קולוקוויום וסמינרים

כדי להצטרף לרשימת תפוצה של קולוקוויום מדעי המחשב, אנא בקר בדף מנויים של הרשימה.


Computer Science events calendar in HTTP ICS format for of Google calendars, and for Outlook.

Academic Calendar at Technion site.

קולוקוויום וסמינרים בקרוב

  • Decentralized Monetary Policy for Crypocurrencies

    דובר:
    אלון שטיירמן, הרצאה סמינריונית למגיסטר
    תאריך:
    יום שני, 21.1.2019, 11:00
    מקום:
    טאוב 601
    מנחה:
    Prof. E. Ben-Sasson

    The rapid increase in the popularity of cryptocurrencies brought with it not only questions regarding the quality of the technology itself, but also the economic phenomena surrounding them. Bitcoin in particular has proven itself to be a very volatile commodity~[Baek and Elbeck, Applied Economics 2015], and a poor currency, according to the definition of money [Kocherlakota, Journal of Economic Theory]. This has given rise to numerous alternative cryptocurrency models, which set about to improve upon their roles as a currency. One inherent problem presented by fixed-capped cryptocurrencies, such as Bitcoin, is that of lost-coins. Lacking a private-key restoration scheme, it is impossible to transfer funds out of an electronic wallet upon losing its private key. Since fixed-capped cryptocurrencies impose a strict monetary policy, by which the ultimate amount of coins in circulation is preset at a constant value, the phenomenon of lost coins has a debilitating effect on the system, effectively reducing the number of actual coins in circulation over time. In this paper, we introduce the problem and possible solutions that allow lost coins to be recirculated into the system, in a way that doesn't violate its security.

  • Pixel Club: Isospectralization, Or How To Hear Shape, Style, and Correspondence

    דובר:
    אריאנה רמפיני (אונ' רומא)
    תאריך:
    יום שני, 21.1.2019, 11:30
    מקום:
    חדר 337, בניין טאוב למדעי המחשב

    The question whether one can recover the shape of a geometric object from its Laplacian spectrum (‘hear the shape of the drum’) is a classical problem in spectral geometry with a broad range of implications and applications. While theoretically the answer to this question is negative (there exist examples of iso-spectral but non-isometric manifolds) little is known about the practical possibility of using the spectrum for shape reconstruction and optimization. In this talk, I will introduce a numerical procedure called isospectralization, consisting of deforming one shape to make its Laplacian spectrum match that of another. By implementing isospectralization using modern differentiable programming techniques, we showed that the *practical* problem of recovering shapes from the Laplacian spectrum is solvable. I will finally exemplify the applications of this procedure in some of the classical and notoriously hard problems in geometry processing, computer vision, and graphics such as shape reconstruction, style transfer, and non-isometric shape matching.

  • Pixel Club: On The Resistance of Neural Networks to Label Noise

    דובר:
    אמנון דורי (אונ' תל-אביב)
    תאריך:
    יום שלישי, 22.1.2019, 11:30
    מקום:
    חדר 337, בניין טאוב למדעי המחשב

    Neural Networks have been shown to be remarkably resistant to label noise. This means that you can train a network using a data set that contains a significant fraction of wrongly-labelled samples, and the network will still be able to accurately predict the label for a previously unseen sample. Our results show that a main factor in explaining this resistance is that networks learn from a local group of training samples, similarly to a K-Nearest Neighbors algorithm. We are able to provide a mathematical expression for the expected accuracy of a network given the noise level, for a general family of noise types. We also show that the extent of the resistance depends strongly on how localized the noise is.

    Shared work with: Oria Ratzon, Prof. Shai Avidan and Dr. Raja Giryes at Tel-Aviv University, school of Electrical Engineering.

  • יריד פרוייקטים ב-IoT, אנדרואיד, כופרה ורשתות

    Project Fair in IoT, Android, Ransomware and Networks

    תאריך:
    יום שלישי, 22.1.2019, 12:30
    מקום:
    האולום השקוף , בית הסטודנט

    מעבדות הפקולטה למדעי המחשב: המעבדה לפיתוח תוכנה ומערכות (SSDL), המעבדה לסייבר ואבטחת מידע (CYBER) וקשורת מחשבים (LCCN) מזמינות אתכם לבקר ביריד הפרוייקטים השנתי בתחומי IOT, אנדרואיד, כופרה ורשתות, שיתקיים ביום שלישי, 22 בינואר 2019, החל מ-12:30 באולם השקוף בבית הסטודנט .

    כולם מוזמנים!

    הפרוייקטים המציגים

  • ערב חשיפה לרשתות תקשורת

    Exposure Evening to Communication Networks

    תאריך:
    יום שלישי, 22.1.2019, 18:30
    מקום:
    חדר 337, בניין טאוב למדעי המחשב

    המעבדה לתקשורת מחשבים (LCCN) בפקולטה למדעי המחשב מזמינה אתכם לערב חשיפה לרשתות תקשורת - להכיר מקרוב את פעילות המעבדה, המחקרים והפרוייקטים שהיא מציעה ואת הצוות המוביל אותם, לשמוע הרצאות ולהשתתף בדיונים בנושא, כמפורט במודעה המצורפת.

    האירוע יתקיים ביום שלישי, 22 בינואר 2019, בשעה 18:30, בחדר 337 (קומה 3).

    מוזמנים סטודנטים לכל התארים המתעניינים בתחום.

  • Applying Machine Learning for Identifying Attacks at Run-time

    דובר:
    נורית דביר, הרצאה סמינריונית למגיסטר
    תאריך:
    יום שני, 28.1.2019, 10:00
    מקום:
    טאוב 601
    מנחה:
    Prof. O. Grumberg and Prof. S. Markovitch

    With the increase in malicious activity over the Internet, it has become extremely important to build tools for automatic detection of such activity. There have been attempts to use machine learning to detect network attacks, but the difficulty in obtaining positive (attack) examples, led to using one-class methods for anomaly detection. In this work we present a novel framework for using multiclass learning to induce an attack detector that identifies attacks at run time. We designed a network simulator that is used to produce network activity. The simulator includes an attacker that stochastically violates the normal activity, yielding positive as well as negative examples. We have also designed a set of features that withstand changes in the network topology. Given the set of tagged feature vectors, we can then apply a learning algorithm to produce a multiclass attack detector. Our framework allows the user to define a cost matrix for specifying the cost for each type of detection error (predicting some value for a run, when its real tag is another value). We tested our framework in a wide variety of network topologies and in different setups, including transfer learning and dynamic networks. In addition, we also referred to how to choose the router(s) that will act as monitor(s) and predict the label of a run. The presented framework will enable any organization to defend itself with an attack detector that is automatically adapted to its particular setting. Please note: the seminar will be given in Hebrew.