An algoritmh for the estimation of 3D surfaces using competitive learning

This paper addresses the estimation of object boundaries from a set of 3D points. An extension of the constrained clustering algorithm developed by Abrantes and Marques in the context of edge linking is presented. The object surface is approximated using rectangular meshes and simplex nets. Centroid-based forces are used for attracting the model nodes towards the data, using competitive learning methods. It is shown that competitive learning improves the model performance in the presence of concavities and allows to discriminate close surfaces. The proposed model is evaluated using synthetic data and medical images (MRI and ultrasound images).

Techniques for batch reinforcement learning in robotics

This thesis addresses the Batch Reinforcement Learning methods in Robotics. This sub-class of Reinforcement Learning has shown promising results and has been the focus of recent research. Three contributions are proposed that aim to extend the state-of-art methods allowing for a faster and more stable learning process, such as required for learning in Robotics. The Q-learning update-rule is widely applied, since it allows to learn without the presence of a model of the environment. However, this update-rule is transition-based and does not take advantage of the underlying episodic structure of collected batch of interactions. The Q-Batch update-rule is proposed in this thesis, to process experiencies along the trajectories collected in the interaction phase. This allows a faster propagation of obtained rewards and penalties, resulting in faster and more robust learning. Non-parametric function approximations are explored, such as Gaussian Processes. This type of approximators allows to encode prior knowledge about the latent function, in the form of kernels, providing a higher level of exibility and accuracy. The application of Gaussian Processes in Batch Reinforcement Learning presented a higher performance in learning tasks than other function approximations used in the literature. Lastly, in order to extract more information from the experiences collected by the agent, model-learning techniques are incorporated to learn the system dynamics. In this way, it is possible to augment the set of collected experiences with experiences generated through planning using the learned models. Experiments were carried out mainly in simulation, with some tests carried out in a physical robotic platform. The obtained results show that the proposed approaches are able to outperform the classical Fitted Q Iteration.

Leicestershire Adult Learning Service: Improving students' experience by increasing independent study

Leicestershire Adult Learning Service’s lead tutor Sarabjit Borrill has been using blended learning effectively in apprentice training for several years. Building on what she has learned in that time, she made 2015/ 16 the year to explore similar approaches with Skills for life students studying GCSE English.

eLearning in the context of a one-year postgraduate course in macroscopy in pathology: benefits of a multifaceted and pluripotent concept

Performing Macroscopy in Pathology implies to plan and implement methods of selection, description and collection of biological material from human organs and tissues, actively contributing to the clinical pathology analysis by preparing macroscopic report and the collection and identification of fragments, according to the standardized protocols and recognizing the criteria internationally established for determining the prognosis. The Macroscopy in Pathology course is a full year program with theoretical and pratical components taught by Pathologists. It is divided by organ/system surgical pathology into weekly modules and includes a practical "hands-on" component in Pathology Departments. The students are 50 biomedical scientists aged from 22 to 50 years old from all across the country that want to acquire competences in macroscopy. A blended learning strategy was used in order to: give students the opportunity to attend from distance; support the contents, lessons and the interaction with colleagues and teachers; facilitate the formative/summative assessment.

Fallbasiertes E-Learning durch dynamische Verknüpfung von Fallstudien und Fachinhalten. Neue Diskussionsansätze zu einem vernachlässigten Konzept

Der Einsatz von Fallstudien kann als wichtiges Bindeglied zur Verknüpfung von Theorie und Praxis betrachtet werden. Fallstudien ermöglichen die Anwendung theoretischen Grundlagenwissens und die Entwicklung überfachlicher Kompetenzen. Damit können sie einen wichtigen Beitrag zur beruflichen Handlungskompetenz genau dort leisten, wo praktische Erfahrungen im Rahmen der Aus-und Weiterbildung nicht möglich sind. Der Einsatz von Fallstudien sollte aus diesem Grund nicht nur den „klassischen“ Anwendungsdisziplinen wie den Rechtswissenschaften, der Betriebswirtschaftslehre oder der Psychologie vorbehalten sein. Auch im Bereich der Informatik können sie eine wichtige Ergänzung zu den bisher eingesetzten Methoden darstellen. Das im Kontext des Projekts New Economy1 entwickelte und hier vorgestellte Konzept zur didaktischen und technischen Aufbereitung von Fallstudien am Beispiel der IT-Aus- und Weiterbildung soll diese Diskussion anregen. Mit Hilfe des vorgestellten Ansatzes ist es möglich, unterschiedliche methodische Zugänge zu einer Fallstudie für eine computerbasierte Präsentation automatisch zu generieren und mit fachlichen Inhalten zu verknüpfen. Damit ist ein entscheidender Mehrwert gegenüber den bisherigen statischen und in sich geschlossenen Darstellungen gegeben. Der damit zu erreichende Qualitätssprung im Einsatz von Fallstudien in der universitären und betrieblichen Aus- und Weiterbildung stellt einen wichtigen Beitrag zur praxisorientierten Gestaltung von Blended Learning-Ansätzen dar.(DIPF/Orig.)

Combination of Evolutionary Algorithms with Experimental Design, Traditional Optimization and Machine Learning

Evolutionary algorithms alone cannot solve optimization problems very efficiently since there are many random (not very rational) decisions in these algorithms. Combination of evolutionary algorithms and other techniques have been proven to be an efficient optimization methodology. In this talk, I will explain the basic ideas of our three algorithms along this line (1): Orthogonal genetic algorithm which treats crossover/mutation as an experimental design problem, (2) Multiobjective evolutionary algorithm based on decomposition (MOEA/D) which uses decomposition techniques from traditional mathematical programming in multiobjective optimization evolutionary algorithm, and (3) Regular model based multiobjective estimation of distribution algorithms (RM-MEDA) which uses the regular property and machine learning methods for improving multiobjective evolutionary algorithms.

The development of an eBook for teaching and learning mathematics

Mobile devices, smartphones, phablets and tablets, are widely avail‐ able. This is a generation of digital natives. We cannot ignore that they are no longer the same students for which the education system was designed tradition‐ ally. Studying math is many times a cumbersome task. But this can be changed if the teacher takes advantage of the technology that is currently available. We are working in the use of different tools to extend the classroom in a blended learning model. In this paper, it is presented the development of an eBook for teaching mathematics to secondary students. It is developed with the free and open standard EPUB 3 that is available for Android and iOS platforms. This specification supports video embedded in the eBook. In this paper it is shown how to take advantage of this feature, making videos available about lectures and problems resolutions, which is especially interesting for learning mathematics.

An investigation of two real time machine learning techniques that could enhance the adaptability of game AI agents

Developers strive to create innovative Artificial Intelligence (AI) behaviour in their games as a key selling point. Machine Learning is an area of AI that looks at how applications and agents can be programmed to learn their own behaviour without the need to manually design and implement each aspect of it. Machine learning methods have been utilised infrequently within games and are usually trained to learn offline before the game is released to the players. In order to investigate new ways AI could be applied innovatively to games it is wise to explore how machine learning methods could be utilised in real-time as the game is played, so as to allow AI agents to learn directly from the player or their environment. Two machine learning methods were implemented into a simple 2D Fighter test game to allow the agents to fully showcase their learned behaviour as the game is played. The methods chosen were: Q-Learning and an NGram based system. It was found that N-Grams and QLearning could significantly benefit game developers as they facilitate fast, realistic learning at run-time.

Digital literacy matters. Increasing workforce productivity through blended English language programmes

Current workplace demands newer forms of literacies that go beyond the ability to decode print. These involve not only competence to operate digital tools, but also the ability to create, represent, and share meaning in different modes and formats; ability to interact, collaborate and communicate effectively using digital tools, and engage critically with technology for developing one’s knowledge, skills, and full participation in civic, economic, and personal matters. This essay examines the application of the ecology of resources (EoR) model for delivering language learning outcomes (in this case, English) through blended classroom environments that use contextually available resources. The author proposes the implementation of the EoR model in blended learning environments to create authentic and sustainable learning environments for skilling courses. Applying the EoR model to Indian skilling instruction contexts, the article discusses how English language and technology literacy can be delivered using contextually available resources through a blended classroom environment. This would facilitate not only acquisition of language and digital literacy outcomes, but also consequent content literacy gain to a certain extent. This would ensure satisfactory achievement of not only communication/language literacy and technological literacy, but also active social participation, lifelong learning, and learner autonomy.

Collaborative technologies, higher order thinking and self-sufficient learning: A case study of adult learners

The inclusion of online elements in learning environments is becoming commonplace in Post Compulsory Education. A variety of research into the value of such elements is available, and this study aims to add further evidence by looking specifically at the use of collaborative technologies such as online discussion forums and wikis to encourage higher order thinking and self-sufficient learning. In particular, the research examines existing pedagogical models including Salmon’s five-stage model, along with other relevant literature. A case study of adult learners in community-based learning centres forms the basis of the research, and as a result of the findings, an arrow model is suggested as a framework for online collaboration that emphasises the learner, mentions pre-course preparation and then includes three main phases of activity: post, interact and critique. This builds on Salmon’s five-stage model and has the benefit of being flexible and responsive, as well as allowing for further development beyond the model, particularly in a blended learning environment.

Best-practice model for technology enhanced learning in the creative arts

This paper presents a best-practice model for the redesign of virtual learning environments (VLEs) within creative arts to augment blended learning. In considering a blended learning best-practice model, three factors should be considered: the conscious and active human intervention, good learning design and pedagogical input, and the sensitive handling of the process by trained professionals. This study is based on a comprehensive VLE content analysis conducted across two academic schools within the creative arts at one Post-92 higher education (HE) institution. It was found that four main barriers affect the use of the VLE within creative arts: lack of flexibility in relation to navigation and interface, time in developing resources, competency level of tutors (confidence in developing online resources balanced against other flexible open resources) and factors affecting the engagement of ‘digital residents’. The experimental approach adopted in this study involved a partnership between the learning technology advisor and academic staff, which resulted in a VLE best-practice model that focused directly on improving aesthetics and navigation. The approach adopted in this study allowed a purposive sample of academic staff to engage as participants, stepping back cognitively from their routine practices in relation to their use of the VLE and questioning approaches to how they embed the VLE to support teaching and learning. The model presented in this paper identified a potential solution to overcome the challenges of integrating the VLE within creative arts. The findings of this study demonstrate positive impact on staff and student experience and provide a sustainable model of good practice for the redesign of the VLE within creative disciplines.

Integrating Machine Learning Paradigms for Predictive Maintenance in the Fourth Industrial Revolution era

In the last decade, manufacturing companies have been facing two significant challenges. First, digitalization imposes adopting Industry 4.0 technologies and allows creating smart, connected, self-aware, and self-predictive factories. Second, the attention on sustainability imposes to evaluate and reduce the impact of the implemented solutions from economic and social points of view. In manufacturing companies, the maintenance of physical assets assumes a critical role. Increasing the reliability and the availability of production systems leads to the minimization of systems’ downtimes; In addition, the proper system functioning avoids production wastes and potentially catastrophic accidents. Digitalization and new ICT technologies have assumed a relevant role in maintenance strategies. They allow assessing the health condition of machinery at any point in time. Moreover, they allow predicting the future behavior of machinery so that maintenance interventions can be planned, and the useful life of components can be exploited until the time instant before their fault. This dissertation provides insights on Predictive Maintenance goals and tools in Industry 4.0 and proposes a novel data acquisition, processing, sharing, and storage framework that addresses typical issues machine producers and users encounter. The research elaborates on two research questions that narrow down the potential approaches to data acquisition, processing, and analysis for fault diagnostics in evolving environments. The research activity is developed according to a research framework, where the research questions are addressed by research levers that are explored according to research topics. Each topic requires a specific set of methods and approaches; however, the overarching methodological approach presented in this dissertation includes three fundamental aspects: the maximization of the quality level of input data, the use of Machine Learning methods for data analysis, and the use of case studies deriving from both controlled environments (laboratory) and real-world instances.

Development and characterization of deep learning techniques for neuroimaging data

Deep learning methods are extremely promising machine learning tools to analyze neuroimaging data. However, their potential use in clinical settings is limited because of the existing challenges of applying these methods to neuroimaging data. In this study, first a data leakage type caused by slice-level data split that is introduced during training and validation of a 2D CNN is surveyed and a quantitative assessment of the model’s performance overestimation is presented. Second, an interpretable, leakage-fee deep learning software written in a python language with a wide range of options has been developed to conduct both classification and regression analysis. The software was applied to the study of mild cognitive impairment (MCI) in patients with small vessel disease (SVD) using multi-parametric MRI data where the cognitive performance of 58 patients measured by five neuropsychological tests is predicted using a multi-input CNN model taking brain image and demographic data. Each of the cognitive test scores was predicted using different MRI-derived features. As MCI due to SVD has been hypothesized to be the effect of white matter damage, DTI-derived features MD and FA produced the best prediction outcome of the TMT-A score which is consistent with the existing literature. In a second study, an interpretable deep learning system aimed at 1) classifying Alzheimer disease and healthy subjects 2) examining the neural correlates of the disease that causes a cognitive decline in AD patients using CNN visualization tools and 3) highlighting the potential of interpretability techniques to capture a biased deep learning model is developed. Structural magnetic resonance imaging (MRI) data of 200 subjects was used by the proposed CNN model which was trained using a transfer learning-based approach producing a balanced accuracy of 71.6%. Brain regions in the frontal and parietal lobe showing the cerebral cortex atrophy were highlighted by the visualization tools.

Robotic Manipulation of DLOs for Wiring Harness Assembly: a Machine Learning Approach

In recent times, a significant research effort has been focused on how deformable linear objects (DLOs) can be manipulated for real world applications such as assembly of wiring harnesses for the automotive and aerospace sector. This represents an open topic because of the difficulties in modelling accurately the behaviour of these objects and simulate a task involving their manipulation, considering a variety of different scenarios. These problems have led to the development of data-driven techniques in which machine learning techniques are exploited to obtain reliable solutions. However, this approach makes the solution difficult to be extended, since the learning must be replicated almost from scratch as the scenario changes. It follows that some model-based methodology must be introduced to generalize the results and reduce the training effort accordingly. The objective of this thesis is to develop a solution for the DLOs manipulation to assemble a wiring harness for the automotive sector based on adaptation of a base trajectory set by means of reinforcement learning methods. The idea is to create a trajectory planning software capable of solving the proposed task, reducing where possible the learning time, which is done in real time, but at the same time presenting suitable performance and reliability. The solution has been implemented on a collaborative 7-DOFs Panda robot at the Laboratory of Automation and Robotics of the University of Bologna. Experimental results are reported showing how the robot is capable of optimizing the manipulation of the DLOs gaining experience along the task repetition, but showing at the same time a high success rate from the very beginning of the learning phase.

Deep learning based style transfer for low altitude aerial imagery

Unmanned Aerial Vehicle (UAVs) equipped with cameras have been fast deployed to a wide range of applications, such as smart cities, agriculture or search and rescue applications. Even though UAV datasets exist, the amount of open and quality UAV datasets is limited. So far, we want to overcome this lack of high quality annotation data by developing a simulation framework for a parametric generation of synthetic data. The framework accepts input via a serializable format. The input specifies which environment preset is used, the objects to be placed in the environment along with their position and orientation as well as additional information such as object color and size. The result is an environment that is able to produce UAV typical data: RGB image from the UAVs camera, altitude, roll, pitch and yawn of the UAV. Beyond the image generation process, we improve the resulting image data photorealism by using Synthetic-To-Real transfer learning methods. Transfer learning focuses on storing knowledge gained while solving one problem and applying it to a different - although related - problem. This approach has been widely researched in other affine fields and results demonstrate it to be an interesing area to investigate. Since simulated images are easy to create and synthetic-to-real translation has shown good quality results, we are able to generate pseudo-realistic images. Furthermore, object labels are inherently given, so we are capable of extending the already existing UAV datasets with realistic quality images and high resolution meta-data. During the development of this thesis we have been able to produce a result of 68.4% on UAVid. This can be considered a new state-of-art result on this dataset.