Kolloquium Interaktion
Im Rahmen dieses Forschungskolloquiums werden aktuelle Forschungsarbeiten im Bereich Interaktion vorgestellt.
Aktuelle Vorträge:
06.02.2023 (12:30 Uhr)
Ort: G29-R307
Vortragender: Prof. Kalyanmoy Deb, University Distinguished Professor, Michigan State University, USA
Titel: Evolutionary Multi-Criterion Optimization: An Emerging Computational Problem-Solving Tool
Abstract: Most problems in science, engineering and commerce involve more than one conflicting criteria to be simultaneously optimized. Despite the vast literature on scalarizing multiple criteria into one, evolutionary optimization methods of treating them as truly multi-criterion problems in a Pareto sense produce a number of additional benefits to the users. Their ability to find and maintain multiple trade-off solutions with a flexible and customizable framework provides vital knowledge about the problem in addition to the optimal solutions themselves. In this lecture, we shall present a few popular and state-of-the-art algorithms, demonstrate their advantages on a number of real-world practical problems from engineering and society, and introduce some recent research topics. Additionally, the use of machine learning algorithms and human knowledge in enhancing their performance, and the use of multi-criterion algorithms in enhancing performance of machine learning methods will be discussed.
Bio-sketch: Kalyanmoy Deb is University Distinguished Professor and Koenig Endowed Chair Professor at Department of Electrical and Computer Engineering in Michigan State University, USA. Prof. Deb's research interests are in evolutionary optimization and their application in multi-criterion optimization, modeling, and machine learning. He is and has been a visiting professor at various universities across the world including University of Skövde in Sweden, Aalto University in Finland, Nanyang Technological University in Singapore, and IITs in India. He was awarded IEEE Evolutionary Computation Pioneer Award for his sustained work in multi-objective optimization, Infosys Prize, TWAS Prize in Engineering Sciences, CajAstur Mamdani Prize, Distinguished Alumni Award from IIT Kharagpur, Edgeworth-Pareto award, Bhatnagar Prize in Engineering Sciences, and Bessel Research award from Germany. He is fellow of ACM, IEEE, and ASME. He has published over 600 research papers with Google Scholar citation of almost 180,000 with h-index 129. More information about his research contribution can be found from https://www.coin-lab.org.
Vergangene Vorträge:
13.12.2022 (10:00 Uhr)
Ort: G29-R035
Vortragender: Prof. Dr. Shohei Hidaka, Japan Advanced Institute of Science and Technology
Titel: A mathematical theory of 3D vision of line drawings
Abstract: Visual perception, receiving a two-dimensional (2D) visual input, often constructs the three-dimensional (3D) perceptual image. Although there are generally multiple structures in the external world that give an equivalent two-dimensional retinal image, the perceptual process naturally and easily infers only one 3D structure as the solution (for example, a Necker Cube). However, the following problems are not obvious at all: what kind of structure can be obtained as a 3D perceptual image from certain 2D information, and why do we get a three-dimensional perceptual image instead of a two-dimensional one. In the present study, we investigate this problem by untangling the Necker Cube phenomenon, and propose a novel theory of three-dimensional visual perception from the viewpoint of the efficiency of information coding.
Among the possible structures that can yield the 2D retinal image of the Necker Cube, the structure of the typical three-dimensional perceptual image of the Necker Cube maximizes the symmetry (in group theory). This maximization of symmetry is characterized by the pairs of adjoint functors (in category theory). Therefore, according to this proposed theory, “the Necker Cube” in the three-dimensional space is perceived as the most efficient encoding of the two-dimensional retinal image.
02.12.2019 (16:00 Uhr)
Ort: G29-301
Vortragender: Prof. Dr. Hisao Ishibuchi, Southern University of Science and Technology, China
Titel: Evolutionary Many-Objective Optimization
Abstract: Recently, evolutionary many-objective optimization has been a hot research topic in the evolutionary multi-objective optimization (EMO) community. In this talk, first I briefly explain the popularity of evolutionary multi-objective and many-objective research using some statistics. Next I explain the following difficulties in evolutionary many-objective optimization: (i) Pareto dominance does not work well in fitness evaluation, (ii) A huge number of solutions are needed to approximate the entire Pareto front, (iii) Visualization of obtained solutions is difficult, (vi) Selection of a single final solution is difficult, (v) Observation of search behavior of many-objective algorithms is difficult, and (vi) Usefulness of crossover is degraded.
Then I explain some difficulties in performance evaluations of evolutionary multi-objective and many-objective algorithms. The difficulties includes the design of computational experiments (e.g., specification of the population size), the choice of test problems and the specification of performance indicators. Finally, I suggest some promising future research directions.
Short Bio: Hisao Ishibuchi received the BS and MS degrees from Kyoto University in 1985 and 1987, respectively. In 1992, he received the Ph. D. degree from Osaka Prefecture University. Since April 2017, he is with Department of Computer Science and Engineering, Southern University of Science Technology, Shenzhen, as a Chair Professor. He received Best Paper Awards from GECCO 2004, HIS-NCEI 2006, FUZZ-IEEE 2009, WAC 2010, SCIS & ISIS 2010, FUZZ-IEEE 2011, ACIIDS 2015, GECCO 2017, 2018 and EMO 2019. He also received a 2007 JSPS (Japan Society for the Promotion of Science) Prize, a 2019 IEEE CIS Fuzzy Systems Pioneer Award, and a 2020 IEEE Trans. on Evolutionary Computation Outstanding Paper Award. He was the IEEE CIS Vice-President for Technical Activities (2010-2013).
Currently, he is the Editor-in-Chief of IEEE CI Magazine (2014-2019) and an IEEE CIS AdCom member (2014-2019). He is also an Associate Editor of IEEE Trans. on Evolutionary Computation, IEEE Access, IEEE Trans. on Cybernetics, and Memetic Computing. He is an IEEE Fellow. In 2018, he was selected in the “Thousand Talents Program” in China.
11.10.2019 (13:00 Uhr)
Ort: G29-301
Vortragender: Dipl. Ing. Thomas Heine, Project Manager Active & Healthy Ageing,
http://weimar.ipc.uni-tuebingen.de
Titel: Das LebensPhasenHaus - Innovationen in einer unterjüngenden Gesellschaft
Abstract: Gemeinsam Innovationen schaffen. Das LebensPhasenHaus (LPH) in Tübingen ist ein Ort für Forschung, Demonstration und Wissenstransfer. Es wurde von 2014 an, über drei Jahre hinweg, mit einer Anschubfinanzierung des Landes Baden-Württemberg (Ministerium für Arbeit und Sozialordnung, Familie, Frauen und Senioren; Ministerium für Finanzen und Wirtschaft; Ministerium für Wissenschaft, Forschung und Kunst) gefördert und öffnete 2015 für die Öffentlichkeit.
Als Kooperation zwischen Wissenschaftlern der Universität und des Universitätsklinikums Tübingen, der Industrie- und Handelskammer Reutlingen, Wirtschaftsunternehmen der Region, Interessensverbänden sowie Experten aus dem Gesundheits- und Pflegebereich sollen im Verlauf von zehn Jahren barrierefreie Wohn- und Freiraumkonzepte, altersgerechte Assistenzsysteme und die damit einhergehenden Dienstleistungen, digitale Informations- und Kommunikationstechnologien sowie die intelligente Vernetzung der Systeme untereinander getestet, validiert, demonstriert und letztendlich erlebbar gemacht werden.
Dieses Ökosystem aus verschiedensten Vertretern aus Politik, Wirtschaft, Wissenschaft und Gesellschaft ist die Grundlage für den Innovationshub LPH. Auf diese Weise können die zentralen Herausforderungen der Unterjüngung der Gesellschaft ganzheitlich und nachhaltig behandelt werden.
Eindrücke, Projekte, Erfahrungen, Hürden und Fallen sowie verschiedenste Momente rund um die Aktivitäten des LebensPhasenHauses.
30.08.2019 (13:00 Uhr)
Ort: G29-301
Vortragender: Dr.-Ing. Dipl.-Inf. Ronald Böck, FEIT IIKT-Cognitive Systeme, www.kognitivesysteme.de
Titel: Automatic Speech Recognition in Relation to Enhanced Human-Machine Interaction
Abstract: Currently, speech-based technologies and devices attract more and more attention allowing a hands-free controlling of and interaction with such systems. Besides the content, further information on the interlocutor’s states and traits can be derived from any interaction, especially from speech input. For this, the textual representation of spoke utterances as well as the acoustics can be analysed to assess the interaction partner.
The talk presents fundamental approaches in speech recognition and an overview on relevant features extracted from the acoustics. Further, emotional models and concepts, utilised in emotion recognition from speech, are introduced and the relation to labelling are discussed. This allows considerations of automatic assessments of emotions and dispositions from speech. Finally, the option for investigations on group-level interaction in the multi-agent-human-machine interaction are presented.
27.06.2019 (13:30 Uhr)
Ort: G29-224
Vortragender: Dr. Andre Mastmeyer, habil., Universität zu Lübeck, Institut für Medizinische Informatik, www.imi.uni-luebeck.de
Titel: Aktuelle Methoden zur 3D-Rekonstruktion von Brachytherapiekathetern in intraoperativen MRT-Bilddaten
Abstract: Die externe Strahlentherapie mit anschließender High-Dose-Rate-(HDR)-Brachytherapie ist der Standard für die Behandlung von Prostatatumoren oder gynäkologischen Krebserkrankungen. Der verbesserte Weichteilkontrast durch die Magnetresonanztomographie (MRT) liefert eine wertvolle, verbesserte Weichteilkontrastierung zur Diagnose und Behandlung dieser Krebsarten.
Jedoch ist im Gegensatz zur Computertomographie (CT)-Bildgebung das Erscheinungsbild der Brachytherapiekatheter, durch die kleine Strahlenquellen zum Tumor gebracht werden, in den Bildern oft variabel und dunkel kontrastiert. Verwechslungen mit anderen ähnlich dargestellten Strukturen ist das Hauptproblem bei der Rekonstruktion. Die Identifikation dieser Katheterbahnen bis zu einer Lochschablone außen am Körper des Patienten ist wichtig, um die richtigen Einschublöcher für die Strahler zu wählen.
In diesem Beitrag wird über eine semiautomatische, modellbasierte und eine vollautomatische Methode des maschinellen Lernens zur Katheterrekonstruktion berichtet, welche in der Situation vieler (bis zu 40) eng zusammenliegender Brachytherapiekatheter vielversprechende Ergebnisse liefern.
21.06.2019 (13:00 Uhr)
Ort: G29-301
Vortragender: Jun.-Prof. Dr.-Ing. Marc Herrlich, University of Kaiserslautern (TUK), Department of Electrical and Computer Engineering, Serious Games Engineering, https://sge.eit.uni-kl.de/en
Titel: Realitätsbasierte Interaktion mit medizinischen Bilddaten
Abstract: Computer und Technik umgeben uns beinahe jederzeit und überall. Computer erleichtern viele Tätigkeiten und schaffen neue Möglichkeiten. Auch im medizinischen Bereich und insbesondere in der Chirurgie sind bildgestützte Verfahren inzwischen eine wichtige Grundlage für viele Eingriffe. Trotz der großen Erfolge der Computertechnik ist die Interaktion mit diesen Systemen für die Benutzer*innen jedoch auch häufig eine frustrierende oder kognitiv belastende Erfahrung und setzt oftmals eine langwierige Lernphase und die Kenntnis vieler Konzepte voraus, die mit der eigentlichen Anwendungsdomäne nur wenig zu tun haben. Außerdem werden die verschiedenen menschlichen Sinne und Interaktionsmöglichkeiten durch WIMP-Interaktionsansätze nicht zu ihrem vollen Potenzial ausgeschöpft.
In diesem Vortrag wird anhand ausgewählter Arbeiten aufgezeigt, wie realitätsbasierte Interaktionsansätze den Umgang mit medizinischen Bilddaten verändern und verbessern können.
04.06.2019 (13:00 Uhr)
Vortragende: Frau Dr. Annabel Latham, Manchester Metropolitan University, UK Ort: G29-301
Thema: Automated Profiling of Individual Traits: Modelling Learning Styles with Oscar Conversational Intelligent Tutoring System
Abstract: Use of computational intelligence methods for automated user profiling have been widely publicised recently following the Cambridge Analytica / FaceBook scandal, with implications for the ethics and governance of tracking data held on social media and other platforms. Debate over the validity of psychological models of personality and learning styles is not new, however adaptive and targeted online advertisements that rely on such models are big business. This talk explores the application of intelligent systems to user profiling in the online learning domain. It will describe research to develop methods for profiling individual traits in a learning context, introducing a Conversational Intelligent Tutoring System called Oscar and the experiments to automatically predict each individual learner’s preferred style in order to provide an adaptive learning experience. Using the Felder-Silverman model of learning styles, a set of typical behaviours is mapped to a set of variables to capture each learner’s behaviour during an adaptive conversational tutorial. The complexities of capturing data implicitly during a real-time tutoring conversation in a live teaching/learning environment will be discussed. A number of methods and algorithms (e.g. rule-based, MLP neural networks, decision trees, fuzzy decision trees) were applied to the behaviour dataset to determine the best predictions for each of the 4 dimensions of learning style, and for attribute selection to reduce time/complexity for application in real-time tutoring conversations.
21.02.2019
Vortragender: Antonio Krüger
Titel: Designing human-centered body-worn User Interfaces
Abstract: User Interfaces move towards our bodies. They have already started to leave the desktops, are occupying our pockets and are attached to our wrist, head and skin. Designing appropriate User Interfaces for these new device classes come with particular challenges associated with the restrictions and novel possibilities of this development. An important aspect is that of perceptual equivalence, meaning that these UI need to be aware of the human's attention, their ergonomic restrictions and the cognitive costs associated to the interaction itself. At the same time they need to be able to support and induce novel types of perceptions as well. Since body-worn User Interfaces can be used in a plentitude of different situations there are several interesting technical design challenges that need to be addressed. This includes Augmented (AR) and Virtual Reality (VR) set-ups where these UI have the potential to improve the user experience. In this talk, I will present examples of our own endeavor to better understand the design challenges grounded to the cognitive and technical requirements of body-worn interfaces. I will describe an interaction-framework for wrist-worn devices that we have developed, present a novel calibration-free eye-tracker that has the potential to inform a perceptual model of the user's current attention, and discuss a novel VR controller aiming to improve weight perception of virtual objects. Finally, I will discuss an in-situ body-worn training device for runners, that uses electric muscle stimulation to actuate correct foot postures.
07.02.2019 (15:00 Uhr in Raum 301, G29)
Image Thresholding using Multilevel Quantum Systems
Vortragender: Prof. Dr. Siddhartha Bhattacharyya, RCC Institute of Information Technology, Kolkata, Indien
Abstract: Cluster analysis is a popular technique whose aim is to segregate a set of data points into groups, called clusters where the number of clusters are predefined. Thresholding remains the simplest method for image segmentation. The determination of the appropriate threshold levels in an image is a challenging proposition in the computer vision community. Different metaheuristics are widely used for solving complex optimization problems. In this lecture, the use of the multilevel quantum logic and metaheuristic techniques is explored to design multilevel quantum inspired metaheuristics, which can be applied to optimize the thresholding process.
Brief Bio: Siddhartha Bhattacharyya is currently a Senior Research Scientist in VSB Technical University of Ostrava, Ostrava, Czechia. He is also the Principal of RCC Institute of Information Technology, Kolkata, India. In addition, he is also serving as a Full Professor of Computer Application of the institute. Prior to this, he was a Full Professor of Information Technology of RCC Institute of Information Technology, Kolkata, India. He served as the Head of the Department from March, 2014 to December, 2016. Prior to this, he was an Associate Professor of Information Technology of RCC Institute of Information Technology, Kolkata, India from 2011-2014. Before that, he served as an Assistant Professor in Computer Science and Information Technology of University Institute of Technology, The University of Burdwan, India from 2005-2011. He was a Lecturer in Information Technology of Kalyani Government Engineering College, India during 2001-2005.
He did his BS in Physics, B. Tech. and M. Tech. from University of Calcutta, India in 1995, 1998 and 2000 respectively. He completed PhD in Computer Science and Engineering from Jadavpur University, India in 2008. He is the recipient of several accolades and awards, both at the national and international levels. He is a co-author of 4 books and the co-editor of 20 books and has more than 200 research publications in international journals and conference proceedings. He has got two PCTs to his name.
His research interests include soft computing, pattern recognition, multimedia data processing, hybrid intelligence and quantum computing.
Siddhartha is a life fellow of OSI, fellow of IETE and IEI, India. He is also a senior member of IEEE, IETI, and ACM. He is a life member of ISRD, CSI, ISTE, IUPRAI and CEGR. He is a member of IET, IRSS, IAENG, CSTA, IAASSE, IDES, ISSIP and SDIWC.
07.02.2019
Vortragender: Jens Krüger
Titel: All In For Neurostimulation
Abstract: Was haben 8-bit Mikroprozessoren, Supercomputer, iPads, iPhones, Workstations und VR Systeme gemeinsam? Alle verbrauchen Strom . und alle diese Geräte (und noch Einiges mehr an Hardware und Software) verwenden wir um eine integrierte Pipeline zur tiefen Hirnstimulation (engl. Deep Brain Stimulation, DBS) zu bauen. Ziel ist es, von der ersten Datenakquise über die präoperative Planung, der Operation selbst, bis hin zur postoperativen Nachversorgung alle Schritte der klinischen Praxis durch Assistenzsysteme zu verbessern. In diesem Vortrag werde ich dazu den bisherigen Stand unserer Entwicklungen vorstellen und über aktuelle Forschungsthemen und offene Fragestellungen sprechen. Zu Letzterem werde ich besonders auf die praktische Nutzbarkeit von VR in klinischen Routinen eingehen.
17.01.2019
Vortragende: Christian Geiger, Anastasia Treskunov
Titel: Mixed Reality Interaction in the Wild"- über die Entwicklung innovativer Mensch-Technik Schnittstellen jenseits von Labor und Schreibtisch
Abstract: Die Arbeitsgruppe Mixed Reality & Visualisierung der Hochschule Düsseldorf (www.mirevi.de) arbeitet an innovativen Nutzererfahrungen entlang des Mixed Reality Kontinuums. Ein besonderer Fokus liegt auf der praktischen Anwendbarkeit der Ergebnisse in allen Lebensräumen für möglichst unterschiedliche Nutzertypen. Dazu arbeitet das MIREVI-Team in einem externen Labor mit zwei wirtschaftlichen Partnern aus den Bereichen Medientechnik und 3D-Design. Der Vortrag gibt eine Übersicht aktueller Arbeiten und fokussiert dabei insbesondere auf Aktivitäten im öffentlichen Raum und an der Schnittstelle digitaler Medien und Gesundheit. Synergien zwischen Kunst, Technik und Wirtschaft und die durchgängige Nutzerzentrierung beim Entwurf von Mixed Reality Inhalten charakterisieren die Arbeiten des MIREVI-Teams. Aktuellstes Vorhaben der Düsseldorfer ist der Aufbau eines LivingLabs um immersive Technologien für ein gesundes Leben zu untersuchen.
22.11.2018
Vortragender: Eike Langbehn
Titel: Perceptual Limitations and Illusions in Virtual Reality
Abstract: Immersive technologies enable us to experience computer-mediated realities through several sensory channels. Being immersed with all of our senses usually enhances the sense of presence. But virtual reality is also being accompanied by some issues of the human perception, e.g. spatio-temporal misperception in terms of distance, size and speed estimation. In this talk, I want to show how these limitations of the human perceptual system can be identified and, furthermore, how these limitations might be exploited for the development of (super-)natural and perceptually-inspired user interfaces. Especially, locomotion techniques can benefit from spatial illusions to support real walking in a confined tracking space.
Mittwoch, den 19.07.2017 (15:30 Uhr in Raum 301, G29)
Breaking the Billion Variable Barrier in Optimization
Prof. Kalyanmoy Deb, Koenig Endowed Chair Professor, Department of Electrical and Computer Engineering, Michigan State University, East Lansing, USA
Abstract:
Optimization methods and practices are around for more than 50 years, but they are still criticized for their "curse of dimensionality". In this talk, we shall look at a specific large-dimensional integer-valued resource allocation problem from practice and review the performance of well-known softwares, such as IBM's CPLEX, on the problem. Thereafter, we shall present a population-based heuristic search algorithm that has the ability to recombine short-sized building blocks, despite having overlapping variable linkage, to form larger-sized building blocks. The process is eventually able to solve a billion-variable version of the problem to near-optimality in a polynomial computational time, making the application one of the largest size optimization problems ever solved.
Bio-sketch of the Speaker:
Kalyanmoy Deb is Koenig Endowed Chair Professor at Department of Electrical and Computer Engineering in Michigan State University, USA. Prof. Deb's research interests are in evolutionary optimization and their application in multi-criterion optimization, modeling, and machine learning. He has worked at various universities across the world including IITs in India, University of Dortmund and Karlsruhe Institute of Technology in Germany, Aalto University in Finland, University of Skovde in Sweden, Nanyang Technological University in Singapore. He was awarded Infosys Prize, TWAS Prize in Engineering Sciences, CajAstur Mamdani Prize, Distinguished Alumni Award from IIT Kharagpur, Edgeworth-Pareto award, Bhatnagar Prize in Engineering Sciences, and Bessel Research award from Germany. He has been just awarded IEEE CIS's "EC Pioneer Award". He is fellow of IEEE, ASME, and three Indian science and engineering academies. He has published over 475 research papers with Google Scholar citation of over 100,000 with h-index 102. He is in the editorial board on 20 major international journals. More information about his research contribution can be found from http://www.egr.msu.edu/~kdeb.
12.06.2018 (10:30 Uhr in Raum G29-035)
Dynamic multi-objective optimization: introduction, issues and future directions
Vortragende: Frau Dr. Marde Helbig
Abstract:
Most optimization problems in real-life have more than one objective, with at least two objectives in conflict with one another and at least one objective that changes over time. These kinds of optimization problems are referred to as dynamic multi-objective optimization (DMOO) problems.
Most research in multi-objective optimization has been conducted on static problems and most research on dynamic problems has been conducted on single-objective optimization. The goal of a DMOO algorithm (DMOA) is to find an optimal set of solutions that is as close as possible to the true set of solutions, and a diverse set of solutions. However, in addition to these goals a DMOA has to track the changing set of optimal solutions over time.
This talk will introduce the field of DMOO by discussing: benchmark functions and performance measures that have been proposed and the issues related to each of these; changes required to static MOO algorithms to solve DMOO; challenges in the DMOO field that are not yet addressed, such as incorporating a decision maker’s preference in DMOO and visualizing the behaviour of DMOAs; real-world applications; and emerging research fields that provide interesting research opportunities.
Biography:
Dr Mardé Helbig is a senior lecturer in the Department of Computer Science at the University of Pretoria (UP). Her research focuses on solving dynamic multi-objective optimization problems (DMOOPs) using computational intelligence algorithms. She is a member of the South African Young Academy of Science (SAYAS), as well as a member of the executive committee of SAYAS, Exceptional Young Researcher Award from UP, and Emerging Research Excellence Award and Promising Young Researcher Excellence Award from the CSIR Meraka Institute.
31.01.2017 (17:00 Uhr in Raum 301)
Rolling Horizon Evolutionary Methods for General Video Game Playing
Dr. Diego Perez Liebana, University of Essex, UK
Abstract:
Games have been used as benchmarks for research in Computational Intelligence for several decades, typically applying state of the art techniques to specific games in order to achieve a high quality of play. Breakthroughs like combining Monte Carlo Tree Search and Deep Learning in the game of Go last year demonstrate the efficiency and popularity of this methodology. However, there's another trend in recent years that tries to push our knowledge and understanding of Artificial General Intelligence, and one of the means to achieve this is tackling the problem of General (Video) Game Playing. MCTS techniques have generally dominated this problem, but recent research has started looking at Evolutionary Algorithms and their potential at matching tree search level of play or even outperforming these methods. This talk will first introduce the problem of General Video Game Playing, via our General Video Game AI framework and competition, to later describe Rolling Horizon Evolution, a technique that has been proven useful for creating agents that are able to play any game is given to it. It will also describe recent works and improvements on this technique, to finally discuss promising ideas for improving these methods in the future.
Bio:
Diego is a Lecturer in Computer Games and Artificial Intelligence at the University of Essex (Colchester, UK). He achieved a PhD in Computer Science from the same institution (2015) and a MSc degree in Computer Science from University Carlos III (Madrid, Spain; 2007). His research is centred in the application of Artificial Intelligence to games, Reinforcement Learning and Evolutionary Computation. At the moment, Diego is especially interested on General Video Game Playing, which involves the creation of content and agents that play any real-time game that is given to it. He has published in the field of Game AI, in the main conferences and journals of the field of Computational Intelligence in Games and Evolutionary Computation and has also organised international competitions for the Game AI research community, such as the Physical Travelling Salesman Competition, and the General Video Game AI Competition, held in IEEE (WCCI, CIG) and ACM (GECCO) International Conferences. Diego has experience in the videogames industry as a game programmer (Revistronic; Madrid, Spain), with titles published for both PC and consoles and worked as a software engineer (Game Brains; Dublin, Ireland), where he was in charge of developing AI tools that can be applied to the latest industry videogames.
23.11.2016 (14:00 Uhr in Raum 301)
Software-Defined Multicast for Over-the-Top and Overlay-based Live Streaming in ISP Networks
Jun.-Prof. Dr. David Hausheer, TU Darmstadt
Abstract:
The scalable and efficient support of over-the-top (OTT) applications such as video streaming poses a variety of challenges to today's Internet Service Providers (ISPs). Software-defined networking (SDN) is a novel concept that can help ISPs to deal with these challenges. In this talk, we will present Software-Defined Multicast (SDM), an SDN-based cross-layer approach enabling ISPs to offer network layer multicast support for OTT and overlay-based live streaming as a service. SDM is specifically tailored towards the needs of P2P-based video stream delivery originating from outside the ISP network and can easily be integrated with existing streaming systems. Prototypical evaluations show significantly improved network layer transmission efficiencies when compared to other overlay streaming mechanisms, down to a level as low as for IP multicast, at linearly bounded costs.
15.09.2016 (17:00 Uhr in Raum 307)
Ground Truth Bias in External Cluster Validity Indices
Prof. Jim Bezdeck
Abstract:
This talk begins with a short review of clustering that emphasizes external cluster validity indices (CVIs). A method for generalizing external pair-based CVIS (e.g., the crisp Rand and Jacard indices) to evaluate soft partitions is described and illustrated. Three types of validation experiments conducted with synthetic and real world labeled data are discussed: "best c" (internal validation with labeled data), and "best I/E" (agreement between an internal and external CVI pair). As is always the case in cluster validity, conclusions based on empirical evidence are at the mercy of the data, so the reported results might be invalid for different data sets and/or clustering models and algorithms. But much more importantly, we discovered during these tests that some external cluster validity indices are also at the mercy of the distribution of the ground truth itself. We believe that our study of this surprising fact is the first systematic analysis of a largely unknown but very important problem ~ bias due to the distribution of the ground truth partition. Specifically, in addition to the well known bias in many external CVIs caused by monotonic dependency on c, the number of clusters in candidate partitions, there are two additional kinds of bias that can be caused by an unusual distribution of the clusters in the ground truth partition provided with labeled data. The most important ground truth bias is caused by imbalance (unequally sized labeled subsets). We demonstrate these effects with randomized experiments on 25 pair-based external CVIs. Then we provide a theoretical analysis of bias due to ground truth for several CV is by relating Rand's index to the Havrda-Charvat quadratic entropy.
Bio:
Jim received the PhD in Applied Mathematics from Cornell University in 1973. Jim is past president of NAFIPS (North American Fuzzy Information Processing Society), IFSA (International Fuzzy Systems Association) and the IEEE CIS (Computational Intelligence Society as the NNC): founding editor the Int'l. Jo. Approximate Reasoning and the IEEE Transactions on Fuzzy Systems: Life fellow of the IEEE and IFSA; and a recipient of the IEEE 3rd Millennium, CIS Fuzzy Systems Pioneer, and technical field award Rosenblatt medals, and the IPMU Kempe de Feret Award. Jim retired in 2007, and will be coming to a university near you soon. Jim's interests: woodworking, optimization, motorcycles, pattern recognition, cigars, clustering in big data, fishing, co-clustering, blues music, wireless sensor networks, gardening, cluster validity, poker and visual clustering.
28.06.2016 (17:00 Uhr in Raum 301)
flora robotica - wie Roboter und Pflanzen zum Bio-Hybrid verschmelzen
Prof. Dr. Heiko Hamann, Universität Paderborn
Im EU Horizon 2020 Projekt “flora robotica” untersuchen wir die Vorteile der Kombination eines verteilten Robotersystems und Pflanzen. Potentielle Anwendungsmöglichkeiten reichen vom modernen Gewächshaus bis zur mechanischen Unkrautbekämpfung. Wir konzentrieren uns aber darauf, architektonische Artefakte in Form eines bio-hybriden Systems wachsen zu lassen. Roboter und Pflanzen sollen eine Einheit bilden, Synergien nutzen und tendenziell miteinander verschmelzen. Dieses System zeichnet sich durch gleichberechtigte Rollen zwischen Pflanze, Roboter und Mensch aus. Zeitweise sollen die Roboter Wachstum und Bewegung der Pflanzen steuern, es soll aber auch einen Informationsfluss von den Pflanzen an die Roboter geben. Zusätzlich interagiert der Mensch mit dem flora robotica System. Ich präsentiere unsere ersten Ergebnisse zur Steuerung von Pflanzen durch Roboter, die dafür entwickelte Hardware und unsere Vision.
07.06.2016 (16:00 in Raum 301)
Schwarmintelligenz - von Bienen und Algorithmen
Prof. Dr. habil. Martin Middendorf (Universität Leipzig)
Die Schwarmintelligenz hat sich in den letzten Jahren zu einem sehr vielfältigen und interdisziplinären Forschungsgebiet entwickelt. Im Vortrag stellen wir ausgewählte neuere Arbeiten aus unserer Forschungsgruppe zu diesem Thema vor. Insbesondere geht es um grundlegende algorithmische Ansätze zur Optimierung mit Schwarmverfahren, um Methoden zur Visualisierung des Verhaltens von Schwarmverfahren sowie um Kommunikationsnetzwerke bei Honigbienen.
Vortragende: Frau Dr. Marde Helbig
Zeit und Raum: Dienstag den 12.06.2018 um 10:30 Raum G29-035
Title: Dynamic multi-objective optimization: introduction, issues and future directions
Abstract:
Most optimization problems in real-life have more than one objective, with at least two objectives in conflict with one another and at least one objective that changes over time. These kinds of optimization problems are referred to as dynamic multi-objective optimization (DMOO) problems.
Most research in multi-objective optimization has been conducted on static problems and most research on dynamic problems has been conducted on single-objective optimization. The goal of a DMOO algorithm (DMOA) is to find an optimal set of solutions that is as close as possible to the true set of solutions, and a diverse set of solutions. However, in addition to these goals a DMOA has to track the changing set of optimal solutions over time.
This talk will introduce the field of DMOO by discussing: benchmark functions and performance measures that have been proposed and the issues related to each of these; changes required to static MOO algorithms to solve DMOO; challenges in the DMOO field that are not yet addressed, such as incorporating a decision maker’s preference in DMOO and visualizing the behaviour of DMOAs; real-world applications; and emerging research fields that provide interesting research opportunities.
Biography:
Dr Mardé Helbig is a senior lecturer in the Department of Computer Science at the University of Pretoria (UP). Her research focuses on solving dynamic multi-objective optimization problems (DMOOPs) using computational intelligence algorithms. She is a member of the South African Young Academy of Science (SAYAS), as well as a member of the executive committee of SAYAS, Exceptional Young Researcher Award from UP, and Emerging Research Excellence Award and Promising Young Researcher Excellence Award from the CSIR Meraka Institute.