Reviewerly/exHarmony huggingface dataset:
Self-Adaptive Logit Balancing for Deep Learning Robustness in Computer Vision

With wide applications of machine learning algorithms, machine learning security has become a significant issue. The vulnerability to adversarial perturbations exists in most machine learning algorithms, including cutting-edge deep neural networks. The standard adversarial perturbation defence techniques with adversarial training need to generate adversarial examples during the training process, which require high computational costs. This paper proposed a novel defence method using self-adaptive logit balancing and Gaussian noise boost training. This method can improve the robustness of deep neural networks without high computational cost and achieve competitive results compared with the adversarial training methods. Meanwhile, this defence method enables deep learning systems to have proactive and reactive defence during the operation. A sub-classifier is trained to determine whether the system is under attack and detect attack algorithms via the patterns of the Log-Softmax values. It can achieve high accuracy for detecting clean inputs and adversarial examples created by seven attack methods.
Convolutional Neural Networks Analysis Using Concentric-Rings Interactive Visualization

The goal of this paper is to present the interactive web visualization technique DeepRings. The technique has a radial design, using concentric rings to represent the layers of a deep learning model, where each circular ring encodes the feature maps of that layer. The proposed technique allows to perform analysis of tasks over time regarding a single model or a comparison between two distinct models, thus contributing to a better understanding of the behavior of such models. The design supports several training methods designed to solve Computer Vision tasks, like supervised learning and self-supervised learning, as well as reinforcement learning. Additional charts highlight similarity metrics, and interaction techniques such as filtering help reduce the analysis data. Finally, preliminary evaluations were conducted with domain experts highlighting positive points and aspects that can be improved, suggesting avenues for future work.
Recognition of Dynamically Varying PRI Modulation via Deep Learning and Recurrence Plot

Recognition of pulse repetition interval (PRI) modulation is a fundamental task in the interpretation of radar's intents. However, the existing PRI modulation recognition methods mainly focus on single-label classification of PRI sequences. The prerequisite for the effectiveness of these methods is that the PRI sequences have been perfectly divided according to different modulation types before identification, while the actual situation is that radar pulses reach the receiver continuously, and there is no completely reliable method to achieve this division in the case of non-cooperative reception. Based on the above actual needs, this paper implements an algorithm based on the recurrence plot technique and the multi-target detection model, which does not need to divide the PRI sequence in advance, and compared with the sliding window method, it can more effectively realize the recognition of the dynamically varying PRI modulation.
Double-Image Encryption Through Compressive Sensing and Discrete Cosine Stockwell Transform

This paper introduces a double-image encryption algorithm using compressive sensing and discrete cosine Stockwell transform (DCST). The source images are transformed using a two-level discrete wavelet transform to sparsify the signals, and two different measurement matrices (MM) are employed to measure the sparse coefficient of the signal. MM are generated through logistic map which provides better reconstruction quality and security. Thereafter, both sampled images are combined to enlarge a single image, and then zig-zag confusion is applied to permute the information. To increase the security level, DCST is also applied. Experimental analysis has been done to prove the effectiveness of the proposed algorithm.
Deep learning and visual perception

In the field of autonomous driving, substantial achievements have been made in pattern recognition relying on machine learning and deep learning. In recent years, considerable progress has been made in the autonomous driving perception method based on deep learning. This chapter will introduce the application of deep learning in unmanned driving from the perspective of deep feedforward neural networks in as straightforward a manner as possible.
Teaching Adults: Learning Languages in the Pandemic Span

All over the world the COVID-19 pandemic has led to the urgent transfer to distance education or blended learning in various educational establishments. As a result of such consequences of the governments’ requirements to prevent the virus spreading those of isolation, social distancing and quarantine, both students and academicians unexpectedly and instantly were forced to switch to distance education within days. Based on the changes, the present study is to analyze the challenges adult learners face and to offer ways to meet them, as well as to devise and develop measures providing language promotion sustainability in the pandemic. These measures are referred to in the present study as a cushion grid could serve as an imperative of the pandemic span style (a teaching style which is seen by the authors as an umbrella term for methods and approaches used for distant teaching during the COVID-19 pandemic) in teaching adult learners. The study is based on analysing the variables producing e-learning satisfaction among the adult learners of the UNICO Language Centre of Siberian Federal University. The Centre was established on the TEMPUS (Trans-European Mobility Programme for University Studies) programme which encourages higher education institutions in the EU Member States and partner countries to engage in structured cooperation to implement Joint European Projects (JEPs) with a clear set of objectives. On completion of the project activities the UNICO (University-Career-Opportunities) Centre was transformed into a sustainable educational establishment where the adult learners could take a non-formal course of studies. The study group included 92 learners of the UNICO Language Centre of Siberian Federal University. The results demonstrated two learning imperatives: those of the social and the linguistic one.
Opt2ada: An Universal Method for Single-Image Low-Light Enhancement

In this paper, it is proposed that the task of single-image low-light enhancement can be accomplished by a straightforward method named Opt2Ada in which a series of operations are integrated including an \textbf{opt}imized illuminance map generation, an \textbf{ada}ptive illuminance enhancement, and an \textbf{ada}ptive global scaling. A rich variety of criteria is used for the evaluation, including full-reference, no-reference, and semantic metrics. Extensive experiments on real-world low-light images clearly verify the superiority of Opt2Ada against recently proposed state-of-the-art enhancing algorithms in RGB color space including both traditional and deep learning methods. Opt2Ada neither relies on specific architecture engineering nor dataset collection and preparation. It is not data-driven and its parameters are universal resulting to a generalization capability better than that of the data-driven methods. Moreover, we deploy Opt2Ada on various mobile phones, and find that a native CPU implementation can reach 3-9 frames per second. Since our method is simple and effective, this makes it convenient to be developed by any programming language and imbedded into the pre-processing subroutines for high-level machine vision tasks.
Exploring the Third Wave of Feminism Through Hierarchical Clustering and Sentiment Analysis

Although much had changed throughout the feminist movement during the first wave (the 1800s–1960s) and second wave (1960s–1980s), with the inclusion of distinct minority groups into the idea, most of the contemporary approaches had been developed within the third wave. The purpose of this study is twofold: First, to investigate the works published during the third wave of feminism through unsupervised clustering methods. Second, to determine the driving emotional structures for each of these consequent clusters using sentiment analysis. To conduct the analyses, sizeable data is gathered using the literature published after the mid-1990s. The data is then cleaned and prepared according to the bag of words methodology before the usage of the hierarchical clustering technique. As a final step, sentiment analysis based on Plutchik’s Wheel of Emotion has been used to illustrate the magnitudes of eight distinct emotions. Results demonstrate that there are clear points of distinction between the 3 waves’ sentiment analysis and main ideas. One of the significant findings was the 3rd wave’s feminism understanding being not only about women but all types of disadvantaged minority groups. Another important finding is that the aggression level of the ideas that do not change during the 3 waves increases significantly over time. This research contributes to the literature by providing an objective framework to analyze how the feminism ideology is evolved.
Active Anomaly Detection Based on Deep One-Class Classification

Active learning has been utilized as an efficient tool for anomaly detection to unburden in obtaining annotated datasets and to improve performance by leveraging feedback from experts. However, most of the existing studies are focused on helping experts identify as many abnormal data samples as possible, which is a sub-optimal approach for one-class classification-based deep anomaly detection.In this paper, we tackle two essential problems of active learning for Deep SVDD: query strategy and semi-supervised learning method. First, rather than solely identifying anomalies, our query strategy selects uncertain samples according to an adaptive boundary in a feature space without data-dependent hyper-parameters. Second, we combine the base model, Deep SVDD, with Noise contrastive estimation to effectively incorporate both labeled normal and abnormal data. The proposed method enables robust anomaly detection by constructing a discriminative representation. We demonstrate the effectiveness of our methods on six anomaly detection benchmark datasets.
Fonts That Fit the Music: A Multimodal Design Trend Analysis of Lyric Videos

Lyric videos, or kinetic typography videos, are music videos showing lyric text in synchronization with the music. The purpose of this paper is to quantitatively and qualitatively analyze lyric videos to understand their design trends via three modalities: word motion, font style, and music style. These trends will not only be helpful as hints for designing new lyric videos but also be meaningful to quantitatively reveal the thought processes of the video design professionals. To achieve this, we needed to develop or utilize several technologies. First, we developed a lyric word tracking method to capture the motion of individual lyric words. The proposed method uses the lyric text as the guiding information for word tracking to overcome the difficulties arising from the various word appearances and motions. Second, we developed a font style estimator to quantify the appearance of each word as a feature vector. Finally, we employed a music style estimator to quantify the mood of the music, e.g., &#x201C;techno&#x201D; and &#x201C;fast.&#x201D; We then analyzed feature vectors of these three style modalities collected at 3,494 time points in 100 lyric videos. After revealing the trend of each modality via k-means, we conducted a co-occurrence analysis to understand the correlation between each modality pair. Our experimental results indicate that such a cluster-wise co-occurrence analysis can capture interesting trends hidden in lyric video designs.
Enhancing Emotional Experience by Building Emotional Virtual Characters in VR Exhibitions

Virtual digital exhibitions attract public attention as they can provide an immersive aesthetic experience augmented by virtual reality (VR) technologies. However, since each visitor is placed in an isolated virtual environment while using VR devices, the exhibitions lose the immediate emotional perception between visitors. In order to enable visitors to perceive the communicative value of the digital exhibitions, increasing the user’s feeling of immersion and engagement in the virtual environment is crucial. Existing experience designs of VR exhibitions put more effort into developing the imitation of physical exhibited space, rather than display instant emotional facial expressions of visitors. Thus, a supporting system design on how to enhance the visitor’s real-time emotional communication in the VR exhibitions experience is needed at present. An emotional color label of visitors in exhibitions and an emotaion recognition and display model in the virtual environment are proposed to alleviate this issue. Our research has the potential to enhance the user’s emotional experience and engagement in VR exhibitions and other forms of virtual digital exhibition.
Improved Cerebral Images Semantic Segmentation Using Advanced Approaches of Deep Learning

Reliable detection of brain tumors is a challenging task, even with the proper acquisition of Magnetic resonance imaging (MRI) images. Computer-aided detection (CADe) systems can reduce the workload of physicians and minimize the time required for accurate segmentation of illnesses. CADe systems for brain tumors comprises two principles stages: pre-processing of MRI images, and segmentation to define the region of interest (ROI). This paper describes the application of deep learning to detect tumors on brain MRI images where the number of defective samples available is small, which is fairly common in many real practical applications and can negatively affect the model performance. In the presented study, the Generative Adversarial Networks (GANs) learned how to synthesize realistic images to improve the training of DL models. We also explored CyclaGAN architecture via hyperparameter tuning and performed image segmentation for brain tumor. The proposed CADe system was validated on Figshare dataset of brain MRI images. Experimental results show that, the data augmentation method can increase the segmentation performance efficiently and effectively.
Grammar Evolution with Machine Learning Guided Transfer for Capacitated Arc Routing Problem

The capacitated arc routing problem (CARP) is a well-known combinatorial optimization problem and the hyper-heuristic method is a cutting-edge technique to solve it. However, most of the existing heuristic-based methods do not take advantage of the correlation between problems to improve efficiency. In this paper, we propose an efficient grammar evolution (GE) based hyper-heuristic method with Machine Learning Guided (MLG) transfer for CARP. In the proposed MLG, machine learning model is built with the individuals from multiple source problems to predict the quality of new individual. A replacement mechanism based on this model are developed to facilitate the GE evolution and improve the efficiency. Experiments with two CARP benchmark data sets are conducted to examine proposed method and the results indicate that MLG outperforms the standard GE and some state-of-the-art individual transfer methods in multi-source scenarios.
Production Prediction Modeling of Industrial Processes for Energy Saving: A Novel Cnn-Based Transformer Integrating Boruta Algorithm

Industrial process data collected by sensors have characteristics of high dimensionality, non-linearity and dynamics. Consequently, the selection extraction can reduce the dimension of the data and remove irrelevant variables of constructing the production prediction model of industrial processes. Therefore, a novel production prediction model using the Boruta algorithm integrating the convolutional neural network-based Transformer (BCT) is proposed. The Boruta algorithm maps nonlinear high-dimensional data to a low-dimensional space to select features that are meaningful to the yield of the industrial process. Then, the features are extracted adaptively using a convolutional neural network (CNN), which are encoded based on the transformer layer to learn relevant information about the different representation spaces. Furthermore, a linear layer with highway connections is employed to obtain prediction results. Finally, the BCT method is applied to establish a realistic production prediction model of actual liquefied petroleum gas plants. Compared with back propagation neural networks, the radial basis function, the extreme learning machine and the transformer based on the CNN, the BCT method achieves a state-of-the-art level. Furthermore, the BCT method provides the operation guidance on the actual LPG production process with increasing the LPG yield by 17.21%, which can improve production efficiency and reducing energy consumption.
A Hybrid Model of Cellular Automata, DNA Sequences, and Hyper-Chaotic System for Color Image Encryption

In this manuscript, the Chen hyper-chaos system is used to generate random sequences, and using them some arrays for image permutation are created. Also, these sequences are used in a random selection of cellular automata ‎rules,‎ ‎a random selection of‎ ‎DNA coding rules‎, ‎key stream generation‎, ‎and random removal from ‎among‎ created arrays‎. ‎Because these random sequences have several different uses‎, ‎separate computation time is‎ ‎not required‎. ‎Therefore‎, ‎temporal convolution is reduced‎. ‎Each of the color layers of the‎ ‎original image for image permutation becomes a one-dimensional vector ‎and ‎the ‎permutation ‎is ‎done ‎based ‎on ‎random ‎removal ‎from ‎among ‎created ‎arrays‎‎. ‎Then‎, ‎DNA‎, ‎cellular automata‎, ‎and key‎ ‎stream created are used in the diffusion phase to change the brightness level of the pixels to‎ ‎new values‎. ‎‎‎ ‎The optimal entropy value obtained‎ ‎in this method is $7.9991$‎.
Quaternion Fractional Order Weighted Generalized Laguerre–Fourier Moments and Moment Invariants for Color Image Analysis

Orthogonal moments based on Laguerre polynomials, especially the quaternion representation of fractional-order orthogonal moments have attracted interest in the imaging field. However, the existing quaternion fractional-order color orthogonal moments (QFr-COMs) based on Laguerre polynomials confront two challenges: Most of the existing algorithms are based on Cartesian coordinate space, hindering the direct realization of the geometric invariance of rotation and the scaling of color image moments, and in the few algorithms that directly apply the polynomial in the Cartesian coordinate system to polar coordinate space, the polynomial does not satisfy orthogonality in polar coordinate space; thus, the constructed image moment strictly belongs to nonorthogonal moments. In this work, we introduce a fast and reliable computing method based on quaternion fractional order weighted generalized Laguerre–Fourier moments (QFr-WGLFMs) and the geometric invariance of the moments. Theoretical analyses and experimental results showed that the proposed QFr-WGLFMs offer enhanced image reconstruction and pattern recognition compared with the existing quaternion fractional-order-based orthogonal moments.
Local Stochastic Factored Gradient Descent for Distributed Quantum State Tomography

We propose a distributed Quantum State Tomography (QST) protocol, named Local Stochastic Factored Gradient Descent (Local SFGD), to learn the low-rank factor of a density matrix over a set of local machines. QST is the canonical procedure to characterize the state of a quantum system, which we formulate as a stochastic non-convex smooth optimization problem. Physically, the estimation of a low-rank density matrix helps characterizing the amount of noise introduced by quantum computation. Theoretically, we prove the local convergence of Local SFGD for a general class of restricted strongly convex/smooth loss functions. Local SFGD converges locally to a small neighborhood of the global optimum at a linear rate with a constant step size, while it locally converges exactly at a sub-linear rate with diminishing step sizes. With a proper initialization, local convergence results imply global convergence. We validate our theoretical findings with numerical simulations of QST on the Greenberger-Horne-Zeilinger (GHZ) state.
Ml-Prdf：A Syndrome Differentiation Model of Traditional Chinese Medicine Based on Pcc-Mlrf and Multi-Label Deep Forest

Syndrome differentiation and treatment is the basic principle of traditional Chinese medicine (TCM) to recognize and treat diseases. Accurate syndrome differentiation could provide a reliable basis for treatment, therefore, establishing a scientific intelligent syndrome differentiation method is of great significance to the modernization of TCM. With the development of artificial intelligence technology, this study proposes a method based on improved multi-label deep forest to achieve effective syndrome differentiation analysis of TCM under small-scale samples, named ML-PRDF. A multi-label deep forest model based on PCC-MLRF algorithm is designed to enhance the representativeness of features in the model, express original information with fewer features, and achieve optimal classification accuracy. The computation results obtained by our proposed model on the TCM dataset are 0.5168±0.0073 (Average precision), 0.0102±0.0004 (Hamming loss), 0.6367±0.0125 (One error), 0.1540±0.0034 (Coverage), 0.1506±0.0073 (Ranking loss). Compared with other algorithms, it has higher accuracy in syndrome differentiation of TCM, and through comparative studies, it can be seen that PCC-MLRF algorithm can better select representative features. ML-PRDF algorithm can effectively improve the performance of TCM syndrome differentiation and provide useful reference for the development of TCM syndrome differentiation task.
A Discussion About Independence and Correlation in the Framework of Coherent Lower Conditional Probability

Aim of the paper is to put under the right perspective the concepts of independence and correlation in the framework of coherent lower conditional probability, taking suitably into account also events whose lower probability is zero or one.