Events: Data- och informationsteknik events at Chalmers University of TechnologyThu, 25 Apr 2019 11:15:29 +0200 road seminar: Electromagnetic simulations<p>Kollektorn, lecture room, Kemivägen 9, MC2-huset</p><p>​Welcome to a seminar on Electromagnetic simulations, on 8 May</p>Programme<br />16.00-16.15 Welcome<br />16.15-16.45 Magnus Olsson, COMSOL: COMSOL Multiphysics – Electromagnetics and much more<br />16.45-17.00 Martin Alm, Huawei: Simulation of the air interface in mobile communications<br />17.00-17.15 Best microwave master thesis award 2018<br />17.15-17.45 Erik Abenius, ESI Group: Large array antenna modelling using macro basis functions in CEM One<br />17.45-18.00 Hans Hjelmgren, Chalmers: Simulation of memory effects in high frequency power transistors<br />18.00-20.00 Get-together and food<br /><div><br /></div> <div>Please register at the latest on 4 May, to <a href=""></a><br /></div> for Master students at Computer Science and Engineering 2019<p>CSE department lunchroom. Follow directions to conference room 8103</p><p>You are cordially invited to an afternoon of talks and mingle. We will discuss all important aspects of postgraduate life: from the start, to the perils and joys of the research itself. This will be of interest to all who consider applying for a PhD position in the future. Even if you have not thought about doing a PhD, you might find it interesting to learn about PhD studies in general, and about PhD studies at the CSE department in particular.</p><br />This event is a great opportunity to learn about PhD studies first-hand from the people who run the PhD education at CSE, from current PhD students, and from PhD supervisors. We will talk about applying for doctoral studies, the life of a doctoral student, and how supervisors support their students and help them to develop into independent researchers. You will also get a chance to mingle with PhD students and researchers from the department. If you have any interest in doing research, you should not miss this opportunity. <hr /> <b><br />Schedule<br /></b> <ul> <li><strong>15:15 Welcome talk,</strong> <a href="/sv/personal/redigera/Sidor/agneta-nilsson.aspx">Agneta Nilsson</a><br /> Agneta is vice head of department, and as such responsible for all aspects of the PhD education. <br /><br /></li> <li><strong>15:25 How we nurture PhD students</strong>, Prof. <a href="/en/Staff/Pages/philippas-tsigas.aspx">Philippas Tsigas<br /><br /></a> </li> <li><strong>15:40 Being a PhD student at Computer Science and Engineering</strong>, N.N.<br /><br /></li> <li><strong>15:50 The supervisor's perspective</strong>, Prof. <a href="/en/staff/Pages/andrei.aspx">Andrei Sabelfeld<br /><br /></a> </li> <li><strong>16:05 Brief presentation of open PhD positions at the department. <br /><br /></strong></li> <li><strong>16:20 or so, Coffee and mingle.</strong></li></ul> <br />There will be also other researchers and PhD students, as well as study administrators to talk to and ask questions. <hr /> How to get to the CSE lunch room: Enter the EDIT-building through the D&amp;IT main entrance on Rännvägen (not on Hörsalsvägen). Then go upstairs or take the lift to the 6th floor. Enter the corridor with the teachers' offices and walk until you see a door to the stair case on the right. Enter the stair case and walk up the stairs. At the end of the stairs, you will find the entrance to the lunch room. Alternatively, you can follow signs to conference room 8103. <p><br /></p> Participation is free. <br /><p>All Chalmers and GU students enrolled in a computing related Masters Programme are welcome. All researchers and PhD students of the CSE department are very welcome, too! </p> <div><br />Register via choodle at the latest on 3rd of May 2019.<br /><a href="">Link to choodle</a></div> <div><br /></div> <div>Looking forward to your presence at the mingle, the CSE Doctoral School </div> Najdataei, Computer Science and Engineering<p>EB, lecture hall, Hörsalsvägen 11, EDIT trappa C, D och H</p><p>Parallel Data Streaming Analytics in the Context of Internet of Things</p> <div><br /><img src="/SiteCollectionImages/Institutioner/DoIT/Profile%20pictures/NS/Hannaneh.gif" class="chalmersPosition-FloatRight" alt="" style="margin:5px" />We are living in an increasingly connected world, where the ubiquitously sensing technologies enable inter-connection of physical objects, as part of Internet of Things (IoT), and provide continuous massive amount of data. As this growth soars, benefits and challenges come together, which requires development of right tools in order to extract valuable information from data. </div> <div>For that, new techniques (e.g. data stream processing) have emerged to perform continuous single pass analysis and enhance parallelism. However, employing such techniques is not a trivial task due to its challenges such as partial knowledge of the data and the trade-off between parallelism and consistency. Moreover, depending on the source, data volumes may fluctuate over time which requires the degree of parallelism to be adapted in runtime. In this work, we contribute to the design of computational infrastructures and development of tools to address these challenges. In this regard, we focus on two problem domains. First, we target continuous data analysis and particularly focus on data clustering, as a significant representative problem, to extract information from massive data, generated by high-rate sensors. We propose Lisco, a single-pass continuous Euclidean distance-based clustering which exploits the inherent ordering of the spatial and temporal data, and its parallel counterpart, P-Lisco, to enhance pipeline- and data-parallelism. These algorithms provide high throughput of results with low latency, through pushing the processing closer to the data sources. Moreover we provide a framework, DRIVEN, that performs a continuous bounded error approximation to compress the volumes of data, and then transmits the compressed data to next layers of the IoT architecture to perform clustering on it, in a continuous fashion, using generalized form of Lisco. The compression in data retrieval speeds up the transmission of the data while preserving very similar clustering quality as raw data transmission. In the second domain, we target the elasticity in data streaming to utilize computational resources in runtime regarding the data rate fluctuations. For that, we provide a stream processing framework, STRETCH, and introduce the concept of virtual shared-nothing parallelization that is able to adapt the resources, maximize the throughput and latency, and preserve determinism. Thorough experimental evaluations on architectures representative of high-end servers and of resource-constrained embedded devices indicate the scalability benefits of all proposed algorithms. </div> <br /> Aoudi, Computer Science and Engineering<p>ED, lecture hall, EDIT-building</p><p>​Departure-Based Intrusion Detection</p><div><img src="/SiteCollectionImages/Institutioner/DoIT/Profile%20pictures/NS/W-Aoudi.gif" class="chalmersPosition-FloatRight" alt="" style="margin:5px" />Industrial Control Systems (ICS) combine information technology with operation technology to monitor or control physical industrial processes via computer-based programs and often operate on critical infrastructures. As such, compromised or maliciously operated ICS can cause devastating consequences on society at large. To meet efficiency requirements, ICS are becoming increasingly connected to corporate networks and to the Internet, thereby elevating the risk of cyberattacks. </div> <div>Resilient and sustainable highly connected ICS therefore require a serious consideration of proper security measures. Securing ICS solely from an IT perspective, while necessary, proves insufficient because, at the physical layer, the critical process would remain unmonitored and therefore vulnerable to sabotage by the attackers. The recent years have witnessed an increased interest in process-level intrusion detection where the process network connecting field devices is monitored for malicious behavior. One prominent approach in the literature proposes to build a model of the physical process, which is then used to compare a predicted state with the actual state in the hope of identifying attacks. Building and using a predictive model of the physical process, however, is non trivial, domain specific, and prone to detection inaccuracies due to noise in the process data. </div> <div>This thesis introduces a novel model-free approach to detecting cyberattacks on ICS by monitoring the process network in real time and deciding when the system operation is departing from normal dynamics. The proposed process-aware stealthy-attack detection mechanism processes raw sensor measurements to capture the dynamics of the underlying control system during a training phase, and then during a detection phase, it measures the extent to which current sensor observations conform with the estimated dynamics. The thesis provides a comprehensive treatment of the introduced method by thoroughly discussing its theoretical basis, proving its efficacy through extensive experiments on various systems, and, finally, demonstrating its applicability to real environments.</div> <br /> Robillard, Computer Science and Engineering<p>EA, lecture hall, Hörsalsvägen 11, EDIT trappa C, D och H</p><p>​Deductive Program Analysis with First-Order Theorem Provers</p>