Reviewerly/exHarmony huggingface dataset:
Dual-manifold regularized regression models for feature selection based on hesitant fuzzy correlation

In this paper, three novel frameworks based on the widespread regression methods Ridge, LASSO and Elastic Net are established to perform the task of feature selection. The suggested frameworks, which benefit from the joint advantages of the dual-manifold learning and the hesitant fuzzy correlation (HFC), utilize the concept of the hesitant fuzzy correlation matrix (HFCM) of the features and samples. In order to compute the HFCM, a fuzzy clustering algorithm is applied to samples (or features), and a hesitant fuzzy set on each sample (or each feature) is obtained after projecting the cluster membership functions on different samples (or features). Then, two kinds of HFCMs are calculated for samples of each class and the whole of features, respectively. In specific, a feature manifold regularization term based on the HFCM among features is added to the objective function in order to preserve the similarity between the features and the feature weights. Furthermore, a sample manifold regularization term is also considered for the purpose of preserving the local correlation among the samples of each class. Eventually, a set of experiments are conducted on twenty-four datasets in order to validate the performance of each method. The results confirm that the proposed approaches are effective to select suitable features in terms of both the number of selected features and the classification accuracy.
Axillary lymph node metastasis prediction by contrast-enhanced computed tomography images for breast cancer patients based on deep learning

When doctors use contrast-enhanced computed tomography (CECT) images to predict the metastasis of axillary lymph nodes (ALN) for breast cancer patients, the prediction performance could be degraded by subjective factors such as experience, psychological factors, and degree of fatigue. This study aims to exploit efficient deep learning schemes to predict the metastasis of ALN automatically via CECT images. A new construction called deformable sampling module (DSM) was meticulously designed as a plug-and-play sampling module in the proposed deformable attention VGG19 (DA-VGG19). A dataset of 800 samples labeled from 800 CECT images of 401 breast cancer patients retrospectively enrolled in the last three years was adopted to train, validate, and test the deep convolutional neural network models. By comparing the accuracy, positive predictive value, negative predictive value, sensitivity and specificity indices, the performance of the proposed model is analyzed in detail. The best-performing DA-VGG19 model achieved an accuracy of 0.9088, which is higher than that of other classification neural networks. As such, the proposed intelligent diagnosis algorithm can provide doctors with daily diagnostic assistance and advice and reduce the workload of doctors. The source code mentioned in this article will be released later. • This is the first research to predict ALN by CECT dataset via deep learning. • We compare different traditional machine learning algorithms and convolutional neural network structures. • Further improve the deformable convolutional network and design a plug-and-play deformable sampling module.
An efficient multistage phishing website detection model based on the CASE feature framework: Aiming at the real web environment

Phishing has become a favorite method of hackers for committing data theft and continues to evolve. As long as phishing websites continue to operate, many more people and companies will suffer privacy leaks or financial losses. Therefore, the demand for fast and accurate phishing website detection grows stronger. However, the existing phishing detection methods do not fully analyze the features of phishing, and the performance and efficiency of the models only apply to certain limited datasets and need to be improved to be applied to the real web environment. This paper fully considers the social engineering principles of phishing, proposes a comprehensive and interpretable CASE feature framework and designs a multistage phishing detection model to effectively detect phishing sites, especially in the real web environment, where high efficiency and performance and extremely low false alarm rates are required. To fully verify the proposed method, two kinds of data experiments were carried out. One was the comparative experiments among different features and different detection models on CASE, which covers both classic machine learning and deep learning algorithms based on a constructed complex dataset. The other was a one-year phishing discovery experiment in the real web environment. The proposed method achieves better detection results under the premise of significantly shortening the execution time and works well in real phishing discovery, which proves its high practicability in reality.
Secure, Privacy Preserving, and Verifiable Federating Learning Using Blockchain for Internet of Vehicles

Internet of Vehicles (IoV) has been sought as a solution to realize an Intelligent Transportation System (ITS) for efficient traffic management. Data driven ITS requires learning from vehicular data and provide vehicles with timely information to support a wide range of safety and infotainment ITS applications. IoV is vulnerable to multitude of cyber-attacks and privacy concerns. Federated learning (FL) is on the verge of delivering the collaborative learning by exchanging learning model parameters instead of actual data, which is expected to provide privacy in IoV. However, despite featuring an inherently secure and privacy-preserving framework, FL is still vulnerable to poisoning and reverse engineering attacks. Blockchain technology (BC) has already demonstrated a zero-trust, fully secure, distributed, and auditable information recording and sharing paradigm. In this article, we present a practical prospect of blockchain empowered federated learning to realize fully secure, privacy preserving, and verifiable FL for the IoV that is capable of providing secure and trustworthy ITS services.
Meet-in-the-Middle Attacks Revisited: Key-Recovery, Collision, and Preimage Attacks

At EUROCRYPT 2021, Bao et al. proposed an automatic method for systematically exploring the configuration space of meet-in-the-middle (MITM) preimage attacks. We further extend it into a constraint-based framework for finding exploitable MITM characteristics in the context of key-recovery and collision attacks by taking the subtle peculiarities of both scenarios into account. Moreover, to perform attacks based on MITM characteristics with nonlinear constrained neutral words, which have not been seen before, we present a procedure for deriving the solution spaces of neutral words without solving the corresponding nonlinear equations or increasing the overall time complexities of the attack. We apply our method to concrete symmetric-key primitives, including SKINNY, ForkSkinny, Romulus-H, Saturnin, Grøstl, WHIRLPOOL, and hashing modes with AES-256. As a result, we identify the first 23-round key-recovery attack on SKINNY-n-3n and the first 24-round key-recovery attack on ForkSkinny-n-3n in the single-key model. Moreover, improved (pseudo) preimage or collision attacks on round-reduced WHIRLPOOL, Grøstl, and hashing modes with AES-256 are obtained. In particular, employing the new representation of the AES key schedule due to Leurent and Pernot (EUROCRYPT 2021), we identify the first preimage attack on 10-round AES-256 hashing.
PatchDB: A Large-Scale Security Patch Dataset

Security patches, embedding both vulnerable code and the corresponding fixes, are of great significance to vulnerability detection and software maintenance. However, the existing patch datasets suffer from insufficient samples and low varieties. In this paper, we construct a large-scale patch dataset called PatchDB that consists of three components, namely, NVD-based dataset, wild-based dataset, and synthetic dataset. The NVD-based dataset is extracted from the patch hyperlinks indexed by the NVD. The wild-based dataset includes security patches that we collect from the commits on GitHub. To improve the efficiency of data collection and reduce the effort on manual verification, we develop a new nearest link search method to help find the most promising security patch candidates. Moreover, we provide a synthetic dataset that uses a new oversampling method to synthesize patches at the source code level by enriching the control flow variants of original patches. We conduct a set of studies to investigate the effectiveness of the proposed algorithms and evaluate the properties of the collected dataset. The experimental results show that PatchDB can help improve the performance of security patch identification.
Making machine learning trustworthy

Safety, transparency, and fairness are essential for high-stakes uses of machine learning
Action recognition via pose-based graph convolutional networks with intermediate dense supervision

Pose-based action recognition has drawn considerable attention recently. Existing methods exploit the joint position to extract body-part features from the activation maps of the backbone CNN to assist human action recognition. However, there are two limitations: (1) the body-part features are independently used or simply concatenated to obtain a representation, where the prior knowledge about the structured correlations between body parts are not fully exploited; (2) the backbone CNN, from which the body-part features are extracted, is "lazy". It always contents itself with identifying patterns from the most discriminative areas of the input, which causes no information on the features extracted from other areas. This consequently hampers the performance of the followed aggregation process and makes the model easy to be misled by the training data bias. To address these problems, we encode the body-part features into a human-based spatiotemporal graph and employ a light-weight graph convolutional module to explicitly model the dependencies between body parts. Besides, we introduce a novel intermediate dense supervision to promote the backbone CNN to treat all regions equally, which is simple and effective, without extra parameters and computations. The proposed approach, namely, the pose-based graph convolutional network (PGCN), is evaluated on three popular benchmarks, where our approach significantly outperforms the state-of-the-art methods.
A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

This paper discusses the valuable role recommender systems may play in cybersecurity. First, a comprehensive presentation of recommender system types is presented, as well as their advantages and disadvantages, possible applications and security concerns. Then, the paper collects and presents the state of the art concerning the use of recommender systems in cybersecurity; both the existing solutions and future ideas are presented. The contribution of this paper is two-fold: to date, to the best of our knowledge, there has been no work collecting the applications of recommenders for cybersecurity. Moreover, this paper attempts to complete a comprehensive survey of recommender types, after noticing that other works usually mention two–three types at once and neglect the others.
High-Speed FPGA Implementation of SIKE Based on an Ultra-Low-Latency Modular Multiplier

The supersingular isogeny key encapsulation (SIKE) protocol, as one of the post-quantum protocol candidates, is widely regarded as the best alternative for curve-based cryptography. However, the long latency, caused by the serial large-degree isogeny computation which is dominated by modular multiplications, has made it less competitive than most popular post-quantum candidates. In this paper, we propose a high-speed and low-latency architecture for our recently presented optimized SIKE algorithm. Firstly, we design a new field arithmetic logic unit (FALU) with many algorithmic transformations and architectural optimizations. Especially, for the FALU, an extremely low-latency modular multiplier is devised based on a modified algorithm by fully parallelizing and highly optimizing the small-size multipliers and the reduction submodules. Secondly, we develop a compact control logic and update the instructions based on the benchmark provided in the newest SIKE library, fitting well with our design. Thirdly, an efficient memory access method is proposed by scheduling the input and output of the arithmetic logic unit (ALU) in two identical RAMs, which can significantly reduce the latency. Finally, we code the proposed architectures using the Verilog language and integrate them into the SIKE library. The implementation results on a Xilinx Virtex-7 FPGA show that for SIKEp751, our design only costs 9.3 ms with a frequency of 155.8 MHz, about 2× faster than the state-of-the-art, and achieves the best area efficiency among existing works. Particularly, the modular multiplier merely needs 16 clock cycles, reducing the delay by nearly one order of magnitude with a small factor of increase in hardware resource.
Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networks

Representations are crucial for a robot to learn effective navigation policies. Recent work has shown that mid-level perceptual abstractions, such as depth estimates or 2D semantic segmentation, lead to more effective policies when provided as observations in place of raw sensor data (e.g., RGB images). However, such policies must still learn latent three-dimensional scene properties from mid-level abstractions. In contrast, high-level, hierarchical representations such as 3D scene graphs explicitly provide a scene's geometry, topology, and semantics, making them compelling representations for navigation. In this work, we present a reinforcement learning framework that leverages high-level hierarchical representations to learn navigation policies. Towards this goal, we propose a graph neural network architecture and show how to embed a 3D scene graph into an agent-centric feature space, which enables the robot to learn policies that map 3D scene graphs to a platform-agnostic control space (e.g., go straight, turn left). For each node in the scene graph, our method uses features that capture occupancy and semantic content, while explicitly retaining memory of the robot trajectory. We demonstrate the effectiveness of our method against commonly used visuomotor policies in a challenging multi-object search task. These experiments and supporting ablation studies show that our method leads to more effective object search behaviors, exhibits improved long-term memory, and successfully leverages hierarchical information to guide its navigation objectives.
Secure Fully Homomorphic Authenticated Encryption

Homomorphic authenticated encryption allows implicit computation on plaintexts using corresponding ciphertexts without losing privacy, and provides authenticity of the computation and the resultant plaintext of the computation when performing a decryption. However, due to its special functionality, the security notions of the homomorphic authenticated encryption is somewhat complicated and the construction of fully homomorphic authenticated encryption has never been given. In this work, we propose a new security notion and the first construction of fully homomorphic authenticated encryption. Our new security notion is a unified definition for data privacy and authenticity of homomorphic authenticated encryption. Moreover, our security notion is simpler and stronger than the previous ones. To realize our new security notion, we also suggest a construction of fully homomorphic authenticated encryption via generic construction. We combine a fully homomorphic encryption and two homomorphic authenticators, one fully homomorphic and one OR-homomorphic, to construct a fully homomorphic authenticated encryption that satisfies our security notion. Our construction requires its fully homomorphic encryption to be indistinguishable under chosen plaintext attacks and its homomorphic authenticators to be unforgeable under selectively chosen plaintext queries. Our construction also supports multiple datasets and amortized efficiency. For efficiency, we also construct a multi-dataset fully homomorphic authenticator scheme, which is a variant of the first fully homomorphic signature scheme. Our multi-dataset fully homomorphic authenticator scheme satisfies the security requirement of our generic construction above and supports amortized efficiency.
A Survey on Low-Resource Neural Machine Translation

Neural approaches have achieved state-of-the-art accuracy on machine translation but suffer from the high cost of collecting large scale parallel data. Thus, a lot of research has been conducted for neural machine translation (NMT) with very limited parallel data, i.e., the low-resource setting. In this paper, we provide a survey for low-resource NMT and classify related works into three categories according to the auxiliary data they used: (1) exploiting monolingual data of source and/or target languages, (2) exploiting data from auxiliary languages, and (3) exploiting multi-modal data. We hope that our survey can help researchers to better understand this field and inspire them to design better algorithms, and help industry practitioners to choose appropriate algorithms for their applications.
Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network

Deep learning (DL) is becoming popular as a new tool for many applications in wireless communication systems. However, for many classification tasks (e.g., modulation classification) it has been shown that DL-based wireless systems are susceptible to adversarial examples; adversarial examples are well-crafted malicious inputs to the neural network (NN) with the objective to cause erroneous outputs. In this paper, we extend this to regression problems and show that adversarial attacks can break DL-based power allocation in the downlink of a massive multiple-input-multiple-output (maMIMO) network. Specifically, we extend the fast gradient sign method (FGSM), momentum iterative FGSM, and projected gradient descent adversarial attacks in the context of power allocation in a maMIMO system. We benchmark the performance of these attacks and show that with a small perturbation in the input of the NN, the white-box attacks can result in infeasible solutions up to 86%. Furthermore, we investigate the performance of black-box attacks. All the evaluations conducted in this work are based on an open dataset and NN models, which are publicly available.
Skeletor: Skeletal Transformers for Robust Body-Pose Estimation

Predicting 3D human pose from a single monoscopic video can be highly challenging due to factors such as low resolution, motion blur and occlusion, in addition to the fundamental ambiguity in estimating 3D from 2D. Approaches that directly regress the 3D pose from independent images can be particularly susceptible to these factors and result in jitter, noise and/or inconsistencies in skeletal estimation. Much of which can be overcome if the temporal evolution of the scene and skeleton are taken into account. However, rather than tracking body parts and trying to temporally smooth them, we propose a novel transformer based network that can learn a distribution over both pose and motion in an unsupervised fashion. We call our approach Skeletor. Skeletor overcomes inaccuracies in detection and corrects partial or entire skeleton corruption. Skeletor uses strong priors learn from on 25 million frames to correct skeleton sequences smoothly and consistently. Skeletor can achieve this as it implicitly learns the spatio-temporal context of human motion via a transformer based neural network. Extensive experiments show that Skeletor achieves improved performance on 3D human pose estimation and further provides benefits for downstream tasks such as sign language translation.
Inter-Domain Adaptation Label for Data Augmentation in Vehicle Re-Identification

Vehicle re-identification (Re-ID) methods often fail to achieve robust performance due to insufficient training data and domain diversities. Although state-of-the-art methods apply image-to-image translation or web data to achieve data augmentation, the construct of new datasets will not only introduce noise, but also undergo a mismatch issue with the source domain. Moreover, the label noise of cross-domain data in existing label distribution technologies cannot be alleviated. In this paper, a multi-domain joint learning with inter-domain adaptation label smoothing regularization (IALSR) is proposed using a semi-supervised learning framework. The overall framework consists of two parts. In one part, a multi-domain joint network (MJNet) is proposed to learn multiple vehicle attributes simultaneously. The output of the training model is employed to group several inter-domain subsets, which are regarded as different domains. To adapt to domain diversities, style transfer models are learned for each pair of subsets to generate free and rich data as a novel data augmentation approach. In the other part, IALSR, which preserves self-similarity and domain-transitivity, is designed to smooth the noise of style-transferred data. Upon our basis, we further introduce the web data to verify the superiority of the IALSR. The results of extensive experimental on two large-scale vehicle Re-ID datasets demonstrate that the proposed approach is superior to other state-of-the-art ones.
CSI-Based Human Activity Recognition With Graph Few-Shot Learning

Human activity recognition (HAR) based on channel state information (CSI) plays an increasingly important role in the research of human&#x2013;computer interaction. Many CSI HAR models based on traditional machine learning methods and deep learning methods have encountered two challenges. A lot of CSI activity data is needed to train the HAR models, which is time consuming. When the indoor environment or scene changes, the recognition accuracy of the model drops significantly, so it is necessary to recollect data to train the model. The existing few-shot learning-based method can solve the above problems to some extent, but when there are more kinds of new activities or fewer shots, the recognition accuracy will decrease significantly. In this article, considering the relationship between various activity data, a graph-based few-shot learning method with dual attention mechanism (CSI-GDAM) is proposed to perform CSI-based HAR. The model uses a feature extraction layer, including the convolutional block attention module (CBAM), to extract activity-related information in CSI data. The difference and inner product of the feature vector of the CSI activity samples are used to realize the graph convolutional network with a graph attention mechanism. The experiments proved that under the learning task of recognizing new activities in the new environment, the recognition accuracy rates reached 99.74&#x0025; and 98.42&#x0025; in the 5-way 5-shot and 5-way 1-shot cases, respectively. The proposed method is also compared with other few-shot learning and transfer learning methods.