Selecting candidate labels for hierarchical document clusters using association rules

One way to organize knowledge and make its search and retrieval easier is to create a structural representation divided by hierarchically related topics. Once this structure is built, it is necessary to find labels for each of the obtained clusters. In many cases the labels have to be built using only the terms in the documents of the collection. This paper presents the SeCLAR (Selecting Candidate Labels using Association Rules) method, which explores the use of association rules for the selection of good candidates for labels of hierarchical document clusters. The candidates are processed by a classical method to generate the labels. The idea of the proposed method is to process each parent-child relationship of the nodes as an antecedent-consequent relationship of association rules. The experimental results show that the proposed method can improve the precision and recall of labels obtained by classical methods. © 2010 Springer-Verlag.

Statistical model applied to NetFlow for network intrusion detection

The computers and network services became presence guaranteed in several places. These characteristics resulted in the growth of illicit events and therefore the computers and networks security has become an essential point in any computing environment. Many methodologies were created to identify these events; however, with increasing of users and services on the Internet, many difficulties are found in trying to monitor a large network environment. This paper proposes a methodology for events detection in large-scale networks. The proposal approaches the anomaly detection using the NetFlow protocol, statistical methods and monitoring the environment in a best time for the application. © 2010 Springer-Verlag Berlin Heidelberg.

Anfis applied to the prediction of surface roughness in grinding of advanced ceramics

This paper introduces a methodology for predicting the surface roughness of advanced ceramics using Adaptive Neuro-Fuzzy Inference System (ANFIS). To this end, a grinding machine was used, equipped with an acoustic emission sensor and a power transducer connected to the electric motor rotating the diamond grinding wheel. The alumina workpieces used in this work were pressed and sintered into rectangular bars. Acoustic emission and cutting power signals were collected during the tests and digitally processed to calculate the mean, standard deviation, and two other statistical data. These statistics, as well the root mean square of the acoustic emission and cutting power signals were used as input data for ANFIS. The output values of surface roughness (measured during the tests) were implemented for training and validation of the model. The results indicated that an ANFIS network is an excellent tool when applied to predict the surface roughness of ceramic workpieces in the grinding process.

Domain reduction using GRASP construction phase for transmission expansion planning problem

This paper proposes a new strategy to reduce the combinatorial search space of a mixed integer linear programming (MILP) problem. The construction phase of greedy randomized adaptive search procedure (GRASP-CP) is employed to reduce the domain of the integer variables of the transportation model of the transmission expansion planning (TM-TEP) problem. This problem is a MILP and very difficult to solve specially for large scale systems. The branch and bound (BB) algorithm is used to solve the problem in both full and the reduced search space. The proposed method might be useful to reduce the search space of those kinds of MILP problems that a fast heuristic algorithm is available for finding local optimal solutions. The obtained results using some real test systems show the efficiency of the proposed method. © 2012 Springer-Verlag.

A malware detection system inspired on the human immune system

Malicious programs (malware) can cause severe damage on computer systems and data. The mechanism that the human immune system uses to detect and protect from organisms that threaten the human body is efficient and can be adapted to detect malware attacks. In this paper we propose a system to perform malware distributed collection, analysis and detection, this last inspired by the human immune system. After collecting malware samples from Internet, they are dynamically analyzed so as to provide execution traces at the operating system level and network flows that are used to create a behavioral model and to generate a detection signature. Those signatures serve as input to a malware detector, acting as the antibodies in the antigen detection process. This allows us to understand the malware attack and aids in the infection removal procedures. © 2012 Springer-Verlag.

How bioinformatics enables livestock applied sciences in the genomic era

This review paper presents the three main approaches currently used in livestock genomic sciences where the bioinfomatics plays a critical role. They are named as Genomic Selection (GS), Genome Wide Association Study (GWAS) and Signatures of Selection (SS). The subsides for the construction of this article were generated in a current project (started in 2011), so called Zebu Genome Consortium (ZGC), which joins researchers from different institutions and countries, aiming to scientifically explore genomic information of Bos taurus indicus cattle breeds and deliver useful information to breeders and academic community, specially from the tropical regions of the world. © 2012 Springer-Verlag.

A proposed neural control for the trajectory tracking of a nonholonomic mobile robot with disturbances

In this paper, a trajectory tracking control problem for a nonholonomic mobile robot by the integration of a kinematic neural controller (KNC) and a torque neural controller (TNC) is proposed, where both the kinematic and dynamic models contains disturbances. The KNC is a variable structure controller (VSC) based on the sliding mode control theory (SMC), and applied to compensate the kinematic disturbances. The TNC is a inertia-based controller constituted of a dynamic neural controller (DNC) and a robust neural compensator (RNC), and applied to compensate the mobile robot dynamics, and bounded unknown disturbances. Stability analysis with basis on Lyapunov method and simulations results are provided to show the effectiveness of the proposed approach. © 2012 Springer-Verlag.

BBA: A binary bat algorithm for feature selection

Feature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be in-viable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the bats behaviour, which has never been applied to this context so far. The wrapper approach combines the power of exploration of the bats together with the speed of the Optimum-Path Forest classifier to find the set of features that maximizes the accuracy in a validating set. Experiments conducted in five public datasets have demonstrated that the proposed approach can outperform some well-known swarm-based techniques. © 2012 IEEE.

3D geovisualisation techniques applied in spatial data mining

The increase in the number of spatial data collected has motivated the development of geovisualisation techniques, aiming to provide an important resource to support the extraction of knowledge and decision making. One of these techniques are 3D graphs, which provides a dynamic and flexible increase of the results analysis obtained by the spatial data mining algorithms, principally when there are incidences of georeferenced objects in a same local. This work presented as an original contribution the potentialisation of visual resources in a computational environment of spatial data mining and, afterwards, the efficiency of these techniques is demonstrated with the use of a real database. The application has shown to be very interesting in interpreting obtained results, such as patterns that occurred in a same locality and to provide support for activities which could be done as from the visualisation of results. © 2013 Springer-Verlag.

Abordagens bio-inspiradas aplicadas ao estudo da cognição: um encontro entre biologia, psicologia e filosofia

Pós-graduação em Filosofia - FFC

Análise biomecânica instrumental da técnica de judô Morote Seoi Nage, através de uma metodologia de treinamento

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Medidas de deslocamento e vibrações através de imagens de câmeras digitais

Pós-graduação em Engenharia Mecânica - FEIS

Avaliação do método de classificação baseada no objeto em imagens de alta resolução espacial aplicado para o monitoramento de faixa de dutos: estudo de caso no entorno da refinaria de Capuava em Mauá - SP

The metropolitan region of São Paulo is the most populous of the country, this happens because of its great importance in the national economy and the job opportunities that are offered to the population. These factors result in intense population growth and urban expansion, reaching some non-habitable places of the metropolis, as areas of pipelines, which are very important for the transportation of natural gas, oil and its derivatives. Before the population growth of the region, these sites were unoccupied, do not presenting problems for the population. However, with the disorderly occupation is generated great anthropogenic pressure on the pipeline stitches, causing risks to people who are around them. Therefore it is extremely important to monitor the strip of pipelines through products and techniques of remote sensing and geoprocessing, enabling, through high spatial resolution images, identification of objects or phenomena that occur on Earth's surface that can alter the functioning and safety of pipelines. Therefore, this study aims to monitor a stretch of the area of the pipeline mesh GASPAL/OSVAT and Capuava Refinery (RECAP), located on the outskirts of the metropolitan area of São Paulo in the city of Mauá, who suffer great human pressure, proving thus the techniques of remote sensing and geographic information system (GIS) as effective tools for monitoring phenomena occurred in urban areas of great complexity. The monitoring was done by object-based classification applied in orbital images Ikonos II and RapidEye, of high spatial resolution and, image processing, detection of objects, segmentation, classification and editing were developed through the eCognition and ArcGis softwares. To determine the statistical accuracy of the mapping of the land cover of the stretch of pipeline in Maua, the results were analyzed by error matrix... (Complete abstract click electronic access below)

Ridge-based fingerprint matching using hough transform

Despite the efficacy of minutia-based fingerprint matching techniques for good-quality images captured by optical sensors, minutia-based techniques do not often perform so well on poor-quality images or fingerprint images captured by small solid-state sensors. Solid-state fingerprint sensors are being increasingly deployed in a wide range of applications for user authentication purposes. Therefore, it is necessary to develop new fingerprint-matching techniques that utilize other features to deal with fingerprint images captured by solid-state sensors. This paper presents a new fingerprint matching technique based on fingerprint ridge features. This technique was assessed on the MSU-VERIDICOM database, which consists of fingerprint impressions obtained from 160 users (4 impressions per finger) using a solid-state sensor. The combination of ridge-based matching scores computed by the proposed ridge-based technique with minutia-based matching scores leads to a reduction of the false non-match rate by approximately 1.7% at a false match rate of 0.1%. © 2005 IEEE.

Is Competition Making a Comeback? Discovering Methods to Keep Female Adolescents Engaged in STEM: A Phenomenological Approach

The decreasing number of women who are graduating in the Science, Technology, Engineering and Mathematics (STEM) fields continues to be a major concern. Despite national support in the form of grants provided by National Science Foundation, National Center for Information and Technology and legislation passed such as the Deficit Reduction Act of 2005 that encourages women to enter the STEM fields, the number of women actually graduating in these fields is surprisingly low. This research study focuses on a robotics competition and its ability to engage female adolescents in STEM curricula. Data have been collected to help explain why young women are reticent to take technology or engineering type courses in high school and college. Factors that have been described include attitudes, parental support, social aspects, peer pressure, and lack of role models. Often these courses were thought to have masculine and “nerdy” overtones. The courses were usually majority male enrollments and appeared to be very competitive. With more female adolescents engaging in this type of competitive atmosphere, this study gathered information to discover what about the competition appealed to these young women. Focus groups were used to gather information from adolescent females who were participating in the First Lego League (FLL) and CEENBoT competitions. What enticed them to participate in a curriculum that data demonstrated many of their peers avoided? FLL and CEENBoT are robotics programs based on curricula that are taught in afterschool programs in non-formal environments. These programs culminate in a very large robotics competition. My research questions included: What are the factors that encouraged participants to participate in the robotics competition? What was the original enticement to the FLL and CEENBoT programs? What will make participants want to come back and what are the participants’ plans for the future? My research mirrored data of previous findings such as lack of role models, the need for parental support, social stigmatisms and peer pressure are still major factors that determine whether adolescent females seek out STEM activities. An interesting finding, which was an exception to previous findings, was these female adolescents enjoyed the challenge of the competition. The informal learning environments encouraged an atmosphere of social engagement and cooperative learning. Many volunteers that led the afterschool programs were women (role models) and a majority of parents showed support by accommodating an afterschool situation. The young women that were engaged in the competition noted it was a friendly competition, but they were all there to win. All who participated in the competition had a similar learning environment: competitive but cooperative. Further research is needed to determine if it is the learning environment that lures adolescent females to the program and entices them to continue in the STEM fields or if it is the competitive aspect of the culminating activity. Advisors: James King and Allen Steckelberg