Autism spectrum disorder (ASD) affects many children and limits their social interaction, communication, and behavioral skills. Regular follow-ups with qualified professionals assist in the patients' development through sessions and progress evaluations. This progress is manually recorded by professionals, which can lead to errors in analysis. To assist with these annotations, an automation process is proposed using pose estimation and object detection techniques in computer vision to detect interaction between participants in therapy sessions with children with ASD. For this purpose, the Yolov8 object detector and Yolov8-Pose estimator are applied. Subsequently, interaction detection is performed using the results obtained from the previous predictions. Heuristics were introduced to compare these techniques. To enhance performance, solutions were explored to address the limitations encountered in object detection and pose estimation predictions. The results demonstrated a 15.8% improvement in precision for the pose estimation heuristic compared to the bounding box heuristic, achieving satisfactory performance for the proposed approach.

Effective exploration abilities are fundamental for robot swarms, especially when small, inexpensive robots are employed (e.g., micro- or nano-robots). Random walks are often the only viable choice if robots are too constrained regarding sensors and computation to implement state-of-the-art solutions. However, identifying the best random walk parameterisation may not be trivial. Additionally, variability among robots in terms of motion abilities—a very common condition when precise calibration is not possible—introduces the need for flexible solutions. This study explores how random walks that present chaotic or edge-of-chaos dynamics can be generated. We also evaluate their effectiveness for a simple exploration task performed by a swarm of simulated Kilobots. First, we show how Random Boolean Networks can be used as controllers for the Kilobots, achieving a significant performance improvement compared to the best parameterisation of a Lévy-modulated Correlated Random Walk. Second, we demonstrate how chaotic dynamics are beneficial to maximise exploration effectiveness. Finally, we demonstrate how the exploration behavior produced by Boolean Networks can be optimized through an Evolutionary Robotics approach while maintaining the chaotic dynamics of the networks achieving 7.6% of improvement compared to the baseline.

The vessels are powered by propellers that convert engine power into movement. Optimizing propeller design involves numerous variables like diameter and blade count, making exact methods impractical. Meta-heuristics, such as evolutionary algorithms, offer a promising solution. This study introduces a novel marine propeller optimization approach. It decomposes the optimization process and proposes a new fitness function to overcome previous limitations. Based on two continuous optimization algorithms, the approach was compared to the state-of-the-art differential evolution algorithm. Results from ferry-boat propeller design experiments show the proposed approach achieves approximately 1% higher efficiency than the baseline study at 7.0 and 7.5-knot speeds. The proposed approach succeeds at 8.0 and 8.5 knots, where the baseline failed. Additionally, decomposition reduces execution time by executing in six threads.

Urban research faces challenges in understanding and describing city regions, which are essential for urban planning and tourism management. Traditional methods rely on predefined areas and non-human-readable representations. This paper presents a new unsupervised approach that overcomes these limitations using a data-driven method with Instruction-tuned Large Language Models (ILLMs). Our technique dynamically identifies urban regions with similar features and generates human-readable descriptions. We validate this method using Flickr images from Pisa, Italy, and our results show that it effectively captures the semantic features of urban regions and generates comprehensible textual descriptions.

Exposing an evolutionary algorithm that is used to evolve robot controllers to variable conditions is necessary to obtain solutions which are robust and can cross the reality gap. However, we do not yet have methods for analyzing and understanding the impact of the varying morphological conditions which impact the evolutionary process, and therefore for choosing suitable variation ranges. By morphological conditions, we refer to the starting state of the robot, and to variations in its sensor readings during operation due to noise. In this paper, we introduce a method that permits us to measure the impact of these morphological variations and we analyze the relation between the amplitude of variations, the modality with which they are introduced, and the performance and robustness of evolving agents. Our results demonstrate that (i) the evolutionary algorithm can tolerate morphological variations which have a very high impact, (ii) variations affecting the actions of the agent are tolerated much better than variations affecting the initial state of the agent or of the environment, and (iii) improving the accuracy of the fitness measure through multiple evaluations is not always useful. Moreover, our results show that morphological variations permit generating solutions which perform better both in varying and non-varying conditions.

In this workshop we will present, through live demos, novel technological tools such as interactive "smart" toys for social play and AI-based monitoring tools, developed to support the early treatment of Autism Spectrum Disorder (ASD). We will discuss the potential of such devices, describing also our promising results with ASD children. The goal of the workshop is to involve the specialist audience in the co-design of some potential aspects of the overall system. In particular, we are interested in collecting feedback and proposals on possible further uses of the system such as additional play activities to stimulate social behaviour; improvements of the monitoring tools; potential use of the tools in developmental disorders other than ASD.

Non-intrusive vision-assisted methods that can recognize complex human behaviors can help the diagnosis and treatment of neurological disorders where stimming behaviors are prominent, such as Autism Spectrum Disorder (ASD). Machine learning methods, especially those related to computer vision are a promising direction for this kind of application, however, the effectiveness of these methods depends on the existence of good-quality datasets. Building a dataset in this domain is challenging due to privacy matters and also to the need to conduct experiments mostly with children. Therefore, we propose a consolidated, annotated, and standardized Stimming Behavior Dataset. This unified dataset can enable researchers to have access to a larger pool of data, classified and annotated for the stimming behaviors, enabling a faster and more streamlined approach when working with this kind of video collection. The Autism Stimming Behavior Dataset - ASBD11https://github.com/OckerGui/Stimming-Behavior-Dataset can be used for the development of machine learning algorithms that can detect behavioral patterns associated with autism spectrum disorder and other applications where this kind of analysis is important. The dataset is comprised of 165 annotated short duration clips, based on excerpts from 155 distinct publicly accessible Youtube videos., with 154 unique subjects of ages spanning from toddlers to young adults and divided into 48 female and 106 male individuals. The average duration of the clips is 10.6 seconds. The annotated clips show not only the URL and class of the video but the moment where the relevant behavior starts and its duration. In addition to providing a unified dataset, this paper also includes predictions of each category based on a classic I3D model trained in the Kinetics 400 dataset, which serves as a baseline for the action-recognition methods and analysis on the previous source datasets and models, providing insights for future action-recognition-based systems that can be used to assist in decisions regarding ASD diagnosis and treatment.

Co-clustering is a specific type of clustering that addresses the problem of finding groups of objects without necessarily considering all attributes. This technique has shown to have more consistent results in high-dimensional sparse data than traditional clustering. In trajectory co-clustering, the methods found in the literature have two main limitations: first, the space and time dimensions have to be constrained by user-defined thresholds; second, elements (trajectory points) are clustered ignoring the trajectory sequence, assuming that the points are independent among them. To address the limitations above, we propose a new trajectory co-clustering method for mining semantic trajectory co-clusters. It simultaneously clusters the trajectories and their elements taking into account the order in which they appear. This new method uses the element frequency to identify candidate co-clusters. Besides, it uses an objective cost function that automatically drives the co-clustering process, avoiding the need for constraining dimensions. We evaluate the proposed approach using a real-world publicly available dataset. The experimental results show that our proposal finds frequent and meaningful contiguous sequences revealing mobility patterns, thereby the most relevant elements.

In the last decade, trajectory classification has received significant attention. The vast amount of data generated on social media, the use of sensor networks, IOT devices and other Internet-enabled sources allowed the semantic enrichment of mobility data, making the classification task more challenging. Existing trajectory classification methods have mainly considered space, time and numerical data, ignoring the semantic dimensions. Only recently proposed methods as Movelets and MASTERMovelets can handle all types of dimensions. MASTERMovelets is the only method that automatically discovers the best dimension combination and subtrajectory size for trajectory classification. However, although it outperformed the state-of-the-art in terms of accuracy, MASTERMovelets is computationally expensive and results in a high dimensionality problem, which makes it unfeasible for most real trajectory datasets that contain a big volume of data. To overcome this problem and enable the application of the movelets approach on large datasets, in this paper we propose a new high-performance method for extracting movelets and classifying trajectories, called HiPerMovelets (High-performance Movelets). Experimental results show that HiPerMovelets is 10 times faster than MASTERMovelets, reduces the high-dimensionality problem, is more scalable, and presents a high classification accuracy in all evaluated datasets with both raw and semantic trajectories.

Logic synthesis is a fundamental step in hardware design whose goal is to find structural representations of Boolean functions while minimizing delay and area. If the function is completely-specified, the implementation accurately represents the function. If the function is incompletely-specified, the implementation has to be true only on the care set. While most of the algorithms in logic synthesis rely on SAT and Boolean methods to exactly implement the care set, we investigate learning in logic synthesis, attempting to trade exactness for generalization. This work is directly related to machine learning where the care set is the training set and the implementation is expected to generalize on a validation set. We present learning incompletely-specified functions based on the results of a competition conducted at IWLS 2020. The goal of the competition was to implement 100 functions given by a set of care minterms for training, while testing the implementation using a set of validation minterms sampled from the same function. We make this benchmark suite available and offer a detailed comparative analysis of the different approaches to learning.

This work evaluates the use of Decision Trees (DTs) methods for a fast logic minimization of Boolean functions. The proposed DT approach is compared to traditional Espresso logic minimizer and the minimization algorithms available in the ABC tool. The methods are compared with respect to the execution time, number of nodes and number of logic levels. The DT methods proved to be a faster alternative, reducing time by an average of 52% and 5.5% when compared to Espresso and ABC respectively, while keeping competitive results in terms of AIG depth and number of nodes. Additionally, in order to obtain smaller circuits at the cost of approximate results we tested DTs with limited tree depth. The trade-offs between synthesis time, circuit area and accuracy are also discussed. Compared to ABC, limiting the maximum tree depth leads to time savings of up to 52%, up to 86% less number of nodes, and up to 48% lower AIG depth, while maintaining acceptable accuracy results.

We demonstrate how evaluating candidate solutions in a limited number of stochastically varying conditions that vary over generations at a moderate rate is an effective method for developing high quality robust solutions. Indeed, agents evolved with this method for the ability to solve an extended version of the double-pole balancing problem, in which the initial state of the agents and the characteristics of the environment in which the agents are situated vary, show the ability to solve the problem in a wide variety of environmental circumstances and for prolonged periods of time without the need to readapt. The combinatorial explosion of possible environmental conditions does not prevent the evolution of robust solutions. Indeed, exposing evolving agents to a limited number of different environmental conditions that vary over generations is sufficient and leads to better results with respect to control experiments in which the number of experienced environmental conditions is greater. Interestingly the exposure to environmental variations promotes the evolution of convergent strategies in which the agents act so to exhibit the required functionality and so to reduce the complexity of the control problem.

Previous evolutionary studies demonstrated how robust solutions can be obtained by evaluating agents multiple times in variable environmental conditions. Here we demonstrate how agents evolved in environments that vary across generations outperform agents evolved in environments that remain fixed. Moreover, we demonstrate that best performance is obtained when the environment varies at a moderate rate across generations, that is, when the environment does not vary every generation but every N generations. The advantage of exposing evolving agents to environments that vary across generations at a moderate rate is due, at least in part, to the fact that this condition maximizes the retention of changes that alter the behavior of the agents, which in turn facilitates the discovery of better solutions. Finally, we demonstrate that moderate environmental variations are advantageous also from an evolutionary computation perspective, that is, from the perspective of maximizing the performance that can be achieved within a limited computational budget.

In this paper we show how evolving robots can develop behaviors displaying a modular organization characterized by semi-discrete and semi-dissociable sub-behavioral units playing different functions. In our experiments, the development of differentiated behaviors is not realized through the sub-division of the control system into modules and/or through the utilization of differentiated training processes. Instead, it simply originates as a consequence of the adaptive advantage provided by the possibility to display and use functionally specialized behaviors. These are selected by evolution not only with respect to their capability to perform a given sub-function but also with respect to the capability to support smooth and effective transition with other behaviors. This is achieved by having different co- adapted behaviors and by evaluating the variation affecting the behaviors on the basis of the impact they have on the overall performance of the robots. Moreover this process enables the development of t...

In this paper an automated platform for immersive multiprojection visualization of manufacturing processes is proposed. It admits scenarios with dynamic components and allows Virtual Reality collaborative visualization among geographically distributed users, through multi-CAVE devices. Modules for modeling, converting, visualizing and interacting composes the platform. The proposed system can be applied to CAD projects, models and simulations used in industry. The ideas discussed are then validated through the study of a real case related to the Shipbuilding and Offshore Industries.