BeSocratic: An Intelligent Tutoring System for the Recognition, Evaluation, and Analysis of Free-form Student Input

This dissertation describes a novel intelligent tutoring system, BeSocratic, which aims to help fill the gap between simple multiple-choice systems and free-response systems. BeSocratic focuses on targeting questions that are free-form in nature yet defined to the point which allows for automatic evaluation and analysis. The system includes a set of modules which provide instructors with tools to assess student performance. Beyond text boxes and multiple-choice questions, BeSocratic contains several modules that recognize, evaluate, provide feedback, and analyze student-drawn structures, including Euclidean graphs, chemistry molecules, computer science graphs, and simple drawings. Our system uses a visual, rule-based authoring system which enables the creation of activities for use within science, technology, engineering, and mathematics classrooms. BeSocratic records each action that students make within the system. Using a set of post-analysis tools, teachers have the ability to examine both individual and group performances. We accomplish this using hidden Markov model-based clustering techniques and visualizations. These visualizations can help teachers quickly identify common strategies and errors for large groups of students. Furthermore, analysis results can be used directly to improve activities through advanced detection of student errors and refined feedback. BeSocratic activities have been created and tested at several universities. We report specific results from several activities, and discuss how BeSocratic\u27s analysis tools are being used with data from other systems. We specifically detail two chemistry activities and one computer science activity: (1) an activity focused on improving mechanism use, (2) an activity which assesses student understanding of Gibbs energy, and (3) an activity which teaches students the fundamentals of splay trees. In addition to analyzing data collected from students within BeSocratic, we share our visualizations and results from analyzing data gathered with another educational system, PhET

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Molecular Signature as Optima of Multi-Objective Function with Applications to Prediction in Oncogenomics

Náplní této práce je teoretický úvod a následné praktické zpracování tématu Molekulární signatura jako optimální multi-objektivní funkce s aplikací v predikci v onkogenomice. Úvodní kapitoly jsou zaměřeny na téma rakovina, zejména pak rakovina prsu a její podtyp triple negativní rakovinu prsu. Následuje literární přehled z oblasti optimalizačních metod, zejména se zaměřením na metaheuristické metody a problematiku strojového učení. Část se odkazuje na onkogenomiku a principy microarray a také na statistiku a s důrazem na výpočet p-hodnoty a bimodálního indexu. Praktická část je pak zaměřena na konkrétní průběh výzkumu a nalezené závěry, vedoucí k dalším krokům výzkumu. Implementace vybraných metod byla provedena v programech Matlab a R, s využitím dalších programovacích jazyků a to konkrétně programů Java a Python.Content of this work is theoretical introduction and follow-up practical processing of topic Molecular signature as optima of multi-objective function with applications to prediction in oncogenomics. Opening chapters are targeted on topic of cancer, mainly on breast cancer and its subtype Triple Negative Breast Cancer. Succeeds the literature review of optimization methods, mainly on meta-heuristic methods for multi-objective optimization and problematic of machine learning. Part is focused on the oncogenomics and on the principal of microarray and also to statistics methods with emphasis on the calculation of p-value and Bimodality Index. Practical part of work consists from concrete research and conclusions lead to next steps of research. Implementation of selected methods was realised in Matlab and R, with use of other programming languages Java and Python.

Knowledge extraction from unstructured data and classification through distributed ontologies

The World Wide Web has changed the way humans use and share any kind of information. The Web removed several access barriers to the information published and has became an enormous space where users can easily navigate through heterogeneous resources (such as linked documents) and can easily edit, modify, or produce them. Documents implicitly enclose information and relationships among them which become only accessible to human beings. Indeed, the Web of documents evolved towards a space of data silos, linked each other only through untyped references (such as hypertext references) where only humans were able to understand. A growing desire to programmatically access to pieces of data implicitly enclosed in documents has characterized the last efforts of the Web research community. Direct access means structured data, thus enabling computing machinery to easily exploit the linking of different data sources. It has became crucial for the Web community to provide a technology stack for easing data integration at large scale, first structuring the data using standard ontologies and afterwards linking them to external data. Ontologies became the best practices to define axioms and relationships among classes and the Resource Description Framework (RDF) became the basic data model chosen to represent the ontology instances (i.e. an instance is a value of an axiom, class or attribute). Data becomes the new oil, in particular, extracting information from semi-structured textual documents on the Web is key to realize the Linked Data vision. In the literature these problems have been addressed with several proposals and standards, that mainly focus on technologies to access the data and on formats to represent the semantics of the data and their relationships. With the increasing of the volume of interconnected and serialized RDF data, RDF repositories may suffer from data overloading and may become a single point of failure for the overall Linked Data vision. One of the goals of this dissertation is to propose a thorough approach to manage the large scale RDF repositories, and to distribute them in a redundant and reliable peer-to-peer RDF architecture. The architecture consists of a logic to distribute and mine the knowledge and of a set of physical peer nodes organized in a ring topology based on a Distributed Hash Table (DHT). Each node shares the same logic and provides an entry point that enables clients to query the knowledge base using atomic, disjunctive and conjunctive SPARQL queries. The consistency of the results is increased using data redundancy algorithm that replicates each RDF triple in multiple nodes so that, in the case of peer failure, other peers can retrieve the data needed to resolve the queries. Additionally, a distributed load balancing algorithm is used to maintain a uniform distribution of the data among the participating peers by dynamically changing the key space assigned to each node in the DHT. Recently, the process of data structuring has gained more and more attention when applied to the large volume of text information spread on the Web, such as legacy data, news papers, scientific papers or (micro-)blog posts. This process mainly consists in three steps: \emph{i)} the extraction from the text of atomic pieces of information, called named entities; \emph{ii)} the classification of these pieces of information through ontologies; \emph{iii)} the disambigation of them through Uniform Resource Identifiers (URIs) identifying real world objects. As a step towards interconnecting the web to real world objects via named entities, different techniques have been proposed. The second objective of this work is to propose a comparison of these approaches in order to highlight strengths and weaknesses in different scenarios such as scientific and news papers, or user generated contents. We created the Named Entity Recognition and Disambiguation (NERD) web framework, publicly accessible on the Web (through REST API and web User Interface), which unifies several named entity extraction technologies. Moreover, we proposed the NERD ontology, a reference ontology for comparing the results of these technologies. Recently, the NERD ontology has been included in the NIF (Natural language processing Interchange Format) specification, part of the Creating Knowledge out of Interlinked Data (LOD2) project. Summarizing, this dissertation defines a framework for the extraction of knowledge from unstructured data and its classification via distributed ontologies. A detailed study of the Semantic Web and knowledge extraction fields is proposed to define the issues taken under investigation in this work. Then, it proposes an architecture to tackle the single point of failure issue introduced by the RDF repositories spread within the Web. Although the use of ontologies enables a Web where data is structured and comprehensible by computing machinery, human users may take advantage of it especially for the annotation task. Hence, this work describes an annotation tool for web editing, audio and video annotation in a web front end User Interface powered on the top of a distributed ontology. Furthermore, this dissertation details a thorough comparison of the state of the art of named entity technologies. The NERD framework is presented as technology to encompass existing solutions in the named entity extraction field and the NERD ontology is presented as reference ontology in the field. Finally, this work highlights three use cases with the purpose to reduce the amount of data silos spread within the Web: a Linked Data approach to augment the automatic classification task in a Systematic Literature Review, an application to lift educational data stored in Sharable Content Object Reference Model (SCORM) data silos to the Web of data and a scientific conference venue enhancer plug on the top of several data live collectors. Significant research efforts have been devoted to combine the efficiency of a reliable data structure and the importance of data extraction techniques. This dissertation opens different research doors which mainly join two different research communities: the Semantic Web and the Natural Language Processing community. The Web provides a considerable amount of data where NLP techniques may shed the light within it. The use of the URI as a unique identifier may provide one milestone for the materialization of entities lifted from a raw text to real world object

The development and evaluation of software to foster professional development in educational assessment

This dissertation sought to answer the question: Is it possible to build a software tool that will allow teachers to write better multiple-choice questions? The thesis proceeded from the finding that the quality of teaching is very influential in the amount that students learn. A basic premise of this research, then, is that improving teachers will improve learning. With this foundation, the next question became what area of teaching to improve. The literature on educational assessment indicated that teachers lack competence at effective assessment, particularly in the area of multiple-choice question generation. It is likely that improvement in this area would yield large gains in educational achievement by students. Several areas of literature including teacher professional development, modification of health-related behaviors, and the information systems theories of captology and structuration theory were synthesized to develop a general model for designing systems to foster teacher professional development. This model was then applied to design and build a tool, QuesGen a web-based system to help teachers write better multiple-choice questions. The tool was evaluated. Quantitative and qualitative results are presented, their implications discussed, and future steps are laid out

Optimax 2016 : peer observation of facilitation

In August 2016, a 3-week research Summer School was delivered at University of Salford. The Summer School, known as ‘OPTIMAX’ was in its fourth year of delivery. Previous iterations were held in the Netherlands (2015), Portugal (2014) and Salford (2013). The purpose of OPTIMAX is to facilitate collaborative international and interdisciplinary research between university academics and students. This offers an exceptional opportunity not only for students, but also for tutors who want to develop their facilitation skills. The project reported here used tutor observers (i.e. tutors who attend the summer school, in an observational capacity only, to develop their own skills as teachers) to observe, identify and reflect on a range of facilitation practices for managing the diverse OPTIMAX research groups. The project presents a description of the peer-observation method we used and highlights a number of findings related to facilitator strategies that appeared to influence group dynamics and learning. These observations are then used to make recommendations about how OPTIMAX tutors can be prepared for their facilitation experience

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Optimising image quality for medical imaging

OPTIMAX 2016 was held at the University of Salford in Greater Manchester. It is the fourth summer school of OPTIMAX with other renditions having been organized at the University of Salford (2013), ESTeSL, Lisbon (2014) and Hanze UAS, Groningen (2015). For OPTIMAX 2016, 72 people participated from eleven countries, comprising PhD, MSc and BSc students as well as tutors from the seven European partner universities. Professional mix was drawn from engineering, medical physics/ physics and radiography. OPTIMAX 2016 was partly funded by the partner universities and partly by the participants. Two students from South Africa and two from Brazil were invited by Hanze UAS (Groningen) and ESTeSL (Lisbon). One student from the United Kingdom was funded by the Nuffield Foundation. The summer school included lectures and group projects in which experimental research was conducted in five teams. Each team project focus varied and included: optimization of full spine curvature radiography in paediatrics; ultrasound assessment of muscle thickness and muscle cross-sectional area: a reliability study; the Influence of Source-to-Image Distance on Effective Dose and Image Quality for Mobile Chest X-rays; Impact of the anode heel effect on image quality and effective dose for AP Pelvis: A pilot study; and the impact of pitch values on Image Quality and radiation dose in an abdominal adult phantom using CT. OPTIMAX 2016 culminated in a poster session and a conference, in which the research teams presented their posters and oral presentations. This book comprises of two sections, the first four chapters concern generic background information which has value to summer school organization and also theory on which the research projects were built. The second section contains the research papers in written format. The research papers have been accepted for the ECR conference, Vienna, 2017 as either oral presentations or posters

Low-level analysis of microarray data

This thesis consists of an extensive introduction followed by seven papers (A-F) on low-level analysis of microarray data. Focus is on calibration and normalization of observed data. The introduction gives a brief background of the microarray technology and its applications in order for anyone not familiar with the field to read the thesis. Formal definitions of calibration and normalization are given. Paper A illustrates a typical statistical analysis of microarray data with background correction, normalization, and identification of differentially expressed genes (among thousands of candidates). A small analysis on the final results for different number of replicates and different image analysis software is also given. Paper B introduces a novel way for displaying microarray data called the print-order plot, which displays data in the order the corresponding spots were printed to the array. Utilizing these, so called (microtiter-) plate effects are identified. Then, based on a simple variability measure for replicated spots across arrays, different normalization sequences are tested and evidence for the existence of plate effects are claimed. Paper C presents an object-oriented extension with transparent reference variables to the R language. It is provides the necessary foundation in order to implement the microarray analysis package described in Paper F. Paper D is on affine transformations of two-channel microarray data and their effects on the log-ratio log-intensity transform. Affine transformations, that is, the existence of channel biases, can explain commonly observed intensity-dependent effects in the log-ratios. In the light of the affine transformation, several normalization methods are revisited. At the end of the paper, a new robust affine normalization is suggested that relies on iterative reweighted principal component analysis. Paper E suggests a multiscan calibration method where each array is scanned at various sensitivity levels in order to uniquely identify the affine transformation of signals that the scanner and the image-analysis methods introduce. Observed data strongly support this method. In addition, multiscan-calibrated data has an extended dynamical range and higher signal-to-noise levels. This is real-world evidence for the existence of affine transformations of microarray data. Paper F describes the aroma package – An R Object-oriented Microarray Analysis environment – implemented in R and that provides easy access to our and others low-level analysis methods. Paper G provides an calibration method for spotted microarrays with dilution series or spike-ins. The method is based on a heteroscedastic affine stochastic model. The parameter estimates are robust against model misspecification