Nayat Sánchez-Pi is the Director of the Inria Chile Research Center. We asked her about her research and about the OcéanIA project which she leads. The aim of the OcéanIA project is to develop new artificial intelligence and mathematical modeling tools to contribute to the understanding of the oceans and their role in regulating and sustaining the biosphere, and tackling the climate change. I have been working in the area of artificial intelligence and machine learning for more than 15 years now. During this time I have always had an interest in finding ways of taking the state-of-the-art of my area of research and applying it to have a direct impact on the real world.

Encoder-decoder style deep convolutional neural networks (CNN) are able to extract features directly from images, mix them with scalar inputs within a general low-dimensional latent space, and generate outputs which represent complex physical phenomenon. One challenge faced by deep learning methods is modeling large non-stationary systems whose characteristics change quickly with time for which re-training is not feasible. In this paper we present a method for adaptive tuning of the low-dimensional latent space of deep encoder-decoder style CNNs based on real-time feedback to compensate for unknown and fast distribution shifts. We demonstrate the approach for predicting the properties of a time-varying charged particle beam in a particle accelerator whose initial distribution and components (accelerating electric fields and focusing magnetic fields) are quickly changing with time and may not be measurable during operations. Our method utilizes the low-dimensional latent space basis directly to generate new outputs and therefore does not require access to new input beam distributions for re-training, which is important for large systems such as particle accelerators where input distribution measurements interrupt normal operations.

Deep Neural Network (DNN), one of the most powerful machine learning algorithms, is increasingly leveraged to overcome the bottleneck of effectively exploring and analyzing massive data to boost advanced scientific development. It is not a surprise that cloud computing providers offer the cloud-based DNN as an out-of-the-box service. Though there are some benefits from the cloud-based DNN, the interaction mechanism among two or multiple entities in the cloud inevitably induces new privacy risks. This survey presents the most recent findings of privacy attacks and defenses appeared in cloud-based neural network services. We systematically and thoroughly review privacy attacks and defenses in the pipeline of cloud-based DNN service, i.e., data manipulation, training, and prediction. In particular, a new theory, called cloud-based ML privacy game, is extracted from the recently published literature to provide a deep understanding of state-of-the-art research. Finally, the challenges and future work are presented to help researchers to continue to push forward the competitions between privacy attackers and defenders.

Most successful computer vision models transform low-level features, such as Gabor filter responses, into richer representations of intermediate or mid-level complexity for downstream visual tasks. These mid-level representations have not been explored for event cameras, although it is especially relevant to the visually sparse and often disjoint spatial information in the event stream. By making use of locally consistent intermediate representations, termed as superevents, numerous visual tasks ranging from semantic segmentation, visual tracking, depth estimation shall benefit. In essence, superevents are perceptually consistent local units that delineate parts of an object in a scene. Inspired by recent deep learning architectures, we present a novel method that employs lifetime augmentation for obtaining an event stream representation that is fed to a fully convolutional network to extract superevents. Our qualitative and quantitative experimental results on several sequences of a benchmark dataset highlights the significant potential for event-based downstream applications.

Questions of fairness, robustness, and transparency are paramount to address before deploying NLP systems. Central to these concerns is the question of reliability: Can NLP systems reliably treat different demographics fairly and function correctly in diverse and noisy environments? To address this, we argue for the need for reliability testing and contextualize it among existing work on improving accountability. We show how adversarial attacks can be reframed for this goal, via a framework for developing reliability tests. We argue that Figure 1: How DOCTOR can integrate with existing reliability testing -- with an emphasis on interdisciplinary system development workflows. Test (left) and system collaboration -- will enable rigorous development (right) take place in parallel, separate and targeted testing, and aid in the enactment teams. Reliability tests can thus be constructed independent and enforcement of industry standards. of the system development team, either by an internal "red team" or by independent auditors.

Access to informative databases is a crucial part of notable research developments. In the field of domestic audio classification, there have been significant advances in recent years. Although several audio databases exist, these can be limited in terms of the amount of information they provide, such as the exact location of the sound sources, and the associated noise levels. In this work, we detail our approach on generating an unbiased synthetic domestic audio database, consisting of sound scenes and events, emulated in both quiet and noisy environments. Data is carefully curated such that it reflects issues commonly faced in a dementia patients environment, and recreate scenarios that could occur in real-world settings. Similarly, the room impulse response generated is based on a typical one-bedroom apartment at Hebrew SeniorLife Facility. As a result, we present an 11-class database containing excerpts of clean and noisy signals at 5-seconds duration each, uniformly sampled at 16 kHz. Using our baseline model using Continues Wavelet Transform Scalograms and AlexNet, this yielded a weighted F1-score of 86.24 percent.

This paper presents a new network intrusion detection system (NIDS) based on Graph Neural Networks (GNNs). GNNs are a relatively new sub-field of deep neural networks, which have the unique ability to leverage the inherent structure of graph-based data. Training and evaluation data for NIDSs are typically represented as flow records, which can naturally be represented in a graph format. This establishes the potential and motivation for exploring GNNs for the purpose of network intrusion detection, which is the focus of this paper. E-GraphSAGE, our proposed new approach is based on the established GraphSAGE model, but provides the necessary modifications in order to support edge features for edge classification, and hence the classification of network flows into benign and attack classes. An extensive experimental evaluation based on six recent NIDS benchmark datasets shows the excellent performance of our E-GraphSAGE based NIDS in comparison with the state-of-the-art.

Ever wondered what happens to a selfie you upload on a social media site? Activists and researchers have long warned about data privacy and said that photographs uploaded on the Internet may be used to train artificial intelligence (AI) powered facial recognition tools. These AI-enabled tools (such as Clearview, AWS Rekognition, Microsoft Azure, and Face) could in turn be used by governments or other institutions to track people and even draw conclusions such as the subject's religious or political preferences. Researchers have come up with ways to dupe or spoof these AI tools from being able to recognise or even detect a selfie, using adversarial attacks – or a way to alter input data that causes a deep-learning model to make mistakes. Two of these methods were presented last week at the International Conference of Learning Representations (ICLR), a leading AI conference that was held virtually.

Fractal's Computer Vision solution for Underwriting & Claims selected as one of the top solutions to be presented at the event to be attended by more than 400 P&C Execs Fractal's Chief Practice Officer for Insurance and Technology, Sankar Narayanan, will be speaking at "Tech Showcase: Computer Vision for Underwriting & Claims". The event will bring together over 400 top executives and insiders for discussions and presentations on some of the most pressing needs and innovative solutions. The talk will feature Fractal's Image and Video Analytics (IVA) solution and demonstrate how it enables #betterdecisions for P&C Insurers. IVA is Fractal's deep-learning based solution that is used by insurers to analyze images/videos/complex documents at scale, during underwriting and claims stages. During underwriting, it is used to assess value and estimate coverage with precision and during claims it is used to assess severity and estimate settlement amount, at speed.

The relevance of the Key Information Extraction (KIE) task is increasingly important in natural language processing problems. But there are still only a few well-defined problems that serve as benchmarks for solutions in this area. To bridge this gap, we introduce two new datasets (Kleister NDA and Kleister Charity). They involve a mix of scanned and born-digital long formal English-language documents. In these datasets, an NLP system is expected to find or infer various types of entities by employing both textual and structural layout features. The Kleister Charity dataset consists of 2,788 annual financial reports of charity organizations, with 61,643 unique pages and 21,612 entities to extract. The Kleister NDA dataset has 540 Non-disclosure Agreements, with 3,229 unique pages and 2,160 entities to extract. We provide several state-of-the-art baseline systems from the KIE domain (Flair, BERT, RoBERTa, LayoutLM, LAMBERT), which show that our datasets pose a strong challenge to existing models. The best model achieved an 81.77% and an 83.57% F1-score on respectively the Kleister NDA and the Kleister Charity datasets. We share the datasets to encourage progress on more in-depth and complex information extraction tasks.