Abstract
IEEE 1687 standard (IJTAG), as an extension to the IEEE 1149.1, facilitates efficient access to embedded instruments by supporting reconfigurable scan networks. Specifically, IJTAG allows each IP to be wrapped by a test data register (TDR) whose access is controlled by a segment insertion bit (SIB) or a scan-mux control bit (SCB). Because the TDRs and the SIB/SCB network are typically not public, but critical for accessing embedded instruments, they might be used for illegitimate purposes, such as dumping credential data and reverse engineering IP design. Machine learning has been proposed to detect such attacks, but the large number of instruments and parallel execution enabled by the IJTAG produce high-dimensional data, which poses a challenge to on-chip detection. In this paper, we propose to reduce the high-dimensional but sparse data using a low-density parity-check (LDPC) matrix. Experiments using a modified version of the OpenSPARC T2 to include IJTAG functionality demonstrate that the use of feature reduction eliminates 91% of the features, leading to 43% reduction in circuit size without affecting detection accuracy. Also, the on-chip detector adds moderate overhead (similar to 8%) to the IJTAG.

Abstract
Many real-world robotic scenarios require performing task planning to decide courses of actions to be executed by (possibly heterogeneous) robots. A classical centralized planning approach that considers in the same search space all combinations of robots and goals could lead to inefficient solutions that do not scale well. Multi-Agent Planning (MAP) provides a good framework to solve this kind of tasks efficiently. Some MAP techniques have proposed to previously assign goals to agents (robots) so that the planning effort decreases. However, these techniques do not scale when the number of agents and goals grow, as in most real world scenarios with big maps or goals that cannot be reached by subsets of robots. In this paper we propose to help the computation of which goals should be assigned to each agent by using Actuation Maps (AMs). Given a map, AMs can determine the regions each agent can actuate on. They help on alleviating the effort of MAP techniques knowing which goals can be tackled by each agent, as well as cheaply estimating the cost of using each agent to achieve every goal. Experiments show that when information extracted from AMs is provided to the Multi Agent planner, goal assignment is significantly faster, speeding-up the planning process considerably. Experiments also show that this approach greatly outperforms classical centralized planning.

Abstract
The Braçal –Malhada mining region, 30km NE of Aveiro included several silver-lead mines that operated until mid 20th century. Mineralization consists on sulphides (galena, sphalerite, pyrite) in quartz veins occurring in the complex Porto-Tomar shear zone, Fig. 1. Veins develop in a 10km wide area, show different orientations, are found in granites, schists and even quartzite, and can pass through different rock types with no alteration. Exploration work in the area dates from the 1950´s and 70’s. This study compiles recent geological mapping, preliminary regional geochemical and geophysical surveys. It aims to obtain a general information on the area, delineate regions for further studies, that is, fieldwork with closer grids, complementary methods (such as electromagnetics, induced polarization), etc. Because of space limitations, only selected maps are given herein. Geophysics consisted on preliminary magnetic and gravimetric mapping. Residual, gradient, second derivative and Euler deconvolution magnetic and gravimetric maps were produced, interpreted against known geology, tectonics and interesting features for further exploration work are revealed. A stream sediments sampling geochemical campaign, overall 101 samples, was carried out and allowed identifying the main geochemical association of elements. Finally, geophysical and geochemical data are integrated to provide an enhanced view of the area.

Abstract

Abstract
Procedural modeling techniques reduce the effort of creating large virtual cities. However, current methodologies do not allow direct user control over the generated models. Associated with this problem, we face the additional problem related to intrinsic ambiguity existing in user selections. In this paper, we propose to address this problem by using a genetic algorithm to generalize user-provided point-and-click selections of building elements. From a few user-selected elements, the system infers new sets of elements that potentially correspond to the user's intention, including the ones manually selected. These sets are obtained by queries over the shape trees generated by the procedural rules, thus exploiting shape semantics, hierarchy and geometric properties. Our system also provides a complete selection-action paradigm that allows users to edit procedurally generated buildings without necessarily explicitly writing queries. The pairs of user selections and procedural operations (the actions) are stored in a tree-like structure, which is easily evaluated. Results show that the selection inference is capable of generating sets of shapes that closely match the user intention and queries are able to perform complex selections that would be difficult to achieve in other systems. User studies confirm this result.

Abstract
Study of the adoption of non-traditional tools as support for Higher Education curricula in Electrical Engineering and Computers. We highlight the use of blended-learning, interactive and remote virtual laboratories, computer simulation, and methodologies, such as Active Learning and Problem Based Learning and their applications in the curricular units of the course. The study is a literature review with the systematization and presentation of the findings through a conceptual map. We concluded that the initiatives that have resorted to new technologies in engineering degrees, as well as reports of similar experiments on this topic, are reduced, not formalized in curricula, and ad hoc.