Reviewerly/exHarmony huggingface dataset:
MMF: a fault detection model for UAVs based on Multimodal Model Fusion

Abstract Fault detection of UAVs is one of the main technical means to ensure the normal flight of UAVs. Traditional methods are based on a single model for anomaly detection, but a single model cannot learn the features of UAVs from multiple perspectives. Multi-model Neural Networks can learn features of the data from multiple perspectives. Inspired by this, a Multimodal Model Fusion (MMF) based UAV fault detection model is proposed in this paper. The MMF model consists of three parts: firstly, a Multilayer Perceptron (MLP) and a Convolutional Neural Network (CNN) are used to obtain nonlinear features and key features through parallel computation of homologous UAV data; secondly, key features and nonlinear features are merged to obtain a fusion vector; finally, a fully connected layer is used to compute the fusion vector, and then the results are output by a Softmax classifier. The experimental results show that the accuracy of the MMF model is higher than that of the single-model by about 2% to 7% on the ALFA dataset.
R Stylo and the authorship determination of <i>Henry V</i>

Abstract Over 25 years, Thomas Merriam has argued that Henry V was co-authored by Shakespeare and Christopher Marlowe, and in his most recent publication ‘Is it time to reconsider Henry V’ (2023), he established differences in word length, which gives clear evidence. This article makes use of the R Stylo suite of stylometric tools and employs the Rolling Delta, Rolling Classify, and the General Imposters methods, all of which obtain the same result that Shakespeare used a Marlowe pretext in his composition of Henry V.
UKRAINIAN DIGITAL ART DURING THE FULL-SCALE RUSSIAN-UKRAINIAN WAR

The purpose of the article is to find out how the full-scale Russian-Ukrainian war and today's technological innovations affected the artistic community, the themes and the creative methods of Ukrainian digital art. The authors of the investigation, on the one hand, consider the digital works of Nikita Titov, Albina Yaloza, Kateryna Lisova, Mykhailo Rai, Oksana Chepelyk, Yulia Shibirkina, Zibra AI, ZViT, Beata Kurkul and Maria Sharlai, and others, and on the other hand, they highlight the biggest events related to the demonstration of digital art, in particular, “Artists support Ukraine”, “Meta History: Museum of War”, “Peace Letters to Ukraine”, “Art on the battlefront”, “UBIENNALE”, “Ukrainian wartime poster”, and others. The analysis of the collected material revealed that the vast majority of war posters are works of digital art, which in general has become one of the leading media in the work of Ukrainian artists. On the one hand, this was the result of a number of steps taken by the Ukrainian authorities aimed at liberalizing the cryptocurrency market, and encouraging computer game developers to fundraise with the help of NFT in support of Ukraine, on the other hand, the threat of Russian air attacks prompted representatives of the creative class to transfer a significant part of their activity to online, and see the benefits of digital art, which, compared to most traditional media, requires significantly less effort and resources to create, securely store, sell and display. The use of neural networks as a tool to create digital art has become one of the trends in war-themed art, but no catastrophic changes for visual art and design have been detected yet. A significant historical event after February 24, 2022 was the unprecedented support of Ukraine from the world cultural community, in particular, international developers of computer games, as well as famous artists of contemporary art, including Marina Abramovych, Ron Arad, Banksy, Francesco Vezzoli, Nan Goldin, Douglas Gordon, Ivan Messak, Lorenzo Quinn, Luke Tuymans, and others.
DB-COVIDNet: A Defense Method against Backdoor Attacks

With the emergence of COVID-19 disease in 2019, machine learning (ML) techniques, specifically deep learning networks (DNNs), played a key role in diagnosing the disease in the medical industry due to their superior performance. However, the computational cost of deep learning networks (DNNs) can be quite high, making it necessary to often outsource the training process to third-party providers, such as machine learning as a service (MLaaS). Therefore, careful consideration is required to achieve robustness in DNN-based systems against cyber-security attacks. In this paper, we propose a method called the dropout-bagging (DB-COVIDNet) algorithm, which works as a robust defense mechanism against poisoning backdoor attacks. In this model, the trigger-related features will be removed by the modified dropout algorithm, and then we will use the new voting method in the bagging algorithm to achieve the final results. We considered AC-COVIDNet as the main inducer of the bagging algorithm, which is an attention-guided contrastive convolutional neural network (CNN), and evaluated the performance of the proposed method with the malicious COVIDx dataset. The results demonstrated that DB-COVIDNet has strong robustness and can significantly reduce the effect of the backdoor attack. The proposed DB-COVIDNet nullifies backdoors before the attack has been activated, resulting in a tremendous reduction in the attack success rate from 99.5% to 3% with high accuracy on the clean data.
Onboarding with confidence in open research (s03e16)

🔈This is a transcript of the Open Update podcast. Listen to the original audio on Anchor.fm. [00:00:00] &lt;strong&gt;Chris Hartgerink:&lt;/strong&gt; Welcome back to the Open Update.
Thermal State Preparation via Rounding Promises

A promising avenue for the preparation of Gibbs states on a quantum computer is to simulate the physical thermalization process. The Davies generator describes the dynamics of an open quantum system that is in contact with a heat bath. Crucially, it does not require simulation of the heat bath itself, only the system we hope to thermalize. Using the state-of-the-art techniques for quantum simulation of the Lindblad equation, we devise a technique for the preparation of Gibbs states via thermalization as specified by the Davies generator. In doing so, we encounter a severe technical challenge: implementation of the Davies generator demands the ability to estimate the energy of the system unambiguously. That is, each energy of the system must be deterministically mapped to a unique estimate. Previous work showed that this is only possible if the system satisfies an unphysical &amp;apos;rounding promise&amp;apos; assumption. We solve this problem by engineering a random ensemble of rounding promises that simultaneously solves three problems: First, each rounding promise admits preparation of a &amp;apos;promised&amp;apos; thermal state via a Davies generator. Second, these Davies generators have a similar mixing time as the ideal Davies generator. Third, the average of these promised thermal states approximates the ideal thermal state.
Ante el repliegue de la pandemia se vislumbra una sociedad creativa

In a globalized world it is essential to promote a creative society, where citizens can live according to their true nature. To visualize a creative society is to walk towards fair, comprehensive and human horizon; this creative society must have the 5 Ss: Healthy (Sana in Spanish) Sustainable, Sensitive, Solidary and Synergistic, basic characteristics if we intend to innovate, cooperate and share. To generate a creative society, educational centers will have to change their culture and start promoting, among other resources, creativity, resilience and an entrepreneurial attitude. Three great challenges are proposed: 1) Redefine the identity of the human being. 2) Acquire the commitment to build a creative society. 3) Develop the spiritual dimension.
Caminos para la (trans)formación docente en tiempos de pandemia a la luz del pensamiento complejo: una revisión bibliográfica sistemática

Currently we face the challenges of an educational scenario affected by the drastic effects of the pandemic of COVID-19, the continuing education of teachers shows itself as possible spaces for reflection of praxis, attesting to the emergence of (trans) formations arising from a paradigmatic change that comes to base learning and integrative strategies (Alves, 2016) during the pandemic context. To this end, complex thinking, anchored in the work of Edgar Morin, shows itself as a light for the proposed discussions. As a methodological action, we conducted a systematic review of integrative literature in order to investigate the academic productions carried out during the pandemic period related to teacher education and supported by the theoretical foundations of complex thinking. It is possible to notice that this is a new paradigm that has been the basis of few productions in the last few months directly related to the theme. Among the studies found, one can notice the relevance of these spaces as promoters of the necessary transformations for the education of the future built in the present.
Blockchain-based privacy and security model for transactional data in large private networks

Cyberphysical systems connect physical devices and large private network environments in modern communication systems. A fundamental worry in the establishment of large private networks is mitigating the danger of transactional data privacy breaches caused by adversaries using a variety of exploitation techniques. This study presents a privacy-preserving architecture for ensuring the privacy and security of transaction data in large private networks. The proposed model employs digital certificates, RSA-based public key infrastructure, and the blockchain to address user transactional data privacy concerns. The model also guarantees that data in transit remains secure and unaltered and that its provenance remains authentic and secure during node-to-node interactions within a large private network. The proposed model has increased the encryption speed by about 17 times, while the decryption process is expedited by 4 times. Therefore, the average overall acceleration obtained was 16.5. Both the findings of the security analysis and the performance analysis demonstrate that the proposed model can safeguard transactional data during communications on large private networks more effectively and securely than the existing solutions.
Pulse-shape effects in qubit dynamics demonstrated on an IBM quantum computer

We present a study of the coherent interaction of a qubit with a pulse-shaped external field of a constant carrier frequency. We explore, theoretically and experimentally, the transition line profile (the dependence of the transition probability on the detuning) for five different pulse shapes: Rectangular, Gaussian, hyperbolic-secant, squared hyperbolic-secant, and exponential. The theoretical description for all cases but ${\mathrm{sech}}^{2}$ is based on the analytical solutions to the Schr\"odinger equation or accurate approximations available in the literature. For the ${\mathrm{sech}}^{2}$ pulse we derive an analytical expression for the transition probability using the Rosen-Zener conjecture, which proves very accurate. In addition, the same conjecture turns out to provide very accurate approximations for the Gaussian and exponential pulses. The experimental results are obtained with one of IBMQ's quantum processors. An excellent agreement between theory and experiment is observed, demonstrating some pulse-shape-dependent fine features of the transition probability profile. The mean absolute error (a measure of the accuracy of the fit) features an improvement by a factor of 4 to 7 for the analytic models compared to the commonly used Lorentzian fits. Moreover, the uncertainty of the qubit's resonance frequency is reduced by a factor of 4 for the analytic models compared to the Lorentzian fits. These results demonstrate both the accuracy of the analytic modeling of quantum dynamics and the excellent coherent properties of IBMQ's qubit.
A School to Remember: Analyzing School Best Practices in the Perspective of Digital Transformation of Schools

The digital transformation of schools aims to ensure a systematic process for adapting primary and secondary education to the level of technology innovations, preparing the next generation for new opportunities and demands. At the same time, digital transformation has to ensure complex organizational alignments beyond simple equipment implementation. School education is a conservative and slow-moving system, so a more in-depth analysis of the existing best practices can provide smooth digital transformation paths. The present research aims to explore and analyze best practices from the perspective of digital transformation in schools. Using a bottom-up approach, in total 436 best practices are collected from 120 students enrolled in Computer Science programs at Sofia University. These students surmounted one of the most competitive university entrance exams in the country, ensuring the validity and relevance of their best practices. The study covers experiences from 85 primary and secondary schools in 42 locations in Bulgaria. The structuring of the paper is as follows. It starts with a literature overview of school digital transformation and presents the DigiLEAD project approach. Then, the methodology of the study is discussed and best practices are classified by origin (teacher, school or policy-level practices) and by impact (five-level model of quality education). The discussion part proposes different models for adopting digital technologies in schools, taking in consideration that almost 60% of the best practices come from individual teachers, teaching approaches and personal commitment. In the conclusions are summarized the key lessons learned and the limitations of the study.
Point-of-Interest Recommendations Based on Immediate User Preferences and Contextual Influences

With the development of various location-based social networks (LSBNs), personalized point-of-interest (POI) recommendations have become a recent research hotspot. Current recommendation methods tend to mine user preferences from their historical check-in records but overlook interest deviations caused by real-time geographic environments and immediate interests present in the records, failing to meet users’ real-time and accurate needs. Therefore, this paper proposes a composite preference-based recommendation model (CPRM) for personalized POI recommendation. This method first extracts multi-factor contextual features, constructs a dual-layer attention network (DLAN) to capture long and short-term preferences, combines real-time geographic scenarios to uncover user immediate preferences, and then weights and fuses these three types of preferences to generate user composite preferences. Finally, a prediction function is employed to obtain the Top-N recommendation list. The experiments on two classic datasets, Foursquare and Gowalla, affirm the effectiveness of the model presented in this paper and offer a novel approach for providing personalized POI recommendations to users.
Social intimacy and skewed love: A study of the attachment relationship between internet group young users and a digital human

Interactions between human beings and digital humans have become a new network phenomenon, and these relationships have gradually become a topic of research. There is still a lack of sufficient research on whether and what kind of attachment relationship exists in these situations. Based on this problem, in this study, a digital human was designed that was oriented to social software and put into chat groups for interaction and research. A questionnaire survey, case analysis, and netnography analysis were used to collect and examine relevant data. The study found a correlation between the type of attachment of users and the degree of attachment to the digital human. In addition, users who were heavily dependent on the network were more likely to try to complete their attachment with the digital human. Attachment with the digital human was able to calm the users’ emotional intensity. This attachment was considered as close to a skewed desire projection. Through the intermediary of a digital human, Internet users have been better able to fulfill some of their own desires.
Write What You Want: Applying Text-to-Video Retrieval to Audiovisual Archives

Audiovisual (AV) archives, as an essential reservoir of our cultural assets, are suffering from the issue of accessibility. The complex nature of the medium itself made processing and interaction an open challenge still in the field of computer vision, multimodal learning, and human-computer interaction, as well as in culture and heritage. In recent years, with the raising of video retrieval tasks, methods in retrieving video content with natural language (text-to-video retrieval) gained quite some attention and have reached a performance level where real-world application is on the horizon. Appealing as it may sound, such methods focus on retrieving videos using plain visual-focused descriptions of what has happened in the video and finding videos such as instructions. It is too early to say such methods would be the new paradigms for accessing and encoding complex video content into high-dimensional data, but they are indeed innovative attempts and foundations to build future exploratory interfaces for AV archives (e.g., allow users to write stories and retrieve related snippets in the archive, or encoding video content at high-level for visualisation). This work filled the application gap by examining such text-to-video retrieval methods from an implementation point of view and proposed and verified a classifier-enhanced workflow to allow better results when dealing with in-situ queries that might have been different from the training dataset. Such a workflow is then applied to the real-world archive from Télévision Suisse Romande (RTS) to create a demo. At last, a human-centred evaluation is conducted to understand whether the text-to-video retrieval methods improve the overall experience of accessing AV archives.
Binary Semantic Pattern Rules for Chinese-English Machine Translation Based on Machine Learning Algorithms

With the increase of internationalization and the exponential growth of intercultural communication, the importance of interlingual translation has become increasingly prominent. Machine translation has been a booming area of research as technology has advanced. Due to the complexity of language ability and limited understanding of language laws, there are challenges for machine translation. This paper focused on how to construct and apply binary semantic pattern rules through machine learning to improve the translation effect in Chinese-English machine translation. The research results of this paper would contribute to the further development and improvement of Chinese-English machine translation technology. In order to produce high-quality translation results, research in machine translation has recognized the need to analyze and understand the semantics of natural language. To address the important issue of lexical and syntactic ambiguity, representations of binary semantic pattern rules have been developed to formally describe these rules. Based on this, this paper designed and implemented a corpus-based binary semantic rule extraction and optimization algorithm, which used machine learning algorithms to automatically detect the semantic rules of two or more than two phrases in the Chinese corpus, and then automatically optimized and converted them according to the statistical results, and realized the design of Chinese-English machine translation system. The article evaluated the quality of machine translation to test the effectiveness of machine translation binary semantic pattern rules based on machine learning algorithms. The study found that compared with the rule set A, the rule sets B and C obtained automatically by the rule mining algorithm had significantly improved accuracy, both reaching more than 90%. This showed that the binary semantic pattern rule mining algorithm and optimization algorithm proposed in this paper were reasonable.
Cryptographic Grade Chaotic Random Number Generator Based on Tent-Map

In recent years, there has been an increasing interest in employing chaotic-based random number generators for cryptographic purposes. However, many of these generators produce sequences that lack the necessary strength for cryptographic systems, such as Tent-Map. However, these generators still suffer from common issues when generating random numbers, including issues related to speed, randomness, lack of statistical properties, and lack of uniformity. Therefore, this paper introduces an efficient pseudo-random number generator, called State-Based Tent-Map (SBTM), based on a modified Tent-Map, which addresses this and other limitations by providing highly robust sequences suitable for cryptographic applications. The proposed generator is specifically designed to generate sequences with exceptional statistical properties and a high degree of security. It utilizes a modified 1D chaotic Tent-Map with enhanced attributes to produce the chaotic sequences. Rigorous randomness testing using the Dieharder test suite confirmed the promising results of the generated keystream bits. The comprehensive evaluation demonstrated that approximately 97.4% of the tests passed successfully, providing further evidence of the SBTM’s capability to produce sequences with sufficient randomness and statistical properties.
Human Activity Recognition Using Ensemble Neural Networks and The Analysis of Multi-Environment Sensor Data Within Smart Environments

The significant focus and potential value of Human Activity Recognition (HAR) technologies based on non-invasive ambient sensors have been attributed to the advancement of Artificial Intelligence (AI) and the widespread adoption of sensors. Due to the proactive engagement of human activities and the utilization of Machine Learning (ML) techniques that depend on domain expertise, developing a standardized model for comprehending the everyday actions of diverse individuals has significant challenges. A technique for recognizing the user's everyday activities in multi-tenant intelligent environments has been developed. This methodology considers data feature limits and recognition approaches and is designed to limit sensor noise during human activities. This work aims at enhancing the quality of a publicly accessible HAR dataset to facilitate data-driven HAR.Additionally, the paper proposes a novel ensemble of neural networks (NN) as a data-driven HAR classifier. A Spatial Proximity Matrix (SPM)uses ambient sensors to facilitate contextawareness and mitigate data noise. The proposed method, named Homogeneous Ensemble Neural Network and Multi-environment Sensor Data (HENN-MSD), leverages a combination of a homogeneous ensemble NN and multi-environment sensor data to identify what individuals do in daily life accurately. The study featured the generation and integration of four fundamental models using the support-function fusion approach. This method included the computation of an output decision score for each basis classifier. The analysis of a comparative experiment conducted on the CASAS dataset indicates that the proposed HENN-MSD technique exhibits superior performance compared to the state-of-the-art methods in terms of accuracy (96.57%) in HAR.