Naturaren kume ezkutuak

Artikulu honetan zulo beltzak ditugu aztergai. Astro bitxi horiek behar bezala ulertzeko erlatibitate orokorra ezinbestekoa denez, artikuluaren lehen orrietan teoria horren inguruko ideia nagusienak aztertzen dira. Segidan, izarren heriotza hartzen da hizpide. Izar handienak zulo beltz bihurtzen direla ikusiko dugu. Zulo beltzek dauzkaten hainbat ezaugarri harrigarri ere lantzen dira artikuluan. Atal horretan zulo beltzen lurrinketak hartzen du lekurik garrantzitsuena. Bertan, kuantikaren oinarrizko kontzeptu batzuk azaldu ostean, zulo beltzak ez direla hain beltzak erakusten da. Azkenik, singularitateen mundu ilunean murgilduko da irakurlea. Dauden argi-izpi bakan horien atzetik joaten saiatuko gara, eta ameslarienentzat, denbora-makinen inguruko xehetasun batzuk ere landuko dira

Egocentric Vision-based Action Recognition: A survey

[EN] The egocentric action recognition EAR field has recently increased its popularity due to the affordable and lightweight wearable cameras available nowadays such as GoPro and similars. Therefore, the amount of egocentric data generated has increased, triggering the interest in the understanding of egocentric videos. More specifically, the recognition of actions in egocentric videos has gained popularity due to the challenge that it poses: the wild movement of the camera and the lack of context make it hard to recognise actions with a performance similar to that of third-person vision solutions. This has ignited the research interest on the field and, nowadays, many public datasets and competitions can be found in both the machine learning and the computer vision communities. In this survey, we aim to analyse the literature on egocentric vision methods and algorithms. For that, we propose a taxonomy to divide the literature into various categories with subcategories, contributing a more fine-grained classification of the available methods. We also provide a review of the zero-shot approaches used by the EAR community, a methodology that could help to transfer EAR algorithms to real-world applications. Finally, we summarise the datasets used by researchers in the literature.We gratefully acknowledge the support of the Basque Govern-ment's Department of Education for the predoctoral funding of the first author. This work has been supported by the Spanish Government under the FuturAAL-Context project (RTI2018-101045-B-C21) and by the Basque Government under the Deustek project (IT-1078-16-D)

Embedding-based real-time change point detection with application to activity segmentation in smart home time series data

[EN]Human activity recognition systems are essential to enable many assistive applications. Those systems can be sensor-based or vision-based. When sensor-based systems are deployed in real environments, they must segment sensor data streams on the fly in order to extract features and recognize the ongoing activities. This segmentation can be done with different approaches. One effective approach is to employ change point detection (CPD) algorithms to detect activity transitions (i.e. determine when activities start and end). In this paper, we present a novel real-time CPD method to perform activity segmentation, where neural embeddings (vectors of continuous numbers) are used to represent sensor events. Through empirical evaluation with 3 publicly available benchmark datasets, we conclude that our method is useful for segmenting sensor data, offering significant better performance than state of the art algorithms in two of them. Besides, we propose the use of retrofitting, a graph-based technique, to adjust the embeddings and introduce expert knowledge in the activity segmentation task, showing empirically that it can improve the performance of our method using three graphs generated from two sources of information. Finally, we discuss the advantages of our approach regarding computational cost, manual effort reduction (no need of hand-crafted features) and cross-environment possibilities (transfer learning) in comparison to others.This work was carried out with the financial support of FuturAALEgo (RTI2018-101045-A-C22) granted by Spanish Ministry of Science, Innovation and Universities

Image captioning for effective use of language models in knowledge-based visual question answering

Integrating outside knowledge for reasoning in visio-linguistic tasks such as visual question answering (VQA) is an open problem. Given that pretrained language models have been shown to include world knowledge, we propose to use a unimodal (text-only) train and inference procedure based on automatic off-the-shelf captioning of images and pretrained language models. More specifically, we verbalize the image contents and allow language models to better leverage their implicit knowledge to solve knowledge-intensive tasks. Focusing on a visual question answering task which requires external knowledge (OK-VQA), our contributions are: (i) a text-only model that outperforms pretrained multimodal (image-text) models of comparable number of parameters; (ii) confirmation that our text-only method is specially effective for tasks requiring external knowledge, as it is less effective in standard a VQA task (VQA 2.0); and (iii) our method attains results in the state-of-the-art when increasing the size of the language model. We also significantly outperform current multimodal systems, even though augmented with external knowledge. Our qualitative analysis on OK-VQA reveals that automatic captions often fail to capture relevant information in the images, which seems to be balanced by the better inference ability of the text-only language models. Our work opens up possibilities to further improve inference in visio-linguistic tasks.Ander is funded by a PhD grant from the Basque Government (PRE_2021_2_0143). This work is based upon work partially supported by the Ministry of Science and Innovation of the Spanish Government (DeepKnowledge project PID2021-127777OB-C21), and the Basque Government (IXA excellence research group IT1570-22)

A Comparative Analysis of Human Behavior Prediction Approaches in Intelligent Environments

Behavior modeling has multiple applications in the intelligent environment domain. It has been used in different tasks, such as the stratification of different pathologies, prediction of the user actions and activities, or modeling the energy usage. Specifically, behavior prediction can be used to forecast the future evolution of the users and to identify those behaviors that deviate from the expected conduct. In this paper, we propose the use of embeddings to represent the user actions, and study and compare several behavior prediction approaches. We test multiple model (LSTM, CNNs, GCNs, and transformers) architectures to ascertain the best approach to using embeddings for behavior modeling and also evaluate multiple embedding retrofitting approaches. To do so, we use the Kasteren dataset for intelligent environments, which is one of the most widely used datasets in the areas of activity recognition and behavior modeling.This work was carried out with the financial support of FuturAAL-Ego (RTI2018-101045-A-C22) and FuturAAL-Context (RTI2018-101045-B-C21) granted by Spanish Ministry of Science, Innovation and Universities

Naturaren kume ezkutuak

Artikulu honetan zulo beltzak ditugu aztergai. Astro bitxi horiek behar bezala ulertzeko erlatibitate orokorra ezinbestekoa denez, artikuluaren lehen orrietan teoria horren inguruko ideia nagusienak aztertzen dira. Segidan, izarren heriotza hartzen da hizpide. Izar handienak zulo beltz bihurtzen direla ikusiko dugu. Zulo beltzek dauzkaten hainbat ezaugarri harrigarri ere lantzen dira artikuluan. Atal horretan zulo beltzen lurrinketak hartzen du lekurik garrantzitsuena. Bertan, kuantikaren oinarrizko kontzeptu batzuk azaldu ostean, zulo beltzak ez direla hain beltzak erakusten da. Azkenik, singularitateen mundu ilunean murgilduko da irakurlea. Dauden argi-izpi bakan horien atzetik joaten saiatuko gara, eta ameslarienentzat, denbora-makinen inguruko xehetasun batzuk ere landuko dira

Ezagutzan oinarritutako giza jardueren eredu dinamiko eta pertsonalizatuak ikasten

Being able to recognise human activities by means of sensor and computational devices can be a key competence in order to achieve human centred technologies. For that purpose, it is mandatory to build computational models of the activities which have to be recognised. There are two major approaches for activity modelling:the data-driven and the knowledge-driven approaches. Both of them have advantages and drawbacks. The objective of this work is to combine both modelling approaches with the aim of building dynamic and personalised activity models, using generic knowledge-based models. This would allow implementing modelling processes which can adapt themselves to the evolution of specific people.; Gizakietara egokitutako teknologiak garatzeko, ezinbestekoa da makinek giza jarduerak antzemateko gaitasuna izatea, sentsoreak eta konputazio-gailuak erabiliz. Horretarako, antzeman nahi diren jarduera horien eredu konputazionalak sortu behar dira. Gaur egun, jarduera-ereduak sortzeko garaian, bi joera nagusi aurki daitezke: datuetan oinarritutako ereduak eta ezagutzan oinarritutakoak. Biek ere abantaila eta desabantailak dituzte. Lan honen helburua da bi joerak elkartzea eredu dinamiko eta pertsonalizatuak lortzeko, ezagutzan oinarritutako eredu orokor batzuetatik hasita. Modu horretan, pertsona bakoitzaren bilakaerara egokitutako modelatze-prozesuak lor daitezke