Fedoram: A Federated Oblivious RAM Scheme

Instant messaging (IM) applications, even with end-to-end encryption enabled, pose privacy issues due to metadata and pattern leakage. Our goal is to develop a model for a privacy preserving IM application, by designing an IM application that focuses on hiding metadata and discussion patterns. To solve the issue of privacy preservation through the obfuscation of metadata, cryptographic constructions like Oblivious Random Access Machines (ORAM) have been proposed in recent years. However, although they completely hide the user access patterns, they incur high computational costs, often resulting in excessively slow performance in practice. We propose a new federated model, FedORAM, which is the first ORAM scheme that uses a federation of servers to hide metadata for an IM use case. In order to investigate the trade-off between security and performance, we propose two versions of FedORAM: Weak FedORAM and Strong FedORAM. Strong FedORAM uses a tree-based federation architecture to ensure strong obliviousness, but with an increased overhead cost. Weak FedORAM has a more simple federated architecture that only uses Oblivious Transfer (OT) to increase communication speed, but with security consequences. Our results show that both constructions are faster than a similar client-server ORAM scheme. Furthermore, Weak FedORAM has a response time of less than 2 seconds per message for a middle-sized federation

Source localization in reverberant rooms using Deep Learning and microphone arrays

International audienceSound sources localization (SSL) is a subject of active research in the field of multi-channel signal processing since many years, and could benefit from the emergence of data-driven approaches. In the present paper, we present our recent developments on the use of a deep neural network, fed with raw multichannel audio in order to achieve sound source localization in reverberating and noisy environments. This paradigm allows to avoid the simplifying assumptions that most traditional localization methods incorporate using source models and propagating models. However, for an efficient training process, supervised machine learning algorithms rely on large-sized and precisely labelled datasets. There is therefore a critical need to generate a large number of audio data recorded by microphone arrays in various environments. When the dataset is built either with numerical simulations or with experimental 3D soundfield synthesis, the physical validity is also critical. We therefore present an efficient tensor GPU-based computation of synthetic room impulse responses using fractional delays for image source models, and analyze the localization performances of the proposed neural network fed with this dataset, which allows a significant improvement in terms of SSL accuracy over the traditional MUSIC and SRP-PHAT methods

TimeScaleNet : a Multiresolution Approach for Raw Audio Recognition using Learnable Biquadratic IIR Filters and Residual Networks of Depthwise-Separable One-Dimensional Atrous Convolutions

International audienceIn the present paper, we show the benefit of a multi-resolution approach that allows to encode the relevant information contained in unprocessed time domain acoustic signals. TimeScaleNet aims at learning an efficient representation of a sound, by learning time dependencies both at the sample level and at the frame level. The proposed approach allows to improve the interpretability of the learning scheme, by unifying advanced deep learning and signal processing techniques. In particular, TimeScaleNet's architecture introduces a new form of recurrent neural layer, which is directly inspired from digital IIR signal processing. This layer acts as a learnable passband biquadratic digital IIR filterbank. The learnable filterbank allows to build a time-frequency-like feature map that self-adapts to the specific recognition task and dataset, with a large receptive field and very few learnable parameters. The obtained frame-level feature map is then processed using a residual network of depthwise separable atrous convolutions. This second scale of analysis aims at efficiently encoding relationships between the time fluctuations at the frame timescale, in different learnt pooled frequency bands, in the range of [20 ms ; 200 ms]. TimeScaleNet is tested both using the Speech Commands Dataset and the ESC-10 Dataset. We report a very high mean accuracy of 94.87 ± 0.24% (macro averaged F1-score : 94.9 ± 0.24%) for speech recognition, and a rather moderate accuracy of 69.71 ± 1.91% (macro averaged F1-score : 70.14 ± 1.57%) for the environmental sound classification task

The Role of SOC in Ensuring the Security of IoT Devices: A Review of Current Challenges and Future Directions

The growing popularity and deployment of Internet of Things (IoT) devices has led to serious security concerns. The integration of a security operations center (SOC) becomes increasingly important in this situation to ensure the security of IoT devices. In this article, we will present a summary of IoT device security issues, their vulnerabilities, a review of current challenges to keep these devices secure, and discuss the role that SOC can bring in protecting IoT devices while considering the challenges encountered and the directions to consider when implementing a reliable SOC for IoT monitoring

Timescalenet: a multiresolution approcha for raw audio recognition

International audienceIn recent years, the use of Deep Learning techniques in audio signal processing has led the scientific community to develop machine learning strategies that allow to build efficient representations from raw waveforms for machine hearing tasks. In the present paper, we show the benefit of a multi-resolution approach : TimeScaleNet aims at learning an efficient representation of a sound, by learning time dependencies both at the sample level and at the frame level. At the sample level, TimeScaleNet's architecture introduces a new form of recurrent neural layer that acts as a learnable passband biquadratic digital IIR filterbank and self-adapts to the specific recognition task and dataset, with a large receptive field and very few learnable parameters. The obtained frame-level feature map is then processed using a residual network of depthwise separable atrous convolutions. This second scale of analysis allows to encode the time fluctuations at the frame timescale, in different learnt pooled frequency bands. In the present paper, TimeScaleNet is tested using the Speech Commands Dataset. We report a very high mean accuracy of 94.87±0.24% (macro averaged F1-score : 94.9 ± 0.24%) for this particular task

Redescrição de três espécies de Artemita Walker (Diptera, Stratiomyidae) do Brasil

The Stratiomyidae genus Artemita Walker 1854 is represented in the Neotropical Region by 14 species, 6 of which occur in Brazil. The knowledge on the morphology of the species must still be worked. In the present study, male and female of A. brasiliana Lindner, 1964 and females of A. convexa (Walker, 1854) and A. hieroglyphica (Wiedemann, 1830) are redescribed, with the description of their terminalia

Redescription of three species of Artemita Walker (Diptera, Stratiomyidae) from Brazil

Os Stratiomyidae do gênero Artemita Walker 1854 estão representados na Região Neotropical por 14 espécies, das quais 6 ocorrem no Brasil. O conhecimento sobre a morfologia das espécies ainda precisa ser trabalhado. Neste estudo, macho e fêmea de A. brasiliana Lindner, 1964 e as fêmeas de A. convexa (Walker, 1854) e A. hieroglyphica (Wiedemann, 1830) são redescritos, com descrição de suas terminálias.The Stratiomyidae genus Artemita Walker 1854 is represented in the Neotropical Region by 14 species, 6 of which occur in Brazil. The knowledge on the morphology of the species must still be worked. In the present study, male and female of A. brasiliana Lindner, 1964 and females of A. convexa (Walker, 1854) and A. hieroglyphica (Wiedemann, 1830) are redescribed, with the description of their terminalia

Structural and Functional Imaging Studies in Chronic Cannabis Users: A Systematic Review of Adolescent and Adult Findings

Background: The growing concern about cannabis use, the most commonly used illicit drug worldwide, has led to a significant increase in the number of human studies using neuroimaging techniques to determine the effect of cannabis on brain structure and function. We conducted a systematic review to assess the evidence of the impact of chronic cannabis use on brain structure and function in adults and adolescents. Methods: Papers published until August 2012 were included from EMBASE, Medline, PubMed and LILACS databases following a comprehensive search strategy and pre-determined set of criteria for article selection. Only neuroimaging studies involving chronic cannabis users with a matched control group were considered. Results: One hundred and forty-two studies were identified, of which 43 met the established criteria. Eight studies were in adolescent population. Neuroimaging studies provide evidence of morphological brain alterations in both population groups, particularly in the medial temporal and frontal cortices, as well as the cerebellum. These effects may be related to the amount of cannabis exposure. Functional neuroimaging studies suggest different patterns of resting global and brain activity during the performance of several cognitive tasks both in adolescents and adults, which may indicate compensatory effects in response to chronic cannabis exposure. Limitations: However, the results pointed out methodological limitations of the work conducted to date and considerable heterogeneity in the findings. Conclusion: Chronic cannabis use may alter brain structure and function in adult and adolescent population. Further studies should consider the use of convergent methodology, prospective large samples involving adolescent to adulthood subjects, and data-sharing initiatives

Mitigação das emissões de amônia por zeólitas naturais durante a compostagem de dejetos de suínos

The objective of this work was to evaluate the efficiency of the natural zeolites clinoptilolite and stilbite to mitigate ammonia (NH3) losses during the initial phase of pig slurry (PS) composting. Three experiments were performed in pilot scale, each lasting 14 days. The zeolites were added to the PS in increasing rates, from 5 to 20% (w/v), on a substrate consisting of a mixture of sawdust (70%) and shavings (30%). Three applications of PS + zeolites were performed per experiment, followed by a turning. The substrate went through three additional turnings, between applications. The zeolites reduced NH3 emissions and their efficiency was directly related to the applied rate. Clinoptilolite was more efficient than stilbite. In the average of the three experiments, clinoptilolite reduced in 24 to 76% NH3 emissions. The results show the high potential of natural zeolites, mainly clinoptilolite, in mitigating NH3 volatilization during PS composting.O objetivo deste trabalho foi avaliar a eficiência das zeólitas naturais clinoptilolita e estilbita em mitigar as perdas de amônia (NH3) na fase inicial da compostagem de dejetos líquidos de suínos (DLS). Foram conduzidos três experimentos em escala piloto, com duração de 14 dias cada um. As zeólitas foram adicionadas aos DLS em doses crescentes, de 5 a 20% (m/v), sobre substrato constituído pela mistura de serragem (70%) e maravalha (30%). Foram realizadas três aplicações de DLS + zeólitas por experimento, seguidas de revolvimento. O substrato passou por outros três revolvimentos entre as aplicações. As zeólitas reduziram as emissões de NH3 e a sua eficiência foi diretamente relacionada à dose aplicada. A clinoptilolita apresentou maior eficiência do que a estilbita. Na média dos três experimentos, a clinoptilolita reduziu em 24 a 76% as emissões de NH3. Os resultados evidenciam o alto potencial de zeólitas naturais, principalmente da clinoptilolita, em mitigar a volatilização de NH3 durante a compostagem de DLS

Zebrafish Larvae Exhibit Rheotaxis and Can Escape a Continuous Suction Source Using Their Lateral Line

Zebrafish larvae show a robust behavior called rheotaxis, whereby they use their lateral line system to orient upstream in the presence of a steady current. At 5 days post fertilization, rheotactic larvae can detect and initiate a swimming burst away from a continuous point-source of suction. Burst distance and velocity increase when fish initiate bursts closer to the suction source where flow velocity is higher. We suggest that either the magnitude of the burst reflects the initial flow stimulus, or fish may continually sense flow during the burst to determine where to stop. By removing specific neuromasts of the posterior lateral line along the body, we show how the location and number of flow sensors play a role in detecting a continuous suction source. We show that the burst response critically depends on the presence of neuromasts on the tail. Flow information relayed by neuromasts appears to be involved in the selection of appropriate behavioral responses. We hypothesize that caudally located neuromasts may be preferentially connected to fast swimming spinal motor networks while rostrally located neuromasts are connected to slow swimming motor networks at an early age