Abstract 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, multichannel, and nonlinear signal with noise that problematizes identifying PD symptoms. A few studies have employed fractal dimension (FD) to extract distinguishing PD features from EEG signals. However, no exploratory study exists, as per our knowledge, on the efficiency of the different FD measures. We aim to conduct a comparative analysis of the various FDs that, as feature extraction measures, can discriminate PD patients who are ON and OFF medication from healthy controls using ML architecture. This study has implemented and analyzed several techniques for segmentation, feature extraction, and ML models. The results show that k-nearest neighbors (KNN) classifier with Higuchi FD and 90% overlap for segmented window delivers the highest accuracies, yielding a mean accuracy of $$99.65\pm 0.15\%$$ 99.65 ± 0.15 % for PD patients ON medication and $$99.45\pm 0.18\%$$ 99.45 ± 0.18 % for PD patients OFF medication, respectively. The model accurately identifies the signs of the disease in resting-state EEG with almost equivalent accuracy in both OFF and ON medication patients. To enhance the interpretability in our study, we leveraged XGB’s feature importance to generate brain topographic plots. This integration of explainable AI (XAI) enhanced the transparency and comprehensibility of our model’s classifications. Additionally, a comparison between the performance of FD and a few entropy measures has also been drawn to validate the significance of FD as a superior feature extraction measure. This study contributes to the body of knowledge with an architectural pipeline for detecting PD in resting-state EEG while emphasizing fractal dimension as an effective way of extracting salient features from EEG signals.

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 the work is the use of Semantic Web principles to develop a recommendation model for the feature selection algorithm. As a result, dataset meta-features are modeled in a domain ontology, and a set of Semantic Web rule language (SWRL) predictive rules have been proposed to recommend a feature selection algorithm. The result of this research is a feature selection algorithm recommendation based on the data characteristics and quality (FSDCQ) ontology, which not only helps with recommendations but also finds the data points with data quality violations. An experiment is conducted on the classification datasets from the UCI repository to evaluate the proposed ontology. The usefulness and effectiveness of the proposed method is evaluated by comparing it with the widely used method in the literature for the recommendation. Results show that the ontology-based recommendations are equally good as the widely used recommendation model, which is k-NN, with added benefits.

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 tries to address the recommendation of a feature selection algorithm based on dataset characteristics and quality. The research uses three types of dataset characteristics along with data quality metrics. The main contribution of the work is the utilization of Semantic Web techniques to develop a novel system that can aid in robust feature selection algorithm recommendations. The system’s strength lies in assisting users of machine learning algorithms by providing more relevant feature selection algorithms for the dataset using an ontology called Feature Selection algorithm recommendation based on Data Characteristics and Quality (FSDCQ). Results are generated using six different feature selection algorithms and four types of classifiers on ten datasets from UCI repository. Recommendations take the form of “Feature selection algorithm X is recommended for dataset i, as it performed better on dataset j, similar to dataset i in terms of class overlap 0.3, label noise 0.2, completeness 0.9, conciseness 0.8 units". While the domain-specific ontology FSDCQ was created to aid in the task of algorithm recommendation for feature selection, it is easily applicable to other meta-learning scenarios.

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 the development of robust constructs of mental workload (MWL). By drawing a parallel between CLT and MWL, as well as by integrating relevant theories and measurement techniques from these two fields, this paper is aimed at investigating the reliability, validity and sensitivity of three existing self-reporting mental workload measures when applied to long learning sessions, namely, the NASA Task Load index, the Workload Profile and the Rating Scale Mental Effort, in a typical university classroom. These measures were aimed at serving for the evaluation of two instructional conditions. Evidence suggests these selected measures are reliable and their moderate validity is in line with results obtained within Ergonomics. Additionally, an analysis of their sensitivity by employing the descriptive Harrell-Davis estimator suggests that the Workload Profile is more sensitive than the Nasa Task Load Index and the Rating Scale Mental Effort for long learning sessions.

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 formal properties. However, limited research exists on the examination and comparison of its inferential capacity in real-world modelling tasks and against other knowledge-representation and non-monotonic reasoning methods. This study is focused on a novel comparison between defeasible argumentation and non-monotonic fuzzy reasoning when applied to the representation of the ill-defined construct of human mental workload and its assessment. Different argument-based and non-monotonic fuzzy reasoning models have been designed considering knowledge-bases of incremental complexity containing uncertain and conflicting information provided by a human reasoner. Findings showed how their inferences have a moderate convergent and face validity when compared respectively to those of an existing baseline instrument for mental workload assessment, and to a perception of mental workload self-reported by human participants. This confirmed how these models also reasonably represent the construct under consideration. Furthermore, argument-based models had on average a lower mean squared error against the self-reported perception of mental workload when compared to fuzzy-reasoning models and the baseline instrument. The contribution of this research is to provide scholars, interested in formalisms on knowledge-representation and non-monotonic reasoning, with a novel approach for empirically comparing their inferential capacity.

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 examination of their inferential capacity. Thus, this paper focuses on a comparison of three knowledge-driven approaches employed for non-monotonic reasoning, namely expert systems, fuzzy reasoning and defeasible argumentation. A knowledge-representation and reasoning problem has been selected: modelling and assessing mental workload. This is an ill-defined construct, and its formalisation can be seen as a reasoning activity under uncertainty. An experimental work was performed by exploiting three deductive knowledge bases produced with the aid of experts in the field. These were coded into models by employing the selected techniques and were subsequently elicited with data gathered from humans. The inferences produced by these models were in turn analysed according to common metrics of evaluation in the field of mental workload, in specific validity and sensitivity. Findings suggest that the variance of the inferences of expert systems and fuzzy reasoning models was higher, highlighting poor stability. Contrarily, that of argument-based models was lower, showing a superior stability of its inferences across knowledge bases and under different system configurations. The originality of this research lies in the quantification of the impact of defeasible argumentation. It contributes to the field of logic and non-monotonic reasoning by situating defeasible argumentation among similar approaches of non-monotonic reasoning under uncertainty through a novel empirical comparison.

This paper investigates the efficiency of two instructional design conditions: a traditional design based on the direct instruction approach to learning and its extension with a collaborative activity based upon the community of inquiry approach to learning. This activity was built upon a set of textual trigger questions to elicit cognitive abilities and support knowledge formation. A total of 115 students participated in the experiments and a number of third-level computer science classes where divided in two groups. A control group of learners received the former instructional design while an experimental group also received the latter design. Subsequently, learners of each group individually answered a multiple-choice questionnaire, from which a performance measure was extracted for the evaluation of the acquired factual, conceptual and procedural knowledge. Two measures of mental workload were acquired through self-reporting questionnaires: one uni-dimensional and one multidimensional. These, in conjunction with the performance measure, contributed to the definition of a measure of efficiency. Evidence showed the positive impact of the added collaborative activity on efficiency.

Catastrophic forgetting has a significant negative impact in reinforcement learning. The purpose of this study is to investigate how pseudorehearsal can change performance of an actor-critic agent with neural-network function approximation. We tested agent in a pole balancing task and compared different pseudorehearsal approaches. We have found that pseudorehearsal can assist learning and decrease forgetting.

In this paper, we investigate how dynamic properties of reputation can influence the quality of users' ranking. Reputation systems should be based on rules that can guarantee high level of trust and help identify unreliable units. To understand the effectiveness of dynamic properties in the evaluation of reputation, we propose our own model (DIB-RM) that utilizes three factors: forgetting, cumulative, and activity period. In order to evaluate the model, we use data from StackOverflow which also has its own reputation model. We estimate similarity of ratings between DIB-RM and the StackOverflow reputation model to test our hypothesis. We use two values to calculate our metrics: DIB-RM reputation and historical reputation. We found out that historical reputation gives better metric values. Our preliminary results are presented for different sets of values of the aforementioned factors in order to analyze how effectively the model can be used for modeling reputation systems.

Cognitive Load Theory (CLT) has been conceived for instructional designers eager to create instructional resources that are presented in a way that encourages the activities of the learners and optimise their performance, thus their learning. Although it has been researched for many years, it has been criticised because of its theoretical clarity and its methodological approach. In particular, one fundamental and open problem is the measurement of its cognitive load types and the measurement of the overall cognitive load of learners during learning tasks. This paper is aimed at investigating the reliability, validity and sensitivity of existing mental workload assessment techniques, borrowed from the discipline of Ergonomics, when applied to the field of Education, Teaching and Learning. In details, a primary research involved the application of three subjective mental workload assessment techniques, namely the NASA Task Load Index, the Workload Profile and the Rating Scale Mental Effort, in a typical third-level classroom for the evaluation of two instructional design conditions. The Cognitive Theory of Multimedia Learning and its design principles have been used as the underlying theoretical framework for the design of the two conditions. Evidence strongly suggests that the three selected mental workload measures are highly reliable within Education and their moderate validity is in line with results obtained in Ergonomics.

Social constructivism is grounded on the construction of information with a focus on collaborative learning through social interactions. However, it tends to ignore the human mental architecture, pillar of cognitivism. A characteristic of cognitivism is that instructional designs built upon it are generally explicit, contrarily to constructivism. This position paper proposes a novel learning task that is aimed at combining both the approaches through the use of trigger questions in a collaborative activity executed after a traditional delivery of instructions. To evaluate this new task, a metric of efficiency based upon a measure of mental workload and a measure of performance is proposed. The former measure is taken from Ergonomics, and two well know subjective self-reporting mental workload assessment techniques are envisioned. The latter measure is taken from an objective quantitative assessment of the performance of learners employing concept maps.

Mental workload is a design concept borrowed from Ergonomics with a significant adoption in the aviation and automobile industries. Nowadays, the consideration of this construct is also taking place in many modern clinical working environments for designing interacting and complex systems that impose ever greater cognitive demand on operators and less physical load. Measuring mental workload is essential for improving the interaction human-system, enhancing performance, reducing the operator's error and increasing safety. However, defining, measuring, assessing mental workload and understanding how this impinges on performance are still open problems. This secondary research is firstly aimed at introducing the construct of mental workload, its foundations, measurements techniques as well as applications in medicine. It then discusses open problems for applied research and eventually, it concludes with a list of challenges for scholars and practitioners. The goal is to provide the reader with a picture of the state of the science of mental workload in medicine and clinical domains with an eye towards future research.

In clinical settings, Human-computer systems need to be designed in a way that medical errors are reduced and patient care is enhanced. Inspection methods are usually employed in HCI to assess usability of interactive systems. However, they do not consider the state of the operator while executing a task, the surrounding environment and the task demands. It is argued that assessing performance of operators is fundamental for designing optimal systems with which healthcare can be effectively delivered. The aim of our solution is to assess performance of operators employing the notion of Mental Workload (MWL) this being a construct believed to strongly correlate with performance. The proposal is to develop a model for MWL assessment using supervised machine learning. This model will be evaluated via user studies involving clinicians and operators interacting with a set of medical systems. Assessments of MWL will be compared and validated with objective indexes of performance such as error rate and task execution time.

Human mental workload (MWL) has gained importance in the last few decades as an important design concept. It is a multifaceted complex construct mainly applied in cognitive sciences and has been defined in many different ways. Although measuring MWL has potential advantages in interaction and interface design, its formalisation as an operational and computational construct has not sufficiently been addressed. This research contributes to the body of knowledge by providing an extensible framework built upon defeasible reasoning, and implemented with argumentation theory (AT), in which MWL can be better defined, measured, analysed, explained and applied in different human–computer interactive contexts. User studies have demonstrated how a particular instance of this framework outperformed state-of-the-art subjective MWL assessment techniques in terms of sensitivity, diagnosticity and validity. This in turn encourages further application of defeasible AT for enhancing the representation of MWL and improving the quality of its assessment.

The first aim of this article is to provide readers informally with the basic notions of defeasible and non-monotonic reasoning, logics borrowed from artificial intelligence. It then describes argumentation theory, a paradigm for implementing defeasible reasoning in practice as well as the common multi-layer schema upon which argument-based models are usually built. The second aim is to describe the selection of argument-based applications in the medical and health-care sectors. Finally, the paper will conclude with a summary of the features, which make defeasible reasoning and argumentation theory attractive, that emerge from the applications under review. The target reader is a medical or health-care practitioner, with limited skills in formal knowledge representation and logic, interested in enhancing evidence modelling and aggregation.

Argumentation theory (AT) has been gaining momentum in the health care arena thanks to its intuitive and modular way of aggregating clinical evidence and taking rational decisions. The basic principles of argumentation theory are described and demonstrated in the breast cancer recurrence problem. It is shown how to represent available clinical evidence in arguments, how to define defeat relations among them and how to create a formal argumentation framework. Argumentation semantics are then applied over the built-framework to compute arguments justification status. It is demonstrated how this process can enhance the clinician decision-making process. A encouraging predictive capacity is compared against the accuracy rate of well-established machine learning techniques confirming the potential of argumentation theory in health care.

A pilot study is reported to identify an improved method of evaluating digital user interfaces in health care. Experience and developments from the aviation industry and the NASA-TLX mental workload assessment tools are applied in conjunction with Nielsen heuristics for evaluating an Electronic Health Record System in an Irish hospital. The NASA-TLX performs subjective workload assessments on operators working with various human-computer systems. Results suggest that depending on the cognitive workload and the working context of users, the usability will differ for the same digital interface. We conclude that incorporating the NASA-TLX with Nielsen's heuristics offers a more reliable method in design and evaluation of digital user interfaces in clinical environments, since the healthcare work context is taken into account. Improved interfaces can be expected to reduce medical errors and improve patient care.