Events: Informations- och kommunikationsteknikhttp://www.chalmers.se/sv/om-chalmers/kalendariumUpcoming events at Chalmers University of TechnologyWed, 03 Jun 2020 14:56:59 +0200http://www.chalmers.se/sv/om-chalmers/kalendariumhttps://www.chalmers.se/en/departments/cse/calendar/Pages/Licentiate-Seminar-Christos-Profentzas.aspxhttps://www.chalmers.se/en/departments/cse/calendar/Pages/Licentiate-Seminar-Christos-Profentzas.aspxChristos Profentzas, Computer Science and Engineering<p>Zoom, link above</p><p>​Enhancing Trust in Devices and Transactions of the Internet of Things</p><br /><div>With the rise of the Internet of Things (IoT), billions of smart embedded devices will interact frequently. These interactions will produce billions of transactions. With IoT, users can utilize their phones, home appliances, wearables, or any other wireless embedded device to conduct transactions. For example, a smart car and a parking lot can utilize their sensors to negotiate the fees of a parking spot. The success of IoT applications highly depends on the ability of wireless embedded devices to cope with a large number of transactions. However, these devices face significant constraints in terms of memory, computation, and energy capacity.</div> <p></p> <br /><div>With our work, we target the challenges of accurately recording IoT transactions from resource-constrained devices. We identify three domain-problems: a) malicious software modification, b) non-repudiation of IoT transactions, and c) inability of IoT transactions to include sensors readings and actuators. The motivation comes from two key factors. First, with Internet connectivity, IoT devices are exposed to cyber-attacks. Internet connectivity makes it possible for malicious users to find ways to connect and modify the software of a device. Second, we need to store transactions from IoT devices that are owned or operated by different stakeholders.</div> https://www.chalmers.se/en/departments/cse/calendar/Pages/Licentiate-Seminar-Victor-Åberg.aspxhttps://www.chalmers.se/en/departments/cse/calendar/Pages/Licentiate-Seminar-Victor-%C3%85berg.aspxVictor Åberg, Computer Science and Engineering<p>Zoom, link above</p><p>CMOS Data Converters for Closed-Loop mmWave Transmitters</p><div><br /></div> <div>With the increased amount of data consumed in mobile communication systems, new solutions for the infrastructure are needed. Massive multiple input multiple output (MIMO) is seen as a key enabler for providing this increased capacity. With the use of a large number of transmitters, the cost of each transmitter must be low. Closed-loop transmitters, featuring high-speed data converters is a promising option for achieving this reduced unit cost. <br /></div> <div>In this thesis, both digital-to-analog (D/A) and analog-to-digital (A/D) converters suitable for wideband operation in millimeter wave (mmWave) massive MIMO transmitters are demonstrated. A 2×6 bit radio frequency digital-to-analog converter (RF-DAC)-based in-phase quadrature (IQ) modulator is demonstrated as a compact building block, that to a large extent realizes the transmit path in a closed-loop mmWave transmitter. The evaluation of an successive-approximation register (SAR) analog-to-digital converter (ADC) is also presented in this thesis. Methods for connecting simulated and measured performance has been studied in order to achieve a better understanding about the alternating comparator topology. </div> <div>These contributions show great potential for enabling closed-loop mmWave transmitters for massive MIMO transmitter realizations.</div>https://www.chalmers.se/en/departments/cse/calendar/Pages/FP-seminar-series-200608.aspxhttps://www.chalmers.se/en/departments/cse/calendar/Pages/FP-seminar-series-200608.aspxChalmers Online Functional Programming Seminar Series<p>Online, link above.</p><p>​Speaker: Phil Wadler, University of Edinburgh</p><h2 class="chalmersElement-H2">​Featherweight Go</h2> <p>We describe a design for generics in Go inspired by previous work on Featherweight Java by Igarashi, Pierce, and Wadler. Whereas subtyping in Java is nominal, in Go it is structural, and whereas generics in Java are defined via erasure, in Go we use monomorphisation. Although monomorphisation is used widely, we are one of the first to formalise it. Our design also supports a solution to The Expression Problem. </p> <p><br /></p> <p>Joint work with: Robert Griesemer, Raymond Hu, Wen Kokke, Julien Lange, Ian Lance Taylor, Bernardo Toninho, and Nobuko Yoshida. <span style="display:inline-block"></span></p> <p><br /></p>https://www.chalmers.se/en/departments/cse/calendar/Pages/Licentiate-Seminar-Elisabet-Lobo-Vesga.aspxhttps://www.chalmers.se/en/departments/cse/calendar/Pages/Licentiate-Seminar-Elisabet-Lobo-Vesga.aspxElisabet Lobo Vesga, Computer Science and Engineering<p>Zoom, link above</p><p>​A Programming Language for Data Privacy with Accuracy Estimations</p><div>Differential privacy offers a formal framework for reasoning about the privacy and accuracy of computations on private data. It also offers a rich set of building blocks for constructing private data analyses. When carefully calibrated, these analyses simultaneously guarantee the privacy of the individuals contributing their data, and the accuracy of the data analyses results, inferring useful properties about the population. The compositional nature of differential privacy has motivated the design and implementation of several programming languages aimed at helping a data analyst in programming differentially private analyses. </div> <div><br /></div> <div>However, most of the programming languages for differential privacy proposed so far provide support for reasoning about privacy but not for reasoning about the accuracy of data analyses. To overcome this limitation, in this work we present DPella, a programming framework providing data analysts with support for reasoning about privacy, accuracy, and trade-offs. The distinguishing feature of DPella is a novel component that statically tracks the accuracy of different data analyses. In order to make tighter accuracy estimations, this component leverages taint analysis for automatically inferring statistical independence of the different noise quantities added for guaranteeing privacy. We evaluate our approach by implementing several classical queries from the literature and showing how data analysts can figure out the best manner to calibrate privacy to meet the accuracy requirements. </div> <br />https://www.chalmers.se/en/departments/cse/calendar/Pages/Thesis-Defence-Rashida-Kasauli.aspxhttps://www.chalmers.se/en/departments/cse/calendar/Pages/Thesis-Defence-Rashida-Kasauli.aspxRashida Kasauli, Computer Science and Engineering<p>Online defence, link above</p><p>Requirements Engineering that Balances Agility of Teams and System-level Information Needs at Scale</p><p>​</p> <p>Large-scale companies are increasingly adopting agile methods in order to meet customer demands and keep up with the competition. The adoption of agile methods comes with challenges as such large companies need to accommodate different development cycles of hardware and software, and are usually subject to regulation and safety concerns.<br /> <br />Also, these companies develop products that are increasingly software-driven and constantly changing. For such software-intensive systems, requirements engineering is essential for successful development. Requirements engineering traditionally involves upfront and detailed analysis which can be at odds with agile development methods. <br /><br />This thesis contributes new knowledge about the challenges related to requirements engineering in large-scale agile systems development as well as solutions. This contribution has been achieved through a series of empirical studies that discover the information needs and related knowledge pertinent to systems development. <br /><br />The new knowledge and approaches presented in this thesis can be used to inform processes in large-scale agile system development. Thereby helping to address the gap between agile and traditional methods, and ultimately combating many challenges relating to coordination and knowledge management. We hope that this research will help large-scale companies to be more successful in their adoption of agile methods. <br /></p>https://www.chalmers.se/en/centres/chair/events/Pages/AI-Talks-Tony-Jebara.aspxhttps://www.chalmers.se/en/centres/chair/events/Pages/AI-Talks-Tony-Jebara.aspxPOSTPONED: AI Talks: Tony Jebara<p>Lecture Hall Palmstedt, university building, Chalmersplatsen 2, Campus Johanneberg</p><p>​Vice President of Machine Learning at Spotify.</p><p><br /><strong></strong></p> <p><strong><span style="color:rgb(255, 51, 0)">Due to increased concerns over the COVID-19 virus this seminar has been postponed until the fall 2020.</span><br /></strong></p>https://www.chalmers.se/en/departments/cse/calendar/Pages/FP-seminar-series-200615.aspxhttps://www.chalmers.se/en/departments/cse/calendar/Pages/FP-seminar-series-200615.aspxChalmers Online Functional Programming Seminar Series<p>Online, link above.</p><p>​Speaker: Robby Findler, Northwestern University</p><h2 class="chalmersElement-H2">Concolic Testing with Higher-Order Inputs </h2> Concolic testing, dating back to the mid 2000s, has proven to be an effective, automatic testing technique. Roughly, a concolic tester monitors the program's behavior, collecting information that connects the branches that the program takes with the original inputs to the program. It then uses that information (with the help of an SMT solver) to try to construct new inputs to force the program to take different branches, in an attempt to uncover problems with the program. <br /> <br /> Most of the existing work on concolic testing focuses on the situation where the inputs to the program being tested are flat values (e.g., numbers) instead of higher-order values (e.g., objects or functions). Higher-order inputs, however, present challenges for concolic testing because the interplay between the input and the program being tested is much more complex. <br /> <br /> In this talk, I'll discuss the way we generalize concolic testing to higher-order inputs, explaining why it is challenging and how we answer the challenge. The talk will be example driven and I will try to bring across the intuition for how we conceptualize the generalization to higher-order inputs and the results we have so far. <br /> <br /> (Joint work with Shu-Hung You and Christos Dimoulas.) <br />https://www.chalmers.se/en/centres/chair/events/Pages/SAIS-Workshop-2020-.aspxhttps://www.chalmers.se/en/centres/chair/events/Pages/SAIS-Workshop-2020-.aspxSAIS Workshop 2020<p>Online</p><p>The 32nd annual workshop of the Swedish Artificial Intelligence Society (SAIS) will be held as an online conference on June 16 – 17, 2020.</p><p><br /><a href="/en/centres/chair/events/sais2020/Pages/default.aspx">​Read more at the conference website</a><br /></p>https://www.chalmers.se/en/departments/cse/calendar/Pages/Licentiate-Seminar-Albin-Eldstal-Ahrens.aspxhttps://www.chalmers.se/en/departments/cse/calendar/Pages/Licentiate-Seminar-Albin-Eldstal-Ahrens.aspxAlbin Eldstål-Ahrens, Computer Science and Engineering<p>Online seminar</p><p>Reducing Memory Traffic with Approximate Compression</p><div><br /></div> <div>Memory bandwidth is a critical resource in modern systems and has an increasing demand. The large number of on-chip cores and specialized accelerators improves the potential processing throughput but also calls for higher data rates. In addition, new emerging data-intensive applications further increase memory traffic. On the other hand, memory bandwidth is pin limited and power constrained and is therefore more difficult to scale. </div> <div>This thesis proposes lossy memory compression as a means to reduce data volumes by exploiting the ability of applications to tolerate approximations in parts of their datasets. A reduction of off-chip memory traffic leads to reduced memory latency, which in turn improves the performance and energy efficiency of the system. </div> <div>The first part of this thesis introduces Approximate Value Reconstruction (AVR), which combines a low-complexity downsampling compressor with an LLC design able to co-locate compressed and uncompressed data. Two separate thresholds are employed to limit the error introduced by approximation. For applications that tolerate aggressive approximation in large fractions of their data, AVR reduces memory traffic by up to 70%, execution time by up to 55%, and energy costs by up to 20% introducing up to 1.2% error to the application output. </div> <div>The second part of this thesis proposes Memory Squeeze (MemSZ), introducing a parallelized implementation of the more advanced Squeeze (SZ) compression method. Furthermore, MemSZ improves on the error limiting capability of AVR by keeping track of life-time accumulated error. An alternate memory compression architecture is also proposed, which utilizes 3D-stacked DRAM as a last-level cache. MemSZ improves execution time, energy and memory traffic over AVR by up to 15%, 9%, and 64%, respectively.</div> <br />https://www.chalmers.se/en/centres/chair/events/Pages/AI-Talks-Thomas-Schön.aspxhttps://www.chalmers.se/en/centres/chair/events/Pages/AI-Talks-Thomas-Sch%C3%B6n.aspxOnline AI Talks: Thomas Schön<p>Online, Zoom</p><p>AI for researc​h and some new results on deep regression</p><p><br /></p> <p>Welcome to a AI Talks under the theme AI for research and some new results on deep regression, with the speaker <a href="http://user.it.uu.se/~thosc112/biography.html">Thomas B. Schön​</a> who is Professor of the Chair of Automatic Control in the Department of Information Technology at Uppsala University. </p> <p><br /></p> <p><strong>Abstract:</strong> This talk has two loosely connected parts: In the first part we discuss (using fairly concrete examples) how AI/ML can be used for research in classical areas of natural science and medicine. The illustrations stem from physics, medicine and biology that we have worked with over the past 2-3 years. The idea is to make a case for using AI/ML as a tool in research within more classical academic disciplines. This is tightly linked to the AI strategy of Uppsala University that I will briefly introduce as well. In the second part (which is perhaps slightly more technical) we develop a new approach to deep regression. While deep learning-based classification is generally addressed using standardized approaches, a wide variety of techniques are employed when it comes to regression. We have developed a new and general deep regression method with a clear probabilistic interpretation. We obtain good performance on several computer vision regression tasks (including a new state-of-the-art result on visual tracking).</p> <p><strong><br /></strong></p> <p>The talk will be given via zoom. Please register for this seminar, at the latest on 17th June. Only registered participants will receive the link to follow the seminar via zoom.</p> <p><strong></strong></p> <h2 class="chalmersElement-H2" style="font-family:&quot;open sans&quot;, sans-serif"><a href="https://ui.ungpd.com/Surveys/1904c716-d5d9-4bd4-8f05-665a0acae645">Register here​</a></h2> <p></p> <hr /> <h3 class="chalmersElement-H3">​About the speaker</h3> <p><img src="/SiteCollectionImages/Centrum/CHAIR/events/AI_Talks_ThomasSchon_180px.jpg" class="chalmersPosition-FloatLeft" alt="" style="margin:5px 15px" />Thomas B. Schön is Professor of the Chair of Automatic Control in the Department of Information Technology at Uppsala University <span style="background-color:initial">and has recently been appointed Beijer Professor of Artificial Intelligence at the same university</span><span style="background-color:initial">. He received the PhD degree in Automatic Control in Feb. 2006, the MSc degree in Applied Physics and Electrical Engineering in Sep. 2001,  the BSc degree in Business Administration and Economics in Jan. 2001, all from Linköping University. He has held visiting positions with the University of Cambridge (UK), the University of Newcastle (Australia) and Universidad Técnica Federico Santa María (Valparaíso, Chile). In 2018, he was elected to The Royal Swedish Academy of Engineering Sciences (IVA) and The Royal Society of Sciences at Uppsala. He received the Tage Erlander prize for natural sciences and technology in 2017 and the Arnberg prize in 2016, both awarded by the Royal Swedish Academy of Sciences (KVA). He was awarded the Automatica Best Paper Prize in 2014, and in 2013 he received the best PhD thesis award by The European Association for Signal Processing. He received the best teacher award at the Institute of Technology, Linköping University in 2009. He is a Senior member of the IEEE and a fellow of the ELLIS society.</span></p> <span></span><p></p> <p>Bio from: <a href="http://user.it.uu.se/~thosc112/biography.html">http://user.it.uu.se/~thosc112/biography.html</a></p> ​​