OpenRaiser/Intern-Atlas huggingface dataset:
Sovereign Smartphone: To Enjoy Freedom We Have to Control Our Phones
Venue: arXiv cs.CR 2021
Year: 2021

The majority of smartphones either run iOS or Android operating systems. This has created two distinct ecosystems largely controlled by Apple and Google - they dictate which applications can run, how they run, and what kind of phone resources they can access. Barring some exceptions in Android where different phone manufacturers may have influence, users, developers, and governments are left with little to no choice. Specifically, users need to entrust their security and privacy to OS vendors and accept the functionality constraints they impose. Given the wide use of Android and iOS, immediately leaving these ecosystems is not practical, except in niche application areas. In this work, we draw attention to the magnitude of this problem and why it is an undesirable situation. As an alternative, we advocate the development of a new smartphone architecture that securely transfers the control back to the users while maintaining compatibility with the rich existing smartphone ecosystems. We propose and analyze one such design based on advances in trusted execution environments for ARM and RISC-V.
On the computational and statistical complexity of over-parameterized matrix sensing
Venue: arXiv cs.LG 2021
Year: 2021

We consider solving the low rank matrix sensing problem with Factorized Gradient Descend (FGD) method when the true rank is unknown and over-specified, which we refer to as over-parameterized matrix sensing. If the ground truth signal $\mathbf{X}^* \in \mathbb{R}^{d*d}$ is of rank $r$, but we try to recover it using $\mathbf{F} \mathbf{F}^\top$ where $\mathbf{F} \in \mathbb{R}^{d*k}$ and $k>r$, the existing statistical analysis falls short, due to a flat local curvature of the loss function around the global maxima. By decomposing the factorized matrix $\mathbf{F}$ into separate column spaces to capture the effect of extra ranks, we show that $\|\mathbf{F}_t \mathbf{F}_t - \mathbf{X}^*\|_{F}^2$ converges to a statistical error of $\tilde{\mathcal{O}} ({k d σ^2/n})$ after $\tilde{\mathcal{O}}(\frac{σ_{r}}σ\sqrt{\frac{n}{d}})$ number of iterations where $\mathbf{F}_t$ is the output of FGD after $t$ iterations, $σ^2$ is the variance of the observation noise, $σ_{r}$ is the $r$-th largest eigenvalue of $\mathbf{X}^*$, and $n$ is the number of sample. Our results, therefore, offer a comprehensive picture of the statistical and computational complexity of FGD for the over-parameterized matrix sensing problem.
Designing an Encoder for StyleGAN Image Manipulation
Venue: arXiv cs.CV 2021
Year: 2021

Recently, there has been a surge of diverse methods for performing image editing by employing pre-trained unconditional generators. Applying these methods on real images, however, remains a challenge, as it necessarily requires the inversion of the images into their latent space. To successfully invert a real image, one needs to find a latent code that reconstructs the input image accurately, and more importantly, allows for its meaningful manipulation. In this paper, we carefully study the latent space of StyleGAN, the state-of-the-art unconditional generator. We identify and analyze the existence of a distortion-editability tradeoff and a distortion-perception tradeoff within the StyleGAN latent space. We then suggest two principles for designing encoders in a manner that allows one to control the proximity of the inversions to regions that StyleGAN was originally trained on. We present an encoder based on our two principles that is specifically designed for facilitating editing on real images by balancing these tradeoffs. By evaluating its performance qualitatively and quantitatively on numerous challenging domains, including cars and horses, we show that our inversion method, followed by common editing techniques, achieves superior real-image editing quality, with only a small reconstruction accuracy drop.
Decoding of (Interleaved) Generalized Goppa Codes
Venue: arXiv cs.IT 2021
Year: 2021

Generalized Goppa codes are defined by a code locator set $\mathcal{L}$ of polynomials and a Goppa polynomial $G(x)$. When the degree of all code locator polynomials in $\mathcal{L}$ is one, generalized Goppa codes are classical Goppa codes. In this work, binary generalized Goppa codes are investigated. First, a parity-check matrix for these codes with code locators of any degree is derived. A careful selection of the code locators leads to a lower bound on the minimum Hamming distance of generalized Goppa codes which improves upon previously known bounds. A quadratic-time decoding algorithm is presented which can decode errors up to half of the minimum distance. Interleaved generalized Goppa codes are introduced and a joint decoding algorithm is presented which can decode errors beyond half the minimum distance with high probability. Finally, some code parameters and how they apply to the Classic McEliece post-quantum cryptosystem are shown.
ChainCQG: Flow-Aware Conversational Question Generation
Venue: arXiv cs.AI 2021
Year: 2021

Conversational systems enable numerous valuable applications, and question-answering is an important component underlying many of these. However, conversational question-answering remains challenging due to the lack of realistic, domain-specific training data. Inspired by this bottleneck, we focus on conversational question generation as a means to generate synthetic conversations for training and evaluation purposes. We present a number of novel strategies to improve conversational flow and accommodate varying question types and overall fluidity. Specifically, we design ChainCQG as a two-stage architecture that learns question-answer representations across multiple dialogue turns using a flow propagation training strategy.ChainCQG significantly outperforms both answer-aware and answer-unaware SOTA baselines (e.g., up to 48% BLEU-1 improvement). Additionally, our model is able to generate different types of questions, with improved fluidity and coreference alignment.
The Discrepancy Attack on Polyshard-ed Blockchains
Venue: arXiv cs.CR 2021
Year: 2021

Sharding, i.e. splitting the miners or validators to form and run several subchains in parallel, is known as one of the main solutions to the scalability problem of blockchains. The drawback is that as the number of miners expanding each subchain becomes small, it becomes vulnerable to security attacks. To solve this problem, a framework, named as \textit{Polyshard}, has been proposed in which each validator verifies a coded combination of the blocks introduced by different subchains, thus helping to protect the security of all subchains. In this paper, we introduce an attack on Polyshard, called \textit{the discrepancy} attack, which is the result of malicious nodes controlling a few subchains and dispersing different blocks to different nodes. We show that this attack undermines the security of Polyshard and is undetectable in its current setting.
Toward a Rational and Ethical Sociotechnical System of Autonomous Vehicles: A Novel Application of Multi-Criteria Decision Analysis
Venue: arXiv cs.CY 2021
Year: 2021

The expansion of artificial intelligence (AI) and autonomous systems has shown the potential to generate enormous social good while also raising serious ethical and safety concerns. AI technology is increasingly adopted in transportation. A survey of various in-vehicle technologies found that approximately 64% of the respondents used a smartphone application to assist with their travel. The top-used applications were navigation and real-time traffic information systems. Among those who used smartphones during their commutes, the top-used applications were navigation and entertainment. There is a pressing need to address relevant social concerns to allow for the development of systems of intelligent agents that are informed and cognizant of ethical standards. Doing so will facilitate the responsible integration of these systems in society. To this end, we have applied Multi-Criteria Decision Analysis (MCDA) to develop a formal Multi-Attribute Impact Assessment (MAIA) questionnaire for examining the social and ethical issues associated with the uptake of AI. We have focused on the domain of autonomous vehicles (AVs) because of their imminent expansion. However, AVs could serve as a stand-in for any domain where intelligent, autonomous agents interact with humans, either on an individual level (e.g., pedestrians, passengers) or a societal level.
The Impact of Sampling and Rule Set Size on Generated Fuzzy Inference System Predictive Accuracy: Analysis of a Software Engineering Data Set
Venue: arXiv cs.SE 2021
Year: 2021

Software project management makes extensive use of predictive modeling to estimate product size, defect proneness and development effort. Although uncertainty is acknowledged in these tasks, fuzzy inference systems, designed to cope well with uncertainty, have received only limited attention in the software engineering domain. In this study we empirically investigate the impact of two choices on the predictive accuracy of generated fuzzy inference systems when applied to a software engineering data set: sampling of observations for training and testing; and the size of the rule set generated using fuzzy c-means clustering. Over ten samples we found no consistent pattern of predictive performance given certain rule set size. We did find, however, that a rule set compiled from multiple samples generally resulted in more accurate predictions than single sample rule sets. More generally, the results provide further evidence of the sensitivity of empirical analysis outcomes to specific model-building decisions.
Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computation
Venue: arXiv cs.IT 2021
Year: 2021

Federated learning (FL) as a promising edge-learning framework can effectively address the latency and privacy issues by featuring distributed learning at the devices and model aggregation in the central server. In order to enable efficient wireless data aggregation, over-the-air computation (AirComp) has recently been proposed and attracted immediate attention. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To combat this effect, the concept of dynamic learning rate (DLR) is proposed in this work. We begin our discussion by considering multiple-input-single-output (MISO) scenario, since the underlying optimization problem is convex and has closed-form solution. We then extend our studies to more general multiple-input-multiple-output (MIMO) case and an iterative method is derived. Extensive simulation results demonstrate the effectiveness of the proposed scheme in reducing the aggregate distortion and guaranteeing the testing accuracy using the MNIST and CIFAR10 datasets. In addition, we present the asymptotic analysis and give a near-optimal receive beamforming design solution in closed form, which is verified by numerical simulations.
DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attacks
Venue: arXiv cs.CV 2021
Year: 2021

State-of-the-art object detectors are vulnerable to localized patch hiding attacks, where an adversary introduces a small adversarial patch to make detectors miss the detection of salient objects. The patch attacker can carry out a physical-world attack by printing and attaching an adversarial patch to the victim object. In this paper, we propose DetectorGuard as the first general framework for building provably robust object detectors against localized patch hiding attacks. DetectorGuard is inspired by recent advancements in robust image classification research; we ask: can we adapt robust image classifiers for robust object detection? Unfortunately, due to their task difference, an object detector naively adapted from a robust image classifier 1) may not necessarily be robust in the adversarial setting or 2) even maintain decent performance in the clean setting. To build a high-performance robust object detector, we propose an objectness explaining strategy: we adapt a robust image classifier to predict objectness for every image location and then explain each objectness using the bounding boxes predicted by a conventional object detector. If all objectness is well explained, we output the predictions made by the conventional object detector; otherwise, we issue an attack alert. Notably, 1) in the adversarial setting, we formally prove the end-to-end robustness of DetectorGuard on certified objects, i.e., it either detects the object or triggers an alert, against any patch hiding attacker within our threat model; 2) in the clean setting, we have almost the same performance as state-of-the-art object detectors. Our evaluation on the PASCAL VOC, MS COCO, and KITTI datasets further demonstrates that DetectorGuard achieves the first provable robustness against localized patch hiding attacks at a negligible cost (<1%) of clean performance.
Optimizing RRH Placement Under a Noise-Limited Point-to-Point Wireless Backhaul
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we study the deployment decisions and location optimization for the remote radio heads (RRHs) in coordinated distributed networks in the presence of a wireless backhaul. We implement a scheme where the RRHs use zero-forcing beamforming (ZF-BF) for the access channel to jointly serve multiple users, while on the backhaul the RRHs are connected to their central units (CUs) through point-to-point wireless links. We investigate the effect of this scheme on the deployment of the RRHs and on the resulting achievable spectral efficiency over the access channel (under a backhaul outage constraint). Our results show that even for noise-limited backhaul links, a large bandwidth must be allocated to the backhaul to allow freely distributing the RRHs in the network. Additionally, our results show that distributing the available antennas on more RRHs is favored as compared to a more co-located antenna system. This motivates further works to study the efficiency of wireless backhaul schemes and their effect on the performance of coordinated distributed networks with joint transmission.
Designing a Cyber-security Culture Assessment Survey Targeting Critical Infrastructures During Covid-19 Crisis
Venue: arXiv cs.CR 2021
Year: 2021

The paper at hand presents the design of a survey aiming at the cyber-security culture assessment of critical infrastructures during the COVID-19 crisis, when living reality was heavily disturbed and working conditions fundamentally affected. The survey is rooted in a security culture framework layered into two levels, organizational and individual, further analyzed into 10 different security dimensions consisted of 52 domains. An in-depth questionnaire building analysis is presented focusing on the aims, goals, and expected results. It concludes with the survey implementation approach while underlining the framework's first application and its revealing insights during a global crisis.
A Verified Decision Procedure for Univariate Real Arithmetic with the BKR Algorithm
Venue: arXiv cs.LO 2021
Year: 2021

We formalize the univariate fragment of Ben-Or, Kozen, and Reif's (BKR) decision procedure for first-order real arithmetic in Isabelle/HOL. BKR's algorithm has good potential for parallelism and was designed to be used in practice. Its key insight is a clever recursive procedure that computes the set of all consistent sign assignments for an input set of univariate polynomials while carefully managing intermediate steps to avoid exponential blowup from naively enumerating all possible sign assignments (this insight is fundamental for both the univariate case and the general case). Our proof combines ideas from BKR and a follow-up work by Renegar that are well-suited for formalization. The resulting proof outline allows us to build substantially on Isabelle/HOL's libraries for algebra, analysis, and matrices. Our main extensions to existing libraries are also detailed.
Boost AI Power: Data Augmentation Strategies with unlabelled Data and Conformal Prediction, a Case in Alternative Herbal Medicine Discrimination with Electronic Nose
Venue: arXiv cs.LG 2021
Year: 2021

Electronic nose has been proven to be effective in alternative herbal medicine classification, but due to the nature of supervised learning, previous research heavily relies on the labelled training data, which are time-costly and labor-intensive to collect. To alleviate the critical dependency on the training data in real-world applications, this study aims to improve classification accuracy via data augmentation strategies. The effectiveness of five data augmentation strategies under different training data inadequacy are investigated in two scenarios: the noise-free scenario where different availabilities of unlabelled data were considered, and the noisy scenario where different levels of Gaussian noises and translational shifts were added to represent sensor drifts. The five augmentation strategies, namely noise-adding data augmentation, semi-supervised learning, classifier-based online learning, Inductive Conformal Prediction (ICP) online learning and our novel ensemble ICP online learning proposed in this study, are experimented and compared against supervised learning baseline, with Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) as the classifiers. Our novel strategy, ensemble ICP online learning, outperforms the others by showing non-decreasing classification accuracy on all tasks and a significant improvement on most simulated tasks (25out of 36 tasks,p<=0.05). Furthermore, this study provides a systematic analysis of different augmentation strategies. It shows at least one strategy significantly improved the classification accuracy with LDA (p<=0.05) and non-decreasing classification accuracy with SVM in each task. In particular, our proposed strategy demonstrated both effectiveness and robustness in boosting the classification model generalizability, which can be employed in other machine learning applications.
Real-World Super-Resolution of Face-Images from Surveillance Cameras
Venue: arXiv cs.CV 2021
Year: 2021

Most existing face image Super-Resolution (SR) methods assume that the Low-Resolution (LR) images were artificially downsampled from High-Resolution (HR) images with bicubic interpolation. This operation changes the natural image characteristics and reduces noise. Hence, SR methods trained on such data most often fail to produce good results when applied to real LR images. To solve this problem, we propose a novel framework for generation of realistic LR/HR training pairs. Our framework estimates realistic blur kernels, noise distributions, and JPEG compression artifacts to generate LR images with similar image characteristics as the ones in the source domain. This allows us to train a SR model using high quality face images as Ground-Truth (GT). For better perceptual quality we use a Generative Adversarial Network (GAN) based SR model where we have exchanged the commonly used VGG-loss [24] with LPIPS-loss [52]. Experimental results on both real and artificially corrupted face images show that our method results in more detailed reconstructions with less noise compared to existing State-of-the-Art (SoTA) methods. In addition, we show that the traditional non-reference Image Quality Assessment (IQA) methods fail to capture this improvement and demonstrate that the more recent NIMA metric [16] correlates better with human perception via Mean Opinion Rank (MOR).
Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning
Venue: arXiv cs.AI 2021
Year: 2021

For highly automated driving above SAE level~3, behavior generation algorithms must reliably consider the inherent uncertainties of the traffic environment, e.g. arising from the variety of human driving styles. Such uncertainties can generate ambiguous decisions, requiring the algorithm to appropriately balance low-probability hazardous events, e.g. collisions, and high-probability beneficial events, e.g. quickly crossing the intersection. State-of-the-art behavior generation algorithms lack a distributional treatment of decision outcome. This impedes a proper risk evaluation in ambiguous situations, often encouraging either unsafe or conservative behavior. Thus, we propose a two-step approach for risk-sensitive behavior generation combining offline distribution learning with online risk assessment. Specifically, we first learn an optimal policy in an uncertain environment with Deep Distributional Reinforcement Learning. During execution, the optimal risk-sensitive action is selected by applying established risk criteria, such as the Conditional Value at Risk, to the learned state-action return distributions. In intersection crossing scenarios, we evaluate different risk criteria and demonstrate that our approach increases safety, while maintaining an active driving style. Our approach shall encourage further studies about the benefits of risk-sensitive approaches for self-driving vehicles.
Zero-shot Learning with Deep Neural Networks for Object Recognition
Venue: arXiv cs.CV 2021
Year: 2021

Zero-shot learning deals with the ability to recognize objects without any visual training sample. To counterbalance this lack of visual data, each class to recognize is associated with a semantic prototype that reflects the essential features of the object. The general approach is to learn a mapping from visual data to semantic prototypes, then use it at inference to classify visual samples from the class prototypes only. Different settings of this general configuration can be considered depending on the use case of interest, in particular whether one only wants to classify objects that have not been employed to learn the mapping or whether one can use unlabelled visual examples to learn the mapping. This chapter presents a review of the approaches based on deep neural networks to tackle the ZSL problem. We highlight findings that had a large impact on the evolution of this domain and list its current challenges.
Committee selection in DAG distributed ledgers and applications
Venue: arXiv cs.DC 2021
Year: 2021

In this paper, we propose several solutions to the committee selection problem among participants of a DAG distributed ledger. Our methods are based on a ledger intrinsic reputation model that serves as a selection criterion. The main difficulty arises from the fact that the DAG ledger is a priori not totally ordered and that the participants need to reach a consensus on participants' reputation. Furthermore, we outline applications of the proposed protocols, including: (i) self-contained decentralized random number beacon; (ii) selection of oracles in smart contracts; (iii) applications in consensus protocols and sharding solutions. We conclude with a discussion on the security and liveness of the proposed protocols by modeling reputation with a Zipf law.