Dinei Florencio

Bio: Dinei Florencio is an academic researcher from Microsoft. The author has contributed to research in topics: Password & Motion compensation. The author has an hindex of 51, co-authored 208 publications receiving 8641 citations. Previous affiliations of Dinei Florencio include MediaTek & Sarnoff Corporation.

A large-scale study of web password habits

Abstract: We report the results of a large scale study of password use andpassword re-use habits. The study involved half a million users over athree month period. A client component on users' machines recorded a variety of password strength, usage and frequency metrics. This allows us to measure or estimate such quantities as the average number of passwords and average number of accounts each user has, how many passwords she types per day, how often passwords are shared among sites, and how often they are forgotten. We get extremely detailed data on password strength, the types and lengths of passwords chosen, and how they vary by site. The data is the first large scale study of its kind, and yields numerous other insights into the role the passwords play in users' online experience.

Improved spread spectrum: a new modulation technique for robust watermarking

Abstract: This paper introduces a new watermarking modulation technique, which we call improved spread spectrum (ISS). When compared with traditional spread spectrum (SS), the signal does not act as a noise source, leading to significant gains. In some examples, performance improvements over SS are 20 dB in signal-to-noise ratio (SNR) or ten or more orders of magnitude in the error probability. The proposed method achieves roughly the same noise robustness gain as quantization index modulation (QIM) but without the amplitude scale sensitivity of QIM. Our proposed ISS is as robust in practice as traditional SS.

CROWDMOS: An approach for crowdsourcing mean opinion score studies

Abstract: MOS (mean opinion score) subjective quality studies are used to evaluate many signal processing methods. Since laboratory quality studies are time consuming and expensive, researchers often run small studies with less statistical significance or use objective measures which only approximate human perception. We propose a cost-effective and convenient measure called crowdMOS, obtained by having internet users participate in a MOS-like listening study. Workers listen and rate sentences at their leisure, using their own hardware, in an environment of their choice. Since these individuals cannot be supervised, we propose methods for detecting and discarding inaccurate scores. To automate crowdMOS testing, we offer a set of freely distributable, open-source tools for Amazon Mechanical Turk, a platform designed to facilitate crowdsourcing. These tools implement the MOS testing methodology described in this paper, providing researchers with a user-friendly means of performing subjective quality evaluations without the overhead associated with laboratory studies. Finally, we demonstrate the use of crowdMOS using data from the Blizzard text-to-speech competition, showing that it delivers accurate and repeatable results.

Point cloud attribute compression with graph transform

Abstract: Compressing attributes on 3D point clouds such as colors or normal directions has been a challenging problem, since these attribute signals are unstructured. In this paper, we propose to compress such attributes with graph transform. We construct graphs on small neighborhoods of the point cloud by connecting nearby points, and treat the attributes as signals over the graph. The graph transform, which is equivalent to Karhunen-Loeve Transform on such graphs, is then adopted to decorrelate the signal. Experimental results on a number of point clouds representing human upper bodies demonstrate that our method is much more efficient than traditional schemes such as octree-based methods.

Context-based routing in multi-hop networks

Abstract: Context-based routing in multi-hop networks involves using a context-based routing metric. In a described implementation, respective path values are calculated for respective ones of multiple paths using the context-based routing metric. A path is selected from the multiple paths responsive to the calculated path values. Data is transmitted over at least one link of the selected path. In an example embodiment, the context-based routing metric is ascertained responsive to an estimated service interval (ESI) of a bottleneck link of each path of the multiple paths. In another example embodiment, the context-based routing metric is ascertained responsive to an expected resource consumption (ERC) metric. In an example embodiment of path selection, the path is selected using a context-based path pruning (CPP) technique that involves maintaining multiple local contexts at each intermediate node, with each local context representing at least one partial path.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Thinking fast and slow.

Abstract: In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Digital Watermarking

Abstract: Digital watermarking is a key ingredient to copyright protection. It provides a solution to illegal copying of digital material and has many other useful applications such as broadcast monitoring and the recording of electronic transactions. Now, for the first time, there is a book that focuses exclusively on this exciting technology. Digital Watermarking covers the crucial research findings in the field: it explains the principles underlying digital watermarking technologies, describes the requirements that have given rise to them, and discusses the diverse ends to which these technologies are being applied. As a result, additional groundwork is laid for future developments in this field, helping the reader understand and anticipate new approaches and applications.

[서평]「Applied Cryptography」

Abstract: The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

New edge-directed interpolation

Abstract: This paper proposes an edge-directed interpolation algorithm for natural images. The basic idea is to first estimate local covariance coefficients from a low-resolution image and then use these covariance estimates to adapt the interpolation at a higher resolution based on the geometric duality between the low-resolution covariance and the high-resolution covariance. The edge-directed property of covariance-based adaptation attributes to its capability of tuning the interpolation coefficients to match an arbitrarily oriented step edge. A hybrid approach of switching between bilinear interpolation and covariance-based adaptive interpolation is proposed to reduce the overall computational complexity. Two important applications of the new interpolation algorithm are studied: resolution enhancement of grayscale images and reconstruction of color images from CCD samples. Simulation results demonstrate that our new interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional linear interpolation.