Reviewerly/exHarmony huggingface dataset:
RDCP: A Real Time Sea Fog Intensity and Visibility Estimation Algorithm

A number of accidents at sea are primarily caused by low visibility due to sea fog. Therefore, it is important to estimate sea fog intensity and visibility in real-time in the ocean environment. Visibility meters utilize optical sensors rather than visional information, so that the measured visibility data occasionally includes errors. Moreover, visibility meters have significant costs so that it is not viable to install them at various locations. Therefore, this paper proposes an algorithm called RDCP (Reduced Dark Channel Prior), which provides reliable estimation at a low cost by processing images captured from cameras, since they receive identical information as human eyes. For the estimation, the RDCP algorithm firstly acquires dark channels from an ocean image, then applies an optimized threshold value to the dark channels and crops out the sky region in the ocean image. For the estimation performance evaluation, 320 raw images captured from cameras at four different ports in Republic of Korea are used. Considering facilities in oceans, which are usually operated in power-limited environment, the processing time performance of PDCP is also examined. The experiments demonstrate that the RDCP algorithm provides reliable estimation performance in real-time.
A New Security Proof for Twin-Field Quantum Key Distribution (QKD)

Twin-field QKD (TF-QKD) protocols allow for increased key rates over long distances when compared to standard QKD protocols. They are even able to surpass the PLOB bound without the need for quantum repeaters. In this work, we revisit a previous TF-QKD protocol and derive a new, simple, proof of security for it. We also look at several variants of the protocol and investigate their performance, showing some interesting behaviors due to the asymmetric nature of the protocol.
Dual encoding DDS‐UNet liver tumour segmentation based on multi‐scale deep and shallow feature fusion

Abstract The fusion and utilization of multi‐scale deep and shallow features are of great significance in liver tumour segmentation. This study proposes a dual encoding DDS‐UNet liver tumour segmentation method based on multi‐scale deep and shallow feature fusion, aiming to fully achieve the fusion and utilization of deep and shallow features and achieve accurate segmentation. The proposed method mainly consists of residual convolution module fusion residual convolution (FRC), dual encoding end fusion module dual encoding end fusion (DEF), and skip connection fusion module jump connection fusion module (JCF). In the residual convolution module, a layer by layer fused residual convolution is used instead of traditional convolution to achieve better training. In the dual encoding end fusion module, multi‐scale feature fusion at the end provides more comprehensive contextual information, solves the loss of spatial geometric information. The skip connection fusion module reduces the interference of invalid features by changing the weights of spatial attention and channel attention on important features. The Dice coefficient, average intersection to union ratio, accuracy, recall, and accuracy indicators tested on the LiTS dataset were 90.37%, 90.16%, 93.78%, 94.91%, and 98.84%, respectively, which are superior to many advanced liver tumor segmentation methods.
Augmenting the Impact of STEAM Education by Developing a Competence Framework for STEAM Educators for Effective Teaching and Learning

STEAM (science, technology, engineering, arts, and mathematics) education is gaining increasing attention worldwide, with many initiatives being implemented to promote its adoption and effectiveness; thus, its successful integration into educational systems has become increasingly critical. Educators will play a central role in this integration; that is why it is critical to assess their needs, map the necessary roles and competences, and provide the means to guide their professional development in a systematic way. To address these requirements, our study introduces the STEAM Competence Framework for Educators (STEAMComp Edu), as a culmination of literature reviews, expert consultations, and empirical validation by 302 educational professionals, policymakers, and scholars. The framework includes 41 vital competences, organized across 14 competence areas, from five broad educators’ perspectives. STEAMComp Edu captures STEAM educators’ roles, from teaching methods to community engagement, and stands as a pivotal tool in enhancing STEAM education quality and effectiveness. This paper also highlights STEAMComp Edu’s significance in the professional development of educators and focuses on the framework’s value in developing self-assessment tools and in designing STEAM educator occupational profiles that are aligned with the ESCO (European Skills, Competences, Qualifications, and Occupations) standards, thereby contributing to a more structured and recognized approach in the field of STEAM education.
From words to paragraphs: modeling sentiment dynamics in ‘notes from underground’ with GPT-4 via descriptive methods and differential equations

This study examines how the sentiment values in the first part of the book entitled as “Underground” of Fyodor Dostoevsky’s “Notes from Underground” change from words to sentences to paragraphs. Using the GPT-4 language model, we conducted a descriptive analysis of standardized sentiment values and calculated cumulative sentiment trajectories over the text. We then created differential equation models to model the sentiment tones using regression analysis. Our findings suggest that sentiment becomes less negative from words to paragraphs, indicating that context moderates negativity. Paragraph sentiment was also more stable with lower variability. There was a narrative arc of initial decline followed by an upward turn in sentiment. Paragraphs had the highest baseline sentiment, suggesting that they are able to capture more nuanced context. Paragraphs lost short-term sentiment quickly but retained longterm sentiment longest, aligning with paragraphs maintaining overall text sentiment over time. These findings suggest that there are complex dynamics between linguistic units contributing to perceived stability of sentiment. Quantitative decay rates are useful indicators but do not fully characterize sentiment stability.
An Improved YOLOv5 Algorithm for Drowning Detection in the Indoor Swimming Pool

In order to mitigate the risk of irreversible drowning injuries, this study introduces an enhanced YOLOv5 algorithm aimed at improving the efficacy of indoor swimming pool drowning detection and facilitating the timely rescue of endangered individuals. To simulate drowning and swimming positions accurately, four swimmers were deliberately chosen and observed, with monitoring conducted by drones flying above the swimming pool. The study was approved by the ethics committee of our institution, with the registration number 2022024. The images captured by the drones underwent a meticulous evaluation, and only those deemed suitable were selected to construct the self-made dataset, comprising a total of 8572 images. Furthermore, two enhancements were implemented in the YOLOv5 algorithm. Firstly, the inclusion of the ICA module strengthened category classification and the localization of water behavioral postures, which is improved from the coordinated attention module (CA). Secondly, the PAN module was replaced with the bi-directional feature pyramid network (BiFPN). Subsequently, the improved YOLOv5 algorithm underwent training using the self-made dataset. Evaluation of the algorithm’s performance revealed a notably improved detection accuracy rate, recall rate, and an impressive mean Average Precision (mAP) score of 98.1%, 98.0%, and 98.5%, respectively. Our paper introduces the improved YOLOv5 algorithm, surpassing the original YOLOv5 algorithm in terms of recognition accuracy for instances of drowning.
TDLearning: Trusted Distributed Collaborative Learning Based on Blockchain Smart Contracts

Massive amounts of data drive the performance of deep learning models, but in practice, data resources are often highly dispersed and bound by data privacy and security concerns, making it difficult for multiple data sources to share their local data directly. Data resources are difficult to aggregate effectively, resulting in a lack of support for model training. How to collaborate between data sources in order to aggregate the value of data resources is therefore an important research question. However, existing distributed-collaborative-learning architectures still face serious challenges in collaborating between nodes that lack mutual trust, with security and trust issues seriously affecting the confidence and willingness of data sources to participate in collaboration. Blockchain technology provides trusted distributed storage and computing, and combining it with collaboration between data sources to build trusted distributed-collaborative-learning architectures is an extremely valuable research direction for application. We propose a trusted distributed-collaborative-learning mechanism based on blockchain smart contracts. Firstly, the mechanism uses blockchain smart contracts to define and encapsulate collaborative behaviours, relationships and norms between distributed collaborative nodes. Secondly, we propose a model-fusion method based on feature fusion, which replaces the direct sharing of local data resources with distributed-model collaborative training and organises distributed data resources for distributed collaboration to improve model performance. Finally, in order to verify the trustworthiness and usability of the proposed mechanism, on the one hand, we implement formal modelling and verification of the smart contract by using Coloured Petri Net and prove that the mechanism satisfies the expected trustworthiness properties by verifying the formal model of the smart contract associated with the mechanism. On the other hand, the model-fusion method based on feature fusion is evaluated in different datasets and collaboration scenarios, while a typical collaborative-learning case is implemented for a comprehensive analysis and validation of the mechanism. The experimental results show that the proposed mechanism can provide a trusted and fair collaboration infrastructure for distributed-collaboration nodes that lack mutual trust and organise decentralised data resources for collaborative model training to develop effective global models.
Development and validation of a test for measuring primary school students' effective use of <scp>ICT</scp>: The <scp>ECC‐ICT</scp> test

Abstract Background A practical test that measures the information and communication technology (ICT) skills students need for effectively using ICT in primary education has yet to be developed (Oh et al., 2021). This paper reports on the development, validation, and reliability of a test measuring primary school students' ICT skills required for effectively using ICT (the ECC‐ICT test). Objectives Based on existing literature, three ICT use domains were identified for effectively using ICT: Effective , collaborative , and creative use of ICT. For these three domains, 24 corresponding teaching objectives were identified from a widely used digital literacy framework. Thirty‐four test items cover these teaching objectives in an online test. Methods A mixed‐method approach was used for the ECC‐ICT test. Four pilot rounds ( n =25) implemented qualitative interviews for cognitive validity and refining the test items, followed by a qualitative usability study ( n =6) . Confirmatory factor analysis and ANOVA provided quantitative insight into the large‐scale test administration ( n =575) . Results and Conclusions Composite reliability of our conceptual 3‐factor confirmatory model showed that the test reliably measured primary school effective use of ICT ( ω = 0.82), collaborative use of ICT ( ω = 0.80) and creative use of ICT ( ω = 0.64). Convergent validity (ranging from 0.41 to 0.46) was acceptable. Internal consistency (ranging from 0.84 to 0.91) and discriminant validity (HTMT values below 0.90) are good. ANOVA results show that mean test scores are higher for students in higher grade levels ( p &lt; 0.001). The post hoc Bonferroni results show that most grade‐by‐grade comparisons are significant ( p &lt; 0.001).
A unified framework for backpropagation-free soft and hard gated graph neural networks

Abstract We propose a framework for the definition of neural models for graphs that do not rely on backpropagation for training, thus making learning more biologically plausible and amenable to parallel implementation. Our proposed framework is inspired by Gated Linear Networks and allows the adoption of multiple graph convolutions. Specifically, each neuron is defined as a set of graph convolution filters (weight vectors) and a gating mechanism that, given a node and its topological context, generates the weight vector to use for processing the node’s attributes. Two different graph processing schemes are studied, i.e., a message-passing aggregation scheme where the gating mechanism is embedded directly into the graph convolution, and a multi-resolution one where neighboring nodes at different topological distances are jointly processed by a single graph convolution layer. We also compare the effectiveness of different alternatives for defining the context function of a node, i.e., based on hyperplanes or on prototypes, and using a soft or hard-gating mechanism. We propose a unified theoretical framework allowing us to theoretically characterize the proposed models’ expressiveness. We experimentally evaluate our backpropagation-free graph convolutional neural models on commonly adopted node classification datasets and show competitive performances compared to the backpropagation-based counterparts.
The use of augmented reality technologies in tourism businesses from the perspective of UTAUT2

Purpose Augmented reality is one of the technologies that developed and became widespread, especially after the 2000s. It is seen that innovations such as 3D virtual reality glasses, artificial intelligence-supported smart chatbots that communicate with customers in a virtual environment, keyless hotel systems, the Internet of Things, augmented reality applications, smart personal assistants, digital infrastructure concepts and face recognition systems are used in tourism businesses. The place and use of augmented reality technologies in tourism enterprises are discussed in this study. This study was evaluated from the perspective of UTAUT2. The purpose of this study is to examine the usability of augmented reality technologies in tourism businesses. Design/methodology/approach Within the scope of the study, interviews were held with augmented reality application developers, digital marketing agencies and accommodation business managers. The obtained data were processed with the MAXQDA analysis program. Findings It has been determined that augmented reality is a useful and informative technological tool for tourists, it can help sales, promotion and marketing activities, and its usability potency in accommodation businesses is high. Originality/value When the literature is reviewed, it is understood that the studies that apply to the opinions of the application developers and the managers of the accommodation establishments are limited. Therefore, this study has been prepared to contribute to the literature in this respect. This study examines and discusses the use of augmented reality technologies in tourism enterprises, the benefits and difficulties they will create from the perspective of application developers and accommodation business managers and their place in tourism enterprises. This study was evaluated from the perspective of UTAUT2. There is a need for research developed using UTAUT models in technology acceptance studies. In this respect, it will contribute to the literature.
DMCH: A Deep Metric and Category-Level Semantic Hashing Network for Retrieval in Remote Sensing

The effectiveness of hashing methods in big data retrieval has been proved due to their merit in computational and storage efficiency. Recently, encouraged by the strong discriminant capability of deep learning in image representation, various deep hashing methodologies have emerged to enhance retrieval performance. However, maintaining the semantic richness inherent in remote sensing images (RSIs), characterized by their scene intricacy and category diversity, remains a significant challenge. In response to this challenge, we propose a novel two-stage deep metric and category-level semantic hashing network termed DMCH. First, it introduces a novel triple-selection strategy during the semantic metric learning process to optimize the utilization of triple-label information. Moreover, it inserts a hidden layer to enhance the latent correlation between similar hash codes via a designed category-level classification loss. In addition, it employs additional constraints to keep bit-uncorrelation and bit-balance of generated hash codes. Furthermore, a progressive coarse-to-fine hash code sorting scheme is used for superior fine-grained retrieval and more effective hash function learning. Experiment results on three datasets illustrate the effectiveness and superiority of the proposed method.
Simulation of Dynamic Performance of DeFi Protocol Based on Historical Crypto Market Behavior

In a rapidly evolving and often volatile crypto market, the ability to use historical data for simulations provides a more realistic assessment of how decentralized finance (DeFi) protocols might perform. This insight is crucial for participants, developers, and investors seeking to make informed decisions. This paper presents a comprehensive study evaluating the dynamic performance of a newly developed DeFi protocol—NOLUS. The main objective of this paper is to present and analyze the built realistic model of the platform. This model could be successfully used to analyze the stability of the platform under different environmental influences by performing various simulations and conducting experiments with different parameters that could not be realized with the real platform. In the article, the key components of the platform are presented in detail and the main dependencies between them are clarified, in addition to the ways of forming multiple variables, and the complex relations between them in the real protocol are explained. The main finding from the experimental part of the study is that the performance of the protocol representation accounts for the expected system behavior. Hence the system simulation could be successfully used to reveal essential protocol behaviors resulting from potential shifts in the crypto market environment and to optimize the protocol’s hyper parameters.
The ambiguity of BERTology: what do large language models represent?

Abstract The field of “BERTology” aims to locate linguistic representations in large language models (LLMs). These have commonly been interpreted as representing structural descriptions (SDs) familiar from theoretical linguistics, such as abstract phrase-structures. However, it is unclear how such claims should be interpreted in the first place. This paper identifies six possible readings of “linguistic representation” from philosophical and linguistic literature, concluding that none has a straight-forward application to BERTology. In philosophy, representations are typically analyzed as cognitive vehicles individuated by intentional content. This clashes with a prevalent mentalist interpretation of linguistics, which treats SDs as (narrow) properties of cognitive vehicles themselves. I further distinguish between three readings of both kinds, and discuss challenges each brings for BERTology. In particular, some readings would make it trivially false to assign representations of SDs to LLMs, while others would make it trivially true. I illustrate this with the concrete case study of structural probing: a dominant model-interpretation technique. To improve the present situation, I propose that BERTology should adopt a more “LLM-first” approach instead of relying on pre-existing linguistic theories developed for orthogonal purposes.
Predictive Modeling of Light–Matter Interaction in One Dimension: A Dynamic Deep Learning Approach

The mathematical modeling and the associated numerical simulation of the light–matter interaction (LMI) process are well-known to be quite complicated, particularly for media where several electronic transitions take place under electromagnetic excitation. As a result, numerical simulations of typical LMI processes usually require a high computational cost due to the involvement of a large number of coupled differential equations modeling electron and photon behavior. In this paper, we model the general LMI process involving an electromagnetic interaction medium and optical (light) excitation in one dimension (1D) via the use of a dynamic deep learning algorithm where the neural network coefficients can precisely adapt themselves based on the past values of the coefficients of adjacent layers even under the availability of very limited data. Due to the high computational cost of LMI simulations, simulation data are usually only available for short durations. Our aim here is to implement an adaptive deep learning-based model of the LMI process in 1D based on available temporal data so that the electromagnetic features of LMI simulations can be quickly decrypted by the evolving network coefficients, facilitating self-learning. This enables accurate prediction and acceleration of LMI simulations that can run for much longer durations via the reduction in the cost of computation through the elimination of the requirement for the simultaneous computation and discretization of a large set of coupled differential equations at each simulation step. Our analyses show that the LMI process can be efficiently decrypted using dynamic deep learning with less than 1% relative error (RE), enabling the extension of LMI simulations using simple artificial neural networks.
Path Planning of Logistic Robot Using Method of Vector Marks Tree Generation

The authors of this article analyzed the problem of logistic robotics. This paper presents a method for robot navigation in a known environment. The method consists of two steps. The first step is to model the system, assign vector marks to the prominent edges of the virtual environment map, and direct the robot to reach these marks. The second step is to enable the robot to execute a specific task based on the given paths and deal with the local obstacles avoidance independently. The identification of the prominent point, the computation of the vector mark, and optimal path calculation are performed on the computer model using colored Petri nets in the software ‘Centaurus CPN’. The proposed approach was extended to simulate the work of a logistic robot, which has to take boxes and deliver them to certain places in storages. The experimental investigation has shown that the simulated mobile robots with the proposed navigation system were efficiently moving along the planned path. The analysis of the vector tree reveals that it takes 0.389 s to compute and graphically represent it. The occupation of certain places in storages is visualized and shown in experimental graphics.