Reviewerly/exHarmony huggingface dataset:
Miper-MVS: Multi-scale iterative probability estimation with refinement for efficient multi-view stereo

Multi-view stereo reconstruction aims to construct 3D scenes from multiple 2D images. In recent years, learning-based multi-view stereo methods have achieved significant results in depth estimation for multi-view stereo reconstruction. However, the current popular multi-stage processing method cannot solve the low-efficiency problem satisfactorily owing to the use of 3D convolution and still involves significant amounts of calculation. Therefore, to further balance the efficiency and generalization performance, this study proposed a multi-scale iterative probability estimation with refinement, which is a highly efficient method for multi-view stereo reconstruction. It comprises three main modules: 1) a high-precision probability estimator, dilated-LSTM that encodes the pixel probability distribution of depth in the hidden state, 2) an efficient interactive multi-scale update module that fully integrates multi-scale information and improves parallelism by interacting information between adjacent scales, and 3) a Pi-error Refinement module that converts the depth error between views into a grayscale error map and refines the edges of objects in the depth map. Simultaneously, we introduced a large amount of high-frequency information to ensure the accuracy of the refined edges. Among the most efficient methods (e.g., runtime and memory), the proposed method achieved the best generalization on the Tanks & Temples benchmarks. Additionally, the performance of the Miper-MVS was highly competitive in DTU benchmark. Our code is available at https://github.com/zhz120/Miper-MVS.
Networks of Migrants’ Narratives: A Post-authentic Approach to Heritage Visualisation

Digital tools, technologies, and infrastructures have increasingly shaped how knowledge is understood, created, managed, maintained, and shared. In sectors such as cultural heritage, for example, curators and scholars now widely apply computer science approaches to their collections for tasks such as enriching content and providing users with visual access. Although there has been an increasing interest in critical digital visual design and cultural heritage, what still seems to be largely missing is a problematised and critical approach to the application of these techniques. Drawing on critical posthumanities, in this article I propose a new critical framework, the post-authentic framework to digital objects and cultural heritage. The framework advocates transparency and openness as well as the active and reflexive participation of the scholar/curator in the process of knowledge production. To exemplify how the post-authentic framework may be applied to digital research, I explore two use cases: critical visualisation design and critical digital heritage research. For the first use case, I examine the design choices for developing the tool DeXTER, an interactive visualisation app that allows users to explore enriched cultural heritage material interactively. I specifically emphasise the challenges facing product design, with a focus on visualising the ambiguities and uncertainties of Network Analysis and Sentiment Analysis. For the second use case, I explore the potential of DeXTER for testing hypotheses on large textual collections, for example by accessing the layers of meaning attached to referential entities. The use case is the Italian American labour movement from 1898 to 1936. The article concludes with key concepts and methods for applying the post-authentic framework to digital heritage practices and knowledge creation more widely.
Salient-Centeredness and Saliency Size in Computational Aesthetics

We investigate the optimal aesthetic location and size of a single dominant salient region in a photographic image. Existing algorithms for photographic composition do not take full account of the spatial positioning or sizes of these salient regions. We present a set of experiments to assess aesthetic preferences, inspired by theories of centeredness, principal lines, and Rule-of-Thirds. Our experimental results show a clear preference for the salient region to be centered in the image and that there is a preferred size of non-salient border around this salient region. We thus propose a novel image cropping mechanism for images containing a single salient region to achieve the best aesthetic balance. Our results show that the Rule-of-Thirds guideline is not generally valid but also allow us to hypothesize in which situations it is useful and in which it is inappropriate.
An Improved Co-Training and Generative Adversarial Network (Diff-CoGAN) for Semi-Supervised Medical Image Segmentation

Semi-supervised learning is a technique that utilizes a limited set of labeled data and a large amount of unlabeled data to overcome the challenges of obtaining a perfect dataset in deep learning, especially in medical image segmentation. The accuracy of the predicted labels for the unlabeled data is a critical factor that affects the training performance, thus reducing the accuracy of segmentation. To address this issue, a semi-supervised learning method based on the Diff-CoGAN framework was proposed, which incorporates co-training and generative adversarial network (GAN) strategies. The proposed Diff-CoGAN framework employs two generators and one discriminator. The generators work together by providing mutual information guidance to produce predicted maps that are more accurate and closer to the ground truth. To further improve segmentation accuracy, the predicted maps are subjected to an intersection operation to identify a high-confidence region of interest, which reduces boundary segmentation errors. The predicted maps are then fed into the discriminator, and the iterative process of adversarial training enhances the generators’ ability to generate more precise maps, while also improving the discriminator’s ability to distinguish between the predicted maps and the ground truth. This study conducted experiments on the Hippocampus and Spleen images from the Medical Segmentation Decathlon (MSD) dataset using three semi-supervised methods: co-training, semi-GAN, and Diff-CoGAN. The experimental results demonstrated that the proposed Diff-CoGAN approach significantly enhanced segmentation accuracy compared to the other two methods by benefiting on the mutual guidance of the two generators and the adversarial training between the generators and discriminator. The introduction of the intersection operation prior to the discriminator also further reduced boundary segmentation errors.
Three-Dimensional Mapping Technology for Structural Deformation during Aircraft Assembly Process

Owing to the assembly state changes during aircraft assembly processes, assembly force-deformation problem occurs. To obtain the structure shape in the product assembly process efficiently and accurately, a three-dimensional (3D) mapping technology for the structural deformation during the aircraft assembly process is proposed combined with a fiber Bragg grating (FBG) optical fiber sensor and binocular vision measurement system. First, this study established a curvature transformation model using optical fiber monitoring data, obtained the 3D spatial deformation of the product, and completed the unification of the optical fiber wavelength change and spatial 3D point coordinate heterogeneous data. Second, a mesh deformation optimization algorithm based on point-cloud optimization was established. Subsequently, the deformation effects of four mesh deformation models were compared to verify the feasibility and accuracy of HEC-Laplace, and the 3D mapping of the product structure shape in the assembly process was realized. Finally, a cantilever wing model was used to verify the deformation of different loading modes. The results show that the product structure changes can be accurately obtained through the proposed technology, thereby improving the accuracy control and overall assembly quality in the aircraft assembly process and providing a theoretical basis for intelligent aircraft assembly.
A New Quantum Private Protocol for Set Intersection Cardinality Based on a Quantum Homomorphic Encryption Scheme for Toffoli Gate

Set Intersection Cardinality (SI-CA) computes the intersection cardinality of two parties' sets, which has many important and practical applications such as data mining and data analysis. However, in the face of big data sets, it is difficult for two parties to execute the SI-CA protocol repeatedly. In order to reduce the execution pressure, a Private Set Intersection Cardinality (PSI-CA) protocol based on a quantum homomorphic encryption scheme for the Toffoli gate is proposed. Two parties encode their private sets into two quantum sequences and encrypt their sequences by way of a quantum homomorphic encryption scheme. After receiving the encrypted results, the semi-honest third party (TP) can determine the equality of two quantum sequences with the Toffoli gate and decrypted keys. The simulation of the quantum homomorphic encryption scheme for the Toffoli gate on two quantum bits is given by the IBM Quantum Experience platform. The simulation results show that the scheme can also realize the corresponding function on two quantum sequences.
Echo State Network-Enabled Intelligent Smart Sensor Design for Creating a Robotic Nervous System

Introduction: The echo states that networks in the Internet of Things (IoT) are currently being implemented in the widest sense. Echo state networks are fast and efficient recurrent neural networks. This consists of an input layer, a reservoir with many sparsely connected neurons, and an output layer. Issues: In the existing wireless sensor networks, strong mobility may disrupt an existing link between two communicating nodes. There is an inconvenience in data communication, and then it searches for a new node to build a better connection. Methods: To overcome these issues, the recently introduced echo state network (ESN) model opened the way to an extremely efficient approach for designing neural networks for temporal data. The study focuses on the ESN-enabled Intelligent Smart Sensor Design (IS2D) for creating the robotic nervous system with a smart healthcare Digital Nervous System (DNS) using the techniques of IoT, DNS, and Smart Sensor Design and Strain Sensor Fabrication (SSF). Results: Experimental results demonstrate the training set testing against the IS2D, the confusion matrix for ESN outcome, the real-time healthcare monitoring for the DNS, the IS2D sensor accuracy, and the DNS intensity calculation. Discussion: The performance analysis of the proposed model in realistic environments attests to the benefits of energy-centric metrics such as energy consumption, network lifetime, delay, and throughput. Finally, we discuss the challenges and opportunities by summarizing the study and proposing possible future works. The training set testing against the IS2D is based on time count, and the voltage result is estimated. The first portion of the data set should be 11.46% at the initial level. Further, this will increase from 1% to 5%, from 6% to 10%, and from 16% to 28% at the consecutive data set. The confusion matrix for ESN outcome is based on accuracy 28.45% higher than the existing strategies. In this part, the initial accuracy is 8.45% while accessing the initial stage. This value should increase with consecutive data sets from 18.45% to 28.45%.
MMDA: Multi‐person marginal distribution awareness for monocular 3D pose estimation

Abstract Most existing 3D pose representations cannot completely decouple the overlapping two or more human joints of the same type. In this paper, the authors propose a novel 2.5 D representation of the human pose by projecting human joints in 3D space onto the three orthogonal planes. The authors apply for the first time the permutation module to a multi‐person 3D human pose estimation task and use Geometric Constraints Loss (GCL) to guide the learning of the model. The authors overcome the negative effects of the inductive bias of convolutional neural networks (CNNs) by aligning the intermediate feature space with the output feature space. The effectiveness of the authors’ approach is validated on the carnegie mellon university (CMU) panoptic dataset and MuPoTS‐3D dataset. The authors’ proposed representations can effectively decouple the human joints in their selected data from overlapping human joints.
Examining the Effects of a Real-Time, Knowledge-Aware Tool for Academic Writing Assessment

The computer-supported writing assessment (CSWA) has been widely used to reduce instructor workload and provide real-time feedback. Interpretability of CSWA draws extensive attention because it can benefit the validity, transparency, and knowledge-aware feedback of academic writing assessments. This study proposes a novel assessment tool, EduNERScore, which enables raters to identify different types of domain knowledge from unstructured educational texts and strengthen the interpretation of assessment through knowledge-aware evidence. This research uses an educational experiment to examine raters’ validity, efficiency, interpretability and user perception with and without the support of EduNERScore. The findings show that the experimental group with the support of EduNERScore achieves human-expert validity, high efficiency, interpretability and good use perception, compared with the control group. Finally, based on the empirical results, this research discusses the technological and educational implications for developing and applying the interpretable CSWA system in education.
How to obtain translation equivalence of culturally specific concepts in a target language

Abstract As is often claimed, “the central problem of translation practice is that of finding target language translation equivalents” ( Catford 1965 , 21). Translation equivalence occurs when a source language (SL) and a target language (TL) text can be related to the same substance. Therefore, translation failure occurs when a concept in SL cannot be related to the equivalent in TL. Translation failures can be classified into two cases: those where it is impossible to find linguistic equivalence in TL, and those where it is impossible to secure cultural equivalence in TL. This study focuses on the latter, especially how to establish the equivalence of culturally specific concepts from SL to TL. Nobody can understand the meanings of words in a certain culturally specific domain if they do not understand some social institutions, and some history of social practices, in which these words are interpreted. Japanese words sontaku (roughly, surmise) and haragei , (literally, belly art) are examples of those culturally specific concepts. Translating these concepts into a TL text includes the identification of their semantic representation. For this purpose, using frame semantics paves the way. By defining frame elements and relations between them and also by identifying cultural practices, we can have a clear understanding of the concepts, which in turn can be related to the TL terms. It is also worth noting that culture-specific words have varying degrees of untranslatability, which can be defined by comparing synonymous words in terms of the frame semantic approach. This research sheds new light on the method of rendering culturally specific untranslatable concepts exemplified by sontaku and haragei more transparent by drawing on frame semantics.
Out-of-Distribution (OOD) Detection and Generalization Improved by Augmenting Adversarial Mixup Samples

Deep neural network (DNN) models are usually built based on the i.i.d. (independent and identically distributed), also known as in-distribution (ID), assumption on the training samples and test data. However, when models are deployed in a real-world scenario with some distributional shifts, test data can be out-of-distribution (OOD) and both OOD detection and OOD generalization should be simultaneously addressed to ensure the reliability and safety of applied AI systems. Most existing OOD detectors pursue these two goals separately, and therefore, are sensitive to covariate shift rather than semantic shift. To alleviate this problem, this paper proposes a novel adversarial mixup (AM) training method which simply executes OOD data augmentation to synthesize differently distributed data and designs a new AM loss function to learn how to handle OOD data. The proposed AM generates OOD samples being significantly diverged from the support of training data distribution but not completely disjoint to increase the generalization capability of the OOD detector. In addition, the AM is combined with a distributional-distance-aware OOD detector at inference to detect semantic OOD samples more efficiently while being robust to covariate shift due to data tampering. Experimental evaluation validates that the designed AM is effective on both OOD detection and OOD generalization tasks compared to previous OOD detectors and data mixup methods.
A Helium Speech Unscrambling Algorithm Based on Deep Learning

Helium speech, the language spoken by divers in the deep sea who breathe a high-pressure helium–oxygen mixture, is almost unintelligible. To accurately unscramble helium speech, a neural network based on deep learning is proposed. First, an isolated helium speech corpus and a continuous helium speech corpus in a normal atmosphere are constructed, and an algorithm to automatically generate label files is proposed. Then, a convolution neural network (CNN), connectionist temporal classification (CTC) and a transformer are combined into a speech recognition network. Finally, an optimization algorithm is proposed to improve the recognition of continuous helium speech, which combines depth-wise separable convolution (DSC), a gated linear unit (GLU) and a feedforward neural network (FNN). The experimental results show that the accuracy of the algorithm, upon combining the CNN, CTC and the transformer, is 91.38%, and the optimization algorithm improves the accuracy of continuous helium speech recognition by 9.26%.
Critiquing Neocolonial Digital Barriers’ Impact on eLibraries and African Scholarship

Abstract This article offers an overview of the Knowledge Exchange Research Group (KERG) West African Elibrary Collaborative (WAELC) study of West African scholars' systemic restricted access to digitized scholarly databases. WAELC, an ongoing qualitative and quantitative study, explores limited accessibility to current open‐access digital repositories and platforms at some African universities. A practical output of the study is to inform the development of a sustainable institutional repository and support the development of an open‐access multimodal digital platform that will feature scholarly and creative works of global Black people. The WAELC data addresses gaps in previous research on library access and usage at African universities and critically responds to the general African Digital Divide literature. A central focus of this article is to discuss the sociocultural and historical practices and processes that shape current digital access to electronic scholarship in Africa. Foregrounded in a Black feminist autoethnographic approach, the author's research process and researcher positionality were central to developing the WAELC research project. A significant finding of this research is that the systemic inequities framing global knowledge access and production in Global South institutions are reproduced in infrastructures weakened from colonial, neocolonial, and neoliberal social, political, and economic systems.
Blockchain-Based Welfare Distribution Model for Digital Inclusivity

An unprecedented rate of technological advancement, compounded by the COVID-19 pandemic, has expedited our transition to a fully digitalised society. Traditionally, digital inclusion focuses on an individual's ability to connect to and access information from the internet. The adoption of novel technologies such as artificial intelligence and blockchain is little distinguished in the literature from internet accessibility. While these digital solutions present novel opportunities, they may also perpetuate or exacerbate the existing hurdles faced by digitally excluded localities. However, these technologies could also be used to tackle the digital divide. Inspired by the design of Bitcoin, the current study offers a conceptual blockchain-based welfare model that adopts a two-pronged approach to enable the fair distribution of capital and resource allocation across the UK regions. The model offers transparency over institutional processes and improves their trustworthiness while preserving privacy. At the community level, the model assumes the application of economic incentives in order to promote digital inclusivity and stimulate cooperation and competition within local cultures. By mobilising both public institutions and communities, such a holistic model would assist the flow of information between the supply and demand side of the regional economy. This approach may not only help to dissolve the welfare losses arising from the digital divide, but also improve social well-being in all regions.
Optimizing Aviation Maintenance through Algorithmic Approach of Real-Life Data

The aviation industry has been undergoing significant changes in recent years, driven by technological advancements and the increasing demand for safe and efficient air transportation. One area that has seen significant advancements is aircraft maintenance, with the implementation of progressive technologies playing a crucial role. The use of digital technologies has revolutionized the way aircraft maintenance is carried out. For instance, the adoption of predictive maintenance algorithms has enabled airlines to predict when their aircraft will require maintenance, reducing downtime and increasing operational efficiency. This has been made possible by the integration of real-time data. Another technology that has transformed aircraft maintenance is the use of augmented reality and virtual reality. These technologies allow maintenance engineers to carry out procedures with greater accuracy and efficiency, as they can see instructions and parts overlaid on their real-world view. This has been particularly useful for complex and critical tasks, where human error could result in significant consequences. The algorithmization and digitalization of processes is one of the many pillars of the Industry 4.0 concept, which, if used correctly, can dramatically enhance the execution of processes. The algorithmic approach presented in this study can be easily adapted to other industries, and could potentially lead to similar optimizations in other fields. Overall, the implementation of progressive technologies in aircraft maintenance has improved safety, reduced downtime, and increased operational efficiency.
A Concise Relation Extraction Method Based on the Fusion of Sequential and Structural Features Using ERNIE

Relation extraction, a fundamental task in natural language processing, aims to extract entity triples from unstructured data. These triples can then be used to build a knowledge graph. Recently, pre-training models that have learned prior semantic and syntactic knowledge, such as BERT and ERNIE, have enhanced the performance of relation extraction tasks. However, previous research has mainly focused on sequential or structural data alone, such as the shortest dependency path, ignoring the fact that fusing sequential and structural features may improve the classification performance. This study proposes a concise approach using the fused features for the relation extraction task. Firstly, for the sequential data, we verify in detail which of the generated representations can effectively improve the performance. Secondly, inspired by the pre-training task of next-sentence prediction, we propose a concise relation extraction approach based on the fusion of sequential and structural features using the pre-training model ERNIE. The experiments were conducted on the SemEval 2010 Task 8 dataset and the results show that the proposed method can improve the F1 value to 0.902.