Speaker: Prof. Eszter Hargittai, PhD
Host: Prof. Dr. Abraham Bernstein
While digital media have certainly lowered the barriers to sharing one's perspectives and creative content with others, research on online engagement has found considerable differences by user background and Internet skills. Drawing on several survey data sets, this talk will discuss who is most likely to participate online from joining social media platforms to editing Wikipedia entries. The talk will also offer insights on the potential biases that can stem from relying on certainly types of data sets in big data studies.
Prof. Eszter Hargittai, PhD in Sociology from Princeton University is Chair of Internet Use and Society at the University of Zurich. Her research looks at how people may benefit from their digital media uses with a particular focus on how differences in people's Web-use skills influence what they do online. Her work has received awards from several professional associations and has been funded by the US National Science Foundation, several private foundations (e.g., the MacArthur Foundation, the Alfred P. Sloan Foundation) and industry (e.g., Google, Merck, Facebook, Nokia). She is co-editor of Research Confidential: Solutions to Problems Most Social Scientists Pretend they Never Have and, with Christian Sandvig, of Digital Research Confidential: The Secrets of Studying Behavior Online from MIT Press. She has given invited talks in 15 countries on four continents. She tweets @eszter.
Speaker: Prof. Dr. Klaus G. Troitzsch
Host: Prof. Dr. Lorenz Hilty
The aim of this talk is to discuss the possibility of using complex software agents in a simulation model in order to represent and analyse the dynamics of certain types of criminal systems via Agent Based Modelling (ABM), in particular Extortion Racket Systems (ERSs). It presents a simulation model in which agents represent Mafiosi, their victims, the police, the public (mainly in their role as consumers) and a court which — beside extorting and forming Mafia families, denouncing or satisfying Mafia requests, observing, arresting and convicting criminals, compensating victims from confiscated Mafia assets — send each other norm invocation messages to mutually modify their action propensities. Each agent type has a repertoire of norms whose salience the agents calculate from their memories before deciding to take action; besides they also calculate the utility of the actions available in the current situation. The current version of the model is event oriented such that each simulation run tells a story of the rise and possible fall of a Mafia regime in a virtual region. The results of a large number of runs are analysed to find out under which parameter constellations governing the normative behaviour of the software agents the model replicates the macro observations in a number of provinces in Southern Italy which are derived from a database of more than 600 actual cases in Sicily and Calabria and the police and judicial documents generated during the prosecution of these cases. Thus it is possible to show that certain parameterisations of the model generate extortion databases similar to the empirical database although the richness of information generated by the model is much greater than what can be documented empirically. Finally the simulation model is applied to analysing strategies and their effect on the behaviour of the agents and the system as a whole.
Klaus G. Troitzsch was a full professor of computer applications in the social sciences at the University of Koblenz-Landau since 1986 until he officially retired in 2012 (but continues his academic activities). He took his first degree as a political scientist. After eight years in active politics in Hamburg and after having taken his PhD, he returned to academia, first as a senior researcher in an election research project at the University of Koblenz-Landau, from 1986 as full professor of computer applications in the social sciences. His main interests in teaching and research are social science methodology and, especially, modelling and simulation in the social sciences.
Among his early research projects there is the MIMOSE project which developed a declarative functional simulation language and tool for micro and multilevel simulation between 1986 and 1992. Several EU funded projects were devoted to social simulation and policy modelling, the most recent from 2012 to 2015 combining data/text mining and agent-based simulation to analyse the global dynamics of extortion racket systems.
He authored, co-authored, and co-edited several books and many articles in social simulation, and he organised or co-organised a number of national and international conferences in this field. Over nearly three decades he advised and/or supervised more than 55 PhD theses, most of them in the field of social simulation. He offered annual summer and spring courses in social simulation between 1997 and 2009; more recent courses of this kind are now being organised by the European Social Simulation Assiciation and held at different places all over Europe (mostly with his contributions).
Speaker: Prof. Dr. Kasper Hornbaek
Host: Prof. Dr. Chat Wacharamanotham
Human-computer interaction is seeing a wave of user interfaces that use the body in new and more active ways. In this talk, I give an overview of my work on body-based user interfaces. I will present the key idea of my ERC consolidator grant that embodied cognition can drive the development of body-based user interfaces. This will be illustrated with prototypes using electric-muscle stimulation, virtual reality, and mobile computing. And I will present studies of body poses, giving input directly on the skin, and haptic feedback. Together, these prototypes and study findings show some of the promises and pitfalls of body-based user interfaces.
Prof. Dr. Kasper Hornbæk received his M.Sc. and Ph.D. in Computer Science from the University of Copenhagen, in 1998 and 2002, respectively. Since 2014 he has been a professor in computer science at the University of Copenhagen. His core research interests is human-computer interaction, including usability research, shape-changing interfaces, large displays, body-based user interfaces, and information visualization. He serves on the editorial board of ACM Transactions on Human-computer Interaction and has served for more than 10 years as an associate chair for ACM conference on Human Factors in Computing, CHI. His most important research contributions has concerned evaluation of usability and user experience, the benefits and issues of fisheye visualization, and the use of the body for input and output. More information at kasperhornbaek.dk.
Speaker: Prof. Dr. Eng. Carlo Ghezzi
Host: Prof. Dr. Harald C. Gall
Advances in technology, in particular in cyber-physical systems, increasingly enable functionalities that will lead to the development of smart living spaces--from ""smart homes"" to ""smarty cities""--where living conditions for people will be facilitated and enhanced. These systems may be collectively called cyber-physical spaces. They will assist and cooperate with people in their homes, including elder and disabled people. They will enhance operations in public buildings, such as hospitals or courts. They will make public spaces more secure; e.g., airports or stations. They will assist in managing traffic in cities, reducing air pollution, and reducing energy consumption. Needless to say, the design of such cyber-physical spaces is a multidisciplinary endeavor, ranging contributions from Internet-of Things to software engineering to civil engineering and architecture to medical sciences, transportation science, environmental science, energy, ...
The talk will argue that software engineering can bring a unique and fundamental contribution into this multidisciplinary world. It can support the design phase with formal models that integrate existing spatial design notations (such as BIM used by architects and civil engineers; or CityGML, an emerging notation for city and landscape models) with modeling notations that support automatic reasoning and analysis, for example to check compliance with existing regulations or possible security and safety threats, or simulate how the space being designed will behave when operational. Automatic checking of models can also support monitoring the smart space, when it will be operational, and possible automatic reactions to keep the operational smart space aligned with its requirements.
Initial research results in this direction will be presented, along with a possible research agenda. The talk is a call for an interdisciplinary approach to address the problem and presents a discussion of the crucial role that software engineering can have in this area.
Prof. Dr. Eng. Carlo Ghezzi is an ACM Fellow (1999), an IEEE Fellow (2005), a member of the European Academy of Sciences and of the Italian Academy of Sciences. He received the ACM SIGSOFT Outstanding Research Award (2015, the Distinguished Service Award (2006), and the 2018 TCSE Distinguished Education Award from IEEE Computer Society Technical Council on Software Engineering (TCSE). He has been President of Informatics Europe. He has been a member of the program committee of flagship conferences in the software engineering field, such as the ICSE and ESEC/FSE, for which he also served as Program and General Chair. He has done research in programming languages and software engineering for over 40 years and has been a recipient of an ERC Advanced Grant on self-adaptive software systems. He has published over 200 papers in international journals and conferences and co-authored 6 books.
Speaker: Prof. Tina Eliassi-Rad, Ph.D.
Host: Prof. Dr. Ingo Scholtes
Fairness in machine learning is an important and popular topic these days. “Fair” machine learning approaches are supposed to produce decisions that are probabilistically independent of sensitive features (such as gender and race) or their proxies (such as zip codes). Some examples of probabilistically fair measures here include precision parity, true positive parity, and false positive parity across pre-defined groups in the population (e.g., whites vs. non-whites). Most literature in this area frame the machine learning problem as estimating a risk score. For example, Jack’s risk of defaulting on a loan is 8, while Jill's is 2. Recent papers - by Kleinberg, Mullainathan, and Raghavan (arXiv:1609.05807v2, 2016) and Alexandra Chouldechova (arXiv:1703.00056v1 , 2017) - present an impossibility result on simultaneously satisfying three desirable fairness properties when estimating risk scores with differing base rates in the population. I take a boarder notion of fairness and ask the following two questions: Is there such a thing as just machine learning? If so, is just machine learning possible in our unjust world? I will describe a different way of framing the problem and will present some preliminary results.
Prof. Tina Eliassi-Rad, Ph.D., is an Associate Professor of Computer Science at Northeastern University in Boston, MA. She is also on the faculty of Northeastern's Network Science Institute. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University; and before that she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Her research is rooted in data mining and machine learning; and spans theory, algorithms, and applications of massive data from networked representations of physical and social phenomena. Tina's work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, and cyber situational awareness. Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). She received an Outstanding Mentor Award from the Office of Science at the US Department of Energy.