Reviewerly/exHarmony huggingface dataset:
Eﬃcient Light Propagation Algorithm using Quantum Computers

Abstract Quantum algorithms can potentially overcome the boundary of computationally hard problems. One of the cornerstones in modern optics is the beam propagation algorithm, facilitating the calculation of how waves with a particular dispersion relation propagate in time and space. This algorithm solves the wave propagation equation by Fourier transformation, multiplication with a transfer function, and subsequent back transformation. This transfer function is determined from the respective dispersion relation, which can often be expanded as a polynomial. In the case of paraxial wave propagation in free space or picosecond pulse propagation, this expansion can be truncated after the quadratic term. The classical solution to the wave propagation requires $\mathcal{O}(N log N)$ computation steps, where $N$ is the number of points into which the wave function is discretized. Here, we show that the propagation can be performed as a quantum algorithm with $\mathcal{O}((log{}N)^2)$ single-controlled phase gates, indicating exponentially reduced computational complexity. We herein demonstrate this quantum beam propagation method (QBPM) and perform such propagation in both one- and two-dimensional systems for the double-slit experiment and Gaussian beam propagation. We highlight the importance of the selection of suitable observables to retain the quantum advantage in the face of the statistical nature of the quantum measurement process, which leads to sampling errors that do not exist in classical solutions.
The emergence of self-awareness: insights from robotics

The ability for self-related thought is historically considered to be a uniquely human characteristic. Nonetheless, as technological knowledge advances, it comes as no surprise that the plausibility of humanoid self-awareness is not only theoretically explored but also engineered. Could the emerging behavioural and cognitive capabilities in artificial agents be comparable to humans? By employing a cross-disciplinary approach, the present essay aims to address this question by providing a comparative overview on the emergence of self-awareness as demonstrated in early childhood and robotics. It argues that developmental psychologists can gain invaluable theoretical and methodological insights by considering the relevance of artificial agents in better understanding the behavioural manifestations of human self-consciousness.
Recovering coefficients in a system of semilinear Helmholtz equations from internal data

Abstract We study an inverse problem for a coupled system of semilinear Helmholtz equations where we are interested in reconstructing multiple coefficients in the system from internal data measured in applications such as thermoacoustic imaging. We derive results on the uniqueness and stability of the inverse problem in the case of small boundary data based on the technique of first- and higher-order linearization. Numerical simulations are provided to illustrate the quality of reconstructions that can be expected from noisy data.
A Hotspot-Aware Personalized News Recommendation Mechanism Based on DistilBERT-TC-MA

Aiming at the problems of existing news recommendation methods, such as inadequate exploration of the semantic information of news, neglecting potential hotspot features of news, and challenging the balance between user preferences and hotspot features, a hotspot-aware personalized news recommendation model (DistilBERT-TC-MA) is suggested, which integrates the distilled version of BERT (DistilBERT), text convolutional neural network (TextCNN), and multilayer attention (MA). First, it takes full advantage of DistilBERT, TextCNN, and self-attention mechanism to achieve news encoding. Following this, representations of trending news are dynamically aggregated using the attention mechanism, while user preferences are mined utilizing user click history. Finally, in order to successfully accomplish the click prediction of candidate news, the hotspot features, user preferences, and candidate news are ultimately combined using a click predictor. The experimental results of the suggested DistilBERT-TC-MA model on MIND dataset are better than several other advanced methods.
Opportunities for Bicycle Tourism in Zomin, Bulungur and Bakhmal Districts

This article discusses the creation of a web page for tourists to travel in the Zomin, Bulungur, and Bakhmaldistricts, as well as the location of service assistance, bicycle rental, guest houses, restaurants, and, most importantly,determining the months.
Optimal Rates of Approximation by Shallow ReLU$$^k$$ Neural Networks and Applications to Nonparametric Regression

Abstract We study the approximation capacity of some variation spaces corresponding to shallow ReLU $$^k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mi>k</mml:mi> </mml:msup> </mml:math> neural networks. It is shown that sufficiently smooth functions are contained in these spaces with finite variation norms. For functions with less smoothness, the approximation rates in terms of the variation norm are established. Using these results, we are able to prove the optimal approximation rates in terms of the number of neurons for shallow ReLU $$^k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mi>k</mml:mi> </mml:msup> </mml:math> neural networks. It is also shown how these results can be used to derive approximation bounds for deep neural networks and convolutional neural networks (CNNs). As applications, we study convergence rates for nonparametric regression using three ReLU neural network models: shallow neural network, over-parameterized neural network, and CNN. In particular, we show that shallow neural networks can achieve the minimax optimal rates for learning Hölder functions, which complements recent results for deep neural networks. It is also proven that over-parameterized (deep or shallow) neural networks can achieve nearly optimal rates for nonparametric regression.
Artificial Intelligence and future of higher education

Many engaged in the development of Artificial Intelligence (AI) see education as a potential market for sales. They predict that AI will transform teaching, learning and assessment. In describing just how this will occur, AI advocates suggest a combination of individualized learning, adaptive and engaged assessment, and personal support for the learners' journey will be the hallmarks of the new reality for education. Harsh reality suggests something different. Using a simple framework of the potential impact of AI tools and the burden they require; a more incremental adoption is likely. This paper presents both the transformation framework and the more likely incremental approach as the choice for instructors, administrators, and policymakers.
Constructing an Interpretive Structural Model to Unravel the Interconnected Drivers of Teaching Quality in Higher Education

Identifying the primary factors of teaching quality remains a pivotal agenda for informed decision making, strategic planning, and resource allocation. This study builds upon ten key factors derived from previous research and recognizes the inherent complexity within their relationships. Emphasizing the necessity for a structured model, this work employs an interpretive structural modelling (ISM) approach and Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis for constructing a hierarchical model that delineates the interrelationships among the factors influencing teaching quality. The findings indicate the substantial impact of intrinsic factors, particularly teachers' individual and psychological characteristics, on other factors. Additionally, our analysis highlights the critical role of student composition in enhancing overall teaching quality. These insights significantly contribute to the literature by offering valuable guidance to decision makers for maintaining teaching quality within higher education institutions.
Detecting Ambiguities in Requirement Documents Written in Arabic Using Machine Learning Algorithms

The identification of ambiguities in Arabic requirement documents plays a crucial role in requirements engineering. This is because the quality of requirements directly impacts the overall success of software development projects. Traditionally, engineers have used manual methods to evaluate requirement quality, leading to a time-consuming and subjective process that is prone to errors. This study explores the use of machine learning algorithms to automate the assessment of requirements expressed in natural language. The study aims to compare various machine learning algorithms according to their abilities in classifying requirements written in Arabic as decision tree. The findings reveal that random forest outperformed all stemmers, achieving an accuracy of 0.95 without employing a stemmer, 0.99 with the ISRI stemmer, and 0.97 with the Arabic light stemmer. These results highlight the robustness and practicality of the random forest algorithm.
Underwater image enhancement using multi-task fusion

Underwater images are often scattered due to suspended particles in the water, resulting in light scattering and blocking and reduced visibility and contrast. Color shifts and distortions are also caused by the absorption of different wavelengths of light in the water. This series of problems will make the underwater image quality greatly impaired, resulting in some advanced visual work can not be carried out underwater. In order to solve these problems, this paper proposes an underwater image enhancement method based on multi-task fusion, called MTF. Specifically, we first use linear constraints on the input image to achieve color correction based on the gray world assumption. The corrected image is then used to achieve visibility enhancement using an improved type-II fuzzy set-based algorithm, while the image is contrast enhanced using standard normal distribution probability density function and softplus function. However, in order to obtain more qualitative results, we propose multi-task fusion, in which we solve for similarity, then we obtain fusion weights that guarantee the best features of the image as much as possible from the obtained similarity, and finally we fuse the image with the weights to obtain the output image, and we find that multi-task fusion has excellent image enhancement and restoration capabilities, and also produces visually pleasing results. Extensive qualitative and quantitative evaluations show that MTF method achieves optimal results compared to ten state-of-the-art underwater enhancement algorithms on 2 datasets. Moreover, the method can achieve better results in application tests such as target detection and edge detection.
Understanding the dilemma of explainable artificial intelligence: a proposal for a ritual dialog framework

Abstract This paper addresses how people understand Explainable Artificial Intelligence (XAI) in three ways: contrastive, functional, and transparent. We discuss the unique aspects and challenges of each and emphasize improving current XAI understanding frameworks. The Ritual Dialog Framework (RDF) is introduced as a solution for better dialog between AI creators and users, blending anthropological insights with current acceptance challenges. RDF focuses on building trust and a user-centered approach in XAI. By undertaking such an initiative, we aim to foster a thorough Understanding of XAI, capable of resolving the current issues of acceptance and recognition.
A Systematic Review of the Design of Serious Games for Innovative Learning: Augmented Reality, Virtual Reality, or Mixed Reality?

The recent integration of educational technologies and emerging learning approaches into education systems has been driven largely by the pandemic. This paper conducts a systematic review and delves into the new wave of research on serious games designed for innovative learning using augmented reality (AR), virtual reality (VR), and mixed reality (MR). The review was referenced to the review protocol, PRISMA 2020. Using the Scopus Database with a time filter from 2007 to 2023 (27 July), we searched 329 articles and shortlisted 273 relevant studies. Notably, European countries contributed the most (62.9%) to this research area. Among the most frequent keywords, VR (90.9%) was commonly used in AR/VR/MR, while e-learning (95.3%) was among the popular innovative learning approaches. Further research studies are needed to employ AR and MR technologies, as well as other innovative learning approaches, to enable performance evaluation and comparison of various educational technologies and learning approaches. We conducted an in-depth analysis of the relevant studies and their basic characteristics. Additionally, we introduced 15 essential and recently published AR/VR/MR standards to ensure better reliability, quality, and safety of architectures, systems, products, services, and processes. To facilitate performance evaluation and analysis, we surveyed 15 recently published benchmark education datasets. This review suggested four future research directions, including multisensory experiences, generative artificial intelligence, personalization and customization, and real-time interaction.
Efficient Vision Transformer YOLOv5 for Accurate and Fast Traffic Sign Detection

Accurate and fast detection of traffic sign information is vital for autonomous driving systems. However, the YOLOv5 algorithm faces challenges with low accuracy and slow detection when it is used for traffic sign detection. To address these shortcomings, this paper introduces an accurate and fast traffic sign detection algorithm–YOLOv5-Efficient Vision TransFormer(EfficientViT)). The algorithm focuses on improving both the accuracy and speed of the model by replacing the CSPDarknet backbone of the YOLOv5(s) model with the EfficientViT network. Additionally, the algorithm incorporates the Convolutional Block Attention Module(CBAM) attention mechanism to enhance feature layer information extraction and boost the accuracy of the detection algorithm. To mitigate the adverse effects of low-quality labels on gradient generation and enhance the competitiveness of high-quality anchor frames, a superior gradient gain allocation strategy is employed. Furthermore, the strategy introduces the Wise-IoU (WIoU), a dynamic non-monotonic focusing mechanism for bounding box loss, to further enhance the accuracy and speed of the object detection algorithm. The algorithm’s effectiveness is validated through experiments conducted on the 3L-TT100K traffic sign dataset, showcasing a mean average precision (mAP) of 94.1% in traffic sign detection. This mAP surpasses the performance of the YOLOv5(s) algorithm by 4.76% and outperforms the baseline algorithm. Additionally, the algorithm achieves a detection speed of 62.50 frames per second, which is much better than the baseline algorithm.
Amphibole Group Minerals in the Ozren Massif Ophiolites of Bosnia and Herzegovina as Petrogenetic Indicators

The Ozren ophiolite complex (OOC) of the Dinaridic Ophiolite Belt is one of the six ophiolite complexes in Bosnia and Herzegovina. This paper deals with the mineral chemistry of amphiboles determined by electron probe micro-analysis and micro-Raman spectroscopy. The detected amphibole generations and types in mafic, ultramafic, and metamorphic rocks suggest a polystage evolution and are therefore useful petrogenetic indicators of the investigated OOC. Most gabbroic rocks and dolerites contain primary magmatic amphibole1 (magnesio-hornblende to pargasite, occasionally hastingsite) and prismatic to needle-like aggregates of late magmatic amphibole2 (magnesio-hornblende), while plagiogranite contains ferri-winchite and ferro-ferri-winchite as primary magmatic amphibole. Post-magmatic amphiboles were detected in dolerites, troctolites, and lesser in peridotites. The Na-(Ti)-rich amphibole3 (ferri-winchite and ferro-ferri-winchite to katophorite and ferri-katophorite) with amphibole4 (grunerite) rim formed along the grain boundaries of clinopyroxene, amphibole1, and plagioclase in dolerites. A part of these amphiboles grows into amphibole1, 2. Kaersutite to ferri-kaersutite, associated with phlogopite, occur in troctolites and dunites, while Mhbl was detected in harzburgite. The ultramafic rocks (lherzolites, harzburgites, and dunites) and the gabbroic layer are crosscut by clinopyroxene–plagioclase gabbroic and clinopyroxene–plagioclase–amphibole gabbro–dolerite dykes, suggesting ‘dry’ and ‘hydrated’ percolating melts generated in inferred subridge and supra-subduction settings, respectively. The amphibole3 and 4 in gabbros and dolerites and similar amphibole types in ultramafic rocks could be related to inferred arc-type basaltic and plagiogranitic percolating melts and fluids. Low-Al amphibole5 (tremolite and actinolite) and associated chlorite, albite, and clinozoisite represent the ocean-floor alterations in mafic rocks. Amphibole6 (magnesio-hornblende to pargasite) was identified in metamorphic sole amphibolites. Micro-Raman spectroscopy provided typical Raman spectra for the studied amphiboles, highlighting distinct features such as bands related to CMg content, CFe3+ presence, TO4 ring-breathing mode, TiO6 stretching mode, presence &gt; 0.3 apfu of CTi, and TO4 stretching indicating CFe2+ in the structure. Applied amphibole geothermobarometry revealed the formation P–T conditions of amphibole (Amp)1 (avg. 863 °C at 0.23 GPa), Amp2 (avg. 747 °C at 0.17 GPa), Amp in the mantle rocks (avg. 853 °C at 0.64 GPa), Amp5 (avg. 349 °C at 0.03 GPa), and Amp6 (avg. 694 °C at 0.46 GPa).
Mapping of American English vocabulary by grade levels

Abstract We describe a large-scale effort to map English-language vocabulary by U.S. school grade levels. Our motivation is to rapidly expand graded vocabulary resources for work with native English speakers in the USA, while taking into consideration school-related influences rather than relying on just the corpus-frequency approaches. We report on the initial effort of data collection, with mapping of about 22K word forms. We provide comparisons of this mapping to some other recent vocabulary mapping efforts, such as age-of-acquisition. We then describe the efforts to automatically expand this resource by using linguistically motivated variables and corpus-based methods. Our current resource maps more than 126K English word forms to US school grade levels. We also compare a subset of our L1 mapped data to English L2 vocabulary levels, as expressed on the CEFR scale, and find that there is a considerable overlap in the order of vocabulary learning in L1 and L2 English.
Towards a graded lexical inventory of multi-word combinations

Abstract Collocations have become increasingly important in our understanding of foreign language learning. When it comes to setting vocabulary learning goals, concerns about how to address collocations still arise today. This article explores the distribution of collocations in L1 and L2 Spanish production with the ultimate goal of informing the design of graded lexical inventories of multi-word combinations. To do so, we explore three defining properties of collocations in L1 and L2 production data, and across different levels of L2 proficiency: syntactic structure, semantic transparency, and the strength of association. Results indicate that there is an increase of collocational density and diversity, but that isolated features of collocations fail to predict L2 proficiency. Findings suggest the need to evaluate collocation use at a high level of granularity.
Swedish word family resource

Abstract The article introduces a novel lexical resource for Swedish based on word family principles. The development of the Swedish Word Family (SweWF) resource is set into the context of linguistic complexity in second language acquisition. The SweWF is particularly appropriate for that, given that it contains lexical items used in second language corpora, namely, in a corpus of coursebook texts, and in a corpus of learner essays. The main focus of the article is on the construction of the resource with its user interface and on its applicability for research, although it also opens vast possibilities for practical applications for language learning, testing and assessment. We demonstrate the value of the resource through several case studies.
A Complete Coverage Path Planning Approach for an Autonomous Underwater Helicopter in Unknown Environment Based on VFH+ Algorithm

An Autonomous Underwater Helicopter (AUH) is a disk-shaped, multi-propelled Autonomous Underwater Vehicle (AUV), which is intended to work autonomously in underwater environments. The near-bottom area sweep in unknown environments is a typical application scenario, in which the complete coverage path planning (CCPP) is essential for AUH. A complete coverage path planning approach for AUH with a single beam echo sounder, including the initial path planning and online local collision avoidance strategy, is proposed. First, the initial path is planned using boustrophedon motion. Based on its mobility, a multi-dimensional obstacle sensing method is designed with a single beam range sonar mounted on the AUH. The VFH+ algorithm is configured for the heading decision-making procedure before encountering obstacles, based on their range information at a fixed position. The online local obstacle avoidance procedure is simulated and analyzed with variations of the desired heading direction and corresponding polar histograms. Finally, several simulation cases are set up, simulated and compared by analyzing the heading decision in front of different obstacle situations. The simulation results demonstrate the feasibility of the complete coverage path planning approach proposed, which proves that AUH completing a full coverage area sweep in unknown environments with a single beam sonar is viable.
A Lightweight Neural Network Model for Disease Risk Prediction in Edge Intelligent Computing Architecture

In the current field of disease risk prediction research, there are many methods of using servers for centralized computing to train and infer prediction models. However, this centralized computing method increases storage space, the load on network bandwidth, and the computing pressure on the central server. In this article, we design an image preprocessing method and propose a lightweight neural network model called Linge (Lightweight Neural Network Models for the Edge). We propose a distributed intelligent edge computing technology based on the federated learning algorithm for disease risk prediction. The intelligent edge computing method we proposed for disease risk prediction directly performs prediction model training and inference at the edge without increasing storage space. It also reduces the load on network bandwidth and reduces the computing pressure on the server. The lightweight neural network model we designed has only 7.63 MB of parameters and only takes up 155.28 MB of memory. In the experiment with the Linge model compared with the EfficientNetV2 model, the accuracy and precision increased by 2%, the recall rate increased by 1%, the specificity increased by 4%, the F1 score increased by 3%, and the AUC (Area Under the Curve) value increased by 2%.