The coordination and cooperation of all the stakeholders involved is a decisive point for the control and the resolution of problems. In the insecurity events, the resolution should refer to a plan that defines a general framework of the procedures to be undertaken and the instructions to be complied with; also, a more precise process must be defined by the actors to deal with the case represented by the particular problem of the current situation. Indeed, this process has to cope with a dynamic, unstable and unpredictable environment, due to the heterogeneity and multiplicity of stakeholders, and finally due to their possible geographical distribution. In this article, we will present the first steps of validation of a collaborative decision-making approach in the context of crisis situations such as road accidents. This approach is based on ontologies and multi-agent systems.

A new collaborative learning, called split learning, was recently introduced, aiming to protect user data privacy without revealing raw input data to a server. It collaboratively runs a deep neural network model where the model is split into two parts, one for the client and the other for the server. Therefore, the server has no direct access to raw data processed at the client. Until now, the split learning is believed to be a promising approach to protect the client's raw data; for example, the client's data was protected in healthcare image applications using 2D convolutional neural network (CNN) models. However, it is still unclear whether the split learning can be applied to other deep learning models, in particular, 1D CNN. In this paper, we examine whether split learning can be used to perform privacy-preserving training for 1D CNN models. To answer this, we first design and implement an 1D CNN model under split learning and validate its efficacy in detecting heart abnormalities using medical ECG data. We observed that the 1D CNN model under split learning can achieve the same accuracy of 98.9\% like the original (non-split) model. However, our evaluation demonstrates that split learning may fail to protect the raw data privacy on 1D CNN models. To address the observed privacy leakage in split learning, we adopt two privacy leakage mitigation techniques: 1) adding more hidden layers to the client side and 2) applying differential privacy. Although those mitigation techniques are helpful in reducing privacy leakage, they have a significant impact on model accuracy. Hence, based on those results, we conclude that split learning alone would not be sufficient to maintain the confidentiality of raw sequential data in 1D CNN models.

Managing the quality of water for present and future generations of coastal regions should be a central concern of both citizens and public officials. Remote sensing can contribute to the management and monitoring of coastal water and pollutants. Algal blooms are a coastal pollutant that is a cause of concern. Many satellite data, such as MODIS, have been used to generate water-quality products to detect the blooms such as chlorophyll a (Chl-a), a photosynthesis index called fluorescence line height (FLH), and sea surface temperature (SST). It is important to characterize the spatial and temporal variations of these water quality products by using the mathematical models of these products. However, for monitoring, pollution control boards will need nowcasts and forecasts of any pollution. Therefore, we aim to predict the future values of the MODIS Chl-a, FLH, and SST of the water. This will not be limited to one type of water but, rather, will cover different types of water varying in depth and turbidity. This is very significant because the temporal trend of Chl-a, FLH, and SST is dependent on the geospatial and water properties. For this purpose, we will decompose the time series of each pixel into several components: trend, intra-annual variations, seasonal cycle, and stochastic stationary. We explore three such time series machine learning models that can characterize the non-stationary time series data and predict future values, including the Seasonal ARIMA (Auto Regressive Integrated Moving Average) (SARIMA), regression, and neural network. The results indicate that all these methods are effective at modelling Chl-a, FLH, and SST time series and predicting the values reasonably well. However, regression and neural network are found to be the best at predicting Chl-a in all types of water (turbid and shallow). Meanwhile, the SARIMA model provides the best prediction of FLH and SST.

We propose novel dynamic multiscale graph neural networks (DMGNN) to predict 3D skeleton-based human motions. The core idea of DMGNN is to use a multiscale graph to comprehensively model the internal relations of a human body for motion feature learning. This multiscale graph is adaptive during training and dynamic across network layers. Based on this graph, we propose a multiscale graph computational unit (MGCU) to extract features at individual scales and fuse features across scales. The entire model is action-category-agnostic and follows an encoder-decoder framework. The encoder consists of a sequence of MGCUs to learn motion features. The decoder uses a proposed graph-based gate recurrent unit to generate future poses. Extensive experiments show that the proposed DMGNN outperforms state-of-the-art methods in both short and long-term predictions on the datasets of Human 3.6M and CMU Mocap. We further investigate the learned multiscale graphs for the interpretability. The codes could be downloaded from https://github.com/limaosen0/DMGNN.

The ominous spread of coronavirus has bolstered the case for such advances as telemedicine; drones; artificial intelligence/machine learning; Big Data; and more flexible regulation of health care personnel and institutions. During a social conversation via FaceTime, her grandson, a physician, realized Mom was in the early stages of septic shock. A day's delay in treatment might have proven fatal. Similar tales emerge from professional telemedicine doctors. The advantages of telemedicine for, say, a migrant worker family on a remote ranch whose child becomes ill in the wee hours.

Timnit Gebru was wary of being labelled an activist. As a young, black female computer scientist, Gebru – who was born and raised in Addis Ababa, Ethiopia, but now lives in the US – says she'd always been vocal about the lack of women and minorities in the datasets used to train algorithms. She calls them "the undersampled majority", quoting another rising star of the artificial intelligence (AI) world, Joy Buolamwini. But Gebru didn't want her advocacy to affect how she was perceived in her field. "I wanted to be known primarily as a tech researcher. I was very resistant to being pigeonholed as a black woman, doing black woman-y things."

Technology is a tool that helps us live out our God-given callings. This is one of the most important things for us to learn as we engage the topic of technology and artificial intelligence. Because we often see the tremendous power that technology has over our lives, we are tempted to treat technology as more than a tool, as something with a value similar to our own if it is powerful enough or does enough work on its own. Technology will be misused and abused by broken people just like you and me. Nowhere in Scripture is a tool or a technology condemned for being evil.

Reporters Without Borders has found a radical new platform for distributing banned journalism in some of the world's most repressive countries: Minecraft. The advocacy group has opened a new virtual space on a dedicated server for the popular video game called'The Uncensored Library,' accessible to any of Minecraft's 145 million monthly players. Inspired by the neoclassical architecture of ancient Rome and Greece, the library will be filled with books containing the text of news stories that have been censored in their countries of origin. To begin with, the library will be stocked with stories from five countries that rank near the bottom of Reporters Without Borders' World Press Freedom Index, including Egypt, Mexico, Russia, Saudi Arabia, and Vietnam. The stories will be published in English and whichever language they were originally written in.

COVID-19 officially became a global pandemic on Wednesday. As public health officials and governments respond; businesses brace for losses; and events like trade shows, SXSW, and Google's I/O shutter around the world, the disease is also impacting scientific conferences. Ironically, a coronavirus conference got canceled this week, and on Tuesday the International Conference on Learning Representations (ICLR), one of the fastest-growing machine learning conferences in the world, shared that it will now be a virtual event held entirely online. Papers will be presented in prerecorded five-minute videos with a slide deck, while researchers invited to make longer presentations can submit 15-minute videos. In a post about the change to an all-digital conference, organizers called the cancellation of an in-person event an "… opportunity to innovate on how to host an effective remote conference."

Players of a free-to-play game are divided into three main groups: non-paying active users, paying active users and inactive users. A State Space time series approach is then used to model the daily conversion rates between the different groups, i.e., the probability of transitioning from one group to another. This allows, not only for predictions on how these rates are to evolve, but also for a deeper understanding of the impact that in-game planning and calendar effects have. It is also used in this work for the detection of marketing and promotion campaigns about which no information is available. In particular, two different State Space formulations are considered and compared: an Autoregressive Integrated Moving Average process and an Unobserved Components approach, in both cases with a linear regression to explanatory variables. Both yield very close estimations for covariate parameters, producing forecasts with similar performances for most transition rates. While the Unobserved Components approach is more robust and needs less human intervention in regards to model definition, it produces significantly worse forecasts for non-paying user abandonment probability. More critically, it also fails to detect a plausible marketing and promotion campaign scenario.