Aesthetics and brands : crosscultural evaluation of furniture design

Do aesthetics relate with brands? In this work, we try to find out if brands interfere with aesthetics, and we use experimental aesthetics in trying to develop a new brand. The brand will apply to the furniture of Álvaro Siza, a famous Portuguese architect, and our concern was consumer’s assessment of it. We confront his design with the one of other relevant authors, and analyse how consumer’s judgement varies in face of some basic factors. Our approach is market oriented, explores cultural differences, uses WMDS - Weighted Multidimensional Scaling, and we believe it brought us a preliminary, but fundamental understanding of consumers’ opinion, as well as some evidence about such a relationship

Time series motifs statistical significance

Time series motif discovery is the task of extracting previously unknown recurrent patterns from time series data. It is an important problem within applications that range from finance to health. Many algorithms have been proposed for the task of eficiently finding motifs. Surprisingly, most of these proposals do not focus on how to evaluate the discovered motifs. They are typically evaluated by human experts. This is unfeasible even for moderately sized datasets, since the number of discovered motifs tends to be prohibitively large. Statistical significance tests are widely used in bioinformatics and association rules mining communities to evaluate the extracted patterns. In this work we present an approach to calculate time series motifs statistical significance. Our proposal leverages work from the bioinformatics community by using a symbolic definition of time series motifs to derive each motif's p-value. We estimate the expected frequency of a motif by using Markov Chain models. The p-value is then assessed by comparing the actual frequency to the estimated one using statistical hypothesis tests. Our contribution gives means to the application of a powerful technique - statistical tests - to a time series setting.This provides researchers and practitioners with an important tool to evaluate automatically the degree of relevance of each extracted motif.(undefined

Automatically estimating iSAX parameters

The Symbolic Aggregate Approximation (iSAX) is widely used in time series data mining. Its popularity arises from the fact that it largely reduces time series size, it is symbolic, allows lower bounding and is space efficient. However, it requires setting two parameters: the symbolic length and alphabet size, which limits the applicability of the technique. The optimal parameter values are highly application dependent. Typically, they are either set to a fixed value or experimentally probed for the best configuration. In this work we propose an approach to automatically estimate iSAX’s parameters. The approach – AutoiSAX – not only discovers the best parameter setting for each time series in the database, but also finds the alphabet size for each iSAX symbol within the same word. It is based on simple and intuitive ideas from time series complexity and statistics. The technique can be smoothly embedded in existing data mining tasks as an efficient sub-routine. We analyze its impact in visualization interpretability, classification accuracy and motif mining. Our contribution aims to make iSAX a more general approach as it evolves towards a parameter-free method

Multiresolution motif discovery in time series

Time series motif discovery is an important problem with applications in a variety of areas that range from telecommunications to medicine. Several algorithms have been proposed to solve the problem. However, these algorithms heavily use expensive random disk accesses or assume the data can't into main memory. They only consider motifs at a single resolution and are not suited to interactivity. In this work, we tackle the motif discovery problem as an approximate Top-K frequent subsequence discovery problem. We fully exploit state of the art iSAX representation multiresolution capability to obtain motifs at diferent resolutions. This property yields interactivity, allowing the user to navigate along the Top-K motifs structure. This permits a deeper understanding of the time series database. Further, we apply the Top-K space saving algorithm to our frequent subsequences approach. A scalable algorithm is obtained that is suitable for data stream like applications where small memory devices such as sensors are used. Our approach is scalable and disk-eficient since it only needs one single pass over the time series database. We provide empirical evidence of the validity of the algorithm in datasets from diferent areas that aim to represent practical applications.(undefined

Disclosure of environmental matters - Galp Energy

There is a growing number of companies expressing social concerns where environmental worries are also included. Even before legal obliga-tion of environmental disclosures, companies already used to adopt an active stance to safeguard their reputation. However, risk events may have negative impacts on companies’ legitimacy, making it necessary to implement strategies to repair/recover the damaged reputation. This study analyses the annual reports from 2001 to 2011 of the largest Por-tuguese oil company - Galp Energia - in the view of the occurrence of negative events, which, consequently, may have affected the company's reputation. Empirically, legitimacy theory can explain the implementa-tion of strategies to repair reputation. Within this context, most of the existing research is based on common-law countries, with little literature grounded on code-law countries, such as Portugal. Findings have shown that Galp Energia has great concerns about the external perception of society. The company acts immediately after risk events, implementing strategies to minimise the risk impact, especially through the use of cor-rective actions.info:eu-repo/semantics/acceptedVersio

Predicting the Outcome of Cognitive Training in Parkinson’s Disease using Magnetic Resonance Imaging

Motivation: Cognitive impairment is an important symptom of Parkinson’s Disease (PD), usually having a substantial negative impact on the quality of life of patients, families, and caregivers. Cognitive Training (CT) have been proven effective in halting the process of cognitive decline in PD. However, the efficacy of CT is unpredictable from subject to subject. Objective: Investigate the possibility of predicting the outcome of CT in PD patients with Mild Cognitive Impairment using structural and functional Magnetic Resonance Imaging (MRI) data. Methods: Before CT, a sample of 42 PD patients underwent structural and functional MRI. Graph measures were then extracted from their structural and functional con nectomes and used as features for random forest (RFo) and decision tree (DT) machine learning (ML) regression algorithms with and without prior latent component analysis (LCA). CT response was evaluated by assessing the outcomes of the Tower of London task pre- and post-treatment. Finally, the 4 ML models were used to predict CT response and their performances were assessed. Post hoc analyses were conducted to investigate whether these algorithms could predict age using connectomic measures on a sample of 80 PD patients. Results: The performances of the aforementioned algorithms did not differ signifi cantly from the baseline performance predicting the subject-specific CT outcome. The performance of the RFo without LCA differed significantly from the baseline performance in the age prediction task for the sample of 80 patients. Conclusion: Notwithstanding the lack of statistical significance in predicting our xicognitive outcomes, the relative success of the age prediction task points towards the potential of this approach. We hypothesise that bigger sample sizes are needed in order to predict the outcome of CT using ML

Portuguese sentiment analysis applied to a reality show using Twitter and NLP in real time

The motivation for this study was to measure the impact that Twitter publications have on voting and the choosing of winners. To this end, an experimental study was carried out based on a set of data collected from tweets (published on Twitter) related to the reality show “Big Brother - A Revolução”, broadcast on a television station in Portugal, TVI. The procedure adopted for conducting the experiment consisted of creating a completely self-contained service, built from scratch for this project, and the correspondent implementation, in order to allow the collection, storage, cleaning, pre-processing and analysis of as many tweets as possible, as long as they are associated with the program. A tool to analyze the polarity (positive, negative or neutral) of the sentiment was implemented and applied to the phrase (or phrases) contained in the tweet and stored in a database. Then, running in the database, the tweets were divided according to how they referred to one or more competitors. Throughout the time that the reality show existed, the results of this experiment were public presented in daily/weekly summaries and posted on Twitter through a “Twitter bot”.FCT-CEECIND/04331/201