7,356 research outputs found
[3-Chloro-N′-(2-oxidonaphthalen-1-ylÂmethylidene)benzohydrazidato]methanol(methanolato)oxidovanadium(V)
In the title complex, [V(C18H11ClN2O2)(CH3O)O(CH3OH)], the VV ion is coordinated by a tridendate 3-chloro-N′-(2-oxidonaphthalen-1-ylmethylidene)benzohydrazidate ligand, one oxido ligand and by O atoms from a methanol and a methoxide ligand, forming a distorted octaÂhedral geometry. The dihedral angle between the benzene ring and the naphthylÂene ring system is 6.4 (3)°. The deviation of the VV ion from the plane defined by the three donor atoms of the tridentate ligand and the methÂoxy O atom towards the oxido O atom is 0.323 (2) Å. In the crystal, pairs of interÂmolecular O—H⋯N hydrogen bonds form centrosymmetric dimers
[N′-(5-Bromo-2-oxidobenzylÂidene-κO)-2-chloroÂbenzohydrazidato-κ2 N′,O](methanol-κO)(methanoÂlato-κO)oxidoÂvanadium(V)
The VV atom in the title complex, [V(C14H8BrClN2O2)(CH3O)O(CH3OH)], is six-coordinated by one phenolate O, one imine N and one enolic O atom of the hydrazone ligand, one oxide O atom, one methanol O atom and one methoxide O atom in a distorted octaÂhedral geometry. The dihedral angle between the two benzene rings of the hydrazone ligand is 13.2 (3)°. The deviation of the V atom towards the oxide O atom from the plane defined by the three donor atoms of the hydrazone ligand and the methÂoxy O atom is 0.318 (2) Å. Bond lengths are comparable with those observed in similar oxidovanadium(V) complexes with hydrazone ligands. In the crystal, pairs of molÂecules are linked through interÂmolecular O—H⋯N hydrogen bonds, forming dimers
Proving Expected Sensitivity of Probabilistic Programs with Randomized Variable-Dependent Termination Time
The notion of program sensitivity (aka Lipschitz continuity) specifies that
changes in the program input result in proportional changes to the program
output. For probabilistic programs the notion is naturally extended to expected
sensitivity. A previous approach develops a relational program logic framework
for proving expected sensitivity of probabilistic while loops, where the number
of iterations is fixed and bounded. In this work, we consider probabilistic
while loops where the number of iterations is not fixed, but randomized and
depends on the initial input values. We present a sound approach for proving
expected sensitivity of such programs. Our sound approach is martingale-based
and can be automated through existing martingale-synthesis algorithms.
Furthermore, our approach is compositional for sequential composition of while
loops under a mild side condition. We demonstrate the effectiveness of our
approach on several classical examples from Gambler's Ruin, stochastic hybrid
systems and stochastic gradient descent. We also present experimental results
showing that our automated approach can handle various probabilistic programs
in the literature
EavesDroid: Eavesdropping User Behaviors via OS Side-Channels on Smartphones
As the Internet of Things (IoT) continues to evolve, smartphones have become
essential components of IoT systems. However, with the increasing amount of
personal information stored on smartphones, user privacy is at risk of being
compromised by malicious attackers. Although malware detection engines are
commonly installed on smartphones against these attacks, attacks that can evade
these defenses may still emerge. In this paper, we analyze the return values of
system calls on Android smartphones and find two never-disclosed vulnerable
return values that can leak fine-grained user behaviors. Based on this
observation, we present EavesDroid, an application-embedded side-channel attack
on Android smartphones that allows unprivileged attackers to accurately
identify fine-grained user behaviors (e.g., viewing messages and playing
videos) via on-screen operations. Our attack relies on the correlation between
user behaviors and the return values associated with hardware and system
resources. While this attack is challenging since these return values are
susceptible to fluctuation and misalignment caused by many factors, we show
that attackers can eavesdrop on fine-grained user behaviors using a CNN-GRU
classification model that adopts min-max normalization and multiple return
value fusion. Our experiments on different models and versions of Android
smartphones demonstrate that EavesDroid can achieve 98% and 86% inference
accuracy for 17 classes of user behaviors in the test set and real-world
settings, highlighting the risk of our attack on user privacy. Finally, we
recommend effective malware detection, carefully designed obfuscation methods,
or restrictions on reading vulnerable return values to mitigate this attack.Comment: 15 pages, 25 figure
- …