OpenRaiser/Intern-Atlas huggingface dataset:
An Alternative Surrogate Loss for PGD-based Adversarial Testing
Venue: arXiv cs.LG 2019
Year: 2019

Adversarial testing methods based on Projected Gradient Descent (PGD) are widely used for searching norm-bounded perturbations that cause the inputs of neural networks to be misclassified. This paper takes a deeper look at these methods and explains the effect of different hyperparameters (i.e., optimizer, step size and surrogate loss). We introduce the concept of MultiTargeted testing, which makes clever use of alternative surrogate losses, and explain when and how MultiTargeted is guaranteed to find optimal perturbations. Finally, we demonstrate that MultiTargeted outperforms more sophisticated methods and often requires less iterative steps than other variants of PGD found in the literature. Notably, MultiTargeted ranks first on MadryLab's white-box MNIST and CIFAR-10 leaderboards, reducing the accuracy of their MNIST model to 88.36% (with $\ell_\infty$ perturbations of $ε= 0.3$) and the accuracy of their CIFAR-10 model to 44.03% (at $ε= 8/255$). MultiTargeted also ranks first on the TRADES leaderboard reducing the accuracy of their CIFAR-10 model to 53.07% (with $\ell_\infty$ perturbations of $ε= 0.031$).
Danse-doigts, a Fine Motor Game
Venue: arXiv cs.HC 2019
Year: 2019

This paper describes the design, implementation and testing of "Danse-doigts", an edutainment therapeutic application for hemiplegic children. The objective of this program is twofold. Firstly, to allow them to train their fine motor skills on tablet. Secondly, to study the effect of this training on their numerical performance (counting, calculation...). The target population and the objective of evaluating numerical skills influenced the design. The software was developed using standard web technologies but is based on a new parallel programming library written in JavaScript. Applications and libraries are free of charge and easy to install on most tablets.
KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition with Deep Learning
Venue: arXiv cs.CV 2019
Year: 2019

Kuzushiji, a cursive writing style, had been used in Japan for over a thousand years starting from the 8th century. Over 3 millions books on a diverse array of topics, such as literature, science, mathematics and even cooking are preserved. However, following a change to the Japanese writing system in 1900, Kuzushiji has not been included in regular school curricula. Therefore, most Japanese natives nowadays cannot read books written or printed just 150 years ago. Museums and libraries have invested a great deal of effort into creating digital copies of these historical documents as a safeguard against fires, earthquakes and tsunamis. The result has been datasets with hundreds of millions of photographs of historical documents which can only be read by a small number of specially trained experts. Thus there has been a great deal of interest in using Machine Learning to automatically recognize these historical texts and transcribe them into modern Japanese characters. Nevertheless, several challenges in Kuzushiji recognition have made the performance of existing systems extremely poor. To tackle these challenges, we propose KuroNet, a new end-to-end model which jointly recognizes an entire page of text by using a residual U-Net architecture which predicts the location and identity of all characters given a page of text (without any pre-processing). This allows the model to handle long range context, large vocabularies, and non-standardized character layouts. We demonstrate that our system is able to successfully recognize a large fraction of pre-modern Japanese documents, but also explore areas where our system is limited and suggest directions for future work.
Tree-gated Deep Mixture-of-Experts For Pose-robust Face Alignment
Venue: arXiv cs.CV 2019
Year: 2019

Face alignment consists of aligning a shape model on a face image. It is an active domain in computer vision as it is a preprocessing for a number of face analysis and synthesis applications. Current state-of-the-art methods already perform well on "easy" datasets, with moderate head pose variations, but may not be robust for "in-the-wild" data with poses up to 90°. In order to increase robustness to an ensemble of factors of variations (e.g. head pose or occlusions), a given layer (e.g. a regressor or an upstream CNN layer) can be replaced by a Mixture of Experts (MoE) layer that uses an ensemble of experts instead of a single one. The weights of this mixture can be learned as gating functions to jointly learn the experts and the corresponding weights. In this paper, we propose to use tree-structured gates which allows a hierarchical weighting of the experts (Tree-MoE). We investigate the use of Tree-MoE layers in different contexts in the frame of face alignment with cascaded regression, firstly for emphasizing relevant, more specialized feature extractors depending of a high-level semantic information such as head pose (Pose-Tree-MoE), and secondly as an overall more robust regression layer. We perform extensive experiments on several challenging face alignment datasets, demonstrating that our approach outperforms the state-of-the-art methods.
Sound texture synthesis using RI spectrograms
Venue: arXiv cs.SD 2019
Year: 2019

This article introduces a new parametric synthesis method for sound textures based on existing works in visual and sound texture synthesis. Starting from a base sound signal, an optimization process is performed until the cross-correlations between the feature-maps of several untrained 2D Convolutional Neural Networks (CNN) resemble those of an original sound texture. We use compressed RI spectrograms as input to the CNN: this time-frequency representation is the stacking of the real and imaginary part of the Short Time Fourier Transform (STFT) and thus implicitly contains both the magnitude and phase information, allowing for convincing syntheses of various audio events. The optimization is however performed directly on the time signal to avoid any STFT consistency issue. The results of an online perceptual evaluation are also detailed, and show that this method achieves results that are more realistic-sounding than existing parametric methods on a wide array of textures.
Edge Dithering for Robust Adaptive Graph Convolutional Networks
Venue: arXiv cs.LG 2019
Year: 2019

Graph convolutional networks (GCNs) are vulnerable to perturbations of the graph structure that are either random, or, adversarially designed. The perturbed links modify the graph neighborhoods, which critically affects the performance of GCNs in semi-supervised learning (SSL) tasks. Aiming at robustifying GCNs conditioned on the perturbed graph, the present paper generates multiple auxiliary graphs, each having its binary 0-1 edge weights flip values with probabilities designed to enhance robustness. The resultant edge-dithered auxiliary graphs are leveraged by an adaptive (A)GCN that performs SSL. Robustness is enabled through learnable graph-combining weights along with suitable regularizers. Relative to GCN, the novel AGCN achieves markedly improved performance in tests with noisy inputs, graph perturbations, and state-of-the-art adversarial attacks. Further experiments with protein interaction networks showcase the competitive performance of AGCN for SSL over multiple graphs.
Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence
Venue: arXiv cs.LG 2019
Year: 2019

Spiking Neural Networks (SNNs) offer a promising alternative to conventional Artificial Neural Networks (ANNs) for the implementation of on-device low-power online learning and inference. On-device training is, however, constrained by the limited amount of data available at each device. In this paper, we propose to mitigate this problem via cooperative training through Federated Learning (FL). To this end, we introduce an online FL-based learning rule for networked on-device SNNs, which we refer to as FL-SNN. FL-SNN leverages local feedback signals within each SNN, in lieu of backpropagation, and global feedback through communication via a base station. The scheme demonstrates significant advantages over separate training and features a flexible trade-off between communication load and accuracy via the selective exchange of synaptic weights.
You May Not Need Order in Time Series Forecasting
Venue: arXiv cs.LG 2019
Year: 2019

Time series forecasting with limited data is a challenging yet critical task. While transformers have achieved outstanding performances in time series forecasting, they often require many training samples due to the large number of trainable parameters. In this paper, we propose a training technique for transformers that prepares the training windows through random sampling. As input time steps need not be consecutive, the number of distinct samples increases from linearly to combinatorially many. By breaking the temporal order, this technique also helps transformers to capture dependencies among time steps in finer granularity. We achieve competitive results compared to the state-of-the-art on real-world datasets.
GANspection
Venue: arXiv cs.LG 2019
Year: 2019

Generative Adversarial Networks (GANs) have been used extensively and quite successfully for unsupervised learning. As GANs don't approximate an explicit probability distribution, it's an interesting study to inspect the latent space representations learned by GANs. The current work seeks to push the boundaries of such inspection methods to further understand in more detail the manifold being learned by GANs. Various interpolation and extrapolation techniques along with vector arithmetic is used to understand the learned manifold. We show through experiments that GANs indeed learn a data probability distribution rather than memorize images/data. Further, we prove that GANs encode semantically relevant information in the learned probability distribution. The experiments have been performed on two publicly available datasets - Large Scale Scene Understanding (LSUN) and CelebA.
MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension
Venue: arXiv cs.CL 2019
Year: 2019

We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems. In this task, we adapted and unified 18 distinct question answering datasets into the same format. Among them, six datasets were made available for training, six datasets were made available for development, and the final six were hidden for final evaluation. Ten teams submitted systems, which explored various ideas including data sampling, multi-task learning, adversarial training and ensembling. The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than our initial baseline based on BERT.
A Scalable Predictive Maintenance Model for Detecting Wind Turbine Component Failures Based on SCADA Data
Venue: arXiv cs.LG 2019
Year: 2019

In this work, a novel predictive maintenance system is presented and applied to the main components of wind turbines. The proposed model is based on machine learning and statistical process control tools applied to SCADA (Supervisory Control And Data Acquisition) data of critical components. The test campaign was divided into two stages: a first two years long offline test, and a second one year long real-time test. The offline test used historical faults from six wind farms located in Italy and Romania, corresponding to a total of 150 wind turbines and an overall installed nominal power of 283 MW. The results demonstrate outstanding capabilities of anomaly prediction up to 2 months before device unscheduled downtime. Furthermore, the real-time 12-months test confirms the ability of the proposed system to detect several anomalies, therefore allowing the operators to identify the root causes, and to schedule maintenance actions before reaching a catastrophic stage.
On the Beneficial Role of a Finite Number of Scatterers for Wireless Physical Layer Security
Venue: arXiv cs.IT 2019
Year: 2019

We show that for a legitimate communication under multipath quasi-static fading with a reduced number of scatterers, it is possible to achieve perfect secrecy even in the presence of a passive eavesdropper for which no channel state information is available. Specifically, we show that the outage probability of secrecy capacity (OPSC) is zero for a given range of average signal-to-noise ratios (SNRs) at the legitimate and eavesdropper's receivers. As an application example, we analyze the OPSC for the case of two scatterers, explicitly deriving the relationship between the average SNRs, the secrecy rate $R_s$ and the fading model parameters required for achieving perfect secrecy. The impact of increasing the number of scatterers is also analyzed, showing that it is always possible to achieve perfect secrecy in this scenario, provided that the dominant specular component for the legitimate channel is sufficiently large compared to the remaining scattered waves.
The Influence of Trust Score on Cooperative Behavior
Venue: arXiv cs.GT 2019
Year: 2019

The assessment of trust between users is essential for collaboration. General reputation and ID mechanisms may support users' trust assessment. However, these mechanisms lack sensitivity to pairwise interactions and specific experience such as betrayal over time. Moreover, they place an interpretation burden that does not scale to dynamic, large-scale systems. While several pairwise trust mechanisms have been proposed, no empirical research examines trust score influence on participant behavior. We study the influence of showing a partner trust score and/or ID on participants' behavior in a small-group collaborative laboratory experiment based on the trust game. We show that trust score availability has the same effect as an ID to improve cooperation as measured by sending behavior and receiver response. Excellent models based on the trust score predict sender behavior and document participant sensitivity to the provision of partner information. Models based on the trust score for recipient behavior have some predictive ability regarding trustworthiness, but suggest the need for more complex functions relating experience to participant response. We conclude that the parameters of a trust score, including pairwise interactions and betrayal, influence the different roles of participants in the trust game differently, but complement traditional ID and have the advantage of scalability.
WeatherNet: Recognising weather and visual conditions from street-level images using deep residual learning
Venue: arXiv cs.CV 2019
Year: 2019

Extracting information related to weather and visual conditions at a given time and space is indispensable for scene awareness, which strongly impacts our behaviours, from simply walking in a city to riding a bike, driving a car, or autonomous drive-assistance. Despite the significance of this subject, it is still not been fully addressed by the machine intelligence relying on deep learning and computer vision to detect the multi-labels of weather and visual conditions with a unified method that can be easily used for practice. What has been achieved to-date is rather sectorial models that address limited number of labels that do not cover the wide spectrum of weather and visual conditions. Nonetheless, weather and visual conditions are often addressed individually. In this paper, we introduce a novel framework to automatically extract this information from street-level images relying on deep learning and computer vision using a unified method without any pre-defined constraints in the processed images. A pipeline of four deep Convolutional Neural Network (CNN) models, so-called the WeatherNet, is trained, relying on residual learning using ResNet50 architecture, to extract various weather and visual conditions such as Dawn/dusk, day and night for time detection, and glare for lighting conditions, and clear, rainy, snowy, and foggy for weather conditions. The WeatherNet shows strong performance in extracting this information from user-defined images or video streams that can be used not limited to: autonomous vehicles and drive-assistance systems, tracking behaviours, safety-related research, or even for better understanding cities through images for policy-makers.
Scalable Neural Dialogue State Tracking
Venue: arXiv cs.CL 2019
Year: 2019

A Dialogue State Tracker (DST) is a key component in a dialogue system aiming at estimating the beliefs of possible user goals at each dialogue turn. Most of the current DST trackers make use of recurrent neural networks and are based on complex architectures that manage several aspects of a dialogue, including the user utterance, the system actions, and the slot-value pairs defined in a domain ontology. However, the complexity of such neural architectures incurs into a considerable latency in the dialogue state prediction, which limits the deployments of the models in real-world applications, particularly when task scalability (i.e. amount of slots) is a crucial factor. In this paper, we propose an innovative neural model for dialogue state tracking, named Global encoder and Slot-Attentive decoders (G-SAT), which can predict the dialogue state with a very low latency time, while maintaining high-level performance. We report experiments on three different languages (English, Italian, and German) of the WoZ2.0 dataset, and show that the proposed approach provides competitive advantages over state-of-art DST systems, both in terms of accuracy and in terms of time complexity for predictions, being over 15 times faster than the other systems.
Vehicle detection and counting from VHR satellite images: efforts and open issues
Venue: arXiv cs.CV 2019
Year: 2019

Detection of new infrastructures (commercial, logistics, industrial or residential) from satellite images constitutes a proven method to investigate and follow economic and urban growth. The level of activities or exploitation of these sites may be hardly determined by building inspection, but could be inferred from vehicle presence from nearby streets and parking lots. We present in this paper two deep learning-based models for vehicle counting from optical satellite images coming from the Pleiades sensor at 50-cm spatial resolution. Both segmentation (Tiramisu) and detection (YOLO) architectures were investigated. These networks were adapted, trained and validated on a data set including 87k vehicles, annotated using an interactive semi-automatic tool developed by the authors. Experimental results show that both segmentation and detection models could achieve a precision rate higher than 85% with a recall rate also high (76.4% and 71.9% for Tiramisu and YOLO respectively).
Multiple criteria decision-making for lane-change model
Venue: arXiv cs.RO 2019
Year: 2019

Simulation has long been an essential part of testing autonomous driving systems, but only recently has simulation been useful for building and training self-driving vehicles. Vehicle behavioural models are necessary to simulate the interactions between robot cars. This paper proposed a new method to formalize the lane-changing model in urban driving scenarios. We define human incentives from different perspectives, speed incentive, route change incentive, comfort incentive and courtesy incentive etc. We applied a decision-theoretical tool, called Multi-Criteria Decision Making (MCDM) to take these incentive policies into account. The strategy of combination is according to different driving style which varies for each driving. Thus a lane-changing decision selection algorithm is proposed. Not only our method allows for varying the motivation of lane-changing from the purely egoistic desire to a more courtesy concern, but also they can mimic drivers' state, inattentive or concentrate, which influences their driving Behaviour. We define some cost functions and calibrate the parameters with different scenarios of traffic data. Distinguishing driving styles are used to aggregate decision-makers' assessments about various criteria weightings to obtain the action drivers desire most. Our result demonstrates the proposed method can produce varied lane-changing behaviour. Unlike other lane-changing models based on artificial intelligence methods, our model has more flexible controllability.
Establishing an Evaluation Metric to Quantify Climate Change Image Realism
Venue: arXiv cs.LG 2019
Year: 2019

With success on controlled tasks, generative models are being increasingly applied to humanitarian applications [1,2]. In this paper, we focus on the evaluation of a conditional generative model that illustrates the consequences of climate change-induced flooding to encourage public interest and awareness on the issue. Because metrics for comparing the realism of different modes in a conditional generative model do not exist, we propose several automated and human-based methods for evaluation. To do this, we adapt several existing metrics, and assess the automated metrics against gold standard human evaluation. We find that using Fréchet Inception Distance (FID) with embeddings from an intermediary Inception-V3 layer that precedes the auxiliary classifier produces results most correlated with human realism. While insufficient alone to establish a human-correlated automatic evaluation metric, we believe this work begins to bridge the gap between human and automated generative evaluation procedures.
Kuksa: A Cloud-Native Architecture for Enabling Continuous Delivery in the Automotive Domain
Venue: arXiv cs.SE 2019
Year: 2019

Connecting vehicles to cloud platforms has enabled innovative business scenarios while raising new quality concerns, such as reliability and scalability, which must be addressed by research. Cloud-native architectures based on microservices are a recent approach to enable continuous delivery and to improve service reliability and scalability. We propose an approach for restructuring cloud platform architectures in the automotive domain into a microservices architecture. To this end, we adopted and implemented microservices patterns from literature to design the cloud-native automotive architecture and conducted a laboratory experiment to evaluate the reliability and scalability of microservices in the context of a real-world project in the automotive domain called Eclipse Kuksa. Findings indicated that the proposed architecture could handle the continuous software delivery over-the-air by sending automatic control messages to a vehicular setting. Different patterns enabled us to make changes or interrupt services without extending the impact to others. The results of this study provide evidences that microservices are a potential design solution when dealing with service failures and high payload on cloud-based services in the automotive domain.
The Bitlet Model: Defining a Litmus Test for the Bitwise Processing-in-Memory Paradigm
Venue: arXiv cs.AR 2019
Year: 2019

This paper describes an analytical modeling tool called Bitlet that can be used, in a parameterized fashion, to understand the affinity of workloads to processing-in-memory (PIM) as opposed to traditional computing. The tool uncovers interesting trade-offs between operation complexity (cycles required to perform an operation through PIM) and other key parameters, such as system memory bandwidth, data transfer size, the extent of data alignment, and effective memory capacity involved in PIM computations. Despite its simplicity, the model has already proven useful. In the future, we intend to extend and refine Bitlet to further increase its utility.
Robot-Friendly Cities
Venue: arXiv cs.CY 2019
Year: 2019

Robots are increasingly tested in public spaces, towards a future where urban environments are not only for humans but for autonomous systems. While robots are promising, for convenience and efficiency, there are challenges associated with building cities crowded with machines. This paper provides an overview of the problems and some solutions, and calls for greater attention on this matter.