The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world {via} creating new services and applications in a variety of sectors such as environmental monitoring, mobile-health systems, intelligent transportation systems and so on. The {information and communication technology }(ICT) sector is experiencing a significant growth in { data} traffic, driven by the widespread usage of smartphones, tablets and video streaming, along with the significant growth of sensors deployments that are anticipated in the near future. {It} is expected to outstandingly increase the growth rate of raw sensed data. In this paper, we present the CPS taxonomy {via} providing a broad overview of data collection, storage, access, processing and analysis. Compared with other survey papers, this is the first panoramic survey on big data for CPS, where our objective is to provide a panoramic summary of different CPS aspects. Furthermore, CPS {require} cybersecurity to protect {them} against malicious attacks and unauthorized intrusion, which {become} a challenge with the enormous amount of data that is continuously being generated in the network. {Thus, we also} provide an overview of the different security solutions proposed for CPS big data storage, access and analytics. We also discuss big data meeting green challenges in the contexts of CPS.

Bayesian calibration of black-box computer models offers an established framework to obtain a posterior distribution over model parameters. Traditional Bayesian calibration involves the emulation of the computer model and an additive model discrepancy term using Gaussian processes; inference is then carried out using MCMC. These choices pose computational and statistical challenges and limitations, which we overcome by proposing the use of approximate Deep Gaussian processes and variational inference techniques. The result is a practical and scalable framework for calibration, which obtains competitive performance compared to the state-of-the-art.

The problem addressed in this paper is how to calculate the amount of personnel required to ensure the activity of a home health care (HHC) center on a tactical horizon. Design of quantitative approaches for this question is challenging. The number of caregivers has to be determined for each profession in order to balance the coverage of patients in a region and the workforce cost over several months. Unknown demand in care and spatial dimensions, combination of skills to cover a care and individual trips visiting patients make the underlaying optimization problem very hard. Few studies are dedicated to staff dimensioning for HHC compared to patient to nurses assignment/sequencing and centers location problems. We propose an original two-stage approach based on integer linear stochastic programming, that exploits historical medical data. The first stage calculates (near-)optimal levels of resources for possible demand scenarios , while the second stage computes the optimal number of caregiver for each profession to meet a target coverage indicator. For decision-makers, our algorithm gives the number of employees for each category required to satisfy the demand without any recourse (overtime, external resources) with fixed probability and confidence interval. The approach has been tested on various instances built from data of the French agency of hospitalization data (ATIH).

Recent incidents of data breaches call for organizations to proactively identify cyber attacks on their systems. Darkweb/Deepweb (D2web) forums and marketplaces provide environments where hackers anonymously discuss existing vulnerabilities and commercialize malicious software to exploit those vulnerabilities. These platforms offer security practitioners a threat intelligence environment that allows to mine for patterns related to organization-targeted cyber attacks. In this paper, we describe a system (called DARKMENTION) that learns association rules correlating indicators of attacks from D2web to real-world cyber incidents. Using the learned rules, DARKMENTION generates and submits warnings to a Security Operations Center (SOC) prior to attacks. Our goal was to design a system that automatically generates enterprise-targeted warnings that are timely, actionable, accurate, and transparent. We show that DARKMENTION meets our goal. In particular, we show that it outperforms baseline systems that attempt to generate warnings of cyber attacks related to two enterprises with an average increase in F1 score of about 45% and 57%. Additionally, DARKMENTION was deployed as part of a larger system that is built under a contract with the IARPA Cyber-attack Automated Unconventional Sensor Environment (CAUSE) program. It is actively producing warnings that precede attacks by an average of 3 days.

It's the stuff of horror films: an intruder in your house, impossible to find but undeniably somewhere, watching you at your most private moments. Or perhaps it's the plot of a thriller, where you are recruited into international crime without even knowing it, at the behest of smart criminals. If the worst fears about the prevalence of weakly secured smart home gadgets materialise, those terrifying situations could become all too real. As we fill our homes with internet-enabled and smart devices, we are opening ourselves up to attacks that exploit houses themselves – and we might not even realise they are happening. Everything from washing machines to baby monitors is being hooked up to the internet by companies convinced that features such as remote control and artificial intelligence will make our lives easier and safer.

The considerable number of articles cover Machine Learning for cybersecurity and the ability to protect us from cyber attacks. Still, it's important to scrutinize how actually Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) can help in cybersecurity right now and what this hype is all about. First of all, I have to disappoint you. Unfortunately, Machine Learning will never be a silver bullet for cybersecurity compared to image recognition or natural language processing, two areas where Machine Learning is thriving. There will always be a man trying to find weaknesses in systems or ML algorithms and to bypass security mechanisms. What's worse, now hackers are able to use Machine Learning to carry out all their nefarious endeavors. Fortunately, Machine Learning can aid in solving the most common tasks including regression, prediction, and classification.

In the future AI could develop a will of its own, a will that is in conflict with ours. These were some of the final words from the late, great Stephen Hawking that were published earlier this month when he warned about the consequences of unregulated artificial intelligence. Hawking also predicted the rise of the'superhuman' who will initially rely on AI and genetics and then eventually escape earth; hopefully to do a better job on a new planet than we have done to date on ours. It will be some time before superhuman are with us, but AI certainly is and it's completely transforming the world and how humans operate within it. This is not a episode of Black Mirror, these are the times we live in.

Once again, Artificial Intelligence (AI) has become a trendy term that brings either trepi dation or inspiration. At one end of the scale, people fear that the advancement of AI will result in machines taking over our lives. On the other side, people believe that AI is the answer to everything. The first step is to assess whether AI today can take over humanity as we know it. Artificial General Intelligence is still far from being achieved – having an AI system that is able to solve all types of human-level tasks with equal proficiency is a very hard problem.

Fei-Fei Li is a world-renowned expert in the field of artificial intelligence, having risen to become head of Stanford University's AI Lab and the chief scientist for AI at Google Cloud. But when Google LLC began an internal debate last year over how to publicly discuss its AI contract with the U.S. Department of Defense, Li's decision to write a confidential memo on the issue last September might be the second-worst moment in her corporate career. The worst was when it all became public. "Avoid at ALL COSTS any mention or implication of AI," Li wrote to her colleagues. "This is red meat to the media to find all ways to damage Google."

Fei-Fei Li is a world-renowned expert in the field of artificial intelligence, having risen to become head of Stanford University's AI Lab and the chief scientist for AI at Google Cloud. But when Google LLC began an internal debate last year over how to publicly discuss its AI contract with the U.S. Department of Defense, Li's decision to write a confidential memo on the issue last September might be the second-worst moment in her corporate career. The worst was when it all became public. "Avoid at ALL COSTS any mention or implication of AI," Li wrote to her colleagues. "This is red meat to the media to find all ways to damage Google."