Podcasts about ieee international conference

Generative AI tools built on large language models are increasingly "intelligent" yet lack a baby's common sense – the ability to non-verbally generalize to novel situations without additional training. What can developmental science contribute to AI? Tech journalist and former CASBS fellow John Markoff chats with 2023-24 CASBS fellow David Moore, a developmental scientist with expertise in infant cognition, on evaluating the efforts of DARPA's Machine Common Sense program as well as prospects and concerns associated with creating AIs with common sense.DAVID MOORE: Personal website | Claremont Infant Study Center | Wikipedia page | DARPA Machine Common Sense programRelated resource:David Moore, et al. "Leveraging Developmental Psychology to Evaluate Artificial Intelligence," 2022 IEEE International Conference on Development and Learning (ICDL), Nov. 2022. DOI: 10.1109/ICDL53763.2022.9962183Recommended by David Moore:Esther Thelen and Linda B. Smith. A Dynamic Systems Approach to the Development of Cognition and Action. MIT Press, 1994.  Read John Markoff's latest book, Whole Earth: The Many Lives of Stewart Brand  (Penguin Random House, 2022)  Center for Advanced Study in the Behavioral Sciences (CASBS) at Stanford UniversityExplore CASBS: website|Bluesky|X|YouTube|LinkedIn|podcast|latest newsletter|signup|outreach​Human CenteredProducer: Mike Gaetani | Engineer & co-producer: Joe Monzel |

The IEEE International Conference on Robotics and Automation (ICRA) 2023 was held in London, UK from 29 May – 2 June. General Chair, Professor Kaspar Althoefer - a systems engineer on robotics at Queen Mary University of London - sat down with MCE's very own David Stankiewicz to discuss the conference's highlights of the day sessions, publicity and news coverage, sustainability, satellite conference centers, AI's impact on the events industry, and of course…robots!

This episode originally aired on 3/13/2020. New episodes coming soon. Learn about how speed listening to podcasts (or "podfasting") affects our emotions; the health differences between white and brown rice; and the time pi was once almost legally changed to 3.2.Speed listening's effects on emotion by Ashley Hamer (Listener question from S.P.)Rousseau, S. (2020, January 24). I Tried Listening to Podcasts at 3x and Broke My Brain. Medium; OneZero. https://onezero.medium.com/i-tried-listening-to-podcasts-at-3x-and-broke-my-brain-d8823edecb7cLiebenthal, E., Silbersweig, D. A., & Stern, E. (2016). The Language, Tone and Prosody of Emotions: Neural Substrates and Dynamics of Spoken-Word Emotion Perception. Frontiers in Neuroscience, 10. https://doi.org/10.3389/fnins.2016.00506Murray, I. R., & Arnott, J. L. (1993). Toward the simulation of emotion in synthetic speech: A review of the literature on human vocal emotion. The Journal of the Acoustical Society of America, 93(2), 1097–1108. https://doi.org/10.1121/1.405558Philippou-Hubner, D., Vlasenko, B., Bock, R., & Wendemuth, A. (2012). The Performance of the Speaking Rate Parameter in Emotion Recognition from Speech. 2012 IEEE International Conference on Multimedia and Expo. https://doi.org/10.1109/icme.2012.183Kraxenberger, M., Menninghaus, W., Roth, A., & Scharinger, M. (2018). Prosody-Based Sound-Emotion Associations in Poetry. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.01284The health differences between white and brown rice are dead even by Steffie DruckerWhelan, C. (2017, May 25). Brown Rice vs. White Rice: Which Is Better for You? Healthline; Healthline Media. https://www.healthline.com/health/food-nutrition/brown-rice-vs-white-riceAllan, P. (2020, February 19). Is Brown Rice Really That Much Healthier Than White Rice? Lifehacker. https://lifehacker.com/is-brown-rice-really-that-much-healthier-than-white-ric-1820044994Parletta, N. (2018, December 5). Rice is a major source of arsenic exposure. Cosmos Magazine. https://cosmosmagazine.com/biology/rice-is-a-major-source-of-arsenic-exposurePi Was Once Almost Legally Changed to 3.2 by Ashley Hamer: https://curiosity.com/topics/happy-pi-day-how-pi-was-almost-legally-changed-to-32-curiosityWant to learn even more? Head to discovery+ to stream from some of your favorite shows. Go to discoveryplus.com/curiosity to start your 7-day free trial today. Terms apply.

C'est un des gestes qui semble le plus universel : le sourire. Joie, contentement, bonheur… mais pas seulement, le sourire recèle une palette d'émotions beaucoup plus variée. Il y a le sourire faux-cul, la fausse joie, la colère déguisée, le symbole de la sagesse, pensons notamment à Bouddha. Adhérez à cette chaîne pour obtenir des avantages : https://www.youtube.com/channel/UCN4TCCaX-gqBNkrUqXdgGRA/join Pour soutenir financièrement la chaîne, trois choix: 1. Cliquez sur le bouton « Adhérer » sous la vidéo. 2. Patreon: https://www.patreon.com/hndl 3. UTip: https://utip.io/lhistoirenousledira Avec: Laurent Turcot, professeur en histoire à l'Université du Québec à Trois-Rivières, Canada Montage: DeadWill - https://www.youtube.com/c/DEADWILL Script: Laurent Turcot Réalisation: Laurent Turcot Musique issue du site : epidemicsound.com Abonnez-vous à ma chaine: https://www.youtube.com/c/LHistoirenousledira Facebook: https://www.facebook.com/histoirenousledira Instagram: https://www.instagram.com/laurentturcot Les vidéos sont utilisées à des fins éducatives selon l'article 107 du Copyright Act de 1976 sur le Fair-Use. Pour aller plus loin: Colin Jones, The Smile Revolution in Eighteenth-Century Paris, Oxford, Oxford University Press, 2014. Angus Trumble, A Brief History of the Smile, Basic Books, 2004. Georges Minois, Histoire du rire et de la dérision, Paris, Fayard, 2000. Jacques Berlioz, « Le rire. Pourquoi riait-on ? », L'Histoire, no 409, mars 2015. Sabine Melchior-Bonnet, Le rire des femmes. Une histoire de pouvoir, Paris, PUF, 2021, p. 19-23. André Wénin, « Abraham, Sarah et Agar dans le récit de la Genèse. Approche narrative et interprétation », Transversalités, 2017/2 (n° 141), p. 157-172. Stanis Perez, La santé de Louis XIV, une biohistoire du Roi-Soleil, Seyssel, Champ Vallon, 2007. Jean-Jacques Courtine et Claudine Haroche, Histoire du visage, exprimer et taire ses émotions (XVIe-début XIXe siècle), Paris, Payot, 1988. André Gunthert. « Le sourire photographique, ou les révolutions du portrait expressif », Le carnet de recherches d'André Gunthert, 5 avril 2017. https://imagesociale.fr/4275 Jacques Le Goff, « Rire au Moyen Age », Les Cahiers du Centre de Recherches Historiques [En ligne], 3 | 1989, mis en ligne le 13 avril 2009, consulté le 06 novembre 2021. URL : http://journals.openedition.org/ccrh/2918 ; DOI : https://doi.org/10.4000/ccrh.2918 Elise Van der Laan : « Why fashion models don't smile. Aesthetic standards and logics in the field of fashion images 1982-2011 », Amsterdam Institute for Social Science Research, 2015 et «Gender models: changing representations and intersecting roles in Dutch and Italian fashion magazines, 1982–2011 » Journal of Gender Studies, 26, 6, may, 2016, p. 1-17. J. M. Girard and D. McDuff, "Historical Heterogeneity Predicts Smiling: Evidence from Large-Scale Observational Analyses," 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), 2017, pp. 719-7 #histoire #documentaire

Our very special guest for this episode is Dr. Mani Golparvar, chief technology officer of Reconstruct Inc. Dr. Golparvar is leading the way on using AI to help construction companies remotely monitor work, inspect building sites and predict milestones. Ryo interviews Dr. Golparvar and comments on some AI news. We also take a look at some of the best upcoming AI conferences. Register for the Digital Garage/Eisai Open Network Lab program here:   https://onlab.jp/en/programs/biohealth-dementia/  Find out more about AI conferences mentioned in the show here: The IEEE International Conference on Data Mining Dec. 7-10 (online, but originally scheduled for Auckland, New Zealand) https://icdm2021.auckland.ac.nz/  The 28th International Conference on Neural Information Processing Dec. 8-12 (online, but originally scheduled for Bali, Indonesia) https://iconip2021.apnns.org/ 20th IEEE International Conference on Machine Learning and Applications Dec. 13-16 (online) https://www.icmla-conference.org/icmla21/finalprogram.pdf   

In this episode, HFIG talks to Dr. Caroline Cao, a Professor in the Department of Computer Science at IMT Atlantique in France and a Professor of Biomedical, Industrial and Human Factors Engineering at Wright State University, and Dr. Barrett Caldwell, a Professor of Industrial Engineering at Purdue University. We talked about the role of human factors in the design process, how we can educate engineering students and industry professionals about the importance of human factors, and how we should raise awareness among the public about the value of human factors in their everyday lives. Additional resources: Paper by Dr. Caldwell which discussed some of the issues mentioned in the episode: Caldwell, B. S. (2018, September). Considering the future of land grant ergonomics education. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 62, No. 1, pp. 359-363). Sage CA: Los Angeles, CA: SAGE Publications. (link) Paper on the importance of human factors in software design: Meister, D. (1992, October). Relevance of human factors to design of computer software. In [Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics (pp. 218-221). IEEE. (link) Fun fact: SIGCHI history page refers to the first CHI conference in 1982 as “Conference on Human Factors in Computing Systems” in 1982. Episode music: The Happiest Side Of Science by MusicLFiles Link: https://filmmusic.io/song/6905-the-happiest-side-of-science License: http://creativecommons.org/licenses/by/4.0/

Welcome to Episode 4 of TY Skin, where Tonaya and Yianni discuss Acne! Types of acne, how it's managed by Dermal Clinicians, Dermatologists and General Practitioners, risk factors for acne and how health professionals grade/rate acne. TY Skin acknowledges the Traditional Owners of the land on which we are meeting. We pay our respects to their Elders, past and present, and the Aboriginal Elders of other communities who may be here today. Make sure to follow us to stay up to date on our podcasts. If you enjoyed this episode, please make sure to give us a review. Types of Acne: https://banish.com/blogs/article/11-types-of-acne-and-how-to-identify-them Acne Mechanica A.K.A "Maskne" https://drive.google.com/file/d/1Mh9DJ2_Mf6EMa6LUusErrWl_Sapkid4g/view?usp=sharing ------------------------------------------------------------------ Welcome to  TY Skin. We are Tonaya and Yianni: Fourth year Dermal Clinic students interested in everything and anything skin. We are here to unveil, debunk, discuss and divulge the dermal world. If you would like to submit an anonymous question for us to answer, please use the following Google Form: https://forms.gle/agF1fvfYgBTgyjz16 Disclaimer: We do our best to research and give our clinically-thought opinions on dermal-related treatments, products and ideas. Topics discussed are subject to change and all things discussed should be taken as discourse and not advice for listeners. Ensure to do independent research and make the best choice for yourself with the help of health professionals, and qualified Dermal Clinicians/Therapists ------------------------------------------------------------------ Journal Articles for Further Research: Michael E. Bratsis. (2017). Health Wise: Acne: Myth vs. Fact. The Science Teacher, 84(4), 17. Adityan, B., Kumari, R., & Thappa, D. H. (2008). Scoring systems in acne vulgaris coring systems in acne vulgaris. Indian J Dermatol Venereol Leprol, 75(3), 323-326. https://doi.org/10.4103/0378-6323.51258 Alamdari, N., Tavakolian, K., Alhashim, M., & Fazel-Rezai, R. (2016). Detection and classification of acne lesions in acne patients: A mobile application. 2016 IEEE International Conference on Electro Information Technology (EIT). https://doi.org/10.1109/eit.2016.7535331 Fabbrocini, G., Donnarumma, M., & Vastarella, M. (2018). Skin needling in acne scars. Acne Scars, 67-73. https://doi.org/10.1201/9781315179889-8 Andrea L. Zaenglein, Arun L. Pathy, Bethanee J. Schlosser, Ali Alikhan, Hilary E. Baldwin, Diane S. Berson, Whitney P. Bowe, Emmy M. Graber, Julie C. Harper, Sewon Kang, Jonette E. Keri, James J. Leyden, Rachel V. Reynolds, Nanette B. Silverberg, Linda F. Stein Gold, Megha M. Tollefson, Jonathan S. Weiss, Nancy C. Dolan, Andrew A. Sagan, Mackenzie Stern, Kevin M. Boyer, Reva Bhushan. (2016). Guidelines of care for the management of acne vulgaris. Journal of the American Academy of Dermatology, 74(5), 945-973. https://doi.org/10.1016/j.jaad.2015.12.037 Kraft, J., & Freiman, A. (2011). Management of acne. Canadian Medical Association Journal, 183(7), E430-E435. https://doi.org/10.1503/cmaj.090374

Citation:Polanía, Luisa F., and Satyajit Gupte. "Learning Fashion Compatibility Across Apparel Categories for Outfit Recommendation." 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019. Link:https://arxiv.org/pdf/1905.03703

Let's talk about digital identity with Mei Ngan, Scientist at the National Institute of Standards and Technology (NIST). In episode 42, we explore Mei's work at NIST evaluating face recognition biometrics with the Face Recognition Vendor Test (FRVT), how accurate facial recognition actually is, and the effects of different variables on the FRVT – face masks (motivated by the pandemic), face morphing as a current FR vulnerability for identity credentials, demographic differentials, and twins – “the forgotten demographic”. "[Face recognition] technology really has come a long way, especially when you only have half the face available to do recognition with. But with that said though, there still remains certain limitations to the technology – such as being able to differentiate between identical twins, demographic differentials and extremely poor-quality photos." Mei Ngan is a scientist at the National Institute of Standards and Technology (NIST).  Her research focus includes evaluation of face recognition and tattoo recognition technologies.  Mei has authored and co-authored a number of technical publications, including the accuracy of face recognition with face masks, evaluation of face morphing detection algorithms, demographic effects in face recognition, performance of facial age and gender estimation algorithms, and publication of a seminal open tattoo database for developing tattoo recognition research, which she received the Special Contribution Award for at the 2015 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA). Mei was awarded the Department of Commerce Gold Medal Award in 2020 and was a recipient of the 2020 Women in Biometrics Award, a globally recognised award honouring innovative women in the biometrics field. Find out more about Mei's work at nist.gov/programs-projects/face-recognition-vendor-test-frvt Find the FRVT leaderboards at pages.nist.gov/frvt/html/frvt11.html (1:1) and pages.nist.gov/frvt/html/frvt1N.html (1:N). View Women in Identity's webinar with Mei exploring demographic effects in facial recognition here: https://youtu.be/Lni4Pe8dYuk We’ll be continuing this conversation on Twitter using #LTADI – join us @ubisecure!  

Learn about how speed listening to podcasts (or "podfasting") affects our emotions; the health differences between white and brown rice; and the time pi was once almost legally changed to 3.2. Speed listening’s effects on emotion by Ashley Hamer (Listener question from S.P.) Rousseau, S. (2020, January 24). I Tried Listening to Podcasts at 3x and Broke My Brain. Medium; OneZero. https://onezero.medium.com/i-tried-listening-to-podcasts-at-3x-and-broke-my-brain-d8823edecb7c  Liebenthal, E., Silbersweig, D. A., & Stern, E. (2016). The Language, Tone and Prosody of Emotions: Neural Substrates and Dynamics of Spoken-Word Emotion Perception. Frontiers in Neuroscience, 10. https://doi.org/10.3389/fnins.2016.00506  Murray, I. R., & Arnott, J. L. (1993). Toward the simulation of emotion in synthetic speech: A review of the literature on human vocal emotion. The Journal of the Acoustical Society of America, 93(2), 1097–1108. https://doi.org/10.1121/1.405558  Philippou-Hubner, D., Vlasenko, B., Bock, R., & Wendemuth, A. (2012). The Performance of the Speaking Rate Parameter in Emotion Recognition from Speech. 2012 IEEE International Conference on Multimedia and Expo. https://doi.org/10.1109/icme.2012.183  Kraxenberger, M., Menninghaus, W., Roth, A., & Scharinger, M. (2018). Prosody-Based Sound-Emotion Associations in Poetry. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.01284  The health differences between white and brown rice are dead even by Steffie Drucker Whelan, C. (2017, May 25). Brown Rice vs. White Rice: Which Is Better for You? Healthline; Healthline Media. https://www.healthline.com/health/food-nutrition/brown-rice-vs-white-rice  Allan, P. (2020, February 19). Is Brown Rice Really That Much Healthier Than White Rice? Lifehacker. https://lifehacker.com/is-brown-rice-really-that-much-healthier-than-white-ric-1820044994  ‌Parletta, N. (2018, December 5). Rice is a major source of arsenic exposure. Cosmos Magazine. https://cosmosmagazine.com/biology/rice-is-a-major-source-of-arsenic-exposure   Pi Was Once Almost Legally Changed to 3.2 by Ashley Hamer:https://curiosity.com/topics/happy-pi-day-how-pi-was-almost-legally-changed-to-32-curiosity Subscribe to Curiosity Daily to learn something new every day with Cody Gough and Ashley Hamer. You can also listen to our podcast as part of your Alexa Flash Briefing; Amazon smart speakers users, click/tap “enable” here: https://curiosity.im/podcast-flash-briefing See omnystudio.com/listener for privacy information.

Music technology PhD Candidate Tim de Reuse recommends “Unmixer: An Interface for Extracting and Remixing Loops” by Jordan Smith,Yuta Kawasaki, and Masataka Goto, published in the proceedings of ISMIR 2019. Tim and Finn interview Jordan about the origins of this project, the algorithm behind the loop extraction, the importance of repetition in music, and the creative and playful applications of Unmixer. Note: This conversation was recorded in December 2019. Techically issues with some tracks contributed to delays. Apologies for the choppy audio quality. Time Stamps [0:01:40] Project Summary[0:05:05] Demonstration of Unmixer[0:14:27] Origins of the UnMixer project [0:19:44] Factorisation algorithm [0:28:37] Computational and musical objectives for factorisation[0:36:15] The Unmixer web interface[0:41:30] 2nd Demonstration, parameters and track selection[0:49:13] What Unmixer tells us about music Show notes Recommended article:Smith, J, Kawasaki, Y, & Goto, M. (2019) Unmixer: An Interface for Extracting and Remixing Loops. Proceedings of  20th ISMIR meeting, Delft Netherlands.UnMixer website: https://unmixer.ongaaccel.jp/Project webpageInterviewee: Dr. Jordan BL Smith, Research Scientist at Tik Tok.Website, twitter Co-host: PhD Candidate Tim de Reuse, website, twitterPapers cited in the discussion:Smith, J. B., & Goto, M. (2018, April). Nonnegative tensor factorization for source separation of loops in audio. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 171-175). IEEE.Schmidhuber, J. (2009). Simple algorithmic theory of subjective beauty, novelty, surprise, interestingness, attention, curiosity, creativity, art, science, music, jokes. Journal of SICE, 48(1).Rafii, Z., & Pardo, B. (2012). Repeating pattern extraction technique (REPET): A simple method for music/voice separation. IEEE transactions on audio, speech, and language processing, 21(1), 73-84.Music sampled: Daft Punk, Random Access Memories (2013): Doing it Right (ft. Panda Bear)Martin Solveig & Dragonette, Smash (2011): Hello - Single EditMura Masa, Soundtrack To a Death (2014): I've Never Felt So GoodOther references:Madeon's Adventure MachineChocolate Rain by Tay Zonday Credits The So Strangely Podcast is produced by Finn Upham, 2020. The closing music includes a sample of Diana Deutsch's Speech-Song Illusion sound demo 1.

When reporting adverse reactions to drugs, people can choose from a plethora of different terms to describe their experience. But that makes it difficult and time-consuming for analysts to tell how similar two case safety reports are. A new method developed by UMC data scientist Lucie Gattepaille comes to the rescue.This episode is part of the Uppsala Reports Long Reads series – the most topical stories from UMC’s pharmacovigilance magazine, brought to you in audio format. Find the original article here.After the read, Uppsala Reports editor Gerard Ross interviews Lucie on her work behind the scenes and the broader implications of her research for the pharmacovigilance field. Tune in to find out:how natural language processing can help connect related drug and adverse reaction termswhat advantages the new method offers over MedDRA classificationswhich pharmacovigilance tasks could benefit from this new researchWant to know more?Lucie presented her work on vector representations for pharmacovigilance at the IEEE International Conference on Healthcare Informatics in 2019. And here’s some background reading on distributed representations of words and phrases.Join the conversation on social mediaFollow us on Twitter, Facebook or LinkedIn, and share your thoughts about the show with the hashtag #DrugSafetyMatters.Got a story to share?We’re always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!About UMCRead more about Uppsala Monitoring Centre and how we work to make medicines safer for patients.

Gudrun sprach im März 2020 mit Nicole Ludwig. Sie ist eine Kollegin am KIT am Campus Nord und gehört dem Institut für Automation und angewandte Informatik an. Sie war Mitglied des DFG Graduiertenkollegs Energiezustandsdaten Informatikmethoden zur Analyse, Erfassung und Nutzung und ist dabei, ihre Promotion abzuschließen. Im Studium wurde sie von den Themen der Ökonometrie und Statistik eingefangen und von der Freude, aus empirischen Daten verlässliche Ergebnisse ableiten zu können. Sie hat schon in ihrer Bachelorarbeit Maschinelles Lernen für Prognosen benutzt. Deshalb war es sehr spannend für sie, diese Kenntnisse und ihre Freude am Thema in das Graduiertenkolleg zu Energiedaten und Informatik einzubringen. Als Gesellschaft müssen wir in naher Zukunft eine Energieproduktion ohne fossile Brennstoffe erreichen. Es ist jedoch nötig, beim Nutzen von erneuerbaren Energien im Vergleich zu konventioneller Energieerzeugung umzulernen, um einerseits für eine stabile Versorgung von Wirtschaft und Haushalten zu sorgen und andererseits dabei alle Lasten der nötigen Veränderungen fair zu verteilen. Es gibt zwei Möglichkeiten, die Energieproduktion zu optimieren. Zum einen können wir den Produktionszeitplan besser auf die Nachfrage abstimmen. Zum anderen können wir das Verbrauchsverhalten ändern, um eine optimale Versorgungsstrategie zu unterstützen. Traditionell kennt man Prognosen für die Energienachfrage in unterschiedlichen Zeithorizonten und macht diese zur Grundlage für Produktionspläne. Mit einer zunehmenden und sich ändernden Menge an Variablen, die das System beeinflussen, sind perfekte Vorhersagen jedoch sehr unrealistisch und wahrscheinlich nicht der richtige Ansatz für die Zukunft. Man muss sich hierzu nur vor Augen halten, dass die Energieernte sowohl bei Windkraft als auch für Solarstrom stark vom Wetter abhängen. Wenn auch die Wettervorhersage schon sehr viel besser geworden ist, so ist es doch noch nicht möglich, auf ihrer Grundlage hinreichend sichere Vorhersagen für die Energieerzeugung machen zu können. Andererseits gibt es heute auch bessere Möglichkeiten, die Energieabnahme zumindest im Prinzip von außen zu steuern. Das was früher als Nachtstrom die Abnahme von Stromspitzen mit niedrigen Preisen versüßte, kann heute ganz regional und sich täglich anpassend nicht nur in Betrieben sondern sogar im Haushalt steuern, wann beispielsweise die Waschmaschine läuft oder ein Warmwasserspeicher lädt. Bald kann auch die Flotte an E-Fahrzeugen mit ihren Akkumulatoren Energie zum passenden Zeitpunkt abnehmen und auch in Spitzenzeiten wieder abgeben. Die Gesetzgebung ist hier noch nicht so weit wie die technischen Möglichkeiten. Aber man muss sicher auch noch einmal gründlich darüber nachdenken, in welcher Art und Weise man Personen dazu zwingen will, Daten dafür zur Verfügung zu stellen und wie man sie anschließend vor dem Missbrauch dieses Wissens durch Unbefugte schützen kann. Schon heute ist die Energieversorgung viel verwundbarer durch Angriffe von Hackern als wir uns eingestehen wollen. Prinzipiell liegen aber - schon allein in Bezug auf Wetterdaten - aber auch in feingranularem Wissen über Energieverbrauch - sehr viele Daten vor, die man nutzen kann, um neuartige Prognosen zu erarbeiten. Man geht also über von rein Physik-basierten Modellen und Expertenwissen über zu neuronalen Netzen und Datamining. Diese arbeiten natürlich nicht sinnvoll ohne den Expertenblick, denn welche Fragen die vorliegenden Daten sinnvoll und recht sicher beantworten können, ist naiv geplant sicher nicht möglich. Nicole gefällt es gut, an der Schnittstelle sehr unterschiedlicher Wissensgebiete (Wirtschaft, Physik/Meteorologie, Ingenieurswissenschaft und Informatik) zu forschen. Literatur und weiterführende Informationen L. Barth e.a.: How much demand side flexibility do we need? - Analyzing where to exploit flexibility in industrial processes 9th ACM International Conference on Future Energy Systems (ACM e-Energy), 2018, Karlsruhe, Germany J.A. Gonzalez Ordiano e.a.: Concept and benchmark results for Big Data energy forecasting based on Apache Spark. Journal of Big Data 5, Article number: 11 (2018) R.R Appino e.a. : On the use of probabilistic forecasts in scheduling of renewable energy sources coupled to storages Applied Energy 210 (2018) L. Barth e.a.: A comprehensive modelling framework for demand side flexibility in smart grids Computer science - research and development 33,13-23 (2018) M. Lösch: Digitalisierte Stromnetze und Smart Meter in Deutschland, Ein Überblick, Vortrag auf der GPN17, 2017. B. Becker, F. Kern, M. Lösch, I. Mauser, H. Schmeck: Building Energy Management in the FZI House of Living Labs, In Proceedings of the D-A-CH Conference on Energy Informatics (pp. 95-112). Springer International Publishing, 2015. M. Lösch, D. Hufnagel, S. Steuer, T. Faßnacht, H. Schmeck: Demand Side Management in Smart Buildings by Intelligent Scheduling of Heat Pumps, In Proceedings of the IEEE International Conference on Intelligent Energy and Power Systems (IEPS), 2014. T. Fassnacht, M. Lösch, A. Wagner: Simulation Study of a Heuristic Predictive Optimization Scheme for Grid-reactive Heat Pump Operation, In Proceedings of the REHVA Annual Conference, 2015. Podcasts S. Coşkun, G. Thäter: Energy Markets, Gespräch im Modellansatz Podcast, Folge 190, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2018. C. Harvey, G. Thäter: Micro Grids, Gespräch im Modellansatz Podcast, Folge 186, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2018. Z. Ahamed, G. Thäter: Electric Vehicles on the Grid, Gespräch im Modellansatz Podcast, Folge 183, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2018. G. Thäter, B. Pousinho: Weather Generator, Gespräch im Modellansatz Podcast, Folge 148, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2017. M. Lösch, S. Ritterbusch: Smart Meter Gateway Gespräch im Modellansatz Podcast, Folge 135, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2017. M. Völter, V. Hagenmeyer: Stromnetze, ein Überblick, omega tau Podcast, Episode 246, 2017. J. Müller-Quade, A. Rupp, B. Löwe, K. Bao: Kryptographie und Privatssphäre im Stromnetz, Feature von Jan Rähm im KIT.audio Forschungspodcast des Karlsruher Instituts für Technologie, Folge 6, 2017. S. Seier, T. Alexandrin: Mieterstrom-Krimi, Abgrund oder Cliffhanger? Episode 16 im Blindstrom Podcast, 2017. M. Dalheimer, P. Hecko: Der Strom, Folge 5 im Pietcast, 2014.

Learn about how speed listening to podcasts (or "podfasting") affects our emotions; the health differences between white and brown rice; and the time pi was once almost legally changed to 3.2. Speed listening’s effects on emotion by Ashley Hamer (Listener question from S.P.) Rousseau, S. (2020, January 24). I Tried Listening to Podcasts at 3x and Broke My Brain. Medium; OneZero. https://onezero.medium.com/i-tried-listening-to-podcasts-at-3x-and-broke-my-brain-d8823edecb7c  Liebenthal, E., Silbersweig, D. A., & Stern, E. (2016). The Language, Tone and Prosody of Emotions: Neural Substrates and Dynamics of Spoken-Word Emotion Perception. Frontiers in Neuroscience, 10. https://doi.org/10.3389/fnins.2016.00506  Murray, I. R., & Arnott, J. L. (1993). Toward the simulation of emotion in synthetic speech: A review of the literature on human vocal emotion. The Journal of the Acoustical Society of America, 93(2), 1097–1108. https://doi.org/10.1121/1.405558  Philippou-Hubner, D., Vlasenko, B., Bock, R., & Wendemuth, A. (2012). The Performance of the Speaking Rate Parameter in Emotion Recognition from Speech. 2012 IEEE International Conference on Multimedia and Expo. https://doi.org/10.1109/icme.2012.183  Kraxenberger, M., Menninghaus, W., Roth, A., & Scharinger, M. (2018). Prosody-Based Sound-Emotion Associations in Poetry. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.01284  The health differences between white and brown rice are dead even by Steffie Drucker Whelan, C. (2017, May 25). Brown Rice vs. White Rice: Which Is Better for You? Healthline; Healthline Media. https://www.healthline.com/health/food-nutrition/brown-rice-vs-white-rice  Allan, P. (2020, February 19). Is Brown Rice Really That Much Healthier Than White Rice? Lifehacker. https://lifehacker.com/is-brown-rice-really-that-much-healthier-than-white-ric-1820044994  ‌Parletta, N. (2018, December 5). Rice is a major source of arsenic exposure. Cosmos Magazine. https://cosmosmagazine.com/biology/rice-is-a-major-source-of-arsenic-exposure   Pi Was Once Almost Legally Changed to 3.2 by Ashley Hamer:https://curiosity.com/topics/happy-pi-day-how-pi-was-almost-legally-changed-to-32-curiosity Subscribe to Curiosity Daily to learn something new every day with Cody Gough and Ashley Hamer. You can also listen to our podcast as part of your Alexa Flash Briefing; Amazon smart speakers users, click/tap “enable” here: https://curiosity.im/podcast-flash-briefing

Join the discussion on our Discord server As ML plays a more and more relevant role in many domains of everyday life, it's quite obvious to see more and more attacks to ML systems. In this episode we talk about the most popular attacks against machine learning systems and some mitigations designed by researchers Ambra Demontis and Marco Melis, from the University of Cagliari (Italy). The guests are also the authors of SecML, an open-source Python library for the security evaluation of Machine Learning (ML) algorithms. Both Ambra and Marco are members of research group PRAlab, under the supervision of Prof. Fabio Roli.   SecML Contributors Marco Melis (Ph.D Student, Project Maintainer, https://www.linkedin.com/in/melismarco/) Ambra Demontis (Postdoc, https://pralab.diee.unica.it/it/AmbraDemontis)  Maura Pintor (Ph.D Student, https://it.linkedin.com/in/maura-pintor) Battista Biggio (Assistant Professor, https://pralab.diee.unica.it/it/BattistaBiggio) References SecML: an open-source Python library for the security evaluation of Machine Learning (ML) algorithms https://secml.gitlab.io/. Demontis et al., “Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks,” presented at the 28th USENIX Security Symposium (USENIX Security 19), 2019, pp. 321–338. https://www.usenix.org/conference/usenixsecurity19/presentation/demontis W. Koh and P. Liang, “Understanding Black-box Predictions via Influence Functions,” in International Conference on Machine Learning (ICML), 2017. https://arxiv.org/abs/1703.04730 Melis, A. Demontis, B. Biggio, G. Brown, G. Fumera, and F. Roli, “Is Deep Learning Safe for Robot Vision? Adversarial Examples Against the iCub Humanoid,” in 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017, pp. 751–759. https://arxiv.org/abs/1708.06939 Biggio and F. Roli, “Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning,” Pattern Recognition, vol. 84, pp. 317–331, 2018. https://arxiv.org/abs/1712.03141 Biggio et al., “Evasion attacks against machine learning at test time,” in Machine Learning and Knowledge Discovery in Databases (ECML PKDD), Part III, 2013, vol. 8190, pp. 387–402. https://arxiv.org/abs/1708.06131 Biggio, B. Nelson, and P. Laskov, “Poisoning attacks against support vector machines,” in 29th Int'l Conf. on Machine Learning, 2012, pp. 1807–1814. https://arxiv.org/abs/1206.6389 Dalvi, P. Domingos, Mausam, S. Sanghai, and D. Verma, “Adversarial classification,” in Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Seattle, 2004, pp. 99–108. https://dl.acm.org/citation.cfm?id=1014066 Sundararajan, Mukund, Ankur Taly, and Qiqi Yan. "Axiomatic attribution for deep networks." Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017. https://arxiv.org/abs/1703.01365  Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "Model-agnostic interpretability of machine learning." arXiv preprint arXiv:1606.05386 (2016). https://arxiv.org/abs/1606.05386 Guo, Wenbo, et al. "Lemna: Explaining deep learning based security applications." Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2018. https://dl.acm.org/citation.cfm?id=3243792 Bach, Sebastian, et al. "On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation." PloS one 10.7 (2015): E0130140. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130140 

Auke Ijspeert is a professor at the EPFL (the Swiss Federal Institute of Technology at Lausanne), and head of the Biorobotics Laboratory (BioRob). Auke is interested in using numerical simulations and robots to get a better understanding of animal locomotion and movement control, and in using inspiration from biology to design novel types of robots and locomotion controllers.His popular TED Talk of the robot that runs and swims like a salamander has been viewed nearly 2M times and he has won close to 20 awards, including the University of Edinburgh "robot-rugby" competition 1996, the best paper award at the IEEE-RAS Humanoids 2007 conference and the Overall Best Paper Award (out of 1,172 submitted, 689 accepted papers) at the IEEE International Conference on Robotics and Automation (ICRA 2002).He is member of the Board of Reviewing Editors of Science magazine, and associate editor for Soft Robotics and for the International Journal of Humanoid Robotics. He has acted as an associate editor for the IEEE Transactions on Robotics (2009-2013) and as a guest editor for the Proceedings of IEEE, IEEE Transactions on Biomedical Engineering, Autonomous Robots, IEEE Robotics and Automation Magazine, and Biological Cybernetics. He has been the organizer of 6 international conferences and been a program committee member of over 50 conferences.You can listen right here on iTunesIn our wide-ranging conversation, we cover many things, including: * The purpose and value of biomicking robots * Why Auke is worried about drones and robots in future battlefields * The difference between theory and practice for robot design * Why Auke works with AI researchers but not that closely * How we should think about regulating robotics * Why Auke thinks we're still a long way off from AGI * The problem with scientists and researchers forgetting to think about ethics * When we can expect large scale robotics in modern life * Why C3PO isn't an ideal answer to home robotics * How hype cycles drive robotics and tech development and * Why AI research and progress may in fact be slowing down * The reason Auke is so excited and optimistic about autonomous vehicles * Why scientists are fascinated and frustrated with Boston DynamicsMake a Tax-Deductible Donation to Support The DisruptorsThe Disruptors is supported by the generosity of its readers and listeners. If you find our work valuable, please consider supporting us on Patreon, via Paypal or with DonorBox powered by Stripe.Donate

Is AV1 more efficient than HEVC? Dror & Mark get into the middle of a 3 against 1 standoff over whether AV1 is actually more efficient than HEVC. The following blog post first appeared on the Beamr blog at: https://blog.beamr.com/2018/11/23/codec-efficiency-is-in-the-eye-of-the-measurer-podcast/ When it comes to comparing video codecs, it's easy to get caught up in the “codec war” mentality. If analyzing and purchasing codecs was as easy as comparing fuel economy in cars, it would undoubtedly take a lot of friction out of codec comparison, but the reality is that it's not that simple. In Episode 02, The Video Insiders go head-to-head comparing two of the leading codecs in a three against one standoff over whether AV1 is more efficient than HEVC. So, which is more efficient? Listen in to this week's episode, “Codec Efficiency Is in the Eye of the Measurer,” to find out. Want to join the conversation? Reach out to TheVideoInsiders@beamr.com. TRANSCRIPTION (lightly edited to improve readability only) Mark Donnigan: 00:41 Hi everyone I am Mark Donnigan and I want to welcome you to episode two of the Video Insiders. Dror Gill: 00:48 And I am Dror Gill. Hi there. Mark Donnigan: 00:50 In every episode of the Video Insiders we bring the latest inside information about what's happening in the video technology industry from encoding, to packaging, to delivery, and playback, and even the business behind the video business. Every aspect of the video industry is covered in detail on the Video Insiders podcast. Dror Gill: 01:11 Oh yeah, we usually do cover everything from pixels, to blocks, to microblocks, to frames, to sequences. We go all the way up and down the video delivery chain and highlight the most important things you should know before you send any video bits over the wire. Mark Donnigan: 01:28 In our first episode we talked about a very hot topic which asked, “Hasn't this kind of been worn out?” The whole HEVC, AV1 discussion. But I think it was very interesting. I sure enjoyed the talk. What about you Dror? Dror Gill: 01:47 Yeah, yeah, yeah. I sure did. It was great talking about the two leading codecs. I don't want to say the word, codec war. Mark Donnigan: 01:58 No, no, we don't believe in codec wars. Dror Gill: 01:59 We believe in codec peace. Mark Donnigan: 02:00 Yeah, that's true. Why is it so complicated to compare video codecs? Why can't it be as simple as fuel economy of cars, this one gets 20 miles per gallon and that one gets 30 and then I make a decision based on that. Dror Gill: 02:15 I wish it was that simple with video codecs. In video compression you have so many parameters to consider. You have the encoding tools, tools are grouped into what's called profiles and levels, or as AV1 calls them “experiments.” Mark Donnigan: 02:31 Experiments, mm-hmm… Dror Gill: 02:35 When you compare the codecs which profiles and levels do you use. What rate control method? Which specific parameters do you set for each codec? And each codec can have hundreds, and hundreds of parameters. Then there is the question of implementation. Which software implementation of the codec do you use? Some implementations are reference implementations that are used for research, and others are highly performance optimized commercial implementations. Which one do you select for the test? And then, which operating system, what hardware do you run on, and obviously what test content? Because encoding two people talking, or encoding an action scene for a movie, is completely different. Dror Gill: 03:13 Finally, when you come to evaluate your video, what quality measure do you use? There're various objective quality measures and some people use actual human viewers and they assesses subjective quality of the video. On that front also, there're many possibilities that you need to choose from. Mark Donnigan: 03:32 Yeah, so many questions and no wonder the answers are not so clear. I was quite surprised when I recently read three different technical articles published at IBC actually, effectively comparing AV1 versus HEVC and I can assume that each of the authors did their research independently. What was surprising was they came to the exact same conclusion, AV1 has the same compression efficiency as HEVC. This is surprising because some other studies and one in particular (I think we'll talk about) out there says the contrary. So can you explain what this means exactly, Dror. Dror Gill: 04:16 By saying that they have the same compression efficiency, this means that they can reach the same quality at the same bitrate or the other way round. You need the same bitrate to reach that same quality. If you need for example, two and a half megabits per second to encode an HD video file using HEVC at a certain quality, then with AV1 you would need roughly the same bitrate to reach that same quality and this means that AV1 and HEVC provide the same compression level. In other words, this means that AV1 does not have any technical advantage over HEVC because it has the same compression efficiency. Of course that's if we put aside all the loyalty issues but we discussed that last time. Right? Mark Donnigan: 04:56 That's right. The guys who wrote the three papers that I'm referencing are really top experts in the field. It's not seminar work done by a student, not to downplay those papers, but the point is these are professionals. One was written by the BBC in cooperation with the Multimedia and Vision Group at the Queen Mary University of London. I think nobody is going to say that the BBC doesn't know a thing or two about video. The second was written by Ateme, and the third by Harmonic, leading vendors. Mark Donnigan: 05:29 I actually pulled out a couple of phrases from each that I'd like to quote. First the BBC and Queen Mary University, here is a conclusion that they wrote, “The results obtained show in general a similar performance between AV1 and the reference HEVC both objectively and subjectively.” Which is interesting because they did take the time to both do the visual assessment as well as use a quality measure. Mark Donnigan: 06:01 Ateme said, “Results demonstrate AV1 to have equivalent performance to HEVC in terms of both objective and subjective video quality test results.” Dror Gill: 06:10 Yeah, very similar. Mark Donnigan: 06:16 And then here is what Harmonic said, “The findings are that AV1 is not more advantageous today than HEVC on the compression side and much more complex to encode than HEVC.” What do you make of this? Dror Gill: 06:32 I don't know. It sounds pretty bad to me, even two of those papers also analyzed subjective quality so they used actual human viewers to check out the quality. But Mark what if I told you that researchers from the University of Klagenfurt in Austria together with Bitmovin published a paper which showed completely different results. What would you say about that? Mark Donnigan: 06:57 Tell me more. Dror Gill: 06:58 Last month in Athens I was the ICIP conference that's the IEEE International Conference on Image Compression and Image Processing. There was this paper presented by this University in Austria with Bitmovin and their conclusion was, let me quote, “When using weighted PSNR, AV1 performs consistently better for bit rate compared to AVC, HEVC, and VP9.” So they claim AV1 is better than three codecs but specifically it's better than HEVC. And then they have a table in their article that compares AV1 to HEVC for six different video clips. The table shows that with AV1 you get up to 25% lower bitrate at the same quality than HEVC. Dror Gill: 07:43 I was sitting there in Athens last month when they presented this and I was shocked. Mark Donnigan: 07:50 What are the chances that three independent papers are wrong and only this paper got it right? And by the way, the point here is not three against one because presumably there're some other papers. I'm guessing other research floating around that might side with Bitmovin. The point is that three companies who no one is going to say that any of them are not experts and not highly qualified to do a video assessment, came up with such a different result. Tell us what you think is going on here? Dror Gill: 08:28 I was thinking the same thing. How can that be. During the presentation I asked one of the authors who presented the paper a few questions and it turned out that they made some very questionable decisions in all of that sea of possibility that I talked about before. Decisions related to coding tools, codec parameters, and quality measures. Dror Gill: 08:51 First of all, in this paper they didn't show any results of subjective viewing. Only the objective metrics. Now we all know that you should always your eyes, right? Mark Donnigan: 09:03 That's right. Dror Gill: 09:04 Objective metrics, nice numbers, but obviously you need to view the video because that's how the actual viewers are going to assess the (video) quality. The second thing is that they only used the single objective metric and this was PSNR. PSNR, it stands for peak signal-to-noise ratio and basically this measure is a weighted average of the difference in peaks between pixel values of the two images. Dror Gill: 09:30 Now, we're Video Insiders, but even if you're not an insider you know that PSNR is not a very good quality measure because it does not correlate very well with human vision. This is the measure that they choose to look at but what was most surprising is that there is a flag in the HEVC open source encoder which they used that if chosen, the result is improved PNSR. What it does, it turns off some psycho-visual optimizations which make the video look better but reduce the PSNR, and that's turned on by default. So you would expect that they're measuring PSNR they would turn that flag on so you would get higher PSNR. Well, they didn't. They didn't turn the flag on! Mark Donnigan: 10:13 Amazing. Dror Gill: 10:17 Finally, even then AV1 is much slower than HEVC, and they also reported in this data that it was much, much slower than HEVC but still they did not use the slowest encoding standing of HEVC, which would provide the best quality. There's always a trade off between performance and quality. The more tools you employ the better quality you can squeeze out of the video, of course that takes you more CPU cycles but they used for HEVC, the third slowest setting which means this is the third best quality you can get with that codec and not the very best quality. When you handicap an HEVC encoder in this way, it's not surprising that you get such poor results. Dror Gill: 11:02 I think based on all these points everybody can understand why the results of this comparison were quite different than all of the other comparison that were published a month earlier at IBC (by Ateme, BBC, Harmonic). Mark Donnigan: 11:13 It's interesting. Mark Donnigan: 11:14 Another critical topic that we have to cover is performance. If you measure the CPU performance on encoding time of AV1, I believe that it's pretty universally understood that you are going to find it currently is a hundred times slower than HEVC. Is that correct? Dror Gill: 11:32 Yeah, that's right. Typically, you measure the performance of an encoder and FPS which is frames per second. For HEVC it's common to measure an FPM which is frames per minute. Mark Donnigan: 11:42 Frames per minute, (more like) frames per hour, FPH. Dror Gill: 11:45 A year and a half ago or a year ago when there were very initial implementation, it was really FPD or FPH, Frames per hour or per day and you really needed to have a lot of patience, but now after they've done some work it's only a hundred times slower than HEVC. Mark Donnigan: 12:02 Yeah, that's pretty good. They're getting there. But some people say that the open source implementation of AV1 I believe it's AOM ENC. Dror Gill: 12:11 Yeah, AOM ENC. Mark Donnigan: 12:16 ENC exactly has not been optimized for performance at all. One thing I like about speed is either your encoder produces X number of frames per second or per minute, or it doesn't. It's really simple. Here is my next question for you. Proponents of AV1 are saying, “well it's true it's slow but it hasn't been optimized, the open source implementation,” which is to imply that there's a lot of room (for improvement) and that we're just getting started, “don't worry we'll close the gap.” But if you look at the code, and by the way I may be a marketing guy but my formal education is computer science. Mark Donnigan: 13:03 You can see it already includes performance optimizations. I mean eptimizations like MMX, SSE, there's AVX instructions, there's CPU optimization, there's multithreading. It seems like they're already trying to make this thing go faster. So how are they going to close this a hundred X (time) gap? Dror Gill: 13:22 I don't think they can. I mean a hundred X, that's a lot and you know even the AV1 guys they even admit that they won't be able to close the gap. I talked to a few senior people who're involved in the Alliance for Open Media and even they told me that they expect AV1 to five to 10 times more complex than HEVC at the end of the road. In two to three years after all optimization are done, it's still going to be more complex than HEVC. Dror Gill: 13:55 Now, if you ask me why it's so complex I'll tell you my opinion. Okay, this is my personal opinion. I think it's because they invested a lot of effort in side stepping the patents (HEVC). Mark Donnigan: 14:07 Good point. I agree. Dror Gill: 14:07 They need to get that compression efficiency which is the same as HEVC but they need to use algorithms that are not patented. They have methods that use much more CPU resources than the original patent algorithms to reach the same results. You can call it kind of brute force implementation of the same thing to avoid the patent issue. That's my personal opinion, but the end result I think is clear, it's going to be five to 10 times slower than HEVC. It has the same compression efficiency so I think it's quite questionable. This whole notion of using AV1 to get better results. Mark Donnigan: 14:45 Absolutely. If you can encode let's say on a single computer with HEVC a full ABR stack, this is what people want to do. But here we're talking speeds that are so slow let's just try and do (encode) one stream. Literally what you're saying is you'll need five to 10 computers to do the same encode with AV1. I mean, that's just not viable. It doesn't make sense to me. Dror Gill: 15:14 Yeah, why would you invest so much encoding into getting the same results. If you look at another aspect of this, let's talk about hardware encode. Companies that have large data centers, companies that are encoding vast amount of video content are not looking into moving from the traditional software encoding and CPUs and GPUs, to dedicated hardware. We're hearing talks about FPGAs even ASICs … by the way this is a very interesting trend in itself that we'll probably cover in one of the next episodes. But in the context of AV1, imagine a chip that is five to 10 times larger than an HEVC chip and which is the same complexity efficiency. The question I ask again is why? Why would anybody design such a chip, and why would anybody use it when HEVC is available today? It's much easier to encode, royalty issues have been practically solved so you know? Mark Donnigan: 16:06 Yeah, it's a big mystery for sure. One thing I can say is the Alliance for Open Media has done a great service to HEVC by pushing the patent holders to finalize their licensing terms … and ultimately make them much more rational shall we say? Dror Gill: 16:23 Yeah. Mark Donnigan: 16:25 Let me say that as we're an HEVC vendor and speaking on behalf of others (in the industry), we're forever thankful to the Alliance for Open Media. Dror Gill: 16:36 Definitely, without the push from AOM and the development of AV1 we would be stuck with HEVC royalty issue until this day. Mark Donnigan: 16:44 That was not a pretty situation a few years back, wow! Dror Gill: 16:48 No, no, but as we said in the last episode we have a “happy ending” now. (reference to episode 1) Mark Donnigan: 16:52 That's right. Dror Gill: 16:52 Billions of devices support HEVC and royalty issues are pretty much solved, so that's great. I think we've covered HEVC and AV1 pretty thoroughly in two episodes but what about the other codecs? There's VP9, you could call that the predecessor of AV1, and then there's VVC, which is the successor of HEVC. It's the next codec developed by MPEG. Okay, VP9 and VVC I guess we have a topic for our next episode, right? Mark Donnigan: 17:21 It's going to be awesome. Narrator: 17:23 Thank you for listening to the Video Insider podcast a production of Beamr limited. To begin using Beamr codecs today go to beamr.com/free to receive up to 100 hours of no cost HEVC and H.264 transcoding every month.

Glæd jer over at Spækbrættet ikke er et radioprogram med country-musik. I denne anden udgave af vores Tåsinge special fra Flemmings værksted afslører drengene Pedigree (R) som fortalere for falska nyheter og opdager at en bestemt etnisk baggrund beskytter mod selvmord ved country-musik. Vi henviser til livslinjen, hvis det sidste klip bliver for meget for jer.Musical credit: Bensound.com - Bensound.com with "Funky Suspense"Kilder:The Effect of Country Music on Suicide - Steven Stack and Jim Gundlach - Social Forces, Vol. 71, No. 1 (Sep., 1992)Aiming and vaulting: Spider inspired leaping for jumping robots - Hossein Faraji et al. 2016 IEEE International Conference on Robotics and Automation (ICRA)Energy and time optimal trajectories in exploratory jumps of the spider Phidippus regius - Mostafa R. A. Nabawy et al. Nature Scientific Reports 2018Support the show (https://spaekbraettet.10er.app/)

Gudrun is speaking with the portuguese engineer Bruno Pousinho. He has been a student of the Energy Technologies (ENTECH) Master program. This is an international and interdisciplinary program under the label of the European Institute of Innovation and Technology (EIT) inbetween a number of European technical universities. Bruno spent his second master year at the Karlsruhe Institute of Technology (KIT). Gudrun had the role of his supervisor at KIT while he worked on his Master's thesis at the Chair of Renewable and Sustainable Energy Systems (ENS) at TUM in Garching. His direct contact person there was Franz Christange from the group of Prof. Thomas Hamacher. Renewable energy systems are a growing part of the energy mix. In Germany between 1990 and 2016 it grew from 4168 GW to 104024 GW. This corresponds to an annual power consumption share of 3.4% and 31.7%, respectively. But in the related research this means a crucial shift. The conventional centralized synchronous machine dominated models have to be exchanged for decentralized power electronic dominated networks - so-called microgrids. This needs collaboration of mechanical and electrical engineers. The interdisciplinary group at TUM has the goal to work on modeling future microgrids in order to easily configure and simulate them. One additional factor is that for most renewable energy systems it is necessary to have the right weather conditions. Moreover, there is always the problem of reliability. Especially for Photovoltaics (PV) and wind turbines Weather phenomena as solar irradiation, air temperature and wind speed have to be known in advance in order to plan for these types of systems. There are two fundamentally different approaches to model weather data. Firstly the numerical weather and climate models, which provide the weather forecast for the next days and years. Secondly, so-called weather generators. The numerical models are very complex and have to run on the largest computer systems available. For that in order to have a simple enough model for planning the Renewable energy resources (RER) at a certain place weather generators are used. They produce synthetic weather data on the basis of the weather conditions in the past. They do not predict/forecast the values of a specific weather phenomenon for a specific time but provides random simulations whose outputs show the same or very similar distributional properties as the measured weather data in the past. The group in Garching wanted to have a time dynamic analytical model. The model is time continuous which grant it the ability of having any time sampling interval. This means it wanted to have a system of equations for the generation of synthetic weather data with as few as possible parameters. When Bruno started his work, there existed a model for Garching (developped by Franz Christange) with about 60 parameters. The aim of Bruno's work was to reduce the number of parameters and to show that the general concept can be used worldwide, i.e. it can adapt to different weather data in different climate zones. In the thesis the tested points range from 33º South to 40º North. In the synthesis of the weather generator the crucial tool is to use stochastic relations. Mostly the standard normal distribution is applied and shaped for the rate of change and corelation between RER. In particular this means that it describes the fundamental behavior of weather (mean, standard deviation, time- and cross-correlation) and introduces them into white noise in an analytical way. This idea was first introduced for crop estimation by Richardson in 1985. Time-dependence works on different time scales - through days and through seasons, e.g.. In the Analysis it is then necessary to parametrize the measured weather data and to provide a parameter set to the weather model. Bruno started his Master course in Lisbon at Instituto Superior tecnico (IST). In his second year he changed to KIT in Karlsruhe and put his focus on Energy systems. In his thesis he uses a lot of mathematics which he learned during his Bachelor education and had to recall and refresh. The results of the project are published in the open source model 'solfons' in Github, which uses Python and was developed in MATLAB. References F. Christange & T. Hamacher: Analytical Modeling Concept for Weather Phenomena as Renewable Energy Resources, in IEEE International Conference on Renewable Energy Research and Applications (ICRERA), 2016. doi: 10.1109/ICRERA.2016.7884551 P. Ailliot, D. Allard, P. Naveau, C. D. Beaulieu, R. Cedex: Stochastic weather generators, an overview of weather type models, Journal de la Société Française de Statistique, Vol. 156, No 1, pp. 1-14, 2015. C.L. Wiegand, A.J. Richardson: Leaf area, light interception, and yield estimates from spectral components analysis, Agron. J., 76, 543, 1984. solfons: Artificial wheater data for energy system modeling, Software at GitHub. Podcasts S. Seier, T. Alexandrin: Blindstrom - Der Energie Podcast, 2016-2017. M. Völter, V. Hagenmeyer: Stromnetze, ein Überblick, omega tau Podcast, Episode 246, 2017. K. A. Zach, L. Bodingbauer: Energiespeicher, PHS186 in der Physikalischen Soiree, 2013. F. Trieb, T. Pritlive: Energie der Zukunft, RZ033 im Raumzeit Podcast, Metaebene Personal Media, 2012.

Gudrun is speaking with the portuguese engineer Bruno Pousinho. He has been a student of the Energy Technologies (ENTECH) Master program. This is an international and interdisciplinary program under the label of the European Institute of Innovation and Technology (EIT) inbetween a number of European technical universities. Bruno spent his second master year at the Karlsruhe Institute of Technology (KIT). Gudrun had the role of his supervisor at KIT while he worked on his Master's thesis at the Chair of Renewable and Sustainable Energy Systems (ENS) at TUM in Garching. His direct contact person there was Franz Christange from the group of Prof. Thomas Hamacher. Renewable energy systems are a growing part of the energy mix. In Germany between 1990 and 2016 it grew from 4168 GW to 104024 GW. This corresponds to an annual power consumption share of 3.4% and 31.7%, respectively. But in the related research this means a crucial shift. The conventional centralized synchronous machine dominated models have to be exchanged for decentralized power electronic dominated networks - so-called microgrids. This needs collaboration of mechanical and electrical engineers. The interdisciplinary group at TUM has the goal to work on modeling future microgrids in order to easily configure and simulate them. One additional factor is that for most renewable energy systems it is necessary to have the right weather conditions. Moreover, there is always the problem of reliability. Especially for Photovoltaics (PV) and wind turbines Weather phenomena as solar irradiation, air temperature and wind speed have to be known in advance in order to plan for these types of systems. There are two fundamentally different approaches to model weather data. Firstly the numerical weather and climate models, which provide the weather forecast for the next days and years. Secondly, so-called weather generators. The numerical models are very complex and have to run on the largest computer systems available. For that in order to have a simple enough model for planning the Renewable energy resources (RER) at a certain place weather generators are used. They produce synthetic weather data on the basis of the weather conditions in the past. They do not predict/forecast the values of a specific weather phenomenon for a specific time but provides random simulations whose outputs show the same or very similar distributional properties as the measured weather data in the past. The group in Garching wanted to have a time dynamic analytical model. The model is time continuous which grant it the ability of having any time sampling interval. This means it wanted to have a system of equations for the generation of synthetic weather data with as few as possible parameters. When Bruno started his work, there existed a model for Garching (developped by Franz Christange) with about 60 parameters. The aim of Bruno's work was to reduce the number of parameters and to show that the general concept can be used worldwide, i.e. it can adapt to different weather data in different climate zones. In the thesis the tested points range from 33º South to 40º North. In the synthesis of the weather generator the crucial tool is to use stochastic relations. Mostly the standard normal distribution is applied and shaped for the rate of change and corelation between RER. In particular this means that it describes the fundamental behavior of weather (mean, standard deviation, time- and cross-correlation) and introduces them into white noise in an analytical way. This idea was first introduced for crop estimation by Richardson in 1985. Time-dependence works on different time scales - through days and through seasons, e.g.. In the Analysis it is then necessary to parametrize the measured weather data and to provide a parameter set to the weather model. Bruno started his Master course in Lisbon at Instituto Superior tecnico (IST). In his second year he changed to KIT in Karlsruhe and put his focus on Energy systems. In his thesis he uses a lot of mathematics which he learned during his Bachelor education and had to recall and refresh. The results of the project are published in the open source model 'solfons' in Github, which uses Python and was developed in MATLAB. References F. Christange & T. Hamacher: Analytical Modeling Concept for Weather Phenomena as Renewable Energy Resources, in IEEE International Conference on Renewable Energy Research and Applications (ICRERA), 2016. doi: 10.1109/ICRERA.2016.7884551 P. Ailliot, D. Allard, P. Naveau, C. D. Beaulieu, R. Cedex: Stochastic weather generators, an overview of weather type models, Journal de la Société Française de Statistique, Vol. 156, No 1, pp. 1-14, 2015. C.L. Wiegand, A.J. Richardson: Leaf area, light interception, and yield estimates from spectral components analysis, Agron. J., 76, 543, 1984. solfons: Artificial wheater data for energy system modeling, Software at GitHub. Podcasts S. Seier, T. Alexandrin: Blindstrom - Der Energie Podcast, 2016-2017. M. Völter, V. Hagenmeyer: Stromnetze, ein Überblick, omega tau Podcast, Episode 246, 2017. K. A. Zach, L. Bodingbauer: Energiespeicher, PHS186 in der Physikalischen Soiree, 2013. F. Trieb, T. Pritlive: Energie der Zukunft, RZ033 im Raumzeit Podcast, Metaebene Personal Media, 2012.

Zur GPN17 des Entropia e.V. im ZKM - Zentrum für Kunst und Medien und der Hochschule für Gestaltung (HfG) hat Manuel Lösch einen Vortrag zu Smart Meter Gateways gehalten. Manuel promoviert am FZI Forschungszentrum Informatik in Karlsruhe zu intelligenten Stromnetzen und der Flexibilisierung von elektrischen Lasten, um diese netzdienlich zur Verfügung zu stellen. Die Einführung des Smart Meter Gateway wurde mit dem Gesetz zur Digitalisierung der Energiewende Ende 2016 in Deutschland beschlossen. Dabei muss man Smart Meter Gateways deutlich von den so genannten Smart Metern oder intelligenten Zählern unterscheiden, die im Fall der elektrischen Energie den Ferraris-Zähler ablösen werden und den Stromverbrauch digital aufzeichnen und verarbeiten können. Die Kombination von intelligenten Zählern und einem Smart Meter Gateway resultiert in einem Intelligenten Messsystem, das Informationen auch an externe Entitäten wie Energielieferanten oder Netzbetreiber versenden kann. Viele Smart Meter sind mit einer Infrarot-Schnittstelle via Smart Message Language beispielsweise über das Volkszähler-Projekt auslesbar. Neuere Geräte verfügen über eine standardisierte M-Bus-Schnittstelle zur digitalen Datenübertragung zwischen Zähler und z.B. dem Smart Meter Gateway. Grundsätzlich soll die Standardisierung den Kunden das Auslesen erleichtern, damit sie einen besseren Einblick in ihr Energienutzungsverhalten erhalten und Einsparmöglichkeiten erkennen können. Gesetzlich ist sogar vorgeschrieben, dass die Nutzer eines intelligenten Zählers neben dem aktuellen Verbrauchswert sogar einen Einblick bis zwei Jahre in die Vergangenheit erhalten müssen. Bis zum Jahre 2032 sollen nach dem Gesetz zur Digitalisierung der Energiewende alle Haushalte in Deutschland mit mindestens intelligenten Zählern ausgestattet sein, und je nach Haushaltsgröße auch mit Smart Meter Gateways, die die Kommunikation nach extern ermöglichen. Die Basis des Gesetzes ist das dritte Energiepaket der EU von 2009, die den Mitgliedstaaten vorgab eine Smart-Metering-Infrastruktur einzurichten, wenn eine Kosten-Nutzen-Analyse dieses für sinnvoll erachtet. Daher wurde 2013 eine Kosten-Nutzen-Analyse durchgeführt mit dem Ergebnis, dass ein Teil-Rollout für Deutschland sinnvoll ist. So sollen zwar alle Nutzer intelligente Zähler erhalten, jedoch sind die Gateways nur für größere, sog. netzrelevante, Nutzer vorgeschrieben. Die betrachtete Nutzungsgröße kann sich mit größerer Elektromobilität jedoch stark ändern: Mit einem Elektroauto kann der Verbrauch eines kleinen Haushalts sich vervielfachen und auch die dezentrale Stromerzeugung beispielsweise durch Photovoltaik wird einbezogen. Mit der Energiewende hat sich die Belastung der Stromnetze stark geändert. Die bisher auf zentrale Versorgung ausgelegte hierarchische Netztopologie kann durch dezentrale Stromerzeugung stark belastet werden. Zur Entlastung der Netze wurden Betreiber von PV-Anlagen verpflichtet, die Einspeisung zu beschränken, entweder fest auf 70 Prozent der Maximalleistung oder über ein Einspeisemanagement gesteuert über Rundsteuertechnik. Das Smart Meter Gateway arbeitet auf drei Netzbereichen und soll eine sichere Kommunikation zwischen diesen ermöglichen. So gibt es das dem Internet bzw. Wide Area Network zur Kommunikation mit beispielsweise dem Stromanbieter, das Home Area Network für Anwendungen im eigenen Haus und das Local Metrological Network welches die eigentlichen Strom-, Wärme, Gas- und Wasserzähler beinhaltet. Im Home Area Network könnten künftig beispielsweise Geräte wie das Nest Thermostat angeschlossen werden. Dieses kann in den USA heute schon beispielsweise Wärmepumpen oder Klimaanlagen sowohl nach Nutzeranforderung und Netzanforderungen optimiert ansteuern. Dabei werden das Haus oder der Warmwasserspeicher, also bereits vorhandene thermische Energiespeicher, zur Lastverschiebung im "intelligenten" Stromnetz ausgenutzt. Das Konzept dazu ist nicht neu, seit längerer Zeit gibt es bereits den Niederstromtarif, der im Gegensatz zum Hochtarif preiswerter ist und zu Zeiten geringer Netzauslastung zur Verfügung steht. Diese Tarife werden auch heute teilweise noch mit Nachtspeicherheizungen genutzt. Auf Grund energetischer Ineffizienz wurden Speicherheizungen bereits verboten, heute erleben sie aber wieder eine gewisse Renaissance, da sie zur Pufferung überschüssiger Energie herangezogen werden können. Mit der ermöglichten Kommunikation über verschiedene Netzwerkbereiche und der Steuerbarkeit von außen steigen auch die Sicherheitsanforderungen an ein solches System. Daher sind Smart Meter Gateways in Deutschland in eine Public Key Infrastruktur eingebunden, um einen Missbrauch nach Stand der Technik zu unterbinden. Eine sehr wichtige Rolle wird hier dem Smart Meter Gateway Administrator zugeteilt, der durch digitale Zertifikate die Grundlage für die Authentifizierung der verschiedenen Kommunikationspartner setzt. Die innere Sicherheit des Smart Meter Gateway wird durch ein vom BSI zertifiziertes Sicherheitsmodul gewährleistet, das die erforderliche Kryptographie zur sicheren Kommunikation zur Verfügung stellt. Auch in manchen Smartphones werden zusätzliche Chips zur Absicherung verwendet. Auch wenn es zumindest schon einen Anbieter eines zertifizierten Sicherheitsmoduls gibt, haben sich zum Zeitpunkt der Aufnahme zwar acht Gateways zur Zertifizierung beworben, doch hat noch keines die Zertifizierung abgeschlossen, obwohl die Gesetzgeber in diesem Jahr den Start des Rollouts intelligenter Messsysteme geplant haben. Die Wahrung der Privatsphäre ist ein wichtiges Thema bei der Weitergabe von Stromverbrauchsdaten: So konnte mit Hilfe der Messdaten eines Smart Meters bereits erfolgreich bestimmt werden, welcher Film auf einem Fernseher lief. Auf der anderen Seite ist die zeitnahe und regelmäßige Weitergabe von Stromverbrauchsdaten eine sehr wichtige Informationsquelle für die Bewirtschaftung der Bilanzkreise, die wesentlich auf der Erstellung von Prognosen basiert und grundlegend für die Stabilität unseres Stromnetzes ist. Da bei kleineren Verbrauchern noch keine viertelstündlichen Verbrauchsmeldungen erzeugt werden, kommen dort standardisierte Lastprofile zum Einsatz, um den typischen Stromverbrauch von Haushalten und Gewerbebetrieben zu modellieren. Durch die steigende Elektromobilität kann sich in Zukunft durch häusliches Laden der Verbrauch eines Haushalts jedoch deutlich von Standardlastprofilen unterscheiden. Andererseits ergibt der Ladeprozess einen neuen Freiheitsgrad, um Lasten zu verschieben und gerade dann Energie zu verbrauchen, wenn diese im Überfluss vorhanden ist. Zur Koordination vieler kleiner dezentraler Energieerzeuger wurde das Konzept der virtuellen Kraftwerke ins Leben gerufen, mit dem viele kleine Kraftwerke als gemeinsame Institution an Strom- und Regelleistungsmärkten aktiv teilnehmen können. Wenn der tatsächliche Verbrauch sich von den Prognosen stark unterscheidet, so fluktuieren die Preise an der Strombörse stark, es kann an der EPEX am Spotmarkt bei starkem Überangebot sogar zu negativen Strompreisen kommen, da große Kraftwerke sich nur beschränkt regeln lassen. Diese Großhandelspreise können aber aktuell nur zu einem Teil an Endkunden weitergegeben werden, da der tatsächliche Energiepreis nur einen Teil des Endpreises ausmacht; zusätzlich fallen fixe Netzentgelte und Umlagen an. Die Steuerung dezentraler Stromerzeuger und variabler Stromverbraucher (heute v.a. Wärmepumpen und Speicherheizungen) wie oft auch die Straßenbeleuchtung erfolgt an vielen Orten und auch in Karlsruhe durch Rundsteuertechnik, welche Signale im hörbaren Frequenzbereich von 110-2000 Hz über das vorhandene Stromnetz überträgt. Um hier ein Netzsegment zu steuern sind Sendeleistungen teilweise bis im hohen Kilowattbereich erforderlich. Die Rundsteuertechnik ist an vielen Orten auch durch Funkrundsteuertechnik mit Signalen auf Langwelle oder Ultrakurzwelle realisiert. Langwellensignale wie DCF77 können mit Soundkarten empfangen und auch können Langwellen per Audioausgang gesendet werden. Zur GPN17 wurde auch der Gulasch Push Notifier alias GPN-Badge entwickelt, der ebenso zentral die Teilnehmer des Events zum Gulasch rufen sollte. Das Forschungsgebiet von Manuel behandelt die Erschließung von Flexibilität in der Erzeugung und dem Verbrauch elektrischer Energie, mit dem Ziel diese netzdienlich und gewinnbringend in sogenannten "intelligenten Stromnetzen" zur Verfügung zu stellen. Dies untersucht er aktuell im Kontext größerer Liegenschaften, welche als große Endverbraucher oft auch vor Ort über eigene dezentrale Stromerzeuger verfügen. Am FZI House of Living Labs setzt er die Forschung praxisnah um: Das Energiemanagementsystem im FZI House of Living Labs ermöglicht beispielsweise die automatisierte Steuerung der Klimaanlage passend zu Meetings und dem aktuellen Netzzustand. Literatur und weiterführende Informationen M. Lösch: Digitalisierte Stromnetze und Smart Meter in Deutschland, Ein Überblick, Vortrag auf der GPN17, 2017. B. Becker, F. Kern, M. Lösch, I. Mauser, H. Schmeck: Building Energy Management in the FZI House of Living Labs, In Proceedings of the D-A-CH Conference on Energy Informatics (pp. 95-112). Springer International Publishing, 2015. M. Lösch, D. Hufnagel, S. Steuer, T. Faßnacht, H. Schmeck: Demand Side Management in Smart Buildings by Intelligent Scheduling of Heat Pumps, In Proceedings of the IEEE International Conference on Intelligent Energy and Power Systems (IEPS), 2014. T. Fassnacht, M. Lösch, A. Wagner: Simulation Study of a Heuristic Predictive Optimization Scheme for Grid-reactive Heat Pump Operation, In Proceedings of the REHVA Annual Conference, 2015. U. Greveler, P. Glösekötterz, B. Justusy, D. Loehr: Multimedia content identification through smart meter power usage profiles, In Proceedings of the International Conference on Information and Knowledge Engineering (IKE). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing, 2012. Podcasts M. Völter, V. Hagenmeyer: Stromnetze, ein Überblick, omega tau Podcast, Episode 246, 2017. S. Ritterbusch: Digitale Währungssysteme, Gespräch mit G. Thäter im Modellansatz Podcast, Folge 32, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2014. J. Müller-Quade, A. Rupp, B. Löwe, K. Bao: Kryptographie und Privatssphäre im Stromnetz, Feature von Jan Rähm im KIT.audio Forschungspodcast des Karlsruher Instituts für Technologie, Folge 6, 2017. S. Seier, T. Alexandrin: Mieterstrom-Krimi, Abgrund oder Cliffhanger? Episode 16 im Blindstrom Podcast, 2017. M. Dalheimer, P. Hecko: Der Strom, Folge 5 im Pietcast, 2014. GPN17 Special Sibyllinische Neuigkeiten: GPN17, Folge 4 im Podcast des CCC Essen, 2017. Smart Meter Gateway, Gespräch mit S. Ritterbusch im Modellansatz Podcast, Folge 135, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2017

Im Gespräch mit Daniel Koester am Computer Vision for Human-Computer Interaction Lab (cv:hci) geht es um die Erkennung des freien Weges vor unseren Füßen. Das cv:hci befasst sich mit der Fragestellung, wie Menschen mit Computer oder Robotern interagieren, und wie gerade die Bildverarbeitung dazu beitragen kann. Das Thema lässt sich auch gut mit dem Begriff der Anthropromatik beschreiben, der von Karlsruher Informatikprofessoren als Wissenschaft der Symbiose von Mensch und Maschine geprägt wurde und im Institut für Anthropromatik und Robotik am Karlsruher Institut für Technologie (KIT) http://www.kit.edu/ erforscht und gelebt wird. So wurde der ARMAR Roboter, der elektronische Küchenjunge (Video), ebenfalls am Institut an der Fakultät für Informatik  entwickelt. Schon früh stellte sich heraus, dass die Steuerung von Programmierung von Computern mit höheren Programmiersprachen wie Fortran, BASIC oder Logo durch Anlehnung an die menschliche Sprache große Vorteile gegenüber der Verwendung der Maschinensprache besitzt. Damit liegt das Thema ganz natürlich im Bereich der Informatik ist aber auch gleichzeitig sehr interdisziplinär aufgestellt: Das Team des KaMaRo (Folge im Modellansatz Podcast zum KaMaRo und Probabilistischer Robotik) entwickelt den Roboter in einem Team aus den Disziplinen Maschinenbau, Elektrotechnik und Informatik. Mit der Freiflächenerkennung befasst sich Daniel Koester seit seiner Diplomarbeit, wo er die Frage anging, wie die Kurzstreckennavigation für blinde Personen erleichtert werden kann. Hier besteht eine Herausforderung darin, dass zwischen einer Fußgängernavigation und der Umgebungserfassung mit dem Blindenlangstock eine große informative Lücke besteht. Nach Abschaltung der Selective Availability des GPS liegt die erreichbare Genauigkeit bei mehreren Metern, aber selbst das ist nicht immer ausreichend. Dazu sind Hindernisse und Gefahren, wie Baustellen oder Personen auf dem Weg, natürlich in keiner Karte verzeichnet. Dabei können Informationen von anderen Verkehrsteilnehmern Navigationslösungen deutlich verbessern, wie das Navigationssystem Waze demonstriert. Die Erkennung von freien Flächen ist außer zur Unterstützung in der Fußgängernavigation auch für einige weitere Anwendungen sehr wichtig- so werden diese Techniken auch für Fahrassistenzsysteme in Autos und für die Bewegungssteuerung von Robotern genutzt. Dabei kommen neben der visuellen Erfassung der Umgebung wie bei Mobileye auch weitere Sensoren hinzu: Mit Lidar werden mit Lasern sehr schnell und genau Abstände vermessen, Beispiele sind hier das Google Driverless Car oder auch der KaMaRo. Mit Schall arbeiten Sonor-Systeme sehr robust und sind im Vergleich zu Lidar relativ preisgünstig und werden oft für Einparkhilfe verwendet. Der UltraCane ist beispielsweise ein Blindenstock mit Ultraschallunterstützung und der GuideCane leitet mit Rädern aktiv um Hindernisse herum. Mit Radar werden im Auto beispielsweise Abstandsregelungen und Notbremsassistenten umgesetzt. Die hier betrachtete Freiflächenerkennung soll aber keinesfalls den Langstock ersetzen, sondern das bewährte System möglichst hilfreich ergänzen. Dabei war es ein besonderer Schritt von der Erkennung bekannter und zu erlernenden Objekte abzusehen, sondern für eine größere Robustheit und Stabilität gerade die Abwesenheit von Objekten zu betrachten. Dazu beschränken sich die Arbeiten zunächst auf optische Sensoren, wobei Daniel Koester sich auf die Erfassung mit Stereo-Kamerasystemen konzentriert. Grundsätzlich ermöglicht die Analyse der Parataxe eine dreidimensionale Erfassung der Umgebung- dies ist zwar in gewissem Maße auch mit nur einer Kamera möglicht, die sich bewegt, jedoch mit zwei Kameras in definiertem Abstand wird dies deutlich leichter und genauer. Dies entspricht dem verbreiteten stereoskopischen Sehen von Menschen mit Augenlicht, doch mitunter kommt es zu Situationen, dass Kinder bei einem schwächeren Auge das stereoskopische Sehen nicht erlernen- hier können temporär Augenpflaster zum Einsatz kommen. Zur Rekonstruktion der Tiefenkarte aus einem Stereobild müssen zunächst korrespondierende Bildelemente gefunden werden, deren Parallaxenverschiebung dann die Bildtiefe ergibt. Ein Verfahren dazu ist das Block-Matching auf Epipolarlinien. Für ein gutes Ergebnis sollten die beiden Sensoren der Stereo-Kamera gut kalibriert und die Aufnahmen vor der Analyse rektifiziert sein. Die Zuordnung gleicher Bildelemente kann auch als lokale Kreuzkorrelation gesehen werden. Diese Tiefenrekonstruktion ist auch den menschlichen Augen nachempfunden, denen durch geeignete Wiederholung zufälliger Punkte in einem Bild eine räumliche Szene vorgespielt werden kann. Dieses Prinzip wird beim Stereogrammen oder Single Image Random Dot Stereogram (SIRDS)  ausgenutzt. Weiterhin muss man die Abbildungseigenschaften der Kameras berücksichtigen, damit die Parallaxverschiebungen auf horizontalen Linien bleiben. Ebenso müssen Vignettierungen ausgeglichen werden. Algorithmen, die nur lokale Informationen zur Identifikation von Korrespondenzen verwenden, lassen sich sehr gut parallelisieren und damit auf geeigneter Software beschleunigen. Für größere gleichmäßige Flächen kommen diese Verfahren aber an die Grenzen und müssen durch globale Verfahren ergänzt oder korrigiert werden. Dabei leiden Computer und Algorithmen in gewisser Weise auch an der Menge der Daten: Der Mensch ist ausgezeichnet darin, die Bildinformationen auf das eigentlich Wichtige zu reduzieren, der Computer hat damit aber große Schwierigkeiten. Für den Flowerbox-Testdatensatz (2GB) wurden Videos mit 1600x1200 Pixeln aufgelöste und synchronisierte Kameras in Stereo aufgezeichnet. Beispiele für synchronisierte Stereokamera-Systeme im Consumer-Bereich sind die Bumblebee oder das GoPro 3D-System. Die Kameras wurden leicht nach unten gerichtet an den Oberkörper gehalten und damit Aufnahmen gemacht, die dann zur Berechnung des Disparitätenbildes bzw. der Tiefenkarte verwendet wurden. Ebenso wurden die Videos manuell zu jedem 5. Bild gelabeled, um die tatsächliche Freifläche zur Evaluation als Referenz zu haben. Der Datensatz zeigt das grundsätzliche Problem bei der Aufnahme mit einer Kamera am Körper: Die Bewegung des Menschen lässt die Ausrichtung der Kamera stark variieren, wodurch herkömmliche Verfahren leicht an ihre Grenzen stoßen. Das entwickelte Verfahren bestimmt nun an Hand der Disparitätenkarte die Normalenvektoren für die Bereiche vor der Person. Hier wird ausgenutzt, dass bei der Betrachtung der Disparitätenkarte von unten nach oben auf freien Flächen die Entfernung kontinuierlich zunimmt. Deshalb kann man aus der Steigung bzw. dem Gradienten das Maß der Entfernungszunahme berechnen und damit die Ausrichtung und den auf der Fläche senkrecht stehenden Normalenvektor bestimmen. Die bestimmte Freifläche ist nun der zusammenhängende Bereich, bei denen der Normalenvektor ebenso aufrecht steht, wie bei dem Bereich vor den Füßen. Die Evaluation des Verfahrens erfolgte nun im Vergleich zu den gelabelten Daten aus dem Flowerbox-Datensatz. Dies führt auf eine Vierfeld-Statistik für das Verfahren. Im Ergebnis ergab sich eine korrekte Klassifikation für über 90% der Pixel auf Basis der realistischen Bilddaten. Die veröffentlichte Software ist im Blind and Vision Support System (BVS) integriert, in der erforderliche Module in der Form eine Graphen mit einander verknüpft werden können- bei Bedarf auch parallel. Eine ähnliche aber gleichzeitig deutlich umfassendere Architektur ist das Robot Operation System (ROS), das noch viele weitere Aspekte der Robotersteuerung abdeckt. Eine wichtige Bibliothek, die auch stark verwendet wurde, ist OpenCV, mit der viele Aspekte der Bildverarbeitung sehr effizient umgesetzt werden kann. Die Entwicklung der Hardware, gerade bei Mobilgeräten, lässt hoffen, dass die entwickelten Verfahren sehr bald in Echtzeit durchgeführt werden können: So können aktuelle Smartphones Spiele Software des Amiga Heimcomputers in einem interpretierten Javascript Emulator auf der Amiga Software Library auf Archive.org nahezu in Orginalgeschwindigkeit darstellen. Für die Umsetzung von Assistenzsystemen für blinde und sehgeschädigte Menschen ist aber auch immer der Austausch mit Nutzern erforderlich: So sind Freiflächen für sich für blinde Personen zunächst Bereiche ohne Orientierbarkeit, da es keinen tastbaren Anknüpfungspunkt gibt. Hier müssen entweder digitale Linien erschaffen werden, oder die Navigation sich weiter nahe an fühlbaren Hindernissen orientieren. Am cv:hci ist der Austausch durch das angeschlossene Studienzentrum für sehgeschädigte Studierende (SZS) unmittelbar gegeben, wo entwickelte Technik sich unmittelbar dem Alltagsnutzen stellen muss. Die entwickelte Freiflächenerkennung war nicht nur wissenschaftlich erfolgreich, sondern gewann auch einen Google Faculty Research Award und die Arbeitsgruppe wurde in der Lehre für ihr Praktikum den Best Praktikum Award 2015 ausgezeichnet.Literatur und weiterführende Informationen D.Koester: A Guidance and Obstacle Evasion Software Framework for Visually Impaired People, Diplomarbeit an der Fakultät für Informatik, Karlsruher Institut für Technologie (KIT), 2013.D. Koester, B. Schauerte, R. Stiefelhagen: Accessible Section Detection for Visual Guidance, IEEE International Conference on Multimedia and Expo Workshops (ICMEW), 2013.

"Many medical interventions today are qualitatively and quantitatively limited by human physical and cognitive capabilities. Professor Allison Okamura will discuss robotic systems that will extend humans’ ability to improve patient care by minimizing invasiveness and improving accuracy. Allison Okamura, MS '96, PhD '00, is an associate professor in mechanical engineering. Her academic interests include haptics (tactile feedback technology), virtual environments and simulators, medical robotics, neuromechanics, prosthetics, and engineering education. She has served as associate editor of the IEEE Transactions on Haptics, editor of the IEEE International Conference on Robotics and Automation Conference Editorial Board, and co-chair of the IEEE Haptics Symposium. Classes Without Quizzes are presented by the Stanford Alumni Association. Filmed on location at Stanford Reunion Homecoming 2014."

RHex is an all-terrain walking robot that could one day climb over rubble in a rescue mission or cross the desert with environmental sensors strapped to its back. Pronounced "Rex," like the over-excited puppy it resembles when it is bounding over the ground, RHex is short for "robot hexapod," a name that stems from its six springy legs. Legs have an advantage over wheels when it comes to rough terrain, but the articulated legs often found on walking robots require complex, specialized instructions for each moving part. To get the most mobility out of RHex's simple, one-jointed legs, Penn researchers are essentially teaching the robot Parkour. Taking inspiration from human free-runners, the team is showing the robot how to manipulate its body in creative ways to get around all sorts of obstacles. The RHex platform was first developed through a multi-university collaboration more than a decade ago. Graduate student Aaron Johnson and professor Daniel Koditschek, both of the Department of Electrical and Systems Engineering in the School of Engineering and Applied Science, are working on a version of RHex known as XRL, or X-RHex Lite. This lighter and more agile version of the robot, developed in Koditschek's Kod*Lab, a division of Engineering's General Robotics, Automation, Sensing and Perception (GRASP) Lab, is ideal for testing new ways for it to run, jump, and climb. By activating its legs in different sequences, XRL can execute double jumps, flips, and, through a combination of moves, even pull-ups. For the tallest obstacles, the robot can launch itself vertically, hook its front legs on the edge of the object it's trying to surmount, then drag its body up and over. The researchers fully demonstrated this particular maneuver under more controlled conditions in the lab. The paper where Johnson and Koditschek outlined these capabilities—"Toward a Vocabulary of Legged Leaping"—was selected as a finalist for best student paper at the IEEE International Conference on Robotics and Automation in May. "What we want is a robot that can go anywhere, even over terrain that might be broken and uneven," Johnson says. "These latest jumps greatly expand the range of what this machine is capable of, as it can now jump onto or across obstacles that are bigger than it is." For more info: http://www.upenn.edu/spotlights/robot... The scientific paper from the lab: http://kodlab.seas.upenn.edu/Aaron/IC... Video Produced and Directed by Kurtis Sensenig (Twitter: @ksensenig) Text by Evan Lerner Music: "Stretched Out" by Hedgehog's Dilemma

We start with presenting motivation and goals for the Future Internet, and reviewing basics of trust in computing.The Pervasive Trust Foundation (PTF) for the Future Internet is proposed next. This includes presenting motivation for trust foundation for the Future Internet, showing placement of security services and mechanisms within the architecture, and trust considerations for security services.Inefficient operation of the PTF-based architecture is the main obstacle to making such architecture a reality. There are two classes of approaches that can reduce operational costs. First, inherent PTF properties result in automatic cost-saving. Second, additional cost-saving techniques --such as leveraging high-trust enclaves, or using enclave "insurers"-- can be used.The architectural principles presented here are a position statement, and their practical verification will require substantial research efforts. About the speaker: Leszek Lilien is an Assistant Professor of Computer Science at Western Michigan University (WMU). He obtained his Ph.D. degree from the University of Pittsburgh, and was involved in post-doctoral research at Purdue University. His was a faculty at the University of Illinois at Chicago and a tutorial instructor for IEEE Computer Society. He has a diversified R&D experience in industry and some entrepreneurial experience in the United States and Poland.His current research is focused in two areas: (1) opportunistic capability utilization networks a.k.a. oppnets (a specialized kind of ad hoc networks); and (2) trust, privacy and security in open computing systems. In Area 1, he focuses on primitives for oppnets, privacy and security in oppnets, and interoperability of oppnet helper networks and devices. In Area 2, he concentrates on privacy-preserving data dissemination, the role of trust in open computing environments, analysis of computer security paradigms, and security and privacy aspects in pervasive systems, including ad hoc sensor networks and embedded networks. He is an Editorial Board member for the International Journal of Communication Networks and Distributed Systems, The Open Cybernetics & Systemics Journal, and Recent Patents on Computer Science. He chaired and organized two International Workshops on Specialized Ad Hoc Networks and Systems (SAHNS 2007 and SAHNS 2009) in conjunction with the IEEE International Conference on Distributed Computing Systems (ICDCS 2007 and ICDCS 2009). He was a Co-PI for an NSF grant on vulnerability analysis and threat assessment/avoidance. In 2008, he was selected for the Visiting Faculty Research Program (VFRP), U.S. Air Force Research Lab. He is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) and IEEE Computer Society. He is affiliated with the Center for Education and Research in Information Assurance and Security (CERIAS) at Purdue University.