Object Segmentation in Images using EEG Signals

This paper explores the potential of brain-computer interfaces in segmenting objects from images. Our approach is centered around designing an effective method for displaying the image parts to the users such that they generate measurable brain reactions. When an image region, specifically a block of pixels, is displayed we estimate the probability of the block containing the object of interest using a score based on EEG activity. After several such blocks are displayed, the resulting probability map is binarized and combined with the GrabCut algorithm to segment the image into object and background regions. This study shows that BCI and simple EEG analysis are useful in locating object boundaries in images.Comment: This is a preprint version prior to submission for peer-review of the paper accepted to the 22nd ACM International Conference on Multimedia (November 3-7, 2014, Orlando, Florida, USA) for the High Risk High Reward session. 10 page

An Efficient Deep Learning Model To Detect COVID-19 Using Chest X-Ray Images

The tragic pandemic of COVID-19, due to the Severe Acute Respiratory Syndrome coronavirus-2 or SARS-CoV-2, has shaken the entire world, and has significantly disrupted healthcare systems in many countries. Because of the existing challenges and controversies to testing for COVID-19, improved and cost-effective methods are needed to detect the disease. For this purpose, machine learning (ML) has emerged as a strong forecasting method for detecting COVID-19 from chest X-ray images. In this paper, we used a Deep Learning Method (DLM) to detect COVID-19 using chest X-ray (CXR) images. Radiographic images are readily available and can be used effectively for COVID-19 detection compared to other expensive and time-consuming pathological tests. We used a dataset of 10,040 samples, of which 2143 had COVID-19, 3674 had pneumonia (but not COVID-19), and 4223 were normal (not COVID-19 or pneumonia). Our model had a detection accuracy of 96.43% and a sensitivity of 93.68%. The area under the ROC curve was 99% for COVID-19, 97% for pneumonia (but not COVID-19 positive), and 98% for normal cases. In conclusion, ML approaches may be used for rapid analysis of CXR images and thus enable radiologists to filter potential candidates in a time-effective manner to detect COVID-19

A transformational creativity tool to support chocolate designers

A new formulation of the central ideas of Boden's well-established theory on combinational, exploratory and transformational creativity is presented. This new formulation, based on the idea of conceptual space, redefines some terms and includes several types of concept properties (appropriateness and relevance), whose relationship facilitates the computational implementation of the transformational creativity mechanism. The presented formulation is applied to a real case of chocolate designing in which a novel and flavorful combination of chocolate and fruit is generated. The experimentation was conducted jointly with a Spanish chocolate chef. Experimental results prove the relationship between appropriateness and relevance in different frameworks and show that the formulation presented is not only useful for understanding how the creative mechanisms of design works but also facilitates its implementation in real cases to support creativity processes.Postprint (author's final draft

Sosiaalipolitiikka ja alkuperäiskansat Taiwanissa

This dissertation examines the relationship between the colonial state and Indigenous peoples by focusing on the case of elderly care. Studying the Tayal in Taiwan, it investigates aging, care and well-being from the Indigenous paradigm. The aim is to develop the hermeneutic perspective of the Tayal to privilege their voices in reconfiguring the concept of care. Three research questions are posed: 1) What are the “Indigenous problems” represented in long-term care (LTC) policies in Taiwan? 2) How do the Tayal experience care in a care center funded by the state? How do they contest the policies and what visions of care do they have? 3) What are the discrepancies between policy and practice? How do they reflect the relationship between coloniality and indigeneity in multicultural Taiwan? The data consists of policy documents, participant observation, field notes, interviews and personal narratives concerning the everyday experiences of Tayal elders (bnkis). Methodologically, the dissertation employs critical policy analysis and critical ethnography. The dissertation arrives at three main conclusions. First, the identified three frames depoliticize the “problem” of elderly care for the Indigenous peoples and make them “invisible.” Through frames of secludedness and inadequacy, the construction of the Indigenous problem is depicted as caused by their geographical location and by their lack of ability to be service providers or consumers. By contrast, the frame of culture emphasizes unique traditions, allowing more agency but running the risk of imposing an image of static and unchanging indigeneity. Second, the ethnographic analysis shows the strength, resilience and resistance of the bnkis. The idealized “tribal care” promoted in the Day Club, the social center which served as the core location for my fieldwork, turns a blind eye to the fluid, contextual and living Tayal culture, which underlies the kind of care that the bnkis prefer. Investigation of the experiences of bnkis shows that the Day Club is appropriated, repurposed and redefined by the Tayal community to negotiate identities and contest predominant conceptualizations of aging and care. Third, the findings indicate that contrary to Taiwan’s claims to be multicultural and its promise to recognize Indigenous rights, the approach to accommodate Indigenous elders is still predicated on a middle-class, urban, Han-Chinese norm. The novelty of this study lies in its aspiration to develop Indigenous epistemology and Tayal hermeneutics in the context of care. The results contribute to literature in critical policy analysis, care studies, Indigenous studies, critical gerontology and Taiwan studies, as they raise important questions about what indigeneity is and the role that the nation-state plays in the making of social policy for Indigenous elders.Tässä väitöskirjassa tarkastellaan koloniaalisen valtion ja alkuperäiskansojen välistä suhdetta keskittymällä vanhusten hoivaan. Väitöskirjassa tutkitaan Taiwanin atayal-alkuperäiskansaan kuuluvien ikääntymistä, hoivaa ja hyvinvointia alkuperäiskansojen paradigman näkökulmasta. Tavoitteena on kehittää atayalien hermeneutiikkaa, jotta heidän äänensä saataisiin kuuluviin hoivan käsitteen uudelleenmäärittelyssä. Väitöskirjassa esitetään kolme tutkimuskysymystä: 1) Mitä ”alkuperäiskansoihin liittyviä ongelmia” Taiwanin pitkäaikaishoivan politiikassa on? 2) Millaisena atayalit kokevat hoivan valtion rahoittamassa hoivakeskuksessa? Miten he kyseenalaistavat vallitsevaa politiikkaa ja minkälaisia näkemyksiä heillä on hoivasta? 3) Mitä eroja politiikan ja käytännön välillä on? Miten erot heijastavat koloniaalisuuden ja alkuperäiskansalaisuuden välistä suhdetta monikulttuurisessa Taiwanissa? Aineisto koostuu poliittisista asiakirjoista, osallistujien havainnoinnista, kenttämuistiinpanoista, haastatteluista ja henkilökohtaisista tarinoista atayal-alkuperäiskansan vanhusten (bnkis) arjen kokemuksista. Väitöskirjan tutkimusmenetelminä käytetään kriittistä policy- analyysia ja kriittistä etnografiaa. Väitöskirjassa tehdään kolme keskeistä johtopäätöstä. Ensinnäkin tunnistetut kolme kehystä depolitisoivat alkuperäiskansoihin kuuluvien vanhusten hoivan ”ongelman” ja tekevät heistä ”näkymättömiä”. Eristäytyneisyyden ja vaillinaisuuden kehysten läpi tarkasteltuna alkuperäiskansojen hoivaan liittyvien ongelmien katsotaan johtuvan heidän maantieteellisestä sijainnista ja oletetusta kyvyttömyydestä toimia palveluntarjoajina tai -kuluttajina. Kulttuurillinen kehys sitä vastoin korostaa alkuperäiskansojen ainutlaatuisia perinteitä, mikä lisää toimijuutta, mutta vaarana on, että syntyy mielikuva staattisesta ja muuttumattomasta alkuperäiskansasta. Toiseksi etnografinen analyysi tuo esiin bnkisien vahvuuden, sitkeyden ja kestävyyden. Kenttätyön ydinkohteena olleessa päiväkerhossa edistetty ihanteellinen ”heimoperusteinen hoiva” sulkee silmänsä muuttuvalta, kontekstuaaliselta ja elävältä atayal-kulttuurilta, joka luo pohjan bnkisien kaipaamalle hoivalle. Bnkisien kokemuksiin perehtyminen osoittaa, että atayal-yhteisö kuitenkin ottaa haltuun, muotoilee ja määrittelee uudelleen päiväkerhon ja kyseenalaistaa identiteetit sekä ikääntymisen ja hoivan vallitsevat käsitteellistämiset. Kolmanneksi havainnot viittaavat siihen, että huolimatta Taiwanin monikulttuuriseksi julistautumisesta ja lupauksesta tunnustaa alkuperäiskansojen oikeudet, lähestymistapa alkuperäiskansoihin kuuluvien vanhusten hoivaan perustuu edelleen keskiluokkaiseen, urbaaniin ja han-kiinalaiseen normiin. Tämän tutkimuksen uutuus piilee pyrkimyksessä kehittää alkuperäiskansojen epistemologiaa ja atayalien hermeneutiikkaa hoivan viitekehyksessä. Tutkimuksen tulokset tarjoavat panoksensa kriittistä policy-analyysia, hoivatutkimusta, alkuperäiskansojen tutkimusta, kriittistä gerontologiaa ja Taiwan-aiheista tutkimusta koskevaan kirjallisuuteen, sillä ne tuovat esiin tärkeitä kysymyksiä alkuperäiskansoista ja kansallisvaltion roolista alkuperäiskansoihin kuuluvia vanhuksia koskevan sosiaalipolitiikan toteuttamisessa

Anomaly detection in fleet service vehicles: improving object segmentation

Dissertação de mestrado integrado em Engenharia InformáticaThe present dissertation is inserted in a BOSCH project in which the global focus is au tonomous driving. The project is divided in multiple phases, being the main focus of this dissertation object detection and segmentation inside fleet service vehicles. The objective is to detect/segment objects and dirt left inside a vehicle, warning the commuter if they forgot an object inside or the administrators if the vehicles need to be cleaned. To train the models, BOSCH provided an initial dataset containing a small set of annotated images. This dataset contains pictures of a vehicles cockpit with many diverse objects and dirt. One of the goals for BOSCH is to increment this dataset with more images. Hence, in this project several state of the art segmentation methods were thoroughly studied and analysed, with two of them being selected for further exploration: DeepExtremeCut and FgSegNet v2. The main objective is to see to what extent can these methods be used in a semi automatic process to segment more images, thereby increasing the initial dataset. DeepExtremeCut works by using a framework in which, after the model is trained, it allows us to click on four extreme points in the desired object, producing the segmentation. This method produced reliable segmentations, however it requires human intervention both for the initial segmentation and verification of the output. Hence, it was not regarded as a good solution for a future augmentation of the BOSCH dataset. Regarding FgSegNet v2, this later method does not require any initial annotation of the input images. Under this approach only a final verification and possible rectification is required. Therefore, this method meets the requirements defined by BOSCH for a dataset expansion solution. An ablation study is also presented for FgSegNet v2, analysing its three stages: (i) Encoder, (ii) Feature Pooling Module and (iii) Decoder. The result of this study is a proposal of a variation of the aforementioned method called Mod FgSegNet. It was also compared with state of the art methods in public datasets. Three datasets are used for testing: CDNet2014, SBI2015 and CityScapes. In CDNet2014 we got an overall improvement when compared to the state of the art, particularly in the LowFrameRate subset. Regarding SBI2015 the overall results are lower in comparison with the top state of art, while in CityScapes some promising results are presented.A presente dissertação está inserida num projeto da BOSCH, em que o foco global é a condução autónoma. Este projeto foi dividido em múltiplas fases, sendo o foco principal desta dissertação e a detecção e a segmentação de objetos. O objetivo é detectar / segmentar objetos e lixo deixados no interior de um veículo, avisando o passageiro se ele se esqueceu de algum objeto no interior ou os administradores se os veículos precisam de ser limpos. Para lidar com este tópico, vários métodos de segmentação do estado da arte foram exaustivamente estudados e analisados, nos quais dois deles em particular foram explorados, ou seja, o DeepExtremeCut e o FgSegNet. Algumas melhorias foram feitas no desempenho deste último, permitindo um potencial aumento semiautomático no tamanho de um dataset fornecido pela BOSCH, uma vez que não possuía imagens suficientes. Este dataset contém fotos da cabine de um veículo com diferentes objetos e lixo. O DeepExtremeCut funciona através do uso de uma framework no qual, após o treino do modelo, permite clicar em quatro pontos extremos do objeto desejado, produzindo a segmentação. Este método produz segmentações confiáveis, embora não corresponda a uma segmentação automática, visto que existe a necessidade de selecionar todos os objetos, ainda pode ser útil quando a segmentação automática de um método diferente não estiver a funcionar em casos particulares. Em relação ao FgSegNet v2, e apresentado um estudo de ablação, sendo feita uma análise das suas três etapas: (i) Encoder, (ii) Feate Pooling Module e (iii) Decoder. O resultado deste estudo e uma proposta de variação do referido método no dataset da BOSCH, de forma a utilizá-lo potencialmente em vários projetos dentro da empresa, chamado Mod_FgSegNet. Também foi comparado com métodos do estado de arte em datasets públicos. Os três datasets usados para teste são: CDNet 2014, SBI2015 e CityScapes. No CDNet2014, obtivemos uma melhoria geral em comparação com o estado da arte, principalmente no subconjunto LowFrameRate. Em relação ao ao SBI2015, os resultados gerais foram inferiores em comparação com o estado da arte de ponta, enquanto que no CityScapes alguns resultados promissores foram apresentados

Review of feature selection techniques in Parkinson's disease using OCT-imaging data

Several spectral-domain optical coherence tomography studies (OCT) reported a decrease on the macular region of the retina in Parkinson’s disease. Yet, the implication of retinal thinning with visual disability is still unclear. Macular scans acquired from patients with Parkinson’s disease (n = 100) and a control group (n = 248) were used to train several supervised classification models. The goal was to determine the most relevant retinal layers and regions for diagnosis, for which univari- ate and multivariate filter and wrapper feature selection methods were used. In addition, we evaluated the classification ability of the patient group to assess the applicability of OCT measurements as a biomarker of the disease

Review of feature selection techniques in Parkinson's disease using OCT-imaging data

Several spectral-domain optical coherence tomography studies (OCT) reported a decrease on the macular region of the retina in Parkinson’s disease. Yet, the implication of retinal thinning with visual disability is still unclear. Macular scans acquired from patients with Parkinson’s disease (n = 100) and a control group (n = 248) were used to train several supervised classification models. The goal was to determine the most relevant retinal layers and regions for diagnosis, for which univari- ate and multivariate filter and wrapper feature selection methods were used. In addition, we evaluated the classification ability of the patient group to assess the applicability of OCT measurements as a biomarker of the disease

Creativity Support System for cake design

In this paper, a new formulation of Creativity is presented in the context of Creativity Support Systems. This formulation is based on the central ideas of the theory of Boden. It redefines some concepts such as appropriateness and relevance in order to allow the implementation of a support system for creative people. The approach is based on the conceptual space proposed by Boden and formalized by other authors. The presented formulation is applied to a real case in which a new chocolate cake with fruit is design. Data collected from a Spanish chocolate chef has been used to validate the proposed system. Experimental results show that the formulation presented is not only useful for understanding how the creative mechanisms of design works, but also facilitates its implementation in real cases to support creativity processes.Peer ReviewedPostprint (published version

Empirical Analysis ot the Top 800 Cryptocurrencies using Machine Learning Techniques

The International Token Classification (ITC) Framework by the Blockchain Center in Frankfurt classifies 795 cryptocurrency tokens based on their economic, technological, legal and industry categorization. This work analyzes cryptocurrency data to evaluate the categorization with real-world market data. The feature space includes price, volume and market capitalization data. Additional metrics such as the moving average and the relative strengh index are added to get a more in-depth understanding of market movements. The data set is used to build supervised and unsupervised machine learning models. The prediction accuracies varied amongst labels and all remained below 90%. The technological label had the highest prediction accuracy at 88.9% using Random Forests. The economic label could be predicted with an accuracy of 81.7% using K-Nearest Neighbors. The classification using machine learning techniques is not yet accurate enough to automate the classification process. But it can be improved by adding additional features. The unsupervised clustering shows that there are more layers to the data that can be added to the ITC. The additional categories are built upon a combination of token mining, maximal supply, volume and market capitalization data. As a result we suggest that a data-driven extension of the categorization in to a token profile would allow investors and regulators to gain a deeper understanding of token performance, maturity and usage