Reviewerly/exHarmony huggingface dataset:
Control Algorithms, Kalman Estimation and Near Actual Simulation for UAVs: State of Art Perspective

The pervasive use of unmanned aerial vehicles for both commercial and military operations has undergone rapid development in the recent past. When designing unmanned aerial vehicles, it is highly desirable for them to be able to complete their missions with minimal human intervention. Reaching full autonomy requires a reliable and efficient control algorithm that can handle all flight conditions. Due to the confidential nature of UAV design and development, there is a lack of comprehensive literature on the subject. When it comes to the practical application of the ideas presented in the literature, the situation is even bleaker. This research not only examines the flight phases in which controllers and estimators are used for UAVs but also provides an in-depth analysis of the most recent and state-of-the-art control and estimate techniques for UAVs. Research opportunities and challenges specific to UAVs were also examined in this study in an effort to raise the bar for UAV design as a whole and smooth the way for researchers to go from simulation-based research to practical applications. This review paper establishes a foundation that not only investigates the inherent flight dynamics, control architecture, and Kalman estimators utilized in the development of UAVs but also points out the shortcomings that currently exist in research. A number of design considerations for realistic applications and potential studies are presented in the conclusion.
Application Research of Artificial Intelligence in the Innovation of Zhuang Brocade Digital Art: Taking “The Speech of Zhuang Brocade” as an Example

Purpose: Based on the dilemma faced by the digitization of Zhuang Brocade intangible cultural heritage in Guangxi and the analysis of the advantages of artificial intelligence art in the digitization and innovation of intangible cultural heritage, this study explores the application path of the digital inheritance and dissemination of Zhuang Brocade in Guangxi by relying on the current theory and practice of artificial intelligence art, and provides reference significance for the inheritance and dissemination of intangible cultural heritage through artificial intelligence art.Method: Through in-depth analysis of the types, characteristics, and cultural connotations of Zhuang Brocade patterns in Guangxi, machine learning is performed using StyleGAN's adversarial neural network, and digital art works are generated by applying Clip-style.The feasibility of developing digital resources for Zhuang Brocade intangible cultural heritage is explored through artistic practice, and an application process and implementation strategy for digital art innovation are proposed.Result: It is feasible to create NFT digital collections through artificial intelligence art to achieve the application scenarios of digital inheritance, innovation, cross-regional dissemination, and even industrialization of Zhuang Brocade in Guangxi.Conclusion: Artificial intelligence art creation can provide new opportunities for digital cultural dissemination and inheritance of Zhuang Brocade while reflecting its cultural connotations and characteristics, and ensure traceable development while ensuring intellectual property rights.It realizes the continuation and revival of the value of Zhuang Brocade in Guangxi, and provides a certain reference for the inheritance and development of other intangible cultural heritage in the current context of rapid media updates and iterations.
Evaluation of Digital Banking Implementation Indicators and Models in the Context of Industry 4.0: A Fuzzy Group MCDM Approach

Modern technologies have changed human life and created a generation of customers who have different needs compared to the past. Considering Industry 4.0 and its drivers, the implementation of digital banking (DB) has faced various challenges that are caused by emerging trends. Both Industry 4.0 and DB are contemporary concepts, and decision-makers are often faced with uncertainties in their decisions regarding the implementation of DB and its indicators. For this purpose, a novel multi-criteria group decision-making approach has been developed utilizing the best–worst method (BWM) and α-cut analysis as well as trapezoidal fuzzy numbers (TFNs). By reviewing the literature and using experts’ opinions, the DB implementation criteria are determined, and considering an uncertain environment, the criteria are prioritized using the proposed method. Then, the available DB models and alternatives are examined based on the decision criteria and the importance of each criterion. This research contributes to the existing literature by identifying and prioritizing the criteria necessary for the successful implementation of DB, taking into account emerging trends and technological advances driven by Industry 4.0. Subsequently, the study prioritizes the prevalent models of DB based on these criteria. This study proposes a decision-support framework for dealing with ambiguity, lack of information, insufficient knowledge, and uncertainty in decision-making. The framework uses TFNs to account for imprecision and doubt in decision-makers’ preferences. Additionally, the study presents a fuzzy multi-criteria group decision-making approach that enables a group of experts to arrive at more reliable results. The proposed approach can help improve the quality of decision-making in complex and uncertain situations. The results of this research show that human resources, rules and regulations, and customer satisfaction are the most important criteria for implementing DB. In addition, the open, blockchain, and social banking models are the crucial models that significantly cover the implementation criteria for DB.
SQL Query Optimization in Distributed NoSQL Databases for Cloud-Based Applications

A method for query optimization is presented by utilizing Spark SQL, a module of Apache Spark that integrates relational data processing. The goal of this paper is to explore NoSQL databases and their effective usage in conjunction with distributed environments to optimize query execution time, in order to accommodate the user complex demands in a cloud computing setting that necessitate the real-time generation of dynamic pages and the provision of dynamic information. In this work, we investigate query optimization using various query execution paths by combining MongoDB and Spark SQL, aiming to reduce the average query execution time. We achieve this goal by improving the query execution time through a sequence of query execution path scenarios that split the initial query into sub-queries between MongoDB and Spark SQL, along with the use of a mediator between Apache Spark and MongoDB. This mediator transfers either the entire database from MongoDB to Spark, or transfers a subset of the results for those sub-queries executed in MongoDB. Our experimental results with eight different query execution path scenarios and six difference database sizes demonstrate the clear superiority and scalability of a specific scenario.
The Application of Open Science Potentials in Research Processes: A Comprehensive Literature Review

Abstract The aim of this study was to conduct a comprehensive literature review of the dimensions of open science in research processes. A total of four databases and snowball searching were used for the comprehensive literature review during 2011–2020; then, we were able to find 98 studies based on the inclusion criteria. Also, we used thematic method to review the relevant studies and identified three categories of dimensions in the research process, namely (1) the publication and sharing category including open access, open data, transparency and reproducibility, citizen science, and crowd sourcing; (2) the infrastructure and cultural category including open infrastructure, open education, open tools, budget mechanism, open culture, and communication; and (3) governance and evaluation including policies, governance, and the ethical principles associated with open science. Open science emphasizes the efforts to open and make the scientific research process more inclusive so as to engage the inside and outside actors in the research process.
DNA Image Encryption Scheme Based on a Chaotic LSTM Pseudo-Random Number Generator

Deoxyribonucleic Acid (DNA) coding technology is a new research field developed by the combination of computer science and molecular biology, that has been gradually applied in the field of image encryption in recent years. Furthermore, sensitivity to initial conditions, pseudo-random properties, and state ergodicity of coupled chaotic maps can help produce good pseudo-random number generators and meet the requirements of an image encryption system well. In this paper, an image encryption algorithm based on high-dimensional coupled chaotic maps and DNA coding is proposed. A pseudo-random sequence is generated by a long short-term memory (LSTM) architecture using the proposed maps and evaluated through a set of statistical tests to show the high performance of the proposed generator. All intensity values of an input image are converted to a binary sequence, which is scrambled globally by the high-dimensional coupled chaotic maps. The DNA operations are performed on the scrambled binary sequences instead of binary operations to increase the algorithm efficiency. Simulation results and performance analyses demonstrate that the proposed encryption scheme is extremely sensitive to small changes in secret keys, provides high security and can resist differential attack.
GUI Component Detection-Based Automated Software Crash Diagnosis

This study presents an automated software crash-diagnosis technique using a state transition graph (STG) based on GUI-component detection. An STG is a graph representation of the state changes in an application that are caused by actions that are executed in the GUI, which avoids redundant test cases and generates bug-reproduction scenarios. The proposed technique configures the software application STG using computer vision and artificial intelligence technologies and performs automated GUI testing without human intervention. Four experiments were conducted to evaluate the performance of the proposed technique: a detection-performance analysis of the GUI-component detection model, code-coverage measurement, crash-detection-performance analysis, and crash-detection-performance analysis in a self-configured multi-crash environment. The GUI-component detection model obtained a macro F1-score of 0.843, even with a small training dataset for the deep-learning model in the detection-performance analysis. Furthermore, the proposed technique achieved better performance results than the baseline Monkey in terms of code coverage, crash detection, and multi-crash detection.
Providing Meaningful Digital Competence Assessment Feedback for Supporting Teachers Professional Development

Measuring the effectiveness of teaching is a complex and multifaceted process which requires understanding both the context and assessment goal. Based on an OECD report [7] the quality of the educational system relies heavily on the systematic assessment and development of teachers’ competence while focusing on reflection. Although teachers’ digital competence has been a central discussion point for the past decade, researchers are still struggling to understand what are the most effective feedback solutions to support teachers’ digital competence development. The following paper gives and overview of the results of a design study which focused on understanding the digital competence self-assessment feedback loop. Based on the design study we proposed and evaluated a feedback loop prototype which supports teacher’s digital competence and meaningful professional development.
The Design of an Adaptive Tool Supporting Formative Assessment in Data Science Courses

The automated correction of assignments is a task that received large support from artificial intelligence. In previous research, we approached the problem of automatically providing a grade and feedback to students in solving data science exercises, that resulted in the development of the rDSA tool. In this paper, we discuss the first steps towards the development of an adaptive system – based on the rDSA tool – supporting the students’ formative assessment activities. In particular, we present the context of use, the requirements – elicited through a study with a small cohort of students, the models enabling adaptation, and the user interface. Finally, we evaluated the user interface through a further study that involved both qualitative and quantitative measures.
Adapting Single-Image Super-Resolution Models to Video Super-Resolution: A Plug-and-Play Approach

The quality of videos varies due to the different capabilities of sensors. Video super-resolution (VSR) is a technology that improves the quality of captured video. However, the development of a VSR model is very costly. In this paper, we present a novel approach for adapting single-image super-resolution (SISR) models to the VSR task. To achieve this, we first summarize a common architecture of SISR models and perform a formal analysis of adaptation. Then, we propose an adaptation method that incorporates a plug-and-play temporal feature extraction module into existing SISR models. The proposed temporal feature extraction module consists of three submodules: offset estimation, spatial aggregation, and temporal aggregation. In the spatial aggregation submodule, the features obtained from the SISR model are aligned to the center frame based on the offset estimation results. The aligned features are fused in the temporal aggregation submodule. Finally, the fused temporal feature is fed to the SISR model for reconstruction. To evaluate the effectiveness of our method, we adapt five representative SISR models and evaluate these models on two popular benchmarks. The experiment results show the proposed method is effective on different SISR models. In particular, on the Vid4 benchmark, the VSR-adapted models achieve at least 1.26 dB and 0.067 improvement over the original SISR models in terms of PSNR and SSIM metrics, respectively. Additionally, these VSR-adapted models achieve better performance than the state-of-the-art VSR models.
Calibration method for large-field-of-view stereo vision system based on distance-related distortion model

Large-field-of-view stereo vision system lacks flexible and high-precision calibration methods. To this end, we proposed a new distance-related distortion model based calibration method combining 3D points and checkerboards. The experiment indicates that the proposed method has a root mean square of fewer than 0.08 pixels for the reprojection error on the calibration dataset, and the mean relative error of length measurement in a volume of 5.0 m × 2.0 m × 16.0 m is 3.6‰. Compared with other distance-related models, the proposed model has the lowest reprojection error on the test dataset. Besides, in contrast to other calibration methods, our method offers enhanced accuracy and greater flexibility.
Time-conserving deduplicated data retrieval framework for the cloud computing environment

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Chaotic Time Series Prediction of Multi‐Dimensional Nonlinear System Based on Bidirectional LSTM Model

Abstract The current work proposes a hybrid data‐driven model—Convolutional bidirectional long–short term memory (CNN‐BLSTM) for predicting chaotic behavior of three‐coupled Duffing oscillator nonlinear system, in which the CNN is for efficiently extracting the more robust and informative representations of chaotic sequences while the BLSTM is for holding the long‐term dependencies combining the past and future contexts. Different from traditional analytical and numerical approaches, the proposed prediction model features the benefit of focusing on the measured data solely without extensive professional domain knowledge. Additionally, three more recurrent neural network (RNN) models, including simple RNNs, stack LSTMs, and BLSTM, are built and comparisons of generalization performances to the CNN‐BLSTM are conducted. From the findings so far, the CNN‐BLSTM is able to learn the pattern of chaotic time sequence data with less training time and apply the acquired knowledge to the unseen dataset with lower errors. Moreover, the current work decently demonstrates that the proposed model outperforms other three models in terms of stability at different noise levels from two evaluation criteria. The CNN‐BLSTM provides useful guidance for the consideration of predicting multi‐dimensional nonlinear chaotic behavior.
Identifying the idiosyncrasies of behavioral information security discourse and proposing future research directions: A Foucauldian perspective

Information security (InfoSec)–related behaviors have been defined in many different ways in the security literature. Indeed, the coexistence of varying terms describing IS security behaviors could be seen as a conceptual inconsistency. However, we believe that it could also be construed as the sign of a vivid and living field embracing dispersed objects of study without changing the disciplinary discourse itself. To test this belief, we conduct a two-part study. Study 1 analyzes the definitions of InfoSec-related behaviors in the existing security literature. The result of Study 1 uncovers the Foucauldian rules that make possible the discourse on behavioral information security, including the surface of emergence, authorities of delimitation, and the grids of specification. Study 1 also indicates that, although behavioral Information Systems Security (ISS) shares the three rules with other disciplines, it is the only field that studies the relations between them. We therefore propose a disciplinary question that reflects this idiosyncrasy. We then conduct Study 2, using Foucault’s view on preconceptual schemata as a theoretical lens, to identify understudied areas in the ISS literature. We found that the existing literature utilizes two variations of the actor and organization schemata to form or to adapt concepts, and utilize two different approaches to study these two variations, namely, an antecedents approach and a dialectics approach. It is noteworthy that the latter is important but understudied in the literature. We then draw an analogy from moral studies to develop three concepts for future ISS research, namely, Stages of Awareness Development, Security Agency, and Security Disengagement.
Polarization bases compensation towards advantages in satellite-based QKD without active feedback

Abstract Long-distance photonic implementations of quantum key distribution protocols have gained increased interest due to the promise of information-theoretic security against unauthorized eavesdropping. However, a significant challenge in this endeavor is photon-polarization getting affected due to the birefringence of fibers in fiber-based implementations, or variation of reference frames due to satellite movement in long-haul demonstrations. Conventionally, active feedback-based mechanisms are employed for real-time polarization tracking. Here, we propose and demonstrate an alternative approach via a proof-of-principle experiment over an in-lab entanglement-based (BBM92) protocol. In this approach, we perform a quantum state tomography to arrive at optimal measurement bases for any one party resulting in maximal (anti-)correlation in measurement outcomes of both parties. Our polarization-entangled bi-photons have 94% fidelity with a singlet state and a Concurrence of 0.92. By considering a representative 1 ns coincidence window span, we achieve a quantum-bit-error-rate (QBER) of ≈5%, and a key rate of ≈35 Kbps. The performance of our implementation is independent of any local polarization rotation. Finally, using optimization methods we achieve the best trade-off between the key rate, QBER, and balanced key symmetry. Our approach obviates the need for active polarization tracking. It is also applicable to such demonstrations with non-maximally entangled states and prepare-and-measure-based protocols with partially polarized single-photon sources.
Blockchain Enabled Interpolation Based Reversible Data Hiding Mechanism for Protecting Records

A diagnosis can be made using a lot of the crucial information contained in medical snaps. Medical images have become a target for malicious attacks due to the requirement for regular communication in order to provide flexibility and accurate diagnosis. In order to protect medical images, encryption algorithms are used. Because of this, medical photos are encrypted before being transmitted; yet, this is only one layer of security. Reversible Data Hiding (RDH) techniques have recently been used to incorporate private data into medical images. This enables efficient and safe communication, and the secretly contained information—such as personal and medical records—is highly helpful for making medical diagnosis. However, the limited embedding capacity of current RDH systems continues to limit their usefulness. In this study, a Reversible Data Hiding method based on a histogram shifting and interpolation scheme is highlighted. The achievable embedding capacity (EC) for the suggested technique is one bit per pixel (bpp) for both digital and medical images. A blockchain-based system based on three keys is used to encrypt the images. The proposed blockchain mechanism is secure against outside threats. To verify the utility of the suggested strategy, the outcomes are compared to cutting-edge techniques for both digital and medical photos. Along with the hash value of the actual medicinal snaps, the private information is preserved on the blockchain. Due to this, all medical photos transmitted through the suggested blockchain network may be monitored. The experiments and analysis are shows that the proposed scheme has excellent security has attained during the entire process. It also achieved high embedding capacity, PSNR, rate and low SSIM throughout the process of data concealing.
Destroy All Humans: The Dematerialisation of the Designer in an Age of Automation and its Impact on Graphic Design—A Literature Review

Abstract Digital automation is on the rise in a diverse range of industries. The technologies employed here often make use of artificial intelligence (AI) and its common form, machine learning (ML) to augment or replace the work completed by human agents. The recent emergence of a variety of design automation platforms inspired the authors to undertake a review of the research literature on the impact of Automation, AI and ML on visual communication, and its subset practice of graphic design, with a view to understanding the implications for the education of practitioners entering that specific field. This review discovered that there was relatively little research published on the topic but what did exist noted that graphic design as we have known it has an uncertain future. Furthermore, the scant literature argued for a shift in educational and professional focus away from the aesthetic and technical skills required to design visual modes of communication and towards a deeper engagement with the softer, more human skills associated with negotiation, facilitation and judgement. The paucity of literature on this topic suggests to the authors that visual communication design education and the industry are poorly prepared for the impact of automation, AI and ML on them.