Engineering Privacy in Public: Confounding Face Recognition

The objective of DARPA’s Human ID at a Distance (HID) program is to develop automated biometric identification technologies to detect, recognize and identify humans at great distances. While nominally intended for security applications, if deployed widely, such technologies could become an enormous privacy threat, making practical the automatic surveillance of individuals on a grand scale. Face recognition, as the HID technology most rapidly approaching maturity, deserves immediate research attention in order to understand its strengths and limitations, with an objective of reliably foiling it when it is used inappropriately. This paper is a status report for a research program designed to achieve this objective within a larger goal of similarly defeating all HID technologies

Stable isotope chemistry reveals plantdominant diet among early foragers on the Andean Altiplano, 9.0-6.5 cal. ka

Current models of early human subsistence economies suggest a focus on large mammal hunting. To evaluate this hypothesis, we examine human bone stable isotope chemistry of 24 individuals from the early Holocene sites of Wilamaya Patjxa (9.0-8.7 cal. ka) and Soro Mik'aya Patjxa (8.0-6.5 cal. ka) located at 3800 meters above sea level on the Andean Altiplano, Peru. Contrary to expectation, Bayesian mixing models based on the isotope chemistry reveal that plants dominated the diet, comprising 70-95% of the average diet. Paleoethnobotanical data further show that tubers may have been the most prominent subsistence resource. These findings update our understanding of earliest forager economies and the pathway to agricultural economies in the Andean highlands. The findings furthermore suggest that the initial subsistence economies of early human populations adapting to new landscapes may have been more plant oriented than current models suggest. © 2024 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Day-ahead Prediction and Shaping of Dynamic Response of Demand at Bulk Supply Points

Email overload is a recent problem that there is increasingly difficulty people have faced to process the large number of emails received daily. Currently this problem becomes more and more serious and it has already affected the normal usage of email as a knowledge management tool. It has been recognized that categorizing emails into meaningful groups can greatly save cognitive load to process emails and thus this is an effective way to manage email overload problem. However, most current approaches still require significant human input when categorizing emails. In this paper we develop an automatic email clustering system, underpinned by a new nonparametric text clustering algorithm. This system does not require any predefined input parameters and can automatically generate meaningful email clusters. Experiments show our new algorithm outperforms existing text clustering algorithms with higher efficiency in terms of computational time and clustering quality measured by different gauges.<br /

Parallel Unsupervised k-Windows: An Efficient Parallel Clustering Algorithm

Clustering can be defined as the process of partitioning a set of patterns into disjoint and homogeneous meaningful groups (clusters) . There is a growing need for parallel algorithms in this field since databases of huge size are common nowadays. This paper presents a parallel version of a recently proposed algorithm that has the ability to scale very well in parallel environments