Demographic Fairness in Multimodal Biometrics: A Comparative Analysis on Audio-Visual Speaker Recognition Systems

In urban scenarios, biometric recognition technologies are being increasingly adopted to empower citizens with a secure and usable access to personalized services. Given the challenging environmental scenarios, combining evidence from multiple biometrics at a certain step of the recognition pipeline has been often proved to increase the performance of the biometric-enabled recognition system. Despite the increasing accuracy achieved so far, it still remains under-explored how the adopted biometric fusion policy impacts on the quality of the decisions made by the biometric system, depending on the demographic characteristics of the citizen under consideration. In this paper, we investigate the extent to which state-of-the-art multimodal recognition systems based on facial and vocal biometrics are susceptible to unfairness towards legally-protected groups of individuals, characterized by a common sensitive attribute. Specifically, we present a comparative analysis of the performance across groups for two deep learning architectures tailored for facial and vocal recognition, under seven fusion policies that cover different pipeline steps (feature, model, score and decision). Experiments show that, compared to the unimodal systems alone and the other fusion policies, the multimodal system obtained via a fusion at the model step leads to the highest overall accuracy and the lowest disparity across groups

Production of the front-end boards of the LHCb muon system

This note describes the production of the front end boards CARDIAC, for the 1368 MWPC, and CARDIAC-GEM, for the 12 triple-GEM chambers, of the LHCb muon system. The PCB structure and component layout and the production issues, such as component soldering, quality assurance at the company and delivery rates, are described. The performance of these boards will be the subject of a future publication

Recency, Popularity, and Diversity of Explanations in Knowledge-based Recommendation

Modern knowledge-based recommender systems enable the end-to-end generation of textual explanations. These explanations are created from learnt paths between an already experience product and a recommended product in a knowledge graph, for a given user. However, none of the existing studies has investigated the extent to which properties of a single explanation (e.g., the recency of interaction with the already experience product) and of a group of explanations for a recommended list (e.g., the diversity of the explanation types) can influence the perceived explanation quality. In this paper, we summarize our previous work on conceptualizing three novel properties that model the quality of the explanations (linking interaction recency, shared entity popularity, and explanation type diversity) and proposing re-ranking approaches able to optimize for these properties. Experiments on two public data sets showed that our approaches can increase explanation quality according to the proposed properties, while preserving recommendation utility. Source code and data: https://github.com/giacoballoccu/explanation-quality-recsys

Robust reputation independence in ranking systems for multiple sensitive attributes

Ranking systems have an unprecedented influence on how and what information people access, and their impact on our society is being analyzed from different perspectives, such as users’ discrimination. A notable example is represented by reputation-based ranking systems, a class of systems that rely on users’ reputation to generate a non-personalized item-ranking, proved to be biased against certain demographic classes. To safeguard that a given sensitive user’s attribute does not systematically affect the reputation of that user, prior work has operationalized a reputation independence constraint on this class of systems. In this paper, we uncover that guaranteeing reputation independence for a single sensitive attribute is not enough. When mitigating biases based on one sensitive attribute (e.g., gender), the final ranking might still be biased against certain demographic groups formed based on another attribute (e.g., age). Hence, we propose a novel approach to introduce reputation independence for multiple sensitive attributes simultaneously. We then analyze the extent to which our approach impacts on discrimination and other important properties of the ranking system, such as its quality and robustness against attacks. Experiments on two real-world datasets show that our approach leads to less biased rankings with respect to multiple users’ sensitive attributes, without affecting the system’s quality and robustness

Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations

Existing explainable recommender systems have mainly modeled relationships between recommended and already experienced products, and shaped explanation types accordingly (e.g., movie "x"starred by actress "y"recommended to a user because that user watched other movies with "y"as an actress). However, none of these systems has investigated the extent to which properties of a single explanation (e.g., the recency of interaction with that actress) and of a group of explanations for a recommended list (e.g., the diversity of the explanation types) can influence the perceived explaination quality. In this paper, we conceptualized three novel properties that model the quality of the explanations (linking interaction recency, shared entity popularity, and explanation type diversity) and proposed re-ranking approaches able to optimize for these properties. Experiments on two public data sets showed that our approaches can increase explanation quality according to the proposed properties, fairly across demographic groups, while preserving recommendation utility. The source code and data are available at https: //github.com/giacoballoccu/explanation-quality-recsys

XRecSys: A framework for path reasoning quality in explainable recommendation

There is increasing evidence that recommendations accompanied by explanations positively impact on businesses in terms of trust, guidance, and persuasion. This advance has been made possible by traditional models representing user–product interactions augmented with external knowledge modeled as knowledge graphs. However, these models produce textual explanations on top of reasoning paths extracted from the knowledge graph without considering relevant properties of the path entities. In this paper, we present XRecSys, a Python framework for the optimization of the reasoning path selection process according to properties deemed relevant by users (e.g., time relevance of the linking interaction or popularity of the entity linked to the explanation). Our framework leads to a higher reasoning path quality in terms of the considered properties and, consequently, textual explanations more relevant for the users

Consumer Fairness Benchmark in Recommendation

Several mitigation procedures have emerged to address consumer unfairness in personalized rankings. However, evaluating their performance is difficult due to variations in experimental protocols, such as differing fairness definitions, data sets, evaluation metrics, and sensitive attributes. This makes it challenging for scientists to choose a suitable procedure for their practical setting. In this paper, we summarize our previous work on investigating the properties a given mitigation procedure against consumer unfairness should be evaluated on. To this end, we defined eight technical properties and leveraged two public datasets to evaluate the extent to which existing mitigation procedures against consumer unfairness met these properties. Source code and data: https://github.com/jackmedda/Perspective-C-Fairness-RecSys

Presenza di un «Potyvirus» sul Carciofo (<i>Cynara scolymus</i> L.) in Sardegna

A latent virus of artichoke has been isolated in Sardinia (Italy). The virus causes characteristic local lesions on Gomphrena globosa L. and Chenopodium amaranticolor Coste et Reyn., and systemic symptoms on Nicotiana benthamiana Domin. and N. clevelandii Gray. In artichoke crude sap the virus has a longevity in vitro of 20-30 hours, a dilution-end point between 10-3 and 10-4 and a thermal inactivation point between 55 and 60°C. The purification of the virus has been obtained from artichoke with two cicles of differential centrifugation followed by sucrose density gradient centrifugation. The purified suspensions had an ultraviolet light (UV) ab sorption spectrum typical of the nucleoproteins with Emax = 260-262 nm; Emin = 246 nm; and a ratio E280/E260 of 0,85 which suggests a RNA content of the virus of about 5,5%. Electron microscope observation showed that negative stained partially purified virus suspensions are composed of filamentous parti cles with a normal lenght (NL) of 730 nm and a mode of the lenght distributions of 729 nm; moreover, ultrathin sections of tissue fragments from leaves of mechanically inocu1ated Nicotiana benthamiana contained cytoplasmic inclusions of the pinwheel type. In serological tube tests, partially purified virus suspensions reacted with homologous serum (titre 1 : 1024) and a serum immune to an ALV (Artichoke Latent Virus) from Bari. These results have been substantially confirmed by serological tests with the method of «antibody coating of virus parti cles » by immune electron microscopy. I t is obvious that a latent potyvirus is widespread in artichoke growing in Sardinia. However, it cannot be exc1uded the possible occurrence in artichoke plants of a latent carlavirus too

ARE SUSCEPTIBILITY to INFECTIVE ENDOCARDITIS and EFFECTIVENESS of ANTIBIOTIC PROPHYLAXIS LINKED to FLUCTUATIONS of the IMMUNE SYSTEM? A NOVEL HYPOTHESIS

Introduction: An amendment incorporated into the 2007 AHA and 2009 ESC guidelines on infective endocarditis led to a substantial restriction in indications for the administration of antibiotic prophylaxis. This may have resulted in a subsequent steady increase in the number of cases of infective endocarditis worldwide. Methods: It has been hypothesised that susceptibility to infective endocarditis, together with effectiveness of antibiotic prophylaxis, may be linked to fluctuations of the immune system. Throughout a person’s lifetime, individual susceptibility to infective endocarditis may vary in an identical situation of risk. As a consequence, a personalised targeted approach should be adopted when prescribing antibiotic prophylaxis to prevent onset of endocarditis, taking into account a series of factors including age, comorbidities, cortisol levels, and ethnicity. Children affected by bicuspid aortic valve and injection drug users are amongst the newly-emerging higher risk populations. Conclusion: This up-to-dated narrative review summarizes all the available scientific evidence concerning the variable influence of the immune system on susceptibility to infective endocarditis

Decay in chemotaxis systems with a logistic term

This paper is concerned with a general fully parabolic Keller-Segel system, defined in a convex bounded and smooth domain Ω of RN , for N ∈ {2, 3}, with coefficients depending on the chemical concentration, perturbed by a logistic source and endowed with homogeneous Neumann boundary conditions. For each space dimension, once a suitable energy function in terms of the solution is defined, we impose proper assumptions on the data and an exponential decay of such energies is established