Speaker: Prof. Isabelle Augenstein, Ph.D.
Host: Prof. Dr. Rico Sennrich
One of the core challenges in typology is to record properties of languages in a structured way. As a result of manual efforts, typological knowledge bases have emerged, which contains information about languages’ phonological, morphological and syntactic properties; as well as information about language families. Ideally, such typological knowledge bases would provide useful information for multilingual NLP models to learn how to selectively share parameters.
A related area of research suggests a different way of encoding properties of languages, namely to learn language representation vectors directly from text documents. In this talk, I will analyse and contrast these two ways of encoding linguistic properties, as well as present research on how the two can benefit one another.
Prof. Isabelle Augenstein, Ph.D., is a associate professor at the University of Copenhagen, Department of Computer Science, Denmark, where she heads the Copenhagen NLU research group. Her main research interests are weakly supervised and low-resource learning with applications including information extraction, machine reading and fact checking. In the space of computational typology, she is interested in how information about properties of languages can aid multilingual learning, and has investigated language embeddings as well as automatically populating typological knowledge bases.
Speaker: Prof. Nick Arnosti, Ph.D.
Host: Prof. Dr. Sven Seuken
We consider a setting where tickets for an experience are awarded by lottery. Each agent belongs to a group, and will only participate in the experience if accompanied by others in their group. The most widespread mechanism in practice is to allow agents to request multiple tickets (up to some maximum). Agents are then ordered uniformly at random and sequentially allocated tickets until none remain. We show that this approach may result in unfair and inefficient outcomes. One alternative is to ask agents to report their groups and conduct a lottery by group. This is approximately fair and approximately efficient, but verifying identities of each group member may be too cumbersome for many applications. Instead, we propose allowing agents to request any number of tickets, but biasing the lottery against agents with large requests. We show that this approach approximates the group lottery, and therefore leads to outcomes that are approximately fair and approximately efficient.
Prof. Nick Arnosti, Ph.D., is an Assistant Professor at the Columbia Business School, Ney York, U.S.A. He received a PhD in Operations Research from Stanford University in 2016. His research focuses on the allocation of "social goods" which are given away, rather than sold to maximize profit. Examples include seats in public schools, affordable housing, permits for hiking and hunting, and discounted event tickets.
GraalVM is a universal virtual machine that allows just-in-time compilation and execution of applications written in a variety of different programming languages. It is being developed by Oracle Labs in Zurich and other locations around the globe. This talk will first introduce some basic components of GraalVM (e.g. JIT, partial evaluation) and then show how GraalVM uses this concept to turn a language interpreter into a self-optimizing compiler with speculative optimizations. We will show how such a speculative just-in-time compiler can significantly improve the performance of typical data processing tasks like encoding and decoding JSON data. This naturally transitions to the question how a database system can benefit from such techniques, which we will further demonstrate in the context of the Oracle Database Multilingual Engine - the embedding of GraalVM in the Oracle Database.
Dr. Alexander Ulrich is an engineer and researcher at Oracle Labs, Zürich, Switzerland. At Oracle Labs, Alexander works on the efficient integration of data management systems and programming language runtimes.
Prior to joining Oracle Labs, Alexander received his Diploma (M.Sc.) and his PhD in Computer Science from the University of Tübingen. From 2011 until 2016, Alexander was a member of the Database Systems research group at the University of Tuebingen led by Torsten Grust. As a PhD student, Alexander performed research on supporting expressive, nested query calculi on relational systems.
Speaker: Prof. Dr. Alessandro Garcia
Host: Prof. Dr. Alberto Bacchelli
Design degradation implies on a systematic deterioration of various attributes of software quality, including comprehensibility, maintainability and others. Many symptoms in a software, such as smells, are used as a (limited) partial sign of design degradation. Many techniques for combating such degradation symptoms, such as refactoring, have been employed by developers in practice. However, design degradation in a software project is often a very long, slow, and complex process; the same characteristics are observed in the process of combating design degradation. These two processes cannot be truly understood if they are studied in terms of isolated events. For example, focusing on the observation of each smell or each single refactoring that was introduced in a commit does not offer enough context to truly understand how the design as a whole is either degrading or recovering from it. Moreover, activities that contribute to both processes are often: (i) intertwined with other daily activities in software development, such as adding or enhancing features or even fixing bugs, and (ii) intertwined among themselves – e.g., a developer can refactor the code in a commit, but this change may end up inducing new smells (or even bugs) along the next commits. However, most of the existing research tends to study these processes in terms of “isolated events” in an oversimplified manner. This talk intends to pinpoint what is missing or even misleading while we focus on understanding design degradation and its recovery in this manner. This discussion will be supported by examples taken from recent or ongoing research. We will also discuss some ideas for future research in the field.
Prof. Dr. Alessandro Garcia is an Associate Professor at the Informatics Department of the Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil. Alessandro holds a PhD in Informatics from PUC-Rio (2004). He also served as an Assistant Professor at Lancaster University from February 2005 to January 2009. He is currently serving as a member of the Editorial Board of two leading international journals in his field: the Empirical Software Engineering Journal (Springer) and the Journal of Systems and Software (Elsevier). He is the Editor-in-Chief of the Springer Open Access on Journal of Software Engineering Research and Development. He also recently served as a member of the Editorial Board of the IEEE Transactions on Software Engineering. He was PC co-chair of FSE 2018 and PC co-chair of ICSA 2020. He has received several awards and distinctions, such as Rio de Janeiro's Distinguished Scientist (FAPERJ, from 2009 to 2022), CNPq Productivity Grant (from 2009 to 2020), Affiliated Member of the Brazilian Academy of Sciences (from 2009 to 2014), Best Researcher of the Year (Lancaster University, UK, 2006), Best Masters Dissertation of 2000 (Brazilian Society of Computing), amongst others. Several of his articles have received distinctions at major international conferences, including two ACM Distinguished Paper Awards at ICSE (2014 and 2018).
Speaker: Piotr Sapiezynski, Ph.D.
Host: Prof. Dr. Aniko Hannak
The enormous financial success of online advertising platforms is partially due to the precise targeting features they offer. Although researchers and journalists have found many ways that advertisers can target - or exclude - particular groups of users seeing their ads, comparatively little attention has been paid to the implications of the platform's ad delivery process, comprised of the platform's choices about which users see which ads.
It has been hypothesized that this process can "skew" ad delivery in ways that the advertisers do not intend, making some users less likely than others to see particular ads based on their demographic characteristics. In this talk, I demonstrate that such skewed delivery occurs on Facebook, due to market and financial optimization effects as well as the platform's own predictions about the "relevance" of ads to different groups of users.
Our measurements show significant skew in delivery along gender and racial lines for employment and housing ads despite neutral targeting parameters.
Further, we observe political ads delivered mostly to people who already agree with them, also despite neutral targeting.
Importantly, we show this skew in politics is driven by differential pricing - the price of showing a political message to a group of Facebook users depends on the content of that message.
Our results demonstrate previously unknown mechanisms that can lead to potentially discriminatory ad delivery, even when advertisers set their targeting parameters to be highly inclusive. This underscores the need for policymakers and platforms to carefully consider the role of the ad delivery optimization run by ad platforms themselves - and not just the targeting choices of advertisers - in preventing discrimination in digital advertising as well as controlling the filter bubble effects.
I will conclude the talk by showing that the measures that Facebook was forced to undertake so far to limit discrimination in advertising are not sufficient.
Piotr Sapiezynski, Ph.D., is an Associate Research Scientist at the Khoury College of Computer Sciences at the Northeastern University in Boston, Massachusetts, U.S.A. He received a PhD in Network Science/Data Science from Technical University of Denmark.
The core of his work is auditing platforms and their algorithms for fairness and privacy. Together with collaborators he investigates systems that are optimized for corporate profit yet drive many aspects of our daily lives. All too often we find these systems have (possibly unintended but often predictable) side effects that bring harm to individuals and the society.
Before diving into algorithm audits Piotr worked on analyses of behavioral data collected from smartphones to model human mobility, spread of diseases, development of relationships, and to predict life outcomes. This experience made him closely aware of and alert to the privacy risks associated with accumulation of personal data.
Speaker: Prof. Dr. Robert M. Davison
Host: Prof. Dr. Gerhard Schwabe
This presentation reports on an exploratory case study to investigate how warehouse employees work around an ERP software that cannot be used as designed due to work practices required by local conditions. The context involves the local Hong Kong operations of a global retailer of home textiles. Our 29 interviews at the site provide many perspectives about how an inadequate information system failed to support necessary work practices and how employees at the site responded by creating a feral IT system that helped them pursue their business responsibilities and objectives. We draw on a compliance view of technology use to suggest that unreflective compliance can be counterproductive; paradoxically, reflective non-compliance may bring greater benefit to both the organization and its customers.
Prof. Dr. Robert Davison is a Professor of Information Systems at the City University of Hong Kong. His research focuses on the use and misuse of information systems, especially with respect to problem solving, guanxi formation and knowledge management, in Chinese organisations. He has published over 200 articles in a wide variety of our premier journals and conferences. He is particularly known for his scholarship in the domain of action research. He primarily teaches MSc and MBA students in the areas of IT consulting, Knowledge Management and Global Information Systems. Within the AIS, Robert chaired the research ethics committee for many years. He currently chairs the IFIP’s WG 9.4 (Social Implications of Computing in Developing Countries) and is the Editor-in-Chief of both the Information Systems Journal and the Electronic Journal of Information Systems in Developing Countries. Robert travels extensively, seeking to understand how people in different contexts and cultures make sense of their lives with IS. Professionally, he seeks to enhance the inclusion of scholars from the global south within our community. To this end, he frequently travels in developing countries where he offers research seminars and workshops, engaging with local PhD students and scholars. As a researcher and as an editor, he champions local and indigenous perspectives.