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News

  • 2017 Humanoid Application Challenge Winners

    The Autonomous Agents lab has just won first place in the 2017 Humanoid Application Challenge at this year's IEEE International Conference on Robotics and Systems (IROS), IEEE's flagship robotics conference. The Humanoid Application Challenge is intended to be more open-ended than most other robotics competitions, in that entries are judged on dimensions of effectiveness and innovation in a given theme rather than stating a precisely defined goal such as winning a soccer competition. This perspective encourages creative entries that cross boundaries and bring together work from many areas of artificial intelligence that are important to intelligent humanoid robots, including vision, speech understanding, coordination, reasoning, machine learning, and human-robot interaction.

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  • Best Paper Award

    Congratulations to Mohammad Moein Almasi, MSc student with Dr. Hadi Hemmati, for winning the Best Paper Award in the 39th International Conference in Software Engineering (ICSE 2017)! 

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  • New Program Approval Process

    We are moving away from the paper program approval forms you may have used in previous years. Instead, we will be using a fillable PDF form, available online under the "resources" link forms section, that you should fill in and e-mail to advisor@cs.umanitoba.ca. If you wish to have a face to face meeting please indicate this in your e-mail otherwise your form will simply be dealt with electronically and you will receive an e-mail once this has been done. 

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Events

  • M.Sc. Thesis Defense: Kazi Wasif Ahmed

    When: December 15, 2017 @ 11:00am
    Where: E2-528 EITC

    Title: Secure and Efficient Nearest Neighbour Search in High Dimensional Space

    Abstract:

    The attractive features of cloud platforms such as low cost, high availability and scalability are encouraging social networks, health and other service providers to outsource their client data to the cloud. Though there are many advantages of using cloud-based solutions, the privacy of the outsourced data is a major concern. Compromised cloud servers can leak sensitive information about users such as the incident of the iCloud celebrity data leakage. One practical solution to mitigate these concerns is to encrypt or anonymize the data before outsourcing to the cloud. Although encryption protects the data from unauthorized access, it increases the computation complexity to execute the required functions (e.g., similarity or nearest neighbour search), which is the key requirement for different social discovery applications. On the other hand, anonymization supports privacy-preserving fast computation but inefficient anonymization result huge data utility loss. In this thesis, I have designed an efficient approach to perform the secure nearest neighbour search in high dimensional space. The proposed framework utilizes the advantages of Intel Software Guard Extensions (Intel SGX) architecture and efficient anonymization methods to perform the secure nearest neighbour search operation.

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