Reviewerly/exHarmony huggingface dataset:
Selection of Best Fit Hardware Performance Counters to Detect Cache Side-Channel Attacks

Cache side-channel attack is a common threat in cloud environments where caches are shared across co-located tenants. Detection of such attacks in real-time before the attack procedure is completed can enable cloud users to come up with a countermeasure and protect their privacy against these kinds of vulnerabilities. In this work, a real-time cache side-channel attack detection system for cloud systems is presented which leverages hardware performance counters. The combination of two neural networks is trained with long-term time sequences collected via hardware performance counters to learn the normal behavior of benign applications so that anomalies caused by attackers can be detected. This paper primarily examines the selection of best fit hardware performance counters for this purpose. Initial experiments are performed and time series feature extraction and selection methods are applied to preliminary results for the analysis.
A Method Improves Speech Recognition with Contrastive Learning in Low-Resource Languages

Building an effective automatic speech recognition system typically requires a large amount of high-quality labeled data; However, this can be challenging for low-resource languages. Currently, self-supervised contrastive learning has shown promising results in low-resource automatic speech recognition, but there is no discussion on the quality of negative sample sets in speech contrastive learning. In this paper, we propose the false negatives impact elimination (FNIE) method to filter false negative samples and improve the quality of the negative sample set in speech. FNIE compares the support vector with the negative sample vector set and optimizes the corresponding loss function, allowing the model to learn better speech representations and achieve superior results in low-resource speech recognition. Experiments demonstrate that FNIE effectively filters negative samples, enhances the quality of the negative sample set, and improves the accuracy of speech recognition. The quality of the negative sample set significantly affects the model’s learning ability, and using too many negative samples can deteriorate it. In a low-resource setting, our FNIE method achieved a relative improvement of 2.98% in WER on the English dataset, 14.3% in WER on the Uyghur dataset, and 4.04% in CER on the Mandarin dataset compared to the baseline model.
An Automatic Calibration Method for Kappa Angle Based on a Binocular Gaze Constraint

Kappa-angle calibration shows its importance in gaze tracking due to the special structure of the eyeball. In a 3D gaze-tracking system, after the optical axis of the eyeball is reconstructed, the kappa angle is needed to convert the optical axis of the eyeball to the real gaze direction. At present, most of the kappa-angle-calibration methods use explicit user calibration. Before eye-gaze tracking, the user needs to look at some pre-defined calibration points on the screen, thereby providing some corresponding optical and visual axes of the eyeball with which to calculate the kappa angle. Especially when multi-point user calibration is required, the calibration process is relatively complicated. In this paper, a method that can automatically calibrate the kappa angle during screen browsing is proposed. Based on the 3D corneal centers and optical axes of both eyes, the optimal objective function of the kappa angle is established according to the coplanar constraint of the visual axes of the left and right eyes, and the differential evolution algorithm is used to iterate through kappa angles according to the theoretical angular constraint of the kappa angle. The experiments show that the proposed method can make the gaze accuracy reach 1.3° in the horizontal plane and 1.34° in the vertical plane, both of which are within the acceptable margins of gaze-estimation error. The demonstration of explicit kappa-angle calibration is of great significance to the realization of the instant use of gaze-tracking systems.
Dual adversarial models with cross-coordination consistency constraint for domain adaption in brain tumor segmentation

The brain tumor segmentation task with different domains remains a major challenge because tumors of different grades and severities may show different distributions, limiting the ability of a single segmentation model to label such tumors. Semi-supervised models (e.g., mean teacher) are strong unsupervised domain-adaptation learners. However, one of the main drawbacks of using a mean teacher is that given a large number of iterations, the teacher model weights converge to those of the student model, and any biased and unstable predictions are carried over to the student. In this article, we proposed a novel unsupervised domain-adaptation framework for the brain tumor segmentation task, which uses dual student and adversarial training techniques to effectively tackle domain shift with MR images. In this study, the adversarial strategy and consistency constraint for each student can align the feature representation on the source and target domains. Furthermore, we introduced the cross-coordination constraint for the target domain data to constrain the models to produce more confident predictions. We validated our framework on the cross-subtype and cross-modality tasks in brain tumor segmentation and achieved better performance than the current unsupervised domain-adaptation and semi-supervised frameworks.
Determinants of blockchain technology application in primary healthcare delivery: An integrated best-worst approach

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BB84 quantum key distribution transmitter utilising broadband sources and a narrow spectral filter

The secure nature of Quantum Key Distribution (QKD) protocols makes it necessary to ensure that the single photon sources are indistinguishable. Any spectral, temporal or spatial discrepancy between the sources would lead to a breach in the security proofs of the QKD protocols. Traditional, weak-coherent pulse implementations of polarization-based QKD protocols have relied on identical photon sources obtained through tight temperature control and spectral filtering. However, it can be challenging to keep the temperature of the sources stable over time, particularly in a real-world setting, meaning photon sources can become distinguishable. In this work, we present an experimental demonstration of a QKD system capable of achieving spectral indistinguishability, over a 10°C range, using a combination of broadband sources, super-luminescent light emitting diodes (SLEDs), along with a narrow band-pass filter. The temperature stability could be useful in a satellite implementation, where there may be temperature gradients over the payload, particularly on a CubeSat.
Statistical physics of deep neural networks: Initialization toward optimal channels

In deep learning, neural networks serve as noisy channels between input data and its latent representation. This perspective naturally relates deep learning with the pursuit of constructing channels with optimal performance in information transmission and representation. While considerable efforts are concentrated on realizing optimal channel properties during network optimization, we study a frequently overlooked possibility that neural networks can be initialized toward optimal channels. Our theory, consistent with experimental validation, identifies primary mechanics underlying this unknown possibility and suggests intrinsic connections between statistical physics and deep learning. Unlike the conventional theories that characterize neural networks applying the classic mean-field approximation, we offer analytic proof that this extensively applied simplification scheme is not appropriate in studying neural networks as information channels. To fill this gap, we develop a restricted mean-field framework applicable for characterizing the limiting behaviors of information propagation in neural networks without strong assumptions on inputs. Based on it, we propose an analytic theory to prove that mutual information maximization is realized between inputs and propagated signals when neural networks are initialized at dynamic isometry, a case where information transmits via norm-preserving mappings. These theoretical predictions are validated by experiments on real neural networks, suggesting the robustness of our theory against finite-size effects. Finally, we analyze our findings with information bottleneck theory to confirm the precise relations among dynamic isometry, mutual information maximization, and optimal channel properties in deep learning. Our work may lay a cornerstone for promoting deep learning in terms of network initialization and suggest general statistical physics mechanisms underlying diverse deep learning techniques.
Pairs of r-Primitive and k-Normal Elements in Finite Fields

Let $${\mathbb {F}}_{q^n}$$ be a finite field with $$q^n$$ elements and r be a positive divisor of $$q^n-1$$ . An element $$\alpha \in {\mathbb {F}}_{q^n}^*$$ is called r-primitive if its multiplicative order is $$(q^n-1)/r$$ . Also, $$\alpha \in {\mathbb {F}}_{q^n}$$ is k-normal over $${\mathbb {F}}_q$$ if the greatest common divisor of the polynomials $$g_{\alpha }(x) = \alpha x^{n-1}+ \alpha ^q x^{n-2} + \ldots + \alpha ^{q^{n-2}}x + \alpha ^{q^{n-1}}$$ and $$x^n-1$$ in $${\mathbb {F}}_{q^n}[x]$$ has degree k. These concepts generalize the ideas of primitive and normal elements, respectively. In this paper, we consider non-negative integers $$m_1,m_2,k_1,k_2$$ , positive integers $$r_1,r_2$$ and rational functions $$F(x)=F_1(x)/F_2(x) \in {\mathbb {F}}_{q^n}(x)$$ with $$\deg (F_i) \le m_i$$ for $$i\in \{ 1,2\}$$ satisfying certain conditions and we present sufficient conditions for the existence of $$r_1$$ -primitive $$k_1$$ -normal elements $$\alpha \in {\mathbb {F}}_{q^n}$$ over $${\mathbb {F}}_q$$ , such that $$F(\alpha )$$ is an $$r_2$$ -primitive $$k_2$$ -normal element over $${\mathbb {F}}_q$$ . Finally as an example we study the case where $$r_1=2$$ , $$r_2=3$$ , $$k_1=2$$ , $$k_2=1$$ , $$m_1=2$$ and $$m_2=1$$ , with $$n \ge 7$$ .
Point-supervised temporal action localisation based on multi-branch attention

Temporal action localisation is a key research direction for video understanding in the field of computer vision. Current methods of using an attention mechanism only divides the video frame into an action instance frame and a background frame. As a result, the action context, which should belong to the background is misclassified into an action instance In addition, during the training phase of using point-supervised frame-level labels, action samples and background samples are unbalanced. The lack of background samples leads to the reduction of the activation score of the background so that the imbalance of samples will affect the separation of action examples from the background. All these reduce the accuracy of action classification and temporal localisation. Therefore, this paper proposesa multi-branch attention network and a pseudo-background label generation method. Experimental results show that the proposed method can improve the separation effect of action instances, background, and action context. Moreover, the proposed model achieves excellent performance on the THUMOS-14 dataset.
Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters

This paper presents an autonomous unmanned-aerial-vehicle (UAV) tracking system based on an improved long and short-term memory (LSTM) Kalman filter (KF) model. The system can estimate the three-dimensional (3D) attitude and precisely track the target object without manual intervention. Specifically, the YOLOX algorithm is employed to track and recognize the target object, which is then combined with the improved KF model for precise tracking and recognition. In the LSTM-KF model, three different LSTM networks (f, Q, and R) are adopted to model a nonlinear transfer function to enable the model to learn rich and dynamic Kalman components from the data. The experimental results disclose that the improved LSTM-KF model exhibits higher recognition accuracy than the standard LSTM and the independent KF model. It verifies the robustness, effectiveness, and reliability of the autonomous UAV tracking system based on the improved LSTM-KF model in object recognition and tracking and 3D attitude estimation.
Complement-Class Harmonized Naïve Bayes Classifier

Naïve Bayes (NB) classification performance degrades if the conditional independence assumption is not satisfied or if the conditional probability estimate is not realistic due to the attributes of correlation and scarce data, respectively. Many works address these two problems, but few works tackle them simultaneously. Existing methods heuristically employ information theory or applied gradient optimization to enhance NB classification performance, however, to the best of our knowledge, the enhanced model generalization capability deteriorated especially on scant data. In this work, we propose a fine-grained boosting of the NB classifier to identify hidden and potential discriminative attribute values that lead the NB model to underfit or overfit on the training data and to enhance their predictive power. We employ the complement harmonic average of the conditional probability terms to measure their distribution divergence and impact on the classification performance for each attribute value. The proposed method is subtle yet significant enough in capturing the attribute values’ inter-correlation (between classes) and intra-correlation (within the class) and elegantly and effectively measuring their impact on the model’s performance. We compare our proposed complement-class harmonized Naïve Bayes classifier (CHNB) with the state-of-the-art Naive Bayes and imbalanced ensemble boosting methods on general and imbalanced machine-learning benchmark datasets, respectively. The empirical results demonstrate that CHNB significantly outperforms the compared methods.
MIU-Net: MIX-Attention and Inception U-Net for Histopathology Image Nuclei Segmentation

In the medical field, hematoxylin and eosin (H&amp;E)-stained histopathology images of cell nuclei analysis represent an important measure for cancer diagnosis. The most valuable aspect of the nuclei analysis is the segmentation of the different nuclei morphologies of different organs and subsequent diagnosis of the type and severity of the disease based on pathology. In recent years, deep learning techniques have been widely used in digital histopathology analysis. Automated nuclear segmentation technology enables the rapid and efficient segmentation of tens of thousands of complex and variable nuclei in histopathology images. However, a challenging problem during nuclei segmentation is the blocking of cell nuclei, overlapping, and background complexity of the tissue fraction. To address this challenge, we present MIU-net, an efficient deep learning network structure for the nuclei segmentation of histopathology images. Our proposed structure includes two blocks with modified inception module and attention module. The advantage of the modified inception module is to balance the computation and network performance of the deeper layers of the network, combined with the convolutional layer using different sizes of kernels to learn effective features in a fast and efficient manner to complete kernel segmentation. The attention module allows us to extract small and fine irregular boundary features from the images, which can better segment cancer cells that appear disorganized and fragmented. We test our methodology on public kumar datasets and achieve the highest AUC score of 0.92. The experimental results show that the proposed method achieves better performance than other state-of-the-art methods.
Neural Network-Assisted Interferogram Analysis Using Cylindrical and Flat Reference Beams

In this paper, we present the results of a comparative analysis of the sensitivity of interferograms to wavefront aberrations recorded with flat and cylindrical reference beams. Our results show that compared to classical linear interferograms based on flat wavefronts, cylindrical interferograms have at least 10% higher sensitivity for radially asymmetric types of aberrations and a 30% decrease in average absolute error for aberration recognition using a convolutional neural network. The use of cylindrical reference beams leads to an increase in the sensitivity of interferograms for detecting medium and strong aberrations.
FM-STDNet: High-Speed Detector for Fast-Moving Small Targets Based on Deep First-Order Network Architecture

Identifying objects of interest from digital vision signals is a core task of intelligent systems. However, fast and accurate identification of small moving targets in real-time has become a bottleneck in the field of target detection. In this paper, the problem of real-time detection of the fast-moving printed circuit board (PCB) tiny targets is investigated. This task is very challenging because PCB defects are usually small compared to the whole PCB board, and due to the pursuit of production efficiency, the actual production PCB moving speed is usually very fast, which puts higher requirements on the real-time of intelligent systems. To this end, a new model of FM-STDNet (Fast Moving Small Target Detection Network) is proposed based on the well-known deep learning detector YOLO (You Only Look Once) series model. First, based on the SPPNet (Spatial Pyramid Pooling Networks) network, a new SPPFCSP (Spatial Pyramid Pooling Fast Cross Stage Partial Network) spatial pyramid pooling module is designed to adapt to the extraction of different scale size features of different size input images, which helps retain the high semantic information of smaller features; then, the anchor-free mode is introduced to directly classify the regression prediction information and do the structural reparameterization construction to design a new high-speed prediction head RepHead to further improve the operation speed of the detector. The experimental results show that the proposed detector achieves 99.87% detection accuracy at the fastest speed compared to state-of-the-art depth detectors such as YOLOv3, Faster R-CNN, and TDD-Net in the fast-moving PCB surface defect detection task. The new model of FM-STDNet provides an effective reference for the fast-moving small target detection task.
Anomaly detection for high‐dimensional dynamic data stream using stacked habituation autoencoder and union kernel density estimator

Abstract With the rapid development of communication technology, various complex heterogeneous sensor network applications produce a large number of high‐dimensional dynamic data streams, which results in more difficult to anomaly detection than ever before. So, anomaly detection for high‐dimensional dynamic data streams is of a more and more challenging problem. This paper proposes a novel method for detecting anomalies in high‐dimensional dynamic data streams by utilizing several components. Firstly, it uses a stacked habituation autoencoder with habituation physiological mechanism to detect similarity anomalies more easily and capture feature relationships. Secondly, a union kernel density estimator with micro‐cluster is designed to improve online anomaly detection accuracy by estimating the data density. Lastly, candidate anomaly sets and a delayed processing approach are utilized to cope with conceptual drift and evolution in the data stream, allowing the system to adapt to changes in the data over time. Extensive experiments on four high‐dimensional dynamic data streams of the Internet of Things show that the proposed method is very effective.
Mobile Payment Protocol with Deniably Authenticated Property

Mobile payment services have been widely applied in our daily life, where users can conduct transactions in a convenient way. However, critical privacy concerns have arisen. Specifically, a risk of participating in a transaction is the disclosure of personal privacy. This might occur if, for example, the user pays for some special medicine, such as AIDS medicine or contraceptives. In this paper, we propose a mobile payment protocol that is suitable for mobile devices only with limited computing resources. In particular, the user in a transaction can confirm the identity of others in the same transaction while the user cannot show convincing evidence to prove that others also take part in the same transactions. We implement the proposed protocol and test its computation overhead. The experiment results corroborate that the proposed protocol is suitable for mobile devices with limited computing resources.
Multi-Behavior Enhanced Heterogeneous Graph Convolutional Networks Recommendation Algorithm based on Feature-Interaction

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Reconstruction of three-dimensional porous media using multi-scale generative adversarial networks

Numerical simulation methods have been widely used in the reconstruction of porous media, but their applicability usually suffers from the huge hardware burden and computational time. Recently, with the rapid development of deep learning, its powerful ability in feature extraction has been applied to reconstruct porous media. As an important branch of deep learning, generative adversarial network (GAN) has been considered as an effective method to extract features from training images (TIs). Based on GAN, this paper proposes a method for the reconstruction of porous media using multi-scale GAN (multi-GAN), which learns the features of the TI at different scales separately by using a convolutional pyramid structure. The proposed method uses only a single three-dimensional (3D) TI of porous media for reconstruction and the reconstructed images contain structural information of the TI at different scales. Compared with some typical methods, this method has certain advantages in terms of reconstruction quality and efficiency.
A Few-Shot Malicious Encrypted Traffic Detection Approach Based on Model-Agnostic Meta-Learning

Existing malicious encrypted traffic detection approaches need to be trained with many samples to achieve effective detection of a specified class of encrypted traffic data. With the rapid development of encryption technology, various new types of encrypted traffic are emerging and difficult to label. Therefore, it is an urgent problem to train a deep learning model using only a small number of samples to detect new classes of malicious encrypted traffic. This paper proposes a few-shot malicious encrypted traffic detection (FMETD) approach based on model-agnostic meta-learning (MAML), integrating feature selection and classification into an end-to-end framework. The FMETD approach first converts the raw traffic data into two-dimensional grayscale images. Then, FMETD trains a deep learning model (2D-CNN) using the MAML, which is to learn an optimal set of model initialization parameters for the model from a set of classification tasks consisting of grayscale images. The model with this set of parameters can detect new classes of maliciously encrypted traffic data efficiently with a few samples by a few iterations steps. The experimental results show that the FMETD approach has 99.8% accuracy for two-class classification encrypted traffic and 98.5% average accuracy for multi-classification. When the number of grayscale images of each class in the support set and validation set is reduced to 20, the accuracy of our approach to multi-class classification is 97.9% for new classes of traffic.