Reviewerly/exHarmony huggingface dataset:
An improved order-encoded quantum image representation model and its application

Quantum image representation is an important research direction of quantum image processing, which mainly studies the use of quantum properties to encode and store images. At present, most quantum image representation models store image grayscale-related information, but lack histogram-related information. A few quantum representation models that store image histogram information cannot accurately retrieve the image, resulting in the difficulty of quantum image histogram processing. Therefore, this paper proposes an improved order-encoded quantum image representation model ONEQR, which uses the quantum basis state sequence to store the grayscale information, real position information and ascending position information of the image, which can not only solve the problem of inability to accurately retrieve the image, but also store the histogram information of the image, which is convenient for the implementation of the histogram processing algorithm of quantum image; In addition, based on ONEQR, a quantum image representation model MULTI-ONEQR that stores multiple images is proposed, and based on this model, a quantum image histogram specification (HS) algorithm is proposed, which can use quantum properties to complete image HS in parallel. Finally, the simulation experiment of the HS algorithm based on the ONEQR model is carried out to verify the effectiveness of the algorithm.
Blockchain-Based E-Medical Record and Data Security Service Management Based on IoMT Resource

Electronic health records are essential and sensitive since they include vital information and are routinely exchanged across several parties, such as hospitals and private clinics. These data must remain accurate, current, secret, and available only to authorized parties. Integrating these data improves the accuracy and cost-effectiveness of the present health data administration framework. Electronic Medical Records (EMRs) are now kept utilizing the structure of the client/server via whom patient data information is maintained in the hospital. Multiple hospitals use the same database to track a single patient. These limitations prevent a custom health system from providing various associated experts and patients with a cohesive, integrated, secure, and confidential medical history. Modern healthcare systems are distinguished by their complexity and expense. However, this may be mitigated by enhanced health record management and Blockchain technology. The Blockchain’s data availability, confidence, and security characteristics have a bright future in healthcare services, giving solutions to the issues of the traditional customer/server architecture EMR management platform: intricacy, confidence, dependability, compatibility, and anonymity. An e-health record management based on Internet of Medical Things (EHRM-IoMT) is proposed in this paper. This paper explores and analyzes Blockchain efficiency and customer/server paradigms. The findings show that a patient-centred strategy may achieve remarkable success utilizing Blockchain. Moreover, the immutable and accurate data of persons in Blockchain may enable healthcare practitioners to better forecast and aid with diagnosis utilizing the IoMT via machine learning and artificial intelligence.
A Survey on Air-Gap Attacks: Fundamentals, Transport Means, Attack Scenarios and Challenges

Major public institutions and organizations that handle sensitive data frequently enforce strong security policies by implementing network separation policies that segregates their internal work networks and internet network using air gaps to prevent the leakage of confidential information. Such closed networks have long been considered the most secure technique for protecting data; however, studies have shown that they are no longer effective in providing a safe data protection environment. Research on air-gap attacks remains in its infancy stage. Studies have been conducted to check the method and demonstrate the possibility of transmitting data using various transmission media available within the closed network. These transmission media include optical signals such as HDD LEDs, acoustic signals such as speakers, and the electrical signals of power lines. This paper examines various media used for air-gap attacks by analyzing different techniques and their essential functions, strengths, and limitations. The findings of this survey and the follow-up analysis aim to assist companies and organizations in protecting their information by providing an understanding of air-gap attacks and their current trends.
TAN: A Transferable Adversarial Network for DNN-Based UAV SAR Automatic Target Recognition Models

Recently, the unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) has become a highly sought-after topic for its wide applications in target recognition, detection, and tracking. However, SAR automatic target recognition (ATR) models based on deep neural networks (DNN) are suffering from adversarial examples. Generally, non-cooperators rarely disclose any SAR-ATR model information, making adversarial attacks challenging. To tackle this issue, we propose a novel attack method called Transferable Adversarial Network (TAN). It can craft highly transferable adversarial examples in real time and attack SAR-ATR models without any prior knowledge, which is of great significance for real-world black-box attacks. The proposed method improves the transferability via a two-player game, in which we simultaneously train two encoder–decoder models: a generator that crafts malicious samples through a one-step forward mapping from original data, and an attenuator that weakens the effectiveness of malicious samples by capturing the most harmful deformations. Particularly, compared to traditional iterative methods, the encoder–decoder model can one-step map original samples to adversarial examples, thus enabling real-time attacks. Experimental results indicate that our approach achieves state-of-the-art transferability with acceptable adversarial perturbations and minimum time costs compared to existing attack methods, making real-time black-box attacks without any prior knowledge a reality.
Revisiting video super-resolution: you only look outstanding frames

Video super-resolution (VSR) has been improved with various deep learning architectures and datasets that mostly contain clean images. However, most real-world videos are compressed and here arises a critical issue: each frame in a video usually varies in quality. We discover an important but simple coding prior that affects the performance of the existing VSR models because the prior can tell us which frame is outstanding than others in terms of quality, namely outstanding-frames. Exploiting the prior, we propose a method that allows you only look outstanding frames (YOLOF) to enhance the existing VSR models as a universal approach, which feeds VSR models the best quality of frames near the reference frame with given distance. Extensive evaluations with various VSR models show that our YOLOF method enhances existing VSR models substantially without harming original architectures.
Binary Neighborhood Coordinate Descriptor for Circuit Board Defect Detection

Due to the periodicity of circuit boards, the registration algorithm based on keypoints is less robust in circuit board detection and is prone to misregistration problems. In this paper, the binary neighborhood coordinate descriptor (BNCD) is proposed and applied to circuit board image registration. The BNCD consists of three parts: neighborhood description, coordinate description, and brightness description. The neighborhood description contains the grayscale information of the neighborhood, which is the main part of BNCD. The coordinate description introduces the actual position of the keypoints in the image, which solves the problem of inter-period matching of keypoints. The brightness description introduces the concept of bright and dark points, which improves the distinguishability of BNCD and reduces the calculation amount of matching. Experimental results show that in circuit board image registration, the matching precision rate and recall rate of BNCD is better than that of classic algorithms such as scale-invariant feature transform (SIFT) and speeded up robust features (SURF), and the calculation of descriptors takes less time.
Phenomena of Contact and Mixing in the Arbëresh Dialects of San Marzano di San Giuseppe in Salento and Vena di Maida in Calabria

In this work, we will investigate hybridization, borrowing, and grammatical reorganization phenomena in the Arbëresh dialects of San Marzano (Apulia) and Vena di Maida (central Calabria). The data from the Arbëresh of S. Benedetto Ullano (northern Calabria) will be useful to provide a comparative frame. Arbëresh is the name of the Albanian varieties spoken in the villages/cities generally formed in the late fifteenth century by communities fleeing from Albania as a consequence of the Ottoman occupation. The long-time contact with neighboring Romance varieties is reflected in the extended mixing phenomena which characterize the lexicon and the morphosyntactic organization of Arbëresh as a heritage language. This is particularly evident in the two dialects that we investigate in this contribution, where relexification and grammatical reorganization phenomena provide us with an interesting testing ground to explain language variation.
An Exhaustive Literature Review of Hadith Text Mining

The Quran and the hadith of the Prophet are the two sources of legislation for Muslims. Sharia rulings and laws are not only derived from the Quran but also the bulk of them come through hadith. Understanding the hadith, its classification, and verification of its authenticity is vital to reach detailed rulings, as the volume of the hadith is many times greater than the volume of the Quran. As a result, mining in the hadith text is one of the things that has attracted the attention of researchers in the past few years. In this study, we conducted a survey of all the techniques and systems related to the mining of the hadith in its two parts, the Al-Matn and the Al-Sanad. On the other hand, the challenges and obstacles which confronted researchers have been shown; in addition, some suggested tips were highlighted to overcome those challenges. Furthermore, the most essential modern techniques used in the classification of Arabic texts, which gave a high degree of efficiency, were highlighted as milestones for future studies.
Contextual tasks in the course "Elementary Mathematics" for future subject teachers

The authors generalize approaches to understanding the essential characteristics of the concept of &amp;quot;contextual task&amp;quot;, substantiate the role of such tasks for the subject preparation of a future teacher of mathematics. A typology of contextual tasks is given. The article presents examples of contextual tasks in the discipline &amp;quot;Elementary Mathematics&amp;quot; for students studying in the direction of Pedagogical Education profile Mathematics.
Conditional Token: A New Model to Supply Chain Finance by Using Smart Contract in Public Blockchain

This paper defines Conditional Token (CT) as the token with specific conditions and proposes the use functions for its operations in smart contract so that it can be deployed at the public blockchain. If CTs were exchanged to/equivalent to fiat currency once then all conditions are realized, that is, the required performances and obligations/rights are agreed upon. In use, the obligation-type CT can be used as a divisible mortgage or be used as a representation of accounts receivable, accounts payable and vouchers as it is used in accounting. While the rights-type CT can be used as divisible fixed-income bonds or as an investment vehicle. Integrate both types of CTs with a matching methodology can thus be used in any kind of peer-to-peer (P2P) system of the decentralized finance, such as crowdfunding and P2P lending. This paper thus applying this new model to solve the complex issues of supply chain finance. For feasibility, this study concludes CT is the “Verdinglichung Obligatorischer Rechte”, and CTs are better than the current corporate loans in terms of cost and benefits. In addition, it is capable of transferring risk to other investors. In terms of implementation, this paper proposes a system framework and has completed a proof of concept of the system.
The relevance of the opening of a new Master's program "Creative Management" at N.I. Lobachevsky National Research University

Rapid changes in the socio-economic processes taking place at the international, federal and regional levels are pushing the heads of organizations to transform the management model in them. This transformation is associated with the accelerated introduction of digital services in organizations and the development of new products and services. Accordingly, the training of a new generation of managers is also required, whose main focus will be the development of new ideas and their implementation in the company's products, processes and results in conditions of uncertainty and high dynamism of the external environment. This article is devoted to the discussion of the competencies and qualities that such managers should possess, and how these competencies can be formed within the framework of the corresponding master's program.
Multilingual cyberpragmatics in instructional settings. Exploring gender and age effects in Catalan, Spanish and English email requests

The present study focuses on email communication in the multilingual university setting. Previous studies dealing with similar settings point to the lack of politeness markers in students’ email messages [Bjorge, A. (2007). Power distance in English lingua franca email communication. International Journal of Applied Linguistics, 17(1), 60–80], requests tend to be more direct and they may not match with the degree of imposition or social distance involved. Students’ lack of netiquette and the lack of physical presence on the Internet may influence their pragmatic behaviour. In addition to that, other studies [Barón, J., & Ortega, M. (2018). Investigating age differences in e-mail pragmatic performance. System, 78, 148–178] point to age effects and further confirm the preference for direct pragmatic forms. Nevertheless, very few studies have adopted a multilingual perspective. For this reason, we have examined 250 email requests to faculty. Openings and requests produced in Catalan, Spanish or English were analysed. Our main goal was to confirm whether students’ requests would devoid politeness markers and whether openings would show a lack of netiquette. We were also interested in identifying age and gender effects and in finding out if the pragmatic forms chosen would be in line with the politeness orientation of the language involved. Results are in line with previous studies but they also contradict them adding interesting information on the way multilingualism is displayed in email interaction.
Poetry Will Not Optimize; or, What Is Literature to AI?

Abstract Literature, poetry, and other forms of noncommercial creative expression challenge the techno-instrumentalist approaches to language, the predictive language generation, informing NLP (large natural language processing models) such as GPT-3 or -4 as well as, more generally, generative AI (text to image, video, audio). Claims that AI systems automate and expedite creativity reflect industry and research priorities of speed, scale, optimization, and frictionlessness driving much artificial intelligence design and application. But poetry will not optimize; the creative process cannot be reduced to a prompt. Some have noted that literary creations generated or augmented by artificial intelligence at best can offer form without meaning; using a GPT creation prompted by Maya Angelou’s poem “Still I Rise” as a case study, this essay argues that NLP’s predictive language generation and what I call algorithmic ahistoricity can also, more disturbingly, render meaning senseless. In doing so, GPT-3’s literary experiments are not “failed” because they do not meet some moving target of a literary standard, nor because of technological insufficiency, but because it can make it harder for people to name and navigate their realities. The coda explores an example of AI as literary interlocutor and creative engagement beyond optimization.
Joint face completion and super-resolution using multi-scale feature relation learning

Previous research on face restoration often focused on repairing specific types of low-quality facial images such as low-resolution (LR) or occluded facial images. However, in the real world, both the above-mentioned forms of image degradation often coexist. Therefore, it is important to design a model that can repair images that are LR and occluded simultaneously. This paper proposes a multi-scale feature graph generative adversarial network (MFG-GAN) to carry out face restoration in contexts in which both LR and occluded degradation modes coexist, and also to repair images with a single type of degradation. Based on the GAN, the MFG-GAN integrates the graph convolution and feature pyramid networks to restore occluded low-resolution face images to non-occluded high-resolution face images. The MFG-GAN uses a set of customized losses to ensure that high-quality images are generated. In addition, we designed the network in an end-to-end format. We conduct experiments on general face image restoration and facial expression restoration. Experimental results on the public-domain databases show that the proposed approach outperforms state-of-the-art methods in performing face super resolution (up to 4× or 8×) and face completion simultaneously and can recover better facial expression details.
SPinS-FL: Communication-Efficient Federated Subnetwork Learning

Federated learning (FL) is a distributed machine learning method in which edge devices collaboratively train a unified model without disclosing their private training data to others. Unlike data centers, edge devices often stand in low-bandwidth traffic environments, which can be a bottleneck in the training process. To tackle this problem, we first consider a naive FL based on the Edge-Popup algorithm. The Edge-Popup-based neural network constructs a subnetwork by assigning a score to each randomly initialized weight and selecting weights based on the score without updating the weights. Forward propagation, based on the subnetwork consisting of only the live weights with high scores, is performed, and then backpropagation is performed to update scores and search for a subnetwork that achieves high accuracy. This algorithm has two unique properties: first, the score itself is unnecessary for calculating the score's gradient, as long as there is a supermask, a bitmask indicating which weights are participating in the subnetwork then; and second, the intensity of the turnover in the score rankings varies according to the scores' rank range. Exploiting these properties, we propose SPinS-FL, a novel FL method based on the Edge-Popup algorithm. Instead of communicating all the scores with the server, SPinS-FL only communicates some supermasks and a partial set of scores of the weights around the boundary between selected and unselected in the subnetwork; this dramatically improves communication efficiency. Numerical experiments using the CIFAR-10 and CIFAR-100 datasets show that SPinS-FL succeeds in reducing the traffic volume by up to 79.8% compared with communicating all the scores. Moreover, SPinS-FL achieves comparable accuracy as the federated averaging method but with up to 69.9% less traffic cost.
F2MKD: Fog-enabled Federated Learning with Mutual Knowledge Distillation

Federated learning (FL) is a promising technology for achieving privacy-preserving distributed learning. Although most existing studies on FL have provided a single global model for all clients, the resulting single model cannot handle heterogeneous local environments. Therefore, we propose a novel FL framework called fog-enabled federated learning with mutual knowledge distillation (F2MKD), in which the server and each client are ascribed to distinctive models. The remarkable feature of F2MKD is the data collection at the fog servers while ensuring data confidentiality among the fog servers. This study paves the new way for client-friendly, collaborative distributed learning. The code is available at https://github.com/d3-ai/F2MKD.
Training of Perceptual Image Denoising Network with Weighted Sum of IQA Methods

Deep learning-based image denoising methods using mean squared error (MSE) as the loss function produce results of an excessively smoothed image with low perceptual quality. Although advances in image quality assessment (IQA) methods, including those using deep learning, allow us to estimate the perceptual quality of images, an existing study reveals that using such IQA alone as a loss function of the denoising methods does not improve the perceptual quality. This may be because each IQA has a special image (adversarial example) that causes the IQA to malfunction. To avoid this problem and improve the perceptual quality of denoised images, we propose a method for learning denoising methods using a loss function that combines IQA with other IQA or MSE. The experimental results show that the proposed method can improve the quantitative and qualitative perceptual quality of the image by suppressing excessive smoothing of the image.
Real-Time Hash Aggregation for Blockchain System With 3D Sensor Network

Smart cities work as a platform that utilize the information and communication technology to provide public services efficiently. Smart monitoring is one of the important components for providing public services in smart cities. 3D-image sensing technology such as the light detection and ranging (LIDAR) is a promising means of capturing a public space for smart monitoring. However, 3D-image data may be sensitive as it is related to public safety and law enforcement. Blockchain technology may be a solution to this issue. Prior work presented a framework for hash value aggregation in a 3D sensor network to avoid overflow that occurs when handling 3D-image data generated by LIDAR devices in real time. However, the prior work presented only a theoretical result even though it was based on actual measurements of processing delay; the feasibility of the framework needs to be evaluated with a real implementation using real LIDAR sensor units. On the basis of the prior work, we propose a real system that actually measures the time taken to register the hash values at an edge computer and determines the optimal number of aggregated hash values to avoid overflow in registration on a blockchain network. We demonstrate the effectiveness of the proposed system through an experiment using an actual edge computer and multiple LIDAR sensor units.
Accessibility for Virtual Tours: on Designing a Prototype for People with Visual Impairments

Virtual tours are tools that could be effectively used in many different contexts. The COVID-19 pandemic has let emerge how they can be useful and efficacy in those situations where it is not possible to access physical places and attend events. Besides that, they can be used for several goals in different contexts. In fact, in simple words, virtual tours are essentially based on using 3D images and videos, letting the users interact with the virtual environment and its points of interest through them, emphasizing the need for navigation and orienteering mechanisms within the virtual environment. Due to these characteristics, virtual tours represent digital barriers affecting users with disabilities, in particular those people with visual impairments. This paper presents an innovative solution we have adopted by designing and developing a prototype devoted to exploring a University Campus, with accessibility in mind. A preliminary evaluation has been conducted, and the obtained results are presented in the paper, which discusses some potentialities and limitations that emerged,