VIP: Safeguard Value Invariant Property for Thwarting Critical Memory Corruption Attacks

2021 ◽  
Author(s):  
Mohannad Ismail ◽  
Jinwoo Yom ◽  
Christopher Jelesnianski ◽  
Yeongjin Jang ◽  
Changwoo Min
Keyword(s):  
2019 ◽  
Vol 58 (4) ◽  
pp. 458-466
Author(s):  
A. N. Grishkov ◽  
M. N. Rasskazova ◽  
L. L. Sabinina

2018 ◽  
Vol 40 (6) ◽  
pp. 1594-1618
Author(s):  
SEBASTIÁN DONOSO ◽  
ANDREAS KOUTSOGIANNIS ◽  
WENBO SUN

For any measure-preserving system $(X,{\mathcal{B}},\unicode[STIX]{x1D707},T_{1},\ldots ,T_{d})$ with no commutativity assumptions on the transformations $T_{i},$$1\leq i\leq d,$ we study the pointwise convergence of multiple ergodic averages with iterates of different growth coming from a large class of sublinear functions. This class properly contains important subclasses of Hardy field functions of order zero and of Fejér functions, i.e., tempered functions of order zero. We show that the convergence of the single average, via an invariant property, implies the convergence of the multiple one. We also provide examples of sublinear functions which are, in general, bad for convergence on arbitrary systems, but good for uniquely ergodic systems. The case where the fastest function is linear is addressed as well, and we provide, in all the cases, an explicit formula of the limit function.


Author(s):  
DINESH P. MITAL ◽  
GOH WEE LENG

The use of autoregressive models in textual analysis holds great potential. Coupling the technique to a circular neighbourhood set imparts a rotational invariant property to it. This was demonstrated by Kashyap and Khotanzad in their model called the Circular Symmetric Autogressive (CSAR) Random Field model. The short-coming in this very ingenious proposal is that it is set in a background of square pixels and the rotational invariant property of the model fails in cases when the aspect ratio of the pixels are not at unity. This paper proposes a major modification to the CSAR to render the model rotational invariant under all configurations of pixel implementation. It is based on the area segments covered by a circle set in a 3×3 neighbourhood. We call it the Circular Area Autoregressive (CAAR) model. The results obtained from the CAAR showed much better consistency over that of the CSAR when a non-square pixel image was used.


2005 ◽  
Vol 49 (2) ◽  
pp. 187-213 ◽  
Author(s):  
Valentina Klimenok ◽  
Che Soong Kim ◽  
Dmitry Orlovsky ◽  
Alexander Dudin

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