Mirko Marras

Mirko Marras
Università degli studi di Cagliari | UNICA · Department of Mathematics and Computer Science

PhD

About

62
Publications
3,662
Reads
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391
Citations
Introduction
Mirko Marras (1992) is an Assistant Professor at the Department of Mathematics and Computer Science of University of Cagliari (Italy). His research deals with responsible machine learning for user profiling.
Additional affiliations
October 2019 - September 2020
Università degli studi di Cagliari
Position
  • PhD Student
January 2019 - March 2019
New York University
Position
  • PhD Student
September 2018 - December 2018
Universidad de Las Palmas de Gran Canaria
Position
  • PhD Student
Education
October 2016 - December 2019
University of Cagliari
Field of study
  • Computer Science
September 2014 - March 2016
University of Cagliari
Field of study
  • Computer Science
September 2011 - July 2014
University of Cagliari
Field of study
  • Computer Science

Publications

Publications (62)
Preprint
Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainability of their decisions, especially relevant in human-centric fields. We implement five state-of-the-art methodologies for explaining black-box machine learning models (...
Article
Full-text available
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-personalize...
Preprint
Full-text available
Despite the increasing popularity of massive open online courses (MOOCs), many suffer from high dropout and low success rates. Early prediction of student success for targeted intervention is therefore essential to ensure no student is left behind in a course. There exists a large body of research in success prediction for MOOCs, focusing mainly on...
Preprint
Engaging all content providers, including newcomers or minority demographic groups, is crucial for online platforms to keep growing and working. Hence, while building recommendation services, the interests of those providers should be valued. In this paper, we consider providers as grouped based on a common characteristic in settings in which certa...
Preprint
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 ex...
Preprint
In this paper, we propose dictionary attacks against speaker verification - a novel attack vector that aims to match a large fraction of speaker population by chance. We introduce a generic formulation of the attack that can be used with various speech representations and threat models. The attacker uses adversarial optimization to maximize raw sim...
Preprint
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-personalize...
Preprint
Enabling non-discrimination for end-users of recommender systems by introducing consumer fairness is a key problem, widely studied in both academia and industry. Current research has led to a variety of notions, metrics, and unfairness mitigation procedures. The evaluation of each procedure has been heterogeneous and limited to a mere comparison wi...
Article
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...
Article
Full-text available
Online education platforms play an increasingly important role in mediating the success of individuals’ careers. Therefore, while building overlying content recommendation services, it becomes essential to guarantee that learners are provided with equal recommended learning opportunities, according to the platform principles, context, and pedagogy....
Article
Full-text available
Considering the impact of recommendations on item providers is one of the duties of multi-sided recommender systems. Item providers are key stakeholders in online platforms, and their earnings and plans are influenced by the exposure their items receive in recommended lists. Prior work showed that certain minority groups of providers, characterized...
Conference Paper
Full-text available
Interactive simulations allow students to independently explore scientific phenomena and ideally infer the underlying principles through their exploration. Effectively using such environments is challenging for many students and therefore , adaptive guidance has the potential to improve student learning. Providing effective support is, however, als...
Book
The First International Workshop on Enabling Data-Driven Decisions from Learning on the Web (L2D 2021) was held as part of the 14th ACM International Conference on Web Search and Data Mining (WSDM 2021) on March 12, 2021. The workshop collected novel, original research on the state of the art of online education empowered with data mining and machi...
Conference Paper
Full-text available
The design and delivering of platforms for online education is fostering increasingly intense research. Scaling up education online brings new emerging needs related with hardly manageable classes, overwhelming content alternatives, and academic dishonesty while interacting remotely, as examples. However , with the impressive progress of the data m...
Preprint
The human voice conveys unique characteristics of an individual, making voice biometrics a key technology for verifying identities in various industries. Despite the impressive progress of speaker recognition systems in terms of accuracy, a number of ethical and legal concerns has been raised, specifically relating to the fairness of such systems....
Chapter
Providing efficient and effective search and recommendation algorithms has been traditionally the main objective for the industrial and academic research communities. However, recent studies have shown that optimizing models through these algorithms may reinforce the existing societal biases, especially under certain circumstances (e.g., when histo...
Article
Recommender systems learn from historical users’ feedback that is often non-uniformly distributed across items. As a consequence, these systems may end up suggesting popular items more than niche items progressively, even when the latter would be of interest for users. This can hamper several core qualities of the recommended lists (e.g., novelty,...
Book
This book constitutes refereed proceedings of the Second International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, held in April, 2021. Due to the COVID-19 pandemic BIAS 2021 was held virtually. The 11 full papers and 3 short papers were carefully reviewed and selected from 37 submissions. The papers cover topics that go...
Chapter
To allow individuals to complete voice-based tasks (e.g., send messages or make payments), modern automated systems are required to match the speaker’s voice to a unique digital identity representation for verification. Despite the increasing accuracy achieved so far, it still remains under-explored how the decisions made by such systems may be inf...
Preprint
Considering the impact of recommendations on item providers is one of the duties of multi-sided recommender systems. Item providers are key stakeholders in online platforms, and their earnings and plans are influenced by the exposure their items receive in recommended lists. Prior work showed that certain minority groups of providers, characterized...
Preprint
Recommender systems learn from historical data that is often non-uniformly distributed across items, so they may end up suggesting popular items more than niche items. This can hamper user interest and several qualities of the recommended lists (e.g., novelty, coverage, diversity), impacting on the future success of the platform. In this paper, we...
Preprint
Full-text available
Online educational platforms are promising to play a primary role in mediating the success of individuals' careers. Hence, while building overlying content recommendation services, it becomes essential to ensure that learners are provided with equal learning opportunities, according to the platform values, context, and pedagogy. Even though the imp...
Article
The International Workshop on Algorithmic Bias in Search and Recommendation was held on April 14, 2020 in conjuction with the 42nd European Conference on Information Retrieval (ECIR 2020). The scientific program included paper and demo presentations and a final discussion. The keynote was delivered by Prof Chirag Shah. This report presents an overv...
Article
ECIR 2020 ¹ was one of the many conferences affected by the COVID-19 pandemic. The Conference Chairs decided to keep the initially planned dates (April 14-17, 2020) and move to a fully online event. In this report, we describe the experience of organising the ECIR 2020 Workshops in this scenario from two perspectives: the workshop organisers and th...
Preprint
Full-text available
ECIR 2020 https://ecir2020.org/ was one of the many conferences affected by the COVID-19 pandemic. The Conference Chairs decided to keep the initially planned dates (April 14-17, 2020) and move to a fully online event. In this report, we describe the experience of organizing the ECIR 2020 Workshops in this scenario from two perspectives: the worksh...
Chapter
Both search and recommendation algorithms provide results based on their relevance for the current user. In order to do so, such a relevance is usually computed by models trained on historical data, which is biased in most cases. Hence, the results produced by these algorithms naturally propagate, and frequently reinforce, biases hidden in the data...
Conference Paper
Full-text available
With tons of healthcare reviews being collected online, finding helpful opinions among this collective intelligence is becoming harder. Existing literature in this domain usually tackled helpfulness prediction with machine-learning models optimized for binary classification. While they can filter out a subset of reviews, users might be still overwh...
Chapter
From border controls to personal devices, from online exam proctoring to human-robot interaction, biometric technologies are empowering individuals and organizations with convenient and secure authentication and identification services. However, most biometric systems leverage only a single modality, and may face challenges related to acquisition d...
Book
This book constitutes refereed proceedings of the First International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2020, held in April, 2020. Due to the COVID-19 pandemic BIAS 2020 was held virtually. The 10 full papers and 7 short papers were carefully reviewed and seleced from 44 submissions. The papers cover topics that go f...
Conference Paper
In this paper, we assess vulnerability of speaker verification systems to dictionary attacks. We seek master voices, i.e., adversarial utterances optimized to match against a large number of users by pure chance. First, we perform menagerie analysis to identify utterances which intrinsically hold this property. Then, we propose an adversarial optim...
Conference Paper
Social media are providing the humus for the sharing of knowledge and experiences and the growth of community activities (e.g., debating about different topics). The analysis of the user-generated content in this area usually relies on Sentiment Analysis. Word embeddings and Deep Learning have attracted extensive attention in various sentiment dete...
Chapter
Most recommender systems are evaluated on how they accurately predict user ratings. However, individuals use them for more than an anticipation of their preferences. The literature demonstrated that some recommendation algorithms achieve good prediction accuracy, but suffer from popularity bias. Other algorithms generate an item category bias due t...
Chapter
Nowadays, biometric recognition and verification methods are everywhere, trying to face the security issues that constantly affect our digital-every day life. In addition, many special-purpose applications, also need a constant (continuous) verification of the user in order to avoid that a sensitive operation is executed by an impostor; as an examp...
Article
Cloud-connected mobile applications are becoming a popular solution for ubiquitous access to online services, such as cloud data storage platforms. The adoption of such applications has security and privacy implications that are making individuals hesitant to migrate sensitive data to the cloud; thus, new secure authentication protocols are needed....
Conference Paper
Full-text available
The massive amount and variety of city-related data raise equally big challenges to enable citizens to make sense of such data for improving their daily life and fostering collective decision making. The existing dashboards include limited pre-defined use cases which can only address the most common needs of citizens, but do not allow for personali...
Chapter
With the proliferation in number and scale of online courses, several challenges have emerged in supporting stakeholders during their delivery and fruition. Machine Learning and Semantic Analysis can add value to the underlying online environments in order to overcome a subset of such challenges (e.g. classification, retrieval, and recommendation)....
Preprint
Full-text available
The advent of massively interconnected technologies over cities raises equally big challenges regarding interfaces to enable citizens to make sense of urban data for improving their daily life and for fostering online participation. The existing dashboards include only pre-defined and limited use cases which can only address the most common needs o...
Article
Moving towards the next generation of personalized learning environments requires intelligent approaches powered by analytics for advanced learning contexts with enriched digital content. Micro-Learning through Massive Open Online Courses is riding the wave of popularity as a novel paradigm for delivering short educational videos in small pre-organ...
Conference Paper
Multi-class classification aims at assigning each sample to one category chosen among a set of different options. In this paper, we present our work for the development of a novel system for multi-class classification of e-learning videos based on the covered educational subjects. The audio transcripts and the text depicted into visual frames are e...
Article
In recent years, online courses have emerged as a new way to educate students in distance learning settings. However, as the demand increases, educational institutions are facing the challenge of how to prove that online students are who they claim to be during e-learning activities, especially exams. Human proctoring is a non-scalable approach whi...
Article
Evaluating user experience is a challenging task, particularly in e-learning. Existing e-learning systems are limited in their ability of being evaluated based on the user interfaces because current evaluation approaches are usually expensive in time and organization and require active users’ participation. Moreover, a usability assessment is neede...
Chapter
Mobile cloud computing integrates cloud computing into mobile envi-ronments, allowing users to use data, infrastructure, platforms, and applications on the cloud from their mobile devices. However, accessing and exploiting cloud-based resources and services is associated with numerous security implications (e.g. authentication and authorization) wh...