The University of Southampton

- Event

Date:
8th of December, 2017  @  15:30 - 17:00
Venue:
Building 2 (2) - 1085
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2017 Guest Speaker: Professor Katia Sycara
Carnegie Mellon University
Robotics Institute, School of Computer Science
Pittsburgh, Pennsylvania, US
 
Trust in Human Interaction with Robot Systems
 
As robotic platforms become cheaper and more reliable, they are increasingly going to be autonomously interacting with people for multiple tasks ranging from service robots in the home or work, to environmental exploration, search and rescue and crisis response. In all these interactions human trust in the autonomy is a very important ingredient. To engender trust, robots must be social, in the sense of considering social norms in their domain decision making. Additionally, as these agents become more sophisticated and independent via learning and interaction, it is critical for their human counterparts to understand their behaviors, the reasoning process behind those behaviors, and the expected outcomes to properly calibrate their trust in the systems and make appropriate decisions. In other words for human intelligibility of an agent's decisions, the agent needs to be transparent. Developing effective ways for autonomous systems to be socially-aware, trustworthy and transparent faces multiple challenges, foremost that the notion of trust, transparency have no unique definitions in the literature, and the role of social norms and their relations is not well understood. Moreover, human cognitive limitations, algorithmic scalability and opacity of sophisticated algorithms pose additional serious technical difficulties as to amount and type of information provided by the autonomous system to the human for trust based interaction.
 
In this talk, I will present some of our recent work on trust, transparency and social norms. In particular, I will present our trust and transparency framework in the context of human interaction with autonomously coordinating robotic swarms as well as our first attempts at transparency in deep neural networks for reinforcement learning and our work on social norm aware engineered systems.
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- Event

Date:
13th of December, 2018  @  11:30 - 12:15
Venue:
Building 2 (2) - Room 1085
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Making learning more inclusive: supporting friends, colleagues and students
Please support this event hosted by ECS to highlight Disability Awareness Month.
Thursday 13 December, B2/1085, 11:30 - 12:15
Lunch provided
 
 
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- Event

Date:
21st of November, 2018  @  14:00 - 15:00
Venue:
EEE Building (32) - Room 3077
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Uncertainty and variability is intrinsic to a plethora of biological processes that we want to understand, model and predict. In cardiac modelling, sources of uncertainty stem from the experimental error in the measurements from our protocols, lack of knowledge about the underlying mechanisms leading to structural error in our models, variability due to differences in cell and ion channel states due to cells being in different settings and gene expression patterns, and variability due to the inherent stochasticity of some of these processes exhibited at multiple time and spatial scales. To accommodate mathematical/phenomenological models in safety-critical clinical practice and drug development, it is therefore of utmost importance to quantify and propagate these uncertainties to model predictions. Bayesian statistics plays a major role in carrying out uncertainty quantification effectively. However, cardiac models pose a unique set of challenges for Bayesian statistical methods. In this talk I would present Bayesian statistical and modern machine learning approaches towards “forward” (from inputs to model predictions) and “inverse” (from experimental data to model structure) uncertainty quantification in cellular cardiac electropysiological models. Specifically, I would present approaches to overcome the computational and statistical challenges associated with uncertainty quantification in mechanistic models, described by differential equations, and highlight some of the open challenges. Furthermore, I would discuss the potential of modern machine learning techniques such as black-box variational inference and probabilistic programming towards solving the uncertainty quantification problem efficiently. Following are the references accompanying this talk: 1) Sanmitra Ghosh, David Gavaghan, Gary Mirams, “Gaussian process emulation for discontinuous response surfaces with applications for cardiac electrophysiology models”, https://arxiv.org/abs/1805.10020v1 2) Sanmitra Ghosh “Probabilistic Programming for Mechanistic Models (P2M2) tutorial repository”, https://github.com/sanmitraghosh/P2M2
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- Event

Date:
24th of October, 2018  @  14:00 - 15:00
Venue:
EEE Building (32) - Room 3077
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One of the key challenges of visual perception is to extract abstract models of 3D objects and object categories from visual measurements, which are affected by complex nuisance factors such as viewpoint, occlusion, motion, and deformations. In this talk, I will introduce two approaches that, given a large number of images of an object and no other supervision, can factorize image deformations and appearance. I will demonstrate the applicability of this method to articulated objects and deformable objects such as human faces and body by learning embeddings from random synthetic transformations or optical flow correspondences, all without any manual supervision. The talk will cover three recent recent papers: [1] Thewlis, J., Bilen, H., & Vedaldi, A. (2017). Unsupervised learning of object landmarks by factorized spatial embeddings. In International Conference on Computer Vision (ICCV). [2] Thewlis, J., Bilen, H., & Vedaldi, A. (2017). Unsupervised learning of object landmarks by factorized spatial embeddings. In Neural Information Processing Systems (NIPS). [3] Jakab, T., Gupta, A., Bilen, H., & Vedaldi, A. (2018). Conditional Image Generation for Learning the Structure of Visual Objects. (NIPS).
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- Event

Date:
28th of November, 2017  @  12:00 - 13:00
Venue:
Nightingale (67) - E1001
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Professor Chris Baber from the University of Birmingham will speak about Ubiquitous computing.
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- Event

Date:
6th of February, 2019  @  13:00 - 14:00
Venue:
53/4025

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Abstract: The increasing adoption of unmanned systems raises the challenge of the prevention of unauthorised access to them for nancial gain or malicious intent. The problem is exacerbated for maritime systems which are intentionally operated at considerable physical distance from the data or asset owner. The success of a naval mission is subject to the ful lment of a set of operational requirements before and during each voyage. As these requirements depend essentially on the maritime system components and the mission pro le, the e ects of failures can be very signi cant if they are not anticipated. In this paper, we use systems-theoretic process analysis (STPA) to develop a systematic mechanism to analyse the security functionalities of a fully autonomous ship. STPA is a hazard analysis technique capable of identifying potential hazardous design flaws, including software and system design errors and unsafe interactions among multiple system components. As part of the process analysis, we identified potential threats, vulnerabilities and attacks in an autonomous ship. The analyses can be used as a springboard to drive an autonomous ship system architecture and to designing a more e ective and secure system.
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- Event

Date:
5th of December, 2018  @  11:00 - 12:00
Venue:
EEE Building (32) - Room 3077
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WAIS Seminar with Ben Clark at B32-R3077
 
Title: Future Worlds - The on-campus startup accelerator: Change the world with your ideas
 
 
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- Event

Date:
24th of May, 2018  @  16:00 - 17:00
Venue:
New Mountbatten (53) - 4025
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This talk will describe the packaging technique for flexible circuits used in smart textiles (e-textiles)
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- Event

Date:
21st of June, 2018  @  16:00 - 17:00
Venue:
New Mountbatten (53) - 4025
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- Event

Date:
19th of July, 2018  @  16:00 - 17:00
Venue:
New Mountbatten (53) - 4025
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Title: Textile based Wearable Gas sensors Abstract: Contamination of air due to various chemical molecules in smart cities has both short and long-term effects on human health. Wearable sensors allow an individual to monitor the air quality in real time at any location enabling portability. It is important to make the sensor invisible and allow comfort to the user. This is achieved by fixing the device into textiles. This research is about incorporating gas sensors into textiles.
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