Reviewerly/exHarmony huggingface dataset:
An accurate identification method for network devices based on spatial attention mechanism

With the metaverse being the development direction of the next generation Internet, the popularity of intelligent devices, and the maturity of various emerging technologies, more and more intelligent devices try to connect to the Internet, which poses a major threat to the management and security protection of network equipment. At present, the mainstream method of network equipment identification in the metaverse is to obtain the network traffic data generated in the process of device communication, extract the device features through analysis and processing, and identify the device based on a variety of learning algorithms. Such methods often require manual participation, and it is difficult to capture the small differences between similar devices, leading to identification errors. Therefore, we propose a deep learning device recognition method based on a spatial attention mechanism. Firstly, we extract the required feature fields from the acquired network traffic data. Then, we normalize the data and convert it into grayscale images. After that, we add a spatial attention mechanism to CNN and MLP respectively to increase the difference between similar network devices and further improve the recognition accuracy. Finally, we identify devices based on the deep learning model. A large number of experiments were carried out on 31 types of network devices such as web cameras, wireless routers, and smartwatches. The results show that the accuracy of the proposed recognition method based on the spatial attention mechanism is increased by 0.8% and 2.0%, respectively, compared with the recognition method based only on the deep learning model under the CNN and MLP models. The method proposed in this paper is significantly superior to the existing method of device-type recognition based only on a deep learning model.
FDPBoost: Federated differential privacy gradient boosting decision trees

The big data era has led to an exponential increase in data usage, resulting in significantly advancements in data-driven domains and data mining. However, due to privacy and regulatory requirements, sharing data among various institutions is not always possible. Federated learning can help address this problem, but existing studies that combine differential privacy with tree models have shown significant accuracy loss. In this study, we propose a Federated Differential Privacy Gradient Boosting Decision Tree (FDPBoost) that protects the private datasets of different owners while improving model accuracy. We select sensitive features according to the secure feature set indicator, and use an exponential mechanism to protect sensitive features and assign significant weight to the Laplace mechanism to protect leaf node values. Additionally, a distributed two-level boosting framework is designed to allocate the privacy budget between intra-iteration and inter-iteration decision trees while protecting model communication. The FDPBoost is tested on five datasets sourced from the materials and medical domains. Our experiments reveal that FDPBoost achieves competitive accuracy with traditional federated gradient boosting decision trees while also exhibiting a significant reduction in error rate as compared to PPGBDT (Zhao et al.) and FV-tree (Gao et al.). Notably, FDPBoost’s error rate on the tumor-diagnosis dataset is 30% lower than that of FV-tree.
Spanish Political Communication and Hate Speech on Twitter During the Russian Invasion of Ukraine

Since the Russian invasion of Ukraine on February 24, 2022, the polarized discourse of the West vs. Russia seems to have escalated to levels similar to those during the Cold War period. The aim of this article, which is centered on the case of Spain, is to discover to what extent communication from political parties contributed to such polarization by encouraging hate speech. To this end, messages sent by the political parties represented in the Spanish parliament, over the social network Twitter during the first 60 days of the Russian invasion of Ukraine, were analyzed: A total of 3,186 tweets from 67 official accounts, both from these political parties and their main leaders, were coded. The results showed that despite social networks in general—and Twitter in particular—being a favorable environment for the promotion of hate speech, the communication of Spanish political parties was generally characterized by political correctness and moderation. The presence of the main indicators of hate speech analyzed (threats, criticism, ridicule, or insults) was very minor. The present article associates this finding to other variables such as the tone of the tweets (informative, opinionated), their scope (international, national), and engagement (replies, quotes, retweets), among other factors. It is concluded that Spanish political authorities had a socially-responsible behavior in the case analyzed, reinforcing the importance of public diplomacy to counteract hate speech. Since the Russian invasion of Ukraine on February 24, 2022, the polarized discourse West vs. Russia seems to have escalated to levels like those of the so-called Cold War period. The aim of this article, focused on the case of Spain, is to discover to what extent the communication from political parties has contributed to such polarization by encouraging hate speech. To this end, the social network Twitter messages by the political parties represented in the Spanish Parliament during the first 60 days of the Russian invasion of Ukraine were analyzed; a total of 3,186 tweets from 67 official accounts from the political parties and their main leaders were coded. The results show that even though social networks in general -or Twitter in particular- are a favorable environment for the promotion of hate speech, the communication of Spanish political parties was characterized, in general, by political correctness and moderation. It is not in vain that the presence of main indicators of hate speech analyzed (threats, criticism, mockery, or insults) were very minor. The article relates this to other variables such as the tone of the tweets (informative, opinionated), the scope (international, national) or engagement (likes, comments), among others. It is concluded that Spanish political authorities have a socially responsible behavior in the analyzed case, reinforcing the importance of public diplomacy to counteract hate speech.
Deep learning with R

Click to increase image sizeClick to decrease image size Additional informationFundingThis work was supported by National Office for Philosophy and Social Sciences under Grant [21BYY046], Fundamental Research Funds for the Central Universities under Grant [3132022611] and Liaoning Office for Education Sciences Planning under Grant [JG21DB059].
The 'Bad Behavior Index': A Composite Measure of the Development Hindering Behavior of Individuals and Institutions

Composite indices have become a popular tool for providing a quantitative, simplified, and visualized representation of complex phenomena. An example of such is the Human Development Index (HDI) which ranks countries by their level of development. The primary limitation of the HDI is its narrow scope, which hinders its effectiveness at explaining why some nations are more developed than others. The discussion as to why some nations are more developed than others goes back as far as the 14th century, where Ibn Khaldun developed a theory which aims to explain why civilizations rise and fall. Some of the hypotheses which seek to answer this question point to the importance of economic freedoms, absence of corruption, high investment in human capital, and the importance of institutions etc. to development. One hypothesis which has not been properly studied regards the culpability of individual and institutional behavior. The purpose of this study is to introduce a composite measure of the development hindering behavior of individuals and institutions, i.e., the Bad Behavior Index (BBI). The methodology of this study is influenced by the Mazziotta &amp; Pareto framework for composite indices. The index weights have been computed by integrating expert opinion with the Fuzzy Analytic Hierarchy Process (FAHP). The findings of this study suggest that African countries engage in the highest level of bad behavior, which subsequently leads to their poor socio-economic development, whereas Northern countries engage in the least level of bad behavior. The study also finds that the most important drivers for socio-economic development are low levels of corruption, high levels of knowledge creation, strict application of the rule of law, high levels of social cohesion, and high levels of political stability.
Testing the Price Bubbles in Cryptocurrencies using Sequential Augmented Dickey-Fuller (SADF) Test Procedures: A Comparison for Before and After COVID-19

Bubbles in asset prices have attracted the attention of economists for centuries. Extreme increases in asset prices, followed by their sudden decline, create a turbulent effect on the economy and even invite crises in time. For this reason, some measurement techniques have been employed to investigate the price bubbles that may occur. This study explores the possible speculative price bubbles of Bitcoin, Ethereum, and Binance Coin cryptocurrencies, compares them with the pre-and post-COVID-19 period, and examines asymmetric causality relationships between variables. Therefore, we analyzed the price bubbles of these cryptocurrencies using the closing price for daily data between 16.01.2018 and 31.12.2021 by the Supremum Augmented Dickey-Fuller (SADF) and the Hatemi-J (2012) asymmetric causality test. In this context, 1446 observations, 723 of which were before COVID-19 and 723 after COVID-19, were employed in the study. Looking at the SADF analysis results, we detected 103 price bubbles before COVID-19 for the three cryptocurrencies, while we determined 599 price bubbles after COVID-19. The common finding in the asymmetric causality test results is that there is a causality relationship between the negative shocks faced by one cryptocurrency and the positive shocks faced by the other cryptocurrencies.
Analysis of Different Filter Techniques for Image Denoising

Images are very important source in research field as it is easier to convey information through them.There are several resources available to generate high quality images, but presence of noise can degrade these images.Hence image denoising is one of the crucial preprocessing steps in digital image processing.This paper is an attempt to study the effect of different noise types on images and how efficiently denoising techniques can reduce noise.Gaussian noise, poisson noise, salt & pepper noise and speckle noises are the most commonly occurring noise types which are considered to conduct experiments with gray scale images.Denoising techniques applied here are gaussian filter, median filter, wiener filter, bilateral filter, non-local means and bm3d.Results of different noise used on gray scale images compared with the help of quantitative and qualitative performance parameters such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM).
Pharmacy Management System using ML

Pharmacy Management System using ML application helps the pharmacist to manage pharmacy in systematic ways. Pharmacy Management system using ML can make the work easier by giving the details of Medicines. It becomes very difficult in big medical stores to handle the details of the medicine manually so by using this system we can maintain the records of all the medicines. Also, nowadays doctors are very busy so, they tend to scribble unreadable prescribed medicines which leads to the problem of misinterpreting medicine names. Patients are sometimes curious to know information about their prescribed medicines before purchasing them. Therefore, this system recognizes handwritten medicine names and returns a readable digital text of the medicine.
Design of Library Archives Information Management Systems Based on Artificial Intelligence and Multimedia Technology

With the rapid development of information technology, the storage and management of library archives and books must meet the needs of readers. In this process, people's demands for various knowledge and information are increasingly diversified, and realizing the personalized service of library archives has become a better solution. At the same time, there are certain connections between various books, which is the basis for providing personalized services for readers. Through the performance test of the library archives information management system, it showed that all the performance indicators of the system met the required functional requirements. When the number of clients was 50, the memory utilization rate was 25.56%; the number of records was 428, and the average response time was 1058ms. Therefore, it is very necessary to use artificial intelligence technology and multimedia technology to design and build archive information management systems.
Coherent SAT solvers: a tutorial

The coherent Ising machine (CIM) is designed to solve the NP-hard Ising problem quickly and energy efficiently. Boolean satisfiability (SAT) and maximum satisfiability (Max-SAT) are classes of NP-complete and NP-hard problems that are equally important and more practically relevant combinatorial optimization problems. Many approaches exist for solving Boolean SAT, such as quantum annealing and classical stochastic local search (SLS) solvers; however, they all are expected to require many steps to solve hard SAT problems and, thus, require large amounts of time and energy. In addition, a SAT problem can be converted into an Ising problem and solved by an Ising machine; however, we have found that this approach has drawbacks. As well as reviewing existing approaches to solving the SAT problem, we have extended the CIM algorithm and architecture to solve SAT and Max-SAT problems directly. This new technique is termed a coherent SAT solver (CSS). We have studied three implementations of the CSS, all-optical, hybrid optical–digital and all digital (cyber-CSS), and have compared the time-to-solution and energy-to-solution of three machines. The cyber-CSS, which is already implemented using a graphics processing unit (GPU), demonstrates competitive performance against existing SLS solvers such as probSAT. The CSS is also compared with another continuous-time SAT solver known as the CTDS, and the scaling behavior is evaluated for random 3-SAT problems. The hybrid optical–digital CSS is a more performant and practical machine that can be realized in a short term. Finally, the all-optical CSS promises the best energy-to-solution cost; however various technical challenges in nonlinear optics await us in order to build this machine.
Accountable and Distributed Industrial Control Systems with Autonomous Contracts : OCN-DLT: Industry Operations and Control Networks with Distributed Ledger Technology

Industrial Control Systems (ICS) have specific data requirements in terms of Quality of Service (QoS). Lost or delayed critical data, such as control signals, can damage widespread production, such as machine performance. The problem is compounded in the next-generation Industry 4.0, where the operations will be even less human-dependent, involving multiple organizations. Moreover, the communication aspects across multistakeholders require a disaggregated approach contrary to the current closed ICS architecture.This paper presents a framework for decentralization of ICS called Operations and Control Networks (OCN) in which ‘contracts’ are the basic units of abstraction to exchange information with trust and precision between different actors. In the lower layers, High-Precision Communication (HPC) contracts execute control functions with requested service level guarantees in the network. A high-level trusted delegation of tasks is executed as smart contracts using Distributed Ledger Technology (DLT) to enable multi-stakeholder auditability and accountability.
Federated Learning for Distributed NWDAF Architecture

Machine Learning (ML) has been considered to play a key role in processing collected data from the network Functions(NFs) in 5G. Network Data Analytic Function(NWDAF) is a new 5G component that is designed to provide analytics for any Network Functions(NFs). However, sending all data to a central NWDAF instance is extremely time-consuming and it raises concerns about security vulnerabilities and data overload. To tackle these issues, a distributed architecture for NWDAFs is proposed to perform parallel processing such that there are multiple NWDAFs co-located on the edges near the NFs in 5G. Therefore, predictive analytics are generated with considerably less latency service in the 5G. However, existing multi-node ML frameworks for distributed networks are not suitable for this scenario due to security and robustness issues. In order to address this issue, we developed a demonstration that showcases our novel centralized Federated Learning framework. The proposed centralized Federated Learning Framework is specially designed to meet the challenges that are roused in the distributed NWDAF architecture. Eventually, the performance of this framework is compared with current solution and potential candidates in our distributed NWDAF demonstration.
Knowledge graph representation learning model based on meta-information and logical rule enhancements

Existing knowledge graph representation learning (KGRL) models rely on explicit semantic information of triple structure and cannot fully mine the implicit semantic information in the knowledge graph (KG). Aiming to improve KGRL model performance and accuracy, making up for the disadvantages of existing research, we propose Melo (Meta-information and Logical rules), a novel KGRL model that leverages meta-information and logical rules of entities and relations. Melo first utilizes neighborhood structures of entities to obtain meta-information and ontological information, then it mines logical rules from the KG to infer high-confidence triples and expand the training samples. Finally, Melo realizes accurate and reliable representations of entities and relations with help of meta-, logical, and triple structure information. Experimental results on regular and sparse datasets show its enhanced performance when compared with baselines in terms of multiple evaluation metrics. Visualization methods are also utilized to demonstrate how meta-information, logical rules, and triple structure mutually and separately enhance training.
High-order Adams Network (HIAN) for image dehazing

Convolutional Neural Networks (CNN) are widely used in image dehazing. However, existing network frameworks are built based on manual design from practical experience, lacking interpretable result or theoretical guidelines. Recently, residual networks are regarded as the explicit Euler forward approximation of the ODE (Ordinary Differential Equation), and several ODE-inspired networks are proposed based on the low-order explicit Euler schemes. However, on the issues of system stability and training convergence, high-order Implicit Adams Predictor–Corrector (IAPC) methods have proven to be better than low-order explicit Euler methods. Hence, we extend the IAPC method to the High-order Implicit Adams Network (HIAN). To do so, we design a series of Implicit Adams Predictor–Corrector Blocks (IABs) based on the high-order IAPC methods, all of which give better stability and accuracy than the ones designed using the low-order Euler methods. Given that, we further propose the Implicit Adams Predictor–Corrector Module (IAM) by combining the Non-local Sparse Attention (NSA) and Attention Feature Fusion (AFF) with stacked IABs where the NSA explores the mutual-correlation among intermediate features with low computation cost via a sparse constraint, while the AFF fuses intermediate features by reweighting the features from stacked IABs adaptively. Moreover, because manual network design with IABs limits dehazing performance, the Neural Architecture Search (NAS) is used to find an optimal architecture automatically. This resulting design not only is interpretable for image dehazing but also provides a reliable guideline on future network designs. The experiments demonstrate that the proposed method outperforms most existing methods on both synthetic and real images.
Quadratic forms representing large integers only

For a positive integer n, let T(n) be the set of all integers greater than or equal to n. An integral quadratic form f is called tight T(n)-universal if the set of nonzero integers that are represented by f is exactly T(n). Let t(n) be the smallest possible rank over all tight T(n)-universal quadratic forms. In this article, we find all tight T(n)-universal diagonal quadratic forms. We also prove that t(n)∈Ω(log2⁡(n))∩O(n). Explicit lower and upper bounds for t(n) will be provided for some small integer n.
Automated image-based geometric modelling of thick laminates with three-dimensional ply wrinkles

Ply wrinkles are commonly induced during thermal–mechanical manufacturing process of laminated composite structures or components. Fibre misalignment due to ply wrinkles compromises the structural integrity of a composite structure. The damage criticality can be assessed numerically based on non-destructive testing results. Geometric modelling is usually required and it is time-consuming if complex wrinkles have to be incorporated into the finite element model. This study presents an automated image-based three-dimensional geometric reconstruction method for thick laminates with complex ply wrinkles. The geometry of a thick laminate is reconstructed based on the limited inspection images from the sides of laminates. The two-dimensional geometric reconstruction of the detected wrinkled plies is done using a multiple Gaussian functions model or linear interpolation, while the intermediate plies are defined by distance-based interpolation. Based on the reconstructed two-dimensional geometries, the three-dimensional ply wrinkles are modelled via an inverse distance weighting interpolation method. To verify the proposed method, a thick glass fibre laminate with artificially induced wrinkles is manufactured and the cross-sectional geometries of the reconstructed model are compared well with and the realistic inspection data. Progressive failure analysis of a two-dimensional and a three-dimensional reconstructed model are compared in terms of damage progression and load versus nominal strain response to highlight the relevance of modelling three-dimensional wrinkles to obtain more accurate and efficient damage criticality assessments.
Experience in creating a digital educational environment in the Republican Medical College of the Ministry of Health of the Udmurt Republic

The article presents the experience of creating a digital educational environment in a college for all participants in the educational process. The availability of online educational services provides equal opportunities for students of all branches of the College to study, regardless of their location. Creating an electronic database of students, maintaining an electronic document flow of educational and educational-methodical activities, optimizes and automates the maintenance of accounting and reporting documentation for the activities of the College. Target. Optimization and ensuring the availability of educational activities, automation of workflow in the College through the digitalization of the learning process of students and educational and methodological activities of teachers. Results. Creation and use of programs Electronic timetable, Electronic journal, LMS Moodle platform for the implementation of programs using distance learning. Conclusion. The created content of the digital educational environment, including a database of students, an electronic timetable, an electronic journal, an electronic document management, creates convenience in the work of all participants in the educational process, and contributes to the effective management of educational activities.