Physical Sciences and Mathematics Commons^™

Slaves To Big Data. Or Are We?, Mireille Hildebrandt

Mireille Hildebrandt

In this contribution the notion of Big Data is discussed in relation to the monetisation of personal data. The claim of some proponents as well as adversaries, that Big Data implies that ‘n = all’, meaning that we no longer need to rely on samples because we have all the data, is scrutinized and found both overly optimistic and unnecessarily pessimistic. A set of epistemological and ethical issues is presented, focusing on the implications of Big Data for our perception, cognition, fairness, privacy and due process. The article then looks into the idea of user centric personal data management, to …

Reasoning Across Language And Vision In Machines And Humans, Andrei Barbu

Open Access Dissertations

Humans not only outperform AI and computer-vision systems, but use an unknown computational mechanism to perform tasks for which no suitable approaches exist. I present work investigating both novel tasks and how humans approach them in the context of computer vision and linguistics. I demonstrate a system which, like children, acquires high-level linguistic knowledge about the world. Robots learn to play physically-instantiated board games and use that knowledge to engage in physical play. To further integrate language and vision I develop an approach which produces rich sentential descriptions of events depicted in videos. I then show how to simultaneously detect …

Computational Intelligence And Decision Making: A Multidisciplinary Review, Renato Martins Alas, Sukanto Bhattacharya, Kuldeep Kumar

Kuldeep Kumar

The phenomenon of dynamic shift in our society called “speed up” has been part of the modern society since the middle of the eighteenth century. Its progressive development is already and will demand more speed in information processing. To cope with such fast pace demand of processing it is necessary to develop more sophisticated computational representation of the human brain. Computational Cognitive Neuroscience is the only realistic approach in reproducing the fundamental nature of human brain’s neurology. We support the biological computational representation of the human brain, based on fMRI imaging analysis, as more effective in the process of decision …

Computer Sketch Recognition, Richard Steigerwald

Master's Theses

Tens of thousands of years ago, humans drew sketches that we can see and identify even today. Sketches are the oldest recorded form of human communication and are still widely used. The universality of sketches supersedes that of culture and language. Despite the universal accessibility of sketches by humans, computers are unable to interpret or even correctly identify the contents of sketches drawn by humans with a practical level of accuracy.

In my thesis, I demonstrate that the accuracy of existing sketch recognition techniques can be improved by optimizing the classification criteria. Current techniques classify a 20,000 sketch crowd-sourced dataset …

Training An Asymmetric Signal Perceptron In An Artificial Chemistry, Peter Banda

Student Research Symposium

Autonomous learning implemented purely by means of a synthetic chemical system has not been previously realized. Learning promotes reusability, and minimizes the system design to simple input-output specification. In this poster, I present a simulated chemical system, the first full-featured implementation of a perceptron in an artificial (simulated) chemistry, which can successfully learn all 14 linearly separable logic functions. A perceptron is the simplest system capable of learning inspired by the functioning of a biological neuron. My newest model called the asymmetric signal perceptron (ASP) is, as opposed to its predecessors such as the weight-race perceptron (WRP), substantially simpler by …

Analysis Of Uncertain Data: Evaluation Of Given Hypotheses, Anatole Gershman, Eugene Fink, Bin Fu, Jaime G. Carbonell

Jaime G. Carbonell

We consider the problem of heuristic evaluation of given hypotheses based on limited observations, in situations when available data are insufficient for rigorous statistical analysis.

Artificial Immune Systems And Particle Swarm Optimization For Solutions To The General Adversarial Agents Problem, Jeremy Mange

Dissertations

The general adversarial agents problem is an abstract problem description touching on the fields of Artificial Intelligence, machine learning, decision theory, and game theory. The goal of the problem is, given one or more mobile agents, each identified as either “friendly" or “enemy", along with a specified environment state, to choose an action or series of actions from all possible valid choices for the next “timestep" or series thereof, in order to lead toward a specified outcome or set of outcomes. This dissertation explores approaches to this problem utilizing Artificial Immune Systems, Particle Swarm Optimization, and hybrid approaches, along with …

A New Intelligent Classifier For Breast Cancer Diagnosis Based On A Rough Set And Extreme Learning Machine: Rs + Elm, Yilmaz Kaya

Turkish Journal of Electrical Engineering and Computer Sciences

Breast cancer is one of the leading causes of death among women all around the world. Therefore, true and early diagnosis of breast cancer is an important problem. The rough set (RS) and extreme learning machine (ELM) methods were used collectively in this study for the diagnosis of breast cancer. The unnecessary attributes were discarded from the dataset by means of the RS approach. The classification process by means of ELM was performed using the remaining attributes. The Wisconsin Breast Cancer dataset (WBCD), derived from the University of California Irvine machine learning database, was used for the purpose of testing …

Mobile Games With Intelligence: A Killer Application?, Philip Hingston, Clare Bates Congdon, Graham Kendall

Research outputs 2013

Mobile gaming is an arena full of innovation, with developers exploring new kinds of games, with new kinds of interaction between the mobile device, players, and the connected world that they live in and move through. The mobile gaming world is a perfect playground for AI and CI, generating a maelstrom of data for games that use adaptation, learning and smart content creation. In this paper, we explore this potential killer application for mobile intelligence. We propose combining small, light-weight AI/CI libraries with AI/CI services in the cloud for the heavy lifting. To make our ideas more concrete, we describe …

Artificial Intelligence And Data Mining: Algorithms And Applications, Jianhong Xia, Fuding Xie, Yong Zhang, Craig Caulfield

Research outputs 2013

Artificial intelligence and data mining techniques have been used in many domains to solve classification, segmentation, association, diagnosis, and prediction problems. The overall aim of this special issue is to open a discussion among researchers actively working on algorithms and applications. The issue covers a wide variety of problems for computational intelligence, machine learning, time series analysis, remote sensing image mining, and pattern recognition. After a rigorous peer review process, 20 papers have been selected from 38 submissions. The accepted papers in this issue addressed the following topics: (i) advanced artificial intelligence and data mining techniques; (ii) computational intelligence in …

Testing A Distributed Denial Of Service Defence Mechanism Using Red Teaming, Samaneh Rastegari, Philip Hingston, Chiou-Peng Lam, Murray Brand

Research outputs 2013

The increased number of security threats against the Internet has made communications more vulnerable to attacks. Despite much research and improvement in network security, the number of denial of service (DoS) attacks has rapidly grown in frequency, severity, and sophistication in recent years. Thus, serious attention needs to be paid to network security. However, to create a secure network that can stay ahead of all threats, detection and response features are real challenges. In this paper, we look at the the interaction between the attacker and the defender in a Red Team/Blue Team exercise. We also propose a quantitative decision …

Cancer Risk Analysis By Fuzzy Logic Approach And Performance Status Of The Model, Atinç Yilmaz, Kürşat Ayan

Turkish Journal of Electrical Engineering and Computer Sciences

Cancer is the leading life-threatening disease for people in today's world. Although cancer formation is different for each type of cancer, it has been determined by studies and research that stress also triggers cancer types. Early precaution is very important for people who have not fallen ill yet with a disease like cancer that has a high mortality rate and expensive treatment. With this study, we expound that the possibility of developing such disease may be decreased and people could take measures against it. For the 3 cancer types selected as pilot work by introducing a fuzzy logic model, the …

Interpreting Individual Classifications Of Hierarchical Networks, Will Landecker, Michael David Thomure, Luis M.A. Bettencourt, Melanie Mitchell, Garrett T. Kenyon, Steven P. Brumby

Computer Science Faculty Publications and Presentations

Hierarchical networks are known to achieve high classification accuracy on difficult machine-learning tasks. For many applications, a clear explanation of why the data was classified a certain way is just as important as the classification itself. However, the complexity of hierarchical networks makes them ill-suited for existing explanation methods. We propose a new method, contribution propagation, that gives per-instance explanations of a trained network's classifications. We give theoretical foundations for the proposed method, and evaluate its correctness empirically. Finally, we use the resulting explanations to reveal unexpected behavior of networks that achieve high accuracy on visual object-recognition tasks using well-known …