Process mining is a research field focused on the analysis of event data with the aim of extracting insights in processes. Applying process mining techniques on data from smart home environments has the potential to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions. Finding the right event labels to enable application of process mining techniques is however far from trivial, as simply using the triggering sensor as the label for sensor events results in uninformative models that allow for too much behavior (overgeneralizing). Refinements of sensor level event labels suggested by domain experts have shown to enable discovery of more precise and insightful process models. However, there exist no automated approach to generate refinements of event labels in the context of process mining. In this paper we propose a framework for automated generation of label refinements based on the time attribute of events. We show on a case study with real life smart home event data that behaviorally more specific, and therefore more insightful, process models can be found by using automatically generated refined labels in process discovery.

As Torrance observes: '[c]reativity defies precise definition... even if we had a precise conception of creativity, I am certain we would have difficulty putting it into words' [15, p. 43]. Many other authors have expressed similar difficulties [7, 10, 16]. In their review of research into human creativity, Hennessey and Amabile ask a significant follow-on question: 'Even if this mysterious phenomenon can be isolated, quantified, and dissected, why bother? Wouldn't it make more sense to revel in the mystery and wonder of it all?' [11, p. 570] Two answers to this question are offered by Hennessey and Amabile, both of which are identified as desirable: to gain a deeper understanding of creativity and to learn how to boost people's creativity. Creativity can and should be studied and measured scientifically, but the lack of a commonly-agreed understanding causes problems for measurement [10]. Plucker et al. make recommendations about best practice based on their own survey of the creativity literature: 'we argue that creativity researchers must (a) explicitly define what they mean by creativity, (b) avoid using scores of creativity measures as the sole definition of creativity (e.g., creativity is what creativity tests measure and creativity tests measure creativity, therefore we will use a score on a creativity test as our outcome variable), (c) discuss how the definition they are using is similar to or different from other definitions, and (d) address the question of creativity for whom and in what context.' [9, p.92] In short, we need to specify and justify the standards that we use to judge creativity. A more objective and well-articulated account of how creativity is manifested enables researchers to make a worthwhile contribution [8-10]. Particularly, in research we would like to focus on what processes and concepts relevant to creativity are'sufficiently important to warrant study' [17, p. 15], based on an accumulation of the body of work on creativity to date [17].

The session is comprised of a panel discussion with leading researchers debating the current scientific trends in AI and its applications. That is followed by a broader discussion that dives into how the developments in AI affect our lives and society. The Heidelberg Laureate Forum Foundation (HLFF) is driven to foster the opportunity for progressive discourse, and the Hot Topic session is a crucial component of that goal. Today, AI is no longer a brash, cryptic concept taken directly from the pages of science fiction. The developments owed to the technology based on AI have altered what we thought possible and has done so in a much quicker fashion than was predicted.

Advances in machine learning and mobile robotics mean that robots could do your job better than you. That's led to some radical predictions of mass unemployment, much more leisure or a work free future. Queen Elizabeth I denied a patent for a knitting machine over fears it would create unemployment, Ricardo thought technology would lower wages and Keynes famously predicted a 15 hour working week by 2030. Understanding why these beliefs proved to be wrong gives us important insights into why similar claims about robotisation might be incorrect. But automation could nevertheless have sizeable distributional implications and ramifications well beyond the industries in which it's deployed.

CIA Director John Brennan participates in a session at the third annual Intelligence and National Security Summit in Washington, D.C., on Sept. 8, 2016. WASHINGTON -- CIA Director John Brennan warned on Sunday that Russia has "exceptionally capable and sophisticated" computer capabilities and that the U.S. must be on guard. "I think that we have to be very, very wary of what the Russians might be trying to do in terms of collecting information in a cyber realm, as well as what they might want to do with it," he told CBS' "Face the Nation" on the 15th anniversary of the Sept. 11 attacks. On the terrorism threat, Brennan said the U.S. government is much better now at sharing information. He praised Saudi Arabia as "a good example of how foreign intelligence services can work against these terrorist organizations."

During the christmas vacation of 2015, I got a motivational boost again and decided try out Kaggle. So I spent quite some time experimenting with various algorithms for their Homesite Quote Conversion, Otto Group Product Classification and Bike Sharing Demand contests. The main takeaway from this was the experience of iteratively improving the results by experimenting with the algorithms and the data. I learned to trust my logic when doing machine learning. If tweaking a parameter or engineering a new feature seems like a good idea logically, it's quite likely that it actually will help.

A few years ago, Michael Osborne and a colleague at Oxford University caused a stir when they published research suggesting 47 percent of jobs in the U.S. are at risk of being replaced with robot labor. Subsequent studies suggest closer to 10 percent of jobs in developed countries could be automated, which is only marginally less worrying for workers. What isn't in doubt is that advances in algorithms and robotics will transform the workplace, with both rote manual labor and higher-level cognitive tasks soon to be performed by machines. Robotics companies, keen to avoid the insinuation their products take jobs from humans, talk a lot these days about "co-bots" (collaborative robots). Humans and robots will increasingly collaborate, they say, with humans freed to do more productive, fulfilling tasks thanks to machines taking on the grunt work.

A variety of technologies have emerged in the last decade that make it easier and cheaper than ever before to make representations of everyday mobile embodiment. Increasing numbers of people are quantifying and self-tracking their everyday lives recording behavioural, biological and environmental data (Beer, 2016; Neff & Nafus, 2016) using a variety of technologies, for example: • lightweight wearable cameras such as the GoPro allowing users to record footage of their most banal everyday activities; • devices such as the Fitbit and Apple Watch bringing continuous physiological monitoring out of the medical realm and into mainstream culture; • apps like Strava allowing people to quantify their cycling, running and walking activities; • lightweight devices for measuring brain activity (EEG) and stimulation (EDA) becoming sufficiently robust and discreet to be used in non-lab environments. None of the underlying technologies are novel, but as they are made accessible in cheaper and more user-friendly packages, new techniques and sources of data are becoming more readily available for geographical analysis. Engagement with these technologies has created a rapidly expanding area of investigation within geography. The emergence of the quantified-self poses both opportunities and dilemmas for geographical thought. We wish to move past simplistic protests that dismiss such technology as offering another take on Haraway's (1988) 'god trick', presenting partial, and highly situated data as objective truth. Instead, this session will build on the potential identified by Delyser and Sui (2013) to take more inventive approaches toward mobile methods. The focus will be on how these technologies can be engaged with by critical geographers to bring new perspectives to their analysis of everyday embodiment.

That's the idea behind Local Motors' latest vehicle, which features a 3D-printed body, a windshield video screen and no steering wheel. Meanwhile, OX launched the world's first all-terrain flat-pack truck, which can be quickly shipped anywhere in the world. Cannae Corporation announced plans to test an "impossible" zero-exhaust microwave thruster that could revolutionize space travel. And Electra Meccanica launched SOLO, an affordable three-wheeled electric vehicle for one. In energy news, this week Sonos Motors announced plans to debut a solar-powered car within two years, and Soel Yachts unveiled a sun-powered motorboat that glides through the water without making a sound.

Follow Google's TTS system can mimic the human voice. Google has announced that DeepMind can mimic the human voice. While this feat might not seem that impressive given the fact that AI-driven voice synthesis has been around for some time, Google's engine can mimic any type of human voice without little to no interference or input from the user. We can all relate to Windows' XP TTS engine, where the user could instruct the system to read aloud chunks of texts either in a female or male voice. Google-owned artificial intelligence company DeepMind presented a deep neural network that generates amazingly human-like speech.