Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy Worlds: Exploring Values and Design in the Development of the Tor Anonymity Network

This paper explores, through empirical research, how values, engineering practices, and technological design decisions shape one another in the development of privacy technologies. We propose the concept of “privacy worlds” to explore the values and design practices of the engineers of one of the world’s most notable (and contentious) privacy technologies: the Tor network. By following Tor’s design and development we show a privacy world emerging—one centered on a construction of privacy understood through the topology of structural power in the Internet backbone. This central “cipher” discourse renders privacy as a problem that can be “solved” through engineering, allowing the translation and representation of different groups of imagined users, adversaries, and technical aspects of the Internet in the language of the system. It also stabilizes a “flattened,” neutralized conception of privacy, risking stripping it of its political and cultural depth. We argue for an enriched empirical focus on design practices in privacy technologies, both as sites where values and material power are shaped, and as a place where the various worlds that will go on to cluster around them—of users, maintainers, and others—are imagined and reconciled.
Online Blind Audio Source Separation Using Recursive Expectation-Maximization

The challenging problem of online multi-microphone blind audio source separation (BASS) in noisy environment is addressed in this paper. We present a sequential, non-iterative, algorithm based on the recursive EM (REM) framework. In the proposed algorithm, the compete-data, which constitutes the separated sources and residual noise, is estimated in the E-step by applying a multichannel Wiener ﬁlter (MCWF); and the corresponding parameters, comprised of acoustic transfer functions (ATFs) relating the sources and the microphones and power spectral densities (PSDs) of the desired sources, are sequentially estimated in the M-step. The separated speech signals are further enhanced using matched-ﬁlter beamformers. The performance of the algorithm is demonstrated in terms of the separation capabilities, the resulting speech intelligibility and the ability to track the direction of arrival (DOA) of the moving sources.
Diels–Alder Cycloaddition of Biomass-Derived 2,5-Dimethylfuran and Ethylene over Sulfated and Phosphated Metal Oxides for Renewable p-Xylene

In this work, sulfated and phosphated metal oxides were studied as catalysts for the Diels–Alder cycloaddition of biomass-derived 2,5-dimethylfuran (DMF) and ethylene to understand the effect of acid strength on the reaction. Four catalysts with varied acidity, namely sulfated SiO2, sulfated TiO2, phosphated SiO2, and phosphated TiO2, were prepared via wet impregnation using sulfuric acid and phosphoric acid as precursors, and their structural and acid properties were examined using X-ray diffraction, Brunauer–Emmett–Teller analysis, Fourier transform infrared spectroscopy, solid state 31P magic angle spinning nuclear magnetic resonance spectroscopy, and temperature programmed desorption of ammonia. The results revealed that the acidity of the catalysts was largely influenced by the type of the acid functional group and the support as well as the calcination temperature. The conversion of DMF and the selectivity toward p-Xylene (PX) were generally correlated with the total acid site density and the acid–metal oxide interaction strength, which in turn affected the acid strength. Overall, phosphated SiO2 and TiO2 calcined at 773 K were identified as the most active and selective catalysts, exhibiting a high PX selectivity of over 70% and DMF conversion of 80% at 523 K after 6 h. The origin of the stability of the highly active phosphated catalysts was also investigated in detail.
Energy-Efficient Joint Task Offloading and Resource Allocation in OFDMA-Based Collaborative Edge Computing

Mobile edge computing (MEC) is an emergent architecture, which brings computation and storage resources to the edge of mobile network and provides rich services and applications near the end users. The joint problem of task offloading and resource allocation in the multi-user collaborative mobile edge computing network (C-MEC) based on Orthogonal Frequency-Division Multiple Access (OFDMA) is a challenging issue. In this paper, we investigate the offloading decision, collaboration decision, computing resource allocation and communication resource allocation problem in C-MEC. The delay-sensitive tasks of users can be computed locally, offloaded to collaborative devices or MEC servers. The goal is to minimize the total energy consumption of all mobile users under the delay constraint. The problem is formulated as a mixed-integer nonlinear programming (MINLP), which involves the joint optimization of task offloading decision, collaboration decision, subcarrier and power allocation, and computing resource allocation. A two-level alternation method framework is proposed to solve the formulated MINLP problem. In the upper level, a heuristic algorithm is used to handle the collaboration decision and offloading decisions under the initial setting; and in the lower level, the allocation of power, subcarrier, and computing resources is updated through deep reinforcement learning based on the current offloading decision. Simulation results show that the proposed algorithm achieves excellent performance in energy efficient and task completion rate (CR) for different network parameter settings.
Low Frequency-Repetitive Nerve Stimulation and Factors Affecting Decremental Response in Amyotrophic Lateral Sclerosis: A Retrospective Study of 449 Cases

A number of studies have demonstrated that decremental response to low frequency repetitive nerve stimulation (LF-RNS) is frequently observed in amyotrophic lateral sclerosis (ALS). However, due to the small sample size involved in previous studies, large discrepancies exist about the positivity rates of LF-RNS tests and factors affecting decremental response. This retrospective study of 449 cases, the largest sample size ever reported, shows that the overall positivity rate of LF-RNS is 49.9%. 3Hz RNS delivered to the accessory nerve has the best sensitivity and highest positivity rate. It obviously increases in response to upper limb onset, disease progression rate < 0.5 score/month, definite ALS and electromyography positive(EMG(+)) in sternocleidomastoid muscle. There is a linear increase in the decrement percentage of CMAP amplitude at 3Hz RNS delivered to the accessory nerve in response to longer disease duration, longer MUP duration and greater MUP duration increment percentage. These findings substantially advance the understanding of RNS results in ALS patients and effectively instruct clinical application.
Log-Likelihood-Ratio Cost Function as Objective Loss for Speaker Verification Systems

Many recent studies in Speaker Veriﬁcation (SV) have been focused on the design of the most appropriate training loss function, which plays an important role to improve the recognition ability of the systems. However, the veriﬁcation loss functions created often do not take into account the performance measures which are used for the ﬁnal system evaluation. For this reason, this paper presents an alternative approach to optimize the parameters of a neural network using a loss function based on the log-likelihood-ratio cost function (CLLR). This function is an application-independent metric that measures the cost of soft detection decisions over all the operating points. Thus, prior or relevance cost parameters assumptions are not employed to obtain it. Moreover, this metric has a differentiable expression, so no approximation is needed to use it as the objective loss to train a neural network. CLLR function as optimization loss was tested on the RSR2015-Part II database for text-dependent speaker veriﬁcation, providing competitive results without us-ing score normalization and outperforming other similar loss functions as Cross-Entropy combined with Ring Loss, as well as our previous loss function based on an approximation of the Detection Cost Function (DCF).
Veritaa: A distributed public key infrastructure with signature store

Today, the integrity and authenticity of digital documents and data are often hard to verify. Existing public key infrastructures (PKIs) are capable of certifying digital identities but do not provide solutions to store signatures immutably, and the process of certification is often not transparent. We propose Veritaa, a distributed public key infrastructure with an integrated signature store (DPKISS). The central part of Veritaa is the Graph of Trust that manages identity claims and singed declarations between identity claims and document identifiers. An application‐specific distributed ledger is used to store the transactions that form the Graph of Trust immutably. For the distributed certification of identity claims, a reputation system based on signed trust declarations and domain vetting is used. In this work, we have designed and implemented the proposed architecture of Veritaa, created a testbed, and performed several experiments. The experiments show the benefits and the high performance of Veritaa.
Quadrinomial trees with stochastic volatility to value real options

PurposeThe purpose of this article is to propose a detailed methodology to estimate, model and incorporate the non-constant volatility onto a numerical tree scheme, to evaluate a real option, using a quadrinomial multiplicative recombination.Design/methodology/approachThis article uses the multiplicative quadrinomial tree numerical method with non-constant volatility, based on stochastic differential equations of the GARCH-diffusion type to value real options when the volatility is stochastic.FindingsFindings showed that in the proposed method with volatility tends to zero, the multiplicative binomial traditional method is a particular case, and results are comparable between these methodologies, as well as to the exact solution offered by the Black–Scholes model.Originality/valueThe originality of this paper lies in try to model the implicit (conditional) market volatility to assess, based on that, a real option using a quadrinomial tree, including into this valuation the stochastic volatility of the underlying asset. The main contribution is the formal derivation of a risk-neutral valuation as well as the market risk premium associated with volatility, verifying this condition via numerical test on simulated and real data, showing that our proposal is consistent with Black and Scholes formula and multiplicative binomial trees method.
Cholinergic interneurons mediate cocaine extinction through similar plasticity across medium spiny neuron subtypes

Cholinergic interneurons (ChINs) in the nucleus accumbens (NAc) have been implicated in the acquisition and extinction of drug associations, as well as related plasticity in medium spiny neurons (MSNs). However, since most previous work has relied on artificial manipulations, if and how endogenous patterns of cholinergic signaling relate to drug associations is unknown. Moreover, despite great interest in the opposing effects of dopamine on MSN subtypes, whether ChIN-mediated effects are similar or different across MSN subtypes is also unknown. Here, we find that endogenous acetylcholine event frequency during extinction negatively correlates with the strength and persistence of cocaine-context associations across individuals, consistent with effects of artificial manipulation of ChIN activity during extinction. Moreover, ChIN activation during extinction produces a reduction in excitatory synaptic strength on both MSN subtypes, similar to the effect of multiple extinction sessions in the absence of ChIN manipulations. Together, our findings indicate that natural variation in NAc acetylcholine may contribute to individual differences in drug-context extinction by modulating glutamatergic presynaptic strength similarly at both D1R and D2R MSN subtypes. Graphical abstract
How far can membrane characteristic parameters bestow at the vacuum membrane distillation (VMD) performance: Modeling and simulation

ABSTRACT A mathematical model of simultaneous heat and mass transfer was presented to describe for the first time the effects of the variation in the characteristic parameters of the membrane during the VMD process on the permeation flux. The modeling approach was derived by dividing the module axially into multi-cells and a set of nonlinear simultaneous equations representing the VMD process were numerically solved via FSOLVE code using MATLAB® software. The validation of the presented model was estimated by simulation with a wide range of 135 diverse published experimental results related to the influence of various operating conditions, module properties, and membrane characteristics on the water permeation flux through the membrane. A good agreement was obtained between the experimental data and the results of the developed model. The same change in the membrane characteristics during the MD operation reported experimentally in the literature was confirmed by the simulation results of the developed model.
Open-Set Audio Classification with Limited Training Resources Based on Augmentation Enhanced Variational Auto-Encoder GAN with Detection-Classification Joint Training

In this paper, we propose a novel method to address practical problems when deploying audio classification systems in operations that are the presence of unseen sound classes (open-set) and the limitation of training resources. To solve it, a novel method which embeds variational auto-encoder (VAE), data augmentation and detection-classification joint training into conventional GAN networks is proposed. The VAE input to GAN-generator helps to generate realistic outlier samples which are not too far from in-distribution class and hence improve the open-set discrimination capabilities of classifiers. Next, the augmentation enhanced GAN scheme developed in our previous work [4] for close-set audio classification, will help to address the limited training resources by in cooperating the physical data augmentation to work together with traditional GAN produced samples to prevent overfitting and improve the optimization convergences. The detection-classification joint training further steps on advantages of VAE and Augmentation GAN to further improving the performances of detection and classification tasks. The experiments carried out on Google Speech Command database show great improvements of open-set classification accuracy from 62.41% to 88.29% when using only 10% amount of training data.
Hybrid Structure of a ZnO Nanowire Array on a PVDF Nanofiber Membrane/Nylon Mesh for use in Smart Filters: Photoconductive PM Filters

A nanofiber membrane with a high surface-to-volume ratio has advantages in applications such as those used for particulate matter filtration and gas detection. To maximize the potentials of the membrane structure, recent research has been attempted to control nanofiber geometries. In this paper, surface modification of a nanofiber membrane with a metal/ceramic nanostructure is performed to improve multi-functional filter performance, enhancing fine particle filtration and toxic gas absorption. Here, a smart filter is fabricated by electrospinning polyvinylidene difluoride (PVDF) nanofiber onto a nylon mesh and hydrothermal synthesis of ZnO nanoparticles onto a nanowire array on a PVDF nanofiber surface. On the ZnO nanowires–PVDF nanofiber layer filter, the pressure difference (ΔP = 4.13 kPa) is higher than the pure PVDF nanofiber layer. However, the filtration efficiency is 94.3% for a 0.3 μm particle size, which is higher than that of other sizes. Additionally, a ZnO nanowire array with high density on a PVDF nanofiber layer affects sensitivity (S = 39.37), with high resolution. The photocurrent characteristics of a smart filter have the potential for a photo-assisted redox reaction to detect toxic polar molecules in continuous airflow in real-time in indoor environments.
Improving animal-specific radiotherapy quality assurance for kilovoltage X-ray radiotherapy using a 3D printed dog skull water phantom

Background: Accurate dose assessment during animal radiotherapy is beneficial for veterinary medicine and medical education. Aim: To visualize the radiation treatment distribution of orthovoltage X-ray equipment in clinical practice using Monte Carlo simulations and create a dog skull water phantom for animal-specific radiotherapy. Methods: EGSnrc-based BEAMnrc and DOSXYZnrc codes were used to simulate orthovoltage dose distributions. At 10, 20, 30, 40, 50, and 80 mm in a water phantom, the depth dose was measured with waterproof Farmer dosimetry chambers, and the diagonal off-axis ratio was measured with Gafchromic EBT3 film to simulate orthovoltage dose distributions. Energy differences between orthovoltage and linear accelerated radiotherapy were assessed with a heterogeneous bone and tissue virtual phantom. The animal-specific phantom for radiotherapy quality assurance (QA) was created from CT scans of a dog and printed with a three-dimensional printer using polyamide 12 nylon, with insertion points for dosimetry chambers and Gafchromic EBT3 film. Results: Monte Carlo simulated and measured dose distributions differed by no more than 2.0% along the central axis up to a depth of 80 mm. The anode heel effect occurred in shallow areas. The orthovoltage radiotherapy percentage depth dose in bone was >40%. Build-up was >40%, with build-down after bone exit, whereas linear accelerator radiotherapy absorption changed little in the bone. A highly water-impermeable, animal-specific dog skull water phantom could be created to evaluate dose distribution. Conclusion: Animal-specific water phantoms and Monte Carlo simulated pre-treatment radiotherapy are useful QA for orthovoltage radiotherapy and yield a visually familiar phantom that will be useful for veterinary medical education.
Clarifying confusion – Prorocentrum triestinum J.Schiller and Prorocentrum redfieldii Bursa (Prorocentrales, Dinophyceae) are two different species

ABSTRACT The Prorocentrales are a unique group of dinophytes based on several apomorphic traits, but species delimitation is challenging within the group. Prorocentrum triestinum was described by Josef Schiller in 1918 as an important bloom-forming species from Trieste (Mediterranean, Adriatic Sea) with a conspicuous asymmetric outline and a small, asymmetrically located subapical spine. All subsequent records under this name fail to conform to Schiller’s original description. These inconsistencies have their origin in John Dodge’s 1975 revision of Prorocentrum, which placed Prorocentrum redfieldii, a more symmetrical, slender species with a long apical spine, into synonymy under P. triestinum. To clarify this confusion, we collected samples at the type locality of P. triestinum in Trieste and established a strain that is morphologically consistent with the protologue and suitable for use in epitypification. Morphology and rRNA sequence data of this strain were compared with four new strains identified as P. redfieldii from the Mediterranean Sea and the North Atlantic Ocean. Cells of P. triestinum had an asymmetric outline in lateral view and a small, dorso-subapical spine. These features, which are readily resolved by light microscopy, were distinct from those of the nearly symmetrical and slender cells of P. redfieldii, which had a long, apically located spine. The species are nevertheless closely related and share an identical architecture of the periflagellar area with a distinctive, largely reduced accessory pore together with a very small platelet 7. This apomorphy clearly differentiates both species from other species of Prorocentrum. Both species differ in their primary rRNA sequences, and ITS and LSU sequence differences will enable them to be distinguished in future meta-barcoding studies. The present study demonstrates that P. triestinum and P. redfieldii are distinct species and thus contributes to a reliable biodiversity assessment of Prorocentrum. HIGHLIGHTS Prorocentrum triestinum is characterised molecularly for the first time and delimited from P. redfieldii. The identity of important bloom-forming species is clarified. Structural details of the periflagellar area are described.
Adverse Childhood Experiences, Family Social Capital, and Externalizing Behavior Problems: An Analysis Across Multiple Ecological Levels

Some research on childhood adversity is critiqued for emphasizing the experiences of white, middle/upper-middle-class people and not accounting for adversities faced by more diverse populations. Adversities are also often summed up in ways that are unhelpful for targeting interventions to reduce risk of poor outcomes. I examine adversities across ecological levels—child, parent, family, and neighborhood—to determine the risk of externalizing behavior problems (EBP) using a racially diverse sample from the Longitudinal Studies of Child Abuse and Neglect (N = 1058). I consider whether family social capital can offset the effects of adversity across ecological levels. Longitudinal models indicate that adversities across multiple levels and those at the child, parent, and neighborhood levels increase risk of EBP throughout childhood. Cross-sectional models yield that early family social capital is associated with fewer EBP for children with multiple levels of adversity and at the child, parent, family, and neighborhood levels.
How Reliable Are Phonetic Data Collected Remotely? Comparison of Recording Devices and Environments on Acoustic Measurements

The COVID-19 pandemic posed an unprecedented challenge to phonetic research. On-site collection of speech data is difficult, if not impossible. The advancement of technology in mobile devices and online conference platforms offers the opportunity to collect data remotely. This paper aims to answer the question of how reliable speech data collected remotely are based on controlled speech. Seven devices, including smartphones and laptops, were used to record speech simultaneously, locally or on the cloud using ZOOM, both in a sound-attenuated lab and a conference room. Common acoustic measurements were made on these recordings. Local recordings proved to be reliable in duration, but not for recordings made on the cloud. Different devices have comparable performances in F0 and F1. The values acquired by different devices differ a lot for F2 and higher formants, spectral moments, and voice quality measures. These differences can lead to erroneous interpretation of segmental and voice quality contrasts. The recordings made remotely by smartphones and locally using ZOOM can be useful in studying prosody, but should be used with care for segments. Copyright © 2021 ISCA.
A High-Order Finite-Difference Method on Staggered Curvilinear Grids for Seismic Wave Propagation Applications with Topography

We developed a 3D elastic wave propagation solver that supports topography using staggered curvilinear grids. Our method achieves comparable accuracy to the classical fourth-order staggered grid velocity–stress finite-difference method on a Cartesian grid. We show that the method is provably stable using summation-by-parts operators and weakly imposed boundary conditions via penalty terms. The maximum stable timestep obeys a relationship that depends on the topography-induced grid stretching along the vertical axis. The solutions from the approach are in excellent agreement with verified results for a Gaussian-shaped hill and for a complex topographic model. Compared with a Cartesian grid, the curvilinear grid adds negligible memory requirements, but requires longer simulation times due to smaller timesteps for complex topography. The code shows 94% weak scaling efficiency up to 1014 graphic processing units.