The Internet of Things generates massive data streams, with edge computing emerging as a key enabler for online IoT applications and 5G networks. Edge solutions facilitate real-time machine learning inference, but also require continuous adaptation to concept drifts. While extensions of the Very Fast Decision Tree (VFDT) remain state-of-the-art for tabular stream mining, their unregulated growth limit efficiency, particularly in ensemble settings where post-pruning at the individual tree level is seldom applied. This paper presents DFDT, a novel memory-constrained algorithm for online learning. DFDT employs activity-aware pre-pruning, dynamically adjusting splitting criteria based on leaf node activity: low-activity nodes are deactivated to conserve resources, moderately active nodes split under stricter conditions, and highly active nodes leverage a skipping mechanism for accelerated growth. Additionally, adaptive grace periods and tie thresholds allow DFDT to modulate splitting decisions based on observed data variability, enhancing the accuracy–memory–runtime trade-off while minimizing the need for hyperparameter tuning. An ablation study reveals three DFDT variants suited to different resource profiles. Fully compatible with existing ensemble frameworks, DFDT provides a drop-in alternative to standard VFDT-based learners.

State-of-the-art data stream mining has long drawn from ensembles of the Very Fast Decision Tree, a seminal algorithm honored with the 2015 KDD Test-of-Time Award. However, the emergence of large tabular models, i.e., transformers designed for structured numerical data, marks a significant paradigm shift. These models move beyond traditional weight updates, instead employing in-context learning through prompt tuning. By using on-the-fly sketches to summarize unbounded streaming data, one can feed this information into a pre-trained model for efficient processing. This work bridges advancements from both areas, highlighting how transformers' implicit meta-learning abilities, pre-training on drifting natural data, and reliance on context optimization directly address the core challenges of adaptive learning in dynamic environments. Exploring real-time model adaptation, this research demonstrates that TabPFN, coupled with a simple sliding memory strategy, consistently outperforms ensembles of Hoeffding trees, such as Adaptive Random Forest, and Streaming Random Patches, across all non-stationary benchmarks.

The use of personality to predict user preferences or select a job candidate are practical examples of how personality can be used in a variety of fields. But to assess someone's personality is not easy. The most commonly used techniques are personality questionnaires, but they are subject to social desirability bias and false responses, limiting the accuracy of personality assessment. Implicit techniques have also been used, but they require great amounts of user interactions with the system, and most of them only focus on broader personality dimensions. To overcome those limitations, we developed a short-duration mobile serious game, Coin Catcher, as a concept proof to implicitly measure the granular personality trait morality in less than 5 min. The game uses concepts related to normative morality, proposing dilemmas that the player must solve in order to progress in the game. Experiments with real users were conducted ($n=96$), showing morality can be implicitly determined by a simple game in a short period of time, without the bias associated with personality questionnaires. Additionally, correlations with other personality traits, such as altruism, cooperation, modesty, sympathy, and anger were also found, showing the game has the potential to measure several granular personality traits that better characterize a person.

Mining data streams with multi-label outputs poses significant challenges due to evolving distributions, high-dimensional label spaces, sparse label occurrences, and complex label dependencies. Moreover, concept drift affects not only input distributions but also label correlations and imbalance ratios over time, complicating model adaptation. To address these challenges, structured learners are categorized into local and global methods. Local methods break down the task into simpler components, while global methods adapt the algorithm to the full output space, potentially yielding better predictions by exploiting label correlations. This work introduces iHOMER (Incremental Hierarchy Of Multi-label Classifiers), an online multi-label learning framework that incrementally partitions the label space into disjoint, correlated clusters without relying on predefined hierarchies. iHOMER leverages online divisive-agglomerative clustering based on Jaccard similarity and a global tree-based learner driven by a multivariate Bernoulli process to guide instance partitioning. To address non-stationarity, it integrates drift detection mechanisms at both global and local levels, enabling dynamic restructuring of label partitions and subtrees. Experiments across 23 real-world datasets show iHOMER outperforms 5 state-of-the-art global baselines, such as MLHAT, MLHT of Pruned Sets and iSOUPT, by 23%, and 12 local baselines, such as binary relevance transformations of kNN, EFDT, ARF, and ADWIN bagging/boosting ensembles, by 32%, establishing its robustness for online multi-label classification.

Personality plays a crucial role in predicting preferences, behaviors, and interactions. Its importance in accurately characterizing individuals has led to its application in areas such as movies, music, and tourism. Although personality questionnaires have traditionally been used to measure personality, they are prone to biases, such as inflated or false responses. In response to these limitations, serious games emerge as innovative alternatives for assessing personality, studying the player's behavior. This study developed and evaluated Mindful Escape, a short duration mobile serious game, as a concept proof to implicitly measure the personality trait of cooperation. The game adapts concepts from the Prisoner's Dilemma and the Tragedy of the Commons to create an Escape Room environment to encourage both cooperative and competitive interactions. Experiments with real users were performed (\(n\)=78), where significant correlations between the game's metrics and cooperation were identified. Additionally, other traits such as modesty, morality, altruism, and anger, also showed correlations. The game's duration exceeded the planned 5min, averaging ca. 10min, mainly due to difficulties related to gameplay by less experienced users, which needs to be addressed in the future. Nevertheless, the participants’ feedback was highly positive, highlighting the immersive and engaging experience offered by the game. The results show short-duration mobile games offer a viable and unobtrusive method for assessing users' detailed personality traits, paving the way to replace traditional personality questionnaires, and their integration into personality-based systems.

The complexity associated with groups of tourists led to the emergence of Group Recommender Systems (GRS) for tourism. But if generating recommendations for small groups is a complex task, to provide them to large and occasional groups is even more. This complexity is especially due to the group’s heterogeinity, conflicting preferences, the information overload found on the internet and the tourists’ different ways of coping with the information, hindering the recommendation process from the users’ profile construction to the final recommendation of a list of points of interest to visit. In this work, we show how we tackled the identified issues in a GRS prototype, Grouplanner, including the cold-start problem, by predicting the tourists’ preferences based only on their personality and dividing the main group into subgroups of similar personality; by using a Multi-Agent Microservice; a novel dynamic clustering algorithm, d-means, adapted from the k-means algorithm, that does not need to know the number of clusters a priori; and association rules. Using a personality dataset of n=100k users, the proposed d-means algorithm was tested against two baselines (k-means and k-means++), showing better results in the clustering quality and scalability. We were also able to determine a large set of association rules to refine the recommendations, although further improvements are needed. To test the Grouplanner prototype, a simulation with real users (n=35) was conducted. The results showed the subgroups formed were very compact, revealing a very good clustering quality, with an average silhouette of s = 0.91. 11 of the 15 proposed tourist preferences were successfully predicted and used for the preliminary recommendation lists, being 92 % of the participants satisfied with the individual recommendations and 96 % with the group recommendations.

Abstract The railway sector has witnessed a significant surge in condition-based maintenance, thanks to the proliferation of sensing technologies and data-driven methodologies, such as machine learning. However, despite the plethora of algorithms designed to detect and classify track irregularities and wheel out-of-roundness, they often fall short when put to the test in real-world scenarios. These shortcomings typically stem from their inability to meet all four critical requirements for constructing an effective maintenance plan: (R1) suitability of the condition-based maintenance strategy, (R2) availability of relevant data, (R3) proper problem formulation, and (R4) accurate evaluation of data mining methods. In response to the absence of a unified framework and standardized guidelines, this survey delves into the realm of time series sensor data and wheel-track interface components for railway structural health monitoring. This survey aims to bridge this gap by offering an extensive categorization, pinpointing existing challenges, and outlining potential directions for future research. Through these efforts, this survey provides a more thorough and targeted exploration of the subject matter, contributing to the advancement of this field.

Within the big data paradigm, there is an increasing demand for machine learning with automatic configuration of hyperparameters.Although several algorithms have been proposed for automatically learning time-changing concepts, they generally do not scale well to very large databases.In this context, this paper presents an automated green machine learning approach applied to condition-based maintenance with automatic data fusion and density-based anomaly detection based on locality sensitivity hashing.Experiments on numerical simulations of train-track dynamic interactions demonstrate the utility of the approach to detect railway wheel out-of-roundness.This unlocks the full potential of scalable machine learning, paving the way for environment-friendly systems and automated decision-making.

The complexity associated to group recommendations needs strategies to mitigate several problems, such as the group's heterogeinity and conflicting preferences, the emotional contagion phenomenon, the cold-start problem, and the group members’ needs and concerns while providing recommendations that satisfy all members at once. In this demonstration, we show how we implemented a Multi-Agent Microservice to model the tourists in a mobile Group Recommender System for Tourism prototype and a novel dynamic clustering process to help minimize the group's heterogeneity and conflicting preferences. To help solve the cold-start problem, the preliminary tourist attractions preference and travel-related preferences & concerns are predicted using the tourists' personality, considering the tourists’ disabilities and fears/phobias. Although there is no need for data from previous interactions to build the tourists’ profile since we predict the tourists’ preferences, the tourist agents learn with each other by using association rules to find patterns in the tourists' profile and in the ratings given to Points of Interest to refine the recommendations.

Type 2 diabetic patients benefit significantly if the disease is well controlled through behavioral changes, namely adopting a healthy lifestyle. Currently, there is some evidence that technological strategies can help patient self-management. However, few studies have specifically targeted individuals who solely engage in automatic and personalized self-management practices. This study aims to synthesize the literature regarding personalized feedback recommendation systems to promote behavior change without health professionals’ direct intervention for the management of type 2 diabetes and to verify if the use of these systems improves health-related outcomes. A systematic review was performed from inception to April 13, 2021, based on a search conducted in six databases. According to the defined search expression, studies addressing type 2 diabetic patients and recommendation systems were included. In total, 2186 papers were initially identified, but only 22 met the specific inclusion criteria after screening. Discrepancies in the selection of studies were discussed in consensus meetings. To assess the quality of the articles, two tools were employed according to the types of articles retrieved. Selected papers were summarized regarding specific characteristics such as clinical and technological outcomes. Studies incorporating a recommendation system into their technological solution showed a positive effect on the evaluated outcomes, except for those with longer duration, where the effect was not statistically significant. Although most studies did not report the type of system used, expert systems (rule-based) were found to be the most prevalent among those that did. As behaviors are often difficult to change quickly, it is recommended that future studies extend their follow-up timeframe. Nevertheless, the data obtained suggest that technological solutions incorporating a recommendation system show potential to improve health-related outcomes and demonstrated good usability.

The emergence of the Industry 4.0 trend brings automation and data exchange to industrial manufacturing. Using computational systems and IoT devices allows businesses to collect and deal with vast volumes of sensorial and business process data. The growing and proliferation of big data and machine learning technologies enable strategic decisions based on the analyzed data. This study suggests a data-driven predictive maintenance framework for the air production unit (APU) system of a train of Metro do Porto. The proposed method assists in detecting failures and errors in machinery before they reach critical stages. We present an anomaly detection model following an unsupervised approach, combining the Half-Space-trees method with One Class K Nearest Neighbor, adapted to deal with data streams. We evaluate and compare our approach with the Half-Space-Trees method applied without the One Class K Nearest Neighbor combination. Our model produced few type-I errors, significantly increasing the value of precision when compared to the Half-Space-Trees model. Our proposal achieved high anomaly detection performance, predicting most of the catastrophic failures of the APU train system.

A large percentage of the worldwide population is affected by chronic diseases, leading to a burden of the patient and the national healthcare systems. Recommendation systems are used for the personalization of healthcare due to their capacity of performing predictive analyses based on the patient’s clinical data. This systematic literature review presents four research questions to provide an overall state of the art of the use of recommendation systems applied to the healthcare of patients with chronic diseases. Disease management was identified as the main purpose of the systems proposed in the literature. However, few solutions provide support to physicians in the clinical decision-making. Ontologies and rule-based systems were the artificial intelligence techniques most used in the systems since they can easily implement clinical guidelines. Current challenges of these systems include the low adherence, data sparsity, heterogeneous data, and explainability, that affect the success of the recommendation system. The results also show that there are few systems that provide support to patients with multiple chronic conditions. The findings of this literature review should be considered in the development of future recommendation systems that aim to support the management of chronic diseases.

Abstract To travel in leisure is an emotional experience, and therefore, the more the information about the tourist is known, the more the personalized recommendations of places and attractions can be made. But if to provide recommendations to a tourist is complex, to provide them to a group is even more. The emergence of personality computing and personality-aware recommender systems (RS) brought a new solution for the cold-start problem inherent to the conventional RS and can be the leverage needed to solve conflicting preferences in heterogenous groups and to make more precise and personalized recommendations to tourists, as it has been evidenced that personality is strongly related to preferences in many domains, including tourism. Although many studies on psychology of tourism can be found, not many predict the tourists’ preferences based on the Big Five personality dimensions. This work aims to find how personality relates to the choice of a wide range of tourist attractions, traveling motivations, and travel-related preferences and concerns, hoping to provide a solid base for researchers in the tourism RS area to automatically model tourists in the system without the need for tedious configurations, and solve the cold-start problem and conflicting preferences. By performing Exploratory and Confirmatory Factor Analysis on the data gathered from an online questionnaire, sent to Portuguese individuals from different areas of formation and age groups ( n = 1035), we show all five personality dimensions can help predict the choice of tourist attractions and travel-related preferences and concerns, and that only neuroticism and openness predict traveling motivations.

Industry 4.0. has empowered the integration of real-time sensor readings and process knowledge for early identification of faults. In this paper, simulation was used to evaluate the process of classifying data streams as drifting towards defective behavior. Focusing on the challenges imposed by real-word, unlabeled and non-stationary data, the proposed method was assessed by conducting a case-study in the plastic extrusion domain. The knowledge gained from initial tests on generated synthetic data sets was used to isolate symptoms of a clogged screen pack from the concept drifts detected by the Adaptive Windowing algorithm. Experimental results for 40 days of running time of an extruder show that the proposed method helped in identifying a defective screen pack state upon the appearance of defects in the extrudate.

Several research studies have demonstrated the strong influence that online reviews exert on consumers' purchasing decisions. Specifically, those with extreme opinions, both favorable and unfavorable, are often considered more useful. This paper is focused on enhancing the detection of extreme reviews through sentiment analysis. For this purpose, a real scenario is taken into account, using the examples of all classes and dealing with the imbalance between them, which is characteristic in online reviews. The main objective is to carry out the classification with a high certainty and incurring as few errors as possible in relation to the examples belonging to the rest of the classes. Therefore, the emphasis is on the quality of the predictions rather than on the quantity. Using XLNet, we show how the transfer of knowledge extracted from the source domain (i.e., the extreme reviews) improves their detection regarding the overall number of errors made in the target domain (i.e., multi-class classification).

Type 2 diabetes is an increasingly prevalent disease and patients do not always manage the disease properly. Therefore, creating tools that help diabetics self-manage their condition over time is of the utmost importance. Technological tools that include features meeting this population's needs, with an integrated personalized feedback system, in an environment of gamified incentives, may be the way for developing a sustained app's usage. This work aims to present a still in development, novel approach to managing diabetes type 2 with a focus on data analysis from user-inputs, gamification, and personalized coaching, with an accessible user interface. Thus, we will present some functionalities, in particular the theoretical and practical concept that is behind this development, namely the Transtheoretical model of behavior change to create different profiles, to provide customized feedback according to the user's behavioral stage, and also to ascertain if it induces any improvement at the behavioral level.

This paper presents LSHAD, an anomaly detection (AD) method based on Locality Sensitive Hashing (LSH), capable of dealing with large-scale datasets. The resulting algorithm is highly parallelizable and its implementation in Apache Spark further increases its ability to handle very large datasets. Moreover, the algorithm incorporates an automatic hyperparameter tuning mechanism so that users do not have to implement costly manual tuning. Our LSHAD method is novel as both hyperparameter automation and distributed properties are not usual in AD techniques. Our results for experiments with LSHAD across a variety of datasets point to state-of-the-art AD performance while handling much larger datasets than state-of-the-art alternatives. In addition, evaluation results for the tradeoff between AD performance and scalability show that our method offers significant advantages over competing methods.

Predictive maintenance requires the constant monitorization of equipment and the accumulation of data captured from sensors, industrial equipment, and existing management software. This data must be cleaned and processed before being used to train machine learning models that will generate different outputs of interest, such as fault prediction, fault detection, estimation of an equipment’s remaining useful life, among others. Considering these requirements and the different technologies needed to accommodate them, we present an architecture for predictive maintenance, based on existing standard architectures for Industry 4.0, that not only supports the implementation of all stages of predictive maintenance, but is flexible enough to be applied in distinct industrial scenarios. Moreover, the architecture is capable of accommodating both offline and online data pre-processing and machine learning techniques.

Summary One of the most important factors to determine the success of an organization is the quality of decisions made. Supporting a decision‐making process is a complex task, mainly when decision‐makers are dispersed. Group decision support systems (GDSSs) have been studied over the last decades with the goal of providing support to decision‐makers; however, their acceptance by organizations has been difficult. This happens mostly due to usability problems, loss of interaction between decision‐makers, and consequently, loss of information. In this work, we present a web‐based GDSS developed to support groups of decision‐makers, regardless of their geographic location. The system allows the creation of multicriteria problems and the configuration of the preferences, intentions, and interests of each decision‐maker. The presented system uses a multiagent system to combine and process this information, using virtual agents that represent each decision‐maker. We believe that, with this approach, we will proceed in the refinements of a successful GDSS to correctly support decision‐makers while preserving the valuable intelligence and knowledge that can be generated in face‐to‐face meetings. Furthermore, the high level of usability that the system provides will contribute to an easier acceptance and adoption of this kind of systems.

In the fourth industrial revolution, various inter-mediation such as theories, techniques, or implementation would be used.It contains computational intelligence, applied soft computing and fuzzy logic and artificial neural networks, intelligent contents security, model driven architecture and meta-modeling, multimedia contents processing and retrieval, vehicular N/W, big data, intelligence information processing, convergence/complex contents, smart learning, intelligent contents design management, methodology and design theory, intelligent media contents convergence/complex media, social media and collective intelligence, and social media big data analytic.

Personalization is increasingly being perceived as an important factor for the effectiveness of Recommender Systems (RS). This is especially true in the tourism domain, where travelling comprises emotionally charged experiences, and therefore, the more about the tourist is known, better recommendations can be made. The inclusion of psychological aspects to generate recommendations, such as personality, is a growing trend in RS and they are being studied to provide more personalized approaches. However, although many studies on the psychology of tourism exist, studies on the prediction of tourist preferences based on their personality are limited. Therefore, we undertook a large-scale study in order to determine how the Big Five personality dimensions influence tourists' preferences for tourist attractions, gathering data from an online questionnaire, sent to Portuguese individuals from the academic sector and their respective relatives/friends (n=508). Using Exploratory and Confirmatory Factor Analysis, we extracted 11 main categories of tourist attractions and analyzed which personality dimensions were predictors (or not) of preferences for those tourist attractions. As a result, we propose the first model that relates the five personality dimensions with preferences for tourist attractions, which intends to offer a base for researchers of RS for tourism to automatically model tourist preferences based on their personality.

Abstract Supporting group decision‐making when the decision makers are spread around the world is a complex process. The mechanisms of automated negotiation, such as argumentation, can be used in Ubiquitous Group Decision Support Systems (UbiGDSS) to help decision makers find a solution based on their preferences. However, the decision‐making process is much more than just a simple criteria and alternative analysis. There are many cognitive and affective issues that affect the outcome, and these issues should not be ignored; otherwise, the quality of the decision could be compromised. In this paper, we detail an UbiGDSS architecture and explore 2 cognitive and affective methods that are essential to the group decision‐making process. We explain how agents can reason about self‐expertise and other decision makers' credibility, and how agents can verify and react to tendencies throughout the decision‐making process. We intend agents to achieve higher quality and more consensual decisions. In any simulation environment that we tested, agents that analysed credibility, expertise, and/or analysed tendencies always achieved a higher consensus compared to agents that used neither of the proposed methods. Likewise, agents that used neither of the proposed methods or only performed tendencies analysis obtained the worst average satisfaction levels for each simulation environment.

Supporting group decision-making in ubiquitous contexts is a complex task that must deal with a large amount of factors to succeed. Here we propose an approach for an intelligent negotiation model to support the group decision-making process specifically designed for ubiquitous contexts. Our approach can be used by researchers that intend to include arguments, complex algorithms, and agents’ modeling in a negotiation model. It uses a social networking logic due to the type of communication employed by the agents and it intends to support the ubiquitous group decision-making process in a similar way to the real process, which simultaneously preserves the amount and quality of intelligence generated in face-to-face meetings. We propose a new look into this problem by considering and defining strategies to deal with important points such as the type of attributes in the multicriterion problems, agents’ reasoning, and intelligent dialogues.

This paper aims to present a model to support collaborative scheduling in complex dynamic manufacturing environments. The model we propose considers the interaction between an agent based scheduling module (ASM) and a group decision support module (GDSM). The input of the agent based scheduling module is a unique set of criteria weight and the output is set of candidate scheduling solutions, generated according the set of criteria weight and/or by a particular kind of method. Scheduling is a multicriteria decision problem; in practice different schedulers may agree as to the key objectives but differ greatly as to their relative importance in any given situation. The interaction between the scheduling actors will be supported through the group decision support module (GDSM) envisaging the selection of a scheduling solution through the exchange of arguments between the members of the scheduling group