Browsing by Author "Silva, Catarina"
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- Adaptive learning for dynamic environments: A comparative approachPublication . Costa, Joana; Silva, Catarina; Antunes, Mário; Ribeiro, BernardeteNowadays most learning problems demand adaptive solutions. Current challenges include temporal data streams, drift and non-stationary scenarios, often with text data, whether in social networks or in business systems. Various efforts have been pursued in machine learning settings to learn in such environments, specially because of their non-trivial nature, since changes occur between the distribution data used to define the model and the current environment. In this work we present the Drift Adaptive Retain Knowledge (DARK) framework to tackle adaptive learning in dynamic environments based on recent and retained knowledge. DARK handles an ensemble of multiple Support Vector Machine (SVM) models that are dynamically weighted and have distinct training window sizes. A comparative study with benchmark solutions in the field, namely the Learn++.NSE algorithm, is also presented. Experimental results revealed that DARK outperforms Learn++.NSE with two different base classifiers, an SVM and a Classification and Regression Tree (CART).
- Assessing the liquidity in Portuguese hotel companiesPublication . Silva, Catarina; Santos, Luís Lima; Gomes, Conceição; Malheiros, CátiaThe hospitality companies have had substantial growth in the tourism sector which gives them a large part of the revenue generated by the sector. In this regard, its impact, whether negative or positive, is quite high and generates a response to a need felt by agents of the environment in which it operates. As a short-term sustainability indicator, the liquidity level of a company demonstrates its ability to repay its obligations, being a great management support for decision making and anticipation of financial problems that may arise. Considering the volatility of hotel companies, greater importance is given to the study of liquidity. The main liquidity ratios of Portuguese hotels in the 2010-2017 period will be analysed; data was collected on July 4, 2019, on the SABI platform and the original sample is composed of 2161 hotel companies registered with two Portuguese economic activity codes (CAE), “55111 - Hotels with restaurant” and “55121 - Hotels without restaurant”. The assessment of liquidity level will be important to decision makers understand if there are differences between hotels with or without restaurant and among the Portuguese districts were hotels are located. The results of this study are expected to be of assistance to hotel managers as decisions taken within the organization can be more deliberate and informed.
- Assistive Mobile Applications for DyslexiaPublication . Madeira, Jorge; Silva, Catarina; Ferreira, Paula Cristina; Marcelino, LuísThe ability to read is one of the main skills of a human being. However, some of us have reading difficulties, regardless of social status, level of intelligence or education. This disorder is the main characteristic of dyslexia and is maintained throughout life,requiring early and specialized intervention. Dyslexia is defined as a learning disturbance in the area of reading, writing and spelling. Although the numbers of prevalence rely heavily on the type of investigation conducted, several studies indicate that up to 17% of the world population is dyslexic, and that men have greater prevalence. In this work we will address the use of assistive mobile applications for dyslexia by analyzing possible solutions and proposing a prototype of a mobile application that can be used by dyslexic and whilst giving feedback both to the dyslexic him/herself and to the assisting technician or teacher. The implemented prototype focuses the Portuguese language and was tested with Portuguese students with ages between 10 and 12 years old. Preliminary results show that the proposed gamified set of activities, allow dyslexics to improve multisensory perception, constituting an added value facilitator of adaptiveness and learning.
- Deep learning with realtime inference for human detection in search and rescuePublication . Llasag Rosero, Raúl; Grilo, Carlos; Silva, CatarinaHuman casualties in natural disasters have motivated tech- nological innovations in Search and Rescue (SAR) activities. Di cult ac- cess to places where res, tsunamis, earthquakes, or volcanoes eruptions occur has been delaying rescue activities. Thus, technological advances have gradually been nding their purpose in aiding to identify and nd the best locations to put available resources and e orts to improve rescue processes. In this scenario, the use of Unmanned Aerial Vehicles (UAV) and Computer Vision (CV) techniques can be extremely valuable for accelerating SAR activities. However, the computing capabilities of this type of aerial vehicles are scarce and time to make decisions is also rele- vant when determining the next steps. In this work, we compare di erent Deep Learning (DL) imaging detectors for human detection in SAR im- ages. A setup with drone-mounted cameras and mobile devices for drone control and image processing is put in place in Ecuador, where volcanic activity is frequent. The main focus is on the inference time in DL learn- ing approaches, given the dynamic environment where decisions must be fast. Results show that a slim version of the model YOLOv3, while using less computing resources and fewer parameters than the original model, still achieves comparable detection performance and is therefore more appropriate for SAR approaches with limited computing resources.
- Experience of an International Collaborative Project with First Year Programming StudentsPublication . Paterson, James H.; Karhu, Markku; Cazzola, Walter; Illina, Irina; Law, Robert; Malchiodi, Dario; Maximiano, Marisa; Silva, CatarinaThis paper describes an Erasmus Intensive Programme that used international collaboration as a novel pedagogical approach to teaching programming skills to first-year students in a blended learning context using a mixture of virtual environment and intensive teaching. The experience and outcomes of the Programme are evaluated from the viewpoints of the students and instructors and conclusions are drawn on the value and conduct of international student collaborations.
- Exploring the potential of wine industry by-products as source of additives to improve the quality of aquafeedPublication . Câmara, José S.; Lourenço, Sílvia; Silva, Catarina; Lopes, André; Andrade, Carlos; Perestrelo, RosaThe recent growing concern driven by consumer interest in the safety and quality of seafood, has boosted the search for healthy and functional aquafeeds. The current study represents the first approach to assess the potential of volatile composition of the wine industry by products (e.g., grape pomace, grape stems, lees), as additives for improving the quality of fish feeds in terms of organoleptic characteristics (e.g., aroma and flavor) and health benefits. Headspace solid-phase microextraction followed by gas chromatography-mass spectrometry (HS-SPME/GC–MS) was used to establish the volatile profile of wine industry by-products. A total of 153 volatile organic compounds (VOCs), which belong to different chemical families, comprising 36 esters, 31 carbonyl compound, 20 alcohols, 18 terpenoids, 17 acids, 11 furanic compounds, four volatile phenols, two lactones, and 14 miscellaneous, were identified. Esters and terpenoids showed a positive contribution to the aquafeeds aroma with fruity, sweet, green, fresh, and berry notes, whereas some acids (e.g., hexanoic acid) and terpenoids (e.g., limonene) could be used as antimicrobial, antioxidant and antiproliferative agents. Our findings confirmed the potential of wine industry by-products as a rich source of essential compounds to enhance the quality of aquafeeds towards the valorization of winery waste based on the concept of circular economy. Further investigation on the extraction, isolation and purification of VOCs from a natural bio-source will guarantee the safety of the aquafeed and compliance with the requirements of the animal feed industry.
- The impact of longstanding messages in micro-blogging classificationPublication . Costa, Joana; Silva, Catarina; Antunes, Mário; Bernardete RibeiroSocial networks are making part of the daily routine of millions of users. Twitter is among Facebook and Instagram one of the most used, and can be seen as a relevant source of information as users share not only daily status, but rapidly propagate news and events that occur worldwide. Considering the dynamic nature of social networks, and their potential in information spread, it is imperative to find learning strategies able to learn in these environments and cope with their dynamic nature. Time plays an important role by easily out-dating information, being crucial to understand how informative can past events be to current learning models and for how long it is relevant to store previously seen information, to avoid the computation burden associated with the amount of data produced. In this paper we study the impact of longstanding messages in micro-blogging classification by using different training timewindow sizes in the learning process. Since there are few studies dealing with drift in Twitter and thus little is known about the types of drift that may occur, we simulate different types of drift in an artificial dataset to evaluate and validate our strategy. Results shed light on the relevance of previously seen examples according to different types of drift.
- Improving Text Classification Performance with Incremental Background KnowledgePublication . Silva, Catarina; Ribeiro, BernardeteText classification is generally the process of extracting interesting and non-trivial information and knowledge from text. One of the main problems with text classification systems is the lack of labeled data, as well as the cost of labeling unlabeled data. Thus, there is a growing interest in exploring the use of unlabeled data as a way to improve classification performance in text classification. The ready availability of this kind of data in most applications makes it an appealing source of information. In this work we propose an Incremental Background Knowledge (IBK) technique to introduce unlabeled data into the training set by expanding it using initial classifiers to deliver oracle decisions. The defined incremental SVM margin-based method was tested in the Reuters-21578 benchmark showing promising results.
- Improving Visualization, Scalability and Performance of Multiclass Problems with SVM Manifold LearningPublication . Silva, Catarina; Bernardete RibeiroWe propose a learning framework to address multiclass challenges, namely visualization, scalability and performance. We focus on supervised problems by presenting an approach that uses prior information about training labels, manifold learning and support vector machines (SVMs). We employ manifold learning as a feature reduction step, nonlinearly embedding data in a low dimensional space using Isomap (Isometric Mapping), enhancing geometric characteristics and preserving the geodesic distance within the manifold. Structured SVMs are used in a multiclass setting with benefits for final multiclass classification in this reduced space. Results on a text classification toy example and on ISOLET, an isolated letter speech recognition problem, demonstrate the remarkable visualization capabilities of the method for multiclass problems in the severely reduced space, whilst improving SVMs baseline performance.
- Information System for Automation of Counterfeited Documents Images CorrelationPublication . Vieira, Rafael; Silva, Catarina; Antunes, Mário; Assis, AnaForgery detection of official documents is a continuous challenge encountered by documents’ forensic experts. Among the most common counterfeited documents we may find citizen cards, passports and driving licenses. Forgers are increasingly resorting to more sophisticated techniques to produce fake documents, trying to deceive criminal polices and hamper their work. Having an updated past counterfeited documents image catalogue enables forensic experts to determine if a similar technique or material was already used to forge a document. Thus, through the modus operandi characterization is possible to obtain more information about the source of the counterfeited document. In this paper we present an information system to manage counterfeited documents images that includes a two-fold approach: (i) the storage of images of past counterfeited documents seized by questioned documents forensic experts of the Portuguese Scientific Laboratory in a structured database; and (ii) the automation of the counterfeit identification by comparing a given fraudulent document image with the database images of previously catalogued counterfeited documents. In general, the proposed information system aims to smooth the counterfeit identification and to overcome the error prone, manual and time consuming tasks carried on by forensic experts. Hence, we have used a scalable algorithm under the OpenCV framework, to compare images, match patterns and analyse textures and colours. The algorithm was tested on a subset of counterfeited Portuguese citizen cards, presenting very promising results.