Including universal design in a summer camp workshop on robotics

In this paper we will describe a summer camp short-course intended for high-school students with excellent qualifications. The course is addressed to students who are thinking on studying a technical career including a section on universal design for the first time. The department of Mathematics and Computer Science at Universitat de Barcelona will host a workshop on robotics next summer within the context of Campus Científicos de Verano by Fundación Española para la Ciencia y la Tecnología.. High-school students will be selected around Spain based on their qualifications and motivation to attend the workshop. The first activity in the summer camp will be the building of Lego Mindstorms robots. These robots contain several sensors and actuators that can be programmed to do different tasks. One of the robots will be programmed to be able to track a line and another two will be programmed to do a Sumo fight on their own. Students will learn how to use sensors and actuators and code programming algorithms. For the second activity the students will develop a Mobile App with the MIT App Inventor2 software [1] in order to control the robots. In this activity students will learn how to program apps in a simple way to complete their understanding of programming. Taking into account European Higher Education Area requirements for Accessibility in technical careers, this workshop will introduce an innovation; the third activity will consist in the adaptation of the app and robots for multimodal access (including sound and sight redundant warnings) and the readjustment of the app’s buttons for users with motor and visual disabilities (e.g. making the buttons bigger and with non-repeating behaviour). Students attending the summer camp will be introduced to the needs and skills of different user profiles of people with disabilities. After this theoretical introduction, they will experience motor and visual disabilities with simulations inspired by the Inclusive design Toolkit resource [2] ].And finally, they will modify the app based on IEEE RWEP Accessible apps by Ayanna Howard [3] so to maximise the accessibility possibilities of App Inventor. Complementary resources will be made available to those students showing interest in this area, such as RWEP prosthetic hands projects, other toolkits and bibliography. This will serve as a first experience for the students and there is no prevision of including technical aids such as GRID2 or similar [4] due to budget restrictions. There are no students with disabilities registered for this year edition so the course does not seek accessibility for participants as authors. We will consider working on accessibility for participants of the following editions of this workshop, building on past experiences reaching this goal [5] [6] [7]. The main focus of the workshop is to encourage the creative learning of a robots summer camp [8], [9] with the inclusion of universal design as an essential requirement in the design and development of computer applications or systems. With this initiative we want to increase awareness on accessibility requirements for future technical students.PID U

Generalized Stacked Sequential Learning

[eng] Over the past few decades, machine learning (ML) algorithms have become a very useful tool in tasks where designing and programming explicit, rule-based algorithms are infeasible. Some examples of applications where machine learning has been applied successfully are spam filtering, optical character recognition (OCR), search engines and computer vision. One of the most common tasks in ML is supervised learning, where the goal is to learn a general model able to predict the correct label of unseen examples from a set of known labeled input data. In supervised learning often it is assumed that data is independent and identically distributed (i.i.d ). This means that each sample in the data set has the same probability distribution as the others and all are mutually independent. However, classification problems in real world databases can break this i.i.d. assumption. For example, consider the case of object recognition in image understanding. In this case, if one pixel belongs to a certain object category, it is very likely that neighboring pixels also belong to the same object, with the exception of the borders. Another example is the case of a laughter detection application from voice records. A laugh has a clear pattern alternating voice and non-voice segments. Thus, discriminant information comes from the alternating pattern, and not just by the samples on their own. Another example can be found in the case of signature section recognition in an e-mail. In this case, the signature is usually found at the end of the mail, thus important discriminant information is found in the context. Another case is part-of-speech tagging in which each example describes a word that is categorized as noun, verb, adjective, etc. In this case it is very unlikely that patterns such as [verb, verb, adjective, verb] occur. All these applications present a common feature: the sequence/context of the labels matters. Sequential learning (25) breaks the i.i.d. assumption and assumes that samples are not independently drawn from a joint distribution of the data samples X and their labels Y . In sequential learning the training data actually consists of sequences of pairs (x, y), so that neighboring examples exhibit some kind of correlation. Usually sequential learning applications consider one-dimensional relationship support, but these types of relationships appear very frequently in other domains, such as images, or video. Sequential learning should not be confused with time series prediction. The main difference between both problems lays in the fact that sequential learning has access to the whole data set before any prediction is made and the full set of labels is to be provided at the same time. On the other hand, time series prediction has access to real labels up to the current time t and the goal is to predict the label at t + 1. Another related but different problem is sequence classification. In this case, the problem is to predict a single label for an input sequence. If we consider the image domain, the sequential learning goal is to classify the pixels of the image taking into account their context, while sequence classification is equivalent to classify one full image as one class. Sequential learning has been addressed from different perspectives: from the point of view of meta-learning by means of sliding window techniques, recurrent sliding windows or stacked sequential learning where the method is formulated as a combination of classifiers; or from the point of view of graphical models, using for example Hidden Markov Models or Conditional Random Fields. In this thesis, we are concerned with meta-learning strategies. Cohen et al. (17) showed that stacked sequential learning (SSL from now on) performed better than CRF and HMM on a subset of problems called “sequential partitioning problems”. These problems are characterized by long runs of identical labels. Moreover, SSL is computationally very efficient since it only needs to train two classifiers a constant number of times. Considering these benefits, we decided to explore in depth sequential learning using SSL and generalize the Cohen architecture to deal with a wider variety of problems

Data-driven System to Predict Academic Grades and Dropout

Nowadays, the role of a tutor is more important than ever to prevent students dropout and improve their academic performance. This work proposes a data-driven system to extract relevant information hidden in the student academic data and, thus, help tutors to offer their pupils a more proactive personal guidance. In particular, our system, based on machine learning techniques, makes predictions of dropout intention and courses grades of students, as well as personalized course recommendations. Moreover, we present different visualizations which help in the interpretation of the results. In the experimental validation, we show that the system obtains promising results with data from the degree studies in Law, Computer Science and Mathematics of the Universitat de Barcelona

Generalized multi-scale stacked sequential learning for multi-class classification

In many classification problems, neighbor data labels have inherent sequential relationships. Sequential learning algorithms take benefit of these relationships in order to improve generalization. In this paper, we revise the multi-scale sequential learning approach (MSSL) for applying it in the multi-class case (MMSSL). We introduce the error-correcting output codesframework in the MSSL classifiers and propose a formulation for calculating confidence maps from the margins of the base classifiers. In addition, we propose a MMSSL compression approach which reduces the number of features in the extended data set without a loss in performance. The proposed methods are tested on several databases, showing significant performance improvement compared to classical approaches

Quantum computing for market research

The digital ecosystem continues to expand around the world and is revolutionising the way markets are researched. Indeed, consumer experiences are advertised and disseminated through so many channels and media that it has become a major challenge for researchers and marketing practitioners to collect, process and generate valuable information to support strategic and operational decisions. In this article, the authors explore how advances in quantum computing, which can be used to process huge amounts of data quickly and accurately, could offer an unprecedented opportunity for researchers to address the challenges of the digital ecosystem. Three studies are presented to define the state of the art and future expectations of quantum computing in market research and business. By means of a bibliometric analysis of 209 publications and a content analysis of the 30 highest-impact articles, we describe the present landscape, and also forecast the future with the help of in-depth interviews with eight experts. The findings reveal that the US and China are at the forefront of scientific development, but the contributions from four other countries (India, the UK, Canada and Spain) are also in double figures. However, graphical analysis identifies four poles of development: the US orbit, which includes Canada and Spain; the Chinese orbit, which includes India; the UK orbit; and the Australian orbit. In terms of expectations, the experts agree on the opportunities offered by quantum computing, but there is less consensus as to how long it will take to develop

Jornada "Experiències d'Aprenentatge Servei a la Universitat de Barcelona". Desenvolupament i experiències presentades.

La Jornada, titulada “Experiències d’Aprenentatge Servei a la Universitat de Barcelona”, te la finalitat de facilitar una reflexió conjunta sobre l’aplicació de l’ApS a la tasca docent i compartir les experiències desenvolupades a la nostra universitat. L'infome presenta el desenvolupament de la jornada i el recull dels pòsters presentats pels participants.ICE-IDP, Grup ApS (UB

Including universal design in a summer camp workshop on robotics

In this paper we will describe a summer camp short-course intended for high-school students with excellent qualifications. The course is addressed to students who are thinking on studying a technical career including a section on universal design for the first time. The department of Mathematics and Computer Science at Universitat de Barcelona will host a workshop on robotics next summer within the context of Campus Científicos de Verano by Fundación Española para la Ciencia y la Tecnología.. High-school students will be selected around Spain based on their qualifications and motivation to attend the workshop. The first activity in the summer camp will be the building of Lego Mindstorms robots. These robots contain several sensors and actuators that can be programmed to do different tasks. One of the robots will be programmed to be able to track a line and another two will be programmed to do a Sumo fight on their own. Students will learn how to use sensors and actuators and code programming algorithms. For the second activity the students will develop a Mobile App with the MIT App Inventor2 software [1] in order to control the robots. In this activity students will learn how to program apps in a simple way to complete their understanding of programming. Taking into account European Higher Education Area requirements for Accessibility in technical careers, this workshop will introduce an innovation; the third activity will consist in the adaptation of the app and robots for multimodal access (including sound and sight redundant warnings) and the readjustment of the app’s buttons for users with motor and visual disabilities (e.g. making the buttons bigger and with non-repeating behaviour). Students attending the summer camp will be introduced to the needs and skills of different user profiles of people with disabilities. After this theoretical introduction, they will experience motor and visual disabilities with simulations inspired by the Inclusive design Toolkit resource [2] ].And finally, they will modify the app based on IEEE RWEP Accessible apps by Ayanna Howard [3] so to maximise the accessibility possibilities of App Inventor. Complementary resources will be made available to those students showing interest in this area, such as RWEP prosthetic hands projects, other toolkits and bibliography. This will serve as a first experience for the students and there is no prevision of including technical aids such as GRID2 or similar [4] due to budget restrictions. There are no students with disabilities registered for this year edition so the course does not seek accessibility for participants as authors. We will consider working on accessibility for participants of the following editions of this workshop, building on past experiences reaching this goal [5] [6] [7]. The main focus of the workshop is to encourage the creative learning of a robots summer camp [8], [9] with the inclusion of universal design as an essential requirement in the design and development of computer applications or systems. With this initiative we want to increase awareness on accessibility requirements for future technical students.PID U

Data-driven System to Predict Academic Grades and Dropout

Nowadays, the role of a tutor is more important than ever to prevent students dropout and improve their academic performance. This work proposes a data-driven system to extract relevant information hidden in the student academic data and, thus, help tutors to offer their pupils a more proactive personal guidance. In particular, our system, based on machine learning techniques, makes predictions of dropout intention and courses grades of students, as well as personalized course recommendations. Moreover, we present different visualizations which help in the interpretation of the results. In the experimental validation, we show that the system obtains promising results with data from the degree studies in Law, Computer Science and Mathematics of the Universitat de Barcelona

Generalized multi-scale stacked sequential learning for multi-class classification

In many classification problems, neighbor data labels have inherent sequential relationships. Sequential learning algorithms take benefit of these relationships in order to improve generalization. In this paper, we revise the multi-scale sequential learning approach (MSSL) for applying it in the multi-class case (MMSSL). We introduce the error-correcting output codesframework in the MSSL classifiers and propose a formulation for calculating confidence maps from the margins of the base classifiers. In addition, we propose a MMSSL compression approach which reduces the number of features in the extended data set without a loss in performance. The proposed methods are tested on several databases, showing significant performance improvement compared to classical approaches

How can entrepreneurs improve digital market segmentation? A comparative analysis of supervised and unsupervised learning algorithms

The identification of digital market segments to make value-creating propositions is a major challenge for entrepreneurs and marketing managers. New technologies and the Internet have made it possible to collect huge volumes of data that are difficult to analyse using traditional techniques. The purpose of this research is to address this challenge by proposing the use of AI algorithms to cluster customers. Specifically, the proposal is to compare the suitability of supervised algorithms, XGBoost, versus unsupervised algorithms, K-means, for segmenting the digital market. To do so, both algorithms have been applied to a sample of 5 million Spanish users cap tured between 2010 and 2022 by a lead generation start-up. The results show that supervised learning with this type of data is more useful for segmenting markets than unsupervised learning, as it provides solutions that are better suited to entre preneurs' commercial objective