Reviewerly/exHarmony huggingface dataset:
TSR-VFD: Generating temporal super-resolution for unsteady vector field data

We present TSR-VFD, a novel deep learning solution that recovers temporal super-resolution (TSR) of three-dimensional vector field data (VFD) for unsteady flow. In scientific visualization, TSR-VFD is the first work that leverages deep neural nets to interpolate intermediate vector fields from temporally sparsely sampled unsteady vector fields. The core of TSR-VFD lies in using two networks: InterpolationNet and MaskNet, that process the vector components of different scales from sampled vector fields as input and jointly output synthesized intermediate vector fields. To demonstrate our approach’s effectiveness, we report qualitative and quantitative results with several data sets and compare TSR-VFD against vector field interpolation using linear interpolation (LERP), generative adversarial network (GAN), and recurrent neural network (RNN). In addition, we compare TSR-VFD with a lossy compression (LC) scheme. Finally, we conduct a comprehensive study to evaluate critical parameter settings and network designs. • A deep learning framework is proposed for unsteady vector field data interpolation. • Quantitative scores are the best among start-of-the-art approaches. • Rendering qualities for both pathlines and streamlines are closest to the ground truth.
Lagrange–Chebyshev Interpolation for image resizing

Image resizing is a basic tool in image processing, and in literature, we have many methods based on different approaches, which are often specialized in only upscaling or downscaling. In this paper, independently of the (reduced or enlarged) size we aim to get, we approach the problem at a continuous scale where the underlying function representing the image is globally approximated by its Lagrange–Chebyshev I kind interpolation polynomial corresponding to suitable (tensor product) grids of first kind Chebyshev zeros. This is a well-known approximation tool widely used in many applicative fields due to the optimal behavior of the related Lebesgue constants. Here we aim to show how Lagrange–Chebyshev interpolation can be fruitfully applied also for resizing any digital image in both downscaling and upscaling at any desired size. The performance of the proposed method has been tested in terms of the standard SSIM (Structured Similarity Index Measurement) and PSNR (Peak Signal to Noise Ratio) metrics. The results indicate that, in upscaling, it is almost comparable with the classical Bicubic resizing method with slightly better metrics, but in downscaling a much higher performance has been observed in comparison with Bicubic and other recent methods too. Moreover, for all downscaling cases with an odd scale factor, we give a theoretical estimate of the MSE (Mean Squared Error) of the output image produced by our method, stating that it is certainly null (hence PSNR equals infinite and SSIM equals one) if the input image’s MSE is null.
Deep feature extraction for document forgery detection with convolutional autoencoders

Document forgery is a significant problem for ages due to paper-based documents' pervasive use. Classical destructive approaches for this problem, such as chromatography and electrophoresis, cannot be implemented as they flaw the document under analysis. Hyperspectral imaging - non-destructive approach that assists in finding the unique features of an image under investigation through their unique spectral signatures. It captures multiple narrow-band images at the electromagnetic spectrum, which is difficult through conventional imaging. Deep learning approaches for hyperspectral images have attained state-of-the-art results for solving many complex and challenging problems. Supervised classification of hyperspectral images is a tedious task since obtaining image labels and labeling the training data is a time-consuming and expensive process. In this paper, an unsupervised approach for classification of hyperspectral document images is proposed. To propose an unsupervised deep learning approach for ink mismatch detection in hyperspectral document images using spectral features. CAE-LR approach is proposed that uses Convolutional Autoencoder (CAE) for feature extraction and utilizing them for ink mismatch detection through Logistic Regression (LR). We evaluated the performance of CAE-LR on UWA writing ink hyperspectral images dataset for blue and black inks. Artificially similar color inks of different types (2∼5) were mixed in varying proportions to detect ink mismatch. Additionally, results are compared with three machine learning algorithms with variants of each, CNN, and five state-of-art methods used by the researchers. Experimental results illustrated that the CAE-LR outperforms all the above – mentioned approaches by achieving the state of art results, which depicts the efficacy of unsupervised deep learning approach for ink mismatch detection in hyperspectral document images.
The AlexNet, LeNet-5 and VGG NET applied to CIFAR-10

The problem this work trying to solve is applying AlexNet, LeNet-5 and VGG Net to the CIFAR-10 database and tell the features and performance of these structures. Multiple strategies of training have been tested, they are respectively AlexNet, LeNet5, VGG Net. These neural network algorithms are famous since they have achieved high accuracy in competitions. Although they are not designed for training CIFAR-10, it is worth to exploit that whether such an excellent model compatible to different input. Therefore, they will modified and tested the performance on CIFAR-10.
Natural Language Processing (NLP): An Introduction

With the increase in internet usage, the amount of available textual data has also continued to increase rapidly. In addition, the development of stronger computers has enabled the processing of data to become much easier. The tourism field has a strong potential to utilize such data available on the internet; yet, on the other hand, a high proportion of available data is unlabelled and unprocessed. In order to use them effectively, new methods and new approaches are needed. In this regard, the area of Natural Language Processing (NLP) helps researchers to utilize textual data and develop an understanding of text analysis. By using machine learning approaches, text mining potential can expand enormously, leading to deeper insights, a better understanding of social phenomena, and, thus, also a better basis for decision-making. As such, this chapter will provide the reader with the basics of NLP as well as present the text pre-processing procedure in detail.
Differential evolution with two-level adaptive mechanism for numerical optimization

This paper presents a novel differential evolution algorithm by designing two prediction-based mutation operators and a two-level adaptive mechanism. To accelerate the search efficiency of algorithm and enhance the suitability of integrating multiple mutation operators with different characteristics, two new mutation operators are first developed to strengthen the local search ability by making full use of the neighborhood information of each individual to predict its promising region and guide the search process. Meanwhile, a two-level adaptive mechanism is designed to meet the search requirements of both individual and population by utilizing the history search information of its neighbors and that of the whole population simultaneously to assign a more suitable mutation operator for it. Moreover, an enhanced restart operation is further adopted to avoid the invalid search during the evolutionary process. Differing from the existing DE variants, the proposed algorithm incorporates the prediction process in mutation to enhance the exploitation, and simultaneously uses the local information of each individual and the global information of population to adaptively adjust the search capability. Then it could effectively improve the search efficiency and balance the exploration and exploitation. Compared with 21 typical algorithms, the numerical results on the benchmark functions from both IEEE CEC2014 and CEC2017 show that the proposed algorithm has better performance.
An improved AIC onset-time picking method based on regression convolutional neural network

Akaike information criterion, known as AIC, has become one of the most used methods for acoustic emission (AE) signals onset-time picking since it was proposed in 1970s. However in practical applications, the automatic onset-times picking are hard to perform precisely due to the interference of the strong background noise and static noise, which affects the accuracy of AIC picking. In this work, an improved AIC onset-time picking method based on regression convolutional neural network (CNN) is proposed. First, several features of AE signals to be trained are selected manually, and arrival times of AE signals are labeled correspondingly. Then datasets with features and labels are put into the regression CNN model for training and enhancing the connection of the signals in the time domain. Finally, AIC algorithm is applied to obtain the onset times of the signals processed by the trained CNN model. Based on the Hsu-Nielsen source AE data, the stability and performance of the proposed method are tested, analyzed and compared with those of other mainstream detection methods: AIC, short/long term average combined with AIC (STA/LTA-AIC), and floating threshold (FT). The results prove that the accuracy of the proposed method significantly exceeds that of other methods. Meanwhile, especially in low signal-to-noise ratios (SNRs) scenario, the accuracy stability of the improved method has excellent accuracy and stability, which proves that the proposed method has promising onset-time picking performance for AE signals, including signals with low SNR characteristics.
Equalization ensemble for large scale highly imbalanced data classification

The class-imbalance problem has been widely distributed in various research fields. The larger the data scale and the higher the data imbalance, the more difficult the proper classification. For large-scale highly imbalanced data sets, the ensemble method based on under-sampling is one of the most competitive techniques among the existing techniques. However, it is susceptible to improperly sampling strategies, easy to lose the useful information of the majority class, and not easy to generalize the learning model. To overcome these limitations, we propose an equalization ensemble method (EASE) with two new schemes. First, we propose an equalization under-sampling scheme to generate a balanced data set for each base classifier, which can reduce the impact of class imbalance on the base classifiers; Second, we design a weighted integration scheme, where the G-mean scores obtained by base classifiers on the original imbalanced data set are used as the weights. These weights can not only make the better-performed base-classifiers dominate the final classification decision, but also adapt to a variety of imbalanced data sets with different scales while avoiding the occurrence of some extremely bad situations. Experimental results on three metrics show that EASE increases the diversity of base classifiers and outperforms twelve state-of-the-art methods on the imbalanced data sets with different scales.
Impacts of Cues on Learning and Attention in Immersive 360-Degree Video: An Eye-Tracking Study

Immersive 360-degree video has become a new learning resource because of its immersive sensory experience. This study examined the effects of textual and visual cues on learning and attention in immersive 360-degree video by using eye-tracking equipment integrated in a virtual reality head-mounted display. Participants (n = 110) were randomly assigned to one of four conditions: (1) no cues, (2) textual cues in the initial field of view (FOV), (3) textual cues outside the initial FOV, and (4) textual cues outside the initial FOV + visual cues. The results showed that the cues (annotations or annotations + arrows) helped learners achieve better learning outcomes and spend more time focusing on the areas with cues. In addition, the study found a serious imbalance in the distribution of learners' attention in each region of the video. The attention directed to textual cues in the initial FOV is much higher than the attention directed to textual cues outside the initial FOV. Adding visual cues can effectively direct attention to textual cues outside the initial FOV and alleviate the imbalance of attention distribution. Consequently, adding cues to immersive 360-degree video can be an appropriate approach to promote learning and guide attention in immersive 360-degree video learning environments. This study provided new insights into the design and development of immersive 360-degree video instructional resources.
Scene recognition using multiple representation network

In recent years, with the rapid development of convolutional neural networks (CNNs), a series of computer vision tasks have been solved. However, scene recognition is still a difficult and challenging problem due to the complexity of scene images. With the emergence of large-scale scene datasets, a single representation generated by a plain CNN is no longer discriminative enough to describe massive scene images. Therefore, in this paper, we propose a comprehensive representation for scene recognition, including enhanced global scene representation, local salient scene representation, and local contextual object representation. We use two pretrained CNNs to extract original feature maps to construct the multiple representations. Specifically, we adopt class activation mapping (CAM) to find salient regions and extract local scene features and employ a bidirectional long short-term module (LSTM) to encode contextual information of objects existing in a scene. In addition, the multiple representations are generated by an end-to-end trainable model, which we call MRNet (multiple representation network). Experimental results on three publicly available scene recognition datasets demonstrate that our proposed model is superior to state-of-the-art models.
A stochastic extended Rippa’s algorithm for LpOCV

In kernel-based approximation, the tuning of the so-called shape parameter is a fundamental step for achieving an accurate reconstruction. Recently, the popular Rippa’s algorithm Rippa (1999) [1] has been extended to a more general cross validation setting. In this work, we propose a modification of such extension with the aim of further reducing the computational costs. The resulting Stochastic Extended Rippa’s Algorithm (SERA) is first detailed and then tested by means of various numerical experiments, which show its efficacy and effectiveness in different approximation settings.
RD-IOD: Two-Level Residual-Distillation-Based Triple-Network for Incremental Object Detection

As a basic component in multimedia applications, object detectors are generally trained on a fixed set of classes that are pre-defined. However, new object classes often emerge after the models are trained in practice. Modern object detectors based on Convolutional Neural Networks (CNN) suffer from catastrophic forgetting when fine-tuning on new classes without the original training data. Therefore, it is critical to improve the incremental learning capability on object detection. In this article, we propose a novel Residual-Distillation-based Incremental learning method on Object Detection (RD-IOD). Our approach rests on the creation of a triple-network based on Faster R-CNN. To enable continuous learning from new classes, we use the original model as well as a residual model to guide the learning of the incremental model on new classes while maintaining the previous learned knowledge. To better maintain the discrimination between the features of old and new classes, the residual model is jointly trained with the incremental model on new classes in the incremental learning procedure. In addition, a two-level distillation scheme is designed to guide the training process, which consists of (1) a general distillation for imitating the original model in feature space along with a residual distillation on the features in both image level and instance level, and (2) a joint classification distillation on the output layers. To well preserve the learned knowledge, we design a 2-threshold training strategy to guide the learning of a Region Proposal Network and a detection head. Extensive experiments conducted on VOC2007 and COCO demonstrate that the proposed method can effectively learn to incrementally detect objects of new classes, and the problem of catastrophic forgetting is mitigated. Our code is available at https://github.com/yangdb/RD-IOD.
Optimal Model for Fewer-Qubit CNOT Gates With Rydberg Atoms

Fewer-qubit quantum logic gate, serving as a basic unit for constructing universal multiqubit gates, has been widely applied in quantum computing and quantum information. However, traditional constructions for fewer-qubit gates often utilize a multipulse protocol, which inevitably suffers from serious intrinsic errors during the gate execution. In this paper, we report an optimal model about universal two- and three-qubit cnot gates mediated by excitation to Rydberg states with easily accessible van der Waals interactions. This gate depends on a global optimization to implement amplitude- and phase-modulated pulses via genetic algorithm, which can facilitate the gate operation with fewer optical pulses. Compared to conventional multipulse piecewise schemes, our gate can be realized by simultaneous excitation of atoms to the Rydberg states, saving the time for multipulse switching at different spatial locations. Our numerical simulations show that a single-pulse two- (three-) qubit cnot gate is possibly achieved with a fidelity of 99.23% (90.39%) for two qubits separated by $7.10\phantom{\rule{0.2em}{0ex}}\ensuremath{\mu}\mathrm{m}$ when the fluctuation of Rydberg interactions is excluded. Our work is promising for achieving fast and convenient multiqubit quantum computing in the study of neutral-atom quantum technology.
Efficient Out-of-Distribution Detection Using Latent Space of <i>β</i> -VAE for Cyber-Physical Systems

Deep Neural Networks are actively being used in the design of autonomous Cyber-Physical Systems (CPSs). The advantage of these models is their ability to handle high-dimensional state-space and learn compact surrogate representations of the operational state spaces. However, the problem is that the sampled observations used for training the model may never cover the entire state space of the physical environment, and as a result, the system will likely operate in conditions that do not belong to the training distribution. These conditions that do not belong to training distribution are referred to as Out-of-Distribution (OOD). Detecting OOD conditions at runtime is critical for the safety of CPS. In addition, it is also desirable to identify the context or the feature(s) that are the source of OOD to select an appropriate control action to mitigate the consequences that may arise because of the OOD condition. In this article, we study this problem as a multi-labeled time series OOD detection problem over images, where the OOD is defined both sequentially across short time windows (change points) as well as across the training data distribution. A common approach to solving this problem is the use of multi-chained one-class classifiers. However, this approach is expensive for CPSs that have limited computational resources and require short inference times. Our contribution is an approach to design and train a single β -Variational Autoencoder detector with a partially disentangled latent space sensitive to variations in image features. We use the feature sensitive latent variables in the latent space to detect OOD images and identify the most likely feature(s) responsible for the OOD. We demonstrate our approach using an Autonomous Vehicle in the CARLA simulator and a real-world automotive dataset called nuImages.
The Effects of MALL on L2 Pronunciation Learning: A Meta-Analysis

Mobile-assisted language learning has emerged as a valuable tool for L2 pronunciation learning as it offers ample opportunities for learners to perform authentic learning activities anytime and anywhere. However, the extent to which mobile devices may facilitate or enhance L2 pronunciation has yet to be systematically investigated. To address the research gap, the study drew on a meta-analytic framework to examine the overall average effect of mobile devices on L2 pronunciation learning. The meta-analysis consisted of 13 primary studies featured in an experimental vs. control group design published between 2009 and 2020. These studies involved 655 participants and reported 19 effect sizes in the results. The analysis revealed a robust effect size ( d = .66) as captured by a random-effects model, indicating a significant effect concerning the use of mobile devices on L2 pronunciation learning. In the meta-analysis, significant moderators were also identified, and their moderating effects were critically examined. In light of the research findings, it is argued that innovative activities and tasks using mobile devices can be adopted for both language instructors and learners due to their portability, connectivity, and individuality.
Symbolic Abstractions From Data: A PAC Learning Approach

Symbolic control techniques aim to satisfy complex logic specifications. A critical step in these techniques is the construction of a symbolic (discrete) abstraction, a finite-state system whose behaviour mimics that of a given continuous-state system. The methods used to compute symbolic abstractions, however, require knowledge of an accurate closed-form model. To generalize them to systems with unknown dynamics, we present a new data-driven approach that does not require closed-form dynamics, instead relying only the ability to evaluate successors of each state under given inputs. To provide guarantees for the learned abstraction, we use the Probably Approximately Correct (PAC) statistical framework. We first introduce a PAC-style behavioural relationship and an appropriate refinement procedure. We then show how the symbolic abstraction can be constructed to satisfy this new behavioural relationship. Moreover, we provide PAC bounds that dictate the number of data required to guarantee a prescribed level of accuracy and confidence. Finally, we present an illustrative example.
HFL-DP: Hierarchical Federated Learning with Differential Privacy

Federated learning (FL) is a framework of distributed machine learning, which aims to protect data privacy by transferring parameters instead of private data from local clients. Compared with the typical cloud-client architecture, applying FL on a cloud-edge-client hierarchical architecture could train the model faster and achieve better communication-computation trade-offs. However, hierarchical federated learning (HFL) still suffers from privacy leakage by analyzing uploaded parameters from clients or edge servers. To address this problem, we propose a privacy-preserving scheme based on the theory of local differential privacy (LDP), where adding the noise to the shared model parameters before uploading them to edge and cloud servers. According to our analysis by the moment accounting, the proposed algorithm can realize the strict differential privacy guarantee for the layers of clients and edge servers with adjustable privacy protection levels. We evaluate its performance based on the image classification tasks, and the result demonstrates that our theoretical analyses are consistent with simulations.