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The principal–agent problem occurs when one entity (the ‘‘agent’’), is able to make decisions and/or take actions on behalf of another person or entity (the ‘‘principal’’). The agent earnings are regulated under a contract designed by the principal. Under the principal’s point of view, the main goal while designing said contract (and the payment rules incorporated on it) is to align the actions made by the agent to the principal’s own goals. So, in this paper we will define a method that will allow the principal to forecast the remuneration obtained by the agent under an established contract in the insurance sector.

The continuous growth of technologies like 4G or 5G has led to a massive use of mobile devices such as smartphones and tablets. This phenomenon, combined with the fact that people use mobile phones for a longer period of time, results in mobile phones becoming the main source of creation of visual information. However, its reliability as a true representation of reality cannot be taken for granted due to the constant increase in editing software. This makes it easier to alter original content without leaving a noticeable trace in the modification. Therefore, it is essential to introduce forensic analysis mechanisms to guarantee the authenticity or integrity of a certain digital video, particularly if it may be considered as evidence in legal proceedings. This paper explains the branch of multimedia forensic analysis that allows to determine the identification of the source of acquisition of a certain video by exploiting the unique traces left by the camera sensor of the mobile device in visual content. To do this, a technique that performs the identification of the source of acquisition of digital videos from mobile devices is presented. It involves 3 stages: (1) Extraction of the sensor fingerprint by applying the block-based technique. (2) Filtering the strong component of the PRNU signal to improve the quality of the sensor fingerprint. (3) Classification of digital videos in an open scenario, that is, where the forensic analyst does not need to have access to the device that recorded the video to find out the origin of the video. The main contribution of the proposed technique eliminates the details of the scene to improve the PRNU fingerprint. It should be noted that these techniques are applied to digital images and not to digital videos. In this work, we show that it is necessary to take this improvement into account to improve the identification of digital videos. Experimental results are also presented that support the validity of the techniques used and show promising results.

A framework for online detection of masquerade attacks is proposed.At the analysis stage, local alignment algorithms are introduced.At the verification stage, a validation scheme based on the U-test is implemented.For mimicry recognition, the parallel analysis of monitored actions is performed.For evaluating the approach, the SEA dataset is applied. Masquerade attackers are internal intruders acting through impersonating legitimate users of the victim system. Most of the proposals for their detection suggested recognition methods based on the comparison of use models of the protected environment. However recent studies have shown their vulnerability against adversarial attacks based on imitating the behavior of legitimate users. In order to contribute to their identification, this article introduces a novel detection method robust against evasion strategies based on mimicry. The proposal described two levels of information processing: analysis and verification. At the analysis stage, local alignment algorithms are implemented. In this way it is possible to score the similarity between action sequences performed by users, bearing in mind their regions of greatest resemblance. On the other hand, a novel validation scheme based on the statistical non-parametric U-test is implemented. Through this it is possible to refine the labeling of sequences to avoid making hasty decisions when their nature is not sufficiently clear. In order to strengthen their effectiveness against mimicry attacks, the analysis of the monitored sequences is performed in concurrency. This involves partitioning long sequences with two purposes: making subsequences of small intrusions more visible and analyzing new sequences when suspicious situations occur, such as the execution of never before seen commands or the discovery of potentially harmful activities. The proposal has been evaluated from the functional standard SEA and mimicry attacks. Promising experimental results have been shown, demonstrating great precision against conventional masqueraders (TPR=98.3%, FPR=0.77%) and a success rate of 80.2% when identifying mimicry attacks, hence outperforming the best contributions of bibliography.

Every day the use of videos from mobile devices as evidence in legal proceedings is more usual and common.Image source acquisition identification is a branch of digital forensic analysis.We use of sensor pattern noise for source identification.We make a series of experiments which emulate similar situations to those that may occur in reality. Recording videos on smartphones and other mobile devices, given their enormous popularity, is currently very common. The portability of these devices facilitates their use for recording videos in a wide variety of situations, including while witnessing criminal activities. These videos can be later used as evidence in legal proceedings. Therefore, the forensic analysis of videos taken with mobile device videos is important, and could serve for legal and also investigative purposes. It is necessary, however, to use techniques that are quite specific to this type of devices, given some peculiar features of their cameras. In this paper, we will address the issue of video source acquisition identification by presenting a technique based on sensor noise and wavelet transform extraction from video key frames. These frames are extracted using an efficient algorithm that takes their content into account, improving the selection of frames to be analyzed over past proposals. The scheme presented consists of four stages: (1) Key frames extraction, (2) sensor pattern noise extraction, (3) feature extraction, and (4) classifier training and prediction. We also present experimental results that support the validity of the techniques used and show promising results.

Every day the use of images from mobile devices as evidence in legal proceedings is more usual and common.Image source acquisition identification is a branch of digital forensic analysis.We use a combination of hierarchical and flat clustering and the use of Sensor Pattern Noise for source identification.We make a series of experiments which emulate similar situations to those that may occur in reality. Every day the use of images from mobile devices as evidence in legal proceedings is more usual and common. Therefore, forensic analysis of mobile device images takes on special importance. This paper explores the branch of forensic analysis which is based on the identification of the source, specifically on the grouping or clustering of images according to their source acquisition. In contrast with other state of the art techniques for source identification, hierarchical clustering does not involve a priori knowledge of the number of images or devices to be identified or training data for a future classification stage. That is, a grouping by classes with all the input images is performed. The proposal is based on the combination of hierarchical and flat clustering and the use of Sensor Pattern Noise (SPN). There has been a series of experiments which emulate similar situations to those that may occur in reality to test the robustness and reliability of the results of the technique. The results are satisfactory in all the experiments, obtaining high rates of success.

Fuzzy logic regulation of flow was investigated in simulation of a model of the Cleveland Clinic Foundation rotodynamic Cardio Pulmonary Bypass blood pump comprised of impeller model #4079 and volute model #4080. A non-linear model of the pump was derived from pump maps obtained from measurements using a custom made pump dynamometer. Pump speed and generated delta pressure were used as inputs to a fuzzy engine in charge of manipulating the pump's speed; the fuzzy controller was capable of maintaining a setpoint of 6±0.5 l/min when presented with pressure disturbances over a range of ±50 mmHg from the baseline of 100 mmHg.