Dr. Dhaval Vyas, Senior Lecturer in the Co-Innovation Group, School of Information Technology and Electrical Engineering, at the University of Queensland
Working and designing ‘with’ underserved communities requires ethical, sensitive and long-term engagement with people from those communities. Over the past 10 years, I have developed a design approach – called Grounded Design that enables developing technologies appropriate for marginalized and underserved communities. Grounded Design is a bottom-up approach that focuses on people’s needs, values and everyday practices and informs technology design by working ‘with’ communities over different phases of design. In this talk, I will discuss this approach using examples from three distinct communities: 1) a Brisbane-based low socioeconomic status community; 2) a Sri Lankan under-banked community, and 3) a Logan-based e-waste maker community.
I contend that inclusion and empowerment are central to designing technologies for underserved communities. By using strengths-based approaches (where the focus is given to the positive aspects of people’s lives) over deficit-based approaches, researchers can enable agency in community members during the design process as well as bring about positive social impact, among other outcomes
Establishing Communication, Non-Destructive Monitoring and the complexity of an Inverse Problem in a Sensor Network
Prof. Mahendran Velauthapillai, McBride Professor of Computer Science, Computer Science Department, Georgetown University, Washington, DC
Co-operative computations in a network of sensor nodes rely on an established, interference free and repetitive communication between adjacent sensors. This talk analyzes a simple randomized and distributed protocol to establish a periodic communication schedule S where each sensor broadcasts once to communicate to all of its neighbors during each period of S. The result obtained holds for any bounded degree network. The existence of such randomized protocols is not new. Our protocol reduces the number of random bits and the number of transmissions by individual sensors from
? (log2 n) to O (log n) where n is the number of sensor nodes. These reductions conserve power which is a critical resource. Both protocols assume upper bound on the number of nodes n and the maximum number of neighbor’s b. For a small multiplicative (i.e., a factor ? (1)) increase in the resources, our algorithm can operate without an upper bound on b.
Next we consider a sensor network for monitoring an environment. Assume that there is hidden value under each node of the sensor network covering the environment. The true value could represent a crack, material failure, temperature, intensity of vibration etc. However, when a measurement is taken at a node, the sensor measurement results in a readout which is a function of the true value of the node and its immediate neighbors. Now, given the sensor measurements, retrieving the true values is the inverse problem that we consider in this talk.
Dr. Ruvan Weerasinghe, Senior Lecturer, University of Colombo School of Computing
Recent advances in artificial intelligence has sparked debate ranging from euphoric optimism to doomsday pessimism. For the first time in its long history AI has become a popular term in the technology industry at large. It is increasingly becoming harder to distinguish reality from hype. In this talk I will attempt to briefly trace the path AI has taken over the years and where it finds itself today. I will then try to outline the stances that are being taken by different people groups and try to bring them together in helping us think about a rational way of thinking about the future of AI.
Dr. Buddy Liyanage, Information Security Manager, Thames Water, UK
The collection, storage, analysis and use of vast amounts of data is now possible due to the low cost of cloud storage and the use of cloud analysis platforms. Such storage of data, and the subsequent analysis brings about many conflicting risks and opportunities – both for the individual whose data is collected and analyzed, and for the organizations responsible for such activities. Big Data presents us with many challenges, both in the analysis and use of massive data sets, and the subsequent interpretation of the results through AI based intelligent systems. Given the scale of information and potential impact, traditional risk management activities may need to be re-visited.