Reviewerly/exHarmony huggingface dataset:
Toggle toolkit: A tool for conducting experiments in unity virtual environments

This article presents and offers Toggle Toolkit, which is an original collection of Unity scripts designed to control various aspects of interactive 3D experiments. The toolkit enables researchers in different fields to design, conduct and evaluate experiments and include interactive elements in immersive virtual environments. This was achieved by using the internal functionalities of the Unity engine and solutions of our own design. The structure of Toggle Toolkit allows triggers and toggles to be allocated to existing virtual objects and throughout the Unity scene. Once a trigger is executed (with a pre-described action, such as colliding with a virtual object, pressing a key, gazing at an object, etc.), the toggles associated with the trigger are activated and then change the attributes or behaviors of linked objects. All interactive behavior is logged and made available for further statistical analysis. Examples of applications in research are presented and discussed. The Toggle Toolkit’s utility lies in its simplicity and modularity. The Toolkit was especially produced for experimenters with few coding skills and high customization requirements in their experiments. The tool is freely available for use in research and can be enhanced with custom scripts. A video tutorial is provided to facilitate use of the tool. The paper aims to not only introduce beginners to experimentation with VR but also offers more experienced researchers who are potentially interested in using and adjusting the features the Toolkit a deeper insight into its structure.
Multi-Source Training Deep-Learning Side-Channel Attacks

Recently, several deep-learning side-channel attacks on cryptographic algorithms were demonstrated. With the help of a trained deep-learning model, the attacker extracts the key from a few power traces captured from a victim device. However, previous works have shown that the inter-chip variation may significantly reduce the attack success probability. In this paper, we quantify the effect of inter-chip variation on the classification accuracy of Multi-Layer Perceptron (MLP) models. We show that, by training on multiple chips, we can increase the probability of recovering the key from a single trace from 39.95% to 86.07% on average. We also evaluate how the printed circuit board diversity affects the classification accuracy.
An Optimization-Based Motion Planner for Autonomous Maneuvering of Marine Vessels in Complex Environments

The task of maneuvering ships in confined environments is a difficult task for a human operator. One major reason is due to the complex and slow dynamics of the ship which need to be accounted for in order to successfully steer the vehicle. In this work, a two-step optimization-based motion planner is proposed for autonomous maneuvering of ships in constrained environments such as harbors. A lattice-based motion planner is used in a first step to compute a feasible, but suboptimal solution to a discretized version of the motion planning problem. This solution is then used to enable efficient warm-start and as a terminal manifold for a second receding-horizon improvement step. Both steps of the algorithm use a high-fidelity model of the ship to plan feasible and energy-efficient trajectories. Moreover, a novel algorithm is proposed for automatic computation of spatial safety envelopes around the trajectory computed by the lattice-based planner. These safety envelopes are used in the second improvement step to obtain collision-avoidance constraints which complexity scales very well with an increased number of surrounding obstacles. The proposed optimization-based motion planner is evaluated with successful results in a simulation study for autonomous docking problems in a model of the Cape Town harbor.
An intelligent healthcare system for predicting and preventing dengue virus infection

Dengue is a mosquito-borne pandemic viral infection, which transmits to humans from Female Aedes albopictis or Aedes agypti mosquitoes. It progressively deteriorates the health of infected individuals and poses a high threat of human morbidity and mortality. This paper proposes an intelligent healthcare system which identifies, monitors, and alerts dengue virus (DeV) infected individuals and other stakeholders in real-time and control the DeV infection outbreak using cloud computing, internet of things and fog computing paradigms. The proposed system uses Naive Bayesian Network (NBN) for diagnosing the possibly DeV infected individuals and generating real-time alerts for suggesting and alerting the concerned stakeholders for taking on-time necessary actions at the fog subsystem. The proposed system also uses Social Network Analysis at the cloud subsystem, to provide Global Positioning Systems (GPS)-based global risk assessment of the DeV infection on Google Maps (Google-based web map service) and control DeV infection outbreak. The analysis of the experimental results acknowledges the efficiency of the NBN-based DeV infection diagnosis, alert generation, and GPS-based risk assessment functionality, of the proposed system, via various statistical measures and experimental approaches.
Bayesian Safe Learning and Control with Sum-of-Squares Analysis and Polynomial Kernels

We propose an iterative method to safely learn the unmodeled dynamics of a control-affine nonlinear system using Bayesian Gaussian process (GP) models with polynomial kernel functions. The method maintains safety by ensuring that the system state stays within the region of attraction (ROA) of a stabilizing control policy while collecting data. A quadratic programming based exploration control policy is computed to keep the exploration trajectory inside an inner-approximation of the ROA and to maximize the information gained from the trajectory. A prior GP model, which incorporates prior information about the unknown dynamics, is used to construct an initial stabilizing policy. As the GP model is updated with data, it is used to synthesize a new policy and a larger ROA, which increases the range of safe exploration. The use of polynomial kernels allows us to compute ROA inner-approximations and stabilizing control laws for the model using sum-of-squares programming. We also provide a probabilistic guarantee of safety which ensures that the policy computed using the learned model stabilizes the true dynamics with high confidence.
Self-Supervised Face Image Manipulation by Conditioning GAN on Face Decomposition

We present a novel architecture for manipulating facial expressions, head poses, and lighting conditions from a single monocular image. Recent methods based on Generative Adversarial Networks show promising results in expression manipulation. However, the variation is either defined by a limited number of classes or not well suitable for explicit manipulation of different attributes such as pose and lighting conditions. Besides, state-of-the-art methods are mostly focused on frontal faces. Therefore, in this paper, a new Generative Adversarial Network architecture is proposed by explicitly conditioning on the appearance image space which is the product of direct manipulation of facial expressions, light and pose conditions of the face model in 3D space. In addition, the method only requires video sequences for training. Therefore, it is self-supervised. Unlike other face manipulation methods, the proposed method does not require target specific training. Large scale experiments show that our method outperforms state-of-the-art methods for different scenarios.
Multibit quantum digital signature with continuous variables using basis encoding over insecure channels

A quantum digital signature (QDS) is utilized to authenticate classical messages and offers information theoretical security guaranteed by the laws of quantum mechanics. Focusing on signing a one-bit message, previous continuous-variable quantum digital signature (CV-QDS) protocols that shared secret keys are generated by quantum key distribution, and it is only mentioned to suitably iterate the one-bit protocol for a multibit message. Although the CV-QDS protocol is unconditionally secure while sending single-bit messages, iterating the one-bit protocol in a naive way will allow some forgery attacks. In this paper, for signing one-bit and multibit messages, a CV-QDS protocol is proposed, on the condition that nonrepudiation and no forging are guaranteed. For one-bit and multibit schemes, we compare the difference in the distribution stage and messaging stage, analyzing the security as well as summarizing some formulas for calculation. We hope the protocol with continuous variables could further improve the performance of QDS.
Near MDS codes from oval polynomials

A linear code with parameters of the form [n,k,n−k+1] is referred to as an MDS (maximum distance separable) code. A linear code with parameters of the form [n,k,n−k] is said to be almost MDS (i.e., almost maximum distance separable) or AMDS for short. A code is said to be near maximum distance separable (in short, near MDS or NMDS) if both the code and its dual are almost maximum distance separable. Near MDS codes correspond to interesting objects in finite geometry and have nice applications in combinatorics and cryptography. There are many unsolved problems about near MDS codes. It is hard to construct an infinite family of near MDS codes whose weight distributions can be settled. In this paper, seven infinite families of [2m+1,3,2m−2] near MDS codes over GF(2m) and seven infinite families of [2m+2,3,2m−1] near MDS codes over GF(2m) are constructed with special oval polynomials for odd m. In addition, nine infinite families of optimal [2m+3,3,2m] near MDS codes over GF(2m) are constructed with oval polynomials in general. The weight distributions of these near MDS codes are settled.
A novel Dual-Blockchained structure for contract-theoretic LoRa-based information systems

LoRa serves as one of the most deployed technologies in Internet-of-Things-based information systems (IoT-IS), and self-motivated deployment is the key to the rollout of LoRa. Proper incentive can play an important role in encouraging the private deployment of LoRa, increasing coverage and promoting effective management of IoT-IS. However, existing incentive mechanisms have the vulnerabilities of insecure centralized architecture and excessive utility loss of LoRa Controllers and Gateways, due to asymmetric information between private owners of gateways and centralized controller (or service providers). Blockchain-based LoRa networks, as a promising solution, have not been comprehensively studied to address the vulnerabilities, let alone the other issues of security, scalability, and flexibility. In this paper, we propose a novel Dual-Chained LoRa-based information system (LoRa-IS) to provide globally cross-validated security. Behaviors, including state-of-the-art contract-theoretic incentive mechanism and new flow control protocol, can be secured with the tamper-resistance of Blockchains. Being part of the proposed incentive mechanism, the new self-driven flow control allows both the Dual-Chain system and the LoRa network to scale. To the best of our knowledge, the proposed system is the first comprehensive Blockchain-based LoRa-IS combined with contract theory. We also provide analysis and simulations, showing that our system can pay fair incentives under information asymmetry. With the new flow control, the system can optimize network coverage while improving the Blockchain scalability and flexibility.
An Empirical Study on the Relation Between Network Interpretability and Adversarial Robustness

Deep neural networks (DNNs) have had many successes, but they suffer from two major issues: (1) a vulnerability to adversarial examples and (2) a tendency to elude human interpretation. Interestingly, recent empirical and theoretical evidence suggests that these two seemingly disparate issues are actually connected. In particular, robust models tend to provide more interpretable gradients than non-robust models. However, whether this relationship works in the opposite direction remains obscure. With this paper, we seek empirical answers to the following question: can models acquire adversarial robustness when they are trained to have interpretable gradients? We introduce a theoretically inspired technique called Interpretation Regularization (IR), which encourages a model's gradients to (1) match the direction of interpretable target salience maps and (2) have small magnitude. To assess model performance and tease apart factors that contribute to adversarial robustness, we conduct extensive experiments on MNIST and CIFAR-10 with both $$\ell _2$$ and $$\ell _\infty$$ attacks. We demonstrate that training the networks to have interpretable gradients improves their robustness to adversarial perturbations. Applying the network interpretation technique SmoothGrad [59] yields additional performance gains, especially in cross-norm attacks and under heavy perturbations. The results indicate that the interpretability of the model gradients is a crucial factor for adversarial robustness. Code for the experiments can be found at https://github.com/a1noack/interp_regularization .
Adaptive Local Embedding Learning for Semi-Supervised Dimensionality Reduction

Semi-supervised learning as one of most attractive problems in machine learning research field has aroused broad attentions in recent years. In this paper, we propose a novel locality preserved dimensionality reduction framework, named Semi-supervised Adaptive Local Embedding learning (SALE), which learns a local discriminative embedding by constructing a <inline-formula><tex-math notation="LaTeX">$k_1$</tex-math></inline-formula> Nearest Neighbors ( <inline-formula><tex-math notation="LaTeX">$k_1$</tex-math></inline-formula> NN) graph on labeled data, so as to explore the intrinsic structure, i.e., sub-manifolds from non-Gaussian labeled data. Then, mapping all samples into learned embedding and constructing another <inline-formula><tex-math notation="LaTeX">$k_2$</tex-math></inline-formula> NN graph on all embedded data to explore the global structure of all samples. Therefore, the unlabeled data and their corresponding labeled neighbors can be clustered into same sub-manifold, so as to improve the discriminative power of embedded data. Furthermore, we propose two semi-supervised dimensionality reduction methods with orthogonal and whitening constraints based on proposed SALE framework. An efficient alternatively iterative optimization algorithm is developed to solve the NP-hard problem in our models. Extensive experiments conducted on several synthetic and real-world data sets demonstrate the superiorities of our methods on local structure exploration and classification task.
Face Image Inpainting With Evolutionary Generators

Recently, deep learning has become a mainstream method of image inpainting. It can not only restore the image texture, obtain high-level abstract features of images, but also restore semantic images such as human face images. Among these methods, generative adversarial networks (GANs) using autoencoder as the generator have become the promising model for image inpainting. These models implement the end-to-end image inpainting and also generate visually reasonable and clear image structures and textures. However, GANs often have problems with gradient vanishing and model collapse during training, so we propose a Generative Adversarial Network with Evolutionary Generators (EG-GAN) and apply it in face image inpainting. To stabilize the model training process, EG-GAN trains the generator network by evolution, combines two mutation functions as a training objective to update the parameter of generator networks, and produces offspring generators through crossover, using the matcher assists the discriminator to criticize the generated image. Experiments on various face image datasets such as CelebA-HQ and CelebA show that EG-GAN successfully overcomes the gradient vanishing problem, achieves stable and efficient training, and generates visually reasonable images.
Privacy-Preserving Distributed Processing: Metrics, Bounds and Algorithms

Privacy-preserving distributed processing has recently attracted considerable attention.It aims to design solutions for conducting signal processing tasks over networks in a decentralized fashion without violating privacy.Many existing algorithms can be adopted to solve this problem such as differential privacy, secure multiparty computation, and the recently proposed distributed optimization based subspace perturbation algorithms.However, since each of them is derived from different contexts and has different metrics and assumptions, it is hard to choose or design an appropriate algorithm in the context of distributed processing.In order to address this problem, we first propose general mutual information based informationtheoretical metrics that are able to compare and relate these existing algorithms in terms of two key aspects: output utility and individual privacy.We consider two widely-used adversary models, the passive and eavesdropping adversary.Moreover, we derive a lower bound on individual privacy which helps to understand the nature of the problem and provides insights on which algorithm is preferred given different conditions.To validate the above claims, we investigate a concrete example and compare a number of state-of-the-art approaches in terms of the concerned aspects using not only theoretical analysis but also numerical validation.Finally, we discuss and provide principles for designing appropriate algorithms for different applications.
On the Influence of Ageing on Face Morph Attacks: Vulnerability and Detection

Face morphing attacks have raised critical concerns as they demonstrate a new vulnerability of Face Recognition Systems (FRS), which are widely deployed in border control applications. The face morphing process uses the images from multiple data subjects and performs an image blending operation to generate a morphed image of high quality. The generated morphed image exhibits similar visual characteristics corresponding to the biometric characteristics of the data subjects that contributed to the composite image and thus making it difficult for both humans and FRS, to detect such attacks. In this paper, we report a systematic investigation on the vulnerability of the Commercial-Off- The-Shelf (COTS) FRS when morphed images under the influence of ageing are presented. To this extent, we have introduced a new morphed face dataset with ageing derived from the publicly available MORPH II face dataset, which we refer to as MorphAge dataset. The dataset has two bins based on age intervals, the first bin - MorphAge-I dataset has 1002 unique data subjects with the age variation of 1 year to 2 years while the MorphAge-II dataset consists of 516 data subjects whose age intervals are from 2 years to 5 years. To effectively evaluate the vulnerability for morphing attacks, we also introduce a new evaluation metric, namely the Fully Mated Morphed Presentation Match Rate (FMMPMR), to quantify the vulnerability effectively in a realistic scenario. Extensive experiments are carried out using two different COTS FRS (COTS I Cognitec FaceVACS-SDK Version 9.4.2 and COTS II - Neurotechnology version 10.0) to quantify the vulnerability with ageing. Further, we also evaluate five different Morph Attack Detection (MAD) techniques to benchmark their detection performance with respect to ageing.
StacMR: Scene-Text Aware Cross-Modal Retrieval

Recent models for cross-modal retrieval have benefited from an increasingly rich understanding of visual scenes, afforded by scene graphs and object interactions to mention a few. This has resulted in an improved matching between the visual representation of an image and the textual representation of its caption. Yet, current visual representations overlook a key aspect: the text appearing in images, which may contain crucial information for retrieval. In this paper, we first propose a new dataset that allows exploration of cross-modal retrieval where images contain scene-text instances. Then, armed with this dataset, we describe several approaches which leverage scene text, including a better scene-text aware cross-modal retrieval method which uses specialized representations for text from the captions and text from the visual scene, and reconcile them in a common embedding space. Extensive experiments confirm that cross-modal retrieval approaches benefit from scene text and highlight interesting research questions worth exploring further. Dataset and code are available at europe.naverlabs.com/stacmr.
Infrared and Visible Cross-Modal Image Retrieval Through Shared Features

Image retrieval is one of the key techniques of computer vision, and has been studied for a long time. Nevertheless, little attention is paid to infrared and visible cross-modal retrieval which can be widely used in various applications, e.g., infrared and visible surveillance systems. In this paper, we propose a shared features based infrared-visible cross-modal image retrieval method. The similar visual features are extracted from infrared and visible images as the shared features, and the Euclidean distance is used to measure the similarity between these features. The core of the proposed method comes from three aspects: 1) Feature separation network can separate image features into shared features and exclusive features; 2) Maximum Mean Discrepancy (MMD) loss is employed to constrain the distribution of shared features, which can reduce the retrieval error caused by different imaging angles and similarity of infrared images. 3) The cross-layer fusion encoder compensates for the context loss in the convolution of infrared images. Experimental results on the Infrared-Visible dataset demonstrate the proposed method is effective and outperforms the state-of-the-art approaches.
Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning

Automatically recognizing the modulation of radar signals is a necessary survival technique in electronic intelligence systems. In order to avoid the complex process of the feature extracting and realize the intelligent modulation recognition of various radar signals under low signal-to-noise ratios (SNRs), this paper proposes a method based on intrapulse signatures of radar signals using adaptive singular value reconstruction (ASVR) and deep residual learning. Firstly, the time-frequency spectrums of radar signals under low SNRs are improved after ASVR denoising processing. Secondly, a series of image processing techniques, including binarizing and morphologic filtering, are applied to suppress the background noise in the time-frequency distribution images (TFDIs). Thirdly, the training process of the residual network is achieved using TFDIs, and classification under various conditions is realized using the new-trained network. Simulation results show that, for eight kinds of modulation signals, the proposed approach still achieves an overall probability of successful recognition of 94.1% when the SNR is only −8 dB. Outstanding performance proves the superiority and robustness of the proposed method.