Comparing Feature Detectors: A bias in the repeatability criteria, and how to correct it

Most computer vision application rely on algorithms finding local correspondences between different images. These algorithms detect and compare stable local invariant descriptors centered at scale-invariant keypoints. Because of the importance of the problem, new keypoint detectors and descriptors are constantly being proposed, each one claiming to perform better (or to be complementary) to the preceding ones. This raises the question of a fair comparison between very diverse methods. This evaluation has been mainly based on a repeatability criterion of the keypoints under a series of image perturbations (blur, illumination, noise, rotations, homotheties, homographies, etc). In this paper, we argue that the classic repeatability criterion is biased towards algorithms producing redundant overlapped detections. To compensate this bias, we propose a variant of the repeatability rate taking into account the descriptors overlap. We apply this variant to revisit the popular benchmark by Mikolajczyk et al., on classic and new feature detectors. Experimental evidence shows that the hierarchy of these feature detectors is severely disrupted by the amended comparator.Comment: Fixed typo in affiliation

A Hardware Security Solution against Scan-Based Attacks

Scan based Design for Test (DfT) schemes have been widely used to achieve high fault coverage for integrated circuits. The scan technique provides full access to the internal nodes of the device-under-test to control them or observe their response to input test vectors. While such comprehensive access is highly desirable for testing, it is not acceptable for secure chips as it is subject to exploitation by various attacks. In this work, new methods are presented to protect the security of critical information against scan-based attacks. In the proposed methods, access to the circuit containing secret information via the scan chain has been severely limited in order to reduce the risk of a security breach. To ensure the testability of the circuit, a built-in self-test which utilizes an LFSR as the test pattern generator (TPG) is proposed. The proposed schemes can be used as a countermeasure against side channel attacks with a low area overhead as compared to the existing solutions in literature

Apoio à depuração e teste de circuitos mistos compatíveis com a norma IEEE1149.4

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto, Instituto Superior de Engenharia. Instituto Politécnico do Porto. 200

Cycles of Critical Metals : Dissipative Losses and Potential Optimizations

Many materials have a growing importance for our society as well as a growing supply risk, which together is referred to as the criticality of materials. Many metals, particularly those that are fre-quently considered to be critical, are used in dissipative ways along the product life cycle. Concur-rently, there is a lack of consensus in nomenclature on dissipation as well as of methodological ap-proaches to assess dissipation. This has been addressed in this thesis by providing a concise defini-tion and classification of dissipative losses, by implementing this definition into the material flow analysis methodology, and by exemplarily applying the methodology to selected metals, which are most frequently considered to be critical. The analysis focuses on selected applications: indium and gallium in CIGS photovoltaic cells, germanium in polymerization catalysts, and yttrium in thermal barrier coatings in aircraft engines. The outcomes of the prospective case studies, which focus on products used in Germany, include information on the hot spots regarding the occurrence of differ-ent types of dissipative losses and potential optimizations. Such knowledge is of value for achieving a more sustainable materials management which reduces environmental impacts of material use. Based on the methodological description and case study outcomes provided in this thesis, future studies with similar scope may be facilitated significantly

Parallel Natural Language Parsing: From Analysis to Speedup

Electrical Engineering, Mathematics and Computer Scienc

Supporting user interaction and social relationship formation in a collaborative online shopping context

The combination of online shopping and social media allow people with similar shopping interests and experiences to share, comment, and discuss about shopping from anywhere and at any time, which also leads to the emergence of online shopping communities. Today, more people turn to online platforms to share their opinions about products, solicit various opinions from their friends, family members, and other customers, and have fun through interactions with others with similar interests. This dissertation explores how collaborative online shopping presents itself as a context and platform for users\u27 interpersonal interactions and social relationship formation through a series of studies. First, a qualitative interview study shows that online shoppers believe that shopping-related interactions have a positive impact on their social bonds. However, there is uncertainty around the appropriateness of discussing shopping in online marketplaces, forums, and social networking sites between strangers and friends. These uncertainties act as strong deterrents that limit further interactions between users with shared shopping interests. Next, a mix of lab experiments and focus groups demonstrate how informational support and social support affect user participation and relationships, the impact of social structure on interpersonal relationship formation between community members, and the development of desire to be socially connected with others through real-time text conversations on shopping topics. Moreover, a combination of interviews, focus groups, and online survey identify four types of personas to help illustrate the complex nature of user participation and behaviors in online shopping communities: Opportunists, Contributors, Explorers, and Followers. Finally, an online experiment study with 50 participants implements problem-solving tasks to examine users’ relationship building in computer-mediated online shopping groups and the effects of interpersonal relationships on user behaviors in collaborative online shopping contexts. The results suggest that users may develop desire to be socially connected after working on implemented collaborative problem-solving tasks within the group, and the perceived social connectedness may encourage user engagement and contribution behaviors in online shopping groups and communities. The results also show that such help-giving, collaborative tasks lead to developing social capital and facilitating social support that have more significant impacts on user behaviors over the long term

Scalable parallel evolutionary optimisation based on high performance computing

Evolutionary algorithms (EAs) have been successfully applied to solve various challenging optimisation problems. Due to their stochastic nature, EAs typically require considerable time to find desirable solutions; especially for increasingly complex and large-scale problems. As a result, many works studied implementing EAs on parallel computing facilities to accelerate the time-consuming processes. Recently, the rapid development of modern parallel computing facilities such as the high performance computing (HPC) bring not only unprecedented computational capabilities but also challenges on designing parallel algorithms. This thesis mainly focuses on designing scalable parallel evolutionary optimisation (SPEO) frameworks which run efficiently on the HPC. Motivated by the interesting phenomenon that many EAs begin to employ increasingly large population sizes, this thesis firstly studies the effect of a large population size through comprehensive experiments. Numerical results indicate that a large population benefits to the solving of complex problems but requires a large number of maximal fitness evaluations (FEs). However, since sequential EAs usually requires a considerable computing time to achieve extensive FEs, we propose a scalable parallel evolutionary optimisation framework that can efficiently deploy parallel EAs over many CPU cores at CPU-only HPC. On the other hand, since EAs using a large number of FEs can produce massive useful information in the course of evolution, we design a surrogate-based approach to learn from this historical information and to better solve complex problems. Then this approach is implemented in parallel based on the proposed scalable parallel framework to achieve remarkable speedups. Since demanding a great computing power on CPU-only HPC is usually very expensive, we design a framework based on GPU-enabled HPC to improve the cost-effectiveness of parallel EAs. The proposed framework can efficiently accelerate parallel EAs using many GPUs and can achieve superior cost-effectiveness. However, since it is very challenging to correctly implement parallel EAs on the GPU, we propose a set of guidelines to verify the correctness of GPU-based EAs. In order to examine these guidelines, they are employed to verify a GPU-based brain storm optimisation that is also proposed in this thesis. In conclusion, the comprehensively experimental study is firstly conducted to investigate the impacts of a large population. After that, a SPEO framework based on CPU-only HPC is proposed and is employed to accelerate a time-consuming implementation of EA. Finally, the correctness verification of implementing EAs based on a single GPU is discussed and the SPEO framework is then extended to be deployed based on GPU-enabled HPC