927 resultados para Negative Selection Algorithm
Resumo:
This paper analyzes the Nova Student Portfolio (NSP) with the objective to understand performances of the fund. Each investment style has been analyzed (growth, value and momentum) in order to highlight what style allocation contributed positively and which had a negative impact. The results show that the team mainly invested in value stocks, which contributed positively but that its growth investments had a negative impact on the stock picking performance. The stock selection shows a major influence of the value investment style. A statistical approach shows that the market factor was the one explaining the most the NSP returns.
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Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation.
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Due to a combination of a vast agricultural industry and a tremendously growing technical textile industry, Ludvig Svensson identified India as target market for possible expansion through domestic production and supply. However, Svensson needed additional information about the industry structure and key players. Therefore, this project focused on a detailed analysis of the technical textile market and its players by following the international partner selection process. Thereby, five key players were identified as potential partners, as well as the need for additional research to determine alternative entry modes, as the market does not currently seem to be receptive for Svensson products.
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Ship tracking systems allow Maritime Organizations that are concerned with the Safety at Sea to obtain information on the current location and route of merchant vessels. Thanks to Space technology in recent years the geographical coverage of the ship tracking platforms has increased significantly, from radar based near-shore traffic monitoring towards a worldwide picture of the maritime traffic situation. The long-range tracking systems currently in operations allow the storage of ship position data over many years: a valuable source of knowledge about the shipping routes between different ocean regions. The outcome of this Master project is a software prototype for the estimation of the most operated shipping route between any two geographical locations. The analysis is based on the historical ship positions acquired with long-range tracking systems. The proposed approach makes use of a Genetic Algorithm applied on a training set of relevant ship positions extracted from the long-term storage tracking database of the European Maritime Safety Agency (EMSA). The analysis of some representative shipping routes is presented and the quality of the results and their operational applications are assessed by a Maritime Safety expert.
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The present paper reports the precipitation process of Al3Sc structures in an aluminum scandium alloy, which has been simulated with a synchronous parallel kinetic Monte Carlo (spkMC) algorithm. The spkMC implementation is based on the vacancy diffusion mechanism. To filter the raw data generated by the spkMC simulations, the density-based clustering with noise (DBSCAN) method has been employed. spkMC and DBSCAN algorithms were implemented in the C language and using MPI library. The simulations were conducted in the SeARCH cluster located at the University of Minho. The Al3Sc precipitation was successfully simulated at the atomistic scale with the spkMC. DBSCAN proved to be a valuable aid to identify the precipitates by performing a cluster analysis of the simulation results. The achieved simulations results are in good agreement with those reported in the literature under sequential kinetic Monte Carlo simulations (kMC). The parallel implementation of kMC has provided a 4x speedup over the sequential version.
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This paper presents a simulation model, which was incorporated into a Geographic Information System (GIS), in order to calculate the maximum intensity of urban heat islands based on urban geometry data. The method-ology of this study stands on a theoretical-numerical basis (Okeâ s model), followed by the study and selection of existing GIS tools, the design of the calculation model, the incorporation of the resulting algorithm into the GIS platform and the application of the tool, developed as exemplification. The developed tool will help researchers to simulate UHI in different urban scenarios.
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Triple negative breast cancer (TNBC) is a particular immunopathological subtype of breast cancer that lacks expression of estrogen and progesterone receptors (ER/PR) and amplification of the human epidermal growth factor receptor 2 (HER2) gene. Characterized by aggressive and metastatic phenotypes and high rates of relapse, TNBC is the only breast cancer subgroup still lacking effective therapeutic options, thus presenting the worst prognosis. The development of targeted therapies, as well as early diagnosis methods, is vital to ensure an adequate and timely therapeutic intervention in patients with TNBC. This review intends to discuss potentially emerging approaches for the diagnosis and treatment of TNBC patients, with a special focus on nano-based solutions that actively target these particular tumors.
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Novel input modalities such as touch, tangibles or gestures try to exploit human's innate skills rather than imposing new learning processes. However, despite the recent boom of different natural interaction paradigms, it hasn't been systematically evaluated how these interfaces influence a user's performance or whether each interface could be more or less appropriate when it comes to: 1) different age groups; and 2) different basic operations, as data selection, insertion or manipulation. This work presents the first step of an exploratory evaluation about whether or not the users' performance is indeed influenced by the different interfaces. The key point is to understand how different interaction paradigms affect specific target-audiences (children, adults and older adults) when dealing with a selection task. 60 participants took part in this study to assess how different interfaces may influence the interaction of specific groups of users with regard to their age. Four input modalities were used to perform a selection task and the methodology was based on usability testing (speed, accuracy and user preference). The study suggests a statistically significant difference between mean selection times for each group of users, and also raises new issues regarding the “old” mouse input versus the “new” input modalities.
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Coagulase-negative staphylococci (CoNS) are common bacterial colonisers of the human skin. They are often involved in nosocomial infections due to biofilm formation in indwelling medical devices. While biofilm formation has been extensively studied in Staphylococcus epidermidis, little is known regarding other CoNS species. Here, biofilms from six different CoNS species were characterised in terms of biofilm composition and architecture. Interestingly, the ability to form a thick biofilm was not associated with any particular species, and high variability on biofilm accumulation was found within the same species. Cell viability assays also revealed different proportions of live and dead cells within biofilms formed by different species, although this parameter was particularly similar at the intra-species level. On the other hand, biofilm disruption assays demonstrated important inter- and intra-species differences regarding extracellular matrix composition. Lastly, confocal laser scanning microscopy (CLSM) experiments confirmed this variability, highlighting important differences and common features of CoNS biofilms. We hypothesised that the biofilm formation heterogeneity observed was rather associated with biofilm matrix composition than with cells themselves. Additionally, our results indicate that polysaccharides, DNA and proteins are fundamental pieces in the process of CoNS biofilm formation.
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The present study investigated whether oculomotor behavior is influenced by attachment styles. The Relationship Scales Questionnaire was used to assess attachment styles of forty-eight voluntary university students and to classify them into attachment groups (secure, preoccupied, fearful, and dismissing). Eye-tracking was recorded while participants engaged in a 3-seconds free visual exploration of stimuli presenting either a positive or a negative picture together with a neutral picture, all depicting social interactions. The task consisted in identifying whether the two pictures depicted the same emotion. Results showed that the processing of negative pictures was impermeable to attachment style, while the processing of positive pictures was significantly influenced by individual differences in insecure attachment. The groups highly avoidant regarding to attachment (dismissing and fearful) showed reduced accuracy, suggesting a higher threshold for recognizing positive emotions compared to the secure group. The groups with higher attachment anxiety (preoccupied and fearful) showed differences in automatic capture of attention, in particular an increased delay preceding the first fixation to a picture of positive emotional valence. Despite lenient statistical thresholds induced by the limited sample size of some groups (p < 0.05 uncorrected for multiple comparisons), the current findings suggest that the processing of positive emotions is affected by attachment styles. These results are discussed within a broader evolutionary framework.
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Modeling clays have been used in several ecological experiments and have proved to be an important tool to variables control. The objective of our study was to determine if fruit color in isolated and grouped displays influences the fruit selection by birds in the field using artificial fruits. Data were collected in six plots distributed homogeneously in 3 km long trails with a minimum distance of 0.5 km. We used a paired experimental design to establish our experiments, so that all treatments were available to the local bird community in each plot. Overall, red was more pecked than brown and white. Isolated red and brown displays were significantly more pecked than others display. Even though our study was conducted in small spatial scales, artificial fruits appeared to be efficient in register fruit consumption attempts by bird. Although inconclusive about selective forces that sharp the dynamics of fruit color polymorphisms and choice by frugivorous birds, our findings corroborate recent studies wherein birds showed preferences by high- over low-contrast fruit signals.
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Nowadays, the sustainability of buildings has an extreme importance. This concept goes towards the European aims of the Program Horizon 2020, which concerns about the reduction of the environmental impacts through such aspects as the energy efficiency and renewable technologies, among others. Sustainability is an extremely broad concept but, in this work, it is intended to include the concept of sustainability in buildings. Within the concept that aims the integration of environmental, social and economic levels towards the preservation of the planet and the integrity of the users, there are, currently, several types of tools of environmental certification that are applicable to the construction industry (LEED, BREEAM, DGNB, SBTool, among others). Within this context, it is highlighted the tool SBTool (Sustainable Building Tool) that is employed in several countries and can be subject to review in institutions of basic education, which are the base for the formation of the critical masses and for the development of a country. The main aim of this research is to select indicators that can be used in a methodology for sustainability assessment (SBTool) of school buildings in Portugal and in Brazil. In order to achieve it, it will also be analyzed other methodologies that already incorporate parameters directly related with the schools environment, such as BREEAM or LEED.
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The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.
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The Electromagnetism-like (EM) algorithm is a population- based stochastic global optimization algorithm that uses an attraction- repulsion mechanism to move sample points towards the optimal. In this paper, an implementation of the EM algorithm in the Matlab en- vironment as a useful function for practitioners and for those who want to experiment a new global optimization solver is proposed. A set of benchmark problems are solved in order to evaluate the performance of the implemented method when compared with other stochastic methods available in the Matlab environment. The results con rm that our imple- mentation is a competitive alternative both in term of numerical results and performance. Finally, a case study based on a parameter estimation problem of a biology system shows that the EM implementation could be applied with promising results in the control optimization area.
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In this paper, we propose an extension of the firefly algorithm (FA) to multi-objective optimization. FA is a swarm intelligence optimization algorithm inspired by the flashing behavior of fireflies at night that is capable of computing global solutions to continuous optimization problems. Our proposal relies on a fitness assignment scheme that gives lower fitness values to the positions of fireflies that correspond to non-dominated points with smaller aggregation of objective function distances to the minimum values. Furthermore, FA randomness is based on the spread metric to reduce the gaps between consecutive non-dominated solutions. The obtained results from the preliminary computational experiments show that our proposal gives a dense and well distributed approximated Pareto front with a large number of points.