OpenRaiser/Intern-Atlas huggingface dataset:
A Zero-Shot based Fingerprint Presentation Attack Detection System
Venue: arXiv cs.CV 2020
Year: 2020

With the development of presentation attacks, Automated Fingerprint Recognition Systems(AFRSs) are vulnerable to presentation attack. Thus, numerous methods of presentation attack detection(PAD) have been proposed to ensure the normal utilization of AFRS. However, the demand of large-scale presentation attack images and the low-level generalization ability always astrict existing PAD methods' actual performances. Therefore, we propose a novel Zero-Shot Presentation Attack Detection Model to guarantee the generalization of the PAD model. The proposed ZSPAD-Model based on generative model does not utilize any negative samples in the process of establishment, which ensures the robustness for various types or materials based presentation attack. Different from other auto-encoder based model, the Fine-grained Map architecture is proposed to refine the reconstruction error of the auto-encoder networks and a task-specific gaussian model is utilized to improve the quality of clustering. Meanwhile, in order to improve the performance of the proposed model, 9 confidence scores are discussed in this article. Experimental results showed that the ZSPAD-Model is the state of the art for ZSPAD, and the MS-Score is the best confidence score. Compared with existing methods, the proposed ZSPAD-Model performs better than the feature-based method and under the multi-shot setting, the proposed method overperforms the learning based method with little training data. When large training data is available, their results are similar.
Eigenvector Component Calculation Speedup over NumPy for High-Performance Computing
Venue: arXiv cs.PF 2020
Year: 2020

Applications related to artificial intelligence, machine learning, and system identification simulations essentially use eigenvectors. Calculating eigenvectors for very large matrices using conventional methods is compute-intensive and renders the applications slow. Recently, Eigenvector-Eigenvalue Identity formula promising significant speedup was identified. We study the algorithmic implementation of the formula against the existing state-of-the-art algorithms and their implementations to evaluate the performance gains. We provide a first of its kind systematic study of the implementation of the formula. We demonstrate further improvements using high-performance computing concepts over native NumPy eigenvector implementation which uses LAPACK and BLAS.
Dandelion Diagram: Aggregating Positioning and Orientation Data in the Visualization of Classroom Proxemics
Venue: arXiv cs.HC 2020
Year: 2020

In the past two years, an emerging body of HCI work has been focused on classroom proxemics - how teachers divide time and attention over students in the different regions of the classroom. Tracking and visualizing this implicit yet relevant dimension of teaching can benefit both research and teacher professionalization. Prior work has proved the value of depicting teachers' whereabouts. Yet a major opportunity remains in the design of new, synthesized visualizations that help researchers and practitioners to gain more insights in the vast tracking data. We present Dandelion Diagram, a synthesized heatmap technique that combines both teachers' positioning and orientation (heading) data, and affords richer representations in addition to whereabouts - For example, teachers' attention pattern (which directions they were attending to), and their mobility pattern (i.e., trajectories in the classroom). Utilizing various classroom data from a field study, this paper illustrates the design and utility of Dandelion Diagram.
Fast Generation of High Fidelity RGB-D Images by Deep-Learning with Adaptive Convolution
Venue: arXiv cs.CV 2020
Year: 2020

Using the raw data from consumer-level RGB-D cameras as input, we propose a deep-learning based approach to efficiently generate RGB-D images with completed information in high resolution. To process the input images in low resolution with missing regions, new operators for adaptive convolution are introduced in our deep-learning network that consists of three cascaded modules -- the completion module, the refinement module and the super-resolution module. The completion module is based on an architecture of encoder-decoder, where the features of input raw RGB-D will be automatically extracted by the encoding layers of a deep neural-network. The decoding layers are applied to reconstruct the completed depth map, which is followed by a refinement module to sharpen the boundary of different regions. For the super-resolution module, we generate RGB-D images in high resolution by multiple layers for feature extraction and a layer for up-sampling. Benefited from the adaptive convolution operators newly proposed in this paper, our results outperform the existing deep-learning based approaches for RGB-D image complete and super-resolution. As an end-to-end approach, high fidelity RGB-D images can be generated efficiently at the rate of around 21 frames per second.
AlignNet: A Unifying Approach to Audio-Visual Alignment
Venue: arXiv cs.CV 2020
Year: 2020

We present AlignNet, a model that synchronizes videos with reference audios under non-uniform and irregular misalignments. AlignNet learns the end-to-end dense correspondence between each frame of a video and an audio. Our method is designed according to simple and well-established principles: attention, pyramidal processing, warping, and affinity function. Together with the model, we release a dancing dataset Dance50 for training and evaluation. Qualitative, quantitative and subjective evaluation results on dance-music alignment and speech-lip alignment demonstrate that our method far outperforms the state-of-the-art methods. Project video and code are available at https://jianrenw.github.io/AlignNet.
Using Automated Theorem Provers for Mistake Diagnosis in the Didactics of Mathematics
Venue: arXiv cs.AI 2020
Year: 2020

The Diproche system, an automated proof checker for natural language proofs specifically adapted to the context of exercises for beginner's students similar to the Naproche system by Koepke, Schröder, Cramer and others, uses a modification of an automated theorem prover which uses common formal fallacies intead of sound deduction rules for mistake diagnosis. We briefly describe the concept of such an `Anti-ATP' and explain the basic techniques used in its implementation.
Compressive Learning of Generative Networks
Venue: arXiv cs.LG 2020
Year: 2020

Generative networks implicitly approximate complex densities from their sampling with impressive accuracy. However, because of the enormous scale of modern datasets, this training process is often computationally expensive. We cast generative network training into the recent framework of compressive learning: we reduce the computational burden of large-scale datasets by first harshly compressing them in a single pass as a single sketch vector. We then propose a cost function, which approximates the Maximum Mean Discrepancy metric, but requires only this sketch, which makes it time- and memory-efficient to optimize.
Efficient Cloud-based Secret Shuffling via Homomorphic Encryption
Venue: arXiv cs.CR 2020
Year: 2020

When working with joint collections of confidential data from multiple sources, e.g., in cloud-based multi-party computation scenarios, the ownership relation between data providers and their inputs itself is confidential information. Protecting data providers' privacy desires a function for secretly shuffling the data collection. We present the first efficient secure multi-party computation protocol for secret shuffling in scenarios with a central server. Based on a novel approach to random index distribution, our solution enables the randomization of the order of a sequence of encrypted data such that no observer can map between elements of the original sequence and the shuffled sequence with probability better than guessing. It allows for shuffling data encrypted under an additively homomorphic cryptosystem with constant round complexity and linear computational complexity. Being a general-purpose protocol, it is of relevance for a variety of practical use cases.
Image-to-Image Translation with Text Guidance
Venue: arXiv cs.CV 2020
Year: 2020

The goal of this paper is to embed controllable factors, i.e., natural language descriptions, into image-to-image translation with generative adversarial networks, which allows text descriptions to determine the visual attributes of synthetic images. We propose four key components: (1) the implementation of part-of-speech tagging to filter out non-semantic words in the given description, (2) the adoption of an affine combination module to effectively fuse different modality text and image features, (3) a novel refined multi-stage architecture to strengthen the differential ability of discriminators and the rectification ability of generators, and (4) a new structure loss to further improve discriminators to better distinguish real and synthetic images. Extensive experiments on the COCO dataset demonstrate that our method has a superior performance on both visual realism and semantic consistency with given descriptions.
A Study of Potential Code Borrowing and License Violations in Java Projects on GitHub
Venue: arXiv cs.SE 2020
Year: 2020

With an ever-increasing amount of open source software, the popularity of services like GitHub that facilitate code reuse, and common misconceptions about the licensing of open source software, the problem of license violations in the code is getting more and more prominent. In this study, we compile an extensive corpus of popular Java projects from GitHub, search it for code clones and perform an original analysis of possible code borrowing and license violations on the level of code fragments. We chose Java as a language because of its popularity in industry, where the plagiarism problem is especially relevant because of possible legal action. We analyze and discuss distribution of 94 different discovered and manually evaluated licenses in files and projects, differences in the licensing of files, distribution of potential code borrowing between licenses, various types of possible license violations, most violated licenses, etc. Studying possible license violations in specific blocks of code, we have discovered that 29.6% of them might be involved in potential code borrowing and 9.4% of them could potentially violate original licenses.
HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models
Venue: arXiv cs.HC 2020
Year: 2020

In this paper, we present a visual analytics tool for enabling hypothesis-based evaluation of machine learning (ML) models. We describe a novel ML-testing framework that combines the traditional statistical hypothesis testing (commonly used in empirical research) with logical reasoning about the conclusions of multiple hypotheses. The framework defines a controlled configuration for testing a number of hypotheses as to whether and how some extra information about a "concept" or "feature" may benefit or hinder a ML model. Because reasoning multiple hypotheses is not always straightforward, we provide HypoML as a visual analysis tool, with which, the multi-thread testing data is transformed to a visual representation for rapid observation of the conclusions and the logical flow between the testing data and hypotheses.We have applied HypoML to a number of hypothesized concepts, demonstrating the intuitive and explainable nature of the visual analysis.
Interactive Multi-User 3D Visual Analytics in Augmented Reality
Venue: arXiv cs.HC 2020
Year: 2020

This publication reports on a research project in which we set out to explore the advantages and disadvantages augmented reality (AR) technology has for visual data analytics. We developed a prototype of an AR data analytics application, which provides users with an interactive 3D interface, hand gesture-based controls and multi-user support for a shared experience, enabling multiple people to collaboratively visualize, analyze and manipulate data with high dimensional features in 3D space. Our software prototype, called DataCube, runs on the Microsoft HoloLens - one of the first true stand-alone AR headsets, through which users can see computer-generated images overlaid onto real-world objects in the user's physical environment. Using hand gestures, the users can select menu options, control the 3D data visualization with various filtering and visualization functions, and freely arrange the various menus and virtual displays in their environment. The shared multi-user experience allows all participating users to see and interact with the virtual environment, changes one user makes will become visible to the other users instantly. As users engage together they are not restricted from observing the physical world simultaneously and therefore they can also see non-verbal cues such as gesturing or facial reactions of other users in the physical environment. The main objective of this research project was to find out if AR interfaces and collaborative analysis can provide an effective solution for data analysis tasks, and our experience with our prototype system confirms this.
Quality Assessment of Online Automated Privacy Policy Generators: An Empirical Study
Venue: arXiv cs.CR 2020
Year: 2020

Online Automated Privacy Policy Generators (APPGs) are tools used by app developers to quickly create app privacy policies which are required by privacy regulations to be incorporated to each mobile app. The creation of these tools brings convenience to app developers; however, the quality of these tools puts developers and stakeholders at legal risk. In this paper, we conduct an empirical study to assess the quality of online APPGs. We analyze the completeness of privacy policies, determine what categories and items should be covered in a complete privacy policy, and conduct APPG assessment with boilerplate apps. The results of assessment show that due to the lack of static or dynamic analysis of app's behavior, developers may encounter two types of issues caused by APPGs. First, the generated policies could be incomplete because they do not cover all the essential items required by a privacy policy. Second, some generated privacy policies contain unnecessary personal information collection or arbitrary commitments inconsistent with user input. Ultimately, the defects of APPGs may potentially lead to serious legal issues. We hope that the results and insights developed in this paper can motivate the healthy and ethical development of APPGs towards generating a more complete, accurate, and robust privacy policy.
Protecting Consumers Against Personalized Pricing: A Stopping Time Approach
Venue: arXiv cs.CE 2020
Year: 2020

The widespread availability of behavioral data has led to the development of data-driven personalized pricing algorithms: sellers attempt to maximize their revenue by estimating the consumer's willingness-to-pay and pricing accordingly. Our objective is to develop algorithms that protect consumer interests against personalized pricing schemes. In this paper, we consider a consumer who learns more and more about a potential purchase across time, while simultaneously revealing more and more information about herself to a potential seller. We formalize a strategic consumer's purchasing decision when interacting with a seller who uses personalized pricing algorithms, and contextualize this problem among the existing literature in optimal stopping time theory and computational finance. We provide an algorithm that consumers can use to protect their own interests against personalized pricing algorithms. This algorithmic stopping method uses sample paths to train estimates of the optimal stopping time. To the best of our knowledge, this is one of the first works that provides computational methods for the consumer to maximize her utility when decision making under surveillance. We demonstrate the efficacy of the algorithmic stopping method using a numerical simulation, where the seller uses a Kalman filter to approximate the consumer's valuation and sets prices based on myopic expected revenue maximization. Compared to a myopic purchasing strategy, we demonstrate increased payoffs for the consumer in expectation.
Hypergraph Optimization for Multi-structural Geometric Model Fitting
Venue: arXiv cs.CV 2020
Year: 2020

Recently, some hypergraph-based methods have been proposed to deal with the problem of model fitting in computer vision, mainly due to the superior capability of hypergraph to represent the complex relationship between data points. However, a hypergraph becomes extremely complicated when the input data include a large number of data points (usually contaminated with noises and outliers), which will significantly increase the computational burden. In order to overcome the above problem, we propose a novel hypergraph optimization based model fitting (HOMF) method to construct a simple but effective hypergraph. Specifically, HOMF includes two main parts: an adaptive inlier estimation algorithm for vertex optimization and an iterative hyperedge optimization algorithm for hyperedge optimization. The proposed method is highly efficient, and it can obtain accurate model fitting results within a few iterations. Moreover, HOMF can then directly apply spectral clustering, to achieve good fitting performance. Extensive experimental results show that HOMF outperforms several state-of-the-art model fitting methods on both synthetic data and real images, especially in sampling efficiency and in handling data with severe outliers.
Effective Reinforcement Learning through Evolutionary Surrogate-Assisted Prescription
Venue: arXiv cs.NE 2020
Year: 2020

There is now significant historical data available on decision making in organizations, consisting of the decision problem, what decisions were made, and how desirable the outcomes were. Using this data, it is possible to learn a surrogate model, and with that model, evolve a decision strategy that optimizes the outcomes. This paper introduces a general such approach, called Evolutionary Surrogate-Assisted Prescription, or ESP. The surrogate is, for example, a random forest or a neural network trained with gradient descent, and the strategy is a neural network that is evolved to maximize the predictions of the surrogate model. ESP is further extended in this paper to sequential decision-making tasks, which makes it possible to evaluate the framework in reinforcement learning (RL) benchmarks. Because the majority of evaluations are done on the surrogate, ESP is more sample efficient, has lower variance, and lower regret than standard RL approaches. Surprisingly, its solutions are also better because both the surrogate and the strategy network regularize the decision-making behavior. ESP thus forms a promising foundation to decision optimization in real-world problems.
Gradient tracking and variance reduction for decentralized optimization and machine learning
Venue: arXiv cs.LG 2020
Year: 2020

Decentralized methods to solve finite-sum minimization problems are important in many signal processing and machine learning tasks where the data is distributed over a network of nodes and raw data sharing is not permitted due to privacy and/or resource constraints. In this article, we review decentralized stochastic first-order methods and provide a unified algorithmic framework that combines variance-reduction with gradient tracking to achieve both robust performance and fast convergence. We provide explicit theoretical guarantees of the corresponding methods when the objective functions are smooth and strongly-convex, and show their applicability to non-convex problems via numerical experiments. Throughout the article, we provide intuitive illustrations of the main technical ideas by casting appropriate tradeoffs and comparisons among the methods of interest and by highlighting applications to decentralized training of machine learning models.
Ressource Efficient Stabilization for Local Tasks despite Unknown Capacity Links
Venue: arXiv cs.DC 2020
Year: 2020

Self-stabilizing protocols enable distributed systems to recover correct behavior starting from any arbitrary configuration. In particular, when processors communicate by message passing, fake messages may be placed in communication links by an adversary. When the number of such fake messages is unknown, self-stabilization may require huge resources: (a) generic solutions (a.k.a. data link protocols) require unbounded resources, which makes them unrealistic to deploy and (b) specific solutions (e.g., census or tree construction) require $O(n\log n)$ or $O(Δ\log n)$ bits of memory per node, where $n$ denotes the network size and $Δ$ its maximum degree, which may prevent scalability. We investigate the possibility of resource efficient self-stabilizing protocols in this context. Specifically, we present a self-stabilizing protocol for $(Δ+1)$-coloring in any n-node graph, under the asynchronous message-passing model. It is deterministic, it converges in $O(kΔn^2\log n)$ message exchanges, where $k$ is the bound of the link capacity in terms of number of messages, and it uses messages on $O(\log\log n+\logΔ)$ bits with a memory of $O(Δ\logΔ+\log\log n)$ bits at each node. The resource consumption of our protocol is thus almost oblivious to the number of nodes, enabling scalability. Moreover, a striking property of our protocol is that the nodes do not need to know the number, or any bound on the number of messages initially present in each communication link of the initial (potentially corrupted) network configuration. This permits our protocol to handle any future network with unknown message capacity communication links. A key building block of our coloring scheme is a spanning directed acyclic graph construction, that is of independent interest, and can serve as a useful tool for solving other tasks in this challenging setting.
Functional Failure Rate Due to Single-Event Transients in Clock Distribution Networks
Venue: arXiv cs.AR 2020
Year: 2020

With technology scaling, lower supply voltages, and higher operating frequencies clock distribution networks become more and more vulnerable to transients faults. These faults can cause circuit-wide effects and thus, significantly contribute to the functional failure rate of the circuit. This paper proposes a methodology to analyse how the functional behaviour is affected by Single-Event Transients in the clock distribution network. The approach is based on logic-level simulation and thus, only uses the register-transfer level description of a design. Therefore, a fault model is proposed which implements the main effects due to radiation-induced transients in the clock network. This fault model enables the computation of the functional failure rate caused by Single-Event Transients for each individual clock buffer, as well as the complete network. Further, it allows the identification of the most vulnerable flip-flops related to Single-Event Transients in the clock network. The proposed methodology is applied in a practical example and a fault injection campaign is performed. In order to evaluate the impact of Single-Event Transients in clock distribution networks, the obtained functional failure rate is compared to the error rate caused by Single-Event Upsets in the sequential logic.
Sensitivity of Wardrop Equilibria: Revisited
Venue: arXiv cs.GT 2020
Year: 2020

For single-commodity networks, the increase of the price of anarchy is bounded by a factor of $(1+ε)^p$ from above, when the travel demand is increased by a factor of $1+ε$ and the latency functions are polynomials of degree at most $p$. We show that the same upper bound holds for multi-commodity networks and provide a lower bound as well.