Reviewerly/exHarmony huggingface dataset:
Real Time Landmark Detection for Within- and Cross Subject Tracking With Minimal Human Supervision

Landmark detection plays an important role for a variety of image processing and analysis tasks.Current methods rely on either supervised or semi-supervised learning which often requires large labeled training datasets.Also, retrospective addition of further target landmarks after completion of training is difficult in current methods.In this paper we propose a framework that addresses these limitations and allows for landmark detection based on only few examples and for definition of target landmarks after completed training without retraining.Our proposed approach relies on self-supervised training on a withinimage template matching task with regularization by data augmentation.The trained network generalizes to cross-image matching and can thus be extended to example-based landmark detection and tracking.We extensively evaluate the proposed framework on chest X-ray images and abdominal MRI scans and demonstrate high accuracy with only few or even only one labeled example.Additionally we apply it to the task of liver and liver lesion tracking in CINE MRI scans.
High Speed Safe Autonomous Landing Marker Tracking of Fixed Wing Drone Based on Deep Learning

Most of the current research on autonomous landing of Unmanned Aerial Vehicles (UAVs) focus on rotorcrafts, which can fly horizontally over the landing site and then land vertically, with less landing risk.However, some of the unmanned fixed-wing aircrafts take off and land in the sliding mode of wheel landing gears, and their landing stage is difficult to control and easier to failure.In this paper, the autonomous landing of the fixed wing UAVs based on the visual navigation is studied.We propose a deep learning based Vision Transformer Particle Region-based Convolutional Neural Network (VitP-RCNN).Our VitP-RCNN uses Mobile Vision Transformers (MobileViT) as the backbone to accelerate feature extraction.Regarding the innovation of the present study, it is noticed that candidate boxes are chosen as the particles in our methodology, where the confidence is defined as the particle weight, the spatio-temopral correlation between adjacent image is used in video tracking and the Particle Filter theory is employed into the two-stage detection network to construct the present VitP-RCNN.Based on the spatial-temporal correlation, Our VitP-RCNN is efficient in predicting the particle state in the next frame through the state of the previous frame, so as to realize the continuous tracking of the landing marker.The experimental results show that on the Jetson AGX Xavier drone platform, our speed is up to 51FPS, enabling us to perform the landing marker tracking for the autonomous landing of the fixed-wing drones.
Separated Graph Neural Networks for Recommendation Systems

Automatic recommendation has become an increasingly relevant problem for industries, which allows users to discover items that match their tastes and enables the system to target items at the right users. Graph neural networks have attracted many researchers' attention and have become a useful tool for recommendation. However, these models face two major challenges, which are heterogeneous information aggregation and aggregation weight estimation. In this article, we propose a graph neural networks-based recommendation model, i.e., a separated graph neural recommendation (SGNR) model, which achieves high-quality performance. SGNR separates BINs in recommendation systems into two weighted homogeneous networks for users and items, respectively, resolving the heterogeneous information aggregation problem. In addition, a propagation coefficient estimation method is proposed, which combines parametric and nonparametric estimation strategies. And, it is constructed with three characteristics, which are collaborative, side-information constrained, and adaptive. Thereinto, a three-hierarchy attention operator is contained for feature fusion, which optimizes the feature aggregation process via a more sensible and flexible propagation mechanism. Experimental results on four public databases indicate that the proposed methods perform better than the state-of-the-art recommendation algorithms on prediction accuracy in terms of quantitative assessments and achieve readability and interpretability to some extent.
Perturbation Inactivation Based Adversarial Defense for Face Recognition

Deep learning-based face recognition models are vulnerable to adversarial attacks. To curb these attacks, most defense methods aim to improve the robustness of recognition models against adversarial perturbations. However, the generalization capacities of these methods are quite limited. In practice, they are still vulnerable to unseen adversarial attacks. Deep learning models are fairly robust to general perturbations, such as Gaussian noises. A straightforward approach is to inactivate the adversarial perturbations so that they can be easily handled as general perturbations. In this paper, a plug-and-play adversarial defense method, named perturbation inactivation (PIN), is proposed to inactivate adversarial perturbations for adversarial defense. We discover that the perturbations in different subspaces have different influences on the recognition model. There should be a subspace, called the immune space, in which the perturbations have fewer adverse impacts on the recognition model than in other subspaces. Hence, our method estimates the immune space and inactivates the adversarial perturbations by restricting them to this subspace. The proposed method can be generalized to unseen adversarial perturbations since it does not rely on a specific kind of adversarial attack method. This approach not only outperforms several state-of-the-art adversarial defense methods but also demonstrates a superior generalization capacity through exhaustive experiments. Moreover, the proposed method can be successfully applied to four commercial APIs without additional training, indicating that it can be easily generalized to existing face recognition systems. The source code is available at https://github.com/RenMin1991/Perturbation-Inactivate..
BubblEX: An Explainable Deep Learning Framework for Point-Cloud Classification

Point clouds data are nowadays one of the major data sources for describing our environment. Recently, deep architectures have been proposed as a key step in understanding and retrieving semantic information. Despite the great contribution of deep learning in this field, the explainability of these models for 3D data is still fairly unexplored. Explainability, identified as a potential weakness of Deep Neural Networks (DNNs), can help researchers against skepticism, considering that these models are far from being self-explanatory. Although literature provides many examples on the exploitation of Explainable Artificial Intelligence (XAI) approaches with 2D data, only a few studies have attempted to investigate it for 3D DNNs. To overcome these limitations, it is here proposed BubblEX, a novel multimodal fusion framework to learn the 3D point features. BubblEX framework comprises two stages: &#x201C;Visualization Module&#x201D; for the visualisation of features learned from the network in its hidden layers and &#x201C;Interpretability Module&#x201D;, which aims at describing how the neighbour points are involved in the features extraction. For our experiments, it has been used Dynamic Graph CNN (DGCNN), trained on Modelnet40 dataset. The developed framework extends a method for obtaining saliency maps from image data, to deal with 3D point cloud data, allowing the analysis, comparison and contrasting of multiple features. Besides, it permits the generation of visual explanations from any DNN-based network for 3D point cloud classification without requiring architectural changes or re-training. Our findings will be extremely useful for both scientists and non-experts in understanding and improving future AI-based models.
Tree Representation, Growth Rate of Blockchain and Reward Allocation in Ethereum With Multiple Mining Pools

It is interesting but difficult and challenging to study Ethereum with multiple mining pools. One of the main difficulties comes from not only how to represent such a general tree with multiple block branches (or sub-chains) related to the multiple mining pools, but also how to analyze a multi-dimensional stochastic system due to the mining competition among the multiple mining pools. In this paper, we first set up a mathematical representation for the tree with multiple block branches. Then we provide a block classification of Ethereum: Regular blocks (in the main chain), orphan blocks, uncle blocks, stale blocks, and nephew blocks, and give some key ratios and probabilities of generating the different types of blocks by applying the law of large numbers. Based on this, we further discuss the growth rate of blockchain and the reward allocation among the multiple mining pools through applying the renewal reward theorem. Finally, we use some simulation experiments to verify our theoretical results, and show that the approximate computation approaches developed, such as the key ratios and probabilities, the long-term growth rate of blockchain, and the long-term reward allocation (rate) among the multiple mining pools, can have a faster convergence. Therefore, we provide a powerful tool for observing and understanding the influence of the selfish mining attacks on the performance of Ethereum with multiple mining pools. We believe that the methodology and results developed in this paper will shed light on the study of Ethereum with multiple mining pools, such that a series of promising research can be inspired potentially.
Comments on “PassBio: Privacy-Preserving User-Centric Biometric Authentication”

In 2018, Zhou et al. proposed PassBio, a privacy-preserving biometric authentication scheme, in IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (TIFS) (DOI: 10.1109/TIFS.2018.2838540). PassBio delivers its promise and effectively protects users&#x2019; privacy from malicious servers. However, we demonstrate that PassBio may be vulnerable in a more inclusive attack model with malicious clients.
Protecting the trust and credibility of data by tracking forgery trace based on GANs

With the advent of the 5G Internet of Things era, communication and social interaction in our daily life have changed a lot, and a large amount of social data is transmitted to the Internet. At the same time, with the rapid development of deep forgery technology, a new generation of social data trust crisis has also followed. Therefore, how to ensure the trust and credibility of social data in the 5G Internet of Things era is an urgent problem to be solved. This paper proposes a new method for forgery detection based on GANs. We first discover the hidden gradient information in the grayscale image of the forged image and use this gradient information to guide the generation of forged traces. In the classifier, we replace the traditional binary loss with the focal loss that can focus on difficult-to-classify samples, which can achieve accurate classification when the real and fake samples are unbalanced. Experimental results show that the proposed method can achieve a high accuracy on the DeeperForensics dataset and with the highest accuracy is 98%.
Quantum Weakest Preconditions for Reasoning about Expected Runtimes of Quantum Programs

We study expected runtimes for quantum programs. Inspired by recent work on probabilistic programs, we first define expected runtime as a generalisation of quantum weakest precondition. Then, we show that the expected runtime of a quantum program can be represented as the expectation of an observable (in physics). A method for computing the expected runtimes of quantum programs in finite-dimensional state spaces is developed. Several examples are provided as applications of this method, including computing the expected runtime of quantum Bernoulli Factory – a quantum algorithm for generating random numbers. In particular, using our new method, an open problem of computing the expected runtime of quantum random walks introduced by Ambainis et al. (STOC 2001) is solved.
Users’ Perceptions of Technological Features in Augmented Reality (AR) and Virtual Reality (VR) in Fashion Retailing: A Qualitative Content Analysis

This study explores users’ perceptions of technological features in augmented reality (AR) and virtual reality (VR) and analyzes the advantages and disadvantages of technologies (e.g., AR vs. VR) in fashion retailing. The findings are presented from a qualitative approach and content analysis of focus group interviews. Users’ perception of AR technological features consisted of 5 dimensions: augmentation, user control, vividness, responsiveness, and simplicity. Users’ perception of VR technological features consisted of 4 dimensions: telepresence, simulator sickness, visual discomfort, and user control. Practical implications for the application of mixed reality technology in fashion venues are discussed: for AR technology as a part of shopping tools, the advantages of control and simplicity should be taken seriously; for VR, an immersive experience as the main pros facilitated by telepresence, while sickness, followed by visual discomfort as the main simulator cons. This research offers valuable and useful insights into AR and VR as antecedents from the technological aspect and helps marketers develop and formulate new solutions for the application of AR and VR in fashion retailing.
Specification-Guided Learning of Nash Equilibria with High Social Welfare

Abstract Reinforcement learning has been shown to be an effective strategy for automatically training policies for challenging control problems. Focusing on non-cooperative multi-agent systems, we propose a novel reinforcement learning framework for training joint policies that form a Nash equilibrium. In our approach, rather than providing low-level reward functions, the user provides high-level specifications that encode the objective of each agent. Then, guided by the structure of the specifications, our algorithm searches over policies to identify one that provably forms an $$\epsilon $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ϵ</mml:mi></mml:math> -Nash equilibrium (with high probability). Importantly, it prioritizes policies in a way that maximizes social welfare across all agents. Our empirical evaluation demonstrates that our algorithm computes equilibrium policies with high social welfare, whereas state-of-the-art baselines either fail to compute Nash equilibria or compute ones with comparatively lower social welfare.
Practical Side-Channel Attack on Free-Space QKD Systems With Misaligned Sources and Countermeasures

Practical implementations of quantum key distribution (QKD) protocols can introduce additional degrees of freedom in the quantum states that may render them distinguishable to an eavesdropper.This is the case of QKD systems using a different laser source to generate each quantum states, which can lead to temporal, spectral and/or spatial differences among them that can be exploited by a malicious party to extract information of the key.In this work we characterize, and experimentally verify, a sidechannel attack on spatially distinguishable states against free-space QKD systems with misaligned laser sources.Specifically, for those emitting Gaussian beams, which is the most common case in free-space QKD.The attack makes theoretically unsafe any QKD system with any angular misalignment between the laser sources.Finally, we propose two countermeasures to eliminate the spatial distinguishability and secure the key exchange.
Sign language translation based on new continuous sign language dataset

deaf individuals rely heavily on one another’s use of sign language as a means of communication. In this era, the significance of accessibility has become more and more important. However, for most people with normal hearing, learning sign language is a very difficult thing. Many research teams around the world are using Deep Learning to develop translators for sign language recognition. However, there is a lack of larger and more semantically rich sign language datasets. In this article, we propose a new Chinese sign language dataset- 109 unique sentences from 50 sign language signers and 27,250 clips. On the new Chinese sign language dataset, we further propose a sequence-to-sequence deep learning approach in order to demonstrate how deep learning may continually lower the communication barriers that exist between persons who are deaf and those who have normal hearing.
Support Vector Machine Algorithm in Machine Learning

The Support Vector methods was proposed by V.Vapnik in 1965, when he was trying to solve problems in pattern recognition. In 1971, Kimeldorf proposed a method of constructing kernel space based on support vectors. In 1990s, V.Vapnik formally introduced the Support Vector Machine (SVM) methods in Statistical Learning. Since then, SVM has been widely applied in pattern recognition, natural language process and so on. Informally, SVM is a binary classifier. The model is based on the linear classifier with the optimal margin in the feature space and thus the learning strategy is to maximize the margin, which can be transformed into a convex quadratic programming problem. It uses the principle of structural risk minimization instead of empirical risk minimization to fit small data samples. Kernel trick is used to transform non-linear sample space into linear space, decreasing the complexity of algorithm. Even though, it still has broader prospects for development.
Motion detection and direction recognition in a photonic spiking neural network consisting of VCSELs-SA

Motion detection and direction recognition are two important fundamental visual functions among the many cognitive functions performed by the human visual system. The retina and visual cortex are indispensable for composing the visual nervous system. The retina is responsible for transmitting electrical signals converted from light signals to the visual cortex of the brain. We propose a photonic spiking neural network (SNN) based on vertical-cavity surface-emitting lasers with an embedding saturable absorber (VCSELs-SA) with temporal integration effects, and demonstrate that the motion detection and direction recognition tasks can be solved by mimicking the visual nervous system. Simulation results reveal that the proposed photonic SNN with a modified supervised algorithm combining the tempotron and the STDP rule can correctly detect the motion and recognize the direction angles, and is robust to time jitter and the current difference between VCSEL-SAs. The proposed approach adopts a low-power photonic neuromorphic system for real-time information processing, which provides theoretical support for the large-scale application of hardware photonic SNN in the future.
Minipatch: Undermining DNN-Based Website Fingerprinting With Adversarial Patches

Website Fingerprinting (WF) enables a local passive attacker to infer which website a user is visiting over an encrypted connection. Classifiers utilizing deep neural networks (DNNs) automatically extract reliable features and have achieved up to 98&#x0025; accuracy even against Tor. Since DNNs are known to be vulnerable to adversarial examples, several recent studies have exploited adversarial perturbations to defeat WF attacks. These defenses, however, require a high bandwidth overhead that typically exceeds 20&#x0025; of the original traffic, prohibiting them from real-world deployment. Moreover, many studies on WF defense have been criticized for unrealistic assumptions such as full access to the target model and operating on the entire website trace. In this paper, we leverage adversarial patches&#x2014;a special type of adversarial example that perturbs only local parts of the input&#x2014;to control the overhead and enable black-box perturbation. In particular, we propose a new WF defense called <i>Minipatch</i> that injects extremely few dummy packets in real-time traffic to evade the attacker&#x2019;s classifier. Experimental results demonstrate that <i>Minipatch</i> provides over 97&#x0025; protection success rate with less than 5&#x0025; bandwidth overhead, much lower than existing defenses. Moreover, we show that our adversarial patches remain effective in challenging settings, e.g., where dummy packets are injected only on the client-side and where perturbations are applied almost two months later. Finally, we also analyze several potential countermeasures and suggest ways to preserve perturbation effectiveness during deployment.
Styler: learning formatting conventions to repair Checkstyle violations

Ensuring the consistent usage of formatting conventions is an important aspect of modern software quality assurance. While formatting convention violations can be automatically detected by format checkers implemented in linters, there is no satisfactory solution for repairing them. Manually fixing formatting convention violations is a waste of developer time and code formatters do not take into account the conventions adopted and configured by developers for the used linter. In this paper, we present Styler, a tool dedicated to fixing formatting rule violations raised by format checkers using a machine learning approach. For a given project, Styler first generates training data by injecting violations of the project-specific rules in violation-free source code files. Then, it learns fixes by feeding long short-term memory neural networks with the training data encoded into token sequences. Finally, it predicts fixes for real formatting violations with the trained models. Currently, Styler supports a single checker, Checkstyle, which is a highly configurable and popular format checker for Java. In an empirical evaluation, Styler repaired 41% of 26,791 Checkstyle violations mined from 104 GitHub projects. Moreover, we compared Styler with the IntelliJ plugin CheckStyle-IDEA and the machine-learning-based code formatters Naturalize and CodeBuff. We found out that Styler fixes violations of a diverse set of Checkstyle rules (24/25 rules), generates smaller repairs in comparison to the other systems, and predicts repairs in seconds once trained on a project. Through a manual analysis, we identified cases in which Styler does not succeed to generate correct repairs, which can guide further improvements in Styler. Finally, the results suggest that Styler can be useful to help developers repair Checkstyle formatting violations.
Reference architectures modelling and compliance checking

Abstract Reference architectures (RAs) are successfully used to represent families of concrete software architectures in several domains such as automotive, banking, and the Internet of Things. RAs inspire architects when designing concrete architectures, and they help to guarantee compliance with architectural decisions, regulatory requirements, as well as architectural qualities. Despite their importance, reference architectures still suffer from a number of open technical issues, including (i) the lack of a common interpretation, a precise notation for their representation and documentation, and (ii) the lack of conformance mechanisms for checking the compliance of concrete architectures to their related reference architecture, architectural decisions, regulatory requirements, etc. This paper addresses these two issues by introducing a model-driven approach that leverages (i) a domain-independent metamodel for the representation of reference architectures and (ii) the combination of model transformation and weaving techniques for the automatic conformance checking of concrete architectures. We evaluate the applicability, effectiveness, and generalizability of our approach using illustrative examples from the web browsers and automotive domains, including an assessment from an independent practitioner.