Luca Longo

Technological University Dublin - City Campus | TU Dublin · School of Computing

The development of theory, frameworks and tools for Explainable AI (XAI) is a very active area of research these days, and articulating any kind of coherence on a vision and challenges is itself a challenge. At least two sometimes complementary and colliding threads have emerged. The first focuses on the development of pragmatic tools for increasin...

Machine and deep learning have proven their utility to generate data-driven models with high accuracy and precision. However, their non-linear, complex structures are often difficult to interpret. Consequently, many scholars have developed a plethora of methods to explain their functioning and the logic of their inferences. This systematic review a...

Understanding the inferences of data-driven, machine-learned models can be seen as a process that discloses the relationships between their input and output. These relationships consist and can be represented as a set of inference rules. However, the models usually do not explicit these rules to their end-users who, subsequently, perceive them as b...

This chapter begins with an assessment of the nature and characteristics of mental workload and how people have defined it over the years. It looks at the major techniques, their relative advantages and disadvantages and how they are enacted in practical circumstances in the many operational domains to which they can apply. The chapter examines app...

Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics. Uncertainties exist in its characterization, motivating the design and development of computational models, thus recently and actively receiving support from the discipline of Comp...

p>Alzheimer's Disease (AD) and Frontotemporal Dementia (FTD) represent formidable neurodegenerative challenges. Existing research into optimal feature-extraction techniques for discerning pertinent AD/FTD biomarkers from Electroencephalography (EEG) data presents room for enhancement. Addressing this, our study undertakes a comprehensive evaluation...

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements i...

One goal of Explainable Artificial Intelligence (XAI) is to interpret and explain the inferential process of data-driven machine-learned models to make it comprehensible for humans. To reach it, it is necessary to have a reliable tool to collect the opinions of human users about the explanations generated by XAI methods of trained complex models. P...

Variational Autoencoders (VAEs) constitute one of the most significant deep generative models for the creation of synthetic samples. In the field of audio synthesis, VAEs have been widely used for the generation of natural and expressive sounds, such as music or speech. However, VAEs are often considered black boxes and the attributes that contribu...

Electroencephalography (EEG) data has emerged as a promising modality for biometric applications, offering unique and secure personal identification and authentication methods. This research comprehensively compared EEG data pre-processing techniques, focusing on biometric applications. In tandem with this, the study illuminates the pivotal role of...

Reinforcement Learning (RL) has shown promise in optimizing complex control and decision-making processes but Deep Reinforcement Learning (DRL) lacks interpretability, limiting its adoption in regulated sectors like manufacturing, finance, and healthcare. Difficulties arise from DRL’s opaque decision-making, hindering efficiency and resource use, t...

Dimensionality reduction and producing simple representations of electroencephalography (EEG) signals are challenging problems. Variational autoencoders (VAEs) have been employed for EEG data creation, augmentation, and automatic feature extraction. In most of the studies, VAE latent space interpretation is used to detect only the out-of-order dist...

Parkinson's Disease (PD) is an incurable neurological disorder that degenerates the cerebrospinal nervous system and hinders motor functions. Electroencephalography (EEG) signal analysis can provide reliable information regarding PD conditions. However, EEG is a complex, multi-channel, and non-linear signal with noise that problematizes identifying...

Controllable timbre synthesis has been a subject of research for several decades, and deep neural networks have been the most successful in this area. Deep generative models such as Variational Autoencoders (VAEs) have the ability to generate a high-level representation of audio while providing a structured latent space. Despite their advantages, t...

This paper presents a novel framework for structured argumentation, named extend argumentative decision graph xADG. It is an extension of argumentative decision graphs built upon Dung's abstract argumentation graphs. The xADG framework allows for arguments to use boolean logic operators and multiple premises (supports) within their internal structu...

Feature selection plays an important role in machine learning and data mining problems. Identifying the best feature selection algorithm that helps to remove irrelevant and redundant features is a complex task. This research tries to address it by recommending a feature selection algorithm based on dataset meta-features. The main contribution of th...

This open access book constitutes selected papers presented during the 30th Irish Conference on Artificial Intelligence and Cognitive Science, held in Munster, Ireland, in December 2022. The 41 presented papers were thoroughly reviewed and selected from the 102 submissions. They are organized in topical sections on machine learning, deep learning...

Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject’s interview by a skilled psychiatrist. The process needs time and is bound to human errors and bias. Recently, brain connectivity indices have been used in a few pattern recognition meth...

In audio processing applications, the generation of expressive sounds based on high-level representations demonstrates a high demand. These representations can be used to manipulate the timbre and influence the synthesis of creative instrumental notes. Modern algorithms, such as neural networks, have inspired the development of expressive synthesiz...

Non-invasive Visual Stimuli evoked-EEG-based P300 BCIs have gained immense attention in recent years due to their ability to help patients with disability using BCI-controlled assistive devices and applications. In addition to the medical field, P300 BCI has applications in entertainment, robotics, and education. The current article systematically...

Global texture characteristics are powerful tools for solving medical image classification tasks. There are many such characteristics like Grey-Level Co-occurrence Matrices, Grey-Level Run-Length Matrices, Grey-Level Size Zone Matrices, texture matrices and others. However, not all are important when solving particular image classification tasks, w...

Electroencephalography (EEG) signals can be analyzed in the temporal, spatial, or frequency domains. Noise and artifacts during the data acquisition phase contaminate these signals adding difficulties in their analysis. Techniques such as Independent Component Analysis (ICA) require human intervention to remove noise and artifacts. Autoencoders hav...

The current study considers the development of a 5-layer pipeline for identifying and classifying COVID-19-induced lung lesions. Such system is multilayer, built upon convolutional and fully connected neural networks and logistic self-organised forest built using the group method of data handling (GMDH) principles. This pipeline includes a mechanis...

The principal reason for measuring mental workload is to quantify the cognitive cost of performing tasks to predict human performance. Unfortunately, a method for assessing mental workload that has general applicability does not exist yet. This is due to the abundance of intuitions and several operational definitions from various fields that disagr...

Dimensionality reduction and the automatic learning of key features from electroencephalographic (EEG) signals have always been challenging tasks. Variational autoencoders (VAEs) have been used for EEG data generation and augmentation, denoising, and automatic feature extraction. However, investigations of the optimal shape of their latent space ha...

The principal reason for measuring mental workload is to quantify the cognitive cost of performing tasks to predict human performance. Unfortunately, a method for assessing mental workload that has general applicability does not exist yet. This research presents a novel self-supervised method for mental workload modelling from EEG data employing De...

Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that combining deep learning with symbolic reasoning will lead to stronger AI than either paradigm on its own. As successful as deep learning has been, it is generally accepted that even our best deep learning systems are not very good at abstract reasoning. And since reasoning is i...

Explainable Artificial Intelligence (XAI) aims to train data-driven, machine learning (ML) models possessing both high predictive accuracy and a high degree of explainability for humans. Comprehending and explaining the inferences of a model can be seen as a defeasible reasoning process which is expected to be non-monotonic meaning that a conclusio...

Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that combining deep learning with symbolic reasoning will lead to stronger AI than either paradigm on its own. As successful as deep learning has been, it is generally accepted that even our best deep learning systems are not very good at abstract reasoning. And since reasoning is i...

Dealing with uncertain, contradicting, and ambiguous information is still a central issue in Artificial Intelligence (AI). As a result, many formalisms have been proposed or adapted so as to consider non-monotonicity. A non-monotonic formalism is one that allows the retraction of previous conclusions or claims, from premises, in light of new eviden...

Virtual reality (VR) is getting traction in many contexts, allowing users to have a real-life experience in a virtual world. However, its application in the field of Neuroscience, and above all probing newer activity with the analysis of electroencephalographic (EEG) event-related potentials (ERP) is underexplored. This article reviews the state-of...

[This corrects the article DOI: 10.3389/fpsyg.2022.883321.].

Dealing with uncertain, contradicting, and ambiguous information is still a central issue in Artificial Intelligence (AI). As a result, many formalisms have been proposed or adapted so as to consider non-monotonicity, with only a limited number of works and researchers performing any sort of comparison among them. A non-monotonic formalism is one t...

One of the aim of Explainable Artificial Intelligence (XAI) is to equip data-driven, machine-learned models with a high degree of explainability for humans. Understanding and explaining the inferences of a model can be seen as a defeasible reasoning process. This process is likely to be non-monotonic: a conclusion, linked to a set of premises, can...

Many research works indicate that EEG bands, specifically the alpha and theta bands, have been potentially helpful cognitive load indicators. However, minimal research exists to validate this claim. This study aims to assess and analyze the impact of the alpha-to-theta and the theta-to-alpha band ratios on supporting the creation of models capable...

Biometrics is the process of measuring and analyzing human characteristics to verify a given person's identity. Most real-world applications rely on unique human traits such as fingerprints or iris. However, among these unique human characteristics for biometrics, the use of Electroencephalogram (EEG) stands out given its high inter-subject variabi...

Many research works indicate that EEG bands, specifically the alpha and theta bands, have been potentially helpful cognitive load indicators. However, minimal research exists to validate this claim. This study aims to assess and analyze the impact of the alpha-to-theta and the theta-to-alpha band ratios on supporting the creation of models capable...

Data is of high quality if it is fit for its intended use in operations, decision-making, and planning. There is a colossal amount of linked data available on the web. However, it is difficult to understand how well the linked data fits into the modeling tasks due to the defects present in the data. Faults emerged in the linked data, spreading far...

Schizophrenia (SZ) is a chronic mental disorder associated with functional impairment of human brain. Early-stage SZ detection can lead to better treatment, improving the quality of life of patients suffering from this disease. This work proposes an automated computer-aided diagnosis (CAD) system for SZ detection using multichannel EEG activity. Th...

With an increasing amount of structured data on the web, the need to understand and convert it into linked data is growing. One of the most frequent data formats is Comma Separated Value (CSV). However, it is not easy to describe metadata such as the datatype, data quality and data provenance along with it. Therefore, to publish CSV on the web, it...

Feature selection plays an important role in machine learning or data mining problems. Removing irrelevant features increases model accuracy and reduces the computational cost. However, selecting important features is not a simple task as one feature selection algorithm does not perform well on all the datasets that are of interest. This paper trie...

Linked data is often generated from raw data with the help of mapping languages. Complex data transformation is one of the essential parts while uplifting data which either can be implemented as custom solutions or separated from the mapping process. In this paper, we propose an approach of separating complex data transformations from the mapping p...

Instructional efficiency within education is a measurable concept and models have been proposed to assess it. The main assumption behind these models is that efficiency is the capacity to achieve established goals at the minimal expense of resources. This article challenges this assumption by contributing to the body of Knowledge with a novel model...

The effective functioning of data-intensive applications usually requires that the dataset should be of high quality. The quality depends on the task they will be used for. However, it is possible to identify task-independent data quality dimensions which are solely related to data themselves and can be extracted with the help of rule mining/patter...

Explainable Artificial Intelligence (XAI) has experienced a significant growth over the last few years. This is due to the widespread application of machine learning, particularly deep learning, that has led to the development of highly accurate models that lack explainability and interpretability. A plethora of methods to tackle this problem have...

In this early-stage research, a multidisciplinary approach is presented for the detection of propaganda in the media, and for mod-eling the spread of propaganda and disinformation using semantic web and graph theory. An ontology will be designed which has the theoretical underpinnings from multiple disciplines including the social sciences and epid...

In this paper we introduce a novel family of semantics called weakly complete semantics. Differently from Dung's complete semantics, weakly complete semantics employs a mechanism called undecidedness blocking by which the label undecided of an attacking argument is not always propagated to an otherwise accepted attacked argument. The new semantics...

This book includes selecting the articles accepted for presentation and discussion at WCQR2021, held on January 20th to 22nd, 2021 (Virtual Conference). The World Conference on Qualitative Research (WCQR) is an annual event that aims to bring together researchers, academics and professionals, promoting the sharing and discussion of knowledge, new p...

The impact of fatigue on train drivers is one of the most important safety-critical issues in rail. It affects drivers’ performance, significantly contributing to railway incidents and accidents. To address the issue of real-time fatigue detection in drivers, most reliable and applicable psychophysiological indicators of fatigue need to be identifi...

Instructional efficiency within education is a measurable concept and models have been proposed to assess it. The main assumption behind these models is that efficiency is the capacity to achieve established goals at the minimal expense of resources. This article challenges this assumption by contributing to the body of Knowledge with a novel model...

This book constitutes the refereed proceedings of the 5th International Symposium on Human Mental Workload: Models and Applications, H-WORKLOAD 2021, held virtually in November 2021. The volume presents 9 revised full papers, which were carefully reviewed and selected from 16 submissions. The papers are organized in two topical sections on models a...

Cognitive cognitive load theory (CLT) has been conceived for improving instructional design practices. Although researched for many years, one open problem is a clear definition of its cognitive load types and their aggregation towards an index of overall cognitive load. In Ergonomics, the situation is different with plenty of research devoted to t...

This paper proposes a novel machine learning procedure for genome-wide association study (GWAS), named LightGWAS. It is based on the LightGBM framework, in addition to being a single, resilient, autonomous and scalable solution to address common limitations of GWAS implementations found in the literature. These include reliance on massive manual qu...

Argumentation has recently shown appealing properties for inference under uncertainty and conflicting knowledge. However, there is a lack of studies focused on the examination of its capacity of exploiting real-world knowledge bases for performing quantitative, case-by-case inferences. This study performs an analysis of the inferential capacity of...

This book constitutes the refereed proceedings of the 4th International Symposium on Human Mental Workload: Models and Applications, H-WORKLOAD 2020, held in Granda, Spain*, in December 2020. The volume presents one keynote paper as well as 13 revised full papers, which were carefully reviewed and selected from 22 submissions. The papers are organ...

The ultimate goal of Explainable Artificial Intelligence is to build models that possess both high accuracy and degree of explainability. Understanding the inferences of such models can be seen as a process that discloses the relationships between their input and output. These relationships can be represented as a set of inference rules which are u...

Cognitive Load Theory is based upon the assumption that working memory can process only explicit and direct instructions. Therefore, it is believed that inquiries techniques, not employing explicit instructional methods for teaching, are set to fail. This paper aims to fill this gap by extending the traditional direct instruction teaching method, w...

The capacity to assess e manage mental workload is becoming more and more relevant in the current work environments as it helps to prevent work related accidents and achieve better efficiency and productivity. Mental workload is often measured indirectly by inferring its effects on performance, mental states, and psychophysiological indexes. Since...

Knowledge-representation and reasoning methods have been extensively researched within Artificial Intelligence. Among these, argumentation has emerged as an ideal paradigm for inference under uncertainty with conflicting knowledge. Its value has been predominantly demonstrated via analyses of the topological structure of graphs of arguments and its...

Several non-monotonic formalisms exist in the field of Artificial Intelligence for reasoning under uncertainty. Many of these are deductive and knowledge-driven, and also employ procedural and semi-declarative techniques for inferential purposes. Nonetheless, limited work exist for the comparison across distinct techniques and in particular the exa...

Explainable Artificial Intelligence (XAI) has experienced a significant growth over the last few years. This is due to the widespread application of machine learning, particularly deep learning, that has led to the development of highly accurate models but lack explainability and interpretability. A plethora of methods to tackle this problem have b...

Mental workload (MWL) is an imprecise construct, with distinct definitions and no predominant measurement technique. It can be intuitively seen as the amount of mental activity devoted to a certain task over time. Several approaches have been proposed in the literature for the modelling and assessment of MWL. In this paper, data related to two sets...

Now at the forefront of automated reasoning, argumentation has become a key research topic within Artificial Intelligence. It involves the investigation of those activities for the production and exchange of arguments, where arguments are attempts to persuade someone of something by giving reasons for accepting a particular conclusion or claim as e...

In Dung’s abstract semantics, the label undecided is always propagated from the attacker to the attacked argument, unless the latter is also attacked by an accepted argument. In this work we propose undecidedness blocking abstract argumentation semantics where the undecided label is confined to the strong connected component where it was generated...

Reputation systems concern soft security dynamics in diverse areas. Trust dynamics in a reputation system should be stable and adaptable at the same time to serve the purpose. Many reputation mechanisms have been proposed and tested over time. However, the main drawback of reputation management is that users need to share private information to gai...

Artificial Intelligence is one of the fastest growing disciplines, disrupting many sectors. Originally mainly for computer scientists and engineers, it has been expanding its horizons and empowering many other disciplines contributing to the development of many novel applications in many sectors. These include medicine and health care, business and...

Although Cognitive Load Theory (CLT) has been researched for many years, it has been criticised for its theoretical clarity and its methodological approach. A crucial issue is the measurement of three types of cognitive load conceived in the theory, and the assessment of overall human cognitive load during learning tasks. This research study is mot...

Reputation systems concern soft security dynamics in diverse areas. Trust dynamics in a reputation system should be stable and adaptable at the same time to serve the purpose. Many reputation mechanisms have been proposed and tested over time. However, the main drawback of reputation management is that users need to share private information to gai...

Cognitive Load Theory has been conceived for supporting instructional design through the use of the construct of cognitive load. This is believed to be built upon three types of load: intrinsic, extraneous and germane. Although Cognitive Load Theory and its assumptions are clear and well-known, its three types of load have been going through a cont...

Mental workload measurement is a complex multidisciplinary research area that includes both the theoretical and practical development of models. These models are aimed at aggregating those factors, believed to shape mental workload, and their interaction, for the purpose of human performance prediction. In the literature, models are mainly theory-d...

Self-reporting procedures have been largely employed in literature to measure the mental workload experienced by users when executing a specific task. This research proposes the adoption of these mental workload assessment techniques to the task of creating uplift mappings in Linked Data. A user study has been performed to compare the mental worklo...

This book constitutes the refereed proceedings of the Second International Symposium on Human Mental Workload: Models and Applications, H-WORKLOAD 2018, held in Amsterdam, The Netherlands, in September 2018. The 15 revised full papers presented together with one keynote were carefully reviewed and selected from 31 submissions. The papers are organ...