IC3K 2024 (16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management) received 175 paper submissions from 47 countries. To evaluate each submission, a double‐blind paper review was performed by the Program Committee. After a stringent selection process, 37 papers were published and presented as full papers, i.e. completed work (12 The organizing committee included the IC3K Conference Chair: Jorge Bernardino, Polytechnic University of Coimbra, Portugal and the IC3K 2024 Program Chairs: David Aveiro, University of Madeira, NOVA- LINCS and ARDITI, Portugal, Antonella Poggi, Università di Roma "La Sapienza", Italy, Ana Fred, Instituto de Telecomunicações and Instituto Superior Técnico (University of Lisbon), Portugal, Le Gruenwald, University of Oklahoma, School of Computer Science, United States, Elio Masciari, University of Napoli Federico II, Italy and Frans Coenen, University of Liverpool, United Kingdom. At the closing session, the conference acknowledged a few papers that were considered excellent in their class, presenting a "Best Paper Award", "Best Student Paper Award" and "Best Poster Award" for each of the co-located conferences. A short list of presented papers will be selected so that revised and extended versions of these papers will be published by Springer in a CCIS Series Book.

University of Coimbra, CEMMPRE, ARISE, Department of Mechanical Engineering, Coimbra, Portugal. Abstract Bio-inspired soft robots have already shown the ability to handle uncertainty and adapt to unstructured environments. However, their availability is partially restricted by timeconsuming, costly and highly supervised design-fabrication processes, often based on resource intensive iterative workflows. Here, we propose an integrated approach targeting the design and fabrication of pneumatic soft actuators in a single casting step. Molds and sacrificial water-soluble hollow cores are printed using fused filament fabrication (FFF). A heated water circuit accelerates the dissolution of the core's material and guarantees its complete removal from the actuator walls, while the actuator's mechanical operability is defined through finite element analysis (FEA). This enables the fabrication of actuators with non-uniform cross sections under minimal supervision, thereby reducing the number of iterations necessary during the design and fabrication processes. Three actuators capable of bending and linear motion were designed, fabricated, integrated and demonstrated as three different bio-inspired soft robots, an earthworm-inspired robot, a four-legged robot, and a robotic gripper. We demonstrate the availability, versatility and effectiveness of the proposed methods, contributing to accelerating the design and fabrication of soft robots. This study represents a step toward increasing the accessibility of soft robots to people at a lower cost. MAIN TEXT 1. Introduction Soft robots provide a freedom of movement and flexibility similar to animals' soft bodies [1-5], making them an attractive choice for innovative robot designs and applications in unstructured environments that were previously unattainable using rigid-bodied robots [6].

The ThEdu series pursues the smooth transition from an intuitive way of doing mathematics at secondary school to a more formal approach to the subject in STEM education, while favouring software support for this transition by exploiting the power of theorem-proving technologies. What follows is a brief description of how the present volume contributes to this enterprise. The 12th International Workshop on Theorem Proving Components for Educational Software(ThEdu'23), was a satellite event of the 29th international Conference on Automated Deduction (CADE 2023), July 1-4, 2023, Rome, Italy. ThEdu'23 was very successful, with one invited talk, by Yves Bertot (Inria, France), "The challenges of using Type Theory to teach Mathematics", and seven regular contributions. An open call for papers was then issued, to which eight contributions were submitted. Seven submissions have been accepted by our reviewers, who jointly produced at least three careful reports on each of the contributions. The resulting revised papers are collected in the present volume. We, the volume editors, hope that this collection of papers will further promote the development of theorem-proving based software, and that it will allow to improve the mutual understanding between computer scientists, mathematicians and stakeholders in education. PC Chairs:Julien Narboux (University of Strasbourg, France); Walther Neuper (JKU, Johannes Kepler University, Linz, Austria); Pedro Quaresma (University of Coimbra, Portugal)

Feature selection plays a crucial role in improving predictive accuracy by identifying relevant features while filtering out irrelevant ones. This study investigates the importance of effective feature selection in enhancing the performance of classification models. By employing reinforcement learning (RL) algorithms, specifically Q-learning (QL) and SARSA learning, this paper addresses the feature selection challenge. Using the Breast Cancer Coimbra dataset (BCCDS) and three normalization methods (Min-Max, l1, and l2), the study evaluates the performance of these algorithms. Results show that QL@Min-Max and SARSA@l2 achieve the highest classification accuracies, reaching 87% and 88%, respectively. This highlights the effectiveness of RL-based feature selection methods in optimizing classification tasks, contributing to improved model accuracy and efficiency.

In recent years, Convolutional Neural Networks (CNNs) have become the standard class of deep neural network for image processing, classification and segmentation tasks. However, the large strides in accuracy obtained by CNNs have been derived from increasing the complexity of network topologies, which incurs sizeable performance and energy penalties in the training and inference of CNNs. Many recent works have validated the effectiveness of parameter quantization, which consists in reducing the bit width of the network's parameters, to enable the attainment of considerable performance and energy efficiency gains without significantly compromising accuracy. However, it is difficult to compare the relative effectiveness of different quantization methods. To address this problem, we introduce RedBit, an open-source framework that provides a transparent, extensible and easy-to-use interface to evaluate the effectiveness of different algorithms and parameter configurations on network accuracy. We use RedBit to perform a comprehensive survey of five state-of-the-art quantization methods applied to the MNIST, CIFAR-10 and ImageNet datasets. We evaluate a total of 2300 individual bit width combinations, independently tuning the width of the network's weight and input activation parameters, from 32 bits down to 1 bit (e.g., 8/8, 2/2, 1/32, 1/1, for weights/activations). Upwards of 20000 hours of computing time in a pool of state-of-the-art GPUs were used to generate all the results in this paper. For 1-bit quantization, the accuracy losses for the MNIST, CIFAR-10 and ImageNet datasets range between [0.26%, 0.79%], [9.74%, 32.96%] and [10.86%, 47.36%] top-1, respectively. We actively encourage the reader to download the source code and experiment with RedBit, and to submit their own observed results to our public repository, available at https://github.com/IT-Coimbra/RedBit.

Dr Maurizio Tinnirello is an independent researcher, and visiting lecturer in International Relations, Conflict and Security at Northumbria University and the Amsterdam University of Applied Sciences. He has held academic positions in both the Global South and North, and he has also worked as an international researcher and policy consultant on global security and military corruption issues. He has been the Vice-Chair and Program Chair of the Science, Technology and Art in International Relations section at The International Studies Association since 2019. Dr Tinnirello holds a PhD from the School of Politics and International Relations, and an MA in International Conflict Analysis, from the University of Kent, UK. He was a recipient of a Marie Skłodowska-Curie Action Initial Training Award, and a visiting PhD fellow at Coimbra University.

Automated Deduction in Geometry (ADG) is a forum to exchange ideas and views, to present research results and progress, and to demonstrate software tools at the intersection between geometry and automated deduction. Relevant topics include (but are not limited to): polynomial algebra, invariant and coordinate-free methods; probabilistic, synthetic, and logic approaches, techniques for automated geometric reasoning from discrete mathematics, combinatorics, and numerics; interactive theorem proving in geometry; symbolic and numeric methods for geometric computation, geometric constraint solving, automated generation/reasoning and manipulation with diagrams; design and implementation of geometry software, automated theorem provers, special-purpose tools, experimental studies; applications of ADG in mechanics, geometric modelling, CAGD/CAD, computer vision, robotics and education. Traditionally, the ADG conference is held every two years. The previous editions of ADG were held in Nanning in 2018, Strasbourg in 2016, Coimbra in 2014, Edinburgh in 2012, Munich in 2010, Shanghai in 2008, Pontevedra in 2006, Gainesville in 2004, Hagenberg in 2002, Zurich in 2000, Beijing in 1998, and Toulouse in 1996. The 13th edition of ADG was supposed to be held in 2020 in Hagenberg, Austria, but due to the COVID-19 pandemic, it was postponed for 2021, and held online (still hosted by RISC Institute, Hagenberg, Austria), September 15-17, 2021 (https://www.risc.jku.at/conferences/adg2021).

Machine Learning, a recurrent and obvious topic in Science and Technology, will also radically change the way research is carried out in the Social Sciences and the Humanities in a near future. A close cooperation between SSH scholars and computer scientists could have a huge impact on both SSH and STEM (Science, Technology, Engineering and Mathematics) related research topics. On the one hand, social scientists and humanities scholars may not be able to design and implement themselves the machine learning algorithms they need for their research. The role of a linguist, a historian or a social scientist should be thus to help computer scientists outperform current machine learning models by offering them theoretical approaches both could adapt together to improve their accuracy. This conference, organised by the Social Sciences and Humanities Working Group of the Coimbra Group, will explore the practical possibilities Machine Learning offers to selected research fields within SSH, particularly linguistics, literature, musicology, and sociology.