Victor Ciriza

A new tangible user interface for machine learning document review

This paper describes a tool for assisting lawyers and paralegal teams during document review in eDiscovery. The tool combines a machine learning technology (CategoriX) and advanced multi-touch interface capable of not only addressing the usual cost, time and accuracy issues in document review, but also of facilitating the work of the review teams by capitalizing on the intelligence of the reviewers and enabling collaborative work.

Mixed reality for supporting office devices troubleshooting

In this paper we describe the Mixed Reality system that we are developing to facilitate a real-world application, that of collaborative remote troubleshooting of broken office devices. The architecture of the system is centered on a 3D virtual representation of the device augmented with status data of the actual device coming from its internal sensors. The purpose of this paper is to illustrate how this approach supports the interactions required by the remote collaborative troubleshooting activity whilst taking into account technical constraints that come from a real world application. We believe it constitutes an interesting opportunity for using Mixed Reality in this domain.

The Personal Assessment Tool: A System Providing Environmental Feedback to Users of Shared Printers for Providing Environmental Feedback

To face ongoing global warming issues and in general to promote sustainable development, a number of tools have been developed that help people to assess the impact of their behavior on the environment. In this paper we present the Personal Assessment Tool, a system that observes print behavior and aggregates information in ways meant to promote more conscious use of the shared printing resources.

Extreme Volume Detection for Managed Print Services

A managed print service (MPS) manages the printing, scanning and facsimile devices in an enterprise to control cost and improve availability. Services include supplies replenishment, maintenance, repair, and use reporting. Customers are billed per page printed. Data are collected from a network of devices to facilitate management. The number of pages printed per device must be accurately counted to fairly bill the customer. Software errors, hardware changes, repairs, and human error all contribute to “meter reads” that are exceptionally high and are apt to be challenged by the customer were they to be billed. Account managers periodically review data for each device in an account. This process is tedious and time consuming and an automated solution is desired. Exceptional print volumes are not always salient and detecting them statistically is prone to errors owing to nonstationarity of the data. Mean levels and variances change over time and usage is highly auto correlated which precludes simple detection methods based on deviations from an average background. A solution must also be computationally inexpensive and require little auxiliary storage because hundreds of thousands of streams of device data must be processed. We present an algorithm and system for online detection of extreme print volumes that uses dynamic linear models (DLM) with variance learning. A DLM is a state space time series model comprising a random mean level system process and a random observation process. Both components are updated using Bayesian statistics. After each update, a forecasted value and its estimated variance are calculated. A read is flagged as exceptionally high if its value is highly unlikely with respect to a forecasted value and its standard deviation. We provide implementation details and results of a field test in which error rate was decreased from 26.4% to 0.5% on 728 observed meter reads.

User and device localization using probabilistic device log trilateration

This paper describes a method to learn demand models and find relative locations of users and devices based on usage logs only. It therefore allows the monitoring and optimization of infrastructures using a signal that is often already available. Absolute positions can be obtained by combining the usage logs with a small number of hand-labeled positions of users and/or devices.