Mobile technology has gone a long way since the days when devices were produced unchecked, had a small number of capabilities and were of questionable quality. Such current and future devices are expected to revolutionize health care as we know it through mobile health. Not just enabling patients in remote places to get the health care they need, but also enabling everybody to get some treatments at home without visiting the physician's office. This could allow the early release of specific patients from the hospital when the treatment and rehabilitation could be done at home under the remote care of the physician or practitioner while the data is being captured and included in the patient's medical records. This may also revolutionize the existing overall model of the interaction of patients with their physicians and other health care practitioners. In this session, the audience and the panelists will discuss the promises and the challenges of this emerging field.

Cybersecurity is one of the key risks for any business today. Growing attack surface includes amateur threats, such as phishing, and sophisticated distributed denial of service attacks. Prevention is nearly impossible. Given enough time attackers will get in: cost of attack is low and automated probing will find a weakness. Advanced persistent threats show hackers are patient. Defense depends on security analysts who are rare, without training, and with high turnover. AI/ML can help with detection of threat signatures across the corporation and with advisory to security analysts. AI/ML can drive down response times from (hundreds of) hours to seconds and scale analyst effectiveness from 1 or 2 incidents to 1000s. With adequate knowledgebase, it can preserve corporate knowledge and transfer response learnt by AI to new analysts. To advance the adoption of AI/ML applied to cybersecurity, HPE is partnering with IEEE, SEI, and universities around the world. We are defining an annual grand challenge in applying AI/ML to cybersecurity, comprised of: 1) arena/rodeo to operate the challenge; 2) standards for operational metrics (preventing attacks; ensuring AI performs as expected); training data; workflows integrating AI into current processes; and human-machine teaming model; 3) knowledgebase for network traffic capture; policy for AI/ML configuration; AI strategies; execution environment; and capturing security analyst knowledge. Over years, grand challenge will result in a repository of training data, sharing data logs and attack details. As a result it will increase adoption of AI/ML in cybersecurity and reduce attacks.

The modern revolution in Information Technology (IT) has allowed us to interact in advanced ways with a plethora of technological artefacts. This has led to new shapes of user interfaces (UIs) and styles of interaction. Today's UIs range from simple mobile input devices with touchscreens and clickable symbols, to home appliances with physical buttons and smart displays, to immersive environments with augmented and virtual reality. The general feeling is that such an increased interactivity is a positive feature. However, differently from the majority of web and software tools, consumer electronics products are sold for a large part based on the functionalities they offer, and an excess of functionalities is likely to lead to less usable products. In addition, consumer electronics products are becoming more and more networked and part of a large ecosystem of connected devices, and today many of the most serious usability problems are caused by the ecosystem as a whole, not by the UIs of the individual products. Since the majority of end-users of consumer electronics products simply want their tasks done in the best way, a possibility to make such products more usable is the transfer of (part of) the task execution workload from the user to the system, as it is done in so-called smart systems. Then, the possible gain in usability needs to be precisely evaluated. Unfortunately, traditional user evaluation techniques from the Human-Computer Interaction (HCI) field fail to properly measure the usability of consumer electronics products, for which it would be required to observe the end-users conducting their own activities with the products for extended time frames. This is very expensive in terms of the time required to collect the data, strongly limiting the number of user tests that can be performed and, consequently, reducing the reliability of the evaluation techniques. To mitigate the above issues, it is becoming common practice to capture the interactions with UIs during daily use and save them into log files for later analysis using dedicated log studies. In this talk, after having introduced an HCI perspective over consumer electronics, we analyze the transition that is taking place in the HCI field towards the use of log studies in order to build frameworks for capturing actual user behavior (and not recalled behaviors or subjective impressions) and, consequently, effectively evaluating the usability of any kind of modern interactive device.

In recent years machine learning (ML) and Artificial Intelligence (AI) methods have begun to play a more and more enabling role in the sciences and in industry. In particular, the advent of large and/or complex data corpora has given rise to new technological challenges and possibilities. The talk will connect two topics (1) explainable AI (XAI) and (2) ML applications in sciences (e.g. Medicine and Quantum Chemistry) for gaining new insight. Specifically I will first introduce XAI methods (such as LRP) that are now readily available and allow for an understanding of the inner workings of nonlinear ML methods ranging from kernel methods to deep learning methods including LSTMs. In particular XAI allows to unmask clever Hans predictors. Then, ML for Quantum Chemistry is discussed, showing that ML methods can lead to highly useful predictors of quantum mechanical properties of molecules (and materials) reaching quantum chemical accuracies both across chemical compound space and in molecular dynamics simulations. Notably, these ML models do not only speed up computation by several orders of magnitude but can give rise to novel chemical insight. Finally, I will analyse morpholocal and molecular data for cancer diagnosis; also here highly interesting novel insights can be obtained. Note that while XAI is used for gaining a better understanding in the sciences, the introduced XAI techniques are readily useful in other application domains and industry as well.

With the promise of making life easier, more convenient and fun, consumers are turning to more and more devices to simplify their lives. And yet, consumer technology is really just a mosaic of badly integrated applications. At TELUS, putting customers first isn't just about ease of use. Instead, incorporating security and privacy from the onset, or by design, is an absolute necessity to safeguarding our customers' lives.

In the consumer goods industry, manufacturers are generally divided into "white goods" (particularly household appliances), "brown goods" (consumer electronics),"gray goods" (Information and communication technology) and in Germany also in so-called "red goods" (thermal technology). These companies are geared towards the specific needs of their end users (B2C), however, they also need to consider the specific interests of their retailers (B2B). Trendsetting product innovations with great application benefits and tailor-made functionality are topics that help them penetrate the market. The complexity management plays an important role in this process. Only those who rapidly build product and production architectures in line with the market needs and establish at the same time an agile organization can withstand the high pressure of global competition.

We are facing rapid development in every field of technology, yet, the gender issue remains unresolved. Do we make a mountain out of a molehill, or we need to take the matter more seriously? How organizations like IEEE, ACM, or VDE can help? Does it require change at every level of the society, or the engineers can provide a unique solution that will set a rule?

Be active part of this session, and help us find a point of view that is revolutionary.

This tutorial presents a way to identify the principle behind a solution. Through a process of clarifying the problem-solution relationship in technical writing, a better logic for the paper is built. A solid logic directly benefits the author(s) by making reviewers understand the contribution of the paper easily, leading to a higher acceptance rate. It is emphasized that the earlier mentioned clarification process to establish a solid logic often unveils a more general principle behind a solution, which may open a door to another research. Some examples to convince the audience support the value of technical writing.

Optical imaging is unequivocally the most versatile and widely used visualization modality in the life sciences. Yet it has been significantly limited by photon scattering, which complicates the visualization of tissue beyond a few hundred microns. For the past few years, there has been an emergence of powerful new optical and optoacoustic imaging methods that offer high resolution imaging beyond the penetration limits of microscopic methods. The talk discusses progress in multi-spectral opto-acoustic tomography (MSOT) and mesoscopy (MSOM) that bring unprecedented optical imaging performance in visualizing anatomical, physiological and molecular biomarkers. Advances in light technology, detection methods and algorithms allow for highly-performing visualization in biology and medicine through several millimetres to centimetres of tissue and real-time imaging. The talk demonstrates implementations in the time and frequency domain, showcase how it is possible to accurately solve fluence and spectral coloring issues for yielding quantitative measurements of tissue oxygenation and hypoxia and demonstrate quantitative in-vivo measurements of inflammation, metabolism, angiogenesis in label free mode. In parallel, progress with clinical systems and the complementarity with ultrasound imaging, fluorescence molecular imaging and other modalities is discussed. Finally the talk offers insights into new miniaturized detection methods based on ultrasound detection using optical fibers, which could be used for minimally invasive applications.

ABSTRACT Patient care is changing rapidly from a generic to a much more personalized approach. In addition, healthcare systems are shifting their focus from diagnosis to early disease interception and even disease prevention. Such a personalized and preventative approach to healthcare requires disruptive technology solutions to closely monitor one's health.

This talk will discuss the trends in health care and life science technologies that enable early diagnosis, disease prevention and effective personalized therapies. Examples of such technologies are high-precision medical-quality wearables and connected health sensors, and systems to integrate complex lab analytics and diagnostics into a consumer-friendly package.

Bio:

Dr. Peumans holds a Ph.D. in electrical engineering from Princeton University, and a bachelor's and master's degree from the Katholieke Universiteit Leuven. Prior to joining imec, Dr. Peumans was a professor of electrical engineering at Stanford University. He is the recipient of an NSF CAREER award and a Belgian-American Educational Foundation honorary fellowship. He currently leads imec's life science technologies program.

We are leaving the futuristic novels lives. There is a rumors that Elon Musk based some of his visions based on his favorite SciFi books. Join this panel and let us know about your visions and favorite novels.

Robots can be very useful to restore movement abilities of upper and lower limbs. First, they can promote neurorehabilitation as training devices after neurological injuries such as spinal cord injury (SCI), traumatic brain injury and stroke. Second, they can be used as assistive devices to support patients or elders with gait impairments in daily life situations. However, current mechatronic solutions are still too bulky and their sensory technologies and control strategies are still too primitive to allow the correct motion intention. Therefore, they do not provide effective assist-as-needed support and are too inconvenient to use. In this talk I will present new patient-cooperative controllers, intention detection strategies and virtual reality technologies that will improve the rehabilitation of arm movements and gait. I will also present the Cybathlon, which is a new kind of Olympics-style championship, where people with physical disabilities compete against each other at tasks of daily life, with the aid of robotic technologies. The next Cybathlon will take place in Zurich, on May 2nd and 3rd 2020.

Key point of each home-automation is to be controlled by a browser. Any browser. Plus - three levels: Level 1. Connect sensors to computer Level 2. Connect multiple computers to aggregating server Level 3. Present data to end user via browser This tutorial will talk about ways to achieve these three levels. Player 1 - ready to participate? We dare you!!!

With the growing number of connected vehicles, changing customer expectations and the opportunities in creating innovation through the combination of digital information, the management and understanding of data gets more and more important. In this context, the domain specific knowledge, which is needed for standardization of data models is a key asset for a successful adaptation in the long run. The presentation will give an overview of some of the standardization activities at BMW Research in regard to vehicle signals and show examples on how it is already used within the company.

his lecture presents importance and possibilities of phase information in signal enhancement. Phase has not been given as much attention in signal enhancement as its counterpart; i.e. magnitude. This is partially because Wang and Lim experimentally showed in 1982 that except for some conditions, accurate phase does not help improve the SNR. After three decades, signal processing applications have significantly expanded and with these new applications, some of the conditions Wang and Lim did not take into consideration have come up in the spotlight. Two examples of those new applications covered in this talk are impact noise suppression and mechanical noise suppression. Consumer products that need these applications have a huge market today. Different uses of phase information in those applications are presented with audio demonstrations.

Bio:

Akihiko Sugiyama (a.k.a. Ken Sugiyama), affiliated with Yahoo! JAPAN Research, has been engaged in a wide variety of research projects in signal processing such as audio coding and interference/noise control. His team developed the world's first Silicon Audio in 1994, a precursor of iPod. He served as the Chair of Audio and Acoustic Signal Processing Technical Committee, IEEE Signal Processing Society (SPS) [2011-2012], as associate editors for several journals such as IEEE Trans. Signal Processing [1994-1996], as the Secretary and a Member at Large to the Conference Board of SPS [2010-2011], as a member of the Awards Board of SPS [2015-2017], as the Chair of Japan Chapter of SPS [2010-2011], and a member of IEEE Fellow Committee. He was a Technical Program Chair for ICASSP2012. He has contributed to 17 chapters of books and is the inventor of 217 registered patents with more pending applications in the field of signal processing in Japan and overseas. He received 17 awards such as the 2002 IEICE Best Paper Award, the 2006 IEICE Achievement Award, and the 2013 Ichimura Industry Award. He has delivered 117 invited talks in 48 cities of 20 countries. He is Fellow of IEEE and IEICE, and a Distinguished Lecturer for IEEE SPS [2014-2015] and for IEEE CE (Consumer Electronics Society) [2017-2018].

In this instructor-led workshop supported by NVIDIA Deep Learning Institute (DLI), you will explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities. You'll learn the basics of deep learning by training and deploying neural networks. Among other, you will be enabled to (1) implement common deep learning workflows, such as image classification and object detection; (2) experiment with data, training parameters, network structure, and other strategies to increase performance and capability; (3) deploy neural networks to start solving real-world problems. Upon completion, you'll be able to start solving problems on your own with deep learning. Each participant is awarded an official NVIDIA DLI certificate of course completion.

Note: Apply at the registration desk when collecting your badge! You will need your own laptop for the workshop, power and internet connection will be provided.