Reviewerly/exHarmony huggingface dataset:
Colour image encryption scheme based on the real-valued discrete Gabor transform

A new colour image encryption scheme that combines real-valued discrete Gabor transform (RDGT), 3D chaotic system with bit scrambling is proposed. First, the plaintext image is performed by the real-valued discrete Gabor transform. Subsequently, the transformed image undergoes Arnold transform to disorder bit values, and the coefficients of Arnold transform are selected from the chaotic sequence. Finally, the processed image matrix is XORed and scrambled with the 3D chaotic sequence after cyclic shift operation. The proposed algorithm possesses a large key space, and the keys are related to the plaintext image. Since the correlation of the ciphertext image is significantly smaller than that of the original image, the performance of the proposed algorithm is acceptable. Simulation results and performance analyses illustrate that the proposed colour image encryption scheme is feasible and secure.
A novel membrane-inspired evolutionary framework for multi-objective multi-task optimization problems

In recent years, many different membrane-inspired evolutionary algorithms have been proposed to solve various complex optimization problems. Considering membrane systems’ powerful computing performance and parallel capability, it has outstanding potential in solving multi-task optimization problems. However, there is no research to explore the performance of membrane-inspired evolutionary algorithms in solving multi-task optimization problems. In this paper, a novel membrane-inspired evolutionary framework with a hybrid dynamic membrane structure is proposed to solve the multi-objective multi-task optimization problems. First, a novel membrane-inspired two-stage evolution strategy algorithm is proposed as the algorithm in the membrane to improve the convergence of the algorithm and the diversity of multisets. Second, the information molecule concentration vector is proposed to reduce negative information transfer. The information molecule concentration vector is inspired by the binding process of information molecules and receptors and can control the information transfer probability adaptively. Finally, comprehensive experimental results show that the proposed algorithm performs better than most advanced multi-objective evolutionary multitasking algorithms.
Graph Convolutional Network Combined with Semantic Feature Guidance for Deep Clustering

The performances of semisupervised clustering for unlabeled data are often superior to those of unsupervised learning, which indicates that semantic information attached to clusters can significantly improve feature representation capability.In a graph convolutional network (GCN), each node contains information about itself and its neighbors that is beneficial to common and unique features among samples.Combining these findings, we propose a deep clustering method based on GCN and semantic feature guidance (GFDC) in which a deep convolutional network is used as a feature generator, and a GCN with a softmax layer performs clustering assignment.First, the diversity and amount of input information are enhanced to generate highly useful representations for downstream tasks.Subsequently, the topological graph is constructed to express the spatial relationship of features.For a pair of datasets, feature correspondence constraints are used to regularize clustering loss, and clustering outputs are iteratively optimized.Three external evaluation indicators, i.e., clustering accuracy, normalized mutual information, and the adjusted Rand index, and an internal indicator, i.e., the Davidson-Bouldin index (DBI), are employed to evaluate clustering performances.Experimental results on eight public datasets show that the GFDC algorithm is significantly better than the majority of competitive clustering methods, i.e., its clustering accuracy is 20% higher than the best clustering method on the United States Postal Service dataset.The GFDC algorithm also has the highest accuracy on the smaller Amazon and Caltech datasets.Moreover, DBI indicates the dispersion of cluster distribution and compactness within the cluster.
Considerations for Radio Frequency Fingerprinting across Multiple Frequency Channels

Radio Frequency Fingerprinting (RFF) is often proposed as an authentication mechanism for wireless device security, but application of existing techniques in multi-channel scenarios is limited because prior models were created and evaluated using bursts from a single frequency channel without considering the effects of multi-channel operation. Our research evaluated the multi-channel performance of four single-channel models with increasing complexity, to include a simple discriminant analysis model and three neural networks. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models can lead to a deterioration in performance from MCC > 0.9 (excellent) down to MCC < 0.05 (random guess), indicating that single-channel models may not maintain performance across all channels used by the transmitter in realistic operation. We proposed a training data selection technique to create multi-channel models which outperform single-channel models, improving the cross-channel average MCC from 0.657 to 0.957 and achieving frequency channel-agnostic performance. When evaluated in the presence of noise, multi-channel discriminant analysis models showed reduced performance, but multi-channel neural networks maintained or surpassed single-channel neural network model performance, indicating additional robustness of multi-channel neural networks in the presence of noise.
Confidentiality considerations: multimedia signals transmission over different wireless channels utilized efficient secured model

Abstract The confidentiality of highly-sensitive multimedia signals is considered in this paper, it can be enhanced by efficient secured model utilizing several-layer security algorithms. The hybrid data hiding and cryptographic techniques are merged for constructing secured model. The Least Significant Bit (LSB) data hiding steganography is utilized with a 2-D Logistic-based map (Model-I) and second Model-II involves data hiding merging within chaotic-Baker-based image encryption security techniques. ِPerformance analyzing and comparison have been presented utilizing various images for examining the applicability of the different proposed image security scenarios for securing wireless image transmission over noise-free, and noisy channels. Moreover, the proposed algorithms are applied for transmission over Orthogonal Frequency-Division Multiplexing (OFDM) channels, and their performance is evaluated under different conditions of fading environments with utilizing the powerful error control schemes in the case of SUI-3 model channel and randomizing the packet based on the encryption tools. An equalizer is used to mitigate the impact of composite fading. A multi-layer security model using Discrete Wavelet Transform (DWT) steganography with chaotic Baker encryption is proposed to protect highly-sensitive text based data records. The results reveal that it can be used efficiently for protecting highly-sensitive text message and records (text-based data). The timing analysis and comparative study are considered with respect to the previous related works.
Multi-Recipient encryption with keyword search without pairing for cloud storage

Abstract With the rapid development of cloud computing technology and communication technology, cloud storage has become a tool used by people in daily life. Cloud storage service enables users to outsource data to cloud servers and retrieve desired document efficiently. Individual privacy in outsource data are very sensitive and should be prevented from any leakage. Public-key encryption with keyword search (PEKS) scheme resolves this tension, while public-key authentication encryption with keyword search (PAEKS) scheme improve its keyword guessing attacks problem potentially. Whereas, the loss of keyword privacy, the limitation of single user interaction and low efficiency make PEKS/PAEKS schemes far from enough in practical applications.In this paper, we develop a multi-recipient public key encryption scheme with keyword search without pairing (MREKS) for cloud storage under public key infrastructure. The proposed scheme has the merits of supporting multi-recipient keyword search way as well as requiring no expensively bilinear pairing operations under standard model. We present a concrete and efficient construction of MREKS, and prove its security based on discrete logarithm assumptions. Furthermore, we embed the algorithm of data plaintext encryption and decryption into the scheme, which makes the scheme more practical. We show that our scheme enjoys much more efficiency than previous PEKS/PAEKS scheme in the simulation experiment, especially the keyword encryption is optimized by 79.5%.
COMAL: compositional multi-scale feature enhanced learning for crowd counting

Accurately modeling the crowd’s head scale variations is an effective way to improve the counting accuracy of the crowd counting methods. Most counting networks apply a multi-branch network structure to obtain different scales of head features. Although they have achieved promising results, they do not perform very well on the extreme scale variation scene due to the limited scale representability. Meanwhile, these methods are prone to recognize background objects as foreground crowds in complex scenes due to the limited context and high-level semantic information. We propose a compositional multi-scale feature enhanced learning approach (COMAL) for crowd counting to handle the above limitations. COMAL enhances the multi-scale feature representations from three aspects: (1) The semantic enhanced module (SEM) is developed for embedding the high-level semantic information to the multi-scale features; (2) The diversity enhanced module (DEM) is proposed to enrich the variety of crowd features’ different scales; (3) The context enhanced module (CEM) is designed for strengthening the multi-scale features with more context information. Based on the proposed COMAL, we develop a crowd counting network under the encoder-decoder framework and perform extensive experiments on ShanghaiTech, UCF_CC_50, and UCF-QNRF datasets. Qualitative and quantitive results demonstrate the effectiveness of the proposed COMAL.
The Performance Improvement of VLC-OFDM System Based on Reservoir Computing

Nonlinear effects have been restricting the development of high-speed visible light communication (VLC) systems. Neural network (NN) has become an effective means to alleviate the nonlinearity of a VLC system due to its powerful ability to fit complicated functions. However, the complex training process of traditional NN limits its application in high-speed VLC. Without performance penalty, reservoir computing (RC) simplifies the training process of NN by training only part of the network connection weights, and has become an alternative scheme to NN. For the indoor visible light orthogonal frequency division multiplexing (VLC-OFDM) system, this paper studies the signal recovery effect of the pilot-assisted reservoir computing (PA-RC) frequency domain equalization algorithm. The pilot information is added to the feature engineering of RC to improve the accuracy of channel estimation by traditional least squares (LS) algorithm. The performance of 64 quadrature amplitude modulation (QAM) signal under different transmission rates and peak to peak voltage (Vpp) conditions is demonstrated in the experiments. Compared with the traditional frequency domain equalization algorithms, PA-RC can further expand the Vpp range that meets the 7% hard-decision forward error correction (FEC) limit of 3.8 × 10−3. At the rate of 240 Mbps, the BER of the system is reduced by about 90%, and the utilization rate of the available frequency band of the system reaches 100%. The results show that PA-RC can effectively improve the transmission performance of VLC system well, and has strong generalization ability.
Unsupervised Outlier Detection for Mixed-Valued Dataset Based on the Adaptive k-Nearest Neighbor Global Network

Outlier detection aims to reveal data patterns different from existing data.Benefit from its good robustness and interpretability, the outlier detection method for numerical dataset based on k-Nearest Neighbor (k-NN) network has attracted much attention in recent years.However, the datasets produced in many practical contexts tend to contain both numerical and categorical attributes, that are, the datasets with mixed-valued attributes (DMAs).And, the selection of k is also an issue that is worthy of attention for unlabeled datasets.Therefore, an unsupervised outlier detection method for DMA based on an adaptive k-NN global network is proposed.First, an adaptive search algorithm for the appropriate value of k considering the distribution characteristics of datasets is introduced.Next, the distance between mixed-valued data objects is measured based on the Heterogeneous Euclidean-Overlap Metric, and the k-NN of a data object is obtained.Then, an adaptive k-NN global network is constructed based on the neighborhood relationships between data objects, and a customized random walk process is executed on it to detect outliers by using the transition probability to limit behaviors of the random walker.Finally, the effectiveness, accuracy, and applicability of the proposed method are demonstrated by a detailed experiment.
Residual memory inference network for regression tracking with weighted gradient harmonized loss

Recently, the memory mechanism has been widely implemented in target tracking. However, these trackers hardly balance the stability of long-term memory with the plasticity of short-term memory through an elegant and efficient mechanism. A residual memory inference network (RMIT) is proposed to exploit the history of target states and last visual features. Specifically, RMIT consists of a base layer and a residual memory layer by synergizing short-and long-term memories. The base layer can be regarded as Discriminative Correlation Filter (DCF) reformulation that maintains the short-term memory to accommodate rapid appearance changes. The residual memory layer can extend residual learning from the spatial domain to the Spatio-temporal domain via ConvLSTM to obtain long-term memory of the target appearance. To avoid model degradation due to sample imbalance, we introduce a weighted gradient harmonized loss to improve the discrimination of the tracker. Then, response scores can be served as a basis of the adaptive learning strategy to ensure the reliability of memory updates. The proposed method performs favorably and has been extensively validated on six benchmark datasets, including OTB-50/100, TC-128, UAV-123, and VOT-2016/2018 against several advanced methods.
Multi-level self-attentive TDNN: A general and efficient approach to summarize speech into discriminative utterance-level representations

Time delay neural networks (TDNN) have become ubiquitous for voice biometrics and language recognition tasks relying on utterance-level speaker- or language-dependent representations. In this paper, we discuss directions to improve upon the conventional TDNN architecture to render it more generally applicable. More specifically, we explore the utility of performing pooling operations across different levels of the convolutional stack and further propose an approach to efficiently combine such set of representations. We show that the resulting models are more versatile, in the sense that a fixed architecture can be re-used across different tasks, and learned representations are more discriminative. Evaluations are performed across two settings: (1) end-to-end, where spoofing attack detection and spoken language identification are explored, and (2) embedding encoding, where speaker-dependent embeddings are tested in a speaker verification task. Experimental results show the proposed design yielding improvements over the original TDNN architecture, as well as other state-of-the-art spoofing, language and speaker recognition methods.
NEW DIGITAL COMPETENCE FOR SCIENCE TECHNOLOGY AND ENGINEERING EDUCATION

In the years 2019-2021 a significant step was made in terms of distance learning, which would have otherwise, in normal circumstances, taken several years. While the solutions applied were the best in the given situation, the question arises of what these solutions imply in terms of long-term, systematic changes. The presented research touches upon three key areas that characterize today’s schools: motivation, atmosphere, and competences of teachers and teacher-training students. The starting point of this research is the premise that the pre-crisis digital competences are simply not good enough for today's situation, or for the near future. Research results show that such fast-tracked changes and the transition to distance education have led to a significant decrease in the motivation of both, teachers and students, that the general atmosphere has worsened, and that there is a significant decrease in competences to be perceived, especially with regard to social and communication competences. The research shows also that neither teachers nor students are sufficiently qualified to work in the society of the future, in society 5.0. What is needed are actual, real changes in the field of digital literacy and digital competences, also indicated in this research. Keywords: digital literacy, digital competences 1.0, digital competences 2.0, science/technology/engineering literacy, society 5.0
Structure-aware Visualization Retrieval

With the wide usage of data visualizations, a huge number of Scalable Vector Graphic (SVG)-based visualizations have been created and shared online. Accordingly, there has been an increasing interest in exploring how to retrieve perceptually similar visualizations from a large corpus, since it can benefit various downstream applications such as visualization recommendation. Existing methods mainly focus on the visual appearance of visualizations by regarding them as bitmap images. However, the structural information intrinsically existing in SVG-based visualizations is ignored. Such structural information can delineate the spatial and hierarchical relationship among visual elements, and characterize visualizations thoroughly from a new perspective. This paper presents a structure-aware method to advance the performance of visualization retrieval by collectively considering both the visual and structural information. We extensively evaluated our approach through quantitative comparisons, a user study and case studies. The results demonstrate the effectiveness of our approach and its advantages over existing methods.
Image Matching Algorithm Based on the Pattern Recognition Genetic Algorithm

Image matching is an important topic in image processing. Matching technology plays an important role in and is the basis for image understanding. In order to solve the shortcomings of slow image matching and low matching accuracy, a matching method based on improved genetic algorithm is proposed. The main improvement of the algorithm is the use of self-identifying crossover operators for crossover operations to avoid premature population maturity. According to the characteristics of the image data, new intersection and mutation operators are defined by the new coding method. The sampling method is used to initialize the population method, introduce an evolution strategy, reduce the number of iterations, and effectively reduce the amount of calculation. The experimental results show that the algorithm can guarantee the matching accuracy and that the calculation time is much shorter than that of the original algorithm. In addition, the image matching calculation time per frame of the algorithm is basically unchanged, which is convenient for engineering applications.
A Study on the Factors Affecting Intention of Learning Python Programming: For Non-majors in University

In an industry, society gets more interest in software. In accordance with this trend, in the process of composing the university's curriculum, it is increasingly emphasized that problem-based learning through computational thinking and programming ability based on logical thinking is weighted. This study conducted a study on how to identify students' educational characteristics and study intention. In particular, a methodology to explore the study intention for Python programming from the characteristics of each student was reviewed. For this analysis, the relationship between new technology from their point of view and factors is analyzed, factors are identified for methodologies, and statistical methodologies are used to verify them. The purpose of this study is to find improvements for software education operation and to provide help in educational policy decision-making of university members who conduct computer software education.
An Empirical Study of the Performance of Different Optimizers in the Deep Neural Networks

In recent years, the Stochastic Gradient Descent (SGD) has been commonly used as an optimizer in the Conventional Neural Network (CNN) models. While many researchers have adopted CNN models to classify tasks, to the best of our knowledge, different optimizers developed for CNN have not been thoroughly studied and analyzed in the training CNNs. In this paper, attempts have been made to investigate the effects of the various optimizers on the performance of CNN. Two sets of experiments are conducted. First, for the classification of the records on the CIFAR10, MNIST, and Fashion MNIST datasets, a well-known CNN called VGG11 is trained from scratch by four different kinds of optimizers including SGD, Adam, Adadelta, and AdaGrad. Second, by the same four optimizers, a popular CNN architecture called AlexNet is fine-tuned to classify the Persian handwritten words. In both experiments, the results showed that Adam and AdaGrad have a relatively similar behavior and higher performance in comparison to the other two optimizers in terms of training cost and recognition accuracy. Also, the effect of different values of the initial learning rate on the performance of the Adam optimizer is investigated experimentally. The result revealed that lower values lead to converges more rapidly.
You can try without visiting: a comprehensive survey on virtually try-on outfits

Since the last years and until now, technology has made fast progress for many industries, in particularly, garment industry which aims to follow consumer desires and demands. One of these demands is to fit clothes before purchasing them on-line. Therefore, many research works have been focused on how to develop an intelligent apparel industry to ensure the online shopping experience. Image-based virtual try-on is among the most potential approach of virtual fitting that tries on target clothes into customer's image, therefore, it has received considerable research efforts in the recent years. However, there are several challenges involved in development of virtual try-on that make it difficult to achieve naturally looking virtual outfit such as shape, pose, occlusion, illumination cloth texture, logo and text etc. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of virtual try-on. This review first introduces virtual try-on and its challenges followed by its demand in fashion industry. We summarize state-of-the-art image based virtual try-on for both fashion detection and fashion synthesis as well as their respective advantages, drawbacks, and guidelines for selection of specific try-on model followed by its recent development and successful application. Finally, we conclude the paper with promising directions for future research.
Data Privacy Considerations for Central Bank Digital Currencies in Asia–Pacific Countries

Central bank digital currencies (CBDCs) raise various data privacy concerns. This article analyses regulatory frameworks for data privacy related to CBDCs in the Asia–Pacific region.