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.
- Assistive mobile software for public transportationPublication . Silva, João De Sousa E; Silva, Catarina; Marcelino, Luís; Ferreira, Rui; Pereira, AntónioThe need of mobility on public transport for persons with visual impairment is mandatory. While traveling on a public transport, the simple ability to know the current location is almost impossible for such persons. To overcome this hurdle, we developed an assistive application that can alert its user to the proximity of all public transportation stops, giving emphasis to the chosen final stop. The application is adjustable to any transportation system and is particularly relevant to use in public transports that do not have any audio system available. The developed prototype runs on an Android OS device equipped with Global Positioning System (GPS). To ensure the highest possible level of reliability and to make it predictable to users, the application's architecture is free of as much dependencies as possible. Therefore, only GPS, or other localization mechanism, is required. The interface was designed to be suitable not only for talkback (Android's inbuilt screen-reader) aimed at blind users, but also for people with low vision that can still use their sight to check the screen. Thus, it was meant to be graphically simple and unobtrusive. It was tested by visual impaired persons leading to the conclusion that it demonstrates an existing need, and opens a new perspective in public transportation's accessibility.
- Boosting dynamic ensemble’s performance in TwitterPublication . Costa, Joana; Silva, Catarina; Antunes, Mário; Ribeiro, BernardeteMany text classification problems in social networks, and other contexts, are also dynamic problems, where concepts drift through time, and meaningful labels are dynamic. In Twitter-based applications in particular, ensembles are often applied to problems that fit this description, for example sentiment analysis or adapting to drifting circumstances. While it can be straightforward to request different classifiers' input on such ensembles, our goal is to boost dynamic ensembles by combining performance metrics as efficiently as possible. We present a twofold performance-based framework to classify incoming tweets based on recent tweets. On the one hand, individual ensemble classifiers' performance is paramount in defining their contribution to the ensemble. On the other hand, examples are actively selected based on their ability to effectively contribute to the performance in classifying drifting concepts. The main step of the algorithm uses different performance metrics to determine both each classifier strength in the ensemble and each example importance, and hence lifetime, in the learning process. We demonstrate, on a drifted benchmark dataset, that our framework drives the classification performance considerably up for it to make a difference in a variety of applications.
- Choice of Best Samples for Building Ensembles in Dynamic EnvironmentsPublication . Costa, Joana; Silva, Catarina; Antunes, Mário; Ribeiro, BernardeteMachine learning approaches often focus on optimizing the algorithm rather than assuring that the source data is as rich as possible. However, when it is possible to enhance the input examples to construct models, one should consider it thoroughly. In this work, we propose a technique to define the best set of training examples using dynamic ensembles in text classification scenarios. In dynamic environments, where new data is constantly appearing, old data is usually disregarded, but sometimes some of those disregarded examples may carry substantial information. We propose a method that determines the most relevant examples by analysing their behaviour when defining separating planes or thresholds between classes. Those examples, deemed better than others, are kept for a longer time-window than the rest. Results on a Twitter scenario show that keeping those examples enhances the final classification performance.
- Citizens@City Mobile Application for Urban Problem ReportingPublication . Ribeiro, António Miguel; Costa, Rui Pedro; Marcelino, Luís; Silva, CatarinaUrban problems, such as holes in the pavement, poor accesses to wheelchairs or lack of public lighting, are becoming pervasive. Despite the fact that most of these problems directly affect life quality and sometimes even safety, not everyone has the readiness or initiative to report them to the proper authorities. This fact makes these “black spots” difficult to identify and the repairing process slow. Citizens@City is an Android mobile application that allows the general population to play a more active role in the identification of these problems by reporting them to the proper authorities in a simple and fast way. Moreover, citizens will have the possibility to follow the identification and repairing processes, and know at a given moment its status (e.g. identified, repairing scheduled, solved). Additionally, it will also allow the proper authorities to identify and manage the reported problems, from their identification until they are solved.
- Decision support system using mobile app statisticsPublication . Constante, Fabian; Guevara, Juan; Silva, Catarina; Gonçalves, Dulce; Marcelino, LuisNowadays to make the right decision about where and how to request or buy a service, a user is often supported by a mobile device that offers more than simple descriptive data. Nevertheless, not all information on services is fully accessible. The difficulty in keeping track of changes in services’ costs causes delays and can result in waste of time and money. In fact, the decision of which service to use usually involves some level of uncertainty and risk. Hence, the user should have access to some form of decision support system that could be easily available through mobile applications. The power of these devices allows to apply knowledge areas already developed, carrying statistics with dynamic and interactive graphics, thus allowing for a more systematic control of services and corresponding expenses. In this work we analyze the existing related work on mobile decision support systems and propose an architecture of a decision support system using Mobile App Statistics. Tests were carried out with a car fuel app to support the decision of choosing the gas station at each point. Results show that using the additional statistical information provided users can take better decisions during the request of a service.
- 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.
- Distributed Text Classification With an Ensemble Kernel-Based Learning ApproachPublication . Silva, Catarina; Lotric, Uros; Ribeiro, Bernardete; Dobnikar, AndrejConstructing a single text classifier that excels in any given application is a rather inviable goal. As a result, ensemble systems are becoming an important resource, since they permit the use of simpler classifiers and the integration of different knowledge in the learning process. However, many text-classification ensemble approaches have an extremely high computational burden, which poses limitations in applications in real environments. Moreover, state-of-the-art kernel-based classifiers, such as support vector machines and relevance vector machines, demand large resources when applied to large databases. Therefore, we propose the use of a new systematic distributed ensemble framework to tackle these challenges, based on a generic deployment strategy in a cluster distributed environment. We employ a combination of both task and data decomposition of the text-classification system, based on partitioning, communication, agglomeration, and mapping to define and optimize a graph of dependent tasks. Additionally, the framework includes an ensemble system where we exploit diverse patterns of errors and gain from the synergies between the ensemble classifiers. The ensemble data partitioning strategy used is shown to improve the performance of baseline state-of-the-art kernel-based machines. The experimental results show that the performance of the proposed framework outperforms standard methods both in speed and classification.
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