Extitutions and security : movement as code

How can we guarantee the security of our means of transport? How can we strengthen that of our communications? How can we keep our ideology or our market safe? Etc. In this context, the press has recently been asking whether it is possible to be 100% certain that a person is not concealing a dangerous item on passing through an airport control. Nonetheless, technical solutions may be about to alter the aforementioned scenario. Indeed, the authorities in a number of countries have decided to install scanners at airports that allow people to be completely undressed without having to remove their clothes. The protests which have been voiced, quite reasonably, against this measure have been completely stifled by arguments based on security. Just a glance at the press in any European country in the last few days will confirm this affirmation. The example of scanners has not been a random choice to begin this article. It has to do with the fact that airports are truly a unique place to illustrate one of the most characteristic phenomena of our current societies: the need to manage mobility and security in unison. However, from our point of view security is much more than an excuse for legitimising procedures for surveillance and control, or a characteristic feature of contemporary states. It constitutes the very logic of such states. It is characterised by circulation and movement, and it operates as an authentic code that is capable of generating new forms of sociality and regimes of powerComo podemos garantir a segurança em nossos meios de transporte? Como podemos reforçar nossa comunicação? Como podemos manter nossa ideologia ou nosso mercado seguros? Etc. Neste contexto, a mídia impressa tem sido recentemente questionada sobre a possibilidade de estar 100% seguro de que uma pessoa não está ocultando um item perigoso ao passar pelo controle do aeroporto. Não obstante, soluções técnicas podem estar prestes a alterar o referido cenário. Em realidade, as autoridades de vários países decidiram instalar scanners nos aeroportos que permitem visualizar pessoas completamente despidas, sem que retirem a roupa. Os protestos realizados contra esta medida têm sido completamente suprimidos pelos argumentos baseados na segurança. Basta uma olhada sobre a mídia impressa, em qualquer país europeu, nos últimos cinco dias para confirmar esta afirmação. O exemplo dos scanners não é uma escolha aleatória para iniciar este artigo. Diz respeito ao fato de que os aeroportos são verdadeiramente o único lugar para ilustrar um dos fenômenos mais característicos de nossas sociedades atuais: a necessidade de lidar com a mobilidade e a segurança em uníssono. Todavia, de nosso ponto de vista, a segurança é muito mais do que uma desculpa para legitimar procedimentos de vigilância e controle, ou uma característica das condições contemporâneas. É caracterizada pela circulação e movimento e opera como um autêntico código que é capaz de gerar novas formas de sociabilidade e regimes de poder

Lo virtual y lo social

Este texto sostiene que lo virtual no es algo nuevo, algo que observamos como resultado de la proliferaci&oacute;n de las tecnolog&iacute;as de la informaci&oacute;n y la comunicaci&oacute;n.&nbsp; Por el contrario, &ldquo;virtual&rdquo; es una noci&oacute;n con una larga historia. La palabra&nbsp; proviene del lat&iacute;n &quot;virtualis&rdquo;, vocablo derivado de &quot;virtus&quot;:&nbsp; fuerza, potencia.&nbsp; En la filosof&iacute;a escol&aacute;stica, lo virtual es lo que existe potencialmente pero no en acto. &Eacute;sta es la raz&oacute;n por la cual autores como Levy (1998) arguyen que virtual no se opone a lo verdadero, sino a lo real. Este escrito desarrolla el t&eacute;rmino &ldquo;virtual&rdquo; apoy&aacute;ndose en las tesis del mencionado Levy y&nbsp; Michel Serres (1994).&nbsp; En Atlas, Serres caracteriza lo &ldquo;virtual&rdquo; como. La imaginaci&oacute;n, la lengua, el conocimiento, la religi&oacute;n o la ficci&oacute;n son modos de ser-fuera-de-ah&iacute;. Sin embargo, las TIC&rsquo;s permiten que volvamos a descubrir o que reinventemos lo virtual.&nbsp; Algunos de los casos que presentamos aqu&iacute; pretenden ilustrar tanto ese redescubrimiento de lo virtual como dar inteligibilidad a algunos procesos y relaciones que caracterizan nuestro presente. En ese sentido, la noci&oacute;n de &ldquo;virtual&rdquo; es &uacute;til para entender, por ejemplo, la tensi&oacute;n local-global, la creaci&oacute;n de colectividades sin lugar espec&iacute;fico y formas de gobierno inesperadas que se alejan de las implementadas por las viejas instituciones. <br /

Comportement détersif des formulations écologiques dans l'extraction de graisses et de protéines sur du tissu en coton apprêté et traité avec differents adoucissants

En el presente trabajo se efectuó un estudio del comportamiento detersivo producido por una formulación ecológica, aplicada para el lavado de un tejido de algodón aprestado con una resina DMDHEU y tratado con diferentes suavizantes catiónicos, del tipo de amonio silicona y esterquat. A estos tejidos se aplicaron, antes del lavado, dos impurezas del tipo proteína y una tipo aceitosa. Los efectos del detergente, en la extracción de tales impurezas del tejido aprestado y tratado con los diferentes suavizantes, fueron obtenidos con la formulación ecológica completa y con la presencia y ausencia de enzimas y blanqueantes en la misma, en orden a obtener la influencia de estas variables en el comportamiento detergente.In this paper a study of detergency performance with ecological detergent formulation applied to the washing of cotton fabric finished with the resin DMDHEU and treated with different cationic softeners such as quaternary ammonium silicone and esterquat was obtained. Before the fabric washing two protein and one oily soil impurities types were applied. The detergent effect was determined in function of different softeners treatments to finished fabric and with different contents of the complete detergent formulation used with and without bleaching agent and with and without enzymes in order to obtain the influence of these variables in the detergency performance.C’est au cours de ce travail qu’a été réalisée une étude du comportement détersif produit par une formulation écologique, appliquée pour le lavage d’un tissu en coton apprêté avec une résine DMDHEU et traité avec différents adoucissants cationiques, du type silicone d’ammonium et sterquat. Deux impuretés du type protéine et une autre du type huileuse ont été appliquées à ces tissus avant le lavage. Les effets du détergent, sur l’extraction de telles impuretés du tissu apprêté et traité avec les différents adoucissants, ont été obtenus avec la formulation écologique complète et avec la présence et l’absence d’enzymes et de blanchissants dans cette dernière, afin d’obtenir l’influence de ces variables sur le comportement détergent

Aproximació històrica al debat sobre la gestió de l'aigua a la política espanyola dels segles XIX i XX

Our paper gives a panoramic picture of the debates that have taken place in Spain about the distribution of water resources. The historic review starts with the colonial crisis and the emergence of reform projects at the end of the XIXth century and ends with the breakdown of the Second Republic in the XXth century. Our research shows the changes of policies undertaken by different governments a long this period and the kind of actors that got active in the debates about how to fight against the country's dryness and drough

DReAM: Per-task DRAM energy metering in multicore systems

Interaction across applications in DRAM memory impacts its energy consumption. This paper makes the case for accurate per-task DRAM energy metering in multicores, which opens new paths to energy/performance optimizations, such as per-task energy-aware task scheduling and energy-aware billing in datacenters. In particular, the contributions of this paper are (i) an ideal per-task energy metering model for DRAM memories; (ii) DReAM, an accurate, yet low cost, implementation of the ideal model (less than 5% accuracy error when 16 tasks share memory); and (iii) a comparison with standard methods (even distribution and access-count based) proving that DReAM is more accurate than these other methods.This work has been partially supported by the Spanish Ministry of Science and Innovation under grant TIN2012-34557, the HiPEAC Network of Excellence, by the European Research Council under the European Union’s 7th FP, ERC Grant Agreement n. 321253, and by a joint study agreement between IBM and BSC (number W1361154). Qixiao Liu has also been funded by the Chinese Scholarship Council under grant 2010608015.Postprint (published version

Self multi-head attention for speaker recognition

Most state-of-the-art Deep Learning (DL) approaches forspeaker recognition work on a short utterance level. Given thespeech signal, these algorithms extract a sequence of speakerembeddings from short segments and those are averaged to ob-tain an utterance level speaker representation. In this work wepropose the use of an attention mechanism to obtain a discrim-inative speaker embedding given non fixed length speech utter-ances. Our system is based on a Convolutional Neural Network(CNN) that encodes short-term speaker features from the spec-trogram and a self multi-head attention model that maps theserepresentations into a long-term speaker embedding. The atten-tion model that we propose produces multiple alignments fromdifferent subsegments of the CNN encoded states over the se-quence. Hence this mechanism works as a pooling layer whichdecides the most discriminative features over the sequence toobtain an utterance level representation. We have tested thisapproach for the verification task for the VoxCeleb1 dataset.The results show that self multi-head attention outperforms bothtemporal and statistical pooling methods with a18%of rela-tive EER. Obtained results show a58%relative improvementin EER compared to i-vector+PLDAPeer ReviewedPostprint (published version

Self attention networks in speaker recognition

Recently, there has been a significant surge of interest in Self-Attention Networks (SANs) based on the Transformer architecture. This can be attributed to their notable ability for parallelization and their impressive performance across various Natural Language Processing applications. On the other hand, the utilization of large-scale, multi-purpose language models trained through self-supervision is progressively more prevalent, for tasks like speech recognition. In this context, the pre-trained model, which has been trained on extensive speech data, can be fine-tuned for particular downstream tasks like speaker verification. These massive models typically rely on SANs as their foundational architecture. Therefore, studying the potential capabilities and training challenges of such models is of utmost importance for the future generation of speaker verification systems. In this direction, we propose a speaker embedding extractor based on SANs to obtain a discriminative speaker representation given non-fixed length speech utterances. With the advancements suggested in this work, we could achieve up to 41% relative performance improvement in terms of EER compared to the naive SAN which was proposed in our previous work. Moreover, we empirically show the training instability in such architectures in terms of rank collapse and further investigate the potential solutions to alleviate this shortcoming.This work was supported by the Spanish Project ADAVOICE PID2019-107579RB-I00 (MICINN).Peer ReviewedPostprint (published version

Auto-encoding nearest neighbor i-vectors for speaker verification

In the last years, i-vectors followed by cosine or PLDA scoringtechniques were the state-of-the-art approach in speaker veri-fication. PLDA requires labeled background data, and thereexists a significant performance gap between the two scoringtechniques. In this work, we propose to reduce this gap by us-ing an autoencoder to transform i-vector into a new speaker vec-tor representation, which will be referred to as ae-vector. Theautoencoder will be trained to reconstruct neighbor i-vectors in-stead of the same training i-vectors, as usual. These neighbori-vectors will be selected in an unsupervised manner accordingto the highest cosine scores to the training i-vectors. The evalua-tion is performed on the speaker verification trials of VoxCeleb-1 database. The experiments show that our proposed ae-vectorsgain a relative improvement of 42% in terms of EER comparedto the conventional i-vectors using cosine scoring, which fillsthe performance gap between cosine and PLDA scoring tech-niques by 92%, but without using speaker labelsPeer ReviewedPostprint (published version

DReAM: An approach to estimate per-Task DRAM energy in multicore systems

Accurate per-task energy estimation in multicore systems would allow performing per-task energy-aware task scheduling and energy-aware billing in data centers, among other applications. Per-task energy estimation is challenged by the interaction between tasks in shared resources, which impacts tasks’ energy consumption in uncontrolled ways. Some accurate mechanisms have been devised recently to estimate per-task energy consumed on-chip in multicores, but there is a lack of such mechanisms for DRAM memories. This article makes the case for accurate per-task DRAM energy metering in multicores, which opens new paths to energy/performance optimizations. In particular, the contributions of this article are (i) an ideal per-task energy metering model for DRAM memories; (ii) DReAM, an accurate yet low cost implementation of the ideal model (less than 5% accuracy error when 16 tasks share memory); and (iii) a comparison with standard methods (even distribution and access-count based) proving that DReAM is much more accurate than these other methods.Peer ReviewedPostprint (author's final draft

Speaker characterization by means of attention pooling

State-of-the-art Deep Learning systems for speaker verification are commonly based on speaker embedding extractors. These architectures are usually composed of a feature extractor front-end together with a pooling layer to encode variable length utterances into fixed-length speaker vectors. The authors have recently proposed the use of a Double Multi-Head Self Attention pooling for speaker recognition, placed between a CNN-based front-end and a set of fully connected layers. This has shown to be an excellent approach to efficiently select the most relevant features captured by the front-end from the speech signal. In this paper we show excellent experimental results by adapting this architecture to other different speaker characterization tasks, such as emotion recognition, sex classification and COVID-19 detection.Peer ReviewedPostprint (published version