Even though a computer science or computing-oriented degree is unavoidably broken into semesters and courses, we always hope that our students form a holistic picture of the discipline by the time they graduate. Yet we do not have too many opportunities to make this point in a convincing manner. The goal of this working group will be to address a well-defined portion of this problem: revealing the significant connections between algorithmic courses (such as Discrete Math, Data Structures, Algorithms) and systems oriented courses (such as Organization, Computer Networks, Operating Systems, and Hardware) that may be missed by students.

Due to the increasing amount of data and the reduction of costs in 3D data acquisition devices, there has been a growing interest, in developing efficient and robust feature extraction algorithms for 3D shapes, invariants to isometric, topological and noise changes, among others. One of the key tasks for feature extraction in 3D shapes is the interest points detection; where interest points are salient structures, which can be used, instead of the whole object. In this research, we present a new approach to detect interest points in 3D shapes by analyzing the triangles that compose the mesh which represent the shape, in different way to other algorithms more complex such as Harris 3D or HKS. Our results and experiments of repeatability, confirm that our algorithm is stable and robust, in addition, the computational complexity is O(n log n), where n represents the number of faces of the mesh.

To facilitate processing of 3D objects is common to use high-level representations. The interest points are one of them. An interest point should possess a distinctive feature regarding its locality and should be stable in different instances of the object. This article proposes a descriptor based on symmetry (GISIFs) and heat diffusion (HKS). From this features, we select a set of representative points. The GISIFs referenced in this article has not been used to extract local features. We compare our results with the results of other techniques, which make up the state of the art in interest point detection. We use a benchmark that evaluates the accuracy of the selected points with respect to an ideal set of interest points.

Due to the advancement of computing and the power of the new hardware, more economical, it is now feasible to have thousands of images which can be analyzed to allow classification for its shape and/or color. Furthermore, techniques and efficiency of the classification depends on the characteristics to be obtained of images in order to compare and classify them according to their similarity. Some images, such as model cars, planes and boats, can be discriminated by their shape. However, other images such as butterfly species where the shape is similar, the color plays an important role in the discrimination task. In this research we propose a novel approach to extract distinctive features of images by combining the HSV color model and wavelets filters. Furthermore, we investigate the best combination of features color and form. Experiments have shown improved performance by combining the HSV color model with Gabor wavelets.

One mechanism for estimating software quality is through the use of metrics, which are functions that evaluates certain characteristics of the product quality development. A software product can be evaluated from different points of view, and in that sense, the results of the evaluations are numeric vectors, which together describe the quality of the software. This research uses data from NASA's open access which undergo a process of reducing the dimensionality by principal component analysis (PCA), then applied three clustering techniques and evaluates the best grouping using Rand Index. Finally, the top clusters are tested with regression to find the metrics that are related to the error of the Software. The results suggest that groups consisting of software modules whose code source have a higher average of blank lines, show a higher density of error. This could be interpreted as an indication of the order of implementation. On the other hand, shows the presence of a direct relationship between the number of errors in a module with the number of calls functions to other modules. The contribution of this work is related to the use of assessment techniques of clustering, dimensionality reduction, clustering algorithms and regression to discover patterns in software metrics a rigorous manner.