Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the Security of Deepfake Detection

Deepfakes pose growing challenges to the trust of information on the Internet. Thus, detecting deepfakes has attracted increasing attentions from both academia and industry. State-of-the-art deepfake detection methods consist of two key components, i.e., face extractor and face classifier, which extract the face region in an image and classify it to be real/fake, respectively. Existing studies mainly focused on improving the detection performance in non-adversarial settings, leaving security of deepfake detection in adversarial settings largely unexplored. In this work, we aim to bridge the gap. In particular, we perform a systematic measurement study to understand the security of the state-of-the-art deepfake detection methods in adversarial settings. We use two large-scale public deepfakes data sources including FaceForensics++ and Facebook Deepfake Detection Challenge, where the deepfakes are fake face images; and we train state-of-the-art deepfake detection methods. These detection methods can achieve 0.94--0.99 accuracies in non-adversarial settings on these datasets. However, our measurement results uncover multiple security limitations of the deepfake detection methods in adversarial settings. First, we find that an attacker can evade a face extractor, i.e., the face extractor fails to extract the correct face regions, via adding small Gaussian noise to its deepfake images. Second, we find that a face classifier trained using deepfakes generated by one method cannot detect deepfakes generated by another method, i.e., an attacker can evade detection via generating deepfakes using a new method. Third, we find that an attacker can leverage backdoor attacks developed by the adversarial machine learning community to evade a face classifier. Our results highlight that deepfake detection should consider the adversarial nature of the problem.
Age of Information in Relay-Assisted Status Updating Systems

In this paper we consider the age of information (AoI) of a status updating system with a relay, where the updates are delivered to destination either from the direct line or the two-hop link via the relay. An updating packet generated at source is sent to receiver and the relay simultaneously. When the direct packet transmission fails, the relay replaces the source and retransmits the packet until it is eventually obtained at the receiver side. Assume that the propagation delay on each link is one time slot, we determine the stationary distribution of the AoI for three cases: (a) relay has no buffer and the packet delivery from relay cannot be preempted by fresher updates from source; (b) relay has no buffer but the packet substitution is allowable; (c) relay has size 1 buffer and the packet in buffer is refreshed when a newer packet is obtained. The idea is invoking a multiple-dimensional state vector which contains the AoI as a part and constituting the multiple-dimensional AoI stochastic process. We find the steady state of each multiple-dimensional AoI process by solving the system of stationary equations. Once the steady-state distribution of larger-dimensional AoI process is known, the stationary AoI distribution is also obtained as it is one of the marginal distributions of that process’s steady-state distribution. For all the situations, we derive the explicit expression of AoI distribution, and calculate the mean and the variance of the stationary AoI. All the results are compared numerically, including the AoI performance of the non-relay state updating system. Numerical results show that adding the relay The authors are with the School of Information Science and Engineering, Southeast University, Nanjing 210096, China (e-mail: 230179077@seu.edu.cn; yinfeixu@seu.edu.cn). July 6, 2021 DRAFT ar X iv :2 10 7. 01 83 3v 1 [ cs . I T ] 5 J ul 2 02 1 2 IEEE TRANSACTIONS ON COMMUNICATIONS improves the system’s timeliness dramatically, and no-buffer-and-preemption setting in relay achieves both minimal average AoI and AoI’s variance. Thus, for the system model discussed in this paper, to reduce the AoI at receiver there is no need to add the buffer in relay.
IITP at WAT 2021: System description for English-Hindi Multimodal Translation Task

Neural Machine Translation (NMT) is a predominant machine translation technology nowadays because of its end-to-end trainable flexibility. However, NMT still struggles to translate properly in low-resource settings specifically on distant language pairs. One way to overcome this is to use the information from other modalities if available. The idea is that despite differences in languages, both the source and target language speakers see the same thing and the visual representation of both the source and target is the same, which can positively assist the system. Multimodal information can help the NMT system to improve the translation by removing ambiguity on some phrases or words. We participate in the 8th Workshop on Asian Translation (WAT - 2021) for English-Hindi multimodal translation task and achieve 42.47 and 37.50 BLEU points for Evaluation and Challenge subset, respectively.
A Topic Guided Pointer-Generator Model for Generating Natural Language Code Summaries

Code summarization is the task of generating natural language description of source code, which is important for program understanding and maintenance. Existing approaches treat the task as a machine translation problem (e.g., from Java to English) and applied Neural Machine Translation models to solve the problem. These approaches only consider a given code unit (e.g., a method) without its broader context. The lacking of context may hinder the NMT model from gathering sufficient information for code summarization. Furthermore, existing approaches use a fixed vocabulary and do not fully consider the words in code, while many words in the code summary may come from the code. In this work, we present a neural network model named ToPNN for code summarization, which uses the topics in a broader context (e.g., class) to guide the neural networks that combine the generation of new words and the copy of existing words in code. Based on the model we present an approach for generating natural language code summaries at the method level (i.e., method comments). We evaluate our approach using a dataset with 4,203,565 commented Java methods. The results show significant improvement over state-of-the-art approaches and confirm the positive effect of class topics and the copy mechanism.
Are standard Object Segmentation models sufficient for Learning Affordance Segmentation?

Affordances are the possibilities of actions the environment offers to the individual. Ordinary objects (hammer, knife) usually have many affordances (grasping, pounding, cutting), and detecting these allow artificial agents to understand what are their possibilities in the environment, with obvious application in Robotics. Proposed benchmarks and state-of-the-art prediction models for supervised affordance segmentation are usually modifications of popular object segmentation models such as Mask R-CNN. We observe that theoretically, these popular object segmentation methods should be sufficient for detecting affordances masks. So we ask the question: is it necessary to tailor new architectures to the problem of learning affordances? We show that applying the out-of-the-box Mask R-CNN to the problem of affordances segmentation outperforms the current state-of-the-art. We conclude that the problem of supervised affordance segmentation is included in the problem of object segmentation and argue that better benchmarks for affordance learning should include action capacities.
Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning

Point clouds have attracted increasing attention. Significant progress has been made in methods for point cloud analysis, which often requires costly human annotation as supervision. To address this issue, we propose a novel self-contrastive learning for self-supervised point cloud representation learning, aiming to capture both local geometric patterns and nonlocal semantic primitives based on the nonlocal self-similarity of point clouds. The contributions are two-fold: on the one hand, instead of contrasting among different point clouds as commonly employed in contrastive learning, we exploit self-similar point cloud patches within a single point cloud as positive samples and otherwise negative ones to facilitate the task of contrastive learning. On the other hand, we actively learn hard negative samples that are close to positive samples for discriminative feature learning, which are sampled conditional on each anchor patch leveraging on the degree of self-similarity. Experimental results show that the proposed method achieves state-of-the-art performance on widely used benchmark datasets for self-supervised point cloud segmentation and transfer learning for classification.
Scarecrow: A Framework for Scrutinizing Machine Text

Modern neural text generation systems can produce remarkably ﬂuent and grammatical texts. While earlier language models suffered from repetition and syntactic errors, the errors made by contemporary models are often semantic, narrative, or discourse failures. To facilitate research of these complex error types, we introduce a new structured, crowdsourced error annotation schema called S CARECROW . The error categories used in S CARECROW —such as redundancy, common-sense errors, and incoherence—were identiﬁed by combining expert analysis with several pilot rounds of ontology-free crowd annotation to arrive at a schema which covers the error phenomena found in real machine generated text. We use S CARECROW to collect 13k annotations of 1.3k human and machine generate paragraphs of English language news text, amounting to over 41k spans each labeled with its error category, severity, a natural language explanation, and antecedent span (where relevant). We collect annotations for text generated by state-of-the-art systems with varying known performance levels, from GPT-2 Small through the largest GPT-3. We isolate several factors for detailed analysis, including parameter count, training data, and decoding technique. Our results show both expected and surprising differences across these settings. These ﬁndings demonstrate the value of S CARECROW annotations in the assess-ment of current and future text generation systems. We release our complete annotation toolkit and dataset at https://yao-dou. github.io/scarecrow/ .
Class Introspection: A Novel Technique for Detecting Unlabeled Subclasses by Leveraging Classifier Explainability Methods

Detecting latent structure within a dataset is a crucial step in performing analysis of a dataset. However, existing state-of-the-art techniques for subclass discovery are limited: either they are limited to detecting very small numbers of outliers or they lack the statistical power to deal with complex data such as image or audio. This paper proposes a solution to this subclass discovery problem: by leveraging instance explanation methods, an existing classifier can be extended to detect latent classes via differences in the classifier's internal decisions about each instance. This works not only with simple classification techniques but also with deep neural networks, allowing for a powerful and flexible approach to detecting latent structure within datasets. Effectively, this represents a projection of the dataset into the classifier's"explanation space,"and preliminary results show that this technique outperforms the baseline for the detection of latent classes even with limited processing. This paper also contains a pipeline for analyzing classifiers automatically, and a web application for interactively exploring the results from this technique.
Fast Rate Learning in Stochastic First Price Bidding

First-price auctions have largely replaced traditional bidding approaches based on Vickrey auctions in programmatic advertising. As far as learning is concerned, first-price auctions are more challenging because the optimal bidding strategy does not only depend on the value of the item but also requires some knowledge of the other bids. They have already given rise to several works in sequential learning, many of which consider models for which the value of the buyer or the opponents' maximal bid is chosen in an adversarial manner. Even in the simplest settings, this gives rise to algorithms whose regret grows as $\sqrt{T}$ with respect to the time horizon $T$. Focusing on the case where the buyer plays against a stationary stochastic environment, we show how to achieve significantly lower regret: when the opponents' maximal bid distribution is known we provide an algorithm whose regret can be as low as $\log^2(T)$; in the case where the distribution must be learnt sequentially, a generalization of this algorithm can achieve $T^{1/3+ \epsilon}$ regret, for any $\epsilon>0$. To obtain these results, we introduce two novel ideas that can be of interest in their own right. First, by transposing results obtained in the posted price setting, we provide conditions under which the first-price biding utility is locally quadratic around its optimum. Second, we leverage the observation that, on small sub-intervals, the concentration of the variations of the empirical distribution function may be controlled more accurately than by using the classical Dvoretzky-Kiefer-Wolfowitz inequality. Numerical simulations confirm that our algorithms converge much faster than alternatives proposed in the literature for various bid distributions, including for bids collected on an actual programmatic advertising platform.
Effect of Dust Rotational Disruption by Radiative Torques on Radiation Pressure Feedback from Massive Protostars

Radiation pressure on dust is thought to play a crucial role in the formation process of massive stars by acting against gravitational collapse onto the central protostar. However, dust properties in dense regions irradiated by the intense radiation of massive protostars are poorly constrained. Previous studies usually assume the standard interstellar dust model to constrain the maximum mass of massive stars formed by accretion, which appears to contradict dust evolution theory. In this paper, using the fact that stellar radiation exerts on dust simultaneous radiation pressure and radiative torques, we study the effects of grain rotational disruption by radiative torques (RATs) on radiation pressure and explore its implications for massive star formation. For this paper, we focus on the protostellar envelope and adopt a spherical geometry. We find that originally large grains of micron sizes presumably formed in very dense regions can be rapidly disrupted into small grains by RATs due to infrared radiation from the hot dust shell near the sublimation front induced by direct stellar radiation. Owing to the modification of the size distribution by rotational disruption, the radiation pressure opacity can be decreased by a factor of ∼3 from the value expected from the original dust model. However, to form massive stars via spherical accretion, the dust-to-gas mass ratio needs to be reduced by a factor of ∼5 as previously found.
Lightning Stokes solver

Gopal and Trefethen recently introduced"lightning solvers"for the 2D Laplace and Helmholtz equations, based on rational functions with poles exponentially clustered near singular corners. Making use of the Goursat representation in terms of analytic functions, we extend these methods to the biharmonic equation, specifically to 2D Stokes flow. Solutions to model problems are computed to 10-digit accuracy in less than a second of laptop time. As an illustration of the high accuracy, we resolve two or more counter-rotating Moffatt eddies near a singular corner.
Mechanisms of ionizational nonequilibrium in air and nitrogen plasmas

Experimental and numerical investigations are presented of the mechanism and degree of ionizational nonequilibrium in recombining plasmas of air and nitrogen produced by a 50 kW RF plasma torch operating at atmospheric pressure. It is shown that, as electron recombination in molecular plasmas occurs primarily via inherently fast two-body dissociative recombination, ionizational nonequilibrium is ultimately limited by slow neutral recombination. This is unlike the case for atomic plasmas, wherein ionizational nonequilibrium is caused by finite three-body electron recombination rates. Electron and heavy particle concentrations were measured and compared with the results of numerical simulations performed with three reaction mechanisms widely used for air and nitrogen plasma kinetics. The results support the proposed recombination model and provide a preliminary assessment of the kinetic rates of importance in recombining air and nitrogen plasmas
Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective

Learning with graphs has attracted significant attention recently. Existing representation learning methods on graphs have achieved state-of-the-art performance on various graph-related tasks such as node classification, link prediction, etc. However, we observe that these methods could leak serious private information. For instance, one can accurately infer the links (or node identity) in a graph from a node classifier (or link predictor) trained on the learnt node representations by existing methods. To address the issue, we propose a privacy-preserving representation learning framework on graphs from the mutual information perspective. Specifically, our framework includes a primary learning task and a privacy protection task, and we consider node classification and link prediction as the two tasks of interest. Our goal is to learn node representations such that they can be used to achieve high performance for the primary learning task, while obtaining performance for the privacy protection task close to random guessing. We formally formulate our goal via mutual information objectives. However, it is intractable to compute mutual information in practice. Then, we derive tractable variational bounds for the mutual information terms, where each bound can be parameterized via a neural network. Next, we train these parameterized neural networks to approximate the true mutual information and learn privacy-preserving node representations. We finally evaluate our framework on various graph datasets.
Coherent Electromagnetic Emission from Relativistic Magnetized Shocks.

Relativistic magnetized shocks are a natural source of coherent emission, offering a plausible radiative mechanism for fast radio bursts (FRBs). We present first-principles 3D simulations that provide essential information for the FRB models based on shocks: the emission efficiency, spectrum, and polarization. The simulated shock propagates in an e^{±} plasma with magnetization σ>1. The measured fraction of shock energy converted to coherent radiation is ≃10^{-3}σ^{-1}, and the energy-carrying wave number of the wave spectrum is ≃4ω_{c}/c, where ω_{c} is the upstream gyrofrequency. The ratio of the O-mode and X-mode energy fluxes emitted by the shock is ≃0. 4σ^{-1}. The dominance of the X mode at σ≫1 is particularly strong, approaching 100% in the spectral band around 2ω_{c}. We also provide a detailed description of the emission mechanism for both X and O modes.
(1+1)-dimensional Dirac equation in an effective mass theory under the influence of local fermi velocity

We present a new approach to study (1+1)-dimensional Dirac equation in the background of an effective mass M by exploiting the possibility of a positiondependent fermi velocity vF . We explore the resulting structure of the coupled equations and arrive at an interesting constraint of M turning out to be the inverse square of vF . We address several solutions of the effective potential that include the free particle, shifted harmonic oscillator, Morse potential and CPRS potential.
On the minimal driver sets for linear dynamical systems on graphs

Let G = (V,E) be a simple, undirected graph on the vertex set V = {1, 2, . , n} and let A be the adjacency matrix ofG. A non-empty subset S ⊆ V is called a driver set for G if the system ẋ = Ax+BSu, or the pair (A,BS), is controllable, where BS is the (n × k)-matrix with columns ei, i ∈ S. Let D(G) denote the minimum cardinality of a driver set for G and let M(G) denote the maximum of all geometric multiplicities of eigenvalues of A . It is well-known that D(G) ≥ M(G) for all graphs G. Let γ denote the Plücker embedding of Gr(k, n) into P( n k )−1. We prove that in all cases of equality D(G) = M(G) = k a necessary condition for S to be a minimal driver set is the condition that S lies in the support of γ(Eλ) for each eigenspace Eλ of A of maximal dimension k. We classify the minimal driver sets for the path and cycle graphs Pn and Cn for all values of n. Let Sym0(G) be the set of symmetric matrices X = [xij] with zero diagonal and off-diagonal elements xij unequal to zero if and only if (i, j) ∈ E. The pair (G,S) is called strongly Sym0(G)-controllable if (X,BS) is controllable for all X ∈ Sym0(G). We show that this property of (G,S) is respected by the automorphism group Aut(G). We determine all orbits of minimal driver sets S for G = Pn and G = Cn for which (G,S) is strongly Sym0(G)-controllable.
Hadron wave functions in high-energy scattering, form factors and strong decays: The effects of the Lorentz contracted form

In this paper, we study the class of the processes, where dynamics depends essentially on the properties of the hadron wave functions involved in the reactions. In this case, the momentum dependence of the form of the wave functions, imposed by the Lorentz invariance and in particular by the Lorentz contraction, can be tested in the experiment and may strongly influence the resulting cross-sections. One example of such observables is given by the hadron form factors in the case when the large [Formula: see text] behavior is mostly frozen, while the Lorentz contraction of the hadron wave functions is taken into account. Another example, considered earlier, is the strong hadron decay with high-energy emission. In this paper, we study the role of the Lorentz contraction in the high-energy hadron–hadron scattering process at large momentum transfer. For the [Formula: see text] and [Formula: see text] scattering at large [Formula: see text], it is shown that at small [Formula: see text], the picture of two exponential slopes in the differential cross-section, explained previously by the author, remains stable, while the backward scattering cross-section is strongly increased by the Lorentz contraction.
Compressibility effects in free shear layers

High Reynolds number compressible free shear layers were studied experimentally to explore the effects of compressibility on the turbulence field. Previous preliminary results reported by the authors showed that the level and the lateral extent of turbulence fluctuations are reduced as the compressibility, which is characterized by a convective Mach number, is increased. The two convective Mach numbers used in the previous study were relatively close, Mc=0.51 and 0.64, and as a result the conclusions were not concrete. The present results with Mc=0.86 strongly support the earlier results, showing even higher reductions in the level and the lateral extent of Reynolds stresses. The higher‐order moments of turbulence fluctuations such as skewness and flatness are reported, which show that the intermittency resulting from the excursion of large‐scale structures into the free streams at the edge of shear layers was significantly reduced (both in the level and the extent) because of increased Mc. In the develop.