BUSCADOR RECOLECTA

The S3 dataset contains the behavior (sensors, statistics of applications, and voice) of 21 volunteers interacting with their smartphones for more than 60 days. The type of users is diverse, males and females in the age range from 18 until 70 have been considered in the dataset generation. The wide range of age is a key aspect, due to the impact of age in terms of smartphone usage. To generate the dataset the volunteers installed a prototype of the smartphone application in on their Android mobile phones. All attributes of the different kinds of data are writed in a vector. The dataset contains the fellow vectors: Sensors: This type of vector contains data belonging to smartphone sensors (accelerometer and gyroscope) that has been acquired in a given windows of time. Each vector is obtained every 20 seconds, and the monitored features are: - Average of accelerometer and gyroscope values. - Maximum and minimum of accelerometer and gyroscope values. - Variance of accelerometer and gyroscope values. - Peak-to-peak (max-min) of X, Y, Z coordinates. - Magnitude for gyroscope and accelerometer. Statistics: These vectors contain data about the different applications used by the user recently. Each vector of statistics is calculated every 60 seconds and contains : - Foreground application counters (number of different and total apps) for the last minute and the last day. - Most common app ID and the number of usages in the last minute and the last day. - ID of the currently active app. - ID of the last active app prior to the current one. - ID of the application most frequently utilized prior to the current application. - Bytes transmitted and received through the network interfaces. Voice: This kind of vector is generated when the microphone is active in a call o voice note. The speaker vector is an embedding, extracted from the audio, and it contains information about the user's identity. This vector, is usually named "x-vector" in the Speaker Recognition field, and it is calculated following the steps detailed in "egs/sitw/v2" for the Kaldi library, with the models available for the extraction of the embedding. A summary of the details of the collected database. - Users: 21 - Sensors vectors: 417.128 - Statistics app's usage vectors: 151.034 - Speaker vectors: 2.720 - Call recordings: 629 - Voice messages: 2.091

The specific fatty acid profile of fish can vary depending on species, age, diet and other factors. However, in general, fish are generally rich in long-chain polyunsaturated fatty acids (LC-PUFA), such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). Liver, skin, meat and fish mucus can contain different levels and types of fatty acids. In fact, each of these tissues can provide valuable information about the fatty acid profile of the whole fish. In order to establish a non-invasive method to assess the lipid profile of fish tissues, twenty-four European sea bass (Dicentrarchus labrax) were fed two different diets [containing different PUFAs profiles] for 30 days. Samples of cutaneous mucus, skin, flesh and liver were then obtained and their fatty acid profile was analyzed by gas chromatography. Multidimensional scaling (MDS), SIMPER procedure (similarity percentages) and permutation test (PERMANOVA) were performed. The results indicate that there are similarities in the fatty lipid profile of skin and liver, as well as between skin and liver mucus. Relationships have been observed between the fatty acid compositions of the different fish tissues, in particular between liver and skin, where linoleic acid (18:2n-6) is the most significant, which would allow the skin to be used as a proxy to infer the hepatic lipid profile, without the need to sacrifice the animals