Events: Data- och informationsteknik events at Chalmers University of TechnologyMon, 23 Oct 2017 08:18:20 +0200 Mensura 2017<p>Chalmers Conference Center Lindholmen and Ericsson Lindholmen</p><p>​Where academic ideas meet industry practice on Software Measurement topics.</p>​<span class="text-normal page-content">The IWSM Mensura conference is the result of the joining of forces of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement (Mensura). Together they form the conference where new ideas from the world of academic research meet practical improvements from industry on topics of measuring software. Each year practitioners and researchers from all over the world gather together to learn about new developments, test new ideas and exchange possible new solutions and applications. </span> in Reliable Language Technology<p>EB, lecture hall, EDIT trappa C, D och H, EDIT</p><p>​Docent lecture by Krasimir Angelov, Computer Science and Engineering.</p>Natural languages are inherently difficult for computers to understand and interpret. Despite the recent AI boom, reliable and scalable Natural Language Processing (NLP) remains elusive. The state of the art is that to have anything done we still sacrifice either reliability or scalability. Most of the contemporary research starts from a method which is scalable by design and tries to improve its reliability. In this lecture I will give an example of an opposite direction where we start from GF (a reliable by design NLP framework) and we try to improve its scalability. This is an exciting project which intertwines ideas from functional programming, type theory and machine learning. in the hands of the data analyzer<p>Scaniasalen, lecture room, Kårhuset, Campus Johanneberg</p><p>​Speaker: Kobbi Nissim, Georgetown University</p>​ <br /><a href="">Please register for this event</a> so that we can adjust the room booking to the number of participants. <br />The seminar is free of charge.<br /><br /><br /><br /><strong>Abstract:</strong> Differential privacy is a robust concept of privacy. It brings mathematical rigor to the decades-old problem of privacy-preserving analysis of collections of sensitive personal information. Informally, differential privacy requires that the outcome of an analysis would remain stable under any possible change to an individual's information, and hence protects individuals from attackers that try to learn the information particular to them.<br /><br />The rich research of differential privacy has uncovered deep connections to many research areas, including learning theory, cryptography, complexity theory, algorithmic game theory, and statistics. After a brief introduction to differential privacy, we will look intro some of these connections, focusing on how differential privacy can serve as a tool for data analysis, even in cases where privacy is not a goal.<br /><br /><strong>Short Bio:</strong> Prof. Kobbi Nissim is McDevitt Chair in Computer Science, Georgetown University. Nissim’s work is focused on the mathematical formulation and understanding of privacy. His work from 2003 and 2004 with Dinur and Dwork initiated rigorous foundational research of privacy and presented a precursor of Differential Privacy - a definition of privacy in computation that he introduced in 2006 with Dwork, McSherry and Smith. His research studies privacy in various contexts, including statistics, computational learning, mechanism design, social networks, and more recently law and policy. Since 2011, Nissim has been involved with the Privacy Tools for Sharing Research Data project at Harvard, developing privacy-preserving tools for the sharing of social-science data. Nissim was awarded the Godel Prize In 2017, the IACR TCC Test of Time Award in 2016, and the ACM PODS Alberto O. Mendelzon Test-of-Time Award in 2013.<br /> Tossou, Computer Science and Engineering<p>EL41, lecture room, Linsen, EDIT</p><p>​Privacy in the Age of Artificial Intelligence</p>An increasing number of people are using the Internet in their daily life. Indeed, more than 40% of the world population have access to the Internet, while Facebook (one of the top social network on the web) is actively used by more than 1.3 billion users each day (Statista 2017). This huge amount of customers creates an abundance of user data containing personal information. These data are becoming valuable to companies and used in various way to enrich user experience or increase revenue. <br /><br />This has led many citizens and politicians to be concerned about their privacy on the Internet to such an extent that the European Union issued a &quot;Right to be Forgotten&quot; ruling, reflecting the desire of many individuals to restrict the use of their information. As a result, many online companies pledged to collect or share user data anonymously. However, anonymisation is not enough and makes no sense in many cases. For example, an MIT graduate was able to easily re-identify the private medical data of Governor William Weld of Massachusetts from supposedly anonymous records released by the Group Insurance Commission. All she did was to link the insurance data with the publicly available voter registration list and some background knowledge (Ohm 2009). <br /><br />Those shortcomings have led to the development of a more rigorous mathematical framework for privacy: Differential privacy. Its main characteristic is to bound the information one can gain from released data, no matter what side information they have available. <br /><br />In this thesis, we present differentially private algorithms for the multi-armed bandit problem. This is a well known multi round game, that originally stemmed from clinical trials applications and is now one promising solution to enrich user experience in the booming online advertising and recommendation systems. However, as recommendation systems are inherently based on user data, there is always some private information leakage. In our work, we show how to minimise this privacy loss, while maintaining the effectiveness of such algorithms. In addition, we show how one can take advantage of the correlation structure inherent in a user graph such as the one arising from a social network. Camilleri, Computer Science and Engineering<p>HC3, lecture hall, Hörsalsvägen 14, Johanneberg.</p><p>​Contracts and Computation – Formal modelling and analysis for normative natural language</p>​<img src="/SiteCollectionImages/Institutioner/DoIT/Profile%20pictures/ST/John-Camilleri.jpg" class="chalmersPosition-FloatRight" alt="" style="margin:5px" />Whether we are aware of it or not, our digital lives are governed by contracts of various kinds, such as privacy policies, software licenses, service agreements, and regulations. At their essence, normative documents like these dictate the permissions, obligations, and prohibitions of two or more parties entering into an agreement, including the penalties which must be paid when someone breaks the rules. Such documents are often lengthy and hard to understand, and most people tend to agree to these legally binding contracts without ever reading them. <br /><br />Our goal is to create tools which can take a natural language document as input and allow an end user to easily ask questions about its implications, getting back meaningful answers in natural language within a reasonable amount of time. We do this by bringing formal methods to the analysis of normative texts, investigating how they can be effectively modelled and the kinds of automatic processing that these models enable. This thesis includes six research papers by the author which cover the various aspects of this approach: entity recognition and modality extraction from natural language, controlled natural languages and visual diagrams as interfaces for modelling, logical formalisms which can be used for contract representation, and analysis via syntactic filtering, trace evaluation, random testing, and model checking. These components are then combined into a prototype tool for end users, allowing for end-to-end analysis of normative texts in natural language.<br /><br /> Antinyan, Computer Science and Engineering<p>Omega, lecture room, Jupiter, Campus Lindholmen</p><p>​Proactive Software Complexity Assessment</p><img src="/SiteCollectionImages/Institutioner/DoIT/Profile%20pictures/SE/Vard-disp.jpg" class="chalmersPosition-FloatRight" alt="" style="margin:5px" />Software is omnipresent in today’s life. Sophisticated appliances, once being completely mechanical or electrical, now contain software. A mo-dern premium car carries about 100 computers to manage functionalities such as climate control, speed control, and automated parking. Similarly, air-traffic control system manages a vast net of trajectories, altitudes, and signals. Thousands of appliances are pervaded by software. <br /><br />On the surface this provides a fascinating perspective to the future. But beyond the surface it is the challenge of software development. Software companies are receiving thousands of requirements for delivering new functionalities. These requirements turn into millions of lines of machine code by the hands of software developers. And all of these are carried on continuously. Continuous software development has become the prere-quisite of tomorrow’s smarter products. But it has also become the source of prodigious complexity that emerges overwhelmingly in developing software. <br /><br />So many software projects have been fallen because of the overly complex implementations, and so many have run out of budgets because of inconceivable complexity. This thesis is an endeavor to understand this complexity, to measure and analyze its effects, importantly, to apply measurements proactively in practice, so that complexity is controlled before it escalates into inconceivable scales. ACM Symposium on Virtual Reality Software and Technology<p>Chalmers conference center Lindholmen, Gothenburg</p><p>​VRST will provide an opportunity for VR researchers to interact, share new results, show live demonstrations of their work, and discuss emerging directions for the field.</p>​ <br />The ACM Symposium on Virtual Reality Software and Technology (VRST) is an international forum for the exchange of experience and knowledge among researchers and developers concerned with virtual reality software and technology. <br /><br /><a href="">To conference website:</a><br /><br /> Seminar: Transition to future transport<p>Linholmen Conference Centre</p><p>Chalmers Area of Advance Transport invites to an Initiative Seminar with a focus on the transition to future transport.</p>​<a href="/en/areas-of-advance/Transport/calendar/initiative-seminar-2017/Pages/default.aspx"><span><span>Programme and registration &gt;&gt;</span></span></a><span><br /><br /><strong>When</strong>: 15 November 2017, at 9.00-17.00<br /><span><strong>Where</strong>: Lindholmen Science Park, Göteborg<span style="display:inline-block"></span></span><br /></span><span><br />Registration will open 2 October 2017.<br /><br /><span>Organizers: Chalmers University of Technology and Gothenburg University<span style="display:inline-block"></span></span></span><p>Wallenberg Conference Centre, Medicinaregatan 20A</p><p>​5th Swedish Workshop on Data Science</p>The Swedish Workshops on Data Science allow members of a community with common interests to meet in the context of a focused and interactive discussion. SweDS 2017, the fifth Swedish Workshop on Data Science, brings together researchers, practitioners, and opinion leaders with interest in data science. The goal is to further establish this important area of research and application in Sweden, foster the exchange of ideas, and to promote collaboration. As a followup to the very successful previous editions, held at the University of Borås, Stockholm University, Blekinge Institute of Technology and Skövde we plan two days of inspiring talks, discussion sessions, and time for networking. We invite stakeholders from academia, industry, and society to share their thoughts, experiences and needs related to data science. <br /><br />Confirmed Keynote Speakers: <br /><ul><li>Josephine Sullivan, School of Computer Science and Communication, KTH </li> <li>Seif Haridi, Chief Scientist, Swedish Institute of Computer Science </li> <li>Sven Nelander, Department of Immunology, Genetics and Pathology, Uppsala University </li> <li>Erik Elmroth, Computing Science, Umeå University</li></ul> seminar on Digitalisation Security & privacy | Machine Intelligence<p>RunAn, conference hall, Kårhuset, Campus Johanneberg</p><p>​Save the date: 15 March 2018</p>​ <br />Next year’s initiative seminar on Digitalisation we focus on the two themes Security/Privacy and Machine Intelligence. More information will follow.