Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classification

Although Deep Neural Networks (DNNs) have great generalization and prediction capabilities, their functioning does not allow a detailed explanation of their behavior. Opaque deep learning models are increasingly used to make important predictions in critical environments, and the danger is that they make and use predictions that cannot be justified or legitimized. Several eXplainable Artificial Intelligence (XAI) methods that separate explanations from machine learning models have emerged, but have shortcomings in faithfulness to the model actual functioning and robustness. As a result, there is a widespread agreement on the importance of endowing Deep Learning models with explanatory capabilities so that they can themselves provide an answer to why a particular prediction was made. First, we address the problem of the lack of universal criteria for XAI by formalizing what an explanation is. We also introduced a set of axioms and definitions to clarify XAI from a mathematical perspective. Finally, we present the Greybox XAI, a framework that composes a DNN and a transparent model thanks to the use of a symbolic Knowledge Base (KB). We extract a KB from the dataset and use it to train a transparent model (i.e., a logistic regression). An encoder-decoder architecture is trained on RGB images to produce an output similar to the KB used by the transparent model. Once the two models are trained independently, they are used compositionally to form an explainable predictive model. We show how this new architecture is accurate and explainable in several datasets.French ANRT (AssociationNationale Recherche Technologie - ANRT)SEGULA TechnologiesJuan de la Cierva Incorporacion grant - MCIN/AEI by "ESF Investing in your future" I JC2019-039152-IGoogle Research Scholar ProgramDepartment of Education of the Basque Government (Consolidated Research Group MATHMODE) IT1456-2

Validation Techniques for Sensor Data in Mobile Health Applications

Mobile applications have become amust in every user’s smart device, andmany of these applications make use of the device sensors’ to achieve its goal. Nevertheless, it remains fairly unknown to the user to which extent the data the applications use can be relied upon and, therefore, to which extent the output of a given application is trustworthy or not. To help developers and researchers and to provide a common ground of data validation algorithms and techniques, this paper presents a review of the most commonly used data validation algorithms, along with its usage scenarios, and proposes a classification for these algorithms. This paper also discusses the process of achieving statistical significance and trust for the desired output.Portuguese Foundation for Science and Technology UID/EEA/50008/2013COST Action Architectures, Algorithms and Protocols for Enhanced Living Environments (AAPELE) IC130

Capabilities, Limitations and Challenges of Style Transfer with CycleGANs: A Study on Automatic Ring Design Generation

Rendering programs have changed the design process completely as they permit to see how the products will look before they are fabricated. However, the rendering process is complicated and takes a signi cant amount of time, not only in the rendering itself but in the setting of the scene as well. Materials, lights and cameras need to be set in order to get the best quality results. Nevertheless, the optimal output may not be obtained in the rst render. This all makes the rendering process a tedious process. Since Goodfellow et al. introduced Generative Adversarial Networks (GANs) in 2014 [1], they have been used to generate computer-assigned synthetic data, from non-existing human faces to medical data analysis or image style transfer. GANs have been used to transfer image textures from one domain to another. However, paired data from both domains was needed. When Zhu et al. introduced the CycleGAN model, the elimination of this expensive constraint permitted transforming one image from one domain into another, without the need for paired data. This work validates the applicability of CycleGANs on style transfer from an initial sketch to a nal render in 2D that represents a 3D design, a step that is paramount in every product design process. We inquiry the possibilities of including CycleGANs as part of the design pipeline, more precisely, applied to the rendering of ring designs. Our contribution entails a crucial part of the process as it allows the customer to see the nal product before buying. This work sets a basis for future research, showing the possibilities of GANs in design and establishing a starting point for novel applications to approach crafts design.MCIN/AEI IJC2019-039152-IESF Investing in your future IJC2019-039152-IGoogle Research Scholar ProgramBasque Government ELKARTEK program (3KIA project) KK-2020/00049 research group MATHMODE T1294-1

Explaining Aha! moments in artificial agents through IKE-XAI: Implicit Knowledge Extraction for eXplainable AI

During the learning process, a child develops a mental representation of the task he or she is learning. A Machine Learning algorithm develops also a latent representation of the task it learns. We investigate the development of the knowledge construction of an artificial agent through the analysis of its behavior, i.e., its sequences of moves while learning to perform the Tower of Hanoï (TOH) task. The TOH is a well-known task in experimental contexts to study the problem-solving processes and one of the fundamental processes of children’s knowledge construction about their world. We position ourselves in the field of explainable reinforcement learning for developmental robotics, at the crossroads of cognitive modeling and explainable AI. Our main contribution proposes a 3-step methodology named Implicit Knowledge Extraction with eXplainable Artificial Intelligence (IKE-XAI) to extract the implicit knowledge, in form of an automaton, encoded by an artificial agent during its learning. We showcase this technique to solve and explain the TOH task when researchers have only access to moves that represent observational behavior as in human–machine interaction. Therefore, to extract the agent acquired knowledge at different stages of its training, our approach combines: first, a Q-learning agent that learns to perform the TOH task; second, a trained recurrent neural network that encodes an implicit representation of the TOH task; and third, an XAI process using a post-hoc implicit rule extraction algorithm to extract finite state automata. We propose using graph representations as visual and explicit explanations of the behavior of the Q-learning agent. Our experiments show that the IKEXAI approach helps understanding the development of the Q-learning agent behavior by providing a global explanation of its knowledge evolution during learning. IKE-XAI also allows researchers to identify the agent’s Aha! moment by determining from what moment the knowledge representation stabilizes and the agent no longer learns.Region BretagneEuropean Union via the FEDER programSpanish Government Juan de la Cierva Incorporacion - MCIN/AEI IJC2019-039152-IGoogle Research Scholar Gran

Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C1011198) , (Institute for Information & communications Technology Planning & Evaluation) (IITP) grant funded by the Korea government (MSIT) under the ICT Creative Consilience Program (IITP-2021-2020-0-01821) , and AI Platform to Fully Adapt and Reflect Privacy-Policy Changes (No. 2022-0-00688).Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI mode ľs decision-making. Thus, the need for eXplainable AI (XAI) methods for improving trust in AI models has arisen. XAI has become a popular research subject within the AI field in recent years. Existing survey papers have tackled the concepts of XAI, its general terms, and post-hoc explainability methods but there have not been any reviews that have looked at the assessment methods, available tools, XAI datasets, and other related aspects. Therefore, in this comprehensive study, we provide readers with an overview of the current research and trends in this rapidly emerging area with a case study example. The study starts by explaining the background of XAI, common definitions, and summarizing recently proposed techniques in XAI for supervised machine learning. The review divides XAI techniques into four axes using a hierarchical categorization system: (i) data explainability, (ii) model explainability, (iii) post-hoc explainability, and (iv) assessment of explanations. We also introduce available evaluation metrics as well as open-source packages and datasets with future research directions. Then, the significance of explainability in terms of legal demands, user viewpoints, and application orientation is outlined, termed as XAI concerns. This paper advocates for tailoring explanation content to specific user types. An examination of XAI techniques and evaluation was conducted by looking at 410 critical articles, published between January 2016 and October 2022, in reputed journals and using a wide range of research databases as a source of information. The article is aimed at XAI researchers who are interested in making their AI models more trustworthy, as well as towards researchers from other disciplines who are looking for effective XAI methods to complete tasks with confidence while communicating meaning from data.National Research Foundation of Korea Ministry of Science, ICT & Future Planning, Republic of Korea Ministry of Science & ICT (MSIT), Republic of Korea 2021R1A2C1011198Institute for Information amp; communications Technology Planning amp; Evaluation) (IITP) - Korea government (MSIT) under the ICT Creative Consilience Program IITP-2021-2020-0-01821AI Platform to Fully Adapt and Reflect Privacy-Policy Changes2022-0-0068

Chondrogenic differentiation of human mesenchymal stem cells via SOX9 delivery in cationic niosomes

[Abstract] Gene transfer to mesenchymal stem cells constitutes a powerful approach to promote their differentiation into the appropriate cartilage phenotype. Although viral vectors represent gold standard vehicles, because of their high efficiency, their use is precluded by important concerns including an elevated immunogenicity and the possibility of insertional mutagenesis. Therefore, the development of new and efficient non-viral vectors is under active investigation. In the present study, we developed new non-viral carriers based on niosomes to promote the effective chondrogenesis of human MSCs. Two different niosome formulations were prepared by varying their composition on non-ionic surfactant, polysorbate 80 solely (P80), or combined with poloxamer 407 (P80PX). The best niosome formulation was proven to transfer a plasmid, encoding for the potent chondrogenic transcription factor SOX9 in hMSC aggregate cultures. Transfection of hMSC aggregates via nioplexes resulted in an increased chondrogenic differentiation with reduced hypertrophy. These results highlight the potential of niosome formulations for gene therapy approaches focused on cartilage repair.Ministerio de Ciencia e Innovación (España); RTI2018-099389-A-100Ministerio de Ciencia e Innovación (España); RYC2018-025617-IXunta de Galicia; ED431F2021/1

A subset of low density granulocytes is associated with vascular calcification in chronic kidney disease patients

Inflammation is central to chronic kidney disease (CKD) pathogenesis and vascular outcomes, but the exact players remain unidentified. Since low density granulocytes (LDGs) are emerging mediators in inflammatory conditions, we aimed to evaluate whether LDGs may be altered in CKD and related to clinical outcomes as biomarkers. To his end, LDGs subsets were measured in peripheral blood by flow cytometry and confocal microscopy in 33 CKD patients undergoing peritoneal dialysis and 15 healthy controls (HC). Analyses were replicated in an additional cohort. DEF3 (marker of early granulopoiesis) gene expression on PBMCs was quantified by qPCR. Total CD15+ LDGs and both CD14lowCD16+ and CD14−CD16− subsets were expanded in CKD. The relative frequency of the CD14−CD16− subpopulation was higher among the CD15+ pool in CKD. This alteration was stable over-time. The increased CD14−CD16−CD15+ paralleled Kauppila scores and DEF3 expression, whereas no association was found with CD14lowCD16+ CD15+. Both subsets differed in their CD11b, CD10, CD35, CD31, CD62L, IFNAR1 and CD68 expression, FSC/SSC features and nuclear morphology, pointing to different origins and maturation status. In conclusion, LDGs were expanded in CKD showing a skewed distribution towards a CD14−CD16−CD15+ enrichment, in association with vascular calcification. DEF3 expression in PBMC can be a marker of LDG expansion.Fil: Rodríguez Carrio, Javier. Hospital Universitario Central de Asturias. Instituto de Investigación Sanitaria del Principado de Asturias (ISPA). Bone and Mineral Research Unit; España. Universidad de Oviedo; EspañaFil: Carrillo López, Natalia. Hospital Universitario Central de Asturias; EspañaFil: Ulloa, Catalina. Hospital Universitario Central de Asturias; EspañaFil: Seijo, Mariana. Hospital Universitario Central de Asturias; España. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; ArgentinaFil: Rodríguez García, Minerva. Hospital Universitario Central de Asturias; EspañaFil: Rodríguez Suárez, Carmen. Hospital Universitario Central de Asturias; EspañaFil: Díaz-Corte, Carmen. Hospital Universitario Central de Asturias; EspañaFil: Cannata Andía, Jorge B.. Universidad de Oviedo; España. Hospital Universitario Central de Asturias; EspañaFil: Suárez, Ana. Universidad de Oviedo; EspañaFil: Dusso, Adriana. Hospital Universitario Central de Asturias; Españ

Effect of Penetration Enhancers and Safety on the Transdermal Delivery of Apremilast in Skin

The poor water solubility of apremilast (APR) is the main impediment to the penetration of the drug through the skin barrier. The objective of this study was to evaluate the permeability of APR in different solutions enriched with penetration promoters in ex vivo samples of human skin, and additionally assess its tolerance in vivo. To this end, APR solutions with 5% promoter were developed, and the drug's ability to penetrate human abdominal skin samples was evaluated; the coefficients of permeability, cumulated amounts permeated, and flow were some of the parameters evaluated; likewise, the in vitro and in vivo tolerance of the solutions was evaluated. The results obtained showed that the solutions containing squalene as a promoter improved the penetration of APR compared to the other promoters evaluated; in the same way, on an in vitro scale in HaCaT cells, the promoters were not toxic, finding a cell viability greater than 80% at the different dilutions evaluated. In the in vivo tests carried out with the solution that presented the best results (APRSqualene solution), it was observed that it does not cause irritation or erythema on the skin after its colorimetric and histological evaluation of the dorsal region of rats after its application. Squalene becomes an excellent candidate to improve the permeability of the drug in the case of the development of a topical formulation; in addition, it was confirmed that this penetration enhancer is neither toxic nor irritating when in contact with the skin in in vivo tests

The enhancement of porosity of carbon xerogels by using additives

Resorcinol-formaldehyde carbon xerogels were synthesized by means of microwave heating by using precursor solutions with pH values ranging from 3 to 7 and adding various amounts of sodium sulfate, hexadecyltrimethylammonium bromide and Span80. It was found that the amount of additive that can be introduced depends to a large extent on the final pH of the precursor solution. Characterization of the porous structure of the carbon xerogels thus synthesized demonstrated that their porosity was modified by interactions between the additives and the polymeric structure of the xerogels. It is worth noting that carbonaceous materials with a pore size that could not be obtained by merely modifying the pH could be synthesized by adding different types of additive, with the result that a significant improvement of the porous properties of the carbon xerogels was achieved. The addition of sodium sulfate increased the size of the clusters and pores due to repulsive forces created between the additive and resorcinol anions. Hexadecyltrimethylammonium bromide gave rise to a dense branched structure with pores of a small size attributable to forces of attraction between the cations of the additive and resorcinol anions. In contrast, the presence of Span80 in the precursor solution produced a condensation reaction between the resorcinol and the additive, as a result of which the amount of resorcinol available for the sol-gel reaction was reduced.Financial support from the Ministerio de Economía y Competitividad of Spain MINECO (under Projects MAT2011-23733 and IPT-2012-0689-420000) is greatly acknowledged. NRR is also grateful to MINECO for her predoctoral research grant.Peer Reviewe

Apremilast Microemulsion as Topical Therapy for Local Inflammation: Design, Characterization and Efficacy Evaluation

Apremilast (APR) is a selective phosphodiesterase 4 inhibitor administered orally in the treatment of moderate-to-severe plaque psoriasis and active psoriatic arthritis. The low solubility and permeability of this drug hinder its dermal administration. The purpose of this study was to design and characterize an apremilast-loaded microemulsion (APR-ME) as topical therapy for local skin inflammation. Its composition was determined using pseudo-ternary diagrams. Physical, chemical and biopharmaceutical characterization were performed. Stability of this formulation was studied during 90 days. Tolerability of APR-ME was evaluated in healthy volunteers while its anti-inflammatory potential was studied using in vitro and in vivo models. A homogeneous formulation with Newtonian behavior and droplets of nanometric size and spherical shape was obtained. APR-ME released the incorporated drug following a first-order kinetic and facilitated drug retention into the skin, ensuring a local effect. Anti-inflammatory potential was observed for its ability to decrease the production of IL-6 and IL-8 in the in vitro model. This effect was confirmed in the in vivo model histologically by reduction in infiltration of inflammatory cells and immunologically by decrease of inflammatory cytokines IL-8, IL-17A and TNFα. Consequently, these results suggest that this formulation could be used as an attractive topical treatment for skin inflammation