosine (Tyr(356)) and alanine (Ala(353)). In vitro experiments showed that LsbB could interact with both YvjB(MN) and YvjB(MG), but the strength of interaction is significantly less with YvjB(MG). In vivo experiments with immunofluorescently labeled antibody demonstrated that LsbB specifically interacts only with cells carrying YvjB(MN). IMPORTANCE The antimicrobial activity of LsbB bacteriocin depends on the correct interaction with the corresponding receptor in the bacterial membrane of sensitive cells. Membrane-located bacteriocin receptors have essential primary functions, such as cell wall synthesis or sugar transport, and it seems that interaction with bacteriocins is suicidal for cells. This study showed that the C-terminal part of LsbB is crucial for the bacteriocin activity, most probably through adequate interaction with the third transmembrane domain of the YvjB receptor. The conserved Tyr(356) and Ala(353) residues of YvjB are essential for the function of this Zn-dependent membrane-located protease as a bacteriocin receptor

Cross-network Embeddings Transfer for Traffic Analysis

Artificial Intelligence (AI) approaches have emerged as powerful tools to improve traffic analysis for network monitoring and management. However, the lack of large labeled datasets and the ever-changing networking scenarios make a fundamental difference compared to other domains where AI is thriving. We believe the ability to transfer the specific knowledge acquired in one network (or dataset) to a different network (or dataset) would be fundamental to speed up the adoption of AI-based solutions for traffic analysis and other networking applications (e.g., cybersecurity). We here propose and evaluate different options to transfer the knowledge built from a provider network, owning data and labels, to a customer network that desires to label its traffic but lacks labels. We formulate this problem as a domain adaptation problem that we solve with embedding alignment techniques and canonical transfer learning approaches. We present a thorough experimental analysis to assess the performance considering both supervised (e.g., classification) and unsupervised (e.g., novelty detection) downstream tasks related to darknet and honeypot traffic. Our experiments show the proper transfer techniques to use the models obtained from a network in a different network. We believe our contribution opens new opportunities and business models where network providers can successfully share their knowledge and AI models with customers

Negative Regulation of Violacein Biosynthesis in Chromobacterium violaceum

In Chromobacteium violaceum, the purple pigment violacein is under positive regulation by the N-acylhomoserine lactone CviI/R quorum sensing system and negative regulation by an uncharacterized putative repressor. In this study we report that the biosynthesis of violacein is negatively controlled by a novel repressor protein, VioS. The violacein operon is regulated negatively by VioS and positively by the CviI/R system in both C. violaceum and in a heterologous Escherichia coli genetic background. VioS does not regulate the CviI/R system and apart from violacein, VioS, and quorum sensing regulate other phenotypes antagonistically. Quorum sensing regulated phenotypes in C. violaceum are therefore further regulated providing an additional level of control

Chatterbot Tira-Dúvidas do Curso de Informática do IFMS

Este artigo apresenta a criação de um chatterbot com princípios em Inteligência Artificial e Processamento de Linguagem Natural, produzido por estudantes do IFMS. Sua elaboração se deve à observação da falta de informações que a população do Mato Grosso do Sul apresenta em relação à instituição federal e ao Curso Técnico de Nível Médio Integrado em Informática. Desta forma, o objetivo é a criação de uma ferramenta alternativa para a divulgação do curso e da instituição, cujos aspectos antropomórficos a tornam mais atrativa para aos interessados e futuros alunos do técnico em informática

Botulinum G neurotoxin cleaves VAMP/synaptobrevin at a single Ala-Ala peptide bond.

Similarly to other serotypes, botulinum neurotoxin serotype G (BoNT/G) contains the zinc binding motif of zinc endopeptidases. Highly purified preparations of BoNT/G show a zinc-dependent protease activity specific for VAMP/synaptobrevin, a membrane protein of synaptic vesicles. The two neuronal VAMP isoforms are cleaved with similar rates at one Ala-Ala peptide bond present in the same region, out of the several such peptide bonds present in their sequences. This site of cleavage is unique among the eight clostridial neurotoxins. VAMP proteolysis is displayed only after reduction of the single interchain disulfide bond present in the toxin, and it is inhibited by EDTA, o-phenanthroline and captopril