Reviewerly/exHarmony huggingface dataset:
Defending against adversarial machine learning attacks using hierarchical learning: A case study on network traffic attack classification

Machine learning is key for automated detection of malicious network activity to ensure that computer networks and organizations are protected against cyber security attacks. Recently, there has been growing interest in the domain of adversarial machine learning, which explores how a machine learning model can be compromised by an adversary, resulting in misclassified output. Whilst to date, most focus has been given to visual domains, the challenge is present in all applications of machine learning where a malicious attacker would want to cause unintended functionality, including cyber security and network traffic analysis. We first present a study on conducting adversarial attacks against a well-trained network traffic classification model. We show how well-crafted adversarial examples can be constructed so that known attack types are misclassified by the model as benign activity. To combat this, we present a novel defensive strategy based on hierarchical learning to help reduce the attack surface that an adversarial example can exploit within the constraints of the parameter space of the intended attack. Our results show that our defensive learning model can withstand crafted adversarial attacks and can achieve classification accuracy in line with our original model when not under attack.
Reinforcement learning-driven deep question generation with rich semantics

Deep question generation (DQG) refers to generating a complex question from different sentences in context. Existing methods mainly focus on enhancing information extraction based on the encoder–decoder neural networks though they cannot perform well in DQG tasks. To address this issue, we consider combining reinforcement learning with semantic-rich information to generate deep questions in this paper. In particular, we propose a Semantic-Rich Reinforcement Learning Deep Question Generation (SRL-DQG) model, which better utilizes the semantic graphs of document representations based on the Gated Graph Neural Network (GGNN). In order to generate high-quality questions, we also optimize specific objectives via reinforcement learning with consideration of four evaluation factors including naturality, relevance, answerability, and difficulty. Empirical evaluations demonstrate that our SRL-DQG effectively improves the quality of generated questions and achieves superior performance than existing methods in terms of multiple performance metrics. Specifically, we show that several BLEU-n scores were improved by 3.5% to 10% after running SRL-DQG on 6072 samples of HotPotQA.
Mining Statistical Relations for Better Decision Making in Healthcare Processes

An important part of healthcare decision making is to understand how certain actions relate to desired and undesired outcomes. One key challenge is to deal with confounding variables, i.e., variables that influence the relation between actions and outcomes. Existing techniques aim to uncover the underlying statistical relations between actions and outcomes, but either do not account for confounding variables or only consider the process or case level instead of the event level. Therefore, this paper proposes a novel relation mining approach for healthcare processes that 1) explicitly accounts for confounding variables at the event level, and 2) transparently communicates the effect of the confounding variables to the user. We demonstrate the applicability and importance of our approach using two evaluation experiments. We use a real-world healthcare dataset to show that the identified relations indeed provide important input for decision making in healthcare processes. We use a synthetic dataset to illustrate the importance of our approach in the general setting of causal model estimation.
Deep Multi-Scale U-Net Architecture and Label-Noise Robust Training Strategies for Histopathological Image Segmentation

Although the U-Net architecture has been extensively used for the segmentation of medical images, we address two of its shortcomings in this work. Firstly, the accuracy of vanilla U-Net degrades when the target regions for segmentation exhibit significant variations in shape and size. Even though the U-Net already possesses some capability to analyze features at various scales, we propose to explicitly add multi-scale feature maps in each convolutional module of the U-Net encoder to improve the segmentation of histology images. Secondly, the accuracy of a U-Net model also suffers when the annotations for supervised learning are noisy or incomplete. This can happen due to the inherent difficulty for a human expert to identify and delineate all instances of specific pathology very precisely and accurately. We address this challenge by introducing auxiliary confidence maps that emphasize less on the boundaries of the given target regions. Further, we utilize the bootstrapping properties of the deep network to address the missing annotation problem intelligently. In our experiments on a private dataset of breast cancer lymph nodes, where the primary task was to segment germinal centers and sinus histiocytosis, we observed substantial improvement over baselines based on the two proposed schemes.
A post quantum secure multi-party collaborative signature with deterability in the Industrial Internet of Things

Industrial Internet-of-Things (IIoT for short) which is the basis of Industry 4.0, extends Internet connectivity beyond traditional computing devices, for example, smartphones, computers, laptops, webcams, etc., to the physical world for improving accuracy and efficiency while reducing the production cost. However, there exists large numbers of security threats, such as data leakage and privacy threats. To solve these issues, we first present a system model, security requirements and then proposed a post quantum secure multi-party collaborative signature with deterability in the IIoT environment. In this scheme, formal security proofs are given in the random oracle assuming that AMLWE and AMSIS problem are hard. We also demonstrate the potential utility of our proposed scheme by evaluating its performance.
Sec-edge: Trusted blockchain system for enabling the identification and authentication of edge based 5G networks

The 5th generation 5G technology, also known as the 2020 telecommunications system, represents the next exciting phase of the evolution of telecommunications, with the recent successful distribution on nearly every continent. 5G is believed to bring multiple advancements such as greater transmission speeds, ultra-low latency, higher network capacity, etc. These advancements will directly support the industry-5.0 and Internet of Things (IoT) in the coming future. Today, IoT is one of the fastest-growing technologies because of its ability to deliver multiple applications across various devices. The emergence of 5G networks will be a major force for IoT development. Subsequently, Edge computing as an extension of cloud computing has emerged as a promising technology to empower 5G and IoT services due to its close vicinity to IoT Devices. However, specific challenging issues plague the integration of 5G, IoT, and Edge Computing, among which security is one of the biggest concerns. Recently, blockchain has been provided as a foundational and decentralized distributed ledger technology that can be used for solving the challenges of IoT, 5G, and Edge Computing integration. This paper proposes a trusted blockchain system for edge-based 5G networks that eliminates the limitations of integrating the Edge Computing, IoT, and 5G networks. The proposed blockchain system can efficiently identify the edge devices, authenticate the devices, and assign the addresses to the edge devices on a demand basis. Further, the proposed work also provides the secure communication among the edge devices that can protect from DDOS and Side-channel attacks.
The EEG signals encryption algorithm with K-sine-transform-based coupling chaotic system

Telemedicine provides remote online services for digital diagnosis and treatment via the Internet. However, there is a risk of data leakage during transmission. Therefore, data protection is an important challenge for telemedicine. Chaos is widely used in image, audio, and EEG encryption because of its unique characteristics of unpredictability, nonlinearity, and sensitivity to an initial state. However, some chaotic maps have various security issues. To solve these problems, this paper proposes a K-sine-transform-based coupled chaotic system (K-STBCCS), combining any two one-dimensional chaotic mappings to generate a new chaos mapping. To demonstrate the reliability of the system, this paper generates three new chaotic mappings using K-STBCCS and analyzes their performance. Using the chaotic mapping generated by K-STBCCS, this paper further proposes an EEG signal encryption scheme based on the confusion-diffusion principle. The purpose of confusion is to separate adjacent EEG signals, while the purpose of diffusion is to change the value of EEG signals. Among them, the diffusion operation uses positive and negative diffusion to reduce the correlation between the ciphertext and the original signals. The experimental results and security analysis show that the proposed EEG signal encryption scheme performs well and passes the rigorous cryptographic security test.
TESPDA-SEI: Tensor embedding substructure preserving domain adaptation for specific emitter identification

For the specific emitter identification (SEI) with few or no labels, domain adaptation make the model respond quickly with the help of empirical information. However, the more extreme case is that there are so few labeled samples in the source domain that it is difficult to train an excellent recognition model. In fact, it is more valuable to make full use of these limited label information. This work aims at proposing an unsupervised domain adaptation (UDA)-based method to accommodate the typical case of no labels in the target domain and small samples in the source domain when new devices are first introduced. The basic principle is to learn tensor embedding shared feature space and preserving inter-class substructure, which perform feature space mapping under the joint source and target domain led by mapping error minimize in the source domain. Specifically, this tensor embedding substructure preserving domain adaptation (TESPDA) consist of three parts, tensor invariant subspace learning, substructure preserving feature space mapping and pseudo-label prediction, which are used to learn inter-class substructure after tensor space mapping and identify the predict labels for the target domain. Finally, experiments are conducted on the real-word ADS-B dataset to demonstrate the effectiveness of the TESPDA method.
Score-Guided Generative Adversarial Networks

We propose a generative adversarial network (GAN) that introduces an evaluator module using pretrained networks. The proposed model, called a score-guided GAN (ScoreGAN), is trained using an evaluation metric for GANs, i.e., the Inception score, as a rough guide for the training of the generator. Using another pretrained network instead of the Inception network, ScoreGAN circumvents overfitting of the Inception network such that the generated samples do not correspond to adversarial examples of the Inception network. In addition, evaluation metrics are employed only in an auxiliary role to prevent overfitting. When evaluated using the CIFAR-10 dataset, ScoreGAN achieved an Inception score of 10.36 ± 0.15, which corresponds to state-of-the-art performance. To generalize the effectiveness of ScoreGAN, the model was evaluated further using another dataset, CIFAR-100. ScoreGAN outperformed other existing methods, achieving a Fréchet Inception distance (FID) of 13.98.
Security of Bennett–Brassard 1984 Quantum-Key Distribution under a Collective-Rotation Noise Channel

The security analysis of the Ekert 1991 (E91), Bennett 1992 (B92), six-state protocol, Scarani–Acín–Ribordy–Gisin 2004 (SARG04) quantum key distribution (QKD) protocols, and their variants have been studied in the presence of collective-rotation noise channels. However, besides the Bennett–Brassard 1984 (BB84) being the first proposed, extensively studied, and essential protocol, its security proof under collective-rotation noise is still missing. Thus, we aim to close this gap in the literature. Consequently, we investigate how collective-rotation noise channels affect the security of the BB84 protocol. Mainly, we study scenarios where the eavesdropper, Eve, conducts an intercept-resend attack on the transmitted photons sent via a quantum communication channel shared by Alice and Bob. Notably, we distinguish the impact of collective-rotation noise and that of the eavesdropper. To achieve this, we provide rigorous, yet straightforward numerical calculations. First, we derive a model for the collective-rotation noise for the BB84 protocol and parametrize the mutual information shared between Alice and Eve. This is followed by deriving the quantum bit error rate (QBER) for two intercept-resend attack scenarios. In particular, we demonstrate that, for small rotation angles, one can extract a secure secret key under a collective-rotation noise channel when there is no eavesdropping. We observe that noise induced by rotation of 0.35 radians of the prepared quantum state results in a QBER of 11%, which corresponds to the lower bound on the tolerable error rate for the BB84 QKD protocol against general attacks. Moreover, a rotational angle of 0.53 radians yields a 25% QBER, which corresponds to the error rate bound due to the intercept-resend attack. Finally, we conclude that the BB84 protocol is robust against intercept-resend attacks on collective-rotation noise channels when the rotation angle is varied arbitrarily within particular bounds.
Detail-Aware Deep Homography Estimation for Infrared and Visible Image

Homography estimation of infrared and visible images is a highly challenging task in computer vision. Recently, the deep learning homography estimation methods have focused on the plane, while ignoring the details in the image, resulting in the degradation of the homography estimation performance in infrared and visible image scenes. In this work, we propose a detail-aware deep homography estimation network to preserve more detailed information in images. First, we design a shallow feature extraction network to obtain meaningful features for homography estimation from multi-level multi-dimensional features. Second, we propose a Detail Feature Loss (DFL), which utilizes refined features for computation and retains more detailed information while reducing the influence of unimportant features, enabling effective unsupervised learning. Finally, considering that the evaluation indicators of the previous homography estimation tasks are difficult to reflect severe distortion or the workload of manually labelling feature points is too large, we propose an Adaptive Feature Registration Rate (AFRR) to adaptive extraction of image pair feature points to calculate the registration rate. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods on synthetic benchmark dataset and real dataset.
NeuralRoom

We present a novel neural surface reconstruction method called NeuralRoom for reconstructing room-sized indoor scenes directly from a set of 2D images. Recently, implicit neural representations have become a promising way to reconstruct surfaces from multiview images due to their high-quality results and simplicity. However, implicit neural representations usually cannot reconstruct indoor scenes well because they suffer severe shape-radiance ambiguity. We assume that the indoor scene consists of texture-rich and flat texture-less regions. In texture-rich regions, the multiview stereo can obtain accurate results. In the flat area, normal estimation networks usually obtain a good normal estimation. Based on the above observations, we reduce the possible spatial variation range of implicit neural surfaces by reliable geometric priors to alleviate shape-radiance ambiguity. Specifically, we use multiview stereo results to limit the NeuralRoom optimization space and then use reliable geometric priors to guide NeuralRoom training. Then the NeuralRoom would produce a neural scene representation that can render an image consistent with the input training images. In addition, we propose a smoothing method called perturbation-residual restrictions to improve the accuracy and completeness of the flat region, which assumes that the sampling points in a local surface should have the same normal and similar distance to the observation center. Experiments on the ScanNet dataset show that our method can reconstruct the texture-less area of indoor scenes while maintaining the accuracy of detail. We also apply NeuralRoom to more advanced multiview reconstruction algorithms and significantly improve their reconstruction quality.
Production-Ready Face Re-Aging for Visual Effects

Photorealistic digital re-aging of faces in video is becoming increasingly common in entertainment and advertising. But the predominant 2D painting workflow often requires frame-by-frame manual work that can take days to accomplish, even by skilled artists. Although research on facial image re-aging has attempted to automate and solve this problem, current techniques are of little practical use as they typically suffer from facial identity loss, poor resolution, and unstable results across subsequent video frames. In this paper, we present the first practical, fully-automatic and production-ready method for re-aging faces in video images. Our first key insight is in addressing the problem of collecting longitudinal training data for learning to re-age faces over extended periods of time, a task that is nearly impossible to accomplish for a large number of real people. We show how such a longitudinal dataset can be constructed by leveraging the current state-of-the-art in facial re-aging that, although failing on real images, does provide photoreal re-aging results on synthetic faces. Our second key insight is then to leverage such synthetic data and formulate facial re-aging as a practical image-to-image translation task that can be performed by training a well-understood U-Net architecture, without the need for more complex network designs. We demonstrate how the simple U-Net, surprisingly, allows us to advance the state of the art for re-aging real faces on video, with unprecedented temporal stability and preservation of facial identity across variable expressions, viewpoints, and lighting conditions. Finally, our new face re-aging network (FRAN) incorporates simple and intuitive mechanisms that provides artists with localized control and creative freedom to direct and fine-tune the re-aging effect, a feature that is largely important in real production pipelines and often overlooked in related research work.
NeuralMarker

We tackle the problem of estimating correspondences from a general marker, such as a movie poster, to an image that captures such a marker. Conventionally, this problem is addressed by fitting a homography model based on sparse feature matching. However, they are only able to handle plane-like markers and the sparse features do not sufficiently utilize appearance information. In this paper, we propose a novel framework NeuralMarker, training a neural network estimating dense marker correspondences under various challenging conditions, such as marker deformation, harsh lighting, etc. Deep learning has presented an excellent performance in correspondence learning once provided with sufficient training data. However, annotating pixel-wise dense correspondence for training marker correspondence is too expensive. We observe that the challenges of marker correspondence estimation come from two individual aspects: geometry variation and appearance variation. We, therefore, design two components addressing these two challenges in NeuralMarker. First, we create a synthetic dataset FlyingMarkers containing marker-image pairs with ground truth dense correspondences. By training with FlyingMarkers, the neural network is encouraged to capture various marker motions. Second, we propose the novel Symmetric Epipolar Distance (SED) loss, which enables learning dense correspondence from posed images. Learning with the SED loss and the cross-lighting posed images collected by Structure-from-Motion (SfM), NeuralMarker is remarkably robust in harsh lighting environments and avoids synthetic image bias. Besides, we also propose a novel marker correspondence evaluation method circumstancing annotations on real marker-image pairs and create a new benchmark. We show that NeuralMarker significantly outperforms previous methods and enables new interesting applications, including Augmented Reality (AR) and video editing.
Gaussian Blue Noise

Among the various approaches for producing point distributions with blue noise spectrum, we argue for an optimization framework using Gaussian kernels. We show that with a wise selection of optimization parameters, this approach attains unprecedented quality, provably surpassing the current state of the art attained by the optimal transport (BNOT) approach. Further, we show that our algorithm scales smoothly and feasibly to high dimensions while maintaining the same quality, realizing unprecedented high-quality high-dimensional blue noise sets. Finally, we show an extension to adaptive sampling.
FCNN: fusion-based underwater image enhancement using multilayer convolution neural network

Underwater robotics is an important part of oceanography, resource exploration, and marine engineering. The images captured by underwater robots are affected by environmental factors, such as scattering and absorption, which lead to color cast and haze issues. We propose a light-weight fusion-based convolution neural network (FCNN) that improves the visual content of the underwater image. In FCNN, two methods, such as automatic white balancing and contrast limited adaptive histogram equalization, are used in the preprocessing phase. Then the outputs of the preprocessing phase are fed into the multilayer neural network to learn the end-to-end features. Finally, the outputs of the preprocessing phase and multilayer neural network are fused to obtain enhanced image. The performance of FCNN is evaluated based on qualitative, quantitative analysis, and in terms of time complexity. The experimental analysis proves that the FCNN overcomes the existing conventional methods and deep learning-based networks.