Abstract
This article presents a systematic review of Multiple Instance Learning (MIL) applied to image classification, specifically highlighting its applications in medical imaging. Motivated by the need for a comprehensive and up-to-date analysis due to the scarcity of recent reviews, this study uses defined selection criteria to systematically assess the quality and synthesize data from relevant studies. Focusing on MIL, a subfield of machine learning that deals with learning from sets of instances or bags, this review is crucial for medical diagnosis, where accurate lesion detection is a challenge. The review details the methodologies, advances and practical implementations of MIL, emphasizing the attention-grabbing and transformative mechanisms that improve the analysis of medical images. Challenges such as the need for extensive annotated datasets and significant computational resources are discussed. In addition, the review covers three main topics: the characterization of MIL algorithms in various imaging domains, a detailed evaluation of performance metrics, and a critical analysis of data structures and computational resources. Despite these challenges, MIL offers a promising direction for research with significant implications for medical diagnostics, highlighting the importance of continued exploration and improvement in this area.

Abstract
All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller quad skate variant of hockey team sports, it is of great interest to automatically track player’s movements and positions, player’s sticks and, also, making other judgments, such as being able to locate the ball. In this work, we introduce a real-time pipeline composed by an object detection model, created specifically for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and quick motions, our deep learning object detection model effectively identifies and tracks, in real-time, important visual elements such as: ball; players; sticks; referees; crowd; goalkeeper; and goal. Using a curated dataset composed by a collection of videos of rink hockey, comprising 2525 annotated frames, we trained and evaluated the algorithm performance and compare it to state of the art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80%, and presents a good performance in terms of accuracy and speed, according to our results, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected one important event type in rink hockey games, the occurrence of penalties.

Abstract
Extensive computational research has been dedicated to detecting keys and modes in tonal Western music within the major and minor modes. Little research has been dedicated to other modes and musical expressions, such as folk or non-Western music. This paper tackles this limitation by comparing traditional template-based with unsupervised machine-learning methods for diatonic mode detection within folk music. Template-based methods are grounded in music theory and cognition and use predefined profiles from which we compare a musical piece. Unsupervised machine learning autonomously discovers patterns embedded in the data. As a case study, the authors apply the methods to a dataset of Irish folk music called The Session on four diatonic modes: Ionian, Dorian, Mixolydian, and Aeolian. Our evaluation assesses the performance of template-based and unsupervised methods, reaching an average accuracy of about 80%. We discuss the applicability of the methods, namely the potential of unsupervised learning to process unknown musical sources beyond modes with predefined templates. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract

Abstract
Fiducial markers play a fundamental role in various fields in which precise localization and tracking are paramount. In Augmented Reality, they provide a known reference point in the physical world so that AR systems can accurately identify, track, and overlay virtual objects. This accuracy is essential for creating a seamless and immersive AR experience, particularly when prompted to cope with the sub-millimeter requirements of medical and industrial applications. This research article presents a comparative analysis of four fiducial marker tracking algorithms, aiming to assess and benchmark their accuracy and precision. The proposed methodology compares the pose estimated by four algorithms running on Hololens 2 with those provided by a highly accurate ground truth system. Each fiducial marker was positioned in 25 sampling points with different distances and orientations. The proposed evaluation method is not influenced by human error, relying only on a high-frequency and accurate motion tracking system as ground truth. This research shows that it is possible to track the fiducial markers with translation and rotation errors as low as 1.36 mm and 0.015 degrees using ArUco and Vuforia, respectively.

Abstract
As robots have limited power sources. Energy optimization is essential to ensure an extension for their operating periods without needing to be recharged, thus maximizing their uptime and minimizing their running costs. This paper compares the energy consumption of different mobile robotic platforms, including differential, omnidirectional 3-wheel, omnidirectional 4-wheel, and Mecanum platforms. The comparison is based on the RobotAtFactory 4.0 competition that typically takes place during the Portuguese Robotics Open. The energy consumption from the batteries for each platform is recorded and compared. The experiments were conducted in a validated simulation environment with dynamic and friction models to ensure that the platforms operated at similar speeds and accelerations and through a 5200 mAh battery simulation. Overall, this study provides valuable information on the energy consumption of different mobile robotic platforms. Among other findings, differential robots are the most energy-efficient robots, while 4-wheel omnidirectional robots may offer a good balance between energy efficiency and maneuverability.