A Universal Identity Backdoor Attack against Speaker Verification based on Siamese Network

Speaker verification has been widely used in many authentication scenarios. However, training models for speaker verification requires large amounts of data and computing power, so users often use untrustworthy third-party data or deploy third-party models directly, which may create security risks. In this paper, we propose a backdoor attack for the above scenario. Specifically, for the Siamese network in the speaker verification system, we try to implant a universal identity in the model that can simulate any enrolled speaker and pass the verification. So the attacker does not need to know the victim, which makes the attack more flexible and stealthy. In addition, we design and compare three ways of selecting attacker utterances and two ways of poisoned training for the GE2E loss function in different scenarios. The results on the TIMIT and Voxceleb1 datasets show that our approach can achieve a high attack success rate while guaranteeing the normal verification accuracy. Our work reveals the vulnerability of the speaker verification system and provides a new perspective to further improve the robustness of the system.Comment: Accepted by the Interspeech 2022. The first two authors contributed equally to this wor

Application of anaerobic granular sludge for competitive biosorption of methylene blue and Pb(II): Fluorescence and response surface methodology

© 2015 Elsevier Ltd. This study assessed the biosorption of anaerobic granular sludge (AGS) and its capacity as a biosorbent to remove Pb(II) and methylene blue (MB) from multi-components aqueous solution. It emerged that the biosorption data fitted well to the pseudo-second-order and Langmuir adsorption isotherm models in both single and binary systems. In competitive biosorption systems, Pb(II) and MB will suppress each other's biosorption capacity. Spectroscopic analysis, including Fourier transform infrared spectroscopy (FTIR) and fluorescence spectroscopy were integrated to explain this interaction. Hydroxyl and amine groups in AGS were the key functional groups for sorption. Three-dimensional excitation-emission matrix (3D-EEM) implied that two main protein-like substances were identified and quenched when Pb(II) or MB were present. Response surface methodology (RSM) confirmed that the removal efficiency of Pb(II) and MB reached its peak when the concentration ratios of Pb(II) and MB achieved a constant value of 1

Knowledge-Assisted Dual-Stage Evolutionary Optimization of Large-Scale Crude Oil Scheduling

With the scaling up of crude oil scheduling in modern refineries, large-scale crude oil scheduling problems (LSCOSPs) emerge with thousands of binary variables and non-linear constraints, which are challenging to be optimized by traditional optimization methods. To solve LSCOSPs, we take the practical crude oil scheduling from a marine-access refinery as an example and start with modeling LSCOSPs from crude unloading, transportation, crude distillation unit processing, and inventory management of intermediate products. On the basis of the proposed model, a dual-stage evolutionary algorithm driven by heuristic rules (denoted by DSEA/HR) is developed, where the dual-stage search mechanism consists of global search and local refinement. In the global search stage, we devise several heuristic rules based on the empirical operating knowledge to generate a well-performing initial population and accelerate convergence in the mixed variables space. In the local refinement stage, a repair strategy is proposed to move the infeasible solutions towards feasible regions by further optimizing the local continuous variables. During the whole evolutionary process, the proposed dual-stage framework plays a crucial role in balancing exploration and exploitation. Experimental results have shown that DSEA/HR outperforms the state-of-the-art and widely-used mathematical programming methods and metaheuristic algorithms on LSCOSP instances within a reasonable time

Partial nitrification granular sludge reactor as a pretreatment for anaerobic ammonium oxidation (Anammox): Achievement, performance and microbial community

© 2018 Elsevier Ltd Partial nitrification granular sludge was successfully cultivated in a sequencing batch reactor as a pretreatment for anaerobic ammonium oxidation (Anammox) through shortening settling time. After 250-days operation, the effluent NH4+-N and NO2−-N concentrations were average at 277.5 and 280.5 mg/L with nitrite accumulation rate of 87.8%, making it as an ideal influent for Anammox. Simultaneous free ammonia (FA) and free nitrous acid (FNA) played major inhibitory roles on the activity of nitrite oxidizing bacteria (NOB). The MLSS and SVI30 of partial nitrification reactor were 14.6 g/L and 25.0 mL/g, respectively. Polysaccharide (PS) and protein (PN) amounts in extracellular polymeric substances (EPS) from granular sludge were about 1.3 and 2.8 times higher than from seed sludge. High-throughput pyrosequencing results indicated that Nitrosomonas affiliated to the ammonia oxidizing bacteria (AOB) was the predominant group with a proportion of 24.1% in the partial nitrification system

2-Amino-6-(naphthalen-1-yl)-4-phenyl­pyridine-3-carbonitrile

In the title compound, C22H15N3, the naphthyl ring system makes dihedral angles of 67.40 (2) and 59.80 (3)° with the pyridyl and phenyl rings, respectively. In the crystal, the mol­ecules are connected via inter­molecular N—H⋯N hydrogen bonds, forming a three-dimensional network

Integrated photonic qubit quantum computing on a superconducting chip

We study a quantum computing system using microwave photons in transmission line resonators on a superconducting chip as qubits. We show that all control necessary for quantum computing can be implemented by coupling to Josephson devices on the same chip, and take advantage of their strong inherent nonlinearities to realize qubit interactions. We analyze the gate error rate to demonstrate that our scheme is realistic even for Josephson devices with limited decoherence times. A conceptually innovative solution based on existing technologies, our scheme provides an integrated and scalable approach to the next key milestone for photonic qubit quantum computing.Comment: 5 pages, 3 figure

A Fluorescence Approach to Assess the Production of Soluble Microbial Products from Aerobic Granular Sludge under the Stress of 2,4-Dichlorophenol

In this study, a fluorescence approach was used to evaluate the production of soluble microbial products (SMP) in aerobic granular sludge system under the stress of 2,4-dichlorophenol (2,4-DCP). A combined use of three-dimension excitation emission matrix fluorescence spectroscopy (3D-EEM), Parallel factor analysis (PARAFAC), synchronous fluorescence and two-dimensional correlation spectroscopy (2D-COS) were explored to respect the SMP formation in the exposure of different doses of 2,4-DCP. Data implied that the presence of 2,4-DCP had an obvious inhibition on biological nitrogen removal. According to EEM-PARAFAC, two fluorescent components were derived and represented to the presence of fulvic-like substances and humic-like substances in Component 1 and protein-like substances in Component 2. It was found from synchronous fluorescence that protein-like peak presented slightly higher intensity than that of fulvic-like peak. 2D-COS further revealed that fluorescence change took place sequentially in the following order: protein-like fraction > fulvic-like fraction. The obtained results could provide a potential application of fluorescence spectra in the released SMP assessment in the exposure of toxic compound during wastewater treatment