[3-Chloro-N′-(2-oxidonaphthalen-1-yl­methylidene)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 octa­hedral geometry. The dihedral angle between the benzene ring and the naphthyl­ene 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 meth­oxy O atom towards the oxido O atom is 0.323 (2) Å. In the crystal, pairs of inter­molecular O—H⋯N hydrogen bonds form centrosymmetric dimers

[N′-(5-Bromo-2-oxidobenzyl­idene-κO)-2-chloro­benzohydrazidato-κ2 N′,O](methanol-κO)(methano­lato-κO)oxido­vanadium(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 octa­hedral 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 meth­oxy 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 mol­ecules are linked through inter­molecular 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