HQR-Scheme: A High Quality and resilient virtual primary key generation approach for watermarking relational data

Most of the watermarking techniques designed to protect relational data often use the Primary Key (PK) of relations to perform the watermark synchronization. Despite offering high confidence to the watermark detection, these approaches become useless if the PK can be erased or updated. A typical example is when an attacker wishes to use a stolen relation, unlinked to the rest of the database. In that case, the original values of the PK lose relevance, since they are not employed to check the referential integrity. Then, it is possible to erase or replace the PK, compromising the watermark detection with no need to perform the slightest modification on the rest of the data. To avoid the problems caused by the PK-dependency some schemes have been proposed to generate Virtual Primary Keys (VPK) used instead. Nevertheless, the quality of the watermark synchronized using VPKs is compromised due to the presence of duplicate values in the set of VPKs and the fragility of the VPK schemes against the elimination of attributes. In this paper, we introduce the metrics to allow precise measuring of the quality of the VPKs generated by any scheme without requiring to perform the watermark embedding. This way, time waste can be avoided in case of low-quality detection. We also analyze the main aspects to design the ideal VPK scheme, seeking the generation of high-quality VPK sets adding robustness to the process. Finally, a new scheme is presented along with the experiments carried out to validate and compare the results with the rest of the schemes proposed in the literature

A Double Fragmentation Approach for Improving Virtual Primary Key-Based Watermark Synchronization

Relational data watermarking techniques using virtual primary key schemes try to avoid compromising watermark detection due to the deletion or replacement of the relation's primary key. Nevertheless, these techniques face the limitations that bring high redundancy of the generated set of virtual primary keys, which often compromises the quality of the embedded watermark. As a solution to this problem, this paper proposes double fragmentation of the watermark by using the existing redundancy in the set of virtual primary keys. This way, we guarantee the right identification of the watermark despite the deletion of any of the attributes of the relation. The experiments carried out to validate our proposal show an increment between 81.04% and 99.05% of detected marks with respect to previous solutions found in the literature. Furthermore, we found out that our approach takes advantage of the redundancy present in the set of virtual primary keys. Concerning the computational complexity of the solution, we performed a set of scalability tests that show the linear behavior of our approach with respect to the processes runtime and the number of tuples involved, making it feasible to use no matter the amount of data to be protected

Evolution of Embodied Intelligence

We provide an overview of the evolutionary approach to the emergence of artificial intelligence in embodied behavioral agents. This approach, also known as Evolutionary Robotics, builds and capitalizes upon the interactions between the embodied agent and its environment. Although we cover research carried out in several laboratories around the world, the choice of topics and approaches is based on work carried out at EPFL. We describe a large number of experiments including evolution of single robots in environments of increasing complexity, competitive and cooperative evolution, evolution of vi-sion-based systems, evolution of learning, and evolution of electronics and morphologies for autonomous robot

Crystal structure, physicochemical properties, Hirshfeld surface analysis and antibacterial activity assays of transition metal complexes of 6-methoxyquinoline

Five monomeric complexes of Co(ii), Cu(ii), Ni(ii), Zn(ii) and Ag(i) with 6-methoxyquinoline (6-MeOQ) as ligand have been prepared, and their crystal structures have been determined by single X-ray diffractions. The Cu(ii), Ni(ii) and Zn(ii) complexes are formulated as M(6-MeOQ) 2 Cl 2 , completing MN 2 Cl 2 coordination spheres. On the other hand, Co(ii) and Ag(i) compounds are ionic with formulae [Ag(6-MeOQ) 2 ] + NO 3 - and H(6-MeOQ) + [Co(6-MeOQ)Cl 3 ] - (where H(6-MeOQ) + is the protonated ligand). Hirshfeld surface analysis was employed to study the intermolecular interactions in the crystal lattices and from these studies it was found that π-stacking contacts play an important role. Besides, the complexes have been characterized by FTIR, UV-visible and emission spectroscopies. The singlet oxygen production and fluorescence quantum yields were measured for all the complexes employing steady-state methodologies. Finally, the antibacterial activity of the complexes was screened against both Gram-positive and Gram-negative bacteria.Fil: Villa Perez, Cristian. Facultad de Ciencias Exactas, Universidad Nacional de la Plata; ArgentinaFil: Ortega, I.C.. Universidad Nacional de Colombia; ColombiaFil: Vélez Macías, Andrea. Universidad Nacional de Colombia; ColombiaFil: Payán, A. M.. Universidad Nacional de Colombia; ColombiaFil: Echeverría, Gustavo Alberto. Facultad de Ciencias Exactas, Universidad Nacional de la Plata; ArgentinaFil: Soria, Delia Beatriz. Facultad de Ciencias Exactas, Universidad Nacional de la Plata; ArgentinaFil: Valencia Uribe, Gloria Cristina. Universidad Nacional de Colombia; Colombi

Human and machine recognition of transportation modes from body-worn camera images

Computer vision techniques applied on images opportunistically captured from body-worn cameras or mobile phones offer tremendous potential for vision-based context awareness. In this paper, we evaluate the potential to recognise the modes of locomotion and transportation of mobile users, by analysing single images captured by body-worn cameras. We evaluate this with the publicly available Sussex-Huawei Locomotion and Transportation Dataset, which includes 8 transportation and locomotion modes performed over 7 months by 3 users. We present a baseline performance obtained through crowd sourcing using Amazon Mechanical Turk. Humans infered the correct modes of transportations from images with an F1-score of 52%. The performance obtained by five state-of-the-art Deep Neural Networks (VGG16, VGG19, ResNet50, MobileNet and DenseNet169) on the same task was always above 71.3% F1-score. We characterise the effect of partitioning the training data to fine-tune different number of blocks of the deep networks and provide recommendations for mobile implementations