Discovering informative subgroup sets is a core goal in subgroup discovery; however, it remains challenging. Existing methods often require manual, problem-specific parameter tuning, which limits scalability and reproducibility. Many methods also optimize subgroup quality using a single metric, assuming it alone can capture relevance. This is restrictive because subgroup quality is inherently multidimensional, involving trade-offs among accuracy, coverage, and diversity. In addition, candidate subgroups are usually evaluated in isolation, without considering their overlap or overall contribution to the final set. To overcome these issues, we propose DASSD (Dynamic and Adaptive Subgroup Set Discovery), a heuristic algorithm that discovers high-quality, non-redundant subgroup sets. DASSD jointly optimizes global set quality and redundancy using a dual-metric strategy based on information gain and odds ratio. A Minimum Redundancy Maximum Relevance (mRMR) pruning mechanism guides the search toward informative and diverse subgroups. The algorithm also uses a data-driven dynamic threshold to adapt parameters automatically, removing the need for manual tuning and enhancing robustness across datasets. Experiments on multiple benchmark datasets show that DASSD discovers high-quality subgroup sets with minimal redundancy and outperforms state-of-the-art methods under multiobjective evaluation, achieving a roughly 200% improvement in mRMR, while remaining computationally efficient.

Lung cancer remains one of the deadliest cancers and a major public health concern. Although numerous studies have identified various risk factors, further research is essential, particularly in the biological domain. Existing data sources compile biological information on lung cancer and its subtypes but differ in structure and format, complicating data extraction and integration for artificial intelligence (AI) models. Ontologies and semantic technologies address this challenge by enabling the construction of unified knowledge graphs that promote interoperability. Lung-CABO is an ontology specifically designed for lung cancer, supporting the creation of a knowledge graph for risk factor identification and AI applications. Its modular design allows expansion to integrate additional data, such as environmental factors, further enhancing its utility and reusability.

This work introduces the Perceptual Algorithm for Versatile video Encoding using Neural Networks (PAVEN), a subjective video coding algorithm designed to reduce the bit rate in videos encoded with the Versatile Video Coding (VVC) standard without compromising subjective video quality. The algorithm uses a deep learning model specifically trained by the authors to account for the specific characteristics of video signals. The trained model outperforms others in the literature by more accurately identifying areas of the frames where viewers are most likely to focus their attention. The output of the deep learning model is further processed to merge all disjoint areas and adapt the result to the Coding Tree Unit (CTU) size in VVC, allowing for greater compression in less important areas. The results show an average reduction in bit rate of 7% while maintaining the same subjective video quality, validated through viewer interviews using the Mean Opinion Score (MOS) metric.

Video coding standardization organizations have invested significant efforts in achieving greater compression factors over the years. Approved in 2020, the Versatile Video Coding (VVC) standard reduces the bit rate needed to encode a sequence by half compared to its predecessor. However, users today have increasingly demanding requirements, leading to a significant rise in video traffic on the Internet. In this context, perceptual video coding aims to reduce video bit rate by decreasing the objective quality while maintaining the subjective quality. This work presents a novel dataset designed for training models to predict video saliency, i.e., areas in the video to which viewers are more likely to pay attention. The dataset is publicly available. Furthermore, this work also proposes a machine learning model that classifies each Coding Tree Unit (CTU) as salient or not, and adjusts its quality accordingly. The results show that this model has an accuracy of 95% and correctly classifies as salient 98% of the CTUs that are actually salient.

Lung cancer is the leading cause of cancer death. More than 238,340 new cases of lung cancer patients are expected in 2023, with an estimation of more than 127,070 deaths. Choosing the correct treatment is an important element to enhance the probability of survival and to improve patient's quality of life. Cancer treatments might provoke secondary effects. These toxicities cause different health problems that impact the patient's quality of life. Hence, reducing treatments toxicities while maintaining or improving their effectiveness is an important goal that aims to be pursued from the clinical perspective. On the other hand, clinical guidelines include general knowledge about cancer treatment recommendations to assist clinicians. Although they provide treatment recommendations based on cancer disease aspects and individual patient features, a statistical analysis taking into account treatment outcomes is not provided here. Therefore, the comparison between clinical guidelines with treatment patterns found in clinical data, would allow to validate the patterns found, as well as discovering alternative treatment patterns. In this work, we have analyzed a dataset containing lung cancer patients information including patients' data, prescribed treatments and their outcomes. Using a Chi-square test and K-Modes clustering algorithm in combination with Pattern Discovery metrics we identify patterns, within the clusters, based on cancer stage and treatment outcomes. Obtained results are analyzed based on statistical and clinical relevance and compared with lung cancer clinical guidelines. The comparison reveals that all patterns found coincide with clinical guidelines recommendations, assessing the validity of the proposed method for pattern discovery in a clinical dataset.

Lung cancer is the leading cause of cancer death. More than 236,740 new cases of lung cancer patients are expected in 2022, with an estimation of more than 130,180 deaths. Improving the survival rates or the patient's quality of life is partially covered by a common element: treatments. Cancer treatments are well known for the toxic outcomes and secondary effects on the patients. These toxicities cause different health problems that impact the patient's quality of life. Reducing toxicities without a decline on the positive survival effect is an important goal that aims to be pursued from the clinical perspective. On the other hand, clinical guidelines include general knowl-edge about cancer treatment recommendations to assist clinicians. Although they provide treatment recommendations based on cancer disease aspects and individual patient features, a statistical analysis taking into account treatment outcomes is not provided here. Therefore, the comparison between clinical guidelines with treatment patterns found in clinical data, would allow to validate the patterns found, as well as discovering alternative treatment patterns. In this work, we have analyzed a dataset containing lung cancer patients information including patients' data, prescribed treatments and outcomes obtained. Using a Subgroup Discovery method we identify patterns based on cancer stage while relying on treatment outcomes. Results are compared with clinical guide-lines and analyzed based on statistical and medical relevance using Subgroup Discovery metrics.

Lung cancer is the leading cause of cancer death. From the estimation of cases that will be in 2021, more than 230,000 new cases are expected to be of lung cancer patients, with an estimation of more than 131,000 deaths. Improving the survival rates or the patient's quality of life is partially covered by a common element: treatments. Collective knowledge about cancer treatment recommendations is typically included in clinical guidelines, intended to optimize patient care and assist clinicians in lung cancer treatment. These guidelines define a set of treatment paths, where recommendations depend on cancer disease aspects and individual features for a concrete patient. Although oncologists are expected to follow clinical guidelines, the inter and intrapatients' variability of response to the possible treatment combinations, makes it necessary to personalize different treatment-patterns on certain cases. Additionally, clinical guidelines are not frequently updated with new findings or lack a consistent methodology when they are frequently updated. For that reason, the analysis of patterns on both patients treated following the standard of care, or outside it, would allow to validate clinical guidelines and identify potential new treatment recommendations. In this work, we have analysed whether actual treatments prescribed to lung cancer patients follow clinical guidelines or not. Using a machine learning method that provides as output association rules (Apriori), we identify patterns based on cancer stage. These preliminary results show that treatments patterns found mostly match with clinical guidelines recommendations, validating the information included in the consulted guidelines.

Different communication-aware mapping techniques were proposed in recent years for improving the performance of distributed systems based on both, off-chip and on-chip networks. Some of these proposals were based on heuristic search for finding pseudo-optimal assignments of tasks and processing elements. However, the technology integration improvements have allowed a significant increase in the number of network nodes, requiring the acceleration of the heuristic search. In this paper, we propose a comparative study of the local search method used in a communication-aware mapping technique, when implemented on different parallel architectures. We compare the performance provided by a version of the local search method when executed on a single Graphics Processing Unit (GPU) with the one provided by the MPI version executed on a supercomputer with the same theoretical performance of the GPU platform, in order to study a fair scenario. We have considered a GPU based on the Fermi architecture, evaluating the improvements achieved by some new architectural features of this platform. The results show that a mixed parallel implementation on a single GPU outperforms the MPI implementation of the local search method. These results validate the GPU implementation as a very cost-effective accelerator for the local search method.

Simulating the realistic behavior of large crowds of autonomous agents is still a challenge for the computer graphics community. In order to handle large crowds, some scalable architectures have been proposed. Nevertheless, the effective use of distributed systems requires the use of partitioning methods that can properly assign different sets of agents to the existing distributed resources. In this paper, we propose the improvement of the partitioning method for distributed crowd simulations by using irregular shape regions. Concretely, we propose the partition of the virtual world using convex hulls. The performance evaluation results show that the convex Hull method outperforms the rest of the considered methods in terms of both fitness function values and execution times, regardless of the movement pattern followed by the agents. These results show that the shape of the regions in the partition can improve the performance of the partitioning method, rather than the heuristic method used.

Crowd simulation can be considered as a special case of virtual environments where avatars are intelligent agents instead of user-driven entities. These applications require both rendering visually plausible images of the virtual world and managing the behavior of autonomous agents. Although several proposals have focused on the software architectures for these systems, the scalability of crowd simulation is still an open issue. In this paper, we propose a scalable architecture that can manage large crowds of autonomous agents at interactive rates. This proposal consists of enhancing a previously proposed architecture through the efficient parallelization of the action server and the distribution of the semantic database. In this way, the system bottleneck is removed, and new action servers (hosted each one on a new computer) can be added as necessary. The evaluation results show that the proposed architecture is able to fully exploit the underlying hardware platform, regardless of both the number and the kind of computers that form the system. Therefore, this system architecture provides the scalability required for large-scale crowd simulation.

This paper presents a multi-agent framework oriented to animate groups of synthetic humans that properly balance task-oriented and social behaviors. We mainly focus on the social model designed for BDI-agents to display socially acceptable decisions. This model is based on an auction mechanism used to coordinate the group activities derived from the character's roles. The model also introduces reciprocity relations between the members of a group and allows the agents to include social tasks to produce realistic behavioral animations. Furthermore, a conversational library provides the set of plans to manage social interactions and to animate from simple chats to more complex negotiations. The framework has been successfully tested in a 3D dynamic environment while simulation a virtual university bar, where groups of waiters and customers can interact and finally display complex social behaviors (e.g. task passing, reciprocity, planned meetings...).