Reviewerly/exHarmony huggingface dataset:
Machine learning enhanced high dynamic range fringe projection profilometry for in-situ layer-wise surface topography measurement during LPBF additive manufacturing

Fringe Projection Profilometry (FPP) is a cost-effective and non-destructive method, typically used for measuring finer features and reconstructing 3D topography of objects. However, to use the FPP method for measuring the dynamic topography of powder bed and printed layers during Laser Powder Bed Fusion (LPBF) based additive manufacturing (AM) process, unique challenges exist due to the varying material properties and ambient conditions in the build chamber. In this work, we aim to enhance the discernibility, accuracy, and resolution of FPP in the specific application scenario of measuring layer-wise surface topography during LPBF AM by integrating our recently developed LPBF-specific FPP sensing model that features localized sensor calibration and Fourier filter-aided unwrapping with an equipment-based High dynamic range (HDR) method and machine learning (ML) aided FPP data analysis. First, a projector based HDR method is applied to mitigate the shadowing and intensity saturation problems by projecting sinusoidal fringe patterns of varying intensities. Secondly, a ML framework is developed to improve the surface topography measurement accuracy (RMSE from 10.57 μm to 7.49 μm or even 4.35 μm for directly measurable points) and enhance resolution that is currently subjected to hardware limitations (from 38 μm to 5 μm laterally and from 10 μm to 1 μm vertically). Several different types of candidate neural networks (NNs) are trained and tested using the in-situ FPP measurement data and ex-situ standard optical microscopy characterization data. Multiple NN-based models are resulted and compared in terms of their ability to enhance the accuracy and resolution of FPP's end-result (height measurement). By selecting the best-performance NN enabled image super resolution model, the proposed ML integrated HDR FPP method is expected to measure the surface topography of printed layers during LPBF-AM more capably and efficiently, thus advancing the existing state-of-the-art methods towards the desired online inspection of LPBF print defects.
Hardware architecture for CRYSTALS-Kyber post-quantum cryptographic SHA-3 primitives

Once powerful enough quantum computers become feasible, many of the regularly used cryptosystems will be completely useless. Thus, designing quantum-safe cryptosystems to replace current algorithms is more crucial than ever. This paper presents the hardware implementation of one of the fundamental building blocks of all post-quantum cryptographic (PQC) algorithms, which are PQC-primitives, having NIST-PQC-finalists CRYSTALS-Kyber algorithm as a target. This work analyzes Keccak sponge function and the four SHA-3 algorithms used in CRYSTALS-Kyber, realizing the correct processing and handling of input information and integrating the four standards into one implementation for Kyber-III level of security. The synthesis results are provided for 65-nm technology, while Artix7 XC7A75-3 is chosen as the implementation platform. The efficiency and the performance of the proposed architecture are compared in terms of area, frequency, clock cycles, and efficiency with the state-of-the-art.
Cumulative absolute velocity prediction for earthquake early warning with deep learning

Abstract A rapid and accurate estimate of earthquake damage is a key component in a successful earthquake early warning (EEW) system. The cumulative absolute velocity (CAV) is an important and widely used parameter to measure ground motion intensity, but it cannot be correctly estimated via the traditional approach with the limited information available in typical EEW systems. Therefore, current EEW systems cannot effectively use CAV to predict earthquake damage. Herein, a CAV prediction model (DLcav) based on convolutional neural networks was proposed for EEW systems. DLcav is an end‐to‐end solution to continuously predict CAV using arriving seismic waves of increasing length and supplemented with additional auxiliary information. The effectiveness of DLcav to predict CAV was tested based on Japanese ground motion records, and the generalization ability of DLcav was assessed using the ground motion records from Chile. The results demonstrate that DLcav can rapidly predict CAV with good accuracy, which will help better estimate earthquake damage in EEW systems.
Connexive Logic, Probabilistic Default Reasoning, and Compound Conditionals

Abstract We present two approaches to investigate the validity of connexive principles and related formulas and properties within coherence-based probability logic. Connexive logic emerged from the intuition that conditionals of the form if not - A , then A , should not hold, since the conditional’s antecedent not - A contradicts its consequent A . Our approaches cover this intuition by observing that the only coherent probability assessment on the conditional event $${A| \overline{A}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mi>A</mml:mi> <mml:mo>|</mml:mo> </mml:mrow> <mml:mover> <mml:mi>A</mml:mi> <mml:mo>¯</mml:mo> </mml:mover> </mml:mrow> </mml:math> is $${p(A| \overline{A})=0}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>(</mml:mo> <mml:mi>A</mml:mi> <mml:mo>|</mml:mo> <mml:mover> <mml:mi>A</mml:mi> <mml:mo>¯</mml:mo> </mml:mover> <mml:mo>)</mml:mo> <mml:mo>=</mml:mo> <mml:mn>0</mml:mn> </mml:mrow> </mml:math> . In the first approach we investigate connexive principles within coherence-based probabilistic default reasoning, by interpreting defaults and negated defaults in terms of suitable probabilistic constraints on conditional events. In the second approach we study connexivity within the coherence framework of compound conditionals, by interpreting connexive principles in terms of suitable conditional random quantities. After developing notions of validity in each approach, we analyze the following connexive principles: Aristotle’s theses , Aristotle’s Second Thesis , Abelard’s First Principle , and Boethius’ theses . We also deepen and generalize some principles and investigate further properties related to connexive logic (like non-symmetry ). Both approaches satisfy minimal requirements for a connexive logic. Finally, we compare both approaches conceptually.
Error-Detectable Bosonic Entangling Gates with a Noisy Ancilla

Bosonic quantum error correction has proven to be a successful approach for extending the coherence of quantum memories, but to execute deep quantum circuits, high-fidelity gates between encoded qubits are needed. To that end, we present a family of error-detectable two-qubit gates for a variety of bosonic encodings. From a new geometric framework based on a "Bloch sphere" of bosonic operators, we construct ZZL(θ) and exponential-swap(θ) gates for the binomial, four-legged cat, dual-rail, and several other bosonic codes. The gate Hamiltonian is simple to engineer, requiring only a programmable beam splitter between two bosonic qubits and an ancilla dispersively coupled to one qubit. This Hamiltonian can be realized in circuit QED hardware with ancilla transmons and microwave cavities. The proposed theoretical framework was developed for circuit QED but is generalizable to any platform that can effectively generate this Hamiltonian. Crucially, one can also detect first-order errors in the ancilla and the bosonic qubits during the gates. We show that this allows one to reach error-detected gate fidelities at the 0.01% level with today's hardware, limited only by second-order hardware errors.3 MoreReceived 23 December 2022Revised 26 April 2023Accepted 8 May 2023DOI:https://doi.org/10.1103/PRXQuantum.4.020354Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasHarmonic oscillatorOptical quantum information processingQuantum controlQuantum error correctionQuantum gatesQuantum information processing with continuous variablesQuantum information with atoms & lightQuantum information with solid state qubitsQuantum Information
Handwritten Arabic Character Recognition for Children Writing Using Convolutional Neural Network and Stroke Identification

Abstract Automatic Arabic handwritten recognition is one of the recently studied problems in the field of Machine Learning. Unlike Latin languages, Arabic is a Semitic language that forms a harder challenge, especially with the variability of patterns caused by factors such as the writer’s age. Most studies have focused on adults, with only one recent study on children. Moreover, many recent machine-learning methods have focused on using Convolutional Neural Networks (CNNs), a powerful class of neural networks that can extract complex features from images. In this paper, we propose a convolutional neural network (CNN) model that recognizes children’s handwriting with an accuracy of 91% on the Hijja dataset, a recent dataset built by collecting images of Arabic characters written by children, and 97% on the Arabic Handwritten Character Dataset. The results showed a good improvement over the proposed model from the Hijja dataset authors, yet it revealed a bigger challenge to solve for children’s Arabic handwritten character recognition. Moreover, we proposed a new approach using multiple models instead of a single model based on the number of strokes in a character and merged Hijja with AHCD, which achieved an average prediction accuracy of 96%.
Developing early career teachers’ professional digital competence: a systematic literature review

Early-career teachers face numerous challenges when using digital technologies in their educational practices. This systematic literature review explores how early-career teachers develop professional digital competence in practice and identifies some of the contributing factors for this development. Twenty-five publications were selected for inclusion in the review study after a careful search strategy and detailed screening conducted in three popular and relevant databases (Web of Science, Scopus and ERIC). The analysis of the reviewed publications revealed four overarching themes (actively developing professional digital competence, taking advantage of development opportunities, mentoring as a way of development, and professional digital competence interwoven with early-career teachers’ professional identity development). The analysis also revealed five factors (institutional culture, accessibility and availability of resources, governance and leadership, insufficient or lack of technical and pedagogical support and a heavy workload) contributing to early-career teachers’ professional digital competence development. The systematic review offers conclusions and research implications.
Energy efficient dual axis solar tracking system using IOT

With the increasing demand for energy, traditional sources are becoming scarce and the need to transition to non-traditional sources of energy is urgent. Solar energy is an inexhaustible form of energy that can be easily tapped from different parts of the world and converted to electrical energy by using devices such as solar panels. However, the output power of these photovoltaic (PV) panels is not constant as the sun moves from east to west and its angle of incidence varies with the season and time of the day. To maximize energy output from the solar panel, a dual-axis solar tracker (DAST) is necessary to rotate the panel about its horizontal and vertical axes. This system will ensure efficient tracking of the sun and optimal energy output from the solar panel. The proposed system will respond within the 0.2 seconds to store the data in database. The whole 24hours data of solar panel is utilized in the preferred system to analyze the data to validate the robustness of the system. The proposed system uses a microcontroller to adjust the position of the solar panel based on the movement of the sun, while the performance of the tracker is monitored through the use of Internet of Things (IoT) connected via Wi-Fi.
Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm

To address the problems of low accuracy and slow convergence of traditional multilevel image segmentation methods, a symmetric cross-entropy multilevel thresholding image segmentation method (MSIPOA) with multi-strategy improved pelican optimization algorithm is proposed for global optimization and image segmentation tasks. First, Sine chaotic mapping is used to improve the quality and distribution uniformity of the initial population. A spiral search mechanism incorporating a sine cosine optimization algorithm improves the algorithm's search diversity, local pioneering ability, and convergence accuracy. A levy flight strategy further improves the algorithm's ability to jump out of local minima. In this paper, 12 benchmark test functions and 8 other newer swarm intelligence algorithms are compared in terms of convergence speed and convergence accuracy to evaluate the performance of the MSIPOA algorithm. By non-parametric statistical analysis, MSIPOA shows a greater superiority over other optimization algorithms. The MSIPOA algorithm is then experimented with symmetric cross-entropy multilevel threshold image segmentation, and eight images from BSDS300 are selected as the test set to evaluate MSIPOA. According to different performance metrics and Fridman test, MSIPOA algorithm outperforms similar algorithms in global optimization and image segmentation, and the symmetric cross entropy of MSIPOA algorithm for multilevel thresholding image segmentation method can be effectively applied to multilevel thresholding image segmentation tasks.
An Empirical Study Assessing Software Modeling in Alloy

Alloy is a declarative formal modeling language with syntax derived from notations common to object-oriented design and first-order relational logic semantics. To better understand the usability of Alloy, the paper presents the results of an empirical study with 30 participants assessing two types of modeling tasks: bug fixing and model building based on natural language specifications. The participants consisted of both novices and non-novices. Besides accuracy and time to complete tasks, we also examined the correlation between the performance of two cognitive tasks and task performance. Results indicate that overall, non-novices completed the tasks with significantly higher accuracy (54% more accurate) than novices. In the novice group, performing more actions using the Alloy analyzer led to more edits and, eventually, higher scores in the bug fixing tasks. We found that participants of all levels had much difficulty writing a model from scratch, and they did not utilize the analyzer to improve their models. On average, non-novices completed all the tasks 32 minutes faster than novices. Non-novices who performed better on the Alloy tasks had higher mental rotation scores, which indicates the importance of spatial cognition ability in solving Alloy tasks. Overall, we find that there is a definite need to improve the usability of the visualizations in the Alloy Analyzer.
Pedestrian Detection Using Multi-Scale Structure-Enhanced Super-Resolution

Pedestrian detection remains a crucial technology for applications such as autonomous driving and gait recognition and continues to be a prominent research area. Despite the development of advanced pedestrian detection techniques, the challenge of detecting pedestrians in low-resolution images persists in real-life scenarios where low-quality imaging devices are still in use. The objective of this study is to enhance the detection of pedestrians in low-resolution (LR) images by improving image quality through super-resolution techniques. To achieve this goal, we propose an end-to-end Multi-scale Structure-Enhanced Super-Resolution (MsSE-SR) method to enlarge LR images into high-resolution (SR) images and utilize Yolov4 for detection, effectively addressing the issue of low detection performance in LR images. To generate an SR image that can accurately distinguish between foreground and background elements while emphasizing pedestrian characteristics, we employ the stationary wavelet transform (SWT) to decompose the image into low and high-frequency sub-images. These sub-images are then processed through different network structures, enabling the network to reconstruct high-frequency details and low-frequency structures with greater precision. Moreover, we propose a high-to-low subnetwork information transfer (H2LSnIT) that incorporates high-frequency edge information into the low-frequency image structure during the reconstruction process, leading to a more detailed reconstruction of the low-frequency structure. We also propose a novel loss function that leverages the characteristics of wavelet decomposition to enhance the network’s focus on reconstructing the image structure, further improving detection performance. The experimental results demonstrate the effectiveness of the proposed MsSE-SR method in significantly enhancing pedestrian detection performance.
Integration of Library, Museum and Archive Resources: Together or Independently?

The article deals with modern problems of integration of digital resources of libraries, archives and museums. The author identifies and describes changes in the goal-setting system of activities within the framework of joint projects, caused by the digitalization of the internal activities of cultural institutions. The vector of change in the goals is directed from general goals to more practical ones, which include: the formation of an integrated research environment of a humanitarian profile; the development of digital collections as a new information product of libraries; the usage of integrated resources as a means of training and educating various groups of patrons; the creation of a joint virtual platform of historical memory institutions for professional communication and project implementation. Creating a new information product — digital collections — will require significant efforts, primarily a scientific and methodological study of the issues of their organization, taking into account the specifics of information products and their future place in the information consumption market. The creation of such products can be carried out not only by the efforts of the keepers of cultural and historical heritage, but also with the involvement of organizations engaged in artificial intelligence and semantic networks. Among the trends, there is a consistent leveling of the degree of digitalisation of libraries, archives and museums, their self-sufficiency and the desire to develop along their own information trajectories. The article provides examples of the most successful projects in the field of creating digital resources of historical memory institutions. There are identified the basic problems: departmental disunity, the lack of a publicly accessible reliable software and technology environment for interaction, as well as the lack of a system of scientific and methodical procedures and information support for resource integration activities. It is necessary to organize interdisciplinary vocational training centers for personnel to work in an integrated environment.
Relevant Region sampling strategy with adaptive heuristic for asymptotically optimal path planning

Sampling-based planning algorithm is a powerful tool for solving planning problems in high-dimensional state spaces. In this article, we present a novel approach to sampling in the most promising regions, which significantly reduces planning time-consumption. The RRT# algorithm defines the Relevant Region based on the cost-to-come provided by the optimal forward-searching tree. However, it uses the cumulative cost of a direct connection between the current state and the goal state as the cost-to-go. To improve the path planning efficiency, we propose a batch sampling method that samples in a refined Relevant Region with a direct sampling strategy, which is defined according to the optimal cost-to-come and the adaptive cost-to-go, taking advantage of various sources of heuristic information. The proposed sampling approach allows the algorithm to build the search tree in the direction of the most promising area, resulting in a superior initial solution quality and reducing the overall computation time compared to related work. To validate the effectiveness of our method, we conducted several simulations in both SE(2) and SE(3) state spaces. And the simulation results demonstrate the superiorities of proposed algorithm.
Learning the language of QCD jets with transformers

Transformers have become the primary architecture for natural language processing. In this study, we explore their use for auto-regressive density estimation in high-energy jet physics, which involves working with a high-dimensional space. We draw an analogy between sentences and words in natural language and jets and their constituents in high-energy physics. Specifically, we investigate density estimation for light QCD jets and hadronically decaying boosted top jets. Since transformers allow easy sampling from learned densities, we exploit their generative capability to assess the quality of the density estimate. Our results indicate that the generated data samples closely resemble the original data, as evidenced by the excellent agreement of distributions such as particle multiplicity or jet mass. Furthermore, the generated samples are difficult to distinguish from the original data, even by a powerful supervised classifier. Given their exceptional data processing capabilities, transformers could potentially be trained directly on the massive LHC data sets to learn the probability densities in high-energy jet physics.
Towards an Edu-Metaverse of Knowledge: Immersive Exploration of University Courses

Metaverse, an alternative universe for play, work and interaction, has become a captivating topic for academia and industry in recent times. This opens the question on what a metaverse for education, or edu-metaverse, should look like. It is believed that this metaverse for learning should be grounded by a pedagogical theory. Particularly, we propose a constructivist metaverse learning theory with eight actionable principles to guide the edu-metaverse and its applications. With this metaverse learning theory, we further propose the framework for an edu-metaverse; it is essentially walkable yellow pages that connect knowledge. The core idea is to combine the structure of knowledge graphs and the immersion of virtual reality in order to facilitate association, exploration and engagement in learning. Our current prototype for this edu-metaverse vision, K-Cube VR, is also presented. We have tested K-Cube VR for the introduction of course topics to our students and the results indicate that our edu-metaverse framework benefits students by providing a focused environment and structured learning on the topics of a course, akin to a mind map. Overall, in this paper, we present an edu-metaverse design that is rooted in a constructivist pedagogy that already shows promising results from a pilot user study via our metaverse prototype.