OpenRaiser/Intern-Atlas huggingface dataset:
One Billion Audio Sounds from GPU-enabled Modular Synthesis
Venue: arXiv cs.SD 2021
Year: 2021

We release synth1B1, a multi-modal audio corpus consisting of 1 billion 4-second synthesized sounds, paired with the synthesis parameters used to generate them. The dataset is 100x larger than any audio dataset in the literature. We also introduce torchsynth, an open source modular synthesizer that generates the synth1B1 samples on-the-fly at 16200x faster than real-time (714MHz) on a single GPU. Finally, we release two new audio datasets: FM synth timbre and subtractive synth pitch. Using these datasets, we demonstrate new rank-based evaluation criteria for existing audio representations. Finally, we propose a novel approach to synthesizer hyperparameter optimization.
Exploring Uncertainty in Deep Learning for Construction of Prediction Intervals
Venue: arXiv cs.LG 2021
Year: 2021

Deep learning has achieved impressive performance on many tasks in recent years. However, it has been found that it is still not enough for deep neural networks to provide only point estimates. For high-risk tasks, we need to assess the reliability of the model predictions. This requires us to quantify the uncertainty of model prediction and construct prediction intervals. In this paper, We explore the uncertainty in deep learning to construct the prediction intervals. In general, We comprehensively consider two categories of uncertainties: aleatory uncertainty and epistemic uncertainty. We design a special loss function, which enables us to learn uncertainty without uncertainty label. We only need to supervise the learning of regression task. We learn the aleatory uncertainty implicitly from the loss function. And that epistemic uncertainty is accounted for in ensembled form. Our method correlates the construction of prediction intervals with the uncertainty estimation. Impressive results on some publicly available datasets show that the performance of our method is competitive with other state-of-the-art methods.
Multi-view Deep One-class Classification: A Systematic Exploration
Venue: arXiv cs.CV 2021
Year: 2021

One-class classification (OCC), which models one single positive class and distinguishes it from the negative class, has been a long-standing topic with pivotal application to realms like anomaly detection. As modern society often deals with massive high-dimensional complex data spawned by multiple sources, it is natural to consider OCC from the perspective of multi-view deep learning. However, it has not been discussed by the literature and remains an unexplored topic. Motivated by this blank, this paper makes four-fold contributions: First, to our best knowledge, this is the first work that formally identifies and formulates the multi-view deep OCC problem. Second, we take recent advances in relevant areas into account and systematically devise eleven different baseline solutions for multi-view deep OCC, which lays the foundation for research on multi-view deep OCC. Third, to remedy the problem that limited benchmark datasets are available for multi-view deep OCC, we extensively collect existing public data and process them into more than 30 new multi-view benchmark datasets via multiple means, so as to provide a publicly available evaluation platform for multi-view deep OCC. Finally, by comprehensively evaluating the devised solutions on benchmark datasets, we conduct a thorough analysis on the effectiveness of the designed baselines, and hopefully provide other researchers with beneficial guidance and insight to multi-view deep OCC. Our data and codes are opened at https://github.com/liujiyuan13/MvDOCC-datasets and https://github.com/liujiyuan13/MvDOCC-code respectively to facilitate future research.
LAST at CMCL 2021 Shared Task: Predicting Gaze Data During Reading with a Gradient Boosting Decision Tree Approach
Venue: arXiv cs.CL 2021
Year: 2021

A LightGBM model fed with target word lexical characteristics and features obtained from word frequency lists, psychometric data and bigram association measures has been optimized for the 2021 CMCL Shared Task on Eye-Tracking Data Prediction. It obtained the best performance of all teams on two of the five eye-tracking measures to predict, allowing it to rank first on the official challenge criterion and to outperform all deep-learning based systems participating in the challenge.
Cross-Level Cross-Scale Cross-Attention Network for Point Cloud Representation
Venue: arXiv cs.CV 2021
Year: 2021

Self-attention mechanism recently achieves impressive advancement in Natural Language Processing (NLP) and Image Processing domains. And its permutation invariance property makes it ideally suitable for point cloud processing. Inspired by this remarkable success, we propose an end-to-end architecture, dubbed Cross-Level Cross-Scale Cross-Attention Network (CLCSCANet), for point cloud representation learning. First, a point-wise feature pyramid module is introduced to hierarchically extract features from different scales or resolutions. Then a cross-level cross-attention is designed to model long-range inter-level and intra-level dependencies. Finally, we develop a cross-scale cross-attention module to capture interactions between-and-within scales for representation enhancement. Compared with state-of-the-art approaches, our network can obtain competitive performance on challenging 3D object classification, point cloud segmentation tasks via comprehensive experimental evaluation.
A Verified Decision Procedure for Orders in Isabelle/HOL
Venue: arXiv cs.LO 2021
Year: 2021

We present the first verified implementation of a decision procedure for the quantifier-free theory of partial and linear orders. We formalise the procedure in Isabelle/HOL and provide a specification that is made executable using Isabelle's code generator. The procedure is already part of the development version of Isabelle as a sub-procedure of the simplifier.
A unified framework for Hamiltonian deep neural networks
Venue: arXiv cs.LG 2021
Year: 2021

Training deep neural networks (DNNs) can be difficult due to the occurrence of vanishing/exploding gradients during weight optimization. To avoid this problem, we propose a class of DNNs stemming from the time discretization of Hamiltonian systems. The time-invariant version of the corresponding Hamiltonian models enjoys marginal stability, a property that, as shown in previous works and for specific DNNs architectures, can mitigate convergence to zero or divergence of gradients. In the present paper, we formally study this feature by deriving and analysing the backward gradient dynamics in continuous time. The proposed Hamiltonian framework, besides encompassing existing networks inspired by marginally stable ODEs, allows one to derive new and more expressive architectures. The good performance of the novel DNNs is demonstrated on benchmark classification problems, including digit recognition using the MNIST dataset.
Towards an efficient inverse static model of a Festo actuator made of two antagonist muscles for hybrid control of its position and stiffness
Venue: arXiv cs.RO 2021
Year: 2021

The Festo air muscle is today one of the most known commercial version of the so-called McKibben pneumatic artificial muscle. A major advantage of handmade McKibben muscles, as well as its commercial versions, lies in the possibility it offers of realizing antagonist muscle actuator on the model of the biceps-triceps system. If pressures are independently controlled in each artificial muscle, it is then possible to define a position-stiffness control of the antagonist actuator by analogy with natural neural control of antagonist skeletal muscles. Such a control however requires a knowledge model of the actuator making possible a stiffness estimation provided by control pressures, while position closed-loop control is facilitated by a feedforward model of this highly nonlinear actuation device. We discuss this issue in the particular case of the antagonist Festo air muscle actuator, and we propose a simplified static actuator model derived from the classic static theoretical model of the McKibben artificial muscle, simple enough for a future MIMO position-stiffness controller be able to integrate it. If the proposed model highlights the ability of the antagonist Festo muscle actuator to mimic the stiffness neural control by the mean of the sum of pressures inside artificial muscles, it also highlights the difficulty to derive a simple but accurate model of the static force produced by Festo muscle in the full control range of pressure. The reported results suggest that it would be particularly interesting to derive advantage from numerous studies about Festo muscle modelling to go further in order to find such an inverse actuator model including an accurate estimation of the actuator stiffness.
Structured Sparse Non-negative Matrix Factorization with L20-Norm for scRNA-seq Data Analysis
Venue: arXiv cs.LG 2021
Year: 2021

Non-negative matrix factorization (NMF) is a powerful tool for dimensionality reduction and clustering. Unfortunately, the interpretation of the clustering results from NMF is difficult, especially for the high-dimensional biological data without effective feature selection. In this paper, we first introduce a row-sparse NMF with $\ell_{2,0}$-norm constraint (NMF_$\ell_{20}$), where the basis matrix $W$ is constrained by the $\ell_{2,0}$-norm, such that $W$ has a row-sparsity pattern with feature selection. It is a challenge to solve the model, because the $\ell_{2,0}$-norm is non-convex and non-smooth. Fortunately, we prove that the $\ell_{2,0}$-norm satisfies the Kurdyka-Ł{ojasiewicz} property. Based on the finding, we present a proximal alternating linearized minimization algorithm and its monotone accelerated version to solve the NMF_$\ell_{20}$ model. In addition, we also present a orthogonal NMF with $\ell_{2,0}$-norm constraint (ONMF_$\ell_{20}$) to enhance the clustering performance by using a non-negative orthogonal constraint. We propose an efficient algorithm to solve ONMF_$\ell_{20}$ by transforming it into a series of constrained and penalized matrix factorization problems. The results on numerical and scRNA-seq datasets demonstrate the efficiency of our methods in comparison with existing methods.
UAV-Assisted Underwater Sensor Networks using RF and Optical Wireless Links
Venue: arXiv cs.IT 2021
Year: 2021

Underwater sensor networks (UWSNs) are of interest to gather data from underwater sensor nodes (SNs) and deliver information to a terrestrial access point (AP) in the uplink transmission, and transfer data from the AP to the SNs in the downlink transmission. In this paper, we investigate a triple-hop UWSN in which autonomous underwater vehicle (AUV) and unmanned aerial vehicle (UAV) relays enable end-to-end communications between the SNs and the AP. It is assumed that the SN--AUV, AUV--UAV, and UAV--AP links are deployed by underwater optical communication (UWOC), free-space optic (FSO), and radio frequency (RF) technologies, respectively. Two scenarios are proposed for the FSO uplink and downlink transmissions between the AUV and the UAV, subject to water-to-air and air-to-water interface impacts; direct transmission scenario (DTS) and retro-reflection scenario (RRS). After providing the channel models and their statistics, the UWSN's outage probability and average bit error rate (BER) are computed. Besides, a tracking procedure is proposed to set up reliable and stable AUV--UAV FSO communications. Through numerical results, it is concluded that the RSS scheme outperforms the DTS one with about 200% (32%) and 80% (17%) better outage probability (average BER) in the uplink and downlink, respectively. It is also shown that the tracking procedure provides on average 480% and 170% improvements in the network's outage probability and average BER, respectively, compared to poorly aligned FSO conditions. The results are verified by applying Monte-Carlo simulations.
Width Transfer: On the (In)variance of Width Optimization
Venue: arXiv cs.CV 2021
Year: 2021

Optimizing the channel counts for different layers of a CNN has shown great promise in improving the efficiency of CNNs at test-time. However, these methods often introduce large computational overhead (e.g., an additional 2x FLOPs of standard training). Minimizing this overhead could therefore significantly speed up training. In this work, we propose width transfer, a technique that harnesses the assumptions that the optimized widths (or channel counts) are regular across sizes and depths. We show that width transfer works well across various width optimization algorithms and networks. Specifically, we can achieve up to 320x reduction in width optimization overhead without compromising the top-1 accuracy on ImageNet, making the additional cost of width optimization negligible relative to initial training. Our findings not only suggest an efficient way to conduct width optimization but also highlight that the widths that lead to better accuracy are invariant to various aspects of network architectures and training data.
Trend Alert: How a Cross-Platform Organization Manipulated Twitter Trends in the Indian General Election
Venue: arXiv cs.SI 2021
Year: 2021

Political organizations worldwide keep innovating their use of social media technologies. In the 2019 Indian general election, organizers used a network of WhatsApp groups to manipulate Twitter trends through coordinated mass postings. We joined 600 WhatsApp groups that support the Bharatiya Janata Party, the right-wing party that won the general election, to investigate these campaigns. We found evidence of 75 hashtag manipulation campaigns in the form of mobilization messages with lists of pre-written tweets. Building on this evidence, we estimate the campaigns' size, describe their organization and determine whether they succeeded in creating controlled social media narratives. Our findings show that the campaigns produced hundreds of nationwide Twitter trends throughout the election. Centrally controlled but voluntary in participation, this hybrid configuration of technologies and organizational strategies shows how profoundly online tools transform campaign politics. Trend alerts complicate the debates over the legitimate use of digital tools for political participation and may have provided a blueprint for participatory media manipulation by a party with popular support.
KEVLAR-TZ: A Secure Cache for ARM TrustZone
Venue: arXiv cs.CR 2021
Year: 2021

Edge devices are increasingly in charge of storing privacy-sensitive data, in particular implantables, wearables, and nearables can potentially collect and process high-resolution vital signs 24/7. Storing and performing computations over such data in a privacy-preserving fashion is of paramount importance. We present KEVLAR-TZ, an application-level trusted cache designed to leverage ARM TrustZone, a popular trusted execution environment available in consumer-grade devices. To facilitate the integration with existing systems and IoT devices and protocols, KEVLAR-TZ exposes a REST-based interface with connection endpoints inside the TrustZone enclave. Furthermore, it exploits the on-device secure persistent storage to guarantee durability of data across reboots. We fully implemented KEVLAR-TZ on top of the OP-TEE framework, and experimentally evaluated its performance. Our results showcase performance trade-offs, for instance in terms of throughput and latency, for various workloads, and we believe our results can be useful for practitioners and in general developers of systems for TrustZone. KEVLAR-TZ is available as open-source at https://github.com/mqttz/kevlar-tz/.
Downlink Precoding for DP-UPA FDD Massive MIMO via Multi-Dimensional Active Channel Sparsification
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we consider user selection and downlink precoding for an over-loaded single-cell massive multiple-input multiple-output (MIMO) system in frequency division duplexing (FDD) mode, where the base station is equipped with a dual-polarized uniform planar array (DP-UPA) and serves a large number of single-antenna users. Due to the absence of uplink-downlink channel reciprocity and the high-dimensionality of channel matrices, it is extremely challenging to design downlink precoders using closed-loop channel probing and feedback with limited spectrum resource. To address these issues, a novel methodology -- active channel sparsification (ACS) -- has been proposed recently in the literature for uniform linear array (ULA) to design sparsifying precoders, which boosts spectral efficiency for multi-user downlink transmission with substantially reduced channel feedback overhead. Pushing forward this line of research, we aim to facilitate the potential deployment of ACS in practical FDD massive MIMO systems, by extending it from ULA to DP-UPA with explicit user selection and making the current ACS implementation simplified. To this end, by leveraging Toeplitz structure of channel covariance matrices, we extend the original ACS using scale-weight bipartite graph representation to the matrix-weight counterpart. Building upon this, we propose a multi-dimensional ACS (MD-ACS) method, which is a generalization of original ACS formulation and is more suitable for DP-UPA antenna configurations. The nonlinear integer program formulation of MD-ACS can be classified as a generalized multi-assignment problem (GMAP), for which we propose a simple yet efficient greedy algorithm to solve it. Simulation results demonstrate the performance improvement of the proposed MD-ACS with greedy algorithm over the state-of-the-art methods based on the QuaDRiGa channel models.
Spoofing Attacks Against Vehicular FMCW Radar
Venue: arXiv cs.CR 2021
Year: 2021

The safety and security of the passengers in vehicles in the face of cyber attacks is a key element in the automotive industry, especially with the emergence of the Advanced Driver Assistance Systems (ADAS) and the vast improvement in Autonomous Vehicles (AVs). Such platforms use various sensors, including cameras, LiDAR and mmWave radar. These sensors themselves may present a potential security hazard if exploited by an attacker. In this paper we propose a system to attack an automotive FMCW mmWave radar, that uses fast chirp modulation. Using a single rogue radar, our attack system is capable of spoofing the distance and velocity measured by the victim vehicle simultaneously, presenting phantom measurements coherent with the laws of physics governing vehicle motion. The attacking radar controls the delay in order to spoof its distance, and uses phase compensation and control in order to spoof its velocity. After developing the attack theory, we demonstrate the spoofing attack by building a proof-of-concept hardware-based system, using a Software Defined Radio. We successfully demonstrate two real world scenarios in which the victim radar is spoofed to detect either a phantom emergency stop or a phantom acceleration, while measuring coherent range and velocity. We also discuss several countermeasures to the attack, in order to propose ways to mitigate the described attack.
An Event-based Parameter Switching Method for Controlling Cybersecurity Dynamics
Venue: arXiv cs.CR 2021
Year: 2021

This paper proposes a new event-based parameter switching method for the control tasks of cybersecurity in the context of preventive and reactive cyber defense dynamics. Our parameter switching method helps avoid excessive control costs as well as guarantees the dynamics to converge as our desired speed. Meanwhile, it can be proved that this approach is Zeno-free. A new estimation method with adaptive time windows is used to bridge the gap between the probability state and the sampling state. With the new estimation method, several practical experiments are given afterwards.
Sifting out the features by pruning: Are convolutional networks the winning lottery ticket of fully connected ones?
Venue: arXiv cs.LG 2021
Year: 2021

Pruning methods can considerably reduce the size of artificial neural networks without harming their performance. In some cases, they can even uncover sub-networks that, when trained in isolation, match or surpass the test accuracy of their dense counterparts. Here we study the inductive bias that pruning imprints in such "winning lottery tickets". Focusing on visual tasks, we analyze the architecture resulting from iterative magnitude pruning of a simple fully connected network (FCN). We show that the surviving node connectivity is local in input space, and organized in patterns reminiscent of the ones found in convolutional networks (CNN). We investigate the role played by data and tasks in shaping the architecture of pruned sub-networks. Our results show that the winning lottery tickets of FCNs display the key features of CNNs. The ability of such automatic network-simplifying procedure to recover the key features "hand-crafted" in the design of CNNs suggests interesting applications to other datasets and tasks, in order to discover new and efficient architectural inductive biases.
A Chromium-based Memento-aware Web Browser
Venue: arXiv cs.DL 2021
Year: 2021

Web browsers provide a user-friendly means of navigating the web. Users rely on their web browser to provide information about the websites they are visiting, such as the security state. Browsers also provide a user interface (UI) with visual cues about each tab that is open, including icons for if the tab is playing audio or requires authentication to view. However, current browsers do not differentiate between the live web and the past web. If a user loads an archived webpage, known as a memento, they have to rely on UI elements present within the page itself to inform them that the page they are viewing is not the live web. Additionally, memento-awareness extends beyond recognizing a page that has already been archived. The browser should give users the ability to archive live webpages, essentially creating mementos of webpages they found important as they surf the web. In this report, the process to create a proof-of-concept browser that is aware of mementos is presented. The browser is created by adding on to the implementation of the open source web browser by Google, Chromium. Creating this prototype for a Memento-aware Browser shows that the features implemented fit well into the current Chromium implementation. The user experience is enhanced by adding the memento-awareness, and the changes to the Chromium code base are minimal.
NTIRE 2021 Depth Guided Image Relighting Challenge
Venue: arXiv cs.CV 2021
Year: 2021

Image relighting is attracting increasing interest due to its various applications. From a research perspective, image relighting can be exploited to conduct both image normalization for domain adaptation, and also for data augmentation. It also has multiple direct uses for photo montage and aesthetic enhancement. In this paper, we review the NTIRE 2021 depth guided image relighting challenge. We rely on the VIDIT dataset for each of our two challenge tracks, including depth information. The first track is on one-to-one relighting where the goal is to transform the illumination setup of an input image (color temperature and light source position) to the target illumination setup. In the second track, the any-to-any relighting challenge, the objective is to transform the illumination settings of the input image to match those of another guide image, similar to style transfer. In both tracks, participants were given depth information about the captured scenes. We had nearly 250 registered participants, leading to 18 confirmed team submissions in the final competition stage. The competitions, methods, and final results are presented in this paper.