32064388
Password sharing: implications for security design based on social practice
Current systems for banking authentication require that customers not reveal their access codes, even to members of the family. A study of banking and security in Australia shows that the practice of sharing passwords does not conform to this requirement. For married and de facto couples, password sharing is seen as a practical way of managing money and a demonstration of trust. Sharing Personal Identification Numbers (PINs) is a common practice among remote indigenous communities in Australia. In areas with poor banking access, this is the only way to access cash. People with certain disabilities have to share passwords with carers, and PIN numbers with retail clerks. In this paper we present the findings of a qualitative user study of banking and money management. We suggest design criteria for banking security systems, based on observed social and cultural practices of password and PIN number sharing.
206599449
Downloading vs Purchase: Music Industry vs Consumers
The music industry argues that unauthorised downloading of music is detrimentally affecting the industry; a breach of copyright that needs to be stopped. The industry has taken many actions to stamp out unauthorised music downloading, including prosecution of peer-to-peer software users for breach of copyright and against peer-to-peer software suppliers for contributory copyright infringement. Industry commentators have questioned this line of reasoning particularly as there is now significant revenue from legal music downloads in the United States and Europe. This paper draws on a qualitative study of music consumers in Australia to show there is not a clear dichotomy between down- loading and purchase. It is more of a continuum. From the users' perspective, downloading is transformed to the activity of accessing and exploring music. The industry could more fruitfully focus on satisfying this basic aspect of the listening experience in DRM systems in order to work together with their customers.
14570975
DRMs, fair use and users' experience of sharing music
There is a mismatch between the law relating to fair use, personal use and copying; the central thrust of Digital Rights Management (DRM) and users' behavior relating to the listening and sharing of music. This paper reports on the different copyright regimes in the United States and Australia. It describes some of the current DRM systems. Against this background, the paper draws on a qualitative study to explore Australian users' experience of listening to and sharing music. A design for a good DRM has to take into account the schism between the copyright regimes and users' sharing behavior.
16568514
Computing approximate bayes-nash equilibria in tree-games of incomplete information
We provide efficient algorithms for finding approximate Bayes-Nash equilibria (BNE) in graphical, specifically tree, games of incomplete information. In such games an agent's payoff depends on its private type as well as on the actions of the agents in its local neighborhood in the graph. We consider two classes of such games: (1) arbitrary tree-games with discrete types, and (2) tree-games with continuous types but with constraints on the effect of type on payoffs. For each class we present a message passing on the game-tree algorithm that computes an ε-BNE in time polynomial in the number of agents and the approximation parameter 1\ε.
462880
A social reinforcement learning agent
We report on our reinforcement learning work on Cobot, a software agent that resides in the well-known online chat community LambdaMOO. Our initial work on Cobot~\cite{cobotaaai} provided him with the ability to collect {\em social statistics\/} and report them to users in a reactive manner. Here we describe our application of reinforcement learning to allow Cobot to proactively take actions in this complex social environment, and adapt his behavior from multiple sources of human reward. After 5 months of training, Cobot received 3171 reward and punishment events from 254 different Lambda\-MOO users, and learned nontrivial preferences for a number of users. Cobot modifies his behavior based on his current state in an attempt to maximize reward. Here we describe LambdaMOO and the state and action spaces of Cobot, and report the statistical results of the learning experiment.
8708828
Optimal motion estimation from visual and inertial measurements
Cameras and inertial sensors are good candidates to be deployed together for autonomous vehicle motion estimation, since each can be used to resolve the ambiguities in the estimated motion that results from using the other modality alone. We present an algorithm that computes optimal vehicle motion estimates by considering all of the measurements from a camera, rate gyro, and accelerometer simultaneously. Such optimal estimates are useful in their own right, and as a gold standard for the comparison of online algorithms. By comparing the motions estimated using visual and inertial measurements, visual measurements only, and inertial measurements only against ground truth, we show that using image and inertial data together can produce highly accurate estimates even when the results produced by each modality alone are very poor Our test datasets include both conventional and omnidirectional image sequences, and an image sequence with a high percentage of missing data.
49345
CobotDS: a spoken dialogue system for chat
We describe CobotDS, a spoken dialogue system providing access to a well-known interact chat server called LambdaMOO. CobotDS provides real-time, two-way, natural language communication between a phone user and the multiple users in the text environment. We describe a number of the challenging design issues we faced, and our use of summarization, social filtering and personalized grammars in tackling them. We report a number of empirical findings from a small user study.
5635288
Indexing Uncertain Categorical Data
Uncertainty in categorical data is commonplace in many applications, including data cleaning, database integration, and biological annotation. In such domains, the correct value of an attribute is often unknown, but may be selected from a reasonable number of alternatives. Current database management systems do not provide a convenient means for representing or manipulating this type of uncertainty. In this paper we extend traditional systems to explicitly handle uncertainty in data values. We propose two index structures for efficiently searching uncertain categorical data, one based on the R-tree and another based on an inverted index structure. Using these structures, we provide a detailed description of the probabilistic equality queries they support. Experimental results using real and synthetic datasets demonstrate how these index structures can effectively improve the performance of queries through the use of internal probabilistic information.