OpenRaiser/Intern-Atlas huggingface dataset:
Controllable deep melody generation via hierarchical music structure representation
Venue: arXiv cs.SD 2021
Year: 2021

Recent advances in deep learning have expanded possibilities to generate music, but generating a customizable full piece of music with consistent long-term structure remains a challenge. This paper introduces MusicFrameworks, a hierarchical music structure representation and a multi-step generative process to create a full-length melody guided by long-term repetitive structure, chord, melodic contour, and rhythm constraints. We first organize the full melody with section and phrase-level structure. To generate melody in each phrase, we generate rhythm and basic melody using two separate transformer-based networks, and then generate the melody conditioned on the basic melody, rhythm and chords in an auto-regressive manner. By factoring music generation into sub-problems, our approach allows simpler models and requires less data. To customize or add variety, one can alter chords, basic melody, and rhythm structure in the music frameworks, letting our networks generate the melody accordingly. Additionally, we introduce new features to encode musical positional information, rhythm patterns, and melodic contours based on musical domain knowledge. A listening test reveals that melodies generated by our method are rated as good as or better than human-composed music in the POP909 dataset about half the time.
FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning
Venue: arXiv cs.CR 2021
Year: 2021

Homomorphic encryption (HE) is a promising privacy-preserving technique for cross-silo federated learning (FL), where organizations perform collaborative model training on decentralized data. Despite the strong privacy guarantee, general HE schemes result in significant computation and communication overhead. Prior works employ batch encryption to address this problem, but it is still suboptimal in mitigating communication overhead and is incompatible with sparsification techniques. In this paper, we propose FLASHE, an HE scheme tailored for cross-silo FL. To capture the minimum requirements of security and functionality, FLASHE drops the asymmetric-key design and only involves modular addition operations with random numbers. Depending on whether to accommodate sparsification techniques, FLASHE is optimized in computation efficiency with different approaches. We have implemented FLASHE as a pluggable module atop FATE, an industrial platform for cross-silo FL. Compared to plaintext training, FLASHE slightly increases the training time by $\leq6\%$, with no communication overhead.
The Good, The Bad and The Missing: A Narrative Review of Cyber-security Implications for Australian Small Businesses
Venue: arXiv cs.CR 2021
Year: 2021

Small businesses (0-19 employees) are becoming attractive targets for cyber-criminals, but struggle to implement cyber-security measures that large businesses routinely deploy. There is an urgent need for effective and suitable cyber-security solutions for small businesses as they employ a significant proportion of the workforce. In this paper, we consider the small business cyber-security challenges not currently addressed by research or products, contextualised via an Australian lens. We also highlight some unique characteristics of small businesses conducive to cyber-security actions. Small business cyber-security discussions to date have been narrow in focus and lack re-usability beyond specific circumstances. Our study uses global evidence from industry, government and research communities across multiple disciplines. We explore the technical and non-technical factors negatively impacting a small business' ability to safeguard itself, such as resource constraints, organisational process maturity, and legal structures. Our research shows that some small business characteristics, such as agility, large cohort size, and piecemeal IT architecture, could allow for increased cyber-security. We conclude that there is a gap in current research in small business cyber-security. In addition, legal and policy work are needed to help small businesses become cyber-resilient.
User, Robot, Deployer: A New Model for Measuring Trust in HRI
Venue: arXiv cs.RO 2021
Year: 2021

There is an increasing interest in considering, implementing, and measuring trust in human-robot interaction (HRI). Typically, this centres on influencing user trust within the framing of HRI as a dyadic interaction between robot and user. We propose this misses a key complexity: a robot's trustworthiness may also be contingent on the user's relationship with, and opinion of, the individual or organisation deploying the robot. Our new HRI triad model (User, Robot, Deployer), offers novel predictions for considering and measuring trust more completely.
Generative Models for Multi-Illumination Color Constancy
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, the aim is multi-illumination color constancy. However, most of the existing color constancy methods are designed for single light sources. Furthermore, datasets for learning multiple illumination color constancy are largely missing. We propose a seed (physics driven) based multi-illumination color constancy method. GANs are exploited to model the illumination estimation problem as an image-to-image domain translation problem. Additionally, a novel multi-illumination data augmentation method is proposed. Experiments on single and multi-illumination datasets show that our methods outperform sota methods.
Non-terrestrial Communications Assisted by Reconfigurable Intelligent Surfaces
Venue: arXiv cs.IT 2021
Year: 2021

Non-terrestrial communications have emerged as a key enabler for seamless connectivity in the upcoming generation networks. This kind of network can support high data rate communications among aerial platforms (i.e., unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), and satellites) and cellular networks, achieving anywhere and anytime connections. However, there are many practical implementation limitations, especially overload power consumption, high probability of blockage, and dynamic propagation environment. Fortunately, the recent technology reconfigurable intelligent surface (RIS) is expected to be one of the most cost-efficient solutions to address such issues. RIS with low-cost elements can bypass blockages and create multiple line-of-sight (LoS) links, and provide controllable communication channels. In this paper, we present a comprehensive literature review on the RIS-assisted non-terrestrial networks (RANTNs). Firstly, the framework of the RANTNs is introduced with detailed discussion about distinct properties of RIS in NTNs and the two types of RIS, that is, terrestrial RISs (TRISs), and aerial RISs (ARISs), and the classification of RANTNs including RIS-assisted air-to-ground (A2G)/ground-to-air (G2A), ARIS-assisted ground-to-ground (G2G), and RIS-assisted air-to-air (A2A) communications. In combination with next-generation communication technologies, the advanced technologies in RANTNs are discussed. Then we overview the literature related to RANTNs from the perspectives of performance analysis and optimization, followed by the widely used methodologies. Finally, open challenges and future research direction in the context of the RANTNs are highlighted.
Dealing with Distribution Mismatch in Semi-supervised Deep Learning for Covid-19 Detection Using Chest X-ray Images: A Novel Approach Using Feature Densities
Venue: arXiv cs.CV 2021
Year: 2021

In the context of the global coronavirus pandemic, different deep learning solutions for infected subject detection using chest X-ray images have been proposed. However, deep learning models usually need large labelled datasets to be effective. Semi-supervised deep learning is an attractive alternative, where unlabelled data is leveraged to improve the overall model's accuracy. However, in real-world usage settings, an unlabelled dataset might present a different distribution than the labelled dataset (i.e. the labelled dataset was sampled from a target clinic and the unlabelled dataset from a source clinic). This results in a distribution mismatch between the unlabelled and labelled datasets. In this work, we assess the impact of the distribution mismatch between the labelled and the unlabelled datasets, for a semi-supervised model trained with chest X-ray images, for COVID-19 detection. Under strong distribution mismatch conditions, we found an accuracy hit of almost 30\%, suggesting that the unlabelled dataset distribution has a strong influence in the behaviour of the model. Therefore, we propose a straightforward approach to diminish the impact of such distribution mismatch. Our proposed method uses a density approximation of the feature space. It is built upon the target dataset to filter out the observations in the source unlabelled dataset that might harm the accuracy of the semi-supervised model. It assumes that a small labelled source dataset is available together with a larger source unlabelled dataset. Our proposed method does not require any model training, it is simple and computationally cheap. We compare our proposed method against two popular state of the art out-of-distribution data detectors, which are also cheap and simple to implement. In our tests, our method yielded accuracy gains of up to 32\%, when compared to the previous state of the art methods.
Coarse-To-Fine And Cross-Lingual ASR Transfer
Venue: arXiv cs.CL 2021
Year: 2021

End-to-end neural automatic speech recognition systems achieved recently state-of-the-art results, but they require large datasets and extensive computing resources. Transfer learning has been proposed to overcome these difficulties even across languages, e.g., German ASR trained from an English model. We experiment with much less related languages, reusing an English model for Czech ASR. To simplify the transfer, we propose to use an intermediate alphabet, Czech without accents, and document that it is a highly effective strategy. The technique is also useful on Czech data alone, in the style of coarse-to-fine training. We achieve substantial eductions in training time as well as word error rate (WER).
Mapping breakpoint types: an exploratory study
Venue: arXiv cs.SE 2021
Year: 2021

Debugging is a relevant task for finding bugs during software development, maintenance, and evolution. During debugging, developers use modern IDE debuggers to analyze variables, step execution, and set breakpoints. Observing IDE debuggers, we find several breakpoint types. However, what are the breakpoint types? The goal of our study is to map the breakpoint types among IDEs and academic literature. Thus, we mapped the gray literature on the documentation of the nine main IDEs used by developers according to the three public rankings. In addition, we performed a systematic mapping of academic literature over 68 articles describing breakpoint types. Finally, we analyzed the developers understanding of the main breakpoint types through a questionnaire. We present three main contributions: (1) the mapping of breakpoint types (IDEs and literature), (2) compiled definitions of breakpoint types, (3) a breakpoint type taxonomy. Our contributions provide the first step to organize breakpoint IDE taxonomy and lexicon, and support further debugging research.
Classifying Organisms and Artefacts By Their Shapes
Venue: arXiv cs.CV 2021
Year: 2021

We often wish to classify objects by their shapes. Indeed, the study of shapes is an important part of many scientific fields such as evolutionary biology, structural biology, image processing, and archaeology. The most widely-used method of shape analysis, Geometric Morphometrics, assumes that that the mathematical space in which shapes are represented is linear. However, it has long been known that shape space is, in fact, rather more complicated, and certainly non-linear. Diffeomorphic methods that take this non-linearity into account, and so give more accurate estimates of the distances among shapes, exist but have rarely been applied to real-world problems. Using a machine classifier, we tested the ability of several of these methods to describe and classify the shapes of a variety of organic and man-made objects. We find that one method, the Square-Root Velocity Function (SRVF), is superior to all others, including a standard Geometric Morphometric method (eigenshapes). We also show that computational shape classifiers outperform human experts, and that the SRVF shortest-path between shapes can be used to estimate the shapes of intermediate steps in evolutionary series. Diffeomorphic shape analysis methods, we conclude, now provide practical and effective solutions to many shape description and classification problems in the natural and human sciences.
Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction
Venue: arXiv cs.LG 2021
Year: 2021

Accurate prediction of metro passenger volume (number of passengers) is valuable to realize real-time metro system management, which is a pivotal yet challenging task in intelligent transportation. Due to the complex spatial correlation and temporal variation of urban subway ridership behavior, deep learning has been widely used to capture non-linear spatial-temporal dependencies. Unfortunately, the current deep learning methods only adopt graph convolutional network as a component to model spatial relationship, without making full use of the different spatial correlation patterns between stations. In order to further improve the accuracy of metro passenger volume prediction, a deep learning model composed of Parallel multi-graph convolution and stacked Bidirectional unidirectional Gated Recurrent Unit (PB-GRU) was proposed in this paper. The parallel multi-graph convolution captures the origin-destination (OD) distribution and similar flow pattern between the metro stations, while bidirectional gated recurrent unit considers the passenger volume sequence in forward and backward directions and learns complex temporal features. Extensive experiments on two real-world datasets of subway passenger flow show the efficacy of the model. Surprisingly, compared with the existing methods, PB-GRU achieves much lower prediction error.
Impact of Attention on Adversarial Robustness of Image Classification Models
Venue: arXiv cs.CV 2021
Year: 2021

Adversarial attacks against deep learning models have gained significant attention and recent works have proposed explanations for the existence of adversarial examples and techniques to defend the models against these attacks. Attention in computer vision has been used to incorporate focused learning of important features and has led to improved accuracy. Recently, models with attention mechanisms have been proposed to enhance adversarial robustness. Following this context, this work aims at a general understanding of the impact of attention on adversarial robustness. This work presents a comparative study of adversarial robustness of non-attention and attention based image classification models trained on CIFAR-10, CIFAR-100 and Fashion MNIST datasets under the popular white box and black box attacks. The experimental results show that the robustness of attention based models may be dependent on the datasets used i.e. the number of classes involved in the classification. In contrast to the datasets with less number of classes, attention based models are observed to show better robustness towards classification.
SetMargin Loss applied to Deep Keystroke Biometrics with Circle Packing Interpretation
Venue: arXiv cs.CV 2021
Year: 2021

This work presents a new deep learning approach for keystroke biometrics based on a novel Distance Metric Learning method (DML). DML maps input data into a learned representation space that reveals a "semantic" structure based on distances. In this work, we propose a novel DML method specifically designed to address the challenges associated to free-text keystroke identification where the classes used in learning and inference are disjoint. The proposed SetMargin Loss (SM-L) extends traditional DML approaches with a learning process guided by pairs of sets instead of pairs of samples, as done traditionally. The proposed learning strategy allows to enlarge inter-class distances while maintaining the intra-class structure of keystroke dynamics. We analyze the resulting representation space using the mathematical problem known as Circle Packing, which provides neighbourhood structures with a theoretical maximum inter-class distance. We finally prove experimentally the effectiveness of the proposed approach on a challenging task: keystroke biometric identification over a large set of 78,000 subjects. Our method achieves state-of-the-art accuracy on a comparison performed with the best existing approaches.
A Novel Compaction Approach for SBST Test Programs
Venue: arXiv cs.AR 2021
Year: 2021

In-field test of processor-based devices is a must when considering safety-critical systems (e.g., in robotics, aerospace, and automotive applications). During in-field testing, different solutions can be adopted, depending on the specific constraints of each scenario. In the last years, Self-Test Libraries (STLs) developed by IP or semiconductor companies became widely adopted. Given the strict constraints of in-field test, the size and time duration of a STL is a crucial parameter. This work introduces a novel approach to compress functional test programs belonging to an STL. The proposed approach is based on analyzing (via logic simulation) the interaction between the micro-architectural operation performed by each instruction and its capacity to propagate fault effects on any observable output, reducing the required fault simulations to only one. The proposed compaction strategy was validated by resorting to a RISC-V processor and several test programs stemming from diverse generation strategies. Results showed that the proposed compaction approach can reduce the length of test programs by up to 93.9% and their duration by up to 95%, with minimal effect on fault coverage.
Building Compact and Robust Deep Neural Networks with Toeplitz Matrices
Venue: arXiv cs.LG 2021
Year: 2021

Deep neural networks are state-of-the-art in a wide variety of tasks, however, they exhibit important limitations which hinder their use and deployment in real-world applications. When developing and training neural networks, the accuracy should not be the only concern, neural networks must also be cost-effective and reliable. Although accurate, large neural networks often lack these properties. This thesis focuses on the problem of training neural networks which are not only accurate but also compact, easy to train, reliable and robust to adversarial examples. To tackle these problems, we leverage the properties of structured matrices from the Toeplitz family to build compact and secure neural networks.
Extended Object Tracking Using Sets Of Trajectories with a PHD Filter
Venue: arXiv cs.CV 2021
Year: 2021

PHD filtering is a common and effective multiple object tracking (MOT) algorithm used in scenarios where the number of objects and their states are unknown. In scenarios where each object can generate multiple measurements per scan, some PHD filters can estimate the extent of the objects as well as their kinematic properties. Most of these approaches are, however, not able to inherently estimate trajectories and rely on ad-hoc methods, such as different labeling schemes, to build trajectories from the state estimates. This paper presents a Gamma Gaussian inverse Wishart mixture PHD filter that can directly estimate sets of trajectories of extended targets by expanding previous research on tracking sets of trajectories for point source objects to handle extended objects. The new filter is compared to an existing extended PHD filter that uses a labeling scheme to build trajectories, and it is shown that the new filter can estimate object trajectories more reliably.
Crypto Currency Regulation and Law Enforcement Perspectives
Venue: arXiv cs.CR 2021
Year: 2021

This paper provides an overview of how crypto currency and blockchain engineering interacts with the law enforcement. We point out that a large proportion of crypto users are amateur investors and the dominant and the largest segment in crypto crime are simply investment scams (!). We look at various questions of criminal use and misuse of technology, especially in the areas of money laundering or cashing out the profits originating from illicit activities. The aim of the paper is to raise a set of concerns arising in the criminal justice and policing circles, based on the interviews with law enforcement practitioners, and to see how cryptos could be reconciled with public security and safety. We propose a simplified classification of crimes related to crypto currency. We study the development of blockchains in a broader context of applied cryptography and payment technology. Ransomware is a big threat but we also need protection against corporate misconduct or negligence, with untested financial services breaching customer trust or government regulations. Not paying taxes is illegal, but there is more at stake: exposing crypto holders to losing all their savings in scams or thefts. Interestingly, privacy helps to defend on multiple fronts: against social engineering, targeted crime, scams, and also against cybersecurity thefts and hacks.
Optimization and Sampling Under Continuous Symmetry: Examples and Lie Theory
Venue: arXiv cs.DS 2021
Year: 2021

In the last few years, the notion of symmetry has provided a powerful and essential lens to view several optimization or sampling problems that arise in areas such as theoretical computer science, statistics, machine learning, quantum inference, and privacy. Here, we present two examples of nonconvex problems in optimization and sampling where continuous symmetries play -- implicitly or explicitly -- a key role in the development of efficient algorithms. These examples rely on deep and hidden connections between nonconvex symmetric manifolds and convex polytopes, and are heavily generalizable. To formulate and understand these generalizations, we then present an introduction to Lie theory -- an indispensable mathematical toolkit for capturing and working with continuous symmetries. We first present the basics of Lie groups, Lie algebras, and the adjoint actions associated with them, and we also mention the classification theorem for Lie algebras. Subsequently, we present Kostant's convexity theorem and show how it allows us to reduce linear optimization problems over orbits of Lie groups to linear optimization problems over polytopes. Finally, we present the Harish-Chandra and the Harish-Chandra--Itzykson--Zuber (HCIZ) formulas, which convert partition functions (integrals) over Lie groups into sums over the corresponding (discrete) Weyl groups, enabling efficient sampling algorithms.