Reviewerly/exHarmony huggingface dataset:
Research and implementation of modulation recognition based on cascaded feature fusion

Abstract Aiming at the problems of weak robustness of single feature and limited recognition range in modulation recognition, this paper proposes a modulation recognition algorithm based on cascaded feature fusion and multi‐classifier combination, in which time‐frequency map and instantaneous amplitude spectral density features are extracted from low intermediate frequency (IF) signal, constellation map and high‐order cumulant features are extracted from zero IF signal, and a three‐level recognition algorithm is designed through the decision fusion of decision tree, convolutional neural network, and support vector machine. In order to verify the performance of the algorithm, a modulation recognition system is designed and built based on USRP2974. The low IF and zero IF modulation signals are received through two channels, and the RF signals are received and processed in real time with the help of the built‐in CPU of the receiver. The recognition of 13 kinds of analog modulation and digital modulation signals is realized. Under the condition of wireless reception, the recognition rate of this system is more than 93% at 5 to 10 dB.
Developing a Virtual World for an Open-House Event: A Metaverse Approach

The concept of a metaverse, a virtual world that offers immersive experiences, has gained widespread interest in recent years. Despite the hype, there is still a gap in its practical application, especially in the realm of education. This study presents the design and implementation of a metaverse tailored to the needs of education. The goal of this paper is to demonstrate the feasibility of such a system and evaluate its effectiveness. It is crucial to understand the architecture and implementation of a metaverse to effectively customise it for educational purposes. To assess user experience, a field study was conducted, collecting data through questionnaires and qualitative feedback. The results show that users were pleased with the features, player experience, and ease of use.
The multilingual Twitter-discourse on vaccination in Germany during the COVID-19 pandemic

There is evidence that specific segments of the population were hit particularly hard by the Covid-19 pandemic (e.g., people with a migration background). In this context, the impact and role played by online platforms in facilitating the integration or fragmentation of public debates and social groups is a recurring topic of discussion. This is where our study ties in, we ask: How is the topic of vaccination discussed and evaluated in different language communities in Germany on Twitter during the Covid-19 pandemic? We collected all tweets in German, Russian, Turkish, and Polish (i.e., the largest migrant groups in Germany) in March 2021 that included the most important keywords related to Covid-19 vaccination. All users were automatically geocoded. The data was limited to tweets from Germany. Our results show that the multilingual debate on Covid-19 vaccination in Germany does not have many structural connections. However, in terms of actors, arguments, and positions towards Covid-19 vaccination, the discussion in the different language communities is similar. This indicates that there is a parallelism of the debates but no social-discursive integration.
“Let’s Talk About Stealing Sh*t”: Online Socialization and Its Potential to Influence Offline Offending

Social media use has significantly increased in recent years, yet relatively few studies have applied criminological theory to understand how online spaces may influence offline criminal behavior. This study uses content analysis techniques to explore how social media users are exposed to socialization processes on an online forum that promotes shoplifting. Findings indicate that concepts derived from the social learning perspective—such as motivations for shoplifting, techniques for participating in this behavior, and reinforcement for offending—were manifested and spread in this online community. These results suggest that social media users can be exposed to key elements of social learning processes that could impact their offline behavior. The implications of these findings for research and policy are considered.
Ke Dise, Mi Arma? Dialectal Varieties in WhatsApp Digital Norm of Andalusian Adolescent Speakers

This article analyzes the written expression in WhatsApp-type instant messaging applications of Andalusian adolescents who are in the last years of compulsory education in Spain. It aims to establish the relationship between the most relevant characteristics of the digital norm used by Andalusian adolescents in their communications through WhatsApp and the dialectal characteristics of their variety of Spanish. Additionally, this study intends to determine whether the writing norm in instant messaging applications influences the standard orthography of Spanish in school texts. A quantitative methodology has been used, analyzing objective data and carrying out a systematic and structured information collection process. A descriptive research design was employed using textual content analysis. The results show that the dialectal linguistic identity of Andalusian adolescent speakers on WhatsApp is mainly identified with suprasegmental linguistic features such as intonation and rhythm. Textisms related to the loss of the intervocalic -d- stand out, as well as those which reproduce the different realizations of the phonemes /s/ and /ʝ/. These features characterize the norm used by adolescent Andalusian speakers and coincide with the features that distinguish their way of pronouncing Spanish. The results show that these textisms are not related to the standard orthography of Spanish and do not cause non-standard orthography.
Google Digital Tools for Higher Education: Exploitation, Results, Prospects

The modern technologies development opens new perspectives for their integration into education. This trend became advantageous after the COVID-19 pandemic especially in terms of the possibilities of cloud services practice. The purpose of the article is to analyze the possibilities, results and prospects of using Google’s digital tools in the educational process. Theoretical pedagogical research methods are used in the work. The SWOT-analysis made possible to identify weaknesses and strengths. As a result, the peculiarities of using Google services, their use problems and advantages in the educational process were investigated. Modern military challenges in Ukraine have actualized the problem of distance learning. A model of classes in the student environment using the functionality of Google online services was also developed, its use demonstrated an improvement in the creativity level among students of higher education. To conclude, the use of GoogleMeet, Google Drive and other resources significantly diversify the possibilities of distance learning. The further online education practice only actualizes the necessity of digital elements and their opportunities for training use.
VR-PEKS: A Verifiable and Resistant to Keyword Guess Attack Public Key Encryption with Keyword Search Scheme

Public key encryption with keyword search (PEKS) allows users to perform keyword searches of ciphertext on untrusted cloud storage servers, protecting data privacy while sharing data. However, it faces several security problems in practical applications. First, an attacker can launch a keyword guessing attack to obtain keywords of interest to users, causing the leakage of their sensitive information. Second, untrusted cloud servers may return incorrect or incomplete results. In addition, with the continuous development of quantum computers, existing PEKS schemes face the problem of quantum attacks. Since cloud servers are mostly untrusted, verifiable search has become a hot research topic among scholars. However, most of the current schemes are based on bilinear pairing constructions, which are vulnerable to quantum attacks. To solve these problems, we propose a new ciphertext retrieval scheme based on fully homomorphic encryption (FHE), called VR-PEKS. This scheme implements verifiable search and is able to solve the problems of keyword guessing attacks and quantum attacks. We propose to improve the security of the scheme by using the oblivious pseudorandom function to randomize keywords and then encrypt them using FHE. An encrypted verified index structure is constructed and exposed by the data owner, enabling the data recipient to achieve verification of the correctness and integrity of the retrieved results without relying on a trusted third party. We demonstrate the security of the proposed scheme in a stochastic prediction model, and prove that our scheme satisfies keyword ciphertext indistinguishability and keyword trapdoor indistinguishability under adaptive keyword selection attacks. The comparison shows that our scheme is secure and feasible.
Information Entropy Measures for Evaluation of Reliability of Deep Neural Network Results

Deep neural networks (DNN) try to analyze given data, to come up with decisions regarding the inputs. The decision-making process of the DNN model is not entirely transparent. The confidence of the model predictions on new data fed into the network can vary. We address the question of certainty of decision making and adequacy of information capturing by DNN models during this process of decision-making. We introduce a measure called certainty index, which is based on the outputs in the most penultimate layer of DNN. In this approach, we employed iEEG (intracranial electroencephalogram) data to train and test DNN. When arriving at model predictions, the contribution of the entire information content of the input may be important. We explored the relationship between the certainty of DNN predictions and information content of the signal by estimating the sample entropy and using a heatmap of the signal. While it can be assumed that the entire sample must be utilized for arriving at the most appropriate decisions, an evaluation of DNNs from this standpoint has not been reported. We demonstrate that the robustness of the relationship between certainty index with the sample entropy, demonstrated through sample entropy-heatmap correlation, is higher than that with the original signal, indicating that the DNN focuses on information rich regions of the signal to arrive at decisions. Therefore, it can be concluded that the certainty of a decision is related to the DNN's ability to capture the information in the original signal. Our results indicate that, within its limitations, the certainty index can be used as useful tool in estimating the confidence of predictions. The certainty index appears to be related to how effectively DNN heatmaps captured the information content in the signal.
Fake goods

The early 2020s commonplace that everything seems a bit unreal is a material truth. Not only our shoes and handbags, but our makeup, phone chargers, glassware, and medications; our oven mitts and bike helmets and airplane parts; the components in our intravenous drip machines and defibrillators, the brakes of our high-speed trains - all may be counterfeit. Counterfeit goods are generally substandard ones that infringe intellectual property rights. The iconic examples are fake Louis Vuitton handbags and Nike Air Jordan sneakers, but fakes can now be found across the whole spectrum of retail and wholesale consumption (Suthivarakom, 2020). Advances in logistics are generating more counterfeits. The burgeoning volume of containerized shipping is increasing the trade in fakes, since the greater surveillance that would detect them would also slow down the flow of legitimate goods. Beginning around 2018, the rise in online shopping, small parcel delivery, and social media shopping multiplied the sales of counterfeits on platforms such as Amazon, Alibaba, Instagram, and TikTok, ushering in a new era of indiscernible copycats of non-luxury products. Pandemic lockdowns exacerbated the problem, creating demand for counterfeit semiconductors and personal protective equipment. Logistics - typically understood to move existing products - creates markets for new, criminal ones. The desire for speed and convenience has given us a counterfeit world of goods. The glib twentieth-century notion of phoniness in The Graduate's ironic advice to Benjamin Braddock (“there's a great future in plastics”) has taken a darker turn. Now even the plastic is not really plastic.
Face Database Protection via Beautification with Chaotic Systems

The database of faces containing sensitive information is at risk of being targeted by unauthorized automatic recognition systems, which is a significant concern for privacy. Although there are existing methods that aim to conceal identifiable information by adding adversarial perturbations to faces, they suffer from noticeable distortions that significantly compromise visual perception, and therefore, offer limited protection to privacy. Furthermore, the increasing prevalence of appearance anxiety on social media has led to users preferring to beautify their faces before uploading images. In this paper, we design a novel face database protection scheme via beautification with chaotic systems. Specifically, we construct the adversarial face with better visual perception via beautification for each face in the database. In the training, the face matcher and the beautification discriminator are federated against the generator, prompting it to generate beauty-like perturbations on the face to confuse the face matcher. Namely, the pixel changes produced by face beautification mask the adversarial perturbations. Moreover, we use chaotic systems to disrupt the order of adversarial faces in the database, further mitigating the risk of privacy leakage. Our scheme has been extensively evaluated through experiments, which show that it effectively defends against unauthorized attacks while also yielding good visual results.
Prediction-based uncertainty quantification for exchangeable sequences

Prediction has a central role in the foundations of Bayesian statistics and is now the main focus in many areas of machine learning, in contrast to the more classical focus on inference. We discuss that, in the basic setting of random sampling-that is, in the Bayesian approach, exchangeability-uncertainty expressed by the posterior distribution and credible intervals can indeed be understood in terms of prediction. The posterior law on the unknown distribution is centred on the predictive distribution and we prove that it is marginally asymptotically Gaussian with variance depending on the predictive updates, i.e. on how the predictive rule incorporates information as new observations become available. This allows to obtain asymptotic credible intervals only based on the predictive rule (without having to specify the model and the prior law), sheds light on frequentist coverage as related to the predictive learning rule, and, we believe, opens a new perspective towards a notion of predictive efficiency that seems to call for further research. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.
Globalization–Income Inequality Nexus in the Post-Soviet Countries: Analysis of Heterogeneous Dataset Using the Quantiles via Moments Approach

Deglobalization, as opposed to the term globalization, appears in the world order due to local solutions to problems and border controls, ignoring the principles of treaties, trade wars, and the expansion of regionalism. In addition, slowbalization helps shrink the global flow of trade, information, and societal and cultural exchange dynamism. However, this scary global order, as triggered by deglobalization and slowbalization, significantly impacts the income factors of allied nations. Against this background, we aim to investigate whether deglobalization and slowbalization proxied by the influencing magnitudes of globalization dimensions (e.g., globalization de facto and de jure, internet diffusions, and trade openness) impact the income inequality of the 12 post-Soviet countries, considering the panel data during 1991–2019. To this end, we apply the quantiles via moments approach to investigate the time-varying connectedness between variables that have country and data-centric heterogeneities. Our findings depict that deglobalization is futile in affecting the post-Soviet countries’ income dynamics, as globalization negatively affects income inequality in diverse quantiles. Specifically, globalization de facto (globalized policy-implementation spectrum) and internet diffusions have a significantly negative influence on reducing income inequality from low to medium quantiles (q.25–q.75). Globalization de jure (globalized policy-decision spectrum) and trade openness are statistically insignificant in entire quantiles (q.25–q.95), implying the likely existence of slowbalization. Finally, government expenditures and governance quality are monotonically negative in reducing income inequality at all quantiles (q.25–q.95). Therefore, policy suggestions enclose galvanizing globalization potentials in curbing income inequality to keep away the distressful phases of deglobalization and slowbalization.
Full-Reference Image Quality Assessment with Transformer and DISTS

To improve data transmission efficiency, image compression is a commonly used method with the disadvantage of accompanying image distortion. There are many image restoration (IR) algorithms, and one of the most advanced algorithms is the generative adversarial network (GAN)-based method with a high correlation to the human visual system (HVS). To evaluate the performance of GAN-based IR algorithms, we proposed an ensemble image quality assessment (IQA) called ATDIQA (Auxiliary Transformer with DISTS IQA) to give weights on multiscale features global self-attention transformers and local features of convolutional neural network (CNN) IQA of DISTS. The result not only performed better on the perceptual image processing algorithms (PIPAL) dataset with images by GAN IR algorithms but also has good model generalization over LIVE and TID2013 as traditional distorted image datasets. The ATDIQA ensemble successfully demonstrates its performance with a high correlation with the human judgment score of distorted images.
A Wavelet-Based Computational Framework for a Block-Structured Markov Chain with a Continuous Phase Variable

We consider the computing issues of the steady probabilities for block-structured discrete-time Markov chains that are of upper-Hessenberg or lower-Hessenberg transition kernels with a continuous phase set. An effective computational framework is proposed based on the wavelet transform, which extends and modifies the arguments in the literature for quasi-birth-death (QBD) processes. A numerical procedure is developed for computing the steady probabilities based on the fast discrete wavelet transform, and several examples are presented to illustrate its effectiveness.
Foreign direct investment, remittances, real exchange rate, imports, and economic growth in Ghana: An ARDL approach

The main objective of this quantitative study is to ascertain the effect of foreign direct investment, real exchange rate, remittances, and import on economic growth in Ghana. Secondary data on gross domestic product, foreign direct investment, real exchange rate, remittances, import, and gross capital formation from 1980 to 2018 were analyzed. The study employed Autoregressive Distributed Lag for the econometrics analysis. The study found that foreign direct investment, real exchange rate, remittances, import, and gross capital formation cointegrates with economic growth. The main findings are that foreign direct investment, real exchange rate, import, and remittances matter from growth perspective. Remittances have a positive and significant effect on economic growth in Ghana both for the short run and the long run. The study also revealed that foreign direct investment, real exchange rate, and imports have a negative and significant effect on the growth process of Ghana’s economy for both the short run and the long run. The study recommends that the Ministry of Finance, Ghana, financial analysts and other policy makers should undertake steps to reduce imports and attract more remittances inflows to attain long-run economic growth. In addition, the economy must concentrate on viable exchange rate policies such as undervaluation of currency to stimulate sustainable economic growth.
Deep bootstrap for Bayesian inference

For a Bayesian, the task to define the likelihood can be as perplexing as the task to define the prior. We focus on situations when the parameter of interest has been emancipated from the likelihood and is linked to data directly through a loss function. We survey existing work on both Bayesian parametric inference with Gibbs posteriors and Bayesian non-parametric inference. We then highlight recent bootstrap computational approaches to approximating loss-driven posteriors. In particular, we focus on implicit bootstrap distributions defined through an underlying push-forward mapping. We investigate independent, identically distributed (iid) samplers from approximate posteriors that pass random bootstrap weights through a trained generative network. After training the deep-learning mapping, the simulation cost of such iid samplers is negligible. We compare the performance of these deep bootstrap samplers with exact bootstrap as well as MCMC on several examples (including support vector machines or quantile regression). We also provide theoretical insights into bootstrap posteriors by drawing upon connections to model mis-specification. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.
Deep Deformable Artistic Font Style Transfer

The essence of font style transfer is to move the style features of an image into a font while maintaining the font’s glyph structure. At present, generative adversarial networks based on convolutional neural networks play an important role in font style generation. However, traditional convolutional neural networks that recognize font images suffer from poor adaptability to unknown image changes, weak generalization abilities, and poor texture feature extractions. When the glyph structure is very complex, stylized font images cannot be effectively recognized. In this paper, a deep deformable style transfer network is proposed for artistic font style transfer, which can adjust the degree of font deformation according to the style and realize the multiscale artistic style transfer of text. The new model consists of a sketch module for learning glyph mapping, a glyph module for learning style features, and a transfer module for a fusion of style textures. In the glyph module, the Deform-Resblock encoder is designed to extract glyph features, in which a deformable convolution is introduced and the size of the residual module is changed to achieve a fusion of feature information at different scales, preserve the font structure better, and enhance the controllability of text deformation. Therefore, our network has greater control over text, processes image feature information better, and can produce more exquisite artistic fonts.