For the first time in 2021, a major Machine Learning conference will have a track devoted to disaster response. The 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021) has a track on "NLP Applications for Emergency Situations and Crisis Management". I am delighted to be the Senior Area Chair for this track! I've worked in machine learning and disaster response for 20 years and I'm glad that more people are now looking into how machine learning can help people at the most critical times. The majority of what goes into a paper on machine learning for disaster response should be the same as any other paper in applied science: reproducible methods that clearly advance our knowledge of how to deploy and evaluate machine learning technologies. However, there are aspects of disaster response that make some aspects of the science more important and a few aspects that are unique to disaster response.

Relevant research has been standing out in the computing community aiming to develop computational models capable of predicting occurrence of crimes, analyzing contexts of crimes, extracting profiles of individuals linked to crimes, and analyzing crimes according to time. This, due to the social impact and also the complex origin of the data, thus showing itself as an interesting computational challenge. This research presents a computational model for the prediction of homicide crimes, based on tabular data of crimes registered in the city of Bel\'em - Par\'a, Brazil. Statistical tests were performed with 8 different classification methods, both Random Forest, Logistic Regression, and Neural Network presented best results, AUC ~ 0.8. Results considered as a baseline for the proposed problem.

This paper introduces a paradigm of smartphone application based disease diagnostics that may completely revolutionise the way healthcare services are being provided. Although primarily aimed to assist the problems in rendering the healthcare services during the coronavirus pandemic, the model can also be extended to identify the exact disease that the patient is caught with from a broad spectrum of pulmonary diseases. The app inputs Chest X-Ray images captured from the mobile camera which is then relayed to the AI architecture in a cloud platform, and diagnoses the disease with state of the art accuracy. Doctors with a smartphone can leverage the application to save the considerable time that standard COVID-19 tests take for preliminary diagnosis. The scarcity of training data and class imbalance issues were effectively tackled in our approach by the use of Data Augmentation Generative Adversarial Network (DAGAN) and model architecture based as a Convolutional Siamese Network with attention mechanism. The backend model was tested for robustness us-ing publicly available datasets under two different classification scenarios(Binary/Multiclass) with minimal and noisy data. The model achieved pinnacle testing accuracy of 99.30% and 98.40% on the two respective scenarios, making it completely reliable for its users. On top of that a semi-live training scenario was introduced, which helps improve the app performance over time as data accumulates. Overall, the problems of generalisability of complex models and data inefficiency is tackled through the model architecture. The app based setting with semi live training helps in ease of access to reliable healthcare in the society, as well as help ineffective research of rare diseases in a minimal data setting.

Nonparametric supervised learning algorithms represent a succinct class of supervised learning algorithms where the learning parameters are highly flexible and whose values are directly dependent on the size of the training data. In this paper, we comparatively study the properties of four nonparametric algorithms, K-Nearest Neighbours (KNNs), Support Vector Machines (SVMs), Decision trees and Random forests. The supervised learning task is a regression estimate of the time lapse in medical insurance reimbursement. Our study is concerned precisely with how well each of the nonparametric regression models fits the training data. We quantify the goodness of fit using the R-squared metric. The results are presented with a focus on the effect of the size of the training data, the feature space dimension and hyperparameter optimization. The findings suggest k-NN's and SVM's algorithms as better models in predicting welldefined output labels (i.e,

Quantum computers can be used for supervised learning by treating parametrised quantum circuits as models that map data inputs to predictions. While a lot of work has been done to investigate practical implications of this approach, many important theoretical properties of these models remain unknown. Here we investigate how the strategy with which data is encoded into the model influences the expressive power of parametrised quantum circuits as function approximators. We show that one can naturally write a quantum model as a partial Fourier series in the data, where the accessible frequencies are determined by the nature of the data encoding gates in the circuit. By repeating simple data encoding gates multiple times, quantum models can access increasingly rich frequency spectra. We show that there exist quantum models which can realise all possible sets of Fourier coefficients, and therefore, if the accessible frequency spectrum is asymptotically rich enough, such models are universal function approximators.

This work investigates fault-resilient federated learning when the data samples are non-uniformly distributed across workers, and the number of faulty workers is unknown to the central server. In the presence of adversarially faulty workers who may strategically corrupt datasets, the local messages exchanged (e.g., local gradients and/or local model parameters) can be unreliable, and thus the vanilla stochastic gradient descent (SGD) algorithm is not guaranteed to converge. Recently developed algorithms improve upon vanilla SGD by providing robustness to faulty workers at the price of slowing down convergence. To remedy this limitation, the present work introduces a fault-resilient proximal gradient (FRPG) algorithm that relies on Nesterov's acceleration technique. To reduce the communication overhead of FRPG, a local (L) FRPG algorithm is also developed to allow for intermittent server-workers parameter exchanges. For strongly convex loss functions, FRPG and LFRPG have provably faster convergence rates than a benchmark robust stochastic aggregation algorithm. Moreover, LFRPG converges faster than FRPG while using the same communication rounds. Numerical tests performed on various real datasets confirm the accelerated convergence of FRPG and LFRPG over the robust stochastic aggregation benchmark and competing alternatives.

Rarely has a crisis accelerated the adoption of a technology in the manner that is occurring today with AI in the food industry. The business of selling food to consumers is being disrupted to a degree not since the last pandemic, over 100 years ago. It is increasingly apparent that our food system was ill prepared ('anti-fragile') for this Covid-19 induced crisis. With restaurants shuttered, a dramatic return to home cooking, a re-ignition in the meal-kit movement, shut-downs of meat factories and office canteens, and explosion of home delivery it may seem as though the world will never be the same again. This too, of course, will pass, but instead of being a 6 month blip, the continued deconstruction and automation of the food supply process makes it clear that we are entering a new norm, and that returning to the world as we knew it won't be possible.

When you place data in the hands of smart companies, wonderful things happen. This is the birthplace of AI for good. We have entered a world where the available data is bigger than the human brain. Whether that data is leveraged in a car that drives itself, to direct your online shopping behaviour, or to recommend where PPE equipment is needed, it's the next step in data's evolution. AI is prevalent in business today, as it takes data analysis to the next level and helps analyze profound data-driven problems.

WNS (Holdings) Limited (NYSE: WNS), is a leading Business Process Management (BPM) company. We combine our deep industry knowledge with technology and analytics expertise to co-create innovative, digital-led transformational solutions with clients across 10 industries. We enable businesses in Travel, Insurance, Banking and Financial Services, Manufacturing, Retail and Consumer Packaged Goods, Shipping and Logistics, Healthcare, and Utilities to re-imagine their digital future and transform their outcomes with operational excellence. We deliver an entire spectrum of BPM services in finance and accounting, procurement, customer interaction services and human resources leveraging collaborative models that are tailored to address the unique business challenges of each client. We co-create and execute the future vision of 400 clients with the help of our 44,000 employees.

Last October, a couple of days before joining Stanford University as the international policy director at the Cyber Policy Center, Marietje Schaake, a former member of the European Parliament, spoke alongside Eric Schmidt, the ex-C.E.O. of Google, to a large audience of tech employees and academics. It was the keynote event at a conference hosted by the newly launched Stanford Institute for Human-Centered Artificial Intelligence (H.A.I.), at which Schaake would also have a co-appointment. Beneath the scalloped panels of a blond wood ceiling, people sipped coffee and typed on laptops in the plush chairs of a new auditorium at the heart of campus. Schmidt spoke first, striking expected notes. He said that artificial intelligence would power "extraordinary gains" in the next five years and stressed just how central Google--which had helped fund H.A.I.--would be to those advances. He acknowledged that China's use of A.I. for surveillance, especially in the Xinjiang region, was concerning.