3,629 research outputs found
Hard isogeny problems over RSA moduli and groups with infeasible inversion
We initiate the study of computational problems on elliptic curve isogeny
graphs defined over RSA moduli. We conjecture that several variants of the
neighbor-search problem over these graphs are hard, and provide a comprehensive
list of cryptanalytic attempts on these problems. Moreover, based on the
hardness of these problems, we provide a construction of groups with infeasible
inversion, where the underlying groups are the ideal class groups of imaginary
quadratic orders.
Recall that in a group with infeasible inversion, computing the inverse of a
group element is required to be hard, while performing the group operation is
easy. Motivated by the potential cryptographic application of building a
directed transitive signature scheme, the search for a group with infeasible
inversion was initiated in the theses of Hohenberger and Molnar (2003). Later
it was also shown to provide a broadcast encryption scheme by Irrer et al.
(2004). However, to date the only case of a group with infeasible inversion is
implied by the much stronger primitive of self-bilinear map constructed by
Yamakawa et al. (2014) based on the hardness of factoring and
indistinguishability obfuscation (iO). Our construction gives a candidate
without using iO.Comment: Significant revision of the article previously titled "A Candidate
Group with Infeasible Inversion" (arXiv:1810.00022v1). Cleared up the
constructions by giving toy examples, added "The Parallelogram Attack" (Sec
5.3.2). 54 pages, 8 figure
A linear algorithm for the grundy number of a tree
A coloring of a graph G = (V,E) is a partition {V1, V2, . . ., Vk} of V into
independent sets or color classes. A vertex v Vi is a Grundy vertex if it is
adjacent to at least one vertex in each color class Vj . A coloring is a Grundy
coloring if every color class contains at least one Grundy vertex, and the
Grundy number of a graph is the maximum number of colors in a Grundy coloring.
We derive a natural upper bound on this parameter and show that graphs with
sufficiently large girth achieve equality in the bound. In particular, this
gives a linear time algorithm to determine the Grundy number of a tree
The Sources of moitivation and academic motivation levels in Sakarya middle schools : Serdivan - Turkey
06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır
DroTrack: High-speed Drone-based Object Tracking Under Uncertainty
We present DroTrack, a high-speed visual single-object tracking framework for
drone-captured video sequences. Most of the existing object tracking methods
are designed to tackle well-known challenges, such as occlusion and cluttered
backgrounds. The complex motion of drones, i.e., multiple degrees of freedom in
three-dimensional space, causes high uncertainty. The uncertainty problem leads
to inaccurate location predictions and fuzziness in scale estimations. DroTrack
solves such issues by discovering the dependency between object representation
and motion geometry. We implement an effective object segmentation based on
Fuzzy C Means (FCM). We incorporate the spatial information into the membership
function to cluster the most discriminative segments. We then enhance the
object segmentation by using a pre-trained Convolution Neural Network (CNN)
model. DroTrack also leverages the geometrical angular motion to estimate a
reliable object scale. We discuss the experimental results and performance
evaluation using two datasets of 51,462 drone-captured frames. The combination
of the FCM segmentation and the angular scaling increased DroTrack precision by
up to and decreased the centre location error by pixels on average.
DroTrack outperforms all the high-speed trackers and achieves comparable
results in comparison to deep learning trackers. DroTrack offers high frame
rates up to 1000 frame per second (fps) with the best location precision, more
than a set of state-of-the-art real-time trackers.Comment: 10 pages, 12 figures, FUZZ-IEEE 202
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