Time series clustering is the process of grouping time series with respect to their similarity or characteristics. Previous approaches usually combine a specific distance measure for time series and a standard clustering method. However, these approaches do not take the similarity of the different subsequences of each time series into account, which can be used to better compare the time series objects of the dataset. In this paper, we propose a novel technique of time series clustering based on two clustering stages. In a first step, a least squares polynomial segmentation procedure is applied to each time series, which is based on a growing window technique that returns different-length segments. Then, all the segments are projected into same dimensional space, based on the coefficients of the model that approximates the segment and a set of statistical features. After mapping, a first hierarchical clustering phase is applied to all mapped segments, returning groups of segments for each time series. These clusters are used to represent all time series in the same dimensional space, after defining another specific mapping process. In a second and final clustering stage, all the time series objects are grouped. We consider internal clustering quality to automatically adjust the main parameter of the algorithm, which is an error threshold for the segmenta- tion. The results obtained on 84 datasets from the UCR Time Series Classification Archive have been compared against two state-of-the-art methods, showing that the performance of this methodology is very promising.

Saliency methods have emerged as a popular tool to highlight features in an input deemed relevant for the prediction of a learned model. Several saliency methods have been proposed, often guided by visual appeal on image data. In this work, we propose an actionable methodology to evaluate what kinds of explanations a given method can and cannot provide. We find that reliance, solely, on visual assessment can be misleading. Through extensive experiments we show that some existing saliency methods are independent both of the model and of the data generating process. Consequently, methods that fail the proposed tests are inadequate for tasks that are sensitive to either data or model, such as, finding outliers in the data, explaining the relationship between inputs and outputs that the model learned, and debugging the model. We interpret our findings through an analogy with edge detection in images, a technique that requires neither training data nor model. Theory in the case of a linear model and a single-layer convolutional neural network supports our experimental findings.

Abstract-- Prognostics and Health Management (PHM) offers several benefits for predictive maintenance. It predicts the future behavior of a system as well as its Remaining Useful Life (RUL). This RUL is used to planned the maintenance operation to avoid the failure, the stop time and optimize the cost of the maintenance and failure. However, with the development of the industry the assets are nowadays distributed this is why the PHM needs to be developed using the new IT. In our work we propose a PHM solution based on Cyber physical system where the physical side is connected to the analyze process of the PHM which are developed in the cloud to be shared and to benefit of the cloud characteristics Keywords-- Cyber physical systems CPS, Prognostics Health Management PHM, Decision post-prognostics, cloud computing, Internet of Things.

Krystian Woznicki (KW):'Rethinking political agency in an AI-driven world' is the topic of the AMBIENT REVOLTS conference in Berlin on 8–10 November. I would therefore like to begin by asking you about the deployment of algorithms at state borders. You have noted that'in order to learn, to change daily and evolve [they] require precisely the circulations and mobilities that pass through'. This observation is part of your larger argument about how governmentality is less concerned with prohibiting movement than with facilitating it in productive ways. The role of self-learning algorithms would seem to be very significant in this context, since – like capitalism – they also hinge upon movement. When it comes to their thirst for traffic, how do you think that relationship between self-learning algorithms and capitalism? Louise Amoore (LA): Yes, I agree that the role of'self learning' or semi-supervised algorithms is of the utmost relevance in understanding how movement and circulation matters.

New research explores how people think autonomous vehicles should handle moral dilemmas. Here, people walk in front of an autonomous taxi being demonstrated in Frankfurt, Germany, last year. New research explores how people think autonomous vehicles should handle moral dilemmas. Here, people walk in front of an autonomous taxi being demonstrated in Frankfurt, Germany, last year. In the not-too-distant future, fully autonomous vehicles will drive our streets.

Scientists have developed a computer tool that can spot if somebody has filed a fake police statement - based purely on text included in the document. The tool has been rolled out across Spain to support police officers and indicate where further investigations are necessary. And, so far, it has been able to successfully identify false robbery reports with over 80 per cent accuracy. Known as VeriPol, the tool is specific to reports of robbery and can recognise patterns that are more common with false claims, such as the types of items reported stolen, finer details of incidents and descriptions of a perpetrator. The research team, which included computer science experts from Cardiff University and Charles III University of Madrid, believe the tool could save the police time and effort by complementing traditional investigative techniques, whilst also deterring people from filing fake statements in the first place.

Whenever an earthquake strikes, news reports quickly fill in certain details, such as how strong the quake was and where it was centered. That information comes from a networks of seismometers scattered across the planet. Seismometers, though, can be expensive to install and maintain over long periods, and researchers cannot place them everywhere they might like, such as in the densely built and expensive streets of an earthquake-prone city like San Francisco. Some scientists, however, are exploring a different approach, using a sensor that is already widely deployed beneath the streets of towns and cities around the world. That sensor is the common fiber-optic cable, used to carry telephone and Internet traffic.

If forced to choose, who should a self-driving car kill in an unavoidable crash? Should the passengers in the vehicle be sacrificed to save pedestrians? Or should a pedestrian be killed to save a family of four in the vehicle? To get closer to an answer - if that were ever possible - researchers from the MIT Media Lab have analysed more than 40 million responses to an experiment they launched in 2014. Their Moral Machine has revealed how attitudes differ across the world.

When you are a world-renowned pioneer in smells, it's somewhat inevitable you will end up sticking your face into peculiar places: the burned rubber tire of a Chevy lowrider, a rotting hunk of wall insulation from an abandoned home, a cupped palmful of cool water from the Detroit River. It's also inevitable that the trailing documentary crew (sent by the local gallery behind your next odor-based installation) and photographer (sent, in this case, by Engadget) will home in on this money shot, jostling ahead of and around you to capture the famous nose in intimate proximity with prosaic, occasionally distasteful, objects. Along with these very words, those images are a critical way to visualize how Sissel Tolaas, who flew to Detroit from Berlin, does the unique fieldwork that has made her a legend in the colossal yet somewhat invisible world of modern olfaction. Yet there's also no denying that the sight of this -- the sniff shot, ubiquitous in casual Google image searches of Tolaas' name -- is not only curious but also comical. The idea of placing one's grown, adult face in close communion with the fluff spilling out of a blighted house to deeply inhale its surely unhealthy molecules and have them wash over you on an emotional level... well, it's something dogs do. But how else are we, with the linguistic and visual tools at our disposal, supposed to communicate what the great Sissel Tolaas is really about to you, the reader? Anyway, Tolaas hates being shadowed by cameras this way, although she's being a terrific sport about it. On her first day in Detroit, she arrives at a former tobacco factory in Poletown.