A new wave of technologies is changing every aspect of human life–from online shopping and scheduling a ride with Uber to completing wells and managing reservoirs. The stuff of science fiction is becoming science fact. The HR Discussion team, via this article, aims to inform young professionals (YPs) about three trends that will change the way we will work in the future. These three trends are introduced in the form of three key questions for YPs. Everything– and everyone–will be measured, recorded, analyzed, and rated.

Many techniques, using statistics or artificial intelligence, exist that help classify and identify areas on satellite imagery. This includes land use characteristics such as urban spaces, agriculture lands, forests, etc. However, recreating a ground-level image and perspective using satellite imagery has only recently been developed and is now an active area of research. Such work has the potential to not only classify land more accurately but it can also provide a ground-level perspective that indicates how it differs or is like other similar classes. One pioneering technique developed in providing ground-level views from satellite images was developed by the University of California, Merced.

Autonomous Robotics, a subsidiary of listed company Thalassa, is to collaborate with Robert Gordon University to conduct research on swarm technology of autonomous underwater vehicles in the North Sea. The work is supported by the Oil & Gas Innovation Centre. The purpose of this research is to further enhance the capability of the'flying node' system and further reduce the cost and time for ocean bottom seismic surveys. The Swarm Technology research will be performed by Dr Wai-keung Fung and Mr Adham Sabra, who are with the Communications and Autonomous Systems Group within the School of Engineering, with results are expected within 12 months. Chairman Dave Grant said: "ARL are working with RGU to research and create a practical localisation system for the flying node system which will allow the flying nodes to operate in a swarm and move from their initial seabed position to a new seabed location.

There is a larger number of vehicles in the streets The number of traffic jams is rising Tons of greenhouse gases are emitted to the atmosphere The citizens' quality of life is decreasing Daniel H. Stolfi Bio-inspired Computing and Smart Mobility October 2018 1 / 73 5. Scientific and Technological Bases 6. Scientific and Technological Bases Smart Mobility Problems Smart Mobility Problems – The Challenge Long travel times Polluted cities Fuel economy Finding an available car park spot We are focused on Smart Mobility and Smart Environment Daniel H. Stolfi Bio-inspired Computing and Smart Mobility October 2018 2 / 73 7. Scientific and Technological Bases Metaheuristics Metaheuristics Daniel H. Stolfi Bio-inspired Computing and Smart Mobility October 2018 3 / 73 8. Scientific and Technological Bases Microsimulation Traffic Simulators Can be categorized as: Macroscopic Mesoscopic Microscopic After a deep study we selected SUMO (Simulation of Urban MObility) http://dlr.de/ts/sumo/ Daniel H. Stolfi Bio-inspired Computing and Smart Mobility October 2018 4 / 73 9. Scientific and Technological Bases SUMO: Simulation of Urban MObility SUMO Open Source (German Aerospace Center - DLR) Several car following models Maps can be imported from OpenStreetMap Lots of data can be retrieved after the simulation Externally controlled by TraCI Daniel H. Stolfi Bio-inspired Computing and Smart Mobility October 2018 5 / 73 10. Scientific and Technological Bases SUMO: Simulation of Urban MObility Building Mobility Scenarios with SUMO 1 Download the map from OpenStreetMap 2 Clean the irrelevant elements using JOSM 3 Import the city model using NETCONVERT 4 Define its routes using DUAROUTER We call it the experts' solution (computed by SUMO's DUAROUTER) Daniel H. Stolfi Bio-inspired Computing and Smart Mobility October 2018 6 / 73 11. Scientific and Technological Bases Incomplete Maps and Data Incomplete Maps and Data PROBLEM: How reliable are the simulation scenarios? OUR PROPOSAL: Maps imported from OpenStreetMap Vehicular flows calculated according to data published by local councils Flow Generator Algorithm (FGA)* * Original contribution of this PhD thesis Daniel H. Stolfi Bio-inspired Computing and Smart Mobility October 2018 7 / 73 12. Scientific and Technological Bases Incomplete Maps and Data Flow Generator Algorithm (FGA) Contributions: Flow Generator Algorithm Route Generator Set of mobility scenarios Daniel H. Stolfi Bio-inspired Computing and Smart Mobility October 2018 8 / 73 13.

Microearthquakes -- low-intensity earthquakes that register 2.0 or less magnitude on the moment magnitude scale -- rarely cause property damage. And as a result of background noise, small events, and false positives, they're not always picked up by seismic monitoring systems. A possible solution is described in a new paper from the Department of Geophysics at Stanford University, where scientists have developed an AI system -- dubbed Cnn-Rnn Earthquake Detector, or CRED -- that can isolate and identify a range of seismic signals from historical and continuous data. It builds on the work of Harvard and Google, which in August created an AI model capable of predicting the location of aftershocks up to one year after a major earthquake. The researchers' system consists of neural network layers -- interconnected processing nodes that loosely mimic the function of neurons in the brain -- of two types: convolutional neural networks and recurrent neural networks.

Text analytics based on supervised machine learning classifiers has shown great promise in a multitude of domains, but has yet to be applied to Seismology. We test various standard models (Naive Bayes, k-Nearest Neighbors, Support Vector Machines, and Random Forests) on a seismological corpus of 100 articles related to the topic of precursory accelerating seismicity, spanning from 1988 to 2010. This corpus was labelled in Mignan (2011) with the precursor whether explained by critical processes (i.e., cascade triggering) or by other processes (such as signature of main fault loading). We investigate rather the classification process can be automatized to help analyze larger corpora in order to better understand trends in earthquake predictability research. We find that the Naive Bayes model performs best, in agreement with the machine learning literature for the case of small datasets, with cross-validation accuracies of 86% for binary classification. For a refined multiclass classification ('non-critical process' < 'agnostic' < 'critical process assumed' < 'critical process demonstrated'), we obtain up to 78% accuracy. Prediction on a dozen of articles published since 2011 shows however a weak generalization with a F1-score of 60%, only slightly better than a random classifier, which can be explained by a change of authorship and use of different terminologies. Yet, the model shows F1-scores greater than 80% for the two multiclass extremes ('non-critical process' versus 'critical process demonstrated') while it falls to random classifier results (around 25%) for papers labelled 'agnostic' or 'critical process assumed'. Those results are encouraging in view of the small size of the corpus and of the high degree of abstraction of the labelling. Domain knowledge engineering remains essential but can be made transparent by an investigation of Naive Bayes keyword posterior probabilities.

Microsoft is embarking on a five-year, $40m programme to explore how artificial intelligence (AI) can be used to bolster the response of non-governmental organisations (NGOs) to humanitarian disasters. The AI for Humanitarian Action programme will focus on using AI technologies to assist select NGOs throughout the world working on projects in four key areas. These include NGOs and humanitarian organisations involved in responding to natural disasters, child protection incidents, refugee crisis situations and human rights abuses, who Microsoft will support by offering grants and investments. "While global relief organisations scramble to respond to these events, their work by definition is often reactive and difficult to scale. We believe that technology, like artificial intelligence combined with cloud, can be a game changer, helping save more lives, alleviate suffering and restore human dignity by changing the way frontline relief organisations anticipate, predict and better target response efforts," said Microsoft president, Brad Smith, in a blog post announcing the initiative's launch.

Simulation-to-real transfer is an important strategy for making reinforcement learning practical with real robots. Successful sim-to-real transfer systems have difficulty producing policies which generalize across tasks, despite training for thousands of hours equivalent real robot time. To address this shortcoming, we present a novel approach to efficiently learning new robotic skills directly on a real robot, based on model-predictive control (MPC) and an algorithm for learning task representations. In short, we show how to reuse the simulation from the pre-training step of sim-to-real methods as a tool for foresight, allowing the sim-to-real policy adapt to unseen tasks. Rather than end-to-end learning policies for single tasks and attempting to transfer them, we first use simulation to simultaneously learn (1) a continuous parameterization (i.e. a skill embedding or latent) of task-appropriate primitive skills, and (2) a single policy for these skills which is conditioned on this representation. We then directly transfer our multi-skill policy to a real robot, and actuate the robot by choosing sequences of skill latents which actuate the policy, with each latent corresponding to a pre-learned primitive skill controller. We complete unseen tasks by choosing new sequences of skill latents to control the robot using MPC, where our MPC model is composed of the pre-trained skill policy executed in the simulation environment, run in parallel with the real robot. We discuss the background and principles of our method, detail its practical implementation, and evaluate its performance by using our method to train a real Sawyer Robot to achieve motion tasks such as drawing and block pushing.

Abstract--The extraction of geological lineaments from digital satellite data is a fundamental application in remote sensing. The location of geological lineaments such as faults and dykes are of interest for a range of applications, particularly because of their association with hydrothermal mineralization. Although a wide range of applications have utilized computer vision techniques, a standard workflow for application of these techniques to mineral exploration is lacking. We present a framework for extracting geological lineaments using computer vision techniques which is a combination of edge detection and line extraction algorithms for extracting geological lineaments using optical remote sensing data. It features ancillary computer vision techniques for reducing data dimensionality, removing noise and enhancing the expression of lineaments. We test the proposed framework on Landsat 8 data of a mineral-rich portion of the Gascoyne Province in Western Australia using different dimension reduction techniques and convolutional filters. To validate the results, the extracted lineaments are compared to our manual photointerpretation and geologically mapped structures by the Geological Survey of Western Australia (GSWA). The results show that the best correlation between our extracted geological lineaments and the GSWA geological lineament map is achieved by applying a minimum noise fraction transformation and a Laplacian filter. Application of a directional filter instead shows a stronger correlation with the output of our manual photointerpretation and known sites of hydrothermal mineralization. Hence, our framework using either filter can be used for mineral prospectivity mapping in other regions where faults are exposed and observable in optical remote sensing data. IGITAL satellite data with different spatial and spectral resolution are available for almost every locality on the Earth's land surface [1]-[5]. This enables the procurement of detailed information from surficial features and processes at different scales. Linear features are considered as one of the most important surficial features in different fields of study [6]-[8]. R. Scalzo is with the Centre for Translational Data Science, University of Sydney, Sydney, NSW 2006, Australia (email: richard.scalzo@sydney.edu.au). Linear features represent the expression of some degree of linearity of a single or diverse grouping of both natural and cultural features [9], [10].

Titled "New Digital Horizons: Connect, Create, Innovate", the second edition of the event is being jointly organised by Cellular Operators Association of India (COAI), the Department of Telecommunications (DoT) and other government departments. The aim behind IMC2018 is building ideas, sharing knowledge and best practices, forming lasting industry relationships, fostering commercial opportunities, showcasing game changing mobile technology and product trends, providing sectoral insights, industrial solutions, case studies and workshops. The biggest ICTevent in South Asia, comprising of conclave and exhibition will include ministerial and partner programs in Digital India, Smart City, emerging technologies, Make in India projects, skill harmonisation, business innovation and knowledge sharing etc. There will be a technology showcase offering a glimpse into virtual reality, connected cars, m-health, smart wearables, smart home, artificial intelligence, drones, robotics, smart energy, internet of things, block chain, bitcoin, Machine Vision, Cloud Computing Holography, among others. The three day event will cover a wide array of topics, including but not confined to emerging technologies to new digital ecosystems, m-education, digital marketing to e-health, 5G and retail.