Neuro-Symbolic Approaches for Context-Aware Human Activity Recognition

Deep Learning models are a standard solution for sensor-based Human Activity Recognition (HAR), but their deployment is often limited by labeled data scarcity and models' opacity. Neuro-Symbolic AI (NeSy) provides an interesting research direction to mitigate these issues by infusing knowledge about context information into HAR deep learning classifiers. However, existing NeSy methods for context-aware HAR require computationally expensive symbolic reasoners during classification, making them less suitable for deployment on resource-constrained devices (e.g., mobile devices). Additionally, NeSy approaches for context-aware HAR have never been evaluated on in-the-wild datasets, and their generalization capabilities in real-world scenarios are questionable. In this work, we propose a novel approach based on a semantic loss function that infuses knowledge constraints in the HAR model during the training phase, avoiding symbolic reasoning during classification. Our results on scripted and in-the-wild datasets show the impact of different semantic loss functions in outperforming a purely data-driven model. We also compare our solution with existing NeSy methods and analyze each approach's strengths and weaknesses. Our semantic loss remains the only NeSy solution that can be deployed as a single DNN without the need for symbolic reasoning modules, reaching recognition rates close (and better in some cases) to existing approaches

SelfAct: Personalized Activity Recognition based on Self-Supervised and Active Learning

Supervised Deep Learning (DL) models are currently the leading approach for sensor-based Human Activity Recognition (HAR) on wearable and mobile devices. However, training them requires large amounts of labeled data whose collection is often time-consuming, expensive, and error-prone. At the same time, due to the intra- and inter-variability of activity execution, activity models should be personalized for each user. In this work, we propose SelfAct: a novel framework for HAR combining self-supervised and active learning to mitigate these problems. SelfAct leverages a large pool of unlabeled data collected from many users to pre-train through self-supervision a DL model, with the goal of learning a meaningful and efficient latent representation of sensor data. The resulting pre-trained model can be locally used by new users, which will fine-tune it thanks to a novel unsupervised active learning strategy. Our experiments on two publicly available HAR datasets demonstrate that SelfAct achieves results that are close to or even better than the ones of fully supervised approaches with a small number of active learning queries

Technical and environmental characterisation of recycled aggregate for reuse in bricks

Waste mud coming from an aggregate washing plant was formerly used as filling material for a pond, aimed at the recovery of an abandoned quarry. Once completed the filling capacity of the pond, the need for identifying a possible reuse of mud produced by the plant arose in order to avoid landfill disposal. Therefore, mud has been geometrically, physically and chemically characterised for its recovery as construction material. A variety of tests was carried out on mud samples as required by EN technical specifications and by Italian environmental standards, focusing particularly on leaching behaviour. The tested material showed satisfactory physical and chemical properties and a release of pollutants below the limits set by the Italian code. Many mix-designs for the production of unfired bricks made of waste mud, sand and straw, stabilised and non-stabilised with lime, gypsum or cement, were developed. The bricks were tested in order to evaluate mechanical properties and leaching behaviour. Mud bricks provided remarkable compressive strength, even if not suitable for structural elements. The use as interior design to minimise humidity changes and to facilitate a thermal insulation is fostered, thus strengthening the so-called green building economy

Sub-nanomolar detection of biogenic amines by SERS effect induced by hairy Janus silver nanoparticles

Surface enhanced Raman scattering (SERS) is largely used as a transduction method for analytes detection in liquid and vapor phase. In particular, SERS effect was promoted by a plethora of different metal and semiconducting nanoparticles (NPs) and silver and gold nanoparticles appear particularly suitable for this application. Nevertheless, silver nanoparticles intrinsic propensity to aggregate in large clusters reduces the possibility to use naked nanoparticles in SERS applications, for this reason they are usually functionalized with organic molecules. This approach inhibits the aggregation process but, on the other hand, reduces the surficial area of the NPs able to interact with the analyte molecules. In the present work, we propose a simple method to obtain surficial anisotropic Janus silver nanoparticles: octadecylamine was used to stabilize the nanoparticles and to promote the deposition of the silver nanoparticles on a solid substrate. The AgNPs/octadecylamine nanostructures showed the typical “hairy” Janus morphology and a strong SERS effect was observed when two biogenic amines, i. e. 2-phenylethylamine and tyramine, were fluxed on the solid film. SERS phenomenon was studied as a function both of the chemical structure of the fluxed amine and of the distance between the aromatic moiety and the nanoparticle allowing to propose the AgNPs/octadecylamine Janus nanoparticles as an active layer for the detection of phenylethylamine and tyramine in picomolar concentration

RTM process monitoring and strain acquisition by fibre optics

Abstract The development of Resin Transfer Moulding technology for advanced applications requires a detailed analysis and control of the process. Fibres optics and Fibres Bragg Gratings are useful tools to investigate composite structures during their lifetime service. They are here employed for the monitoring of manufacturing phase and the acquisition of strains during product usage in service. The adopted monitoring procedure allows to follow all the stages of the production process, evidencing their possible influences over final laminate characteristics. Resin injection, curing and cooling, mould extraction, sensor position, deformation control during mechanical testing are analysed on the basis of the signal output from fibre optic sensors embedded in a model component

Biocompatible Collagen Paramagnetic Scaffold for Controlled Drug Release

A porous collagen-based hydrogel scaffold was prepared in the presence of iron oxide nanoparticles (NPs) and was characterized by means of infrared spectroscopy and scanning electron microscopy. The hybrid scaffold was then loaded with fluorescein sodium salt as a model compound. The release of the hydrosoluble species was triggered and accurately controlled by the application of an external magnetic field, as monitored by fluorescence spectroscopy. The biocompatibility of the proposed matrix was also tested by the MTT assay performed on 3T3 cells. Cell viability was only slightly reduced when the cells were incubated in the presence of the collagen-NP hydrogel, compared to controls. The economicity of the chemical protocol used to obtain the paramagnetic scaffolds as well as their biocompatibility and the safety of the external trigger needed to induce the drug release suggest the proposed collagen paramagnetic matrices for a number of applications including tissue engeneering and drug delivery

Prevalence of p53 dysregulations in feline oral squamous cell carcinoma and non-neoplastic oral mucosa

Squamous cell carcinoma is the most common malignant oral tumor in cats. The late presentation is one of the factors contributing to the detrimental prognosis of this disease. The immunohistochemical expression of the p53 tumor suppressor protein has been reported in 24% to 65% of feline oral squamous cell carcinomas, but no study has systematically evaluated in this tumor the presence of p53 encoding gene (TP53) mutations. The aim of this retrospective study was to determine whether p53 immunohistochemistry accurately reflects the mutational status of the TP53 gene in feline oral squamous cell carcinoma. Additionally, the prevalence of p53 dysregulation in feline oral squamous cell carcinoma was compared with that of feline non-neoplastic oral mucosa, in order to investigate the relevance of these dysregulations in cancer development. The association between p53 dysregulations and exposure to environmental tobacco smoke and tumor characteristics was further assessed. Twenty-six incisional biopsies of oral squamous cell carcinomas and 10 cases each of lingual eosinophilic granuloma, chronic gingivostomatitis and normal oral mucosa were included in the study. Eighteen squamous cell carcinomas (69%) expressed p53 and 18 had mutations in exons 5\u20138 of TP53. The agreement between immunohistochemistry and mutation analysis was 77%. None of non-neoplastic oral mucosa samples had a positive immunohistochemical staining, while one case each of eosinophilic granuloma and chronic gingivostomatitis harbored TP53 mutations. Unlike previously hypothesized, p53 dysregulations were not associated with exposure to environmental tobacco smoke. These results suggest an important role of p53 in feline oral tumorigenesis. Additionally, the immunohistochemical detection of p53 expression appears to reflect the presence of TP53 mutations in the majority of cases. It remains to be determined if the screening for p53 dysregulations, alone or in association with other markers, can eventually contribute to the early detection of this devastating disease