Reviewerly/exHarmony huggingface dataset:
A novel image encryption/decryption scheme based on integrating multiple chaotic maps

In this paper, a novel framework is presented for chaotic image encryption. The proposed method is based on integrating multiple chaotic maps (e.g., logistic, tent, quadratic, cubic, and Bernoulli) to generate more robust chaotic maps in order to increase the security and privacy needed by applying variable keys. The latter are generated by computing the sine square logistic map and are then applied to generate the chaotic maps employed in our framework. For this, we have performed many experiments to achieve the best period for each chaotic map in which it performed the best encryption. Here, we combine multiple chaotic maps to get a new map that works well when X ∈ [0, 1]. For using a chaotic map in the encryption process, it was necessary to find a way to choose the best of those chaotic maps for encryption. This selection was done with the lowest value for the correlation factor because the smaller value of correlation has an impression of good encryption. We have also noted a clear difference in the influence of one of these maps on some pictures from the others. We chose one of those maps according to the correlation value for each encoding process and compared them. Then, we used a chaotic map of the best of these values for encryption and decryption. Numerical results on various gray images showed the robustness of the proposed method to encrypt and decrypt the images based on the evaluation using different performance analyses. We compared our methods against other well-known approaches, e.g., circular mapping, S-boxes, and S-box with Arnold transform. Our pipeline outperforms those methods. Moreover, our results documented that the proposed scheme has an excellent security level with very low correlation coefficients and good information entropy.
Using Data Sonification to Overcome Science Literacy, Numeracy, and Visualization Barriers in Science Communication

Sharing the complex narratives within scientific data in an intuitive fashion has proven difficult, especially for communicators endeavoring to reach a wide audience comprised of individuals with differing levels of scientific knowledge and mathematical ability. We discuss the application of data sonification – the process of translating data into sound, sometimes in a musical context– as a method of overcoming barriers to science communication. Data sonification can convey large datasets with many dimensions in an efficient and engaging way that reduces scientific literacy and numeracy barriers to understanding the underlying scientific data. This method is particularly beneficial for its ability to portray scientific data to those with visual impairments, who are often unable to engage with traditional data visualizations. We explore the applications of data sonification for science communicators and researchers alike, as well as considerations for making sonified data accessible and engaging to broad audiences with diverse levels of expertise.
A Privacy-Preserving and Verifiable Federated Learning Scheme

Due to the complexity of the data environment, many organizations prefer to train deep learning models together by sharing training sets. However, this process is always accompanied by the restriction of distributed storage and privacy. Federated learning addresses this challenge by only sharing gradients with the server without revealing training sets. Unfortunately, existing research has shown that the server could extract information of the training sets from shared gradients. Besides, the server may falsify the calculated result to affect the accuracy of the trained model. To solve the above problems, we propose a privacy-preserving and verifiable federated learning scheme. Our scheme focuses on processing shared gradients by combining the Chinese Remainder Theorem and the Paillier homomorphic encryption, which can realize privacy-preserving federated learning with low computation and communication costs. In addition, we introduce the bilinear aggregate signature technology into federated learning, which effectively verifies the correctness of aggregated gradient. Moreover, the experiment shows that even with the added verification function, our scheme still has high accuracy and efficiency.
Results and Guidelines From a Repeated-Measures Design Experiment Comparing Standing and Seated Full-Body Gesture-Based Immersive Virtual Reality Exergames: Within-Subjects Evaluation

Although full-body seated exercises have been studied in a wide range of settings (ie, homes, hospitals, and daycare centers), they have rarely been converted to seated exergames. In addition, there is an increasing number of studies on immersive virtual reality (iVR) full-body gesture-based standing exergames, but the suitability and usefulness of seated exergames remain largely unexplored.This study aimed to evaluate the difference between playing a full-body gesture-based iVR standing exergame and seated exergame in terms of gameplay performance, intrinsic motivation, and motion sickness.A total of 52 participants completed the experiment. The order of the game mode (standing and sitting) was counterbalanced. Gameplay performance was evaluated by action or gesture completion time and the number of missed gestures. Exertion was measured by the average heart rate (HR) percentage (AvgHR%), increased HR%, calories burned, and the Borg 6-20 questionnaire. Intrinsic motivation was assessed with the Intrinsic Motivation Inventory (IMI), whereas motion sickness was assessed via the Motion Sickness Assessment Questionnaire (MSAQ). In addition, we measured the fear of falling using a 10-point Likert scale questionnaire.Players missed more gestures in the seated exergame than in the standing exergame, but the overall miss rate was low (2.3/120, 1.9%). The analysis yielded significantly higher AvgHR%, increased HR%, calories burned, and Borg 6-20 rating of perceived exertion values for the seated exergame (all P<.001). The seated exergame was rated significantly higher on peripheral sickness (P=.02) and sopite-related sickness (MSAQ) (P=.004) than the standing exergame. The score of the subscale "value/usefulness" from IMI was reported to be higher for the seated exergame than the standing exergame. There was no significant difference between the seated exergame and standing exergame in terms of intrinsic motivation (interest/enjoyment, P=.96; perceived competence, P=.26; pressure/tension, P=.42) and the fear of falling (P=.25).Seated iVR full-body gesture-based exergames can be valuable complements to standing exergames. Seated exergames have the potential to lead to higher exertion, provide higher value to players, and be more applicable in small spaces compared with standing exergames. However, gestures for seated exergames need to be designed carefully to minimize motion sickness, and more time should be given to users to perform gestures in seated exergames compared with standing exergames.
FROST: Flexible Round-Optimized Schnorr Threshold Signatures

Unlike signatures in a single-party setting, threshold signatures require cooperation among a threshold number of signers each holding a share of a common private key. Consequently, generating signatures in a threshold setting imposes overhead due to network rounds among signers, proving costly when secret shares are stored on network-limited devices or when coordination occurs over unreliable networks. In this work, we present FROST, a Flexible Round-Optimized Schnorr Threshold signature scheme that reduces network overhead during signing operations while employing a novel technique to protect against forgery attacks applicable to similar schemes in the literature. FROST improves upon the state of the art in Schnorr threshold signature protocols, as it can safely perform signing operations in a single round without limiting concurrency of signing operations, yet allows for true threshold signing, as only a threshold t out of n possible participants are required for signing operations, such that $$t\le n$$ . FROST can be used as either a two-round protocol, or optimized to a single-round signing protocol with a pre-processing stage. FROST achieves its efficiency improvements in part by allowing the protocol to abort in the presence of a misbehaving participant (who is then identified and excluded from future operations)—a reasonable model for practical deployment scenarios. We present proofs of security demonstrating that FROST is secure against chosen-message attacks assuming the discrete logarithm problem is hard and the adversary controls fewer participants than the threshold.
Research on Sentiment Classification of Online Travel Review Text

In recent years, the number of review texts on online travel review sites has increased dramatically, which has provided a novel source of data for travel research. Sentiment analysis is a process that can extract tourists’ sentiments regarding travel destinations from online travel review texts. The results of sentiment analysis form an important basis for tourism decision making. Thus far, there has been minimal concern as to how sentiment analysis methods can be effectively applied to improve the effect of sentiment analysis. However, online travel review texts are largely short texts characterized by uneven sentiment distribution, which makes it difficult to obtain accurate sentiment analysis results. Accordingly, in order to improve the sentiment classification accuracy of online travel review texts, this study transformed sentiment analysis into a multi-classification problem based on machine learning methods, and further designed a keyword semantic expansion method based on a knowledge graph. Our proposed method extracts keywords from online travel review texts and obtains the concept list of keywords through Microsoft Knowledge Graph. This list is then added to the review text to facilitate the construction of semantically expanded classification data. Our proposed method increases the number of classification features used for short text by employing the huge corpus of information associated with the knowledge graph. In addition, this article introduces online travel review text preprocessing, keyword extraction, text representation, sampling, establishment classification labeling, and the selection and application of machine learning-based sentiment classification methods in order to build an effective sentiment classification model for online travel review text. Experiments were implemented and evaluated based on the English review texts of four famous attractions in four countries on the TripAdvisor website. Our experimental results demonstrate that the method proposed in this paper can be used to effectively improve the accuracy of the sentiment classification of online travel review texts. Our research attempts to emphasize and improve the methodological relevance and applicability of sentiment analysis for future travel research.
A benchmark of recent population-based metaheuristic algorithms for multi-layer neural network training

Multi-layer neural networks (MLNNs) are extensively used in many industrial applications. Training is the crucial task for MLNNs. While gradient descent-based approaches are most commonly employed here, they suffer from drawbacks such as getting stuck in local optima. One approach to address this is to employ population-based metaheuristic algorithms. Since a variety of such algorithms have been proposed in the literature, in this paper we benchmark the performance of 15 metaheuristic algorithms, including state-of-the-art as well as some of the most recent algorithms, for neural network training. In particular, we evaluate particle swarm optimisation (PSO), differential evolution (DE), artificial bee colony (ABC), imperialist competitive algorithm (ICA), cuckoo search (CS), gravitational search algorithm (GSA), bat algorithm (BA), firefly algorithm (FA), grey wolf optimiser (GWO), ant lion optimiser (ALO), dragonfly algorithm (DA), sine cosine algorithm (SCA), whale optimisation algorithm (WOA), grasshopper optimisation algorithm (GOA), and salp swarm algorithm (SSA), and assess their performance on different classification algorithms.
View synthesis-based light field image compression using a generative adversarial network

Light field (LF) imaging has generated considerable interest owing to its ability to capture both spatial and angular information of light rays simultaneously. However, the extremely large volume of data associated with LF imaging poses challenges to both data storage and transmission. This study addresses this issue by proposing a view synthesis-based LF image compression method using a generative adversarial network (GAN). The primary basis of compression relies on the fact that adjacent sub-aperture images (SAIs) are highly correlated. Accordingly, only sparsely sampled SAIs are transmitted and the others are reconstructed at the decoder side. The proposed sparse SAI sampling method enhances the quality of reconstructed SAIs by considering a fair trade-off between the number of SAIs available for use as priors in the synthesis process and SAI redundancy. The quality of reconstructed SAIs is further enhanced by a GAN-based SAI synthesis method, where the synthesis procedure is broken into disparity estimation and un-sampled SAI estimation components, and the adversarial nature of the jointly trained generative and discriminative networks results in a more accurate generative model. Furthermore, more texture details can be preserved in the synthesized SAIs by adopting a loss function in the GAN model based on perceptual quality. Extensive experimental results demonstrate the superiority of the proposed method relative to several other state-of-the-art compression methods in terms of standard quality metrics and the perceptual quality of the synthetic SAIs at the decoder side.
Epidemiology of measles during the COVID-19 pandemic, a description of the surveillance data, 29 EU/EEA countries and the United Kingdom, January to May 2020

The number of measles cases declined in European Union/European Economic Area countries and the United Kingdom in 2020. Reported cases to The European Centre for Disease Prevention and Control decreased from 710 to 54 between January and May. Epidemic intelligence screening observed a similar trend. Under-diagnoses and under-reporting during the coronavirus disease (COVID-19) pandemic should be ruled out before concluding reduced measles circulation is because of social distancing and any community control measures taken to control COVID-19.
Universal adversarial perturbations against object detection

Despite the remarkable success of deep neural networks on many visual tasks, they have been proved to be vulnerable to adversarial examples. For visual tasks, adversarial examples are images added with visually imperceptible perturbations that result in failure for recognition. Previous works have demonstrated that adversarial perturbations can cause neural networks to fail on object detection. But these methods focus on generating an adversarial perturbation for a specific image, which is the image-specific perturbation. This paper tries to extend such image-level adversarial perturbations to detector-level, which are universal (image-agnostic) adversarial perturbations. Motivated by this, we propose a Universal Dense Object Suppression (U-DOS) algorithm to derive the universal adversarial perturbations against object detection and show that such perturbations with visual imperceptibility can lead the state-of-the-art detectors to fail in finding any objects in most images. Compared to image-specific perturbations, the results of image-agnostic perturbations are more interesting and also pose more challenges in AI security, because they are more convenient to be applied in the real physical world. We also analyze the generalization of such universal adversarial perturbations across different detectors and datasets under the black-box attack settings, showing it’s a simple but promising adversarial attack approach against object detection. Furthermore, we validate the class-specific universal perturbations, which can remove the detection results of the target class and keep others unchanged.
Identification of Active Attacks in Internet of Things: Joint Model- and Data-Driven Automatic Modulation Classification Approach

The Internet of Things (IoT) pervades every aspect of our daily lives and industrial productions since billions of interconnected devices are deployed everywhere of the globe. However, the seamless IoT unveils a number of physical-layer threats, such as jamming and spoofing that decrease the communication performance and the reliability of the IoT systems. As the process of identifying the modulation format of signals corrupted by noise and fading, automatic modulation classification (AMC) plays a vital role in physical-layer security as it can detect and identify the pilot jamming, deceptive jamming, and sybil attacks. In this article, we propose a novel cyclic correntropy vector (CCV)-based AMC method using long short-term memory densely connected network (LSMD). Specifically, cyclic correntropy model-driven feature CCV is first extracted using the received signals as it contains both the second-order and the higher order characteristics of cyclostationary. Then, the extracted CCV feature is put into the data-driven LSMD which mainly consists of long short-term memory (LSTM) network and dense network (DenseNet). Moreover, an additive cosine loss is utilized to train the LSMD for maximizing the interclass feature differences and minimizing the intraclass feature variations. Simulations demonstrate that the proposed CCV-LSMD method yields superior performance than other recent schemes.
A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

Breast cancer is a type of cancer that has risen to be the second cause of death among women.Classification of breast tissues into normal, benign, or malignant depends on the presence of abnormalities like microcalcifications, masses, architectural distortions, and asymmetries.Architectural distortion (AD) is subtle in detection with no association with masses but shows the abnormal arrangement of tissue strands, often in a radial, spiculation, or random pattern.It is widely rated as the third symptom of breast cancer which is the most commonly missed abnormality.Most computational approaches characterizing abnormalities in breast images often concentrate on the detection of microcalcification and masses with architectural distortions appearing as a secondary finding.The subtle nature and a minimal occurrence of architectural distortions may seem to complicate computational approaches for its detection.As a result, little research interest has been recorded in this area.It is widely reported that some cases of recent breast cancer are wrongly diagnosed due to the omission in detecting the presence of architectural distortion at the early stage of the disease.However, we discovered that most computational solutions to early detection of breast cancer are focused mainly on detecting other abnormalities such as masses and microcalcification, which are some evidence of the advanced stage of the disease.To emphasise the little efforts channeled towards detection of AD compared to other abnormalities, this article aims to detail the review of such studies in the last decade.To the best of our knowledge, this study presents the first review which focuses on the detection of architectural distortion (AD) from mammographic images.Furthermore, this article presents a comprehensive review of approaches, advances, and challenges on the computational methods for detecting AD, with the sole aim of advancing the use of deep learning models in detecting AD.Moreover, a comparative study of performance analyses of articles surveyed in this article is investigated.Our findings revealed that about 70% of the existing literature adopted Gabor Filters, while just less than 10% leveraged on the state-ofthe-art performances recorded in computer vision and deep learning, in building outstanding computational models for the detection of AD.The current study also discovered that using a deep learning approach, such as the convolution neural network (CNN) method, can yield a significant increase in performance for the task of detection of architectural distortions.This assertion is based on literature results obtained using the CNN, which generates an accuracy of 99.4% compared to the use of Gabor filters method, which accounts for 95% accuracy.
A Secure Flexible and Tampering-Resistant Data Sharing System for Vehicular Social Networks

Vehicular social networks (VSNs) have emerged as the promising paradigm of vehicular networks that can improve traffic safety, relieve traffic congestion and even provide comprehensive social services by sharing vehicular sensory data. To selectively share the sensory data with other vehicles in the vicinity and reduce the local storage burden of vehicles, the vehicular sensory data are usually outsourced to vehicle cloud server for sharing and searching. However, existing data sharing systems for VSNs can neither provide secure selective one-to-many data sharing and verifiable data retrieval over encrypted data nor ensure that the integrity of retrieved data. In this paper, we propose FTDS, a secure flexible and tampering-resistant data sharing system for VSNs by introducing a novel secure key-aggregate search encryption scheme and a tampering-resistant blockchain technology. With the proposed FTDS system for VSNs, the vehicular sensory data can be selectively shared and retrieved in a fine-grained way. Besides, our system allows vehicle data users to detect any unauthorized manipulation. Then, we present the detailed security analysis to prove that the proposed data sharing system can achieve both selective security and verifiability. We also evaluate its performance and demonstrate that it is efficient and practical for the VSNs scenarios.
Chaotic evolutionary-based image encryption using RNA codons and amino acid truth table

With the increasing use of Internet and mass media, information security has become an important issue in the field of communication. Image encryption is one of the ways to secure information in communications. In this paper, a Chaotic Evolutionary Amino Acid Codons Model (CEAACM) is presented for image encryption. The proposed model consists of four phases. In the first phase, a hidden key is provided using the SHA-256 algorithm for the initial value of the hyper chaos function. In the second phase, the permutation and diffusion of image pixels are done using two bit level permutation and Hyper Chaos Function. In the next phase, RNA and amino acid codons are used for the diffusion operation, and in the fourth phase, the process performed in the previous phases is repeated by the initial population number of the imperialist competition algorithm. Then in the next step, the optimization is performed using the operators of the imperialist competition algorithm. The results show the superiority of the proposed model over other models. In addition, according to the high value of entropy, the proposed model is highly resistant to common attacks.