Reviewerly/exHarmony huggingface dataset:
Assessing primary school preservice teachers’ confidence to apply their TPACK in specific categories of technologies using a self-audit survey

Abstract Internationally, university teacher educators have acted on the requirement that practising teachers need to be operational users of technologies. In response, coursework has been restructured to develop preservice teachers’ (PSTs) use and application of educational technologies for teaching and learning purposes. This paper presents the development and use of a self-audit survey designed to guide primary school PSTs’ self-directed learning and assessments in 10 specific categories of technologies, and to improve their confidence to apply their knowledge. The survey was administered to 296 PSTs in a pre- and post-course design and validated post hoc with Rasch analysis. Pre- and post-course comparison of responses showed significant increases in the PSTs’ self-assessed confidence in their abilities across all ability categories with medium to large effect sizes ( r = .42–.82). The survey had utility, providing each PST with a customised report with which to identify their technology learning needs; it also provided PSTs with the agency to be self-directed in their learning. The survey has the potential to provide teacher educators with fine-grained information regarding which technological pedagogical and content knowledge (TPACK) being currently taught can be adopted, adapted, or rejected to maximise PSTs’ TPACK development.
Measuring the motion of the eardrum using Digital Image Correlation

Abstract The human eardrum is a translucent curved membrane and is in direct contact with the middle ear bones. Studying this complicated biomechanical system remains challenging. Digital image correlation (DIC) is an optical technique that allows measuring the shape and displacement of curved surfaces in 3D. In this work, we discuss the advantages and disadvantages of using DIC to study the eardrum at frequencies around 1 kHz. Special attention is given to the application of speckle patterns required to perform DIC on the translucent eardrum. Additionally, we detail an alternative approach to high-speed cameras: phase-locked light pulses. Using light pulses synced to the phase of the sound, regular DIC cameras can be used at their full resolution. This enables a high spatial resolution to thoroughly investigate the motion of the eardrum at the relevant frequencies.
An efficient image encryption algorithm using a discrete memory-based logistic map with deep neural network

Abstract In the last few years, multimedia technology has made tremendous strides. These days, the Web is frequently used to transfer multimedia content, including audio, video, and photos. However, the Internet is a very vulnerable medium with many security holes. To ensure that multimedia content carried across unprotected channels, like the Internet, is secure and private, several encryption techniques have been proposed. New encryption strategies must be developed because multimedia data streams cannot be encrypted using traditional methods. Therefore, the main goal of the recommended system is to present an analytical research approach for introducing a sophisticated framework wherein the suggested encryption technologies' efficacy is increased through the use of deep neural networks (DNNs). The robustness of the DNN principle is coupled with a discrete memristor-based logistic chaotic map notion for enhanced security performance. In this paper, three distinct encryption algorithms—Arnie cat with an artificial neural network (ANN), Henon map with an ANN, and logistic map with a DNN—are compared for security and performance with the suggested algorithm. Correlation coefficients, information entropy, number of pixels changing rate (NPCR), encryption quality, and encryption duration are the cryptographic analysis parameters examined here. The results show that the recommended implementation enhances security performance without degrading image quality. The proposed algorithm achieves 35.9% of UACI, 99.95% of NPCR, and 7.997231 of entropy.
Traffic sign recognition using CNN and Res-Net

&#x0D; In the realm of contemporary applications and everyday life, the significance of object recognition and classification cannot be overstated. A multitude of valuable domains, including G-lens technology, cancer prediction, Optical Character Recognition (OCR), Face Recognition, and more, heavily rely on the efficacy of image identification algorithms. Among these, Convolutional Neural Networks (CNN) have emerged as a cutting-edge technique that excels in its aptitude for feature extraction, offering pragmatic solutions to a diverse array of object recognition challenges. CNN's notable strength is underscored by its swifter execution, rendering it particularly advantageous for real-time processing. The domain of traffic sign recognition holds profound importance, especially in the development of practical applications like autonomous driving for vehicles such as Tesla, as well as in the realm of traffic surveillance. In this research endeavour, the focus was directed towards the Belgium Traffic Signs Dataset (BTS), an encompassing repository comprising a total of 62 distinct traffic signs. By employing a CNN model, a meticulously methodical approach was obtained commencing with a rigorous phase of data pre-processing. This preparatory stage was complemented by the strategic incorporation of residual blocks during model training, thereby enhancing the network's ability to glean intricate features from traffic sign images. Notably, our proposed methodology yielded a commendable accuracy rate of 94.25%, demonstrating the system's robust and proficient recognition capabilities. The distinctive prowess of our methodology shines through its substantial improvements in specific parameters compared to pre-existing techniques. Our approach thrives in terms of accuracy, capitalizing on CNN's rapid execution speed, and offering an efficient means of feature extraction. By effectively training on a diverse dataset encompassing 62 varied traffic signs, our model showcases a promising potential for real-world applications. The overarching analysis highlights the efficacy of our proposed technique, reaffirming its potency in achieving precise traffic sign recognition and positioning it as a viable solution for real-time scenarios and autonomous systems.
Research on YOLOv5 Vehicle Detection and Positioning System Based on Binocular Vision

Vehicle detection and location is one of the key sensing tasks of automatic driving systems. Traditional detection methods are easily affected by illumination, occlusion and scale changes in complex scenes, which limits the accuracy and robustness of detection. In order to solve these problems, this paper proposes a vehicle detection and location method for YOLOv5(You Only Look Once version 5) based on binocular vision. Binocular vision uses two cameras to obtain images from different angles at the same time. By calculating the difference between the two images, more accurate depth information can be obtained. The YOLOv5 algorithm is improved by adding the CBAM attention mechanism and replacing the loss function to improve target detection. Combining these two techniques can achieve accurate detection and localization of vehicles in 3D space. The method utilizes the depth information of binocular images and the improved YOLOv5 target detection algorithm to achieve accurate detection and localization of vehicles in front. Experimental results show that the method has high accuracy and robustness for vehicle detection and localization tasks.
Path Planning for Fixed-Wing Unmanned Aerial Vehicles: An Integrated Approach with Theta* and Clothoids

Unmanned Aerial Vehicles (UAVs) have emerged as a compelling alternative to manned operations, offering the capability to navigate hazardous environments without risks for human operators. Despite their potential, optimizing UAV missions in complex and unstructured environments remains a pivotal challenge. Path planning becomes a crucial aspect to increase mission efficiency, although it is inherently complex due to various factors such as obstacles, no-fly zones, non-cooperative aircraft, and flight mechanics limitations. This paper presents a path-planning technique for fixed-wing unmanned aerial vehicles (UAVs) based on the Theta* algorithm. The approach introduces innovative features, such as the use of Euler spiral, or clothoids, to serve as connection arcs between nodes, mitigating trajectory discontinuities. The design of clothoids can be linked to the aircraft performance model, establishing a connection between curvature constraints and the specific characteristics of the vehicle. Furthermore, to lower the computational burden, the implementation of an adaptive exploration distance and a vision cone was considered, reducing the number of explored solutions. This methodology ensures a seamless and optimized flight path for fixed-wing UAVs operating in static environments, showcasing a noteworthy improvement in trajectory smoothness. The proposed methodology has been numerically evaluated in several complex test cases as well as in a real urban scenario to prove its effectiveness.
High sensitivity strain measurement on a vibrating saxophone reed using stroboscopic digital image

Abstract In this paper, we present a study of different optical textures and techniques to apply very fine, non-specular reflecting random speckle texture, which makes it possible to measure surface strains with a sensitivity better than 0.01 % strain on a vibrating object. The method is based on triggered stroboscopic digital image correlation, which is able to obtain time-resolved full-field measurements of displacement and strain fields. The method is demonstrated on reeds of the saxophone to investigate their role in sound production and playability. Such high-sensitivity measurements allow the detection of subtle effects, such as artefacts on synthetic saxophone reeds caused by the production process.
AI and the future of human rights in Bangladesh: a call for robust legal and ethical frameworks

Abstract The rise of artificial intelligence (AI) technology presents both opportunities and challenges for the protection of human rights in Bangladesh. While AI has the potential to transform and enhance many areas of society, including healthcare, education, and business, it also raises significant ethical and legal questions that require careful consideration. This paper explores the current state of AI technology in Bangladesh and examines the potential impact on human rights, particularly in the areas of privacy, discrimination, and freedom of expression. The paper argues that while AI can be a valuable tool for promoting human rights, it can also exacerbate existing inequalities and create new forms of discrimination. To ensure that AI is used in a way that upholds human rights, the paper calls for the development of robust legal and ethical frameworks to regulate the use of AI technology. The paper proposes several recommendations for achieving this goal, including the establishment of clear ethical guidelines for the development and deployment of AI systems, the creation of a regulatory body to oversee AI technology in Bangladesh, and the use of impact assessments to identify potential risks and harms associated with AI systems. Overall, the paper emphasizes the need for a collaborative and interdisciplinary approach to addressing the legal and ethical challenges posed by AI technology in Bangladesh. By taking a proactive and holistic approach, Bangladesh can ensure that AI is developed and used in a way that respects and promotes human rights, rather than undermining them. The paper concludes by stressing the importance of ongoing research and dialogue on this critical topic to inform and guide policy and decision-making in the future.
The influence of Ohmic noise on the dynamics of three-spin open quantum system

Abstract The dynamics of open quantum systems under decoherence effects remain a hotly debated topic in the case of the practical deployment of quantum information processing. In this regard, we examine how the three-qubit mixed state is affected by changes in the cut-off frequency of the Ohmic spectral density in a harmonic reservoir. Specifically, the degree of quantum memory-assisted entropic uncertainty (QMA-EU), along with the entanglement, coherence, and purity of the system, is to be demonstrated. A thorough exercise is done to investigate any underlying relationship between the three-qubit quantum characteristics. We show that a harmonic reservoir controlled by Ohmic noise prevails a monotonic-like decay in the current case where, sooner or later, the state becomes completely disentangled, decoherent, and mixed. The QMA-EU has always been found to have an increasing function causing the quantum resourcefulness to be reduced. Although, there is no pathway to avoid the Ohmic noise consequences and complete decay, however, we provided parameterization which would lead to prolonged preservation of quantum correlations with time. Finally, we provide various settings for the tuning of cut-off frequency regarding the Ohmic type bath and state parameters on the initial as well as final levels of quantum features.
Initial correlations in open quantum systems are always detectable

Consider an open quantum system which interacts with its environment. Assuming that the experimenter has access only to the system, an interesting question is whether it is possible to detect initial correlations between the system and the environment by performing measurements only on the system. Various methods have been proposed to detect correlations by local measurements on the system. After reviewing these methods, we will show that initial correlations between the system and the environment are always detectable. In particular, we will show that one can always find a unitary evolution, for the whole system-environment, such that the trace distance method, proposed to witness correlations locally, succeeds. We also find the condition for existence of the optimal unitary evolution, for which the entire correlation is locally detectable. Next, we address the case where the system and the environment interact through a time-independent Hamiltonian. For this case we will see that if the initial correlation can be detected locally at some time $t$, then it can be detected for almost all the other times too. On the other hand, we see that one can find cases for which initial correlations between the system and the environment always remain undetectable even though the unitary evolution, generated by the Hamiltonian, is not factorized.
Avoided level crossings with exponentially closing gaps in quantum annealing

This paper explores the phenomenon of avoided level crossings in quantum annealing, a promising framework for quantum computing that may provide a quantum advantage for certain tasks. Quantum annealing (QA) involves letting a quantum system evolve according to the Schr\"odinger equation, with the goal of obtaining the optimal solution to an optimization problem through measurements of the final state. However, the continuous nature of quantum annealing makes analytical analysis challenging, particularly with regard to the instantaneous eigenenergies. The adiabatic theorem provides a theoretical result for the annealing time required to obtain the optimal solution with high probability, which is inversely proportional to the square of the minimum spectral gap. Avoided level crossings can create exponentially closing gaps, which can lead to exponentially long running times for optimization problems. In this paper, we use a perturbative expansion to derive a condition for the occurrence of an avoided level crossing during the annealing process. We then apply this condition to the MaxCut problem on bipartite graphs. We show that no exponentially small gaps arise for regular bipartite graphs, implying that QA can efficiently solve MaxCut in that case. On the other hand, we show that irregularities in the vertex degrees can lead to the satisfaction of the avoided level crossing occurrence condition. We provide numerical evidence to support this theoretical development, and discuss the relation between the presence of exponentially closing gaps and the failure of quantum annealing.
Phase-error-rate analysis for quantum key distribution with phase postselection

Quantum key distribution (QKD) stands as a pioneering method for establishing information-theoretically secure communication channels by utilizing the principles of quantum mechanics. In the security proof of QKD, the phase error rate serves as a critical indicator of information leakage and directly influences the security of the shared key bits between communicating parties, Alice and Bob. In estimating the upper bound of the phase error rate, phase randomization and subsequent postselection mechanisms serve pivotal roles across numerous QKD protocols. However, the nonzero interval of phase postselection will introduce intrinsic errors, leading to an overestimation of phase error rate. Here we make a precise phase-error-rate analysis for QKD protocols with phase postselection, which eliminates error rate associated with nonzero interval and helps us to accurately bound the amount of information an eavesdropper may obtain. We further apply our analysis in sending-or-not-sending twin-field quantum key distribution (SNS-TFQKD) and mode-pairing quantum key distribution (MP-QKD). The simulation results confirm that our precise phase error analysis can noticeably improve the key rate performance especially over long distances in practice. Note that our method does not require alterations to the existing experimental hardware or protocol steps. It can be readily applied within current SNS-TF-QKD and MP-QKD for higher key rate generation.
Minimality, Necessity and Sufficiency for Argumentation and Explanation

We discuss explanations for formal (abstract and structured) argumentation – the question whether and why a certain argument or claim can be accepted (or not) under various extension-based semantics. We introduce a flexible framework, which can act as the basis for many different types of explanations. For example, we can have simple or comprehensive explanations in terms of arguments for or against a claim, arguments that (indirectly) defend a claim, the evidence (knowledge base) that supports or is incompatible with a claim, and so on. We show how selection based on necessity and sufficiency can be captured in our basic framework and discuss a real-life application.
Advances on similarity via transversal intersection of manifolds

Let A be an n×n real matrix. As shown in the recent paper “The bifurcation lemma for strong properties in the inverse eigenvalue problem of a graph”, Linear Algebra Appl. 648 (2022), 70–87, by S.M. Fallat, H.T. Hall, J.C.-H. Lin, and B.L. Shader, if the manifolds MA={G−1AG:G∈GL(n,R)} and Q(sgn(A)) (consisting of all real matrices having the same sign pattern as A), both considered as embedded submanifolds of Rn×n, intersect transversally at A, then every superpattern of sgn(A) also allows a matrix similar to A. Those authors introduced a condition on A (in terms of certain linear matrix equations) equivalent to the above transversality, called the nonsymmetric strong spectral property (nSSP). In this paper, this transversality property of A is characterized using an alternative, more direct and convenient condition, called the similarity-transversality property (STP). Let X=[xij] be a generic matrix of order n whose entries are independent variables. The STP of A is defined as the full row rank property of the Jacobian matrix of the entries of AX−XA at the zero entry positions of A with respect to the nondiagonal entries of X. This new approach makes it possible to take better advantage of the combinatorial structure of the matrix A, and provides theoretical foundation for constructing matrices similar to a given matrix while the entries have certain desired signs. In particular, several important classes of zero-nonzero patterns and sign patterns that require or allow this transversality property are identified. Examples illustrating many possible applications (such as diagonalizability, number of distinct eigenvalues, nilpotence, idempotence, semi-stability, eigenvalues and their algebraic and geometric multiplicities, Jordan canonical form, minimal polynomial, and rank) are provided.
A robust phase unwrapping method for phase shifting coding high-frequency color fringe projection profilometry

Phase unwrapping is one of the key problems in fringe projection profilometry (FPP). How to achieve unambiguous fringe step order to perform phase unwrapping with few patterns still face a major challenge. We propose a robust phase unwrapping method by using a pair of encoded color fringe patterns, one is a conventional three-step phase-shifting color fringe pattern with equal phase shifts, another is added an additional phase shift valve t(k) to the former, in which t(k) is related to the order of fringe periods. A single-valued solution function for t(k) is constructed and used to determine the unique fringe order. Besides, multiplexing this pair of fringes can help denoise the extracted wrapped phase to improve the accuracy. The simulation and experimental results proved that the proposed method can achieve unambiguous phase unwrapping for high-frequency fringe patterns. The proposed method is also applicable for isolated objects.
Connection-density-aware satellite-ground federated learning via asynchronous dynamic aggregation

With the development of space technology, the use of satellites for Earth observation is becoming more and more common. Among them, low-earth orbit (LEO) satellites have received a lot of attention because of their short return period and low cost. However, traditional satellite systems are unable to process data in orbit due to technical limitations and can only transmit massive amounts of data with useless information back to ground stations and then perform calculations, putting a tremendous strain on downstream bandwidth. Additionally, there are data walls between satellite affiliates, which makes it difficult to share and use data. To address the lack of data processing capabilities and data barriers in conventional satellite systems, as well as to effectively use satellite resources and enhance worldwide collaboration, we proposed a connection density-aware satellite-ground federated learning (FL) framework. According to this structure, satellites serve as workers in federated learning, analyzing data and transmitting the parameters to ground stations for aggregation. The ground station senses the satellite’s connection density during the aggregation process and implements a dynamic aggregation approach for the downstream parameters. Experiments based on real image datasets and on a simulated satellite network validate the effectiveness of the method in terms of execution efficiency and accuracy.