Directional Privacy for Deep Learning

Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method for applying privacy in the training of deep learning models. This applies isotropic Gaussian noise to gradients during training, which can perturb these gradients in any direction, damaging utility. Metric DP, however, can provide alternative mechanisms based on arbitrary metrics that might be more suitable. In this paper we apply \textit{directional privacy}, via a mechanism based on the von Mises-Fisher (VMF) distribution, to perturb gradients in terms of \textit{angular distance} so that gradient direction is broadly preserved. We show that this provides $\epsilon d$-privacy for deep learning training, rather than the $(\epsilon, \delta)$-privacy of the Gaussian mechanism; and that experimentally, on key datasets, the VMF mechanism can outperform the Gaussian in the utility-privacy trade-off

Answering Unanswered Questions through Semantic Reformulations in Spoken QA

Spoken Question Answering (QA) is a key feature of voice assistants, usually backed by multiple QA systems. Users ask questions via spontaneous speech which can contain disfluencies, errors, and informal syntax or phrasing. This is a major challenge in QA, causing unanswered questions or irrelevant answers, and leading to bad user experiences. We analyze failed QA requests to identify core challenges: lexical gaps, proposition types, complex syntactic structure, and high specificity. We propose a Semantic Question Reformulation (SURF) model offering three linguistically-grounded operations (repair, syntactic reshaping, generalization) to rewrite questions to facilitate answering. Offline evaluation on 1M unanswered questions from a leading voice assistant shows that SURF significantly improves answer rates: up to 24% of previously unanswered questions obtain relevant answers (75%). Live deployment shows positive impact for millions of customers with unanswered questions; explicit relevance feedback shows high user satisfaction.Comment: Accepted by ACL 2023 Industry Trac

Follow-on Question Suggestion via Voice Hints for Voice Assistants

The adoption of voice assistants like Alexa or Siri has grown rapidly, allowing users to instantly access information via voice search. Query suggestion is a standard feature of screen-based search experiences, allowing users to explore additional topics. However, this is not trivial to implement in voice-based settings. To enable this, we tackle the novel task of suggesting questions with compact and natural voice hints to allow users to ask follow-up questions. We define the task, ground it in syntactic theory and outline linguistic desiderata for spoken hints. We propose baselines and an approach using sequence-to-sequence Transformers to generate spoken hints from a list of questions. Using a new dataset of 6681 input questions and human written hints, we evaluated the models with automatic metrics and human evaluation. Results show that a naive approach of concatenating suggested questions creates poor voice hints. Our approach, which applies a linguistically-motivated pretraining task was strongly preferred by humans for producing the most natural hints.Comment: Accepted as Long Paper at EMNLP'23 Finding

Nutrient solutions for plant growth and fructan production in Vernonia herbacea

O crescimento limitado de rizóforos de Vernonia herbacea (Asteraceae) em solução de Hoagland levou à necessidade de estabelecer uma solução nutritiva para o cultivo dessa planta, visando ao incremento da biomassa de seus rizóforos ricos em frutanos. Essa solução (denominada Vernonia), constituída de Ca(NO3)2.4H2O 2,5 mmol L-1, KNO3 2,3 mmol L-1, KH2PO4 0,52 mmol L-1, Mg(NO3)2.6H2O 1,7 mmol L-1 e Na2SO4 1,3 mmol L-1, foi comparada com a de Hoagland nas forças iônicas de 50%, 100% e 200%. Foram realizadas duas avaliações para análise de crescimento e conteúdo de frutanos. As plantas não sobreviveram até os dois meses na solução de Hoagland 200%. A solução Vernonia diluída duas vezes (50%) foi a mais eficiente para o incremento de massa seca dos rizóforos e produção de frutanos por planta. Maior crescimento da parte aérea foi verificado nas soluções de Hoagland e Vernonia 100%. Em comparação com a solução de Hoagland, a solução Vernonia é mais pobre em macronutrientes, confirmando a hipótese de que plantas adaptadas a solos oligotróficos são menos exigentes em nutrientes minerais.The limited growth of rhizophores of Vernonia herbacea in Hoagland solution demanded the definition of a nutrient solution for plants of V. herbacea, aiming at the increase of the rhizophore biomass and fructan production. This solution, named Vernonia, is comprised of Ca(NO3)2.4H2O 2.5 mmol L-1, KNO3 2.3 mmol L-1, KH2PO4 0.52 mmol L-1, Mg(NO3)2.6H2O 1.7 mmol L-1 and Na2SO4 1.3 mmol L-1. Its effect on plants was compared to that of Hoagland solution, both with different ionic strengths, 50%, 100% and 200%. The effect of the solutions on plant growth and fructan content was evaluated twice in a six-month period. Plants did not survive up to two months, when cultivated in 200% Hoagland solution. The 50% Vernonia solution was the most effective for rhizophore biomass increase and fructan production per plant. Growth of aerial organs was promoted in 100% Hoagland and Vernonia solutions. Compared to Hoagland, Vernonia solution contains less macronutrients, which confirms the hypothesis that plants adapted to the oligotrophic soils of the cerrado, as V. herbacea, demand less mineral nutrients to achieve full growth.

Sífilis gástrica: desafios do diagnóstico e relato de caso: Gastric syphilis: diagnostic challenges and case report

A sífilis é uma doença de caráter infeccioso, sexualmente transmissível e de manifestações diversas. Dentre elas, a variante gástrica é extremamente rara e de difícil diagnóstico, o que confere elevada morbidade a seus portadores. Nesse sentido, a avaliação criteriosa do histórico médico e dos exames físico, endoscópico e patológico são essenciais para a exclusão de diagnósticos diferenciais, em particular, do Linfoma tipo MALT. Quando diagnosticada corretamente, a sífilis gástrica apresenta bom prognóstico e pode ser tratada de maneira conservadora por meio da antibioticoterapia. Dessa forma, é possível que procedimentos agressivos para a retirada da lesão sejam evitados, o que resulta em uma melhor qualidade de vida para os pacientes e menor lotação dos sistemas de saúde

Adoption of NFV and machine learning to detect and mitigate anomalies in software-defined networks

Uma rede de computadores é dita resiliente quando consegue manter níveis adequados de operação mesmo frente a anomalias, minimizando prejuízos aos usuários. Este trabalho propõe uma coordenação harmônica de diferentes técnicas a fim de promover resiliência para diferentes tipos de anomalias em redes definidas por software (SDN). Em especial, propõe-se que métricas de rede sejam coletadas e agrupadas em perfis, e cada perfil tenha um conjunto de ações que trate os problemas encontrados usando aprendizagem de máquina, virtualização de funções de rede (NFV) e controlador SDN. São abordadas anomalias tipicamente maliciosas, como ataques de negação de serviço, mas também benignas, como balanceamento de tráfego legítimo.A computer network is said to be resilient when it is able to keep appropriate levels of operation even against anomalies, minimising damage to users. This work proposes a harmonic coordination of different techniques in order to promote resilience for different kinds of anomalies in software-defined networks (SDN). In particular, it is proposed the gathering of network metrics and their grouping into profiles, each one having a set of actions to handle the encountered problems using machine learning, network functions virtualisation (NFV) and the SDN controller. Typically malicious anomalies are addressed, like denial of service attacks, as well as benign anomalies, such as legit traffic load balancing

Adoption of NFV and machine learning to detect and mitigate anomalies in software-defined networks

Uma rede de computadores é dita resiliente quando consegue manter níveis adequados de operação mesmo frente a anomalias, minimizando prejuízos aos usuários. Este trabalho propõe uma coordenação harmônica de diferentes técnicas a fim de promover resiliência para diferentes tipos de anomalias em redes definidas por software (SDN). Em especial, propõe-se que métricas de rede sejam coletadas e agrupadas em perfis, e cada perfil tenha um conjunto de ações que trate os problemas encontrados usando aprendizagem de máquina, virtualização de funções de rede (NFV) e controlador SDN. São abordadas anomalias tipicamente maliciosas, como ataques de negação de serviço, mas também benignas, como balanceamento de tráfego legítimo.A computer network is said to be resilient when it is able to keep appropriate levels of operation even against anomalies, minimising damage to users. This work proposes a harmonic coordination of different techniques in order to promote resilience for different kinds of anomalies in software-defined networks (SDN). In particular, it is proposed the gathering of network metrics and their grouping into profiles, each one having a set of actions to handle the encountered problems using machine learning, network functions virtualisation (NFV) and the SDN controller. Typically malicious anomalies are addressed, like denial of service attacks, as well as benign anomalies, such as legit traffic load balancing