University students and faculty have positive perceptions of open/alternative resources and their utilization in a textbook replacement initiative

This is contribution no. 16-114-J from the Kansas Agricultural Experiment Station. The Kansas State University Open/Alternative Textbook Initiative provides grants to faculty members to replace textbooks with open/alternative educational resources (OAERs) that are available at no cost to students. Open educational resources are available for anyone to access, while alternative educational resources are not open. The objective of this study was to determine the perceptions towards OAERs and the initiative, of students enrolled in, and faculty members teaching, courses using OAERs. A survey was sent out to 2,074 students in 13 courses using the OAERs. A total of 524 (25.3%) students completed the survey and a faculty member from each of the 13 courses using OAERs was interviewed. Students rated the OAERs as good quality, preferred using them instead of buying textbooks for their courses, and agreed that they would like OAERs used in other courses. Faculty felt that student learning was somewhat better and it was somewhat easier to teach using OAERs than when they used the traditional textbooks. Nearly all faculty members preferred teaching with OAERs and planned to continue to do so after the funding period. These results, combined with the tremendous savings to students, support the continued funding of the initiative and similar approaches at other institutions.

The three people you need for distance learning

Presentation at M25 Learning Technology Group, FutureLearn, 15 November 2017

Assuring best practice in technology-enhanced learning environments

This paper documents the development and findings of the Good Practice Report on Technology-Enhanced Learning and Teaching funded by the Australian Learning and Teaching Council (ALTC). Developing the Good Practice Report required a meta-analysis of 33 ALTC learning and teaching projects relating to technology funded between 2006 and 2010. This report forms one of 12 completed Good Practice Reports on a range of different topics commissioned by the ALTC and Australian Government Office for Learning and Teaching (OLT). The reports aim to reduce issues relating to dissemination that projects face within the sector by providing educators with an efficient and accessible way of engaging with and filtering through the resources and experiences of numerous learning and teaching projects funded by the ALTC and OLT. The Technology-Enhanced Learning and Teaching Report highlights examples of good practice and provides outcomes and recommendations based on the meta-analysis of the relevant learning and teaching projects. However, in order to ensure the value of these reports is realised, educators need to engage with the reports and integrate the information and findings into their practice. The paper concludes by detailing how educational networks can be utilised to support dissemination.

Learning analytics from distance learning

Presentation at M25 Learning Technology Group, FutureLearn, 15 November 2017

Portfolios for learning; course design and platform choices

Presentation at M25 Learning Technology Group, FutureLearn, 15 November 2017

Operational issues and plans for distance learning at UCL

Presentation at M25 Learning Technology Group, FutureLearn, 15 November 2017

Variational and learning models for image and time series inverse problems

Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.

Methods for integrating machine learning and constrained optimization

In the framework of industrial problems, the application of Constrained Optimization is known to have overall very good modeling capability and performance and stands as one of the most powerful, explored, and exploited tool to address prescriptive tasks. The number of applications is huge, ranging from logistics to transportation, packing, production, telecommunication, scheduling, and much more. The main reason behind this success is to be found in the remarkable effort put in the last decades by the OR community to develop realistic models and devise exact or approximate methods to solve the largest variety of constrained or combinatorial optimization problems, together with the spread of computational power and easily accessible OR software and resources. On the other hand, the technological advancements lead to a data wealth never seen before and increasingly push towards methods able to extract useful knowledge from them; among the data-driven methods, Machine Learning techniques appear to be one of the most promising, thanks to its successes in domains like Image Recognition, Natural Language Processes and playing games, but also the amount of research involved. The purpose of the present research is to study how Machine Learning and Constrained Optimization can be used together to achieve systems able to leverage the strengths of both methods: this would open the way to exploiting decades of research on resolution techniques for COPs and constructing models able to adapt and learn from available data. In the first part of this work, we survey the existing techniques and classify them according to the type, method, or scope of the integration; subsequently, we introduce a novel and general algorithm devised to inject knowledge into learning models through constraints, Moving Target. In the last part of the thesis, two applications stemming from real-world projects and done in collaboration with Optit will be presented.

Applied deep learning and data science with a human-centric and data-centric approach

The term Artificial intelligence acquired a lot of baggage since its introduction and in its current incarnation is synonymous with Deep Learning. The sudden availability of data and computing resources has opened the gates to myriads of applications. Not all are created equal though, and problems might arise especially for fields not closely related to the tasks that pertain tech companies that spearheaded DL. The perspective of practitioners seems to be changing, however. Human-Centric AI emerged in the last few years as a new way of thinking DL and AI applications from the ground up, with a special attention at their relationship with humans. The goal is designing a system that can gracefully integrate in already established workflows, as in many real-world scenarios AI may not be good enough to completely replace its humans. Often this replacement may even be unneeded or undesirable. Another important perspective comes from, Andrew Ng, a DL pioneer, who recently started shifting the focus of development from “better models” towards better, and smaller, data. He defined his approach Data-Centric AI. Without downplaying the importance of pushing the state of the art in DL, we must recognize that if the goal is creating a tool for humans to use, more raw performance may not align with more utility for the final user. A Human-Centric approach is compatible with a Data-Centric one, and we find that the two overlap nicely when human expertise is used as the driving force behind data quality. This thesis documents a series of case-studies where these approaches were employed, to different extents, to guide the design and implementation of intelligent systems. We found human expertise proved crucial in improving datasets and models. The last chapter includes a slight deviation, with studies on the pandemic, still preserving the human and data centric perspective.

Machine learning "as a service" for high energy physics (MLaaS4HEP): evolution of a framework for ML-based physics analyses

The scientific success of the LHC experiments at CERN highly depends on the availability of computing resources which efficiently store, process, and analyse the amount of data collected every year. This is ensured by the Worldwide LHC Computing Grid infrastructure that connect computing centres distributed all over the world with high performance network. LHC has an ambitious experimental program for the coming years, which includes large investments and improvements both for the hardware of the detectors and for the software and computing systems, in order to deal with the huge increase in the event rate expected from the High Luminosity LHC (HL-LHC) phase and consequently with the huge amount of data that will be produced. Since few years the role of Artificial Intelligence has become relevant in the High Energy Physics (HEP) world. Machine Learning (ML) and Deep Learning algorithms have been successfully used in many areas of HEP, like online and offline reconstruction programs, detector simulation, object reconstruction, identification, Monte Carlo generation, and surely they will be crucial in the HL-LHC phase. This thesis aims at contributing to a CMS R&D project, regarding a ML "as a Service" solution for HEP needs (MLaaS4HEP). It consists in a data-service able to perform an entire ML pipeline (in terms of reading data, processing data, training ML models, serving predictions) in a completely model-agnostic fashion, directly using ROOT files of arbitrary size from local or distributed data sources. This framework has been updated adding new features in the data preprocessing phase, allowing more flexibility to the user. Since the MLaaS4HEP framework is experiment agnostic, the ATLAS Higgs Boson ML challenge has been chosen as physics use case, with the aim to test MLaaS4HEP and the contribution done with this work.

Texting and Driving: rilevazione di utilizzo dello smartphone alla guida tramite Object Detection e Machine Learning

Questa tesi si ispira a lavori precedentemente portati avanti da altri studenti e si pone il problema della possibilit\`a di riconoscere se uno smartphone \`e utilizzato da un utente mentre esso si trova alla guida di un'autovettura. In essa verranno presentati vari metodi per risolvere questo problema di Machine Learning, ovvero realizzazione di dataset per l'allenamento di modelli e creazione e allenamento di modelli stessi, dediti al riconoscimento di un problema di classificazione binaria e riconoscimento di oggetti tramite Object Detection. Il cercare di riconoscere se l'utente \`e alla guida o meno, avverr\`a tramite l'output della fotocamera frontale dello smartphone, quindi lavoreremo su immagini, video e frame. Arriveremo a riconoscere la posizione della persona rappresentata da questi fotogrammi tramite un modello di Object Detection, che riconosce cintura e finestrino e determina se sono appartenenti al sedile e alla posizione del conducente o del passeggero. Vedremo alla fine, attraverso un'attenta analisi dei risultati ottenuti su ben 8 video diversi che saranno divisi in molti frame, che si ottengono risultati molto interessanti, dai quali si pu\`o prendere spunto per la creazione di un importante sistema di sicurezza alla guida.

Analysis of human posterior EEG alpha frequency as a function of implicit learning of probabilistic lateralized stimuli in a visual detection task

Alpha oscillatory activity has long been associated with perceptual and cognitive processes related to attention control. The aim of this study is to explore the task-dependent role of alpha frequency in a lateralized visuo-spatial detection task. Specifically, the thesis focuses on consolidating the scientific literature's knowledge about the role of alpha frequency in perceptual accuracy, and deepening the understanding of what determines trial-by-trial fluctuations of alpha parameters and how these fluctuations influence overall task performance. The hypotheses, confirmed empirically, were that different implicit strategies are put in place based on the task context, in order to maximize performance with optimal resource distribution (namely alpha frequency, associated positively with performance): “Lateralization” of the attentive resources towards one hemifield should be associated with higher alpha frequency difference between contralateral and ipsilateral hemisphere; “Distribution” of the attentive resources across hemifields should be associated with lower alpha frequency difference between hemispheres; These strategies, used by the participants according to their brain capabilities, have proven themselves adaptive or maladaptive depending on the different tasks to which they have been set: "Distribution" of the attentive resources seemed to be the best strategy when the distribution probability between hemifields was balanced: i.e. the neutral condition task. "Lateralization" of the attentive resources seemed to be more effective when the distribution probability between hemifields was biased towards one hemifield: i.e., the biased condition task.

Antiproliferative Activity Of Synthetic Fatty Acid Amides From Renewable Resources.

In the work, the in vitro antiproliferative activity of a series of synthetic fatty acid amides were investigated in seven cancer cell lines. The study revealed that most of the compounds showed antiproliferative activity against tested tumor cell lines, mainly on human glioma cells (U251) and human ovarian cancer cells with a multiple drug-resistant phenotype (NCI-ADR/RES). In addition, the fatty methyl benzylamide derived from ricinoleic acid (with the fatty acid obtained from castor oil, a renewable resource) showed a high selectivity with potent growth inhibition and cell death for the glioma cell line-the most aggressive CNS cancer.

Ecological Literacy And Beyond: Problem-based Learning For Future Professionals.

Ecological science contributes to solving a broad range of environmental problems. However, lack of ecological literacy in practice often limits application of this knowledge. In this paper, we highlight a critical but often overlooked demand on ecological literacy: to enable professionals of various careers to apply scientific knowledge when faced with environmental problems. Current university courses on ecology often fail to persuade students that ecological science provides important tools for environmental problem solving. We propose problem-based learning to improve the understanding of ecological science and its usefulness for real-world environmental issues that professionals in careers as diverse as engineering, public health, architecture, social sciences, or management will address. Courses should set clear learning objectives for cognitive skills they expect students to acquire. Thus, professionals in different fields will be enabled to improve environmental decision-making processes and to participate effectively in multidisciplinary work groups charged with tackling environmental issues.

Delivery Of A Urology Online Course Using Moodle Versus Didactic Lectures Methods.

To subjectively and objectively compare an accessible interactive electronic library using Moodle with lectures for urology teaching of medical students. Forty consecutive fourth-year medical students and one urology teacher were exposed to two teaching methods (4 weeks each) in the form of problem-based learning: - lectures and - student-centered group discussion based on Moodle (modular object-oriented dynamic learning environment) full time online delivered (24/7) with video surgeries, electronic urology cases and additional basic principles of the disease process. All 40 students completed the study. While 30% were moderately dissatisfied with their current knowledge base, online learning course delivery using Moodle was considered superior to the lectures by 86% of the students. The study found the following observations: (1) the increment in learning grades ranged from 7.0 to 9.7 for students in the online Moodle course compared to 4.0-9.6 to didactic lectures; (2) the self-reported student involvement in the online course was characterized as large by over 60%; (3) the teacher-student interaction was described as very frequent (50%) and moderately frequent (50%); and (4) more inquiries and requisitions by students as well as peer assisting were observed from the students using the Moodle platform. The Moodle platform is feasible and effective, enthusing medical students to learn, improving immersion in the urology clinical rotation and encouraging the spontaneous peer assisted learning. Future studies should expand objective evaluations of knowledge acquisition and retention.